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FINANCIAL-STABILITY-COMPETITION-AND-EFFICIENCY-IN-LATIN-AMERICAN-AND-CARIBBEAN-BANKING_2014_Journal-of-Applied-Economics.pdf

* Adnan Kasman (corresponding author): Department of Economics, Faculty of Business, Dokuz Eylul University, Izmir, Turkey; [email protected]. Oscar Carvallo: Economic Research Office, Central Bank of Venezuela Caracas, Venezuela; [email protected]. Valuable comments of two anonymous referees and the editor are greatly appreciated. However, any remaining errors are ours.

Journal of Applied Economics. Vol XVII, No. 2 (November 2014), 301-324

FINANCIAL STABILITY, COMPETITION AND EFFICIENCY IN LATIN AMERICAN AND CARIBBEAN BANKING

AdnAn KAsmAn* Dokuz Eylul University

OscAr cArvAllO Central Bank of Venezuela

Submitted May 2013; accepted March 2014

Using a sample of 272 commercial banks from fifteen Latin American countries for the period 2001-2008, we estimate cost and revenue efficiency scores, financial stability scores (Z-scores) and competition scores (Lerner indexes and Boone indicators) at the bank level. The Granger causality technique in dynamic panels is used to establish dynamic relationships among these variables. We find evidence that strongly supports the “quite life” hypothesis, while we also find partial support for causality running in the opposite direction. Moreover, the results suggest that more competition is conducive to greater financial stability (when the revenue efficiency score is used). Banks seem to achieve market power through better efficiency, leverage and earning ability. As size and complexity increase, however, agency problems and increasing risk-taking might start gaining momentum, generating inefficiency and fragility.

JEL classification codes: G21, D24, C23 Key words: financial stability, competition, efficiency, Latin American banking

302 Journal of applied economics

I. Introduction

The global financial crisis of 2007 has not only shaken most of financial markets

and institutions, but also key underlying assumptions regarding financial market

mechanisms. During the run-up to the crisis, it became apparent that bubbles

could survive for long periods of time despite the presence of well-informed and

well-financed rational arbitrageurs. Moreover, the “efficient markets hypothesis”

would rule out such a phenomenon (Abreu and Brunnermeier 2003; Brunnermeier

et al. 2009). For a long time, competition was not thought to be an important

determinant of financial stability. In fact, in most countries standard competition

policy was not applied fully to financial markets. Although competition policy

had been substantially strengthened at the national level over the last couple of

decades, regulators in most countries were complacent about market power in

national markets. The crisis also changed that. Particularly, the largest and more

interconnected financial institutions in developed markets have failed.

The Latin American and Caribbean banking systems have shown an apparent

resilience during the current crisis. The region’s financial systems have undergone

intense structural change since the 1990s. In addition, starting in the nineties,

consolidation and restructuring have changed the competitive environment (Levy-

Yeyati and Micco 2007; Yildirim and Philippatos 2006; Carvallo and Kasman

2005; BIS 2007). However, there are concerns regarding the impact of increased

concentration on the level of competition, performance and the financial stability

of the banking systems in the region.

As the experience of the crisis underscores, a better understanding of links

between stability and competition is a must. Moreover, the studies so far have

focused on structural and aggregated measures of competition, concentration,

stability and efficiency, mostly in a static setting. In the related literature, studies

have paid attention to performance (cost or profit efficiencies) or competition

(and/or risk) of banks. In this paper, we integrate these dimensions in search of

mutual dynamic relationships between bank efficiency, competition and financial

stability in the banking sectors within the region. The sweeping changes in the

global regulatory paradigm will affect the way the region regulates its financial

markets. Competition policy will be part of those changes and solid empirical

evidence to guide it is required.

This paper extends the related literature in several ways. First, we estimate

market power at the bank level by computing Lerner indexes for an unbalanced

financial stability, competition and efficiency in banking 303

sample panel of 272 Latin American and Caribbean banks from fifteen countries

in the period 2001-2008. We also estimate cost and revenue efficiency of banks

operating in the region during the period by taking into account legal and economic

environmental variables. Following Fiordelisi et al. (2011) and Casu and Girardone

(2009), this paper applies a relatively new empirical methodology, Granger-

causality tests in dynamic panels. Regressions using pooled OLS and panel data

models might present endogeneity problems, since the lagged dependent variable

is often correlated with the disturbance term. Thus, we present several regression

estimation methods. In doing so, we test two prevailing hypotheses regarding

stability and competition, namely, the “competition-fragility” and “competition-

stability” hypotheses. Lastly, we investigate the dynamic feedbacks between

efficiency and market power. In this regard, two different views are contrasted:

the so called “structure-conduct-performance” and the “efficient structure”

hypotheses.

The rest of the paper is organized as follows. In Section II, we present a review of

the literature regarding market power, financial stability and efficiency in banking.

In Section III, we discuss the methodology and the econometric specification used

for the estimation of competition indexes, efficiency scores, financial stability and

causality tests. The data and empirical results of the estimations are reported in

Section IV. The paper’s concluding remarks are provided in Section V.

II. Related literature, empirical evidence and research hypotheses

Numerous studies have looked at the determinants of market power in banking

(Berger and Hannan 1989; Claessens and Laeven 2004; Maudos and Nagore

2005). For instance, Claessens and Laeven (2004) use the Panzar and Rosse (1987)

methodology to check for the relevance of indicators of countries’ banking structure

and regulation. They find that contestability along with activity restrictions, rather

than concentration, is the main determinant of market power. Foreign entry and

fewer activity restrictions enhance the competitiveness of banking systems. Maudos

and Nagore (2005) construct bank-level measures of market power (Lerner indexes).

They find an inverted U-shaped relationship between bank size and market power.

More efficient banks enjoy greater market power, as banks seem to pass on to

customer their cost advantages. They also find a positive effect of concentration on

market power, in line with the conventional view.

304 Journal of applied economics

The relationship between competition and financial stability has been a controversial issue long before the current crisis started. Both at theoretical and empirical level, the issue remain ambiguous and unresolved, despite a large body of literature. The most traditional view asserts that competition increases risk - taking as it undermines the “charter value” of banks. Alternatively, banks that enjoy market power can extract monopoly rents that enhance charter value. As increased risk-taking endanger that value, banks have less incentive to engage in it (Keeley 1990). Also, larger banks are supposed to diversify better and be more efficient due to the economies of scale and scope (Boyd and Pescott 1986). However, the traditional “too big to fail” doctrine implies increased risk-taking by the largest banks, which enjoy implicit public guarantees (Mishkin 1999).

The controversy has spilled over to more recent contributions. Larger banks in concentrated systems can easily create capital buffers against macroeconomic and liquidity shocks, improving systemic stability (Boyd et al. 2004). In addition to traditional scale and scope diversification, larger banks can spread their operation geographically to reduce risk (Meon and Weill 2005). The “charter value hypothesis” has been revived on the grounds of increased profit buffers (Allen and Gale 2004; Matutes and Vives 2000; Cordella and Levy-Yeyati 2003). Larger banks engaging in “credit rationing” enhance soundness as they pick fewer investments with higher quality (Boot and Thakor 2000). Based on moral hazard arguments, it can be argued that rates charged by banks with market power can induce increased risk-taking by entrepreneurs (Boyd and De Nicolo 2005). Also, the effect of increased scope and scale economies can be counterbalanced by increased managerial inefficiency (“X-inefficiency”) due to increased complexity (Beck et al. 2006). The empirical evidence reflects these conflicting views. Whereas recent evidence support the “competition - fragility” view (Beck et al. 2006), several studies provide evidence supporting the “competition - stability” hypothesis (De Nicolo et al. 2004; Schaeck and Cihak 2007; Schaeck et al. 2006; Uhde and Heimeshoff 2009). Adding to that, the bulk of the empirical literature uses market concentration measures, which might not necessarily reflect effective competition. Accordingly, what is found to be positively correlated with financial

stability is contestability, rather than the actual level of foreign presence or market

concentration (Barth et al. 2004). 1

1 One of the factors that complicate this picture is the complex nature of the relation between concentration and effective competition. Either on a theoretical or empirical basis, it is becoming clear that they measure different dimensions of the competitive landscape (Matutes and Vives 1996).

financial stability, competition and efficiency in banking 305

Williams (2012) examines the relationship between bank efficiency and market

power to test the so called “quiet life” hypothesis in Latin American banking for

the period 1985-2010.2 He uses an efficiency-adjusted Lerner index, proposed by

Koetter et al. (2012), in addition to the conventional one, to measure market power.

He also produces conventional Lerner indexes in deposits and loans markets. Their

results show that deposit markets are more competitive than lending markets.

Chortareas et al. (2011), like Williams (2012), examine the relationship between

bank efficiency and market power for Latin American banks for the period 1997-

2005. In contrast to Williams (2012) and the present study, they use the Herfindahl-

Hirschman Index (HHI) to measure market power. Their results uncover evidence

supporting the efficient structure hypothesis in Latin American countries. 3

Regarding financial fragility, Berger et al. (2009) examine the competition -

fragility relationship by examining different dimensions of risk. Although banks

with higher market power are found to have overall less risk exposure - consistent

with “competition - fragility” view, banks tend to offset loan risk by increasing

capital ratios. In line with this idea, Maudos and Nagore (2005) examine the

determinants of stability as measured by the Z-scores. They find support for the

“competition - fragility” view when using Lerner indexes as measures of market

power. More recent studies examine the issue in a dynamic context. Uhde and

Heimeshoff (2009) compute Z-scores and concentration indexes in a dynamic

panel context to examine the inter-temporal relationship between consolidation

in European banking and financial stability. They find that banks are more prone

to financial fragility in the Eastern European banking markets exhibiting a lower

level of competitive pressure, fewer diversification opportunities and a higher

fraction of government-owned banks.

Claessens et al. (2001) present evidence with regard to foreign entry, asserting

that it increases bank efficiency by reducing margins. Demirguc-Kunt et al. (2004)

2 The “quiet life” hypothesis was first proposed by Hicks (1935). According to this hypothesis, banks with more market power put less effort in pursuing cost efficiency as the pressure to increase efficiency is absent. Hence, instead of taking advantage of their favorable position by cutting costs, they prefer to enjoy a “quite life”. 3 Several papers in the related literature examine the relationship between market power and concentration. For instance, Bikker and Haaf (2002) provide support for the conventional view that concentration reduces competitiveness. Claessens and Laeven (2004), however, find that competition is not negatively related to market concentration. With respect to Latin American and Caribbean banks, Yildirim and Philippatos (2006) find that regardless of increased concentration and foreign entry, competition as a rule has not been affected in the region. Banks seem to be operating under monopolistic competition conditions.

306 Journal of applied economics

find that concentration has a negative effect on efficiency in less developed banking

systems. Schaeck and Cihak (2010) find that competition enhances efficiency.

In this regard, Zarutskie (2009) and Dick and Lehnert (2010) provide evidence

that competition affect banks’ efficiency by improving specialization, screening,

monitoring and lending ability. More recently, Fiordelisi et al. (2011) use Granger-

causality technique and find that lower cost and revenue efficiency cause higher

bank risk. Using the same methodology, Casu and Girardone (2009) investigate

the relationship between competition and efficiency for a sample of European

banks. They find a positive relationship between market power and efficiency, and

a weak relation running from efficiency to competition.

It is also becoming increasingly clear that efficiency is an important factor

with regard to market power and risk. Different efficiency levels will affect equity,

leverage and risk decisions taken by banks, under regulatory pressure or because

of agency considerations (Hughes and Mester 1998). Also, according to the “quiet

life” hypothesis, firms enjoying market power tend to operate inefficiently rather

than to reap all potential rents (Nickell et al. 1997). Theoretically, then, increased

competition should induce firms’ efficiency. Even though empirical studies have

found that deregulation tends to improve efficiency and performance, few studies have explored the simultaneous relationship (Wilson 1994; Claessens and Laeven 2004; Bikker and Spierdijk 2008). The “quiet life” hypothesis is in the tradition of the “Structure-Conduct-Performance” (SCP) paradigm. The SCP hypothesis argues that market power and limits to competition create an environment that affects bank conduct and performance in socially unfavorable ways. A competing view is the “Efficient Structure” (ES) hypothesis, according to which an industry’s structure arises because of superior operating efficiency by particular firms.4 Carvallo and Kasman (2005) and Kasman, et al. (2005) estimate common stochastic cost and profit frontiers for sixteen Latin American and Caribbean banks countries for the period 1995-1999. They find that concentration is significant and positively related to the cost and profit inefficiency. They also find that foreign banks are more cost and profit efficient in twelve of the sixteen countries in their sample.

4 Berger et al. (2004) provide a complete survey of the “structure-conduct-performance” and “efficient structure” literature.

financial stability, competition and efficiency in banking 307

By using Granger-causality techniques, this paper tries to integrate competing views regarding the relationships among financial stability, efficiency and market power. In particular, we investigate the “competition-stability” versus “competition fragility” controversy, together with the “Structure-Conduct-Performance” versus “Efficient Structure” one. Under the ES hypothesis, we can expect a positive dynamic relationship between our measures of efficiency and financial soundness, and market power. However, under the SCP hypothesis, a negative relationship is expected, which is running from market power to efficiency and soundness. Finally, under the “competition-stability” hypothesis, a dynamic negative relation is expected between our measure of market power and financial soundness. Under the alternative “competition-fragility” view, however, a positive sign is to be

expected.

III. Methodology

A. Efficiency estimations

We estimate cost and revenue efficiencies employing the stochastic frontier model of Battese and Coelli (1995), in which the inefficiency term is drawn from a truncated normal distribution. One of the main features of this model is that it allows controlling for environmental differences across countries and analyzes the effects of these variables on estimated efficiency scores. Moreover, this model allows for a firm-specific and time-varying intercept shift in the distribution of the inefficiency term, and this intercept shift is itself a function of the exogenous environmental variables that vary across countries.

Assuming that costs, for bank i at time t, are function of output Q, input prices W, inefficiency u, and random error v, then cost function can be specified as follows:

, (1)

where C denotes costs if and transformed total revenues if

are independently and identically distributed random errors that are

independently distributed of the are independently distributed, such that

is obtained by truncation (at zero) of the normal distribution with mean

, and variance , that is ; is a (1× m) vector of country-

308 Journal of applied economics

specific environmental variables (GDP growth, GDP per capita, deposit density,

population density, the ratio of average equity to total assets, capitalization of

stock market, inflation, M2 for a proxy of financial deepening) that are allowed to

vary over time.

The inefficiency effects, , in (1) can be specified as follows:

, (2)

where is an (m×1) vector of unknown coefficients of the environmental variables; is defined by the truncation of the normal distribution, such that the point of

truncation is .5 Battese and Coelli (1995) show that when (1) is assumed, the

cost efficiency (or the alternative revenue efficiency) for an individual banking

firm can be defined as follows: 6

, (3)

In modeling the cost (or revenue) function, we adopt a translog functional form since it does not require too many restrictive assumptions about the nature of the technology. A one output and three input cost (or revenue) function is specified

as follows:

5 The model without country-specific environmental variables has some limitations (see for example Dietsch and Lozano-Vivas 2000; Lozano-Vivas et al. 2001; Kasman and Yildirim 2006). The main limitation is that the model is based on the assumption that cross-country efficiency differences are mainly attributable to managerial decisions within the banks. However, different economic and regulatory environments across countries can also explain the differences. 6 Both cost and revenue efficiency measure departure from optimal behavior, but the standard against which their performance is evaluated is different. In the case of revenue efficiency the standard is the revenue frontier. In the case of cost efficiency, the standard is the cost frontier. The failure to maximize revenue stems from either an inability to technically achieve the efficient output levels or to allocate an appropriate output mix, given output prices and the input vector employed. In the case of cost efficiency, the inability relates to sub optimal input utilization and mix. Thus, the efficiency aspect being measured is different.

(4),

financial stability, competition and efficiency in banking 309

where TC ( TR ) is total costs (total revenues) of the banking firm in a given year. Q is the output (total assets), are the input prices (borrowed funds, labor and capital) and TREND is a time trend included to take into account technical change.7 The and are the inefficiency and the error terms, respectively.8 To ensure that the estimated cost frontier is well-behaved, standard restrictions of linear homogeneity in input prices and symmetry of the second order parameters

are imposed.

B. Competition measures estimations

The Lerner index

The Lerner index is a good candidate for a measure of market power that varies at the bank level rather than a concentration proxy at the country level. It can be defined as the difference between the marginal price and marginal cost divided by the marginal price, as follows: 9

, (5)

where is proxied by the ratio of total revenues (interest and non-interest revenues) to total assets. The marginal cost, , is derive from the translog cost function defined in (4). Marginal cost is obtained as follows:

7 The price of labor is calculated as the ratio between personnel expenses and total assets. The price of capital is given by operating costs net of personnel expenses over fixed assets. Finally, the price of funds is calculated by dividing total interest expenses by total purchased funds. Both financial and operating costs are included in the estimation of the cost function. Moreover, we estimate the alternative revenue efficiency scores. In this approach, banks take input and output quantities as given and these measures were selected since prices are often inaccurately measured in banking. The revenue inefficiency scores are estimated using the translog functional model specified in (4). Hence, the bank output and input definitions used is the same as to estimate the cost inefficiency scores. 8 The composite error term is for the revenue function. 9 Koetter et al. (2012) propose an efficiency-adjusted Lerner index and point out that the conventional approach of computing the Lerner index assumes both profit efficiency and cost efficiency. Hence, the estimated price-cost margins could be biased in measuring the true extent of market power.

(4)

310 Journal of applied economics

The Boone indicator

The second measure of competition used in this paper is the Boone indicator.

Boone’s model is based on the idea that competition enhances the performance

of efficient banks and weakens the less efficient ones. This effect is stronger the

higher the competition in the market is. To support this quite intuitive market

characteristic, Boone (2001, 2008) develops a broad set of theoretical models and

proves that more efficient banks (i.e., banks with lower marginal costs) gain higher

market shares. The Boone indicator is estimated by using the following empirical

model:

, (7)

where ms and mc denote the market shares and marginal costs in the loans market,

respectively. In this paper, we also measure the evolution of competition. Hence,

we include time dummies, D, to control factors common to all banks in the market

and specific to each year. The coefficient denotes the Boone indicator. Market

shares increase for banks with lower marginal costs (i.e, ). Hence, an increase

in competition raises the market share of a more efficient bank relative to a less

efficient one. A larger negative value of is an indication of more competitive

conditions in the banking market. However, positive values of are also possible,

implying that the higher a bank’s marginal costs, the more market share it will

earn. In the case of positive , either the market has an extreme level of collusion

or the banks are competing on quality.

C. Financial stability estimation

The Z-score is one of the most commonly used indicators of financial stability,

which measures the distance from insolvency and is calculated as follows:

, (8)

financial stability, competition and efficiency in banking 311

where ROA is the return on assets, EQ/TA denotes the equity to asset ratio and

is the standard deviation of return on assets in the period of time analyzed.

The Z-score increases with profitability and solvency and decreases as the standard

deviation of the return increases. A higher Z-score implies a lower probability of

insolvency (failure), providing a more direct measure of soundness compared other

measures of risk. Using (8) we can generate Z-scores, as an indicator of financial

stability, for each bank and year. Although the expression in the denominator is

constant during the sample period, the expression in the numerator varies every

year.10

D. Testing the relationship among competition, performance and financial stability

We examine the link among competition, performance and financial stability in

the Latin American banking markets in a dynamic Granger-causality framework,

formally specified in (9), (10), and (11) as follows:

, (9)

, (10)

, (11)

where competition is either the Lerner index or the Boone indicator, performance

is given by cost efficiency scores or revenue efficiency scores, and stability by the

10 Due to the limited number of years in our sample data, we could not calculate the standard deviation of the returns using a three-year (or a four-year) rolling time window to allow for variation in the denominator of (8).

312 Journal of applied economics

Z-scores. , and are the random error terms. Equation (9) tests

whether performance (cost or revenue efficiency) and stability (Z-scores) changes

temporally lead to variations in competition (Lerner index or Boone indicator).

Equation (10) examines whether changes in competition and stability temporally

lead to variations in bank performance. Finally, equation (11) tests whether changes

in competition and performance temporally lead to variation in bank stability.

The Granger causality test results are sensitive to the choice of lag length. In

estimating (9) - (11) with OLS, the optimal lag length is determined based on the

Schwarz Information Criterion. The optimal lag length is two, based on this criterion.

As for the system GMM specification, following Casu and Girardone (2009) and

Fiordelisi et al. (2011), we use two lags and estimate an AR(2) process as in the OLS

case. Hence, Granger causality assesses whether the coefficients on the two lags are

jointly statistically different from zero. The “long-run effect” of each variable is also

tested using the restriction that the sum of the lags of each determinant variable is

zero; a rejection of which signifies evidence of a long run effect. 11

IV. Data and empirical results

The sample in this study includes the commercial banks from 15 Latin American and Caribbean countries over the period 2001-2008. The countries included (number of banks in parentheses) are Argentina (50), Bolivia (8), Brazil (59), Colombia (12), Costa Rica (13), Dominican Republic (17), Ecuador (19), El Salvador (7), Honduras (12), Jamaica (5), Panama (19), Paraguay (11), Peru (10), Trinidad and Tobago (6) and Venezuela (24). Bank level data for all countries in the sample were obtained from the Bankscope database and macroeconomic variables were obtained from the World Development Indicators and International Financial Statistics of the IMF. After reviewing the data for reporting errors, inconsistencies,

11 The introduction of a lagged dependent variable among the right hand side variables in (9)-(11) creates an endogeneity problem since the lagged dependent variable is correlated with the disturbance, . To solve this problem, Arellano and Bond (1991) developed a difference GMM estimator for the coefficients in above mentioned equations where the lagged levels of the regressors are the instruments for the equation in first differences. However, Arellano and Bover (1995) and Blundell and Bond (1998) suggest to difference the instruments instead of the regressors in order to make them exogenous to the fixed effects. This leads from the difference GMM to the system GMM estimator, which is a joint estimation of the equation in levels and in first differences. Hence, we use the two-step system GMM estimators with Windmeijer (2005) corrected standard error, along with the OLS and Fixed Effects estimators, to conduct our analysis.

financial stability, competition and efficiency in banking 313

missing values and outliers, an unbalanced panel of 1828 observations is used, which includes 272 commercial banks over the period 2001-2008. 12

To identify the common frontier for the estimation of cost and revenue effi- ciency scores, we chose several geographic, market structure, and financial depth variables which explain the peculiar features of each country’s banking sector. Av- erages of these variables are reported in Table 1. As in Dietsch and Lozano-Vivas (2000), these variables are categorized in three groups. The first group includes measures of density of population, income per capita, and density of demand for each country. The second group includes a concentration ratio and the average capital ratio. A final group includes a proxy for the accessibility of banking servic- es and other environmental variables that are relevant to determine bank efficiency.

Table 1. Average values of environmental variables (2001-2008)

POP INC ($) DEMAND ($) HHI AEQ (%) INT (%) GDPG (%)

MONEY (%)

INF (%)

Argentina 13.87 8021.27 12715.50 894.89 12.12 8.66 4.45 27.54 10.08

Bolivia 8.36 1079.09 2830.71 1698.53 10.00 5.11 3.90 51.11 5.33

Brazil 21.61 3986.86 30817.25 1006.49 9.31 16.86 3.65 49.88 7.12

Colombia 37.51 2786.39 28048.63 1505.24 12.30 7.30 4.31 19.77 6.12

Costa Rica 83.80 4529.47 184342.13 1689.66 13.46 25.40 4.96 22.12 11.28

Dominican Republic 194.18 3062.38 105870.12 1533.70 12.12 16.99 5.29 21.86 15.20

Ecuador 45.79 1544.36 27296.63 1890.99 11.16 4.55 5.28 22.14 9.62

El Salvador 287.55 2429.11 217390.13 2520.27 11.59 4.84 2.81 40.79 4.03

Honduras 56.35 1280.80 21625.194 1503.55 0.124 7.80 5.02 44.92 8.24

Jamaica 182.33 3711.11 342063.13 2116.44 11.93 11.40 1.67 44.32 11.65

Panama 39.12 4510.14 294822.11 1138.51 11.37 3.44 6.64 78.80 2.51

Paraguay 14.38 1375.93 3895.01 1294.22 11.19 7.51 3.72 32.30 8.88

Peru 21.51 2370.54 29824.75 2750.77 10.18 5.11 5.93 28.98 2.41

Trinidad and Tobago 256.59 8895.44 1859157.87 2463.57 13.86 4.32 7.58 38.55 6.54

Venezuela 28.89 4989.43 40994.50 1019.47 16.55 10.93 4.77 21.20 20.95

Note: INC = Income per capita (constant 2000 US$); POP = Density of population; DEMAND = Density of demand (deposits per km2) ; INF = Inflation rate; HHI = Concentration (Herfindahl Index); INT = Money market rate; AEQ = Average capital ratios; GDPG = GDP Growth; MONEY= Money / GDP. Sources: Bankscope IBCA, World Development Indicators; International Financial Statistics, own calculations.

12 For reviewing outliers in the sample, we used two criteria. First, equity should always be positive. Second, all variables should not increase or decrease between two periods dramatically. Banks that fail to meet these criteria for a given year were dropped from the sample.

314 Journal of applied economics

Some descriptive statistics regarding the variables used in the estimations

are displayed in Table 2. As seen in Table 2, the trend of the Lerner index is

upward, suggesting that the market power of Latin American banks has increased

in average during the sample period. The yearly average of the Lerner index ranges

between 0.066 (in 2001) and 0.145 (in 2008). To estimate the Boone indicators we

regress marginal costs, which are obtained from a traslog cost function specified

in (4), on the market share. The coefficient of market share in (7) is the Boone

indicator. The Boone indicators are mostly statistically significant and fluctuate

between 0.046 and -0.879 over the sample period. As seen in Table 2, there was

a small variation in the degree of competition in the region particularly between

2003 and 2008. The estimates of cost efficiency and revenue efficiency scores are

obtained from the stochastic cost function defined as in (4).

The estimated average cost efficiency and revenue efficiency for 15 Latin

American and Caribbean countries over the sample period are 0.834 and 0.763,

respectively. The average estimated cost efficiency scores do not fluctuate widely

during the sample period, reaching the minimum in 2002 (80.7%) and the

maximum in 2008 (85.5%). The average estimated revenue efficiency scores also

do not fluctuate greatly over the sample, ranging from 73.2% to 78.9%.

The last column of Table 2 reports the Z-scores, which combines in one single

indicator the banks’ profitability, equity to total asset ratio and return volatility.

The Z-score is considered as an indicator of financial stability. A higher (lower)

Z-score indicates a lower (higher) probability of insolvency risk. As seen in the

table, the Z-score ratio displays wide variation over the sample period, ranging

from 14.642 to 19.081.

Country specific mean values of the Lerner index, Boone indicators, efficiency

scores and Z-scores are reported in Table 3. The mean values of the five variables by

country show a wide range of variation. When we take year 2008 as the reference

year, the difference between the country with highest market power (Dominican

Republic, with a Lerner index of 0.043) and the country with lowest market power

(Trinidad and Tobago, with a Lerner index of 0.308) is 1 to 7, showing a great

range of variation. It is important to note that countries where Lerner indexes have

increased during the sample period coexist with the countries where the indexes

have decreased. Likewise, the Boone indicators show a wide range of variation.

The highest value of the coefficient (-0.798) in 2008 is observed in Venezuela,

implying that the banking sector in Venezuela is relatively more competitive than

those in other countries in the region. The coefficient takes positive values in

financial stability, competition and efficiency in banking 315

Colombia, Costa Rica, Honduras, Panama, and Trinidad and Tobago in 2008,

implying that these countries have less competitive banking sectors. As mentioned

above, in the case of a positive , either the market has an extreme level of collusion

or the banks are competing on quality. As seen in Table 3, competition levels

decreased between 2002 and 2008 in most of the sampled countries.

Table 2. Mean values of Lerner indices, Boone indicators, efficiency scores and Z-scores

Year Lerner Boone Cost Efficiency Revenue Efficiency Z-score

2001 0.066 (0.256)

-0.879 (1.133)

0.837 (0.072)

0.789 (0.167)

14.642 (13.688)

2002 0.009 (0.585)

0.046 (0.445)

0.807 (0.119)

0.756 (0.189)

19.081 (17.638)

2003 0.043 (0.816)

-0.157 (0.675)

0.823 (0.115)

0.750 (0.181)

19.010 (17.778)

2004 0.083 (0.485)

-0.144 (0.599)

0.826 (0.111)

0.763 (0.171)

18.804 (17.250)

2005 0.064 (0.892)

-0.222 (0.618)

0.834 (0.109)

0.764 (0.181)

18.912 (17.129)

2006 0.121 (0.743)

-0.194 (0.522)

0.842 (0.103)

0.782 (0.169)

18.297 (16.516)

2007 0.149 (0.259)

-0.209 (0.600)

0.847 (0.101)

0.788 (0.189)

17.821 (17.162)

2008 0.145 (0.173)

-0.201 (0.563)

0.855 (0.083)

0.732 (0.189)

17.459 (16.177)

Overall 0.090 (0.609)

-0.245 (0.645)

0.834 (0.106)

0.763 (0.181)

18.353 (16.977)

Note: Figures in parentheses are the standard deviations

As for the Z-score, the difference is almost the same as in the market power

case, with minimum and maximum values in 2008 of 6.123 (Argentina) and 44.138

(Trinidad and Tobago), respectively. As seen in the table, the Z-score has increased

during the sample period in about half of the sample countries.

Table 3 also shows that efficiency results display a wide range of cost and

revenue efficiency scores across countries. All the banking systems display

significant levels of cost (revenue) inefficiency ranging from 28.3% (71.3%) to

9.4% (7.1%).

316 Journal of applied economics

Tables 4 and 5 show regressions results from AR(2) models with pooled

OLS, with panel fixed effects model and with the system GMM estimator. 13

Table 4 incorporates cost efficiency into the equations, whereas Table 5 utilizes

the revenue efficiency estimates instead. At the bottom of each table, we report

specification test results for the GMM estimations.14 According to these tests, all

GMM equations are properly specified. Regarding market power, the Granger-causality results in the first panel of

Table 4 indicate that financial stability positively Granger-cause market power, suggesting that sounder banks are able to develop future market power. The results prevail when the revenue measure of efficiency is used, for the OLS and FE estimation techniques, but not for the SYS-GMM as shown in Table 5. Financial stability is not only related to banks exposure to risk, but also with their ability to cope with it, through capitalization or earning capacity, as reflected in the Z-scores. Hence, banks in the position to handle risk better and improve profitability can be able to gain market power at the expense of those less able to do so. Also, Table 4 shows that for two estimation techniques evidence is provided of the fact that banks with greater cost efficiency are able to gain future market power, as found in Maudos and Nagore (2005). Taken together, both pieces of evidence lend partial support for the “efficient structure” hypothesis.15

Regarding efficiency, however, the second panels of Tables 4 and 5 provide evidence that the Lerner indexes negatively Granger-cause cost and revenue efficiency. Hence, the results support the view that more competition is conducive to greater efficiency. Alternatively, firms enjoying greater market power tend to be less efficient. In the case of the specification incorporating cost efficiency the results are significant for all equations, where the same is true for the GMM and OLS estimations of the revenue efficiency specifications. Overall, these results provide evidence supporting the “quiet life” hypothesis, as discussed above.

13 In accordance to the Hausman test, the random effects model was rejected. 14 The Sargan test is a test on whether the instruments are uncorrelated with the error tem. Moreover, the Arellano- Bond test results also require significant AR(1) serial correlation and lack of AR(2) serial correlation. 15 We also used the Boone indicator as a measure of competition in the regressions. The results indicate that financial stability positively Granger-cause competition, suggesting that sounder banks are operating in a less competitive environment given that lower values of the Boone indicator signify more competition. The results also show that efficiency negatively Granger-causes competition, suggesting that bank efficiency increases in more competitive banking sectors. For the sake of flow and size of the paper, the results are not reported but available upon request from the authors.

financial stability, competition and efficiency in banking 317

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318 Journal of applied economics

Table 4. Estimation Results: Granger causality between market power, risk and cost efficiency

Dependent variable: Lerner Dependent variable: c-eff Dependent variable: Z-score

OLS FE SYS-GMM OLS FE SYS-GMM OLS FE SYS-GMM

Intercept -0.545* (0.143)

-0.826* (0.032)

-0.468** (0.615)

0.206** (0.013)

0.657* (0.032)

0.375* (0.064)

0.137*** (0.143)

2.127** (0.176)

0.854*** (0.480)

1−tLerner 0.045 (0.029)

-0.431* (0.029)

0.011 (0.028)

-0.011* (0.002)

-0.010* (0.003)

-0.008** * (0.005)

-0.049* (0.016)

-0.041** (0.016)

-0.029 (0.024)

2−tLerner 0.083* (0.025)

-0.295* (0.026)

0.047 (0.029)

-0.000 (0.002)

-0.004** (0.002)

-0.000 (0.002)

-0.024*** (0.014)

-0.044* (0.014)

-0.011 (0.011)

1−− teffc 0.564** (0.256)

0.731* (0.278)

0.365 (0.446)

0.686* (0.024)

0.305* (0.028)

0.572* (0.067)

-0.294** (0.141)

0.396* (0.153)

-0.690** (0.096)

2−− teffc 0.115 (0.245)

0.144 (0.254)

0.024 (0.315)

0.081* (0.023)

-0.094* (0.025)

0.008 (0.053)

0.271** (0.135)

0.123 (0.140)

0.096 (0.232)

1−− tscoreZ 0.076*** (0.042)

0.104** (0.050)

0.101** (0.047)

-0.003 (0.004)

-0.001 (0.005)

-0.003 (0.006)

0.701* (0.023)

0.234* (0.027)

0.669* (0.062)

2−− tscoreZ -0.041 (0.042)

0.012 (0.047)

0.003 (0.064)

0.002 (0.004)

0.006 (0.004)

-0.001 (0.005)

0.248* (0.023)

0.014 (0.026)

0.194* (0.050)

M1(p-value) NA NA 0.030* NA NA 0.003* NA NA 0.005*

M2 (p-value) NA NA 0.468 NA NA 0.223 NA NA 0.622 Sargan/Hansen (p-value) NA NA 0.293 NA NA 0.217 NA NA 0.360

Diff- Sargan/Hansen (p-value)

NA NA 0.479 NA NA 0.416 NA NA 0.795

∑ Lerner -0.011* (0.002)

-0.011* (0.001)

-0.008*** (0.057)

0.074* (0.000)

-0.094* (0.000)

-0.04 (0.167)

∑ − effc 0.679* (0.000)

0.875* (0.008)

0.389 (0.459)

-0.023 (0.801)

-0.271 (0.132)

-0.594 (0.180)

∑ − scoreZ 0.035** (0.417)

0.116*** (0.074)

0.104** (0.213)

0.001 (0.512)

0.004 (0.531)

-0.004 (0.138)

Granger Causality:

Lerner (p-value) 0.000* 0.002* 0.052*** 0.001* 0.002* 0.381

effc − (p-value) 0.000* 0.015** 0.708 0.097*** 0.035** 0.106

Z-score (p-value) 0.050** 0.098*** 0.090** 0.697 0.500 0.316

Notes: OLS: ordinary least squares: FE: fixed effects: SYS-GMM: system GMM. The Arellano and Bond dynamic panel system GMM estimations (Stata xtabond2 command) with Windmeijer (2005) corrected standard error (reported in parentheses) are used. *, **, and *** denote significance level at 1%, 5% and 10%, respectively. The variables ∑ Lerner , ∑ − effc , and ∑ − scoreZ are the estimated coefficients for the test that the sum of lagged terms of Lerner, cost efficiency, and risk, respectively, which show long-run impact. The Granger causality test is used to examine the null hypothesis that x doesn’t Granger-cause y. The Sargan/Hansen is a test of the over-identifying restrictions for the GMM estimators. M1 and M2 are tests for the first-order and second-order serial correlation.

financial stability, competition and efficiency in banking 319

Table 5. Estimation Results: Granger causality between market power, risk and revenue efficiency

Dependent variable: Lerner Dependent variable: r-eff Dependent variable: Z-score

OLS FE SYS-GMM OLS FE SYS-GMM OLS FE SYS-GMM

Intercept -0.090 (0.102)

-0.296 (0.416)

0.141 (0.264)

0.044* (0.009)

0.890* (0.041)

0.097* (0.028)

0.254* (0.056)

2.251* (0.228)

0.455 (0.298)

1−tLerner 0.060** (0.028)

-0.423* (0.029)

0.030*** (0.016)

-0.001 (0.002)

-0.001 (0.003)

-0.001 (0.001)

-0.051* (0.016)

-0.044* (0.016)

-0.043*** (0.025)

2−tLerner 0.084* (0.025)

-0.300* (0.026)

0.055** (0.027)

-0.003 (0.002)

-0.003** (0.002)

-0.004** (0.001)

-0.019 (0.014)

-0.038* (0.014)

-0.014 (0.009)

1−− teffr 0.030 (0.380)

0.393 (0.422)

-0.117 (0.183)

0.758* (0.035)

0.128* (0.042)

0.728* (0.049)

-0.163 (0.209)

-0.698* (0.153)

-0.319** (0.147)

2−− teffr 0.080 (0.383)

-0.140 (0.405)

-0.099 (0.283)

0.199* (0.036)

-0.284* (0.040)

0.151* (0.053)

0.013 (0.210)

0.255 (0.221)

0.039 (0.171)

1−− tscoreZ 0.082*** (0.042)

0.108** (0.047)

0.077 (0.049)

0.000 (0.004)

-0.003 (0.005)

0.000 (0.003)

0.688* (0.023)

0.225* (0.027)

0.682* (0.062)

2−− tscoreZ -0.038 (0.042)

0.008 (0.047)

-0.020 (0.052)

-0.005 (0.004)

-0.001 (0.005)

-0.003** (0.001)

0.251* (0.023)

0.019 (0.026)

0.224* (0.045)

M1(p-value) NA NA 0.030* NA NA 0.000* NA NA 0.007*

M2 (p-value) NA NA 0.477 NA NA 0.491 NA NA 0.597

Sargan/Hansen (p-value)

NA NA 0.184 NA NA 0.000* NA NA 0.196

Diff-Sargan/Hansen (p-value)

NA NA 0.221 NA NA 0.530 NA NA 0.418

∑ Lerner -0.004 (0.247)

-0.004 (0.382)

-0.005*** (0.087)

-0.070** (0.000)

-0.082* (0.000)

-0.057*** (0.077)

∑ − effr 0.110 (0.266)

0.254 (0.610)

-0.216 (268)

-0.151* (0.006)

-0.443 (0.104)

-0.281 (0.113)

∑ − scoreZ 0.044** (0.017)

0.118*** (0.072)

0.057 (0.291)

-0.005* (0.005)

-0.003 (0.591)

-0.002 (0.433)

Granger Causality:

Lerner (p-value) 0.385 0.456 0.056*** 0.001* 0.003* 0.077***

effr − (p-value) 0.538 0.645 0.401 0.019** 0.010* 0.042**

Z-score (p-value) 0.021** 0.089*** 0.282 0.016** 0.831 0.129

Notes: OLS: ordinary least squares: FE: fixed effects: SYS-GMM: system GMM. The Arellano and Bond dynamic panel system GMM estimations (Stata xtabond2 command) with Windmeijer (2005) corrected standard error (reported in parentheses) are used. *, **, and *** denote significance level at 1%, 5% and 10%, respectively. The variables ∑ Lerner , ∑ − effc , and ∑ − scoreZ are the estimated coefficients for the test that the sum of lagged terms of Lerner, cost efficiency, and risk, respectively, which show long-run impact. The Granger causality test is used to examine the null hypothesis that x doesn’t Granger-cause y. The Sargan/Hansen is a test of the over-identifying restrictions for the GMM estimators. M1 and M2 are tests for the first-order and second-order serial correlation.

320 Journal of applied economics

As for the financial stability, the third panels of Tables 4 and 5 provides evidence for Granger causality. In Table 5, which uses revenue efficiency, we show that Lerner indexes Granger-cause financial stability, both significantly and negatively, for all estimation techniques. More competition, as represented by lower Lerner indexes, causes more stability. This result strongly supports the “competition-stability” view, as discussed in Mishkin (1999), Boyd and De Nicolo (2006), De Nicolo et al. (2004), Schaeck and Cihak (2007), Schaeck et al. (2006) and, Uhde and Heimeshoff (2009). The result is strengthened in the first two equations of the corresponding panel in Table 4. Although, there is some evidence that competition Granger causes financial stability when cost efficiency is included, this evidence is less robust. Accordingly, our results should be qualified as stating that, when considering the dynamic relationship between competition, stability and revenue efficiency, we find that more competition leads to more financial stability. Our indicator of financial stability, the Z-scores, has three components: profitability, capital and the volatility of returns. Hence, we presume that one possible explanation of the results in Table 5, as opposed to Table 4, is that the revenue efficiency component of financial stability is being better controlled in this setting.

V. Conclusions

In this study, we used a sample of 272 commercial banks from fifteen Latin American countries for the period 2001-2008. We estimated cost and revenue efficiency scores, financial stability scores (Z-scores) and competition scores (Lerner indexes and Boone indicators) at bank level. This allows us to use Granger causality techniques in dynamic panels in order to establish dynamic relationships among the variables.

The results support the view that competition is conducive to greater financial stability, as posed by the “competition-stability” hypothesis, when revenue efficiency is included in the specification. At the same time, we detect complex dynamic processes: sounder banks tend to reach higher market power, lending support to the “efficient structure” hypothesis. The complexity of the dynamics is also underlined, as we also found support for the “quiet life” hypothesis. Market power, as reflected in greater Lerner indexes, seems to be conducive to greater efficiency, both in the cost and revenue sides.

Banks seem to achieve market power through better efficiency, leverage and earning ability. As size and complexity increase, however, we hypothesize that agency problems and increasing risk-taking may start to gain momentum, generating inefficiency and fragility. A competitive environment, however, can prove to be crucial to restraint these tendencies.

financial stability, competition and efficiency in banking 321

As a corollary, we regard that contrary to the conventional view, competition policy has prudential implications. Also, efficiency proves to be relevant for an effective regulation. In particular, the regulatory review of potential stake-holders agency problems and internal governance of banks are relevant. This is particularly important for firms with larger size, complexity and systemic importance. Achieving an appropriate combination of these three dimensions — competition, prudential aspects and governance — is an important key to achieving stronger financial systems.

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Journal of International Money and Finance 45 (2014) 1–16

Contents lists available at ScienceDirect

Journal of International Money and Finance

journal homepage: www.elsevier.com/locate/jimf

Review

Competition and financial stability in European cooperative banks

Franco Fiordelisi a,b,1, Davide Salvatore Mare c,* a Faculty of Economics, University of Rome III, Italy b Bangor Business School, Bangor University, UK c Business School, The University of Edinburgh, 29 Buccleuch Place, Edinburgh EH8 9JS, UK

JEL classification: C23 G21

Keywords: Bank soundness Cooperative banks Competition Financial stability

* Corresponding author. Tel.: þ44 (0)131 651 50 E-mail addresses: [email protected] (F. Fio

1 Faculty of Economics, Via S. D’Amico 77, 00145

http://dx.doi.org/10.1016/j.jimonfin.2014.02.008 0261-5606/� 2014 Elsevier Ltd. All rights reserved

a b s t r a c t

Cooperative banks are a driving force for socially committed business at the local level, accounting for around one fifth of the European Union (EU) bank deposits and loans. Despite their importance, little is known about the relationship between bank stability and competition for these small credit institutions. Does competition affect the stability of cooperative banks? Does the financial stability of banks increase/decrease when competition is higher? We assess the dynamic relationship between competition and bank soundness (both in the short and long run) among Eu- ropean cooperative banks between 1998 and 2009. We obtain three main results. First, we provide evidence in line with the competition-stability view proposed by Boyd and De Nicolò (2005). Bank market power negatively “Granger-causes” banks’ soundness, meaning that there is a positive relationship between competition and stability. Second, we find that this fundamental relationship does not change during the 2007–2009 financial crisis. Third, we show that increased homogeneity in the cooper- ative banking sector positively affects bank soundness. Our find- ings have important policy implications for designing and implementing regulations that enhance the overall stability of the financial system and in particular of the cooperative banking sector.

� 2014 Elsevier Ltd. All rights reserved.

77; fax: þ44 (0)131 651 3197. rdelisi), [email protected] (D.S. Mare). Rome, Italy. Tel.: þ39 065 733 5672; fax: þ39 065 733 5797.

.

F. Fiordelisi, D.S. Mare / Journal of International Money and Finance 45 (2014) 1–162

1. Introduction

Recent regulatory developments toward a more integrated European banking market point to the establishment of a European Banking Union.2 In response to the 2007–2009 financial turmoil, the new European banking union reforms3 introduce the issue of the supervision over highly heterogeneous types of banks. Moreover, international efforts have been coordinated toward the development of a new regulatory framework to control systemic risks (e.g., the Basel Committee’s framework on global systematically important banks). Nevertheless, small banks are different from international com- mercial banks and are important for local economic development. This is not recognised in the existing literature, as little is known about the relationship between bank stability and competition among small credit institutions, particularly given the variety of local structures.

Cooperative banks4 are a driving force for socially committed business at a local level, accounting for around one fifth of the European Union (EU) bank deposits and loans. As stated by the European As- sociation of Co-operative Banks (EACB, 2012, p. 4) “in the unique context of the global financial crisis, this sector demonstrated its robustness and resilience, as well as its ability to act as a key driver for the real economy.” Specifically, in 2012, the EU had 4000 cooperative banks with 72,000 branches, more than 850,000 employees, 56 million members, 217 million clients, 3932 billion Euro in deposits, 4034 billion Euro of loans, and 6951 billion Euro in total assets. Cooperative banks are significantly different from other types of credit institutions in three important aspects: ownership, control, and benefits.5

Specifically, owners of cooperative banks are often also their customers (usually referred to as “members”). Membership is not transferable, is limited to individual equity shares, and is redeemable only at a nominal value. As a consequence, members cannot accumulate votes by purchasing shares on the market. In addition, cooperative banks are characterized by the one-member one-vote principle regardless of the amount of capital owned. EACB (2012) notes that “their unique stakeholders structure brings about efficiency and sound governance: members control the co-operative by exerting checks and balances at each level of the business. This minimizes the organization’s risk, identifies credit- worthiness and gives immediate response to customers’ needs”. As such, cooperative banks usually raise capital through new memberships or retained profits (often a fixed minimum percentage amount established by law). In regard to benefits, cooperative banks aim to maximize members’ value by of- fering products and services along with the distribution of profits. It follows that profit-maximization is not the sole business objective of these credit institutions given their risk profile and business model (e.g., focus on retail banking). In addition, cooperative banks are mainly local or regionally based6 with strong links to the communities they serve.

A number of papers have analysed small credit institutions by focussing on performance (Goddard et al., 2008a; Kontolaimou and Tsekouras, 2010), diversification (Goddard et al., 2008b; Lepetit et al., 2008; Mercieca et al., 2007; McKillop and Wilson, 2011), risk of failure (Fiordelisi and Mare, 2013), and ownership structure (Gorton and Schmid, 1999). A debated issue is whether cooperative banks are more stable than commercial banks. During the recent financial crisis, cooperative banks performed

2 The new framework involves regulatory arrangements designed to mitigate longer-term financial crises through the centralized delivery of EU-wide rules. It engenders a Single Supervisory Mechanism led by the European Central Bank, a Single Resolution Mechanism, a Single Resolution Fund and a Single Rulebook (i.e., unified regulatory framework).

3 The new reforms comprise the Single Supervisory Mechanism (conferring specific supervision tasks on the European Central Bank with the objective to strengthen the Economic and Monetary Union) voted by European Parliament Plenary on September 12, 2013 and the Single Resolution Mechanism. On July 10, 2013 the European Commission proposed a new text and the European Council agreed the general approach on Single Resolution Mechanism on December 19, 2013.

4 Cooperative banks belong to the broader category of financial cooperatives, which also includes other credit institutions such as credit unions, banks set up by other cooperatives (such as The Co-operative Bank in the UK), and building societies.

5 Note that the cooperative credit sector in Europe is not entirely uniform in terms of legal framework, size, and organization. Nevertheless, the distinctive features described differentiate cooperative banks from other types of credit institutions.

6 An exception is the large cooperative networks such as Dutch Rabobank Group or French Crédit Agricole Group. In this case, the local and regional cooperatives are members of a central institution (called APEX) that centralises some services and processes to reap the benefits from economies of scale and scope.

F. Fiordelisi, D.S. Mare / Journal of International Money and Finance 45 (2014) 1–16 3

better than commercial banks, as discussed by Jose Manuel Barroso (President of the European Commission) in 2011: “Co-operative businesses that have stayed faithful to co-operative values and principles and the co-operative banks which rely on members’ funds and are controlled by local people have generally been able to resist the crisis very well.”7 Barroso’s statement is consistent with various papers (Hesse and Cihák, 2007; Ayadi et al., 2010) that provide empirical evidence that cooperative banks are more stable than commercial banks because they have a great deal of soft information (which is hard to collect) on the creditworthiness of members/customers and are therefore less likely to make lending mistakes. Furthermore, size appears to be positively related to systemic risk (Vallascas and Keasey, 2012; De Jonghe, 2010), and the majority of cooperative banks are small, rural credit in- stitutions. Conversely, several studies suggest that cooperative banks are more fragile than commercial banks (Goodhart, 2004; Brunner et al., 2004; Fonteyne, 2007) and have higher default rates. For instance, Fiordelisi and Mare (2013) document that the default rate of Italian cooperative banks was four times higher than that of commercial banks in the period before the financial crisis (1997–2006). We believe supervisory behaviour could be the key to reconciling these opposing views. Specifically, cooperative banks are likely to have less volatile earnings than commercial banks, but in periods of financial stability, supervisors are more inclined to shut down distressed cooperative banks than distressed commercial banks, consistent with a Too-Big-To-Fail policy.

Rather than entering the debate about whether cooperative banks are more fragile than commercial banks,8 this study of the financial stability of cooperative banks investigates three key issues. First, cooperative banks are different from commercial banks, and their stability is influenced by different factors.9 Recent studies have focused on productive performance related to technological development (Kontolaimou and Tsekouras, 2010), performance and risk factors associated with ownership structure (Iannotta et al., 2007), and cost efficiency and financial structure (Girardone et al., 2009). Second, competition is likely to be one of the key factors influencing bank stability, but its influence is probably different for commercial and cooperative banks. Third, it is necessary to account for banking super- visors’ behaviour to assess the link between competition and risk. Surprisingly, whilst there is a substantial literature investigating the link between competition and bank stability among commercial banks, no studies have specifically analysed cooperative banks.

Does competition affect the stability of cooperative banks? Does bank soundness increase/decrease when competition is higher? This study empirically addresses these questions. By analysing a large sample of cooperative banks in the EU between 1998 and 2009, we obtain three main results. First, we show that market power is negatively related to individual bank stability, meaning there is a positive relationship (both in the short and long run) between competition and stability in line with the competition-stability view proposed by Boyd and De Nicolò (2005). Second, we show that the 2007 financial crisis does not change the direction of the relation between competition and stability. Third, we find a statistically significant relationship between the Herding measure and bank stability. This result is particularly interesting for policy makers because the level of industry homogeneity has a positive influence on bank stability.

We estimate competition using the Lerner Index of Monopoly Power, recently used in a variety of studies (Maudos and de Guevara, 2007; Turk Ariss, 2010; Radi�c et al., 2012; among many others). We use both pooled ordinary least squares regressions and fixed-effects panel regressions to control for spurious relationships and country-specific effects in our tests of whether changes in competition predict variations in bank risk measures. We also analyse the impact that multifarious factors have on the competition–risk relationship, such as herding behaviour, the financial crisis, concentration in the loan and deposit markets, and bank-level fundamentals.

The remainder of the paper is structured as follows. Section 2 summarizes the literature and the research hypotheses. Section 3 presents the data and variables employed in the analysis. In Section 4,

7 Source: European Association of Co-operative Banks (2012), http://www.eacb.eu/en/cooperative_banks/what_they_say_ about_us.html.

8 This paper does not aim to discuss cooperative bank fragility. Rey and Tirole (2007), Beck et al. (2009), Hesse and Cihák (2007), and Fonteyne (2007) are useful sources from a theoretical, empirical, and policy perspective, respectively.

9 See Boonstra and Mooij (2012) for a detailed explanation.

F. Fiordelisi, D.S. Mare / Journal of International Money and Finance 45 (2014) 1–164

we discuss the empirical approach. Section 5 summarises the results from the estimations and the robustness checks. Section 6 concludes.

2. Literature review and research hypotheses

We empirically assess whether an increase in competition predicts higher instability among cooperative banks. Many studies have examined this relationship within the commercial banking sector from both theoretical and empirical standpoints.

From a theoretical perspective, there are two concurrent views. The competition-fragility view (see, among others, Marcus, 1984; Keeley, 1990; Allen and Gale, 2004; Beck et al., 2006; Matsuoka, 2013) argues that higher competition leads to more risk in banking and the erosion of bank charter value. In contrast, other papers suggest that higher competition could transform the nature of banking and induce banks to become more relationship-oriented (Boot and Thakor, 2000). As such, the competition-stability view (Boyd and De Nicolò, 2005; De Nicolò and Lucchetta, 2009) challenges the negative effects of concentration. Under this theory, the considerable market power of only a few banks will cause them to raise the interest rates on loans, which will induce adverse selection (risky projects are financed) and moral hazard (risk shifting), with a negative impact on the stability of the banking system.

A recent stream of empirical studies has tried to measure the effects of competition and market power on stability. Several works have tested the relation between banking market structure and risk by focussing on credit risk (Hakenes and Schnabel, 2010; Fiordelisi et al., 2011), interest rate risk (Delis and Kouretas, 2011), or the broader default risk (Repullo, 2004; Schaeck et al., 2009; Berger et al., 2009; Jiménez et al., 2010; Turk Ariss, 2010) and have found mixed evidence. For instance, Boyd et al. (2006) and De Nicoló and Loukoianova (2007) show that financial instability increases in less competitive markets, but Jiménez et al. (2010) find opposite evidence (i.e., risk decreases as bank market power increases). Schaeck et al. (2009) analyse banks operating in 45 nations over 1980–2005 and find that more competitive and more concentrated banking systems are less likely to experience a systemic crisis and have a longer time to crisis. Berger et al. (2009) study a large sample of banks in 23 developed countries and observe that even if an increase in bank market power leads to riskier portfolios, the effect on stability could be offset by a greater franchise value. In an attempt to reconcile the mixed empirical evidence, Beck et al. (2013) show that greater competition is generally associated with a larger impact on banks’ risk-taking activities in countries with more stringent activity restrictions, more herding in revenue structure, less concentrated banking markets, and more generous deposit insurance.

While the extant literature focuses on commercial banking, we find few studies that examine cooperative banking. Cooperative banks are key to the EU economy; hence, it is important to inves- tigate the competition-stability link among these credit institutions. EU policy makers seem to agree, pointing out the importance of these differences when designing new regulations. For example, Michel Barnier, the EU commissioner responsible for the internal market and services, stated in 2011 that “we are totally faithful to Basel’s spirit, letter and level of ambition. But you cannot apply rules to 8200 banks as you would to 20 banks. That is why we take into account the specificities of the European banking sector, with its mutual or co-operative banks and its bank and insurance groups.”10

Only a few papers are loosely related to the research questions we address in this study. The most relevant research is by Liu et al. (2012), who investigate the link between competition and stability among regional banks (which include cooperative banks) in 11 European countries between 2000 and 2008. Without explicitly focussing on cooperative banks, which are substantially different from savings banks, the authors find a positive link between competition and bank stability and show that coop- erative banks have a positive marginal effect on bank stability. Hesse and Cihák (2007) analyse the stability of cooperative, commercial, and savings banks (measured using the Z-score) by estimating a linear regression model with dummy variables capturing different bank types. Without taking into

10 Source: European Association of Co-operative Banks (2012), http://www.eacb.eu/en/cooperative_banks/what_they_say_ about_us.html.

Table 1 Summary statistics.

Variable Symbol Obs Mean Std dev Min Max

Output price P 17,074 0.0571 0.0320 0.0003 2.5915 Marginal cost MC 17,074 0.0271 0.0236 0.0000 1.7527 Funding-adjusted MC FA_MC 17,074 0.0286 0.0275 0.0000 2.0872 Lerner index LER 17,074 0.5228 0.1265 �2.5818 1.0000 Funding-adjusted LER FA_LER 17,074 0.4952 0.1494 �2.5301 1.0000 Herding measure HERD 17,074 0.0672 0.0271 0.0123 0.2386 Concentration loans HHI LOANS 17,074 0.0029 0.0073 0.0001 0.0504 Concentration deposits HHI DEPOSITS 17,074 0.0070 0.0156 0.0001 0.0911 Z-score Z 17,074 17.2572 8.0582 0.1823 158.9526 Z-rob Z_Rob 17,074 17.2514 7.8394 1.4571 162.9922

This table presents the descriptive statistics of our sample of cooperative banks in the European banking system between 1998 and 2009 for the main variables used in the model. It is at first surprising that both measures of market power (i.e., Lerner Index and funding-adjusted Lerner Index) are negative for some observations, though for 97 and 132 observations only (0.6% and 0.8% of the sample respectively). We argue that this could be the case when cooperative banks start operations and bear high fixed costs (e.g., for fixed assets).

F. Fiordelisi, D.S. Mare / Journal of International Money and Finance 45 (2014) 1–16 5

account competition in the banking industry, the authors conclude that cooperative banks are more stable than commercial banks.

Our paper contributes to the existing literature in several ways. First, we focus on cooperative banks, which allows us to use a homogenous data set rather than relying on dichotomous variables to control for differences across different types of banks. Second, we estimate stability and competition at the individual bank level using Z-scores and the Lerner Index, both of which have been used in recent studies (Boyd et al., 2006; De Nicoló and Loukoianova, 2007; Berger et al., 2009). Third, we account for the potential impact of regulatory intervention on the relation between competition and bank risk. Whilst the Too-Big-To-Fail or Too-Important-To-Fail views do not apply to cooperative banks, we recognize that cooperative bank closure policies may suffer from an implicit “Too-Many-To-Fail” problem, as suggested by Acharya and Yorulmazer (2007). Specifically, when the number of bank failures is large, the regulator finds it ex-post optimal to bail out some or all distressed institutions, triggering incentives to herd ex-ante and increasing the risk that many banks may concurrently fail together ex-post. Similar to Beck et al. (2013), we investigate the assumption that competition will have a stronger impact on bank stability in more homogeneous banking system (where herding behaviour is more likely).

3. Data sources and variables

Bank financial statements are taken from the Bureau van Dijk Bankscope database. We restrict our analysis to banks from the five largest cooperative banking sectors in Europe–Austria, France, Germany, Italy, and Spain – over the period of 1998–2009. In 2010, cooperative banks in these five countries accounted for 85% of total assets held by all EU cooperative banks.

To avoid duplication, we consider consolidated data where possible and unconsolidated data otherwise. We also omit banks for which relevant information is not available (e.g., total assets and total costs). After data cleaning, our final sample consists of 17,074 observations for 2529 cooperative banks in Austria, France, Germany, Italy, and Spain (accounting for 4%, 6%, 60%, 28%, and 2% of the observations, respectively). Table 1 reports the sample summary statistics.

Additional information on total market deposits and total market loans for each country comes from the European Central Bank. Data include the total loans and total deposits for all monetary and financial institutions toward unspecified sectors.11

11 We are aware that, as per definition of the European Central Bank, monetary financial institutions includes central banks, resident credit institutions, and other resident financial institutions (e.g., money market funds). Nevertheless, we believe this is a convenient proxy for the amount outstanding of loans and deposits markets in each country.

F. Fiordelisi, D.S. Mare / Journal of International Money and Finance 45 (2014) 1–166

A comprehensive set of variables is considered in the analysis to control for the effect of other factors on the relationship between competition and risk. We include variables such as heterogeneity, market concentration, and the 2007–2009 financial crisis that can directly affect the relationship be- tween stability and competition. We also include factors that could explain bank financial soundness, such as bank-level fundamentals and environmental determinants. Below, we first describe the main variables of interest in our analysis – the Lerner Index and bank stability – and then the other variables we include in the estimation.

3.1. Measuring competition: the Lerner Index

Following recent studies (Maudos and de Guevara, 2007; Casu and Girardone, 2009; Turk Ariss, 2010; among many others), we directly estimate competition using the Lerner Index of Monopoly Power (LER) as a measure of cooperative bank market power. This indicator, which represents the extent to which market power allows firms to fix a price above the marginal cost, is calculated as follows:

LERi;t ¼ Pi;t � MCi;t

Pi;t ; (1)

where Pi,t is the price of the output of bank i at year t, and MCi,t is the marginal cost. Higher index values imply greater market power. The price of output Q is calculated as total revenues (interest plus non- interest income) divided by total assets. In line with recent papers (Berger et al., 2009; Beck et al., 2013), we estimate the conventional marginal cost using a translog cost function with three inputs, one single output, and a time trend. The final specification is as follows:

ln TCi;t ¼ a0 þ a1 ln Q þ a2 2 ln Q2 þ

X3 j ¼ 1

bj ln Pj þ 1 2

X3 j ¼ 1

X3 k ¼ 1

djk ln Pj ln Pk

þ 1 2

X3 k ¼ 1

gj ln Q ln Pj þ s1t þ s2 2 t2 þ s3t � ln Q þ

X3 k ¼ 1

jjt ln Pj þ εit; (2)

where TCi,t is the total costs (i.e., the sum of personnel expenses, other administrative expenses, and other operating expenses), and Q is the cooperative banks’ single output proxied by total assets. P1, P2, and P3 are the prices of the inputs used in the production process: P1 is the price of labour (i.e., personnel expenses over total assets); P2 is the price of physical capital (i.e., other administrative ex- penses plus other operating expenses over total fixed assets); and P3 is the price of borrowed funds (i.e., interest expenses over the sum of total deposits and money market funds). t is a time trend capturing the dynamics of the cost function over time, and a, b, d, g, s, and j are coefficients to be estimated. εit is a two-component error term computed as follows:

εit ¼ uit þ vit; (3) where vit is a two-sided error term, and uit is a one-sided disturbance term representing inefficiency.

12

From Equation (2), the marginal costs can be derived as follows:

MCi;t ¼ TCi;t Qi;t

2 4ba1 þ ba2 ln Q þ

X3 j ¼ 1

bgj ln Pj þ bs3t 3 5; (4)

12 vit is assumed to be independently and identically normally distributed with a mean of zero and a variance of s 2 v, and

independent of uit ¼ {ui exp[�n (t – T)]}, where uit is a one-sided error term capturing the effects of inefficiency and assumed to be half-normally distributed with a mean of zero and a variance of s2u. n is an unknown parameter to be estimated that captures the effect of inefficiency change over time. We apply the common restrictions of standard symmetry and homogeneity of degree one in prices to the translog functional form (see Appendix A for the specification of the restrictions in the case of two inputs).

F. Fiordelisi, D.S. Mare / Journal of International Money and Finance 45 (2014) 1–16 7

MCi,t obtained from Equation (4) is then substituted into Equation (2) to calculate the Lerner Index for bank i at time t, thereby giving us the dynamic change in market power across banks over time. We also calculate a different specification of MC using the funding-adjusted Lerner Index suggested by Maudos and de Guevara (2007) and Turk Ariss (2010). Specifically, MCi,t is derived from the estimation of the cost function that omits funding costs as one of the inputs. This procedure enables us to account for market power that may have previously been exercised in the deposit market. Specifically, by excluding funding costs, we obtain a clean proxy of pricing power that is not affected by market power that had previously originated in the deposit market while banks raise funds. Appendix A summarises the main steps to compute the funding-adjusted Lerner Index.

3.2. Measuring bank stability

We proxy individual bank stability using the Z-score, which has been used extensively in the banking literature (e.g., Boyd et al., 2006; Iannotta et al., 2007; Laeven and Levine, 2009). This measure is computed as the sum of the capital-asset ratio (CAR) and the return on assets (ROA) divided by the standard deviation of the return on assets (sROA). The underlying idea is to capture the number of standard deviations by which returns have to diminish in order to deplete the equity of a bank. Various approaches have been proposed to construct time-varying Z-score measures (for a review, see Lepetit and Strobel, 2013). We follow the methodology used by Hesse and Cihák (2007) to obtain a time- varying measure of individual bank stability: we estimate the Z-score employing sroa calculated using a cross-sectional technique and combine this with current period values of CARt and ROAt for each individual bank. The Z-score is then computed as follows:

Z � scorei;t ¼ ROAi;t þ CARi;t

s � ROAc;t

� ; (5) where ROAi,t is the return on assets for bank i in current period t. CARi,t denotes the capital-asset ratio for bank i in current period t. s(ROAc,t) is computed as the standard deviation of return on assets within each individual country c in current period t. The Z-score provides a measure of bank soundness. Higher values imply a higher degree of solvency, and therefore it gives a direct measure of bank sta- bility. In our analysis, we consider the natural logarithm of the Z-score to smooth out higher values within the distribution.

As it is estimated in (5), the Z-score allows us to have a time-varying measure of bank stability that does not suffer from endogeneity problems.13 Nevertheless, since ROAi,t and s(ROAc,t) are drawn from different distributions, this could create an inconsistency problem. To overcome this issue and get more robust results, we estimate the Z-score similarly to Yeyati and Micco (2007) by using the following equation:

Z Robi;t ¼ m � ROAc;t

� þ CARi;t

s � ROAc;t

� ; (6) where m(ROAc,t) is the mean of the return on assets within each individual country c in current period t. Once more, we take the natural logarithm to smooth out extreme values in the Z-score distribution.

We also calculate two alternative measures of bank risk-taking to get more robust results and avert the presence of a spurious correlation, given that net operating profits are considered both in the computation of the Z-score and the Lerner Index (Beck et al., 2013). Since cooperative banks are mostly oriented toward lending activities (e.g., loans are 58% of total assets)14 rather than non-traditional intermediation activities, we are confident that their stability is strictly related to the quality of their loan portfolio. As such, we use the ratio of loan-loss provisions to total loans and loan-loss provisions to net interest margin to test for this relationship.

13 We would like to thank an anonymous referee for raising this issue and for suggesting several approaches to solving the problem. 14 Source: EACB (2012).

Table 2 Variables definition.

Variables Symbol Definition and calculation method

Z-score Z The ratio synthesizes a measure of overall banking risk. Similarly to Hesse and Cihák (2007), it is computed as the sum of the current period t return on assets (ROA) and the equity ratio (equity over total assets) divided by the standard deviation of ROA computed within each individual country (c) in year t.

Z-Rob Z_Rob Alternative stability measure computed as the sum of return on assets (ROA) calculated at the individual country (c) level in year t and the equity ratio divided by the standard deviation of ROA computed at the individual country (c) level in year t.

lerner index LER The Lerner Index represents the extent to which market power allows the bank to fix a price (P) above its marginal cost (MC).

Funding-adjusted LER FA_LER Lerner Index adjusted for the market power previously originated in the deposit market while raising funds.

Output price P Following recent studies (Berger et al., 2009; Turk Ariss, 2010) and assuming that banks produce an heterogeneous flow of services that is proportional to their dimension, we use banks’ total assets as a proxy of their overall activity (Angelini and Cetorelli, 2003), and we estimate average price as total revenues (interest and non-interest income) on total assets.

Marginal costs MC Marginal cost of the product as described in Section 3.2. Funding-adjusted MC FA_MC Marginal cost of the product as described in Appendix A. Herding measure HERD This is a measure of banking industry heterogeneity

computed, as in Beck et al. (2013), as the within-country standard deviation of the percentage non-interest income (with respect to total assets) per year (t) and per country (c).

Financial crisis FINCIR_LER Interaction terms computed as the product of the Lerner Indexes and a dummy variable for the 2007–2009 financial crisis. The categorical variable takes a value of 1 in 2007– 2009, and 0 otherwise. Both Lerner Indexes are used to build two different interaction terms.

Concentration loans HHI LOANS Concentration Index (Herfindahl–Hirschman Index) calculated as the sum of the squares of the market shares (considering loans) of each bank (i) in a specific country (c) in a determined year (t). We consider one observation per year (t) per country (c) (i.e., 60 values).

Concentration deposits HHI DEPOSIT Concentration Index (Herfindahl–Hirschman Index) calculated as the sum of the squares of the market shares (considering deposits) of each bank (i) in a specific country (c) in a determined year (t). We consider one observation per year (t) per country (c) (i.e., 60 values).

Herd Lerner HERD_LER Mixed measure that combines the banks with the highest tendency to herd (i.e., lowest third of the distribution of HERD) with market monopoly power.

Concentration Lerner loans HHIL_LER Mixed measure that combines banks’ concentration index in the loan market with market monopoly power.

Concentration Lerner deposits HHID_LER Mixed measure that combines banks’ concentration index in the deposit market with market monopoly power.

Bank-level controls Bank controls Liquidity ratio built as cash and due from other banks to total assets. Credit risk ratio built as loan loss provision to interest income margin. Credit orientation built as total loans to total assets.

Time-country dummies Time-countries Interaction terms computed as the product of year dummies and country dummies (i.e., 44 variables).

This table reports the name, symbol and definition of the variables employed in the analysis. The source of data is Bureau van Dijk Bankscope. The market concentration of loans and deposits is computed using data from both Bankscope and the European Central Bank.

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F. Fiordelisi, D.S. Mare / Journal of International Money and Finance 45 (2014) 1–16 9

3.3. Other variables

We compute a herding measure and loan market concentration to control for the effects of other factors on the relationship between stability and competition. The herding measure, as in Beck et al. (2013), is the within-country standard deviation per year of non-interest income (i.e., fees and com- missions) as a share of total assets. It takes into consideration the possible incentives for banks to increase their risk-taking following an increase in competition. If the regulator finds it ex-post optimal to bail out a large number of banks when the number of bank failures is high (Acharya and Yorulmazer, 2007; Brown and Dinç, 2011), cooperative banks are more likely to expand their operations outside their core business in response to an increase in competition. The higher the value of this indicator, the lower is the herding behaviour in the cooperative banking sector. We also compute a combined measure using the interaction between the herding indicator and the Lerner Index. The Herd-Lerner is estimated as the product of a dummy variable and the Lerner Index. The dummy takes a value of one if the banking sector in a country is in the lowest third of the herding measure distribution (i.e., more homogeneous sources of revenues), and zero otherwise.

The Herfindahl–Hirschman Index (HHI) conveys information on the market concentration of loans and deposits. The index is computed annually at the country level because cooperative banks operate both at the regional level (e.g., big cooperatives in Germany and Austria) and the local level (e.g., small rural Italian cooperatives) and in our database it is not available detailed information on the geographical scope of the operations of each credit institution. The higher the value of HHI, the higher is the concentration of the market. We also calculate combined measures using both HHI and the Lerner Index. The HHI-Lerner for loans and the HHI-Lerner for deposits are computed as the product of a dummy and the two Lerner Index measures. The two dummies take a value of one if the banking sector in a country is in the highest third of the HHI distribution (i.e., more concentrated markets), and zero otherwise.

The 2007–2009 financial crisis could have affected the fundamental relationship holding between bank soundness and competition among cooperative banks. On the one hand, small rural cooperative banks might have been shielded from the market jitters in the interbank lending market. On the other hand, the economic crisis that followed might have had a profound impact on the fundamental eco- nomic relationship between cooperative banks and their members. We investigate this effect via an interaction term computed using the Lerner Index and a dummy variable for the 2007–2009 financial crisis.

We also include in the analysis two sets of control variables to limit the problem of spurious re- lationships. First, we consider bank-level fundamentals to account for liquidity risk, credit risk, and asset composition. Second, as in Beck et al. (2013), we control for the dynamic changes in country conditions using an interaction term between year dummies and country dummies (Table 2).

4. Empirical approach

To investigate the relationship between bank competition (measured using the Lerner Index) and stability (measured via the Z-score), we first employ the Granger causality technique. This approach has the advantage of permitting us to test unique time-ordered and signed relationships among pairs of variables.15 Although Granger causality tests have several limitations,16 this approach has been widely used in the economics (e.g., Jaeger and Paserman, 2008; Assenmacher-Wesche and Gerlach, 2008) and banking (e.g., Fiordelisi et al., 2011; Fiordelisi and Molyneux, 2010; Casu and Girardone, 2009; Williams, 2004) literature to analyse intertemporal relationships. Specifically, to disentangle

15 Granger’s (1969, p. 428) notion of causality states that “. yt is causing xt if we are better able to predict xt using all available information than if the information apart from yt had been used.” Granger’s suggestion to regress xt on its own lags and a set of lagged yt has become a standard procedure. If lagged yt provides a statistically significant explanation of xt, then yt “Granger causes” xt. 16 For instance, Granger-testing does not prove economic causation between two variables, but it identifies gross statistical associations.

Table 3 The link between bank stability and competition in cooperative banks: The Granger causality test.

Dependent variable: ln (Z-score)

(1) (2) (3) (4) (5) (6)

Zt�1 0.685*** (0.012)

0.679*** (0.012)

0.633*** (0.012)

0.630*** (0.012)

0.766*** (0.026)

0.767*** (0.026)

Zt�2 �0.014 (0.013)

�0.001 (0.013)

0.019 (0.012)

0.028** (0.012)

0.114*** (0.024)

0.112*** (0.024)

LERt�1 �0.008 (0.056)

0.033 (0.051)

�0.135*** (0.030)

LERt�2 �0.304*** (0.067)

�0.241*** (0.060)

0.083*** (0.025)

FA_LERt�1 0.168*** (0.057)

0.170*** (0.051)

�0.146*** (0.031)

FA_LERt�2 �0.370*** (0.064)

�0.281*** (0.056)

0.089*** (0.027)

HERDt�1 �2.106*** (0.111)

�2.152*** (0.111)

�5.325*** (0.469)

�5.359*** (0.470)

HHI LOANSt�1 9.701*** (1.318)

8.979*** (1.322)

18.301 (13.244)

19.331 (13.165)

HHI DEPOSITSt�1 �3.078*** (0.547)

�3.043*** (0.544)

�9.455 (6.557)

�10.054 (6.516)

Constant 1.149*** (0.035)

1.066*** (0.033)

1.286*** (0.034)

1.222*** (0.031)

0.872*** (0.059)

0.869*** (0.058)

LER (Total) �0.313*** (0.038)

�0.207*** (0.037)

�0.052*** (0.020)

FA_LER (Total) �0.203*** (0.031)

�0.111*** (0.031)

�0.058*** (0.020)

Observations 11,426 11,426 11,426 11,426 11,426 11,426 R-squared 0.382 0.383 0.404 0.404 0.863 0.863 TIME-COUNTRY NO NO NO NO YES YES BANK CONTROLS NO NO NO NO YES YES

Robust standard errors in parentheses: ***p < 0.01, **p < 0.05, *p < 0.1. This table reports the results of the Granger causality test performed using two different specifications of the Lerner Index. In columns (1), (3), and (5) we report the results for the traditional Lerner Index; columns (2), (4), and (6) summarise the results for the funding-adjusted measure. Notice that because we are using two lags in the variables of interest, we lose two time periods for each individual. Therefore, the number of observations drops from 17,074 to 11,426.

F. Fiordelisi, D.S. Mare / Journal of International Money and Finance 45 (2014) 1–1610

the intertemporal relationships between competition and stability, we first analyse the one-sided historical association between competition and bank stability via a simple causality regression using two lags in the variables of interest, with the Z-score as the dependent variable and the Lerner Index as the independent variable. The main quantity of interest is the sum of the coefficients of the two Z-score lags, computed as in Hesse and Cihák (2007). We assess the long-run effect of competition over financial stability by testing for the restriction that the sum of all lagged coefficients of the Z-score is zero: a rejection of the restriction suggests evidence that x has a long-run effect on y.17 To get more robust results, we use two specifications for the Lerner Index (traditional and funded-adjusted), and we control for other exogenous factors that may affect the relationship, namely the homogeneity in bank investment activities (herding measure), the concentration in the deposits and loans markets, the dynamic trend of environmental conditions at the country level, and bank-level fundamentals.

Second, in line with previous studies (for instance, Beck et al., 2013), we analyse the economic causality using both pooled ordinary least squares and panel fixed-effects techniques. We specify the following relationship:

17 Following Casu and Girardone (2009), we also assess the Granger causality as the joint test of the null hypothesis that the two lags are equal to zero. If the probability is less than 10%, then the null hypothesis that x Granger-causes y is rejected at the 10% significance level. Results are available from the authors upon request.

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Zi;t ¼ con þ � u0 þ u1Xi;t�1

� � LERi;t�1 þ gXi;t�1 þ zKi;t�1 þ εi;t; (7)

where the i subscript denotes the cross-sectional dimension across banks, and t denotes the time dimension. Zi,t is the Z-score for bank i at time t. LERi,t-1 is the Lerner Index for bank i lagged one year to minimize any simultaneity problems. Xi,t-1 is the one-year lagged factors that we expect to influence the relationship between competition and stability (i.e., herding measure, Herfindahl–Hirschman Index for deposits and loans, and the 2007–2009 financial crisis). Ki,t are the control variables (as detailed in Section 3.3), and u0 is the coefficient that summarises the strength and the sign of the relationship between bank soundness and competition. u1 is a vector of coefficients that captures the combined effects of competition with factors that influence the relationship between stability and competition. g and z are vectors of coefficients to be estimated, and εi,t indicates robust standard errors clustered at the individual bank level. The pooled ordinary least squares and the panel fixed-effects models allow us to further investigate the economic causality running from competition to bank stability.

5. Results

We first analyse the competition-stability nexus using Granger causality. The aim is to determine whether changes in market power precede financial troubles for European cooperative banks. We begin with a simple two-variable model that relates the two measures of market power (traditional Lerner and funding-adjusted Lerner) to bank soundness, computed as in Hesse and Cihák (2007). Our focus is on the significance and magnitude of the sum of the coefficients of the two lags in the two market power measures. The results in columns (1) and (2) of Table 3 indicate a negative association between market power and bank stability, meaning that a decrease in competition anticipates an increase in bank instability.18 This finding is not robust for specification bias and could also be related to spurious causality. To curb these potential issues, two additional sets of variables are included in the analysis. First, we consider factors that might affect the relationship such as the heterogeneity in banks’ revenues or the concentration in the loan and deposit markets. Second, we control for the bank-level fundamentals and for the dynamic changes in the environment where the banks operate. The results, reported in columns (3) to (6) of Table 3, are consistent with the previous estimation showing a negative statistically significant relationship between banks’ market power and bank stability.19 Note that the long-run effect is lessened by the spurious factors. In addition, the higher R2 in the regressions with the full set of control variables suggests, as noted in Beck et al. (2013), that environmental con- ditions might have a great impact on the cross-country variation in the relationship between bank market power and bank soundness.

We also analyse the relationship between competition and risk in a panel data setting. We introduce a new element in the analysis by investigating whether the 2007–2009 financial crisis has affected the European cooperative banks and the nexus under study. In addition, we run separate regressions with the two measures of market concentration, the herding measure, and all the control variables together. Furthermore, to mitigate the omitted bias problem, in each regression we add bank-level information to control for other potential variables that drive individual bank stability. All the explanatory variables are lagged one year to minimise simultaneity problems.

In all the regressions, we find that bank market power is negatively related to bank stability, meaning that there is a positive relationship between competition and stability: when competition is low (i.e., market power is high), stability is low. This in turn provides evidence in favour of the competition-stability view proposed by Boyd and De Nicolò (2005). Moreover, to investigate if the

18 As noted in Berger (1995), it is possible to have a different sign in the lag coefficients without losing generality in the results. The only potential problem pertains to the serial correlation with lags more than two periods past. Nevertheless, we assume that two lags are sufficient to capture the long-run effect of competition on financial stability. 19 We also investigate a potential feedback relationship running from stability to competition. Results, available from the authors upon request, show that stability is not Granger-causing competition while controlling for spurious causality as the sum of the lagged Z-score is not significant at the 10% level.

Table 4 The link between bank stability and competition in cooperative banks.

Dependent variable

(1) (2) (3) (4) (5)

lnZ lnZ lnZ lnZ lnZ

LERt�1 �0.175*** (0.054) �0.230*** (0.056) �0.147** (0.062) �0.102* (0.058) �0.119*** (0.020) FINCIR_LER 0.573*** (0.022) �0.433*** (0.087) �0.277*** (0.029) HERDt�1 �2.374*** (0.160) �0.436*** (0.138) �1.034*** (0.231) �0.469* (0.249) �0.802*** (0.162) HERD_LERt�1 0.445*** (0.019) 0.124 (0.084) 0.321*** (0.024) HHI LOANSt�1 31.642*** (2.398) 23.003*** (3.655) 113.537*** (11.281) 110.473*** (13.827) 104.407*** (4.406) HHI DEPOSITSt�1 �10.406*** (1.324) �3.518* (2.035) �53.415*** (6.043) �52.106*** (7.654) �50.575*** (2.335) HHIL_LERt�1 �7.042 (6.332) 62.180*** (17.076) 17.724*** (4.755) HHID_LERt�1 �4.266 (3.432) �32.049*** (11.232) �7.354*** (2.411) Constant 2.844*** (0.050) 2.694*** (0.052) 2.607*** (0.081) 2.618*** (0.081) 3.687*** (0.050) Observations 14,070 14,070 14,070 14,070 14,070 R-squared 0.070 0.199 0.506 0.510 0.791 Number of clusters

2395 2395 2395 2395 2395

BANK FE NO NO NO NO YES TIME-COUNTRY NO NO YES YES YES BANK CONTROLS YES YES YES YES YES

Robust standard errors in parentheses: ***p < 0.01, **p < 0.05, *p < 0.1. This table summarises the results of the estimations. Coefficients in columns (1) and (2) are estimated using pooled ordinary least square regressions, whereas in the remaining regressions, a panel-fixed effects technique is used. In regressions (1–5), we control for the bank-level determinants described in Table 2. Additionally, we include individual bank fixed effects (BANK FE) and country-year dummies to account for the dynamic changes in the environmental conditions (TIME-COUNTRY). In re- gressions (1–5), the dependent variable is the natural logarithm of the Z-score built using a method similar to Hesse and Cihák (2007). Notice that because we are using one lag in the variables of interest, we lose one time period for each individual; therefore, the number of observations is reduced from 17,074 to 14,070.

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results are driven by individual country conditions, we also run separate regressions at the country level and obtain qualitatively the same results.20 Our finding is consistent with the results of various studies on commercial banking (Beck et al., 2006; Schaeck et al., 2009, among the others) showing that European cooperative banks become more risky in less competitive markets. This evidence offers useful insights for policy makers undertaking the current redesign of the supervisory approach to European banks.

Turning to the effect of the financial crisis, we obtain contrasting results depending on the speci- fication. On the one hand, if we do not consider time-varying country effects (Table 4, column 2), higher market power during the period seems to induce higher stability. On the other hand, the coefficient on the financial crisis signals higher instability when we consider the full set of controls (Table 4, columns 4 and 5). Nevertheless, and for the aim of our analysis, the most important element is that the crisis seems not to have affected the fundamental relationship between competition and stability in coop- erative banking.

We also account for cooperative bank closure policies and an implicit Too-Many-To-Fail problem (Acharya and Yorulmazer, 2007). Specifically, we investigate the assumption that competition will have a stronger impact on bank stability in more homogeneous banking systems (where herding behaviour is more likely). As such, we introduce the herding measure and a combined measure obtained by interacting the Lerner Index with a dummy capturing the bank herding behaviour. As reported in Table 4, in all regressions the herding measure is negatively related to bank stability, meaning that coop- erative banks tend to become more stable in more homogenous banking markets. Moreover, the positive sign in the combined measure reinforces this presumption. These findings are particularly pertinent to policy makers. First, we show that the level of homogeneity influences the stability of the cooperative banking system. Second, we find support for the view that the expansion of cooperative

20 Results from this analysis are not reported in the paper and are available from the authors upon request.

Table 5 Test of robustness using alternative dependent variables.

Dependent variable

(1) (2) (3) (4) (5)

lnZ lnZ lnZ lnZ lnZ

LERt�1 �0.295*** (0.050) �0.172*** (0.049) �0.100*** (0.016) �0.004*** (0.001) �0.101** (0.043) FINCIR_LER 0.565*** (0.021) �0.401*** (0.080) �0.240*** (0.024) �0.003*** (0.000) �0.039*** (0.008) HERDt�1 �0.415*** (0.126) �0.545*** (0.198) �0.866*** (0.133) �0.016*** (0.003) �0.537*** (0.059) HERD_LERt�1 0.437*** (0.016) 0.089 (0.079) 0.282*** (0.019) 0.003*** (0.000) 0.061*** (0.010) HHI loanst�1 22.367*** (3.008) 111.866*** (12.696) 104.561*** (3.622) �0.103* (0.060) �2.954*** (1.128) HHI depositst�1 �3.355** (1.566) �52.559*** (6.676) �49.669*** (1.919) �0.011 (0.037) 0.530 (0.583) HHIL_LERt�1 �7.904 (5.638) 54.752*** (13.806) 14.883*** (3.909) 0.126 (0.087) 0.061 (1.823) HHID_LERt�1 �3.202 (2.773) �27.953*** (8.659) �7.447*** (1.982) �0.057* (0.029) �0.079 (0.912) Constant 2.754*** (0.048) 2.678*** (0.069) 3.648*** (0.041) 0.011*** (0.001) 0.249*** (0.020) Observations 14,070 14,070 14,070 14,070 14,070 R-squared 0.221 0.569 0.848 0.0281 0.0259 Number of clusters

2395 2395 2395 2395 2395

BANK FE NO NO YES NO NO TIME-COUNTRY NO YES YES NO NO BANK CONTROLS YES YES YES NO NO

Robust standard errors in parentheses: ***p < 0.01, **p < 0.05, *p < 0.1. This table summarises the results of the estimations using alternative dependent variables. Coefficients in columns (1), (4), and (5) are estimated using pooled ordinary least square regressions, whereas in the remaining regressions, a panel-fixed effects technique is used. In regressions (1–3), we control for the bank-level determinants described in Table 2. Additionally, in column (3) we include individual bank fixed effects (BANK FE) and in columns (2) and (3) country-year dummies to account for the dynamic changes in the environmental conditions (TIME-COUNTRY). In regressions (1–3) we employ as the dependent variable the natural logarithm of the Z-score built using a method similar to Yeyati and Micco (2007). In columns (4) and (5) we employ two different dependent variables to check for the robustness of our results. Namely, we use the ratio of loan-loss provisions to total loans (LLPTL) and the variable loan-loss provisions to net interest margin (LLPNIC). Notice that because we are using one lag in the variables of interest, we lose one time period for each individual; therefore, the number of observations is reduced from 17,074 to 14,070.

F. Fiordelisi, D.S. Mare / Journal of International Money and Finance 45 (2014) 1–16 13

banks into non-traditional business lines (i.e., non-interest income activities) could lead to higher insolvency risk.21 Although policy makers should carefully evaluate how diversification could affect the safety and soundness of the overall banking system, more homogenous cooperative banking markets seem to be more stable. The herding measure and the combined measure are strongly related to bank stability: the coefficients are statistically significant at least at the 10% level, and the signs of the co- efficients are stable in the majority of the regressions.

To further enrich our analysis, we consider the effect of concentration in the loan and deposits markets. The one-year lagged Herfindahl–Hirschman Index for loans is positively related to the Z-score, suggesting that bank stability is higher in more concentrated markets. Similarly, when it is statistically significant, the interaction term with the Lerner Index is positively and strongly related to bank sta- bility, meaning that the higher the market power associated with more concentrated markets, the more financially sound banks are. On the deposits side, we observe opposite results. Concentration in the deposits market is negatively related to individual bank soundness; we obtain the same result when we consider the combined effect of market power and market structure.

We use different specifications of the dependent variable to get more robust results. As reported in Table 5 in columns (1–3), we first employ as the dependent variable the natural logarithm of the Z- score built using a method similar to Yeyati and Micco (2007). The results remain qualitatively the same and this further reinforces our previous findings. In addition, the relationship between risk- taking and market power is also confirmed using credit risk measures (Table 5, columns 4 and 5).

21 See Mercieca et al. (2007) for the negative implications of diversification in the case of small European banks and Goddard et al. (2008a, b) for evidence from the U.S. market.

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6. Conclusions

Cooperative banks are a driving force for socially committed business at the local level, accounting for around one fifth of the European banking system. Despite their importance, few if any studies have assessed the relationship between competition and financial stability in the cooperative banking sector. Our paper empirically fills this void examining a large sample of cooperative banks in the Eu- ropean Union between 1998 and 2009.

We show that bank market power negatively Granger-causes banks’ stability, meaning that there is a positive relationship between competition and soundness: when competition is low (i.e., market power is high), stability is low. The positive link between competition and stability is observed both in the short and long run. We also provide empirical evidence that bank soundness is higher in more homogenous markets where the herding behaviour is stronger. Cooperative bank closure policies may suffer from an implicit Too-Many-To-Fail problem, as suggested by Acharya and Yorulmazer (2007), when the banking system is more competitive because herding behav- iour is more likely. This result is particularly interesting for policy makers because it suggests that the level of industry homogeneity influences the stability of the cooperative banking system. We also show that the financial crisis of 2007 has not changed the relationship between competition and stability.

We consider a combination of measures to account for herding behaviour in the case of high mo- nopoly power (combination of herding and Lerner Index) and for concentration within the loan and deposit markets (combination of concentration of the loan market and the Lerner Index). We do not find evidence that the sign of the relationship between stability and market power in cooperative banking is affected by the introduction of these variables into the analysis.

Acknowledgements

The authors wish to thank Alessandro Carretta, Miguel Duran, Iftekhar Hasan, Phil Molyneux, and Ornella Ricci, who kindly provided comments on earlier versions of this paper, Karen DeVivo for professional and timely proofreading. The participants at the 2nd International Conference of the Financial Engineering and Banking Society (FEBS) and the 5th International Conference of the Inter- national Finance and Banking Society (IFABS) provided constructive comments. The authors are particularly grateful to Kees Koedijk (the editor) and to an anonymous referee for their constructive suggestions that have significantly improved the work. All remaining errors and omissions rest with the authors.

Appendix A

As a robustness test, we estimate an alternative measure of the marginal cost in the Lerner Index formula following some recent papers (Maudos and de Guevara, 2007; Turk Ariss, 2010). We compute the marginal cost using a translog cost function for each country separately with two inputs, one single output, and a time trend. The specification is as follows:

ln TCi;t ¼ a0 þ a1 ln Q þ a2 2 ln Q2 þ

X2 j ¼ 1

bj ln Pj þ 1 2

X2 j ¼ 1

X2 k ¼ 1

djk ln Pj ln Pk

þ 1 2

X2 j ¼ 1

gj ln Q ln Pj þ s1t þ s2 2 t2 þ s3t � ln Q þ

X2 j ¼ 1

jjt ln Pj þ εit; (A.1)

where TCi,t is total costs (the sum of personnel expenses, other administrative expenses, and other operating expenses), and Q is the cooperative banks’ single output proxied by total assets. P1 and P2 are respectively the price of labour and the price of physical capital. t is a time trend capturing the dy- namics of the cost function over time. a, b, d, g, s, and j are coefficients to be estimated, and εit is a two- component error term. As in the estimation of equation (2), we apply the common restrictions of

F. Fiordelisi, D.S. Mare / Journal of International Money and Finance 45 (2014) 1–16 15

standard symmetry and homogeneity of degree one in input prices defined as:P2 j¼1bj ¼ 1;

P2 j¼1gj ¼ 0; and ck˛f1; 2g :

P2 j¼1dj;k ¼ 0.

From equation (A.1), the marginal costs are derived as follows:

MCi;t ¼ TCi;t Qi;t

2 4ba1 þ ba2 ln Q þ

X2 j ¼ 1

bgj ln Pj þ bs3t 3 5; (A.2)

Using the funding-adjusted Lerner Index, we curb the potential issue of considering the market power that had previously originated in the deposit market while raising funds.

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  • Competition and financial stability in European cooperative banks
    • 1 Introduction
    • 2 Literature review and research hypotheses
    • 3 Data sources and variables
      • 3.1 Measuring competition: the Lerner Index
      • 3.2 Measuring bank stability
      • 3.3 Other variables
    • 4 Empirical approach
    • 5 Results
    • 6 Conclusions
    • Acknowledgements
    • Appendix A
    • References

How-does-competition-affect-bank-risk-taking-_2013_Journal-of-Financial-Stability.pdf

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Journal of Financial Stability 9 (2013) 185– 195

Contents lists available at SciVerse ScienceDirect

Journal of Financial Stability

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / j f s t a b i l

ow does competition affect bank risk-taking?

abriel Jiménez a, Jose A. Lopez b,∗, Jesús Saurina a

Banco de España, Spain Federal Reserve Bank of San Francisco, United States

r t i c l e i n f o

rticle history: eceived 3 October 2012 eceived in revised form 5 January 2013 ccepted 21 February 2013 vailable online 14 March 2013

EL classification: 21 11

a b s t r a c t

A common assumption in the academic literature and in the supervision of banking systems is that fran- chise value plays a key role in limiting bank risk-taking. As market power is the primary source of franchise value, reduced competition in banking markets has been seen as promoting banking stability. A recent paper by Martínez-Miera and Repullo (MMR, 2010) shows that a nonlinear relationship theoretically exists between bank competition and risk-taking in the loan market. We test this hypothesis using data from the Spanish banking system. After controlling for macroeconomic conditions and bank character- istics, we find support for this nonlinear relationship using standard measures of market concentration in both the loan and deposit markets. When direct measures of market power, such as Lerner indices,

eywords: ank competition ranchise value erner index redit risk inancial stability

are used, the empirical results are more supportive of the original franchise value hypothesis, but only in the loan market. Overall, the results highlight the empirical relevance of the MMR model, even though further analysis across other banking markets is needed.

Published by Elsevier B.V.

B a b r i b c s v d a t k

. Introduction

A standard principle of banking supervision is that increased ompetition among banks could threaten the solvency of partic- lar institutions and hamper the stability of the banking system t an aggregate level. Such competition could erode the franchise alue of a bank and encourage it to pursue riskier policies in an ttempt to maintain its former profits.1 Examples of riskier policies re taking on more credit risk in the loan portfolio, lowering capital evels, or both. These riskier policies should increase the probability f higher non-performing loan ratios and lead to more bank fail- res. In contrast, restrained competition should encourage banks o protect their higher franchise values by pursuing safer policies hat contribute to the stability of the entire banking system. This

franchise value” paradigm has been supported both theoretically nd empirically over time in the banking literature.

∗ Corresponding author. Tel.: +1 415 977 3894. E-mail addresses: [email protected] (G. Jiménez), [email protected]

J.A. Lopez), [email protected] (J. Saurina). 1 The extensive theoretical literature on this topic was started by Keeley (1990)

nd is summarized in Section 2 of this paper. Carletti and Hartmann (2003) as well s Carletti (2008) survey the literature on financial stability and competition.

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572-3089/$ – see front matter. Published by Elsevier B.V. ttp://dx.doi.org/10.1016/j.jfs.2013.02.004

A debate regarding this paradigm was initiated by the work of oyd and De Nicoló (BDN, 2005). In their model, less competition mong banks could result in higher interest rates being charged on usiness loans, which might raise the credit risk of borrowers as a esult of moral hazard issues, as in Stiglitz and Weiss (1981). The ncreased default risk could lead to more problem loans and greater ank instability. The authors argue that this “loan market channel” ould eliminate the trade-off between competition and financial tability implied by the “deposit channel” implied by the franchise alue paradigm; that is, the economic rents that banks earn from epositors provide the only incentives to carry out conservative sset side policies. Their proposed “risk-shifting” paradigm argues hat increased competition across both the loan and deposit mar- ets could lower loan rates, decrease borrower credit risk, and nhance financial stability. In fact, Boyd et al. (2006) as well as e Nicoló and Loukoianova (2007) provide empirical evidence of a ositive relationship between banking market concentration and ank risk-taking.

More recently, Martínez-Miera and Repullo (MMR, 2010) xtend the BDN model by allowing for imperfect correlation across ndividual firms’ default probabilities. Their model also identifies

risk-shifting effect that accounts for fewer firm defaults when oan rates decrease in a more competitive banking environment. owever, since imperfect correlation between firms is now per- itted, there is also a “margin” effect that reduces the interest

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86 G. Jiménez et al. / Journal of F

ayments from performing loans and thus bank revenues. These wo effects work in opposite directions, so that the net effect on ank risk-taking and financial stability is unclear. In their model, he risk-shifting effect is shown to be dominated by the margin ffect in competitive banking environments, such that increased ompetition increases bank failure risk. In a more concentrated anking market, the model suggests that the risk-shifting effect ominates and thus bank failure risk declines with increased ompetition. Overall, the authors show that there is a U-shaped elationship in their model between bank competition, which is easured by the number of banks, and the risk of bank failure. The objective of this paper is to examine empirically whether

he relationship between bank competition and risk-taking is lin- ar, as suggested by both the franchise value and risk-shifting odels (although with opposite signs), or U-shaped as in the MMR odel. We examine this relationship within the context of the

panish banking system. While some papers have used cross- ountry data to examine this relationship, we focus on a single anking system to ensure comparability across both dependent and

ndependent variables. Our analysis of the Spanish banking sys- em permits us to use detailed databases to construct consistent

arket concentration variables, such as Herfindahl–Hirschmann ndexes and the number of banks operating in a market. We also enerate Lerner indexes as alternative measures of market power sing the Banco de España’s interest rate database that contains onthly information about the marginal interest rates charged by

ach bank for several banking products, such as commercial loans nd deposits. Similarly, for our independent variable measure of ank risk, we use the Banco de España’s credit register to obtain onsistent estimates of banks’ commercial non-performing loan atios (NPL), which are an empirical measure of bank risk.

Our empirical results for the Spanish banking market provide upport for the relationships proposed in the MMR model. That s, after controlling for macroeconomic conditions and bank char- cteristics, we find evidence of a nonlinear relationship between anking market competition and bank risk-taking using standard arket concentration measures for both loan and deposit markets. hen Lerner indices are used as measures of bank competition,

he results do not suggest a nonlinear relationship, but do sup- ort the franchise value paradigm directly in the loan market. This esult may be due to the fact that the MMR model is not framed ith respect to such concentration variables. Importantly, while

he empirical relationship between banking market concentration n the Spanish deposit market and bank risk-taking with respect to on-performing loans was found to be nonlinear, the coefficients uggest that the relationship is concave as opposed to the convex elationship found in the loan market, both in theory and in our ata. Further analysis of this deposit market result is necessary.

In summary, we find supportive evidence of a nonlinear rela- ionship between bank market concentration and bank risk-taking, lthough the relationship does not hold across all banking markets nd concentration variables. The paper is organized as follows. Sec- ion 2 contains a brief discussion of the theoretical and empirical iterature on the topic. In Section 3, we present our databases, vari- bles and methodology used to empirically examine the trade-off etween competition and bank risk. Section 4 presents our empir-

cal results, and Section 5 concludes.

. Literature review

.1. Theoretical literature

The “franchise value” paradigm for bank risk-taking, both with nd without government regulation, is well established in the c

al Stability 9 (2013) 185– 195

anking literature. Simply stated, the idea is that banks limit their isk-taking in order to protect the quasi-monopoly rents granted y their government charters. Increased competition would erode hese rents and the value of the charters, which would likely lead o greater bank risk-taking and greater financial instability.

One of the earliest papers in this literature was by Marcus 1984), who used a one-period model to show that franchise value eclines as a bank engages in riskier policies. Chan et al. (1986) howed that increased competition erodes the surplus that banks an earn by identifying high-quality borrowers. The reduction in alue leads banks to reduce their screening of potential borrowers nd, thus, overall portfolio credit quality declines. Keeley (1990), ollowing Furlong and Keeley (1989), used a state preference model ith two periods to show explicitly that a decline in franchise value

ncreases bank risk-taking. Besanko and Thakor (1993) showed hat increased competition erodes informational rents originated rom relationship banking and leads to greater risk-taking by banks. n a context of asymmetric information, Marquez (2002) showed hat an increase in the number of banks in a market disperses the orrower-specific information and results in both higher funding osts and greater access to credit for low-quality borrowers.

Using a dynamic optimization model with an infinite horizon, uárez (1994) showed a trade-off between market power and sol- ency. If the market power of the bank decreases, the incentive to ngage in riskier policies increases significantly. As the franchise alue of the bank is a component of bankruptcy costs, it should ncourage the bank to carry out prudent policies that increase the olvency of the bank.2 Matutes and Vives (1996, 2000) showed n a framework of imperfect competition (i.e., product differentia- ion) that higher market power reduces a bank’s default probability. ellmann et al. (2000) showed in a dynamic model of moral haz- rd that competition can have a negative impact on prudent bank ehavior. Capital requirements are not sufficient to reduce the ambling incentives in the system, and deposit rate controls need o be added as an additional regulatory instrument. Building on hat, Repullo (2004) used a dynamic model of imperfect bank- ng competition to show that more competition (i.e., lower bank

argins) leads to more risk-taking in the absence of regulation, isk-based capital requirements were found to effectively control he risk-shifting incentives in that model.

As an interesting alternative to the franchise value paradigm, oyd and De Nicoló (BDN, 2005) developed a model, modifying ne presented by Allen and Gale (2000), where an increase in bank arket power both in the loan and deposit markets translates into

igher loan rates charged to borrowers. In a moral hazard envi- onment as per Stiglitz and Weiss (1981), entrepreneurs facing igher interest rates on their loans would choose to increase the isk of their investment projects, a practice that would lead to more roblem loans and a higher bankruptcy risk for banks. They find a onotonic declining relationship between competition (measured

s the number of banks lending in a market) and bank risk; that is, s the number of banks and competition increases, the level of bank isk would decline.

Martínez-Miera and Repullo (MMR, 2010) extend the BDN odel by introducing imperfect correlation across borrowing

rms. Under this assumption, two potentially countervailing ffects of bank competition are introduced. As in the BDN model, he “risk-shifting” effect captures the result that more competi-

2 Chan et al. (1986) also consider the franchise value a component of the private ost of bankruptcy.

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hich should lead to potentially greater bank risk and bank failures. his effect is defined as the “margin” effect by the authors. In the MR model, a U-shaped relationship between bank competition

measured as the number of banks) and the risk of bank failure s found to represent the net effect of these two forces. The risk- hifting effect is shown to dominate in very concentrated markets, uch that increased entry improves bank risk measures. In already ompetitive markets, the margin effect dominates such that fur- her entry worsens bank risk. Thus, the lowest degrees of bank risk re obtained in loan markets with moderate levels of competition. he authors importantly found that the results hold whether the ariable of interest is loan supply or pricing, thus expanding the set f circumstances under which the model applies.

.2. Empirical literature

The empirical literature that we address in this paper focuses n the relationship between measures of competition in banking arkets and bank risk. The extant studies use different measures

f bank competition, which often highlight deposit market com- etition. Keeley (1990) measured the degree of bank competition sing Tobin’s q, which is defined as the ratio of a bank’s equity mar- et valuation to its book value. First, he showed that liberalization easures eroded Tobin’s q, controlling for macroeconomic vari-

bles and bank characteristics. Second, he related two measures of ank risk to his measure of market power finding that: (1) a bank’s olvency ratio, defined as the market value of capital divided by the arket value of assets, had a positive relationship (i.e., higher mar-

et power was correlated with greater solvency) and (2) funding osts via large certificates of deposit (CDs) had a negative rela- ionship (i.e., as market power declined, the perceived bankruptcy isk of large banks increased and so did the cost of their uninsured arge CDs). On aggregate, these results support the franchise value aradigm.

Demsetz et al. (1996) showed that U.S. banks with greater mar- et power also have the largest solvency ratios and a lower level of sset risk. Saunders and Wilson (1996), for a sample of U.S. bank nd a period of a century, found support for Keeley’s results in the eriod from 1973 to 1992.3 For a sample of publicly traded U.S. hrifts, Brewer and Saidenberg (1996) found a negative relation- hip between franchise value and risk measured as the volatility f their stock prices. Hellmann et al. (2000) expressed the view hat Japanese financial-market liberalization in the 1990s increased ompetition and reduced the profitability and franchise value of omestic banks, which, jointly with other factors, lead to the East sian financial crisis and a weaker financial system in Japan. Salas nd Saurina (2003) replicated Keeley’s work for Spain, finding a ery significant and robust relationship between Tobin’s q and he solvency and non-performing loan ratios of Spanish banks.4

reater market power was found to be correlated with higher bank olvency ratios and lower credit risk losses. For Italy, Bofondi and obbi (2004) found that a bank’s loan default rate increases as the umber of banks in a market increases.

In contrast, Jayaratne and Strahan (1998) showed that bank erformance, measured using return on assets, return on equity, nd several indicators of credit quality, improved significantly after

3 In fact, Rhoades and Rutz (1982) had already found, using a quite different ethodology, that banks with higher market power (measured using a concen-

ration index) were more risk-averse. 4 The paper contains a detailed overview of regulatory changes in Spain during

he last three decades as well as some description of the institutional setting. In articular, Spain has a pre-funded deposit insurance system based on flat rates on eposits, which is independent of bank risk level. The analysis presented here differs arkedly both in terms of the variables and methodology used.

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estrictions on banks’ geographic expansion were lifted in the U.S. oreover, loan losses decreased sharply after statewide branch-

ng was permitted. Thus, an increase in competition seems to have ad the opposite effect of the franchise value paradigm. Neverthe-

ess, Dick (2006) provides evidence of a positive and significant elationship between banking deregulation and increases in loan osses. Hannan and Prager (1998) showed that liberalization of nterstate branching and operations increased competition in the eposit market and reduced profitability, ceteris paribus Moreover, he literature focusing on new bank entrants finds that increases in oan market competition may increase loan losses due to the win- er’s curse arising from larger degrees of asymmetric information; ee Shaffer (1998).

In the above-mentioned studies, differences in the degree of ank competition were either cross-sectional or caused by key hanges in regulation within one country. Several other studies ave examined this relationship in a cross-country setting. Beck t al. (2006) examine banking data for 69 countries over a 20 year eriod, and they found that more concentrated national banking ystems are subject to a lower probability of systemic banking cri- is and hence are more stable. However, they cast doubts on the ppropriateness of the share of assets of the three largest banks n the banking system of each country (i.e., their C3 measure) and elated measures as proxies for competitiveness in a national bank- ng system. Claessens and Laeven (2004) showed a positive and ignificant relationship between bank concentration measured as 5 and the H-statistic, a measure of the intensity of competition in a arket developed by Panzar and Rosse (1987). Robustness analyses

f this result showed that the relationship between concentration nd the H-statistic could also be insignificant, and they concluded hat bank concentration is not a good summary of the bank com- etitive environment.5 Also using the H-statistic as the measure of ompetitiveness, Levy Yeyati and Micco (2007) found an increase n bank risk as bank competition increased in eight Latin American ountries.

In contrast, Boyd et al. (2006) provided cross-country empirical vidence supporting the risk-shifting model using several meas- res of bank risk – namely a z-score based on bank returns on ssets (ROA), its dispersion measured as �(ROA), and the ratio f equity to total assets – and bank competition measured using

Herfindahl–Hirschmann index (HHI). They examined two data amples: a cross section of around 2500 small, rural banks oper- ting within the U.S. and a panel of about 2700 banks from 134 ountries, excluding Western countries. In both samples, they ound a negative and significant relationship between their z-score nd the HHI; thus, more concentrated banking markets are asso- iated with greater risk of bank failures. Moreover, De Nicoló and oukoianova (2007) found that this result is stronger when bank wnership is taken into account. Also in a cross-country setting, chaek et al. (2006) found that more competitive national banking ystems are less prone to systemic crises based on their analysis of 8 countries over the period from 1980 to 2003 again using the H tatistic.

Berger et al. (2009) found more nuanced results in their study f banking systems across 23 developing countries. In that study, hey examine how several measures of bank-level risk, such as on-performing loans and book-value failure probabilities (i.e., -scores), are affected by a bank-level Lerner index, which is a

ark-up of prices over marginal costs and is generated using a

ranslog cost function. Importantly, the authors include squared ompetition measures in their empirical work to account for

5 A survey of the literature on bank concentration and competition is in Berger t al. (2004).

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88 G. Jiménez et al. / Journal of F

he possible nonlinearities suggested by the MMR model. Their esults provide some support for the theoretical MMR result in hat they found support for both the “competition-fragility” and competition-stability” hypotheses. They found that banks with ore market power typically have lower overall risk measures, hich supports the “competition-fragility” hypothesis, as well as

iskier loan portfolios, a result that is partially offset by their higher apital ratios.

. Data and model description

.1. Data

In this paper, we use different measures of bank market power nd risk-taking to test whether the franchise value paradigm, the isk-shifting paradigm, or both (as per the MMR model) apply to he Spanish banking system. Our dependent variable measure of isk-taking is a bank’s commercial non-performing loan (NPL) ratio, hich is an ex-post measure of credit risk. We focus on commer-

ial credit risk for two reasons. First, the BDN and MMR models re based importantly on the borrowing behavior of commercial rms, and second, credit risk is the primary driver of risk for most anks, although other risks obviously exist. The NPL ratios for Span-

sh banks are obtained from the credit register maintained by the anco de España, which is known as the Central de Información de iesgos (CIR). The CIR contains information on any loan, includ-

ng mortgages and consumer loans, above a minimum threshold of 6000 granted by any bank operating in Spain. Therefore, it con- ains a full census of commercial loans granted in Spain. We have

onthly information starting in 1984, but for practical reasons, we se only annual data from the month of December without loss of enerality.6

As discussed previously, various measures of the degree of bank ompetition have been used in the literature. We use three standard easures in our analysis – C5, HHI, and the number of banks oper-

ting in each market, which is defined as one of the fifty Spanish rovinces – as proxies for market power. Note that the last mea- ure is the one explicitly used in the MMR model. We also construct erner indexes for a variety of banking products. The Lerner index s a commonly used measure of market power that captures the egree to which a firm can increase their marginal price beyond heir marginal cost. This computation requires a proper estimate f the marginal cost of the product. For our analysis, we take advan- age of a database maintained by the Banco de España that records he marginal interest rate each bank charges on an array of banking roducts each month over the period from 1988 through 2003 (see he Appendix for details of these calculations). For our analysis, we alculate Lerner indexes for commercial bank receivables, credit ines, and all loans, including mortgages and consumer loans. We lso compute Lerner indexes for deposits.

Given that our dependent variable is the level of credit risk at ach bank and that the Spanish credit market is segmented geo- raphically into 50 provinces, the concentration measures reflect he degree of concentration each bank faces in each of the regional

arkets where it operates. We construct an aggregate concentra- ion measure for each bank using a weighted average, where the

eights are the market share of commercial loans each bank holds

n each province. If a bank only operates in one province, it faces he concentration indicators of that province; whereas if a bank perates nationwide, it has a nationwide weighted index for each

6 A more detailed description of the CIR database can be found in Jiménez et al. 2006) and Jiménez et al. (2009).

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f the concentration measures.7 Again, the concentration variables efer to the commercial loan market to be consistent with our other isk and competition measures. Finally, in our analysis, we also use

database of bank accounting data to control for individual bank haracteristics, such as return on assets (ROA). We focus on com- ercial and savings banks, which provide 95% of the credit market

o firms.8

Table 1 presents the descriptive statistics for the variables used. e have 1262 bank-year observations for 107 commercial and sav-

ngs banks over the 14-year sample.9 The average NPL ratio is 4.4% ith a large degree of dispersion across banks; ranging from 0% to

bove 38%. There is significant variation over time in this variable ith the median NPL ratio at around 2% at the beginning of the

ample period, rising to a median value of 7% in 1993, and falling to round 1% in more recent years. These time dynamics are related o the Spanish business cycle, which experienced a deep recession round 1993 and two expansion periods before and after 1993. Real nterest rates declined steadily during the period, as the Spanish conomy converged to that of the euro zone countries.

Next, we summarize our various concentration measures as roxies for the degree of bank competition. While there are a rea- onably large number of banks operating in each provincial credit arket, there is a high degree of dispersion, ranging from provinces ith 22 banks to 148 banks in certain years. We do not have ore detailed geographical market breakdowns but, in general, it

s easy to see a significant correlation between the population of he province and the number of banks operating there. Madrid and arcelona, by far the most populated provinces, have a much higher umber of banks. The correlation coefficient for the logged province opulation and the log of the number of banks in the province is sta- le at 0.88 in 1990 and 0.85 in 2000. Across provinces, we observe

variety of patterns regarding the number of banks. For commercial loans, the market share of the first five com-

ercial lenders in each province, denoted as C5, is relatively high ith an average of 58%, ranging from 40% to 74%. Across provinces

nd time, there are occasional jumps in the C5 index as large banks erge. Regarding mergers, we have treated banks merged as two

eparate entities before the merger and as a new one after it. The HHI for commercial loans has an average of around 8, which

oughly implies 12 banks of equal size per market. Since this num- er is well below the average number of 76 banks per province,

large number of banks in each market must have a tiny market hare with only one or a few branches in the province. This fact fur- her points toward the need for careful use of the number of banks s an empirical proxy for competition in a market, even though it s the one often used in theoretical models. The loan HHI shows no lear cross-sectional pattern across provinces.

With respect to our Lerner index measures, the average index or receivables is positive, although relatively small; margins on eceivables are only about 15% of the rates charged once the risk remium has been accounted for. For credit lines, the index is nega- ive on average and zero at the median, suggesting that the median nterest rate on credit lines only covers the funding cost plus the

7 Our robustness tests indicate that this aggregation procedure does not affect he qualitative results of our analysis.

8 Credit cooperatives and specialized lenders are excluded because of the lack of he required data on interest rates.

9 This is the final number of observations used to run the regressions, after taking rst differences and allowing for lags in the instruments. The original number of bservations was 1632. The data is uniformly distributed across the entire 16 years f the sample period from 1988 to 2003.

G. Jiménez et al. / Journal of Financial Stability 9 (2013) 185– 195 189

Table 1 Descriptive statistics for bank-year observations.

Variables Mean S.D. Median Minimum Maximum

NPLit 4.44 4.93 2.66 0.00 38.02 GDPGt 2.92 1.56 2.76 −1.03 5.04 SIZEit 0.70 1.27 0.28 0.00 9.32 LOAN RATIOit 25.41 12.55 23.00 0.08 90.14 ROAit 0.66 1.19 0.72 −16.19 11.08 Number of banksit 75.93 24.77 73.00 22.00 148.00 C5 loansit 57.73 6.60 58.44 40.00 74.25 Her loans firmsit 8.22 1.86 8.09 4.14 15.02 Lerner receivablesit 0.15 0.39 0.19 −7.96 0.64 Lerner credit linesit −0.10 0.50 0.00 −6.09 0.70 Lerner loansit 0.05 0.53 0.11 −12.27 0.52 C5 depositsit 68.00 5.61 67.35 53.70 84.64 Her depositsit 16.77 3.67 16.33 7.58 28.57 Lerner depositsit 0.35 0.11 0.36 −0.49 0.68

NPLit is the commercial non-performing loan ratio of bank i at time t; GDPGt is the real GDP growth rate of the Spanish economy at time t; SIZEit is the market share of bank i at time t in terms of total loans; LOAN RATIOit measures the specialization of firm i at time t in the non-financial sector through the ratio of loans to firms over total loans; ROAit is the return on assets of bank i at time t; Number of banksit is the number of banks that has the representative province for bank i at time t, calculated as the weighted average (by total loans) over all the provinces where the bank grants loans (the other concentration and competition measures are obtained in the same way); C5 denotes the share of the 5 largest banks in the representative province for bank i at time t; Herit is the Herfindahl index of concentration for the representative province of bank i at time t, calculated in each province as the sum of banks’ squared market shares in loans granted in the province; Lernerit is the Lerner index of bank i in year t defined for p cost o T m wh 1

a d t v m s i c a

3

c

R

w a fi m b

l

T a u o a i

r

p c i m a r

t d b l f r t l v e

p a i i l p p i a m

i s l t p w

roduct l of the asset side as (Rl − R)/Rl , where R is the credit risk adjusted marginal he time period analyzed spans from 1988 to 2003. We have 1632 observations fro 07 unique banks (commercial and savings banks).

Table 1 also shows that commercial and savings banks have an verage annual ROA of 0.66% for the period analyzed, with a high egree of heterogeneity. In the sample, we measure bank size using he share of total CIR loans that the bank originated. The average alue is 0.7%, which is relatively small, but the range goes up to a aximum value of 9.3%; thus, we have also heterogeneity in bank

izes. Finally, there is a significant difference in degrees of special- zation in the commercial lending (LOAN RATIO) as some banks oncentrate on commercial lending (as high as 90%), while others lmost do not operate in that market segment.

.2. Model description

To examine the various hypotheses regarding the effect of bank ompetition on bank risk, we estimate the general regression:

ISKit = f (COMPETITION INDEXit , BUSINESS CYCLEt , BANK CONTROL VARIABLESit ), (1)

here the i subscript refers to a bank and the t subscript refers to sample year. The model sets the relationship between the speci- ed bank risk measure and the specified bank market competition easure, controlling for bank characteristics and the state of the

usiness cycle. The actual model specification we examine is:

n (

NPLit 100 − NPLit

) = ̨ + ̌ ln

( NPLit−1

100 − NPLit−1

) + ı1STRUCTUREit

+ ı2STRUCTURE2it + �1GDPGt + �2GDPGt−1 + �1ROAit + �2SIZEit +�3LOAN RATIOit + �i + εit . (2)

he dependent bank risk variable is the log-odds transformation of bank’s NPL ratio, which changes the variable’s support from the nit interval to the real number line. There is a significant degree f persistence in the transformed NPL variable, as indicated by the verage value of the first-order autocorrelation of 0.68. Hence, we

nclude the lagged dependent variable as an explanatory variable.10

We control for business cycle conditions by introducing the cur- ent and lagged values of the annual real GDP growth rate, since

10 See Salas and Saurina (2002) for a more detailed discussion.

t i t t s d

f product l for bank j granted in year t, while it is defined as (R − Rl )/R for deposits. ich, after taking first differences and instrumenting remain 1262 corresponding to

roblem loans develop in line with the business cycle. We also ontrol for the profitability of the bank, its size, and its special- zation in commercial lending using its contemporaneous ROA, its

arket share in terms of CIR total loans (SIZEit), and its percent- ge of total assets that represent commercial loans (LOAN RATIOit), espectively.

Our primary variables of interest are related to the structure of he banking market and the degree of bank market competition, enoted STRUCTUREit. For the loan market, we use the number of anks, C5, HHI as well as our Lerner indexes for receivables, credit

ines and all loans. For the deposit market, we use the Lerner index or total deposits. We include the squared STRUCTUREit term in our egressions to address the hypothesis within the MMR model that he relationship between the number of banks and bank risk is not inear. We include the bank fixed effect �i to control for unobser- able bank characteristics constant over time, and εit is a random rror that has a normal distribution.

In our model specification, positive values for ı1 and ı2 would rovide evidence in support of the risk-shifting paradigm; that is, s market power increases (and competition decreases), bank risk- ness as measured by NPL ratios would also increase. In contrast, f these parameters are negative, increased market power would ead to less bank risk, which is supportive of the franchise value aradigm. If ı1 is negative and ı2 is positive, the results would sup- ort the U-shaped pattern proposed in the MMR model. Note that

f ı1 is positive and ı2 is negative, the results would still support nonlinear pattern, although the model implications would not atch the MMR model directly. Regarding the other explanatory variables, we expect a signif-

cant positive coefficient for the lagged dependent variable and a ignificant negative effect for the GDPG variables, since problem oans should increase in bad times. We do not have clear expec- ations for the bank characteristics. In general, there should be a ositive, long-term relationship between risk and return, but banks ith high NPL ratios might experience significant losses in a par-

icular year. The specialization of a bank should be indicative of mproved monitoring and screening of borrowers, but, at the same

ime, specialized banks might be willing to take more risks. Finally, here is no general support for a certain relationship between the ize of the bank and its risk level. A larger bank benefits from risk iversification but, at the same time, bank managers could take

1 inancial Stability 9 (2013) 185– 195

a t

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it is

th e

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in

th e

p ro

v in

ce ;

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er it

is

th e

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er

in d

ex

o f

b an

k

i

in

y ea

r

t

d efi

n ed

fo r

p ro

d u

ct

l

o f

th e

as se

t

si d

e

as

(R l −

R )/

R l,

w h

er e

R

is

th e

cr ed

it

ri sk

ad ju

st ed

m ar

g in

al

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90 G. Jiménez et al. / Journal of F

dvantage of that in order to push further along the risk profile of he bank.11

It is possible that unobservable bank characteristics are corre- ated with the bank NPL ratios; for example, the risk aversion of ank managers and/or shareholders. In this case, an OLS estimation f model (2) would produce biased parameters due to the lagged ependent variable. To address these estimation problems, we use he Arellano and Bond (1991) procedure to estimate the model in rst-difference form using GMM estimation techniques. We thus reat bank characteristics as endogenous and use their second lag to nstrument for them. We also consider the concentration and mar- et power measures as potentially endogenous and instrument for hem with the second lag. The validity of these instruments is tested sing the standard Hansen test. Since we take first differences, e should observe first-order autocorrelation and no second-order

utocorrelation in the residuals.12

. Empirical results

.1. Correlations

Table 2 presents the pairwise correlations between the vari- bles. We find a negative relationship between all our measures of ank market power and bank’s commercial NPL ratios, our mea- ure of bank risk. The correlations for the different loan market erner indexes range from −0.56 to −0.20, and the correlation for he deposit market Lerner index is −0.09. Both the C5 and HHI

easures for both markets show a negative, although low correla- ion, with ex-post credit risk. Therefore, simple correlation analysis uggests a negative relationship between market power and bank isk, supporting the franchise value paradigm.

As expected, commercial NPL ratios are correlated negatively ith the business cycle. Specialization in commercial lending

s correlated with lower NPL ratios, probably due to enhanced creening and monitoring of borrowers. We find that current prob- em loans have a negative impact on current profitability. The orrelation between size of the bank and risk in business loans eems weak. Bank profitability seems to be inversely related to he number of banks operating in each local market and positively elated to the standard concentration measures as well as mar- et power indicators. However, the absolute value of correlation oefficients is, in general, low and in the range of [0.16, 0.24] for oans and is 0.18 for deposits. The correlation with the number of anks is actually −0.36.

Among concentration measures, there is a strong negative cor- elation between the number of banks operating in a market and he C5 and HHI measures for both loan and deposit markets, ranging rom −0.67 to −0.42. The C5 and HHI measures for both markets re highly correlated (around 0.85) with each other. Across the two arkets, the correlations based on these concentration measures

re also high at +0.82 for the C5 measure and +0.59 for the HHI easure. Therefore, C5 and HHI would seem to be interchange-

ble as concentration proxies. However, a very different picture merges for the Lerner measures of market power. Within mar- ets, the correlations between the Lerner measures and the two oncentration measures drop sharply to between +0.15 and +0.21.

cross the markets, the correlations between the Lerner indexes re quite low at +0.12, suggesting that loan and deposit markets ight behave differently.

11 See, for instance, Hughes et al. (1996) for this last result. 12 Note that we also estimated the model using just two lags as well as all available ags as instruments, but the overall qualitative results were unchanged. T

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it

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er

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ts jt

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is

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su re

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sp ec

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p re

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ta ti

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p ro

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ce

fo w

ay );

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d en

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s

th e

sh ar

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k s’

sq u

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Table 3 Loan market ln

( NPLit

100−NPLit

) = ˛ + ˇ ln

( NPLit−1

100−NPLit−1

) + ı1 STRUCTUREit + ı2 STRUCTURE2it + �1 GDPGt + �2 GDPGt−1 + �1 ROAit + �2 SIZEit + �3 LOAN RATIOit + �i + εit .

STRUCTUREit ln(# banks) C5 loans Her loans firms Lerner receivables Lerner credit lines Lerner loans

Coefficient t-Statistic Coefficient t-Statistic Coefficient t-Statistic Coefficient t-Statistic Coefficient t-Statistic Coefficient t-Statistic

ln(NPLit−1 /(100 − NPLit−1 )) 0.527 7.57*** 0.516 7.33*** 0.557 8.05*** 0.537 9.32*** 0.484 7.19*** 0.546 9.45*** GDPGt −0.143 −11.21*** −0.155 −11.92*** −0.144 −11.74*** −0.140 −12.54*** −0.122 −10.89*** −0.129 −10.74*** GDPGt−1 −0.030 −1.98** −0.025 −1.64* −0.026 −1.85* −0.046 −3.53*** −0.027 −2.20** −0.045 −3.85*** STRUCTUREit −5.842 −0.76 −0.059 −0.72 −0.395 −2.3** −0.636 −3.23*** −1.333 −5.07*** −0.936 −5.24*** STRUCTURE2it 1.627 0.87 0.000 0.57 0.023 2.37

** −0.065 −3.21*** −0.291 −2.46** −0.076 −5.24*** SIZEit −0.688 −2.49** −0.512 −2.54** −0.625 −3.14*** −0.519 −3.09*** −0.497 −3.17*** −0.453 −3.27*** LOAN RATIOit −0.032 −4.35*** −0.033 −3.79*** −0.027 −2.55** −0.026 −3.09*** −0.013 −1.63 −0.017 −1.95* ROAit 0.008 0.19 0.015 0.38 −0.006 −0.16 −0.108 −0.86 −0.013 −0.22 0.075 1.27 No. observations 1262 1262 1262 1155 1155 1155 F test (p-value) 0.000 0.000 0.000 0.000 0.000 0.000 Test 1st order serial correlation

(m1)/p-value −3.86 0.00 −5.12 0.00 −4.15 0.00 −4.63 0.00 −4.34 0.00 −4.34 0.00

Test 2nd order serial correlation (m2)/p-value

−1.54 0.12 −1.63 0.10 −1.49 0.14 −1.39 0.16 −1.22 0.22 −1.36 0.17

Hansen test (p-value) 0.35 0.49 0.19 0.41 0.22 0.29 Bank fixed effects, �i Yes Yes Yes Yes Yes Yes

NPLit is the commercial non-performing loan ratio of bank i at time t; GDPGt is the real GDP growth rate of the Spanish economy at time t; SIZEit is the market share of bank i at time t in terms of total loans; LOAN RATIOit measures the specialization of firm i at time t in the non-financial sector through the ratio of loans to firms over total loans; ROAit is the return on assets of bank i at time t; Number of banksit is the number of banks that has the representative province for bank i at time t, calculated as the weighted average (by total loans) over all the provinces where the bank grants loans (the other concentration and competition measures are obtained in the same way); C5 denotes the share of the 5 largest banks in the representative province for bank i at time t; Herit is the Herfindahl index of concentration for the representative province of bank i at time t, calculated in each province as the sum of banks’ squared market shares in loans granted in the province; Lernerit is the Lerner index of bank i in year t defined for product l of the asset side as (Rl − R)/Rl , where R is the credit risk adjusted marginal cost of product l for bank j granted in year t, while it is defined as (R − Rl )/R for deposits. The time period analyzed spans from 1988 to 2003. We have 1632 observations from which, after taking first differences and instrumenting remain 1262 corresponding to 107 unique banks. Standard errors (SE) of estimated coefficients consistent to any pattern of heteroskedasticity within banks.

* Statistically significant at 10%. ** Statistically significant at 5%.

*** Statistically significant at 1%.

192 G. Jiménez et al. / Journal of Financial Stability 9 (2013) 185– 195

Fig. 1. Empirical relationship between competition measures in the loan market and bank risk-taking. The x-axes in the graphs below correspond to the values of the alternative loan market measures of competition used in our analysis. These axes are ordered from less to more competition, which in most cases causes the numerical indexes to be reversed. The y-axes correspond to bank risk-taking measured as nonperforming loan ratios. The six competition measures presented here are the number of banks in panel A; C5 in panel B; the Herfindahl index in panel C; the Lerner index for receivables in panel D; the Lerner index for credit lines in panel E; and the Lerner index for total loans in panel F. All charts are computed using the correspondent Table 3 estimation results for values of the competition variable between its observed 1% and 99% percentiles. Number of banksit is the number of banks that has the representative province for bank i at time t, calculated as the weighted average (by total loans) over all the provinces where the bank grants loans (the other concentration and competition measures are obtained in the same way); C5 denotes the share of the 5 largest b ex of c p ce; Le ( ranted

i i u b

4

b f t s t a a

c t g l t T a b

i a i

s l t a t t i i

s h t m o b f I ture terms are again of opposite signs, but are now both significant at the 5% level. In fact, their implied U-shaped pattern is strongly supportive of the MMR model, as shown in Fig. 1C.

13 The x-axis values in Figs. 1 and 2 are based on the observed percentiles of the variables’ empirical distributions, starting with the first percentile and ending with the 99th percentile.

14 Note that in the remaining panels of Figs. 1 and 2, concentration decreases along

anks in the representative province for bank i at time t; Herit is the Herfindahl ind rovince as the sum of banks’ squared market shares in loans granted in the provin Rl − R)/Rl , where R is the credit risk adjusted marginal cost of product l for bank j g

Finally, it should be noticed that, in general, market power s procyclical; that is, macroeconomic improvements seem to ncrease market power in the Spanish market. Concentration meas- res are also positively correlated with the business cycle indicator, ut with low values.

.2. Regression results

Table 3 presents the estimation results for our baseline model ased on the Spanish loan market. The table’s six columns dif- er only by the market structure measure used. The validity of he instruments for our specification is satisfactory in all cases, as hown by the Hansen test. Moreover, as expected since we estimate he model in first differences, there is significant first-order serial utocorrelation in the residuals, but no significant second-order utocorrelation.

In all six regressions, the lagged endogenous variable is signifi- ant at the 1% level with a parameter value around 0.5, confirming he persistence shown in the NPL ratios. The contemporaneous GDP rowth rate is negative and significant at the 1% level, while the agged GDP growth rate is always negative but only significant in he last four columns based on the Herfindahl and Lerner measures. he parameters for these lagged variables are, in absolute terms, lways less than half of the contemporaneous ones, indicating that usiness cycle changes quickly influence firms’ problem loans.

For the bank characteristics, larger banks have lower NPL ratios n all six regressions. Thus, it seems that portfolio diversification nd possibly better managerial ability at larger banks play a role n mitigating credit risk within Spain. We find that the more

t a b m c

oncentration for the representative province of bank i at time t, calculated in each rnerit is the Lerner index of bank i in year t defined for product l of the asset side as

in year t, while it is defined as (R − Rl )/R for deposits.

pecialized a bank is in commercial lending, the lower its problem oan ratio in that sector. This result is statistically significant for he first, second and fourth regressions at the 1% level; significant t 5% level for the third one; significant only at the 10% level for he sixth; and insignificant for the fifth. These results suggest hat specialization improves the screening and monitoring abil- ties of banks. Finally, ROA as a measure of bank profitability is nsignificant in the six regressions.

Regarding the structure variables, the first column of Table 3 hows that the number of banks operating in a market does not ave a statistically significant effect on bank risk-taking, even hough the signs on the coefficients are supportive of the MMR

odel. Fig. 1A shows that as competition increases (e.g. the number f banks operating in the market grows), risk taking first declines ut then increases past a certain point.13 A similar outcome holds or the C5 concentration measure in the second column of Table 3.14

n the third column based on the HHI measure for loans, the struc-

he x-axis, and correspondingly, competition increases. For example, in Fig. 1B, s we move along the x-axis, the C5 measure of market share for the five largest anks decreases, which corresponds to increased competition. Similarly, in Fig. 1C, ovement along the x-axis corresponds to declining HHI values and increasing

ompetition.

G. Jiménez et al. / Journal of Financial Stability 9 (2013) 185– 195 193

Table 4 Deposit market ln

( NPLit

100−NPLit

) = ̨ + ̌ ln

( NPLit−1

100−NPLit−1

) + ı1 STRUCTUREit + ı2 STRUCTURE2it + �1 GDPGt + �2 GDPGt−1 + �1 ROAit + �2 SIZEit + �3 LOAN RATIOit + �i + εit .

STRUCTUREit C5 deposits Her deposits Lerner deposits

Coefficient t-Statistic Coefficient t-Statistic Coefficient t-Statistic

ln(NPLit−1 /(100 − NPLit−1 )) 0.512 6.98*** 0.501 6.52*** 0.548 8.64*** GDPGt −0.135 −8.78*** −0.135 −11.18*** −0.152 −14.04*** GDPGt−1 −0.035 −1.94* −0.034 −2.04** −0.002 −0.13 STRUCTUREit 0.500 2.27** 0.231 3.1*** 0.100 0.07 STRUCTURE2it −0.004 −2.32** −0.007 −3.85*** −1.618 −0.89 SIZEit −0.719 −3.30*** −0.598 −3.07*** −0.666 −3.68*** LOAN RATIOit −0.023 −1.86* −0.035 −3.48*** −0.039 −4.60*** ROAit −0.012 −0.27 −0.001 −0.02 −0.062 −0.57 No. observations 1262 1262 1155 F test (p-value) 0.000 0.000 0.000 Test 1st order serial correlation (m1)/p-value −5.04 0.00 −4.92 0.00 −4.31 0.00 Test 2nd order serial correlation (m2)/p-value −1.40 0.16 −1.51 0.13 −1.13 0.26 Hansen test (p-value) 0.25 0.47 0.51 Bank fixed effects, �i Yes Yes Yes

NPLit is the commercial non-performing loan ratio of bank i at time t; GDPGt is the real GDP growth rate of the Spanish economy at time t; SIZEit is the market share of bank i at time t in terms of total loans; LOAN RATIOit measures the specialization of firm i at time t in the non-financial sector through the ratio of loans to firms over total loans; ROAit is the return on assets of bank i at time t; Number of banksit is the number of banks that has the representative province for bank i at time t, calculated as the weighted average (by total loans) over all the provinces where the bank grants loans (the other concentration and competition measures are obtained in the same way); C5 denotes the share of the 5 largest banks in the representative province for bank i at time t; Herit is the Herfindahl index of concentration for the representative province of bank i at time t, calculated in each province as the sum of banks’ squared market shares in loans granted in the province; Lernerit is the Lerner index of bank i in year t defined for product l of the asset side as (Rl − R)/Rl , where R is the credit risk adjusted marginal cost of product l for bank j granted in year t, while it is defined as (R − Rl )/R for deposits. The time period analyzed spans from 1988 to 2003. We have 1632 observations from which, after taking first differences and instrumenting remain 1262 corresponding to 107 unique banks. Standard errors (SE) of estimated coefficients consistent to any pattern of heteroskedasticity within banks.

* Statistically significant at 10%.

m a – c fi v t t

b m a l

m k d i f t

o u r

F a n a A t t p

** Statistically significant at 5%. *** Statistically significant at 1%.

In the last three columns based on the Lerner market power easures, the ı1 and ı2 estimates are both negative and significant

t the 1% level in all cases. An increase in market power for loans measured as an increase in the Lerner indexes for receivables, redit lines, or total loans – produces a decline in the risk pro- les of the banks in our sample. These results support the franchise alue paradigm instead of the U-shaped relationship proposed in he MMR model, at least for these Lerner index-based measures in he Spanish banking sector.

What if the source of market power arises not from bank assets

ut from their liability structures (or market power in the deposit arkets? For instance, more market power over deposits might

llow banks to be more aggressive in the loan market and thus end to riskier borrowers with the short term objective of increasing

i t F t

ig. 2. Empirical relationship between competition measures in the deposit market and lternative loan market measures of competition used in our analysis. These axes and all o umerical indexes to be reversed. t and the y-axes correspond to bank risk-taking measu re C5 in panel A; the Herfindahl index in panel B; and the Lerner index for deposits in pa ll charts are computed using the correspondent Table 4 estimation results for values of

he share of the 5 largest banks in the representative province for bank i at time t; Herit is ime t, calculated in each province as the sum of banks’ squared market shares in loans roduct l of the asset side as (Rl − R)/Rl , where R is the credit risk adjusted marginal cost o

arket share. From a theoretical standpoint, it is unclear how mar- et power over deposits might affect loan underwriting and pricing ecisions. In fact, as mentioned before, correlation between Lerner

ndexes in loan and deposit markets is positive but very low. There- ore, both markets could be separated, contributing to the value of he bank independently.

Table 4 presents our empirical analysis of these questions using ur baseline specification, but with deposit market structure meas- res. The regression results in the first two columns show that the elationship is not linear, fitting the non-linear pattern proposed

n the MMR model. For both, C5 and HHI measures of concentra- ion, the two estimated parameters are significant at the 5% level. ig. 2A and B shows that from a starting point of high concentra- ion in some provincial markets (i.e., on the left-hand side of the

bank risk-taking. The x-axes in the graphs below correspond to the values of the f them are ordered from less to more competition, which in most cases causes the

red as nonperforming loan ratios. The three competition measures presented here nel C.

the competition variable between its observed 1% and 99% percentiles. C5 denotes the Herfindahl index of concentration for the representative province of bank i at

granted in the province; Lernerit is the Lerner index of bank i in year t defined for f product l for bank j granted in year t, while it is defined as (R − Rl )/R for deposits.

1 inanci

x t a r m t v p t t w s

r t f a a t t r b t m w

5

i v a a e s a h N s i a t i p v

e b c m fi i fi t e n t t i c e c r m

e r c i a r b c r m i o fi b

a b r f i

A

e R t t I e w A G g J V h

A

t r t e p

t p i u charged. Failure to take the risk premium into account would result in significant biases in measuring bank market power.17 If the inter- est rate on a loan is denoted as R1, the Lerner index (or gross profit

15 A more detailed description of this database can be found in Martín et al. (2007). Significant changes in the database prevent us from extending the time period consistently after 2003.

16 Under conventional assumptions, Tirole (1988) shows that the Lerner index

94 G. Jiménez et al. / Journal of F

-axis), as we trace a reduction in concentration by moving along he axis, competition increases as does risk. However, soon after, s concentration is further reduced, the level of NPL and thus of isk declines. It could be the case that competition in the deposit arket leads banks to take on more risk. As this market power in

he deposit market decreases, banks should become more conser- ative in their lending and conscious that their favorable funding ositions are disappearing. The third column of Table 4 presents he Lerner index results, which are not statistically significant; i.e., he results suggest that market power in deposits is uncorrelated ith risk. However, the signs of the coefficients are appropriate and

upportive of a nonlinear relationship. Overall, our empirical results are supportive of the nonlinear

elationship between bank market competition and bank risk- aking, as proposed in the MMR model. The results are strongest or the loan market using traditional concentration variables such s the Herfindahl index, whereas the results using Lerner indexes re more supportive of the franchise value hypothesis, for the ime period and banks analyzed in the paper. In deposit markets, raditional concentration measures are supportive of a nonlinear elationship between competition and risk taking in the asset side, ut the relationship is concave. This unexpected result suggests hat further theoretical work is needed to better frame banking

arket competition, when it affects both sides of the balance sheet ith risk-taking behavior just on the asset side.

. Conclusions

In the academic literature and in the actual supervision of bank- ng systems worldwide, the dominant paradigm is that franchise alue plays a key role in limiting the riskiness of individual banks nd hence of banking systems more broadly. That is, bank man- gement and shareholders will typically limit or reduce their risk xposure to preserve the bank’s franchise value. The underlying ource of franchise value is typically assumed to be market power, nd reduced competition (or, equivalently, market concentration) as been considered to promote banking stability. Boyd and De icoló (BDN, 2005) present an alternative view through a the risk-

hifting paradigm, which argues that market concentration could mpact bank stability in different ways, depending on the net effect cross deposit and loan markets. Specifically, the authors suggest hat concentration in the loan market could lead to increased lend- ng rates that both raise the borrowers’ debt loads and default robabilities as well as their incentive to engage in riskier projects ia moral hazard.

More recently, Martínez-Miera and Repullo (MMR, 2010) xtend the BDN model to allow for a nonlinear relationship etween competition and bank risk-taking, such that both the fran- hise value and risk-shifting paradigms could be possible. Their odel also identifies a risk-shifting effect that accounts for fewer

rm defaults when loan rates decrease in a more competitive bank- ng environment. However, since imperfect correlation between rms is now permitted, there is also a “margin” effect that reduces he interest payments from performing loans and thus bank rev- nues. These two effects work in opposite directions, so that the et effect on bank risk-taking and financial stability is unclear. In heir model, the risk-shifting effect is shown to be dominated by he margin effect in competitive banking environments, such that ncreased competition increases bank failure risk. In a more con- entrated banking market, the model suggests that the risk-shifting

ffect dominates and thus bank failure risk declines with increased ompetition. Overall, the authors show that there is a U-shaped elationship in their model between bank competition, which is easured by the number of banks, and the risk of bank failure.

c a i

i

al Stability 9 (2013) 185– 195

Using unique datasets covering the Spanish banking system, we xplicitly examine the relationship between bank competition and isk. Our dependent variable is a bank’s ratio of non-performing ommercial loans (NPL), which is the variable addressed directly n the MMR model. After controlling for macroeconomic conditions nd bank characteristics, we find clear support for this nonlinear elationship using standard measures of market concentration in oth loan and deposit markets. While the relationship between ompetition and risk taking is convex in the loan market, the elationship is unexpectedly concave when examining the deposit arket. When direct measures of market power, such as Lerner

ndices, are used, the empirical results are more supportive of the riginal franchise value hypothesis, but only in the loan market. We nd that the number of banks, which is the measure highlighted in oth models, has little effect on NPL ratios.

The main contribution of our paper is to perform a focused nd precise test of the relationship between bank competition and ank risk using data for the Spanish banking market. Our empirical esults provide support for the MMR model, but also point toward urther extensions in the model to take into account competition n deposit markets and its interaction with loan rates.

cknowledgements

The views expressed here are those of the authors and not nec- ssarily those of the Banco de España, the Eurosystem, the Federal eserve Bank of San Francisco or the Federal Reserve System. We hank the editor Iftekhar Hasan and an anonymous referee for heir helpful comments. We also thank seminar participants at the nternational Monetary Fund, the Federal Reserve Board of Gov- rnors, and several conferences for their comments. In particular, e also thank Antonio Antunes, Rima Turk Ariss, Thorston Beck, llen Berger, Lamont Black, Arnoud Boot, John Boyd, Mark Carey, ianni de Nicoló, Olivier De Jonghe, Robert DeYoung, Enrica Detra- iache, Astrid Dick, Mark Flannery, Christopher James, Kevin James, an Pieter Krahnen, Loretta Mester, Rafael Repullo, Nuno Ribeiro, icente Salas, Joao Santos, Miguel Segoviano, and Javier Suárez for elpful comments.

ppendix. Lerner index calculations

For our paper, we take advantage of another database main- ained by the Banco de España that records the marginal interest ate each bank charges on an array of banking each month over he period from 1988 through 2003. That is, for each bank and ach banking product, we have the average interest rate set on that roduct for new transactions.15

The Lerner index is a commonly used measure of market power hat captures the degree to which a firm can increase their marginal rice beyond their marginal cost.16 The computation of the Lerner

ndex requires a proper estimation of the marginal cost of the prod- ct, which for bank loans requires a measure of the risk premium

an be related to measures of welfare losses such as Harberger’s (1964) triangle. In recent paper, Carbó-Valverde et al. (2008) also use the Lerner index to test the mpact of market power on SME lending constraints. 17 We follow Martín et al. (2006) in what follows to properly construct the Lerner ndexes.

inanci

m R I i t h t

t a z c a f c v p b a b b r t L S

b m f d b a c i c

R

A A

B

B

B

B

B

B

B

r

k r a p p t

B

C

C

C

C

C

D

D

D

F

F

H

H

H

H

J

J

J

K

L

M

M

M

M

M

M

M

P

R

G. Jiménez et al. / Journal of F

argin relative to the market price) is defined as (R1 − R)/R1, where is the marginal cost to the bank of acquiring the funds for the loan.

f we introduce the realistic assumption that the marginal operat- ng costs of loans and deposits are either fixed in the very short erm or impossible to calculate separately, we assume that banks ave a lower bound on the marginal cost for their loans equal to he interest rate offered in the interbank market.18

However, banks must introduce a risk premium into their prices o account for credit risk. Let PD be the probability that a loan, with

normalized face value of one, will default over a specified hori- on, and let LGD be the amount of the loan’s value that the bank annot collect in case of default. If the interbank interest rate r is ssumed to be risk-free, the marginal opportunity cost of the loan or a risk-neutral bank will be the interest rate R that satisfies the ondition that the risk-free value of the loan equals the expected alue of the loan, given the PD and LGD parameters. From this sim- le identity, the marginal cost R = (r + PD − LGD)/(1 − PD − LGD) can e derived. For our calculations, the risk-free interest rate r is the nnual average of the daily interbank rate. The bank-specific, not orrower-specific, PD is obtained directly from the CIR; for a given ank and loan product at the end of year t, PD equals the bank’s atio of defaulted loans divided by total outstanding loans of that ype. Since we do not have bank-specific information regarding GD, we use the value 45% set by the Basel Committee of Banking upervisors for its regulatory capital framework.19

For our analysis, we calculate Lerner indexes for commercial anking receivables and credit lines as well as all loans, including ortgages and consumer loans. We also compute Lerner indexes

or deposits by assuming (1) the separability between loan and eposit pricing and (2) that the interbank rate acts as an upper ound for deposit rates. The Lerner index for deposits is calculated s (r − Rd)/r, where Rd is the bank’s offered rate on deposits. We also alculate an average Lerner index for loans and deposits together n order to consider the possibility that loan and deposit markets annot be separated.

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  • How does competition affect bank risk-taking?
    • 1 Introduction
    • 2 Literature review
      • 2.1 Theoretical literature
      • 2.2 Empirical literature
    • 3 Data and model description
      • 3.1 Data
      • 3.2 Model description
    • 4 Empirical results
      • 4.1 Correlations
      • 4.2 Regression results
    • 5 Conclusions
    • Acknowledgements
    • Appendix Lerner index calculations
    • References

Bank-competition-and-financial-stability-A-comparison-of-commercial-banks-and-mutual-savings-banks-in-Korea_2013_Pacific-Basin-Finance-Journal.pdf

Pacific-Basin Finance Journal 25 (2013) 253–272

Contents lists available at ScienceDirect

Pacific-Basin Finance Journal

journal homepage: www.elsevier.com/locate/pacfin

Bank competition and financial stability: A comparison of commercial banks and mutual savings banks in Korea☆

Jin Q. Jeon a,⁎, Kwang Kyu Lim b

a Dongguk Business School, Dongguk University, 3-26 Pil-dong, Chung-gu, Seoul 100-715, South Korea b Economist, Macroprudential Analysis Department, The Bank of Korea, 110, Namdaemunro 3-Ga, Jung-Gu, Seoul 100-794, South Korea

a r t i c l e i n f o

☆ We thank seminar participants at the Financial St is grateful to the Dongguk University Research Fund ⁎ Corresponding author. Tel.: +82 2 2260 8911.

E-mail addresses: [email protected] (J.Q. Jeon),

0927-538X/$ – see front matter © 2013 Elsevier B.V. http://dx.doi.org/10.1016/j.pacfin.2013.10.003

a b s t r a c t

Article history: Received 5 February 2013 Accepted 8 October 2013 Available online 17 October 2013

In this study, we provide new evidence that the relationship between banking competition and financial stability varies depending on the characteristics of banks. By using a sample of two different types of banks, Korean commercial banks and mutual savings banks, we find that the non-linear relationship between competition and the stability of commercial banks reflects a trade-off between the interest effect and risk-shifting effect. However, consistent with Boyd and De Nicolo (2005), competition has a positive effect on the stability of mutual savings banks with greater business risk and weaker corporate governance. Our results provide important implications on banking competition policy.

© 2013 Elsevier B.V. All rights reserved.

JEL classification: G21 G23 L1

Keywords: Bank competition Stability Risk shifting Mutual saving banks Commercial banks

1. Introduction

Banking literature provides two alternative hypotheses regarding the relationship between bank competition and stability or risk-taking behavior. According to the conventional competition–fragility theory, higher competition in the financial services industry causes financial institutions to lose their market power, leading to a decrease in profitability. In order to recover from financial losses, financial institutions are more likely to invest in riskier portfolios. Consequently, this risk-taking behavior will undermine the stability of financial institutions (Keeley, 1990; Allen and Gale, 2000; Hellmann et al., 2000, etc.).

ability Forum supported by the Bank of Korea for helpful Comments. Jin Q. Jeon for financial support. Remaining errors are our own.

[email protected] (K.K. Lim)

All rights reserved.

254 J.Q. Jeon, K.K. Lim / Pacific-Basin Finance Journal 25 (2013) 253–272

Competition–stability theory, on the other hand, suggests that competition has a potentially positive effect on the stability of financial institutions. According to Boyd and De Nicolo (2005), banks with a greater market share in the loan market, which experience lower competition, tend to impose higher interest rates on their loans. Higher interest rates being charged by banks in a less competitive market may increase the risk-taking behavior of borrowing firms. Boyd and De Nicolo consequently argue that since the risk is ultimately shifted from borrowers to banks in this situation, the default probability of banks increases in the riskiness of bank loans.

A recent study by Martinez-Miera and Repullo (2010) provides partial support for Boyd and De Nicolo's (2005) risk-shifting effect that the greater interest rates that exist in less competitive markets raise the risk of loans and thus make a bank bankruptcy more likely. They, however, additionally take into account the fact that greater interest rates also improve bank profitability, which is known as the interest effect, and suggest that there exists a U-shaped relationship between competition and the risk of bank failure. Thus far, empirical research has produced mixed results on the influence of bank competition on stability (e.g., Berger et al., 2009; Tabak et al., 2012a; Beck et al., 2013).

The purpose of this paper is to investigate the relationship between competition and stability by using a sample of two different types of banks: mutual savings banks (hereafter MSBs) and commercial banks in the Korean depository industry. Although previous studies have indicated the theoretical and empirical links between bank competition and stability, it has not as yet been investigated how this relationship varies across different types of banks. This paper attempts to fill this gap in the literature by examining whether there exist differences in terms of governance structure, loan characteristics and regulatory environments that cause banks to differently interact with industry competition.

The primary difference in the corporate governance structure between MSBs and commercial banks in the Korean markets is that MSB ownership is, in general, concentrated in the hands of a few individuals, who customarily belong to one family, while the ownership of commercial banks is widely dispersed.1 It is also important to note that since there are little mandatory requirements for disclosure, MSBs which provide little information on their financial situation are not affected by market discipline mechanisms. This creates a moral hazard problem in that the major shareholders and senior management are likely to take excessive risks in pursuit of private gains. Moreover, the borrowers of MSBs are mainly small and medium-sized enterprises (hereafter, SME), which possess greater business and credit risks than is true of the borrowers of commercial banks. Under this circumstance, MSBs in less competitive markets are more likely to be exposed to a greater degree of moral hazard and to charge higher interest rates on their loans.2 As a result, in light of the lower level of competition, MSB borrowers are more likely to choose risky projects to repay high interest rates, leading to the high possibility of defaults for both the borrowers and MSBs. As Martinez-Miera and Repullo observe, the risk-shifting effect may overwhelm the interest effect for MSBs.

This study investigates the hypothesis using the quarterly panel data of MSBs and commercial banks from 1999, the end of Asian financial crisis, through to 2011. We first estimate the level of bank competition. Although it is hard to directly measure competition, several possible approaches to this are presented in the banking literature. We follow Boone (2008a, 2008b) which measures competitiveness using operating efficiency, known as the Boone index. The conventional concentration ratio and the Herfindahl–Hirschman Index are also used as supplementary measures for bank competition. We employ the Z-score as a measure of bank stability which can capture how far a given bank is from insolvency. Finally, we estimate pooling regressions, panel regressions, and the difference-in-differences model to examine the relationship between the competitive levels and stability of MSBs and commercial banks.

Our empirical results generally support the hypothesis that the effect of bank competition on stability is different depending on the characteristics of the banks involved. The results of our multiple regressions show that competition has a significant and positive effect on the stability of MSBs with weak corporate governance. On the contrary, competition pressure significantly reduces the stability of commercial banks but this relationship is shown to be non-linear. Consistent with Tabak et al. (2012a), commercial banks are more stable at the very low or very high level of competition, while they are riskier at the medium level of

1 As of March 2011, the average ownership by the largest shareholders of large MSBs is 62.2%, whereas that of small and medium- sized MSBs is 70.4% (Korea Financial Supervisory Service, 2012).

2 It has been documented that the recent distress of MSBs has mainly been caused by excess risk-taking and, in some MSBs, unauthorized appropriation by major shareholders and senior management, and by the failure of effective monitoring on the part of internal and external governance mechanisms (See The Korean Herald, May 2, 2011; Korea Times, May 14, 2012).

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competition. These results are robust across alternative proxies for competition and stability as well as across various model specifications. Therefore, we conclude that, for commercial banks at least, higher competition creates a trade-off between the risk-shifting effect and interest effect, but the risk shifting effect overwhelms the interest effect for MSBs when the market is less competitive. In addition, our difference-in-differences analysis shows that the positive effect of competition on the stability of MSBs became even greater after deregulations by the Korean government and after the global financial crisis.

The paper contributes to the literature in several ways. First, while most of previous studies examine the relationship using either domestic or international banking data and focusing only on the commercial bank industry, it is one of the first works to provide comprehensive evidence on the relationship between bank competition and stability, as conditional on different bank characteristics. Second, the paper can provide further understanding of the influence of bank competition on stability by showing that competition significantly decreases the risk-taking behaviors of MSBs with weaker corporate governance and greater business risk, while it has a non-linear relationship with stability for commercial banks. Third, our evidence can provide important implications on banking competition policy of which the main focus is to ensure financial stability. We argue that competition policy should be applied differently to commercial banks and MSBs. That is, either a very low or a very high degree of competition would be optimal for commercial banks, while a higher degree of competition will significantly decrease the default risk of MSBs. Fourth, it is the first paper to analyze Korean MSBs by using hand-collected data. Thus far, there has been no published empirical research directly examining MSBs, due to the lack of available data that results from the fact that most MSBs are non-listed.3 Finally, while the previous literature has used annual financial data in its analysis, this work aims to derive more reliable results from its empirical tests by using quarterly financial data, which enables us to analyze a larger number of observations. We should, however, be clear about the limitations of the work. Due to the lack of detailed information on the corporate governance of MSBs, the paper does not address how the corporate governance mechanisms of banks affect the relationship between competition and stability, which we leave for future research.

The paper is organized as follows. Section 2 provides a literature review. In Section 3, we describe our sample and empirical methodology. Section 4 analyzes the relationship between competition and stability, while Section 5 concludes.

2. Literature review

2.1. Bank competition and stability

According to the Bank for International Settlement (BIS) and International Monetary Fund (IMF), the world has seen a rapid consolidation of banks during the past decades, which results in a decrease in the number of banks and an increase in the average size of the banks.4 The Korean banking industry also has experienced unprecedented merger and restructuring activity during the last decade. As a result, the number of MSBs and commercial banks has significantly declined leading to a rise in the degree of concentration (for detailed discussions, see Section 2.2).

Several recent studies show that consolidation and the associated change in the size structure of the banking industry significantly affect not only the performance of individual banks but also market-wide competition and stability of banks. Berger et al. (2007) examine the effect of the change in market structure and competition in the banking industry on the performance and stability of banks. They show that the increased presence of large multi-market banks in a local market significantly lowers loan rates indicating higher competition for small business loans. Using a theoretical framework to analyze competition between large multimarket banks and small local banks, Park and Pennacchi (2009) indicate that large banks are likely to set uniform loan rates across their markets, whereas small banks tend to set rates based on the competitive environment within their respective market. They conclude that competition from the large multimarket banks tends to benefit borrowers but harm depositors. Erel (2011) find that bank mergers, on average, reduce loan

3 As of 2011, only 7 out of 105 MSBs were listed in the Korean exchange. 4 See “The banking industry in the emerging market economies: Competition, consolidation, and systemic stability.” (BIS paper 4,

2001) and “Financial sector consolidation in emerging markets” (International Capital Market Report published by the International Monetary Fund, 2001).

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spreads and the effect is larger with an acquirer with larger declines in operating costs. However, the spread is larger when the acquirer and target have significant market overlap and increased market power post merger. Cyree and Spurlin (2012) investigate the effect of the presence of a big bank on the performance of rural and small banks. They find lower profit efficiency for rural one-county banks, while they tend to achieve greater ROA and higher levels of interests and fees from their loans.

Turning now to the relationship between bank competition and stability, theoretical models and empirical results have provided conflicting evidence. According to the theory of competition fragility, a greater level of competition in the banking industry leads to more fragility. On the other hand, in a less competitive market, banks will have numerous lending opportunities and can increase profits and capital ratios, and as such they will be able to withstand any economic shocks and are less likely to take excessive risk. Keeley (1990) developed a theoretical model that predicts that banks with more pressures on their profits have higher incentives to take excessive risk, resulting in a greater probability of defaults. Allen and Gale (2000) report that the number of competitors in a loan market has a positive effect on bank defaults. Hellmann et al. (2000) examine the relationship between competition for deposits, risk taking and regulation, using a dynamic framework model. They argue that removal of interest ceilings on deposits erodes franchise value and motivates moral hazard behavior by banks. The framework of these models is characterized by the fact that bank competition is modeled on the liability side whereas banks' asset allocation decisions are modeled on the asset side, which is not affected by bank competition.

However, more recent papers have taken into account the relationship between banks and borrowers on the asset side of banks. Boyd and De Nicolo (2005), in their theoretical model, assume that borrowing firms entirely determine the risk of projects in the condition of the loan rates set by banks. This paper supports the competition–stability hypothesis by showing that greater concentration (less competition) causes banks to become riskier because the greater loan rate charged by banks with less competition implies a greater bankruptcy risk for borrowing firms. The empirical study by Boyd et al. (2006), who measure market structure by concentration indicators, also shows that, consistent with Boyd and De Nicolo (2005), the probability of bank failure increases by the level of concentration in the bank industry. However, Martinez-Miera and Repullo (2010) point out that the competition–stability view supported by Boyd and De Nicolo does not necessarily hold when the loan defaults are imperfectly correlated. Rather, greater competition decreases the risk taking by borrowing firms and thus reduces the probability of bank defaults, which is called the risk-shifting effect. On the other hand, a decrease in loan rates due to greater competition reduces bank profits, known as the margin effect, which is not considered in Boyd and De Nicolo. As a result, Martinez-Miera et al. suggest a non-linear U-shaped relationship between competition and bank risk taking.

Recent empirical literature on the effect of bank competition on stability has shown mixed results. Berger et al. (2009) examine 8235 banks in 23 developed economies and report the results that support both the competition–fragility and competition–stability views. Specifically, they find that banks with greater market power have riskier loan portfolios but that the risk is traded off by higher capital ratios or other risk-mitigating techniques. Tabak et al. (2012a) use bank data from 10 Latin American countries from 2003 to 2008 and find evidence that the relationship between competition and risk-taking is non-linear. That is, both high and low levels of competition significantly increase bank stability, while the opposite is true under moderate competition. Beck et al. (2013) study cross-country variation in the relationship between bank competition and stability. Specifically, they investigate how heterogeneous regulatory and institutional features affect this relationship across countries. Their paper shows that competition significantly decreases bank stability in countries with stronger activity restrictions and more homogenous market environments. They also find that the deposit insurance policy and efficiency of credit information sharing are important determinants of the negative relationship between stability and competition. Finally, Liu and Wilson (2011) examine whether the effect of competition on stability varies depending on the characteristics of banks in the Japanese market. They found that competition enhances the stability of banks with a lower stability level, but damages the stability of banks with a higher stability level.

Several studies have extended the empirical framework by including the banking system and the government policies. For example, Hakenes and Schnabel (2011) analyze the effects of capital regulation on banking stability when banks compete for loans and deposit. They show that tighter capital regulation increase the probability of loan default, which increase the risk of banks. Tabak et al. (2012b) investigate how bank size and market competition affect bank stability in 17 Latin countries. They show that in concentrated markets a few dominant banks are likely to outperform other banks, while this unequal

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banking system is detrimental for the stability and performance of small banks. Gropp et al. (2011) test the competitive effects of bail-out policies by the government. Their results suggest that government guarantees significantly increase the risk-taking of competitors, while they don't find any evidence on the greater default probability of the protected banks.

2.2. Mutual savings banks and commercial banks in Korea

Based on Mutual Savings Banks Act of 2001, a MSB is classified as a non-bank depositary institution. This type of institution was introduced by the Korean government in order to protect consumers by incorporating private lenders into the financial landscape and rationalizing their business, as well as to act as a financial intermediary for SMEs, which tend to have low credit ratings and sound solvency. MSBs charge higher interest rates than commercial banks so as to expand their business base and to compensate for greater perceived risk. Due to drastic deregulations by the Korean government in order to support microfinance and promote small loan markets, MSBs aggressively expanded their microcredit loans during the mid-2000s. Furthermore, they enjoyed a boom as real estate project financing loans rose sharply as a consequence of the real estate boom. However, from 2007, sluggishness in the real estate business and the global financial crisis increased the defaults in the real estate project financing loans extended by the MSBs. Consequently, as of the end of 2011, MSBs have greater risk and more vulnerable loans than is true of other financial institutions. For example, low-rated loans with a borrower's credit score greater than 7 account for half of the total household loans of MSBs, according to the Bank of Korea.5 The average credit rating of companies borrowing from MSBs is at CC. The proportion of loans given to those whose credit rating is B-rated and above, which is in general considered relatively good, accounts for only 20% of the total corporate loans of MSBs. It has been documented that MSB distress is mainly caused by excess risk-taking practices and, in some MSBs, unauthorized appropriation by major shareholders and senior management, as well as by the failure of effective monitoring on the part of internal and external governance mechanisms.

According to the Korea Federation of Saving Banks (KFSB), the number of MSBs showed a downward trend from 211 at the end of 1998 to 105 at the end of 2010.6 During this period, many MSBs were restructured by license revocations, mergers and acquisitions, as well as purchase and assumptions (P&A). Furthermore, 20 banks (e.g., Samhwa and Busan) were ordered to suspend business between 2011 and 2012. As a result, 93 MSBs were in business as of the end of June 2012. The default problems of these MSBs have not yet been solved in Korea because there are worries about additional defaults in real estate project financing loans and uncertainties throughout the industry, which have been caused by a series of business suspensions of some large-sized MSBs. The KFSB reports that the asset size of MSBs had expanded from 25 trillion won at the end of 2002 to 87 trillion won at its peak at the end of 2010. After then, however, their total asset size diminished very sharply, to approximately 60 trillion won as of the end of 2011.

Korean commercial banks experienced a strong restructuring as a result of the government putting large-scale public funds into insolvent banks from 1997 to 1998, when Korea suffered from a currency crisis. Entering the 2000s, Korean banks also continued to grow larger and larger through mergers. In 2001, the Korean megabanks started with Woori Bank, which was formed as a merger by the Peace Bank of Korea and Hanvit, followed by Kookmin (merged with Housing & Commercial Bank) and Shinhan (merged with Cho Hung). Likewise, City Bank Korea took over KorAm in 2004 and First City Bank of Korea was acquired by a British-based bank, Standard Chartered plc., in April 2005. As a result of a series of restructurings in the Korean banking system since the Asian currency crisis, the number of commercial banks (including regional banks) declined from 26 banks in 1997 to 17 banks in 2000, to 13 banks as of 2012. The Korean banking industry as such experienced large-scale restructuring during this short period. Consequently, this has led to a rise in degree of concentration within the Korean banking industry and concerns have ceaselessly been raised over a marked decline in market competition.

5 In Korea, a borrower's credit score assigned by financial institutions ranges from 1 to 10. A higher credit score indicates a weaker status in terms of repayment capacity. Accordingly, borrowers with a credit score of 7 and above have higher possibility of delinquency than those with lower credit scores.

6 http://www.fsb.or.kr/.

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3. Sample and methodology

3.1. Sample

The sample period for the Korean commercial banks starts in January 1999 and ends in December 2011. For the MSBs, however, it starts from 2003, due to data limitations. We obtain financial data on the population of commercial banks and hand-collect the data for the MSBs, most of which are non-listed firms. The databases that we use include the Data Analysis, Retrieval and Transfer System (DART) and the Financial Statistics Information System (FISIS) maintained by the Korean Financial Supervisory Service (FSS), as well as the Economic Statistics System (ECOS) by the Bank of Korea.7 Since the data from the DART, FISIS and ECOS are not sufficient for our analysis, we collect data based on the reports of each MSB. Even though most empirical studies on banking competition use annual financial data, we use quarterly data because this enables us to provide more reliable results.

Table 1 reports the descriptive statistics for our sample of MSBs and commercial banks. Market shares are calculated based on total assets, total loans, household loans, and commercial loans. The average market share in total assets is 8.489% for MSBs and 5.882% for commercial banks. The size of the commercial banks is greater than that of the MSBs. The natural logarithm of total assets for MSBs is, on average, 7.652, while it is 17.404 for commercial banks. The average profit ratio, calculated as net income divided by total revenues, of MSBs, is −5.599%, which reflects the depression that has existed in the MSB industry since the late 2000s, as is described in Section 2. The greater standard deviations of the profit ratio for MSBs compared to that of the commercial banks suggest that MSBs are less stable than are commercial banks. The loan to deposit ratio and commercial loan to home loan ratio for MSBs are, on average, 83.095% and 8.221 times, respectively, while those for commercial banks are 113.466% and 2.259 times, respectively. The average BIS ratio defined as the ratio of the capital to the risk-adjusted assets is 10.851% for the MSBs and 11.862% for the commercial banks.

3.2. Methodology

We estimate the following regression models in order to examine the effect of bank competition on stability.

7 DAR

Stabilityit ¼ α þ β Competition Measuresit þ XN k¼1

γkitXkit þ eit

i is the indexed banks and t is the index quarters. Our measures for Stability and Competition are

where discussed in more details below. X is the set of N control variables, including the bank-specific and market-related variables. We conduct both OLS and panel analysis to estimate the equations. Based on previous literature, we include the bank size (ln(assets)), profitability (Profit ratio), loan–deposit ratio, (Loan to deposit), and commercial-house loan ratio (Commercial to home loan) as control variables in our regression models. We also include the fluctuation of CD rates (CD volatility) in order to control for the effect of market situations.

Following Laeven and Levine (2009), Boyd et al. (2006), and many others, we use the Z-score as a measure of bank stability. The Z-score is defined as the average of the ROAs and capital ratios divided by the standard deviation of the ROAs.

Z−score ¼ ROA þ Capital Ratio � �

σROA

We calculate the Z-score by using the ROAs and capital ratios for the prior 4 quarters, ln(Z4), as well as the prior 8 quarters, ln(Z8). The Z-score measures how distant a bank is from insolvency, inversely

T: http://dart.fss.or.kr, FISIS: http://efisis,fss,or,kr, ECOS: http://ecos.bok.or.kr/.

Table 1 The sample period starts in January 1999 for commercial banks and in January 2003 for MSBs, and ends in December 2011. We obtain quarterly financial data from the Data Analysis, Retrieval and Transfer System (DART) and Financial Statistics Information System (FISIS) maintained by the Korean Financial Supervisory Service (FSS), Economic Statistics System (ECOS) maintained by the Bank of Korea, and the financial reports by MSBs. Market share is calculated based on total assets, total loans, household loans, and commercial loans. Variable definitions are provided in Appendix A.

N Mean Median St dev

Panel A. MSBs Market share (%) of

Total assets 5890 8.489 4.743 9.991 Total loans 3456 0.955 0.489 1.481 Household loans 3456 0.955 0.541 1.600 Commercial loans 3456 4.557 2.062 6.586

ln(assets) 5890 7.652 7.524 1.089 Profit ratio (%) 3432 −5.599 4.632 76.620 Loan to deposit (%) 5888 83.095 84.623 19.487 Commercial to home loan (x) 3456 8.221 3.564 14.635 BIS (%) 3117 10.851 9.820 16.615

Panel B. commercial banks Market share (%) of

Total assets 884 5.882 4.300 5.110 Total loans 880 5.909 3.416 5.887 Household loans 874 5.950 2.545 8.360 Commercial loans 880 8.156 4.945 7.934

ln(assets) 884 17.404 17.723 1.322 Profit ratio (%) 820 3.225 4.213 13.923 Loan to deposit (%) 833 113.466 97.737 57.881 Commercial to home loan (x) 874 2.259 1.973 1.876 BIS (%) 866 11.862 11.655 3.868

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proportional to the probability of bank defaults. That is, a greater Z-score represents a lower bankruptcy risk.

Our primary measure of bank competition is the Boone index.8 Boone (2008a, 2008b) estimates the level of bank competition by investigating the relationship between bank performance and efficiency, which is measured as marginal costs. According to Boone, when a market becomes more competitive, efficient firms are more greatly rewarded and inefficient firms are more harshly punished, compared to the situation in a less competitive market. Hence, Boone calculates the level of competition by estimating the elasticity of a firm' performance, in terms of its market shares, with respect to its marginal costs, as follows:

8 Giv conduc banking market Hirschm assume small n (Baumo Panzar type of

ln MSitð Þ ¼ α̂ þ β̂ln MCitð Þ

MS denotes the market shares in the loans or total assets of bank i and MC is the marginal costs. The

where estimated coefficient β is interpreted as the profit elasticity, or the Boone index, which is negative, i.e. banks with greater marginal costs lose market share. Since competition enhances this negative relationship, the greater is the bank competition, the more negative is the Boone index.

en that competition cannot be measured directly, indirect measurement techniques have been divided into structure– t–performance and competition–contestability approaches. The structural methods to assess the competitive level in the industry are based on the structure–conduct–performance assumption, which predicts that the number of banks and their shares determine competitive behavior. The conventional measures include the concentration ratio and Herfindahl– an Index. On the contrary, the nonstructural approach, which is based on the competition–contestability theory, does not , a priori, that concentrated markets are less competitive. It suggests that, in the absence of an entry barrier, markets where a umber of firms serve can be nevertheless characterized by competitive equilibrium because of potential short-term entrants l, 1982; Baumol et al., 1982; among others). This non-structural approach uses the measures such as the H statistic from and Rosse (1982, 1987) and the Boone (2008a, 2008b) index, which measure bank competitiveness without considering the market structure.

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Since marginal costs cannot be directly observed, empirical studies approximate the marginal costs by calculating the average variable costs (Schaeck and Cihak, 2010) or by using a translog cost function (Leuvensteijn et al., 2011). In this paper, we follow Leuvensteijn et al. and estimate the following cost function:

9 The

ln C=ω3ð Þ ¼ δ0 þ X

jδjlnyjþ X

j

X kμjklnyjlnyk þ

X kβkln ωk=ω3ð Þ þ

X j

X kγjklnyjln ωk=ω3ð Þ

þ X

j

X kθjkln ω j=ω3

� � ln ωk=ω3ð Þ þ e

C is the bank operating expenses, y is the output including total loans (y1), total securities (y2), and

where non-interest income (y3). w represents the inputs including the prices of labor (w1), of physical capital (w2), and of human capital (w3). By taking the first derivative of the translog cost function with respect to total loans, we obtain the marginal costs of loans as follows:

MC1 ¼ ∂

C ωg

! ∂y1

¼ C ωg y1

0 BB@

1 CCA

∂ln C ωg

! ∂lny1

Finally, we estimate the Boone index for the entire sample period as well as for the specific quarter so that,

ln MSitð Þ ¼ α̂ þ β̂ln MCitð Þ þ eit ln MSitð Þ ¼ α þ

X2011Q4 t¼1999Q1βtln MCitð Þ � Quarter Dummyt þ δtQuarter Dummyt þ εit:

In the first regression, the Boone index for the period of 1999–2011 is obtained. In the second regression, we interact the Boone index with the quarter dummies and control for the quarter effect. The estimation of the Boone index for each quarter is a result of this specification. The market share is mainly based on total assets and total loans, but is also calculated with respect to total house loans and commercial loans. A negative Boone index suggests that an increase in marginal costs decreases market shares and, thus, competitive pressure does in fact exists in the bank industry. In order to make this directly proportional to the level of competition, we employ the opposite of the Boone index (Tabak et al., 2012a). We also use a dummy that takes a value of 1 for a significantly negative Boone index and is otherwise 0, as a proxy for competition.

The conventional measures for market concentration, the concentration ratio (CR, hereafter) and the Herfindahl–Hirschman Index (HHI hereafter), are also employed as supplementary measures for competition. CR is defined as the sum of market shares of the N largest banks in the market.

CRN ¼ X

N i¼1MarketSharei

Although there is no rule for the determination of the number of N, CR4 and CR8 are the most frequently used methods. The HHI is calculated by squaring the market share of each bank and then summing the squares:

HHI ¼ XK i¼1

MarketShareið Þ 2 :

The U.S. Department of Justice and the FTC (Federal Trade Commission) consider a market with a value less than 1500 to be a competitive market place, one with 1500 ~ 2500 to be a moderately concentrated market place, and one with 2500 or higher to be a highly concentrated market place.9

market is perfectly competitive if HHI is 100. See http://www.justice.gov/atr/public/guidelines/hmg-2010.html.

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4. Empirical results

4.1. Analysis of market competition

Table 2 presents the results of the Boone indices for both Korean commercial banks and MSBs, as estimated for the entire sample period. In Panels A and B, we calculate the market shares based on the total assets and total loans. Additionally, given that MSBs concentrate their finance on SMEs, the market share is then re-calculated using the total commercial loans for MSBs and the total household loans for commercial banks in Panel C. The table shows that, for all three cases, the coefficients of the Boone indices, as measures for bank competition, are negatively and significantly correlated with the market shares. The results suggest that, during our sample period, competition pressure did in fact exist for both the commercial bank and the MSB industry in Korea.

In order to construct panel data, which enable us to examine how bank competition affects stability, we estimate the Boone indices on a quarter basis. In Table 3, the market shares are calculated based on total assets as well as total loans. The sample periods start in 1999 for commercial banks and in 2003 for MSBs, due to data availability. The results show that, for both commercial banks and MSBs, the distributions of the Boone indices based on total assets and total loans are qualitatively similar. In the commercial bank industry, Boone indices, coefficients of ln(MC), are generally negative and significant in early 2000 in terms of the market share of total assets. With respect to total loans, however, the Boone indices are significant until 2007, consistent with the fact that commercial banks competed fiercely for household loans in the mid-2000s due to the house price boom at this time. In the MSB industry, there existed considerable market pressure during the mid-2000s when the Korean government relaxed loan regulations drastically in order to support microfinance and promote small loan markets for low-income groups and low-rated SMEs. Likewise, real estate project financing loans increased steeply during this period, as a result of the real estate boom in the mid-2000s.

Table 2 Boone index for the whole period. The table reports the results of the following regression specification for MSBs and commercial banks for the full sample period:

ln MSiτð Þ ¼ α þ βln MCiτð Þ þ eiτ where MS denotes market shares, MC is marginal costs, and the coefficients stand for the Boone index. All tests are based on robust standard errors and the symbols ***, **, and * represent statistical significance at the 1%, 5%, and 10% level, respectively.

MSBs Commercial banks

Coef. t P N |t| Coef. t P N |t|

Panel A. Dependent variable: ln(market share) based on total assets ln(MC) −0.581 −7.20 0.000⁎⁎⁎ −0.348 −7.25 0.000⁎⁎⁎ Intercept 0.010 0.16 0.869 2.080 16.99 0.000⁎⁎⁎

No. of obs 1970 860 F tests 51.82 52.54 R2 0.033 0.064

Panel B. Dependent variable: ln(market share) based on total loans ln(MC) −0.913 −10.57 0.000⁎⁎⁎ −0.432 −9.76 0.000⁎⁎⁎ Intercept −4.377 −64.35 0.000⁎⁎⁎ −2.341 −19.87 0.000⁎⁎⁎ No. of obs 1777 860 F tests 111.78 95.18 R2 0.073 0.104

Panel C. Dependent variable: ln(market share) based on total commercial loans for MSBs and on total household loans for commercial banks

ln(MC) −1.023 −9.36 0.000⁎⁎⁎ −1.005 −12.06 0.000⁎⁎⁎ Intercept −3.144 −36.01 0.000⁎⁎⁎ −1.467 −7.48 0.000⁎⁎⁎ No. of obs 1777 854 F tests 87.55 145.43 R2 0.055 0.223

Table 3 Estimation of quarterly Boone indices. The table reports estimated of quarterly Boone indices from the following regression for MSBs and commercial banks:

ln MSitð Þ ¼ α þ X2011Q4

τ¼1999Q1βτln MCiτð Þ � Quarter Dummyτ þ δτQuarter Dummyτ þ εiτ

where MS denotes market shares, MC is marginal costs, and β represents Boone indices for each quarter. All tests are based on robust standard errors and the symbols ***, **, and * represent statistical significance at the 1%, 5%, and 10% level, respectively.

Commercial banks MSBs

Total assets Total loans Total assets Total loans

Year Quarter Coef. t-Value Coef. t-Value Coef. t-Value Coef. t-Value

1999 1 −1.464 −3.36⁎⁎⁎ −1.202 −2.91⁎⁎⁎ 2 −0.177 −0.49 −0.097 −0.25 3 −0.885 −7.34⁎⁎⁎ −0.816 −6.79⁎⁎⁎ 4 −0.232 −0.56 −0.221 −0.67

2000 1 −1.104 −2.09⁎⁎ −1.085 −2.51⁎⁎ 2 −1.458 −5.76⁎⁎⁎ −1.379 −5.88⁎⁎⁎ 3 −1.000 −10.19⁎⁎⁎ −0.982 −9.91⁎⁎⁎ 4 −0.836 −2.90⁎⁎⁎ −0.848 −3.35⁎⁎⁎

2001 1 −0.713 −1.11 −0.800 −1.41 2 −1.270 −2.24⁎⁎ −1.363 −2.84⁎⁎⁎ 3 −0.831 −3.09⁎⁎⁎ −0.859 −3.09⁎⁎⁎ 4 −1.024 −2.55⁎⁎ −1.196 −3.85⁎⁎⁎

2002 1 −0.832 −1.32 −1.017 −1.92⁎ 2 −1.044 −2.50⁎⁎ −1.235 −3.94⁎⁎⁎ 3 −1.171 −2.47⁎⁎ −1.342 −3.66⁎⁎⁎ 4 −1.132 −3.17⁎⁎⁎ −1.279 −4.69⁎⁎⁎

2003 1 −0.888 −1.93⁎ −1.095 −3.12⁎⁎⁎ 2 −1.158 −3.18⁎⁎⁎ −1.331 −5.14⁎⁎⁎ 3 −0.881 −1.91⁎ −1.096 −3.29⁎⁎⁎ 4 −1.038 −2.54⁎⁎ −1.185 −3.96⁎⁎⁎ −0.151 −0.35 −3.315 −9.01⁎⁎⁎

2004 1 −0.869 −1.31 −1.105 −2.07⁎⁎ −0.275 −0.69 −0.396 −0.94 2 −1.156 −1.96⁎ −1.360 −3.06⁎⁎⁎ −0.568 −1.30 −0.888 −2.03⁎⁎ 3 −1.142 −1.96⁎ −1.336 −3.03⁎⁎⁎ 0.410 0.82 0.296 0.51 4 −0.476 −0.84 −0.794 −1.73⁎ −0.969 −2.23⁎⁎ −1.235 −2.69⁎⁎⁎

2005 1 −0.673 −0.97 −0.960 −1.71⁎ −0.422 −1.16 −0.514 −1.30 2 −0.879 −1.28 −1.150 −2.11⁎⁎ −0.807 −2.67⁎⁎⁎ −1.275 −3.71⁎⁎⁎ 3 −1.024 −1.52 −1.259 −2.40⁎⁎ −0.814 −3.09⁎⁎⁎ −0.980 −3.57⁎⁎⁎ 4 −1.053 −1.69⁎ −1.324 −2.83⁎⁎⁎ −0.981 −3.20⁎⁎⁎ −1.413 −4.04⁎⁎⁎

2006 1 −0.576 −0.88 −0.892 −1.63 −1.127 −3.87⁎⁎⁎ −1.560 −4.85⁎⁎⁎ 2 −0.607 −0.99 −0.895 −1.96⁎ −1.207 −4.62⁎⁎⁎ −1.869 −5.10⁎⁎⁎ 3 −0.806 −1.13 −1.119 −2.01⁎⁎ −1.047 −3.64⁎⁎⁎ −1.185 −4.75⁎⁎⁎ 4 −0.986 −1.35 −1.292 −2.33⁎⁎ −1.560 −3.90⁎⁎⁎ −1.658 −3.84⁎⁎⁎

2007 1 −0.585 −0.94 −0.881 −1.85⁎ −1.409 −3.51⁎⁎⁎ −1.646 −3.82⁎⁎⁎ 2 −0.799 −1.35 −1.012 −2.29⁎⁎ −0.855 −2.18⁎⁎ −1.473 −3.34⁎⁎⁎ 3 −0.611 −1.19 −0.842 −2.09⁎⁎ −0.330 −1.18 −0.689 −3.14⁎⁎⁎ 4 −0.553 −1.01 −0.807 −1.84⁎ −0.022 −0.09 −0.418 −1.60

2008 1 −0.141 −0.38 −0.414 −1.32 2.043 1.95⁎ 1.724 1.90⁎ 2 −0.342 −0.81 −0.609 −1.70⁎ 1.548 1.30 1.173 1.11 3 0.000 0.00 −0.288 −1.02 −1.069 −1.28 −0.930 −1.01 4 0.469 1.44 0.162 0.51 1.133 0.81 1.048 0.72

2009 1 0.439 1.69⁎ 0.125 0.47 −0.125 −0.20 −0.127 −0.20 2 −0.651 −1.12 −0.860 −1.60 0.331 0.27 −0.033 −0.03 3 −0.460 −0.67 −0.749 −1.24 0.358 0.87 −0.025 −0.05 4 −0.618 −1.06 −0.826 −1.56 0.008 0.02 −0.499 −1.13

2010 1 −0.273 −0.52 −0.556 −1.11 −0.228 −0.51 −0.632 −1.64 2 −0.138 −0.23 −0.407 −0.71 −0.808 −1.12 −1.312 −2.00⁎⁎ 3 −0.751 −1.12 −0.991 −1.64 −0.527 −0.75 −0.707 −0.89 4 −1.438 −1.93⁎ −1.637 −2.50⁎⁎ −0.026 −0.02 −0.311 −0.22

2011 1 −0.111 −0.25 −0.253 −0.63 −0.995 −0.77 −1.437 −1.13 2 −0.185 −0.40 −0.344 −0.82 −1.176 −1.06 −1.748 −1.80⁎ 3 0.602 1.40 0.427 1.00 −0.090 −0.08 −0.290 −0.25

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Table 3 (continued)

Commercial banks MSBs

Total assets Total loans Total assets Total loans

Year Quarter Coef. t-Value Coef. t-Value Coef. t-Value Coef. t-Value

4 0.011 0.02 −0.110 −0.19 −0.489 −0.89 −0.972 −1.86⁎ Quarter dummies included Quarter dummies included

R2 0.197 0.274 0.092 0.148

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4.2. Competition and stability in the MSB industry

Before conducting regression analysis, we first take an initial look at the correlations between the independent variables. By obtaining the correlation coefficients between these figures, we can become aware of potential multicollinearity. As reported in Table 4, in the MSB sample, all the correlation coefficients are below around 10%. In the case of the commercial banks, however, the largest coefficient is 36.1%, between the size of firm and loan to deposit ratio. In both cases, we can conclude that the correlation coefficients between explanatory variables are rather small and thus muticollinearity is not likely to affect the results of our regressions.

Using the Boone indices and the concentration measures estimated in the previous sections, we examine the effect of competition on stability in the MSB industry. We conduct OLS regressions as a baseline-model as well as panel analysis to control for the time invariant heterogeneity of each bank. Table 5 reports the results of our regressions, where the dependent variables in Panel A and Panel B are the Z-scores using ROAs and the capital ratios for prior four quarters and for prior eight quarters, respectively. The key independent variables are a dummy for the presence of market pressure (Competition) and the opposite of the Boone indices (Inverse of Boone Index). The significant and positive coefficients for Competition and the Inverse of Boone Index in both the OLS and fixed effects models suggest that the probability of MSB defaults is lower in competitive markets, consistent with our hypothesis that the higher interest rates charged by MSBs in a less competitive market would increase the risk-taking behavior of borrowers, most of whom are SMEs possessing greater business and credit risk (Boyd and De Nicolo, 2005). As was previously noted, Martinez-Miera and Repullo (2010) argue that the risk shifting effect overwhelms the interest effect for MSBs when the market is less competitive.

Note that most of the coefficients of our control variables are signed in accordance with our expectations and prior literature. The profit ratio is positively correlated with MSB stability. The loan to

Table 4 Correlation coefficients matrix. The table presents Pearson correlation coefficients matrix for the variables used in our main analysis. Variable definitions are provided in Appendix A.

Competition Inverse of Boone index ln(assets) Profit ratio Loan to deposit

Panel A. MSBs Competition 1 Inverse of Boone index 0.573 1 ln(assets) −0.134 −0.105 1 Profit ratio 0.057 0.037 −0.015 1 Loan to deposit 0.091 0.040 0.006 0.118 1 Commercial to home loan −0.121 −0.088 0.386 −0.034 0.046

Panel B. Commercial banks Competition 1 Inverse of Boone index 0.894 1 ln(assets) −0.145 −0.155 1 Profit ratio −0.203 −0.164 0.075 1 Loan to deposit −0.168 −0.177 0.361 0.076 1 Commercial to home loan −0.049 −0.061 0.185 0.032 0.244

Table 5 Effects of competition on stability in the MSB industry. OLS and panel regressions are estimated for the stability of MSBs. The dependent variable is the Z-score using the ROAs for prior four or eight quarters. Year dummy is included but the coefficients are not reported. The t-statistics based on robust standard errors are reported in brackets below coefficient values. The symbols ***, **, and * represent statistical significance at the 1%, 5%, and 10% level, respectively. Variable definitions are provided in Appendix A.

Panel A. Analysis of Z-score with the ROAs and capital ratios for prior four quarters

ln(Z4) OLS Fixed effects

Coef. t-Value Coef. t-Value Coef. t-Value Coef. t-Value

Competition 0.4987 [8.88]⁎⁎⁎ 0.5551 [6.87]⁎⁎⁎

Inverse of Boone index 0.2716 [7.92]⁎⁎⁎ 0.2756 [6.18]⁎⁎⁎

ln(assets) −0.0357 [−1.23] −0.0410 [−1.40] 0.0120 [0.08] −0.1399 [−0.97] Profit ratio 0.0042 [2.10]⁎⁎ 0.0043 [2.11]⁎⁎ 0.0035 [1.96]⁎ 0.0036 [1.93]⁎

Loan to deposit 0.0465 [3.92]⁎⁎⁎ 0.0455 [3.72]⁎⁎⁎ 0.0567 [1.59] 0.0544 [1.52] Loan to deposit2 −0.0002 [−3.29]⁎⁎⁎ −0.0002 [−3.03]⁎⁎⁎ −0.0003 [−1.45] −0.0002 [−1.31] Commercial to home loan

0.0019 [1.37] 0.0013 [0.87] −0.0074 [−1.70]⁎ −0.0083 [−1.85]⁎

CD volatility −0.1801 [−2.95]⁎⁎⁎ −0.2921 [−4.98]⁎⁎⁎ −0.1878 [−2.64]⁎⁎⁎ −0.2785 [−3.81]⁎⁎⁎ Intercept −1.7630 [−3.04]⁎⁎⁎ −1.5559 [−2.62]⁎⁎⁎ −2.5086 [−1.25] −1.1059 [−0.55] No. of obs 2137 2137 2137 2137 F/Wald tests 24.95 21.29 14.68 12.38 R2 0.084 0.074 0.072 0.052

Panel B. Analysis of Z-score with the ROAs and capital ratios for prior eight quarters

ln(Z8) OLS Fixed effects

Coef. t-Value Coef. t-Value Coef. t-Value Coef. t-Value

Competition 0.5543 [9.73]⁎⁎⁎ 0.6905 [7.75]⁎⁎⁎

Inverse of Boone index 0.2614 [7.65]⁎⁎⁎ 0.2847 [6.71]⁎⁎⁎

ln(assets) −0.0024 [−0.08] −0.0106 [−0.37] 0.2352 [1.22] −0.0464 [−0.27] Profit ratio 0.0059 [2.17]⁎⁎ 0.0059 [2.15]⁎⁎ 0.0056 [2.06]⁎⁎ 0.0056 [2.05]⁎⁎

Loan to deposit 0.0526 [3.88]⁎⁎⁎ 0.0514 [3.80]⁎⁎⁎ 0.0684 [2.21]⁎⁎ 0.0731 [2.41]⁎⁎

Loan to deposit 2 −0.0002 [−3.16]⁎⁎⁎ −0.0002 [−2.95]⁎⁎⁎ −0.0003 [−1.83]⁎ −0.0003 [−1.88]⁎ Commercial to home loan

0.0002 [0.18] −0.0004 [−0.32] −0.0061 [−1.74]⁎ −0.0075 [−1.92]⁎

CD volatility −0.1574 [−2.51]⁎⁎ −0.2665 [−4.43]⁎⁎⁎ −0.1685 [−2.36]⁎⁎ −0.2626 [−3.61]⁎⁎⁎ Intercept −2.8477 [−4.37]⁎⁎⁎ −2.6309 [−4.05]⁎⁎⁎ −5.6183 [−2.89]⁎⁎⁎ −3.5127 [−1.95]⁎ No. of obs 1764 1764 1764 1764 F/Wald tests 27.87 21.83 16.90 15.14 R2 0.115 0.097 0.090 0.084

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deposit ratio increases the stability of MSBs but the negative effect of the squared loan to deposit ratio suggests that its increasing rate in fact decreases. Market volatility, measured by CD volatility, significantly increases the defaults of MSBs.

In Table 6, we additionally investigate the relationship between the degree of concentration and the stability in the MSB market. According to the structure–conduct–performance view, the number of firms and their market shares determine the competitive level of the industry. The CR and HHI, which were discussed in Section 3.2, are used as proxies of market concentration and, more specifically, CR4 and CR8 represent the concentration ratio of the top four and eight banks, respectively. As in Table 5, we use the Z-scores as the dependent variables and estimate OLS and fixed effects regressions. The results show that the coefficients for the concentration measure are significantly negative, which is consistent with the results in Table 5 given that greater concentration ratios imply less competitive markets. However, we also find that the squared terms of concentration ratios are positively correlated with the Z-scores, which suggests that the relationship between market concentration and MSB stability is in fact non-linear. The coefficients of control variables are signed in a way that is consistent with the results of the previous table.

Table 6 Effects of concentration on stability in the MSB industry. OLS and panel regressions are estimated for the stability of MSBs as a function of the concentration ratio (CR) or Herfindahl–Hirschman index (HHI). The dependent variable is the Z-score using the ROAs and capital ratios for prior four or eight quarters. Year dummy is included but the coefficients are not reported. The t-statistics based on robust standard errors are reported in brackets below coefficient values. The symbols ***, **, and * represent statistical significance at the 1%, 5%, and 10% level, respectively. Variable definitions are provided in Appendix A.

Panel A. Analysis of Z-score with the ROAs and capital ratios for prior four quarters

ln(Z4) OLS Fixed effects

Coef. Coef. Coef. Coef. Coef. Coef. Coef. Coef.

[t-Value] [t-Value] [t-Value] [t-Value] [t-Value] [t-Value] [t-Value] [t-Value]

CR4 −0.1288⁎⁎⁎ −1.8701⁎⁎ −0.1735⁎⁎⁎ −2.1478⁎⁎ [−7.38] [−2.25] [−4.42] [−2.32]

(CR4) 2 0.0274⁎⁎ 0.0314⁎⁎

[2.10] [2.15] HHI −0.0122⁎⁎⁎ −0.5760⁎⁎⁎ −0.0160⁎⁎⁎ −0.5849⁎⁎⁎

[−6.22] [−7.15] [−3.39] [−6.38] HHI2 0.0013⁎⁎⁎ 0.0013⁎⁎⁎

[7.01] [6.21] ln(assets) −0.0309 −0.0303 −0.0344 −0.0311 0.1579 0.0401 0.0774 0.0339

[−1.06] [−1.03] [−1.17] [−1.06] [0.84] [0.73] [0.40] [0.60] Profit ratio 0.0041⁎⁎ 0.0041⁎⁎ 0.0043⁎⁎ 0.0040⁎⁎ 0.0033⁎ 0.0037⁎⁎ 0.0036⁎ 0.0036⁎⁎

[2.05] [2.09] [2.12] [2.04] [1.85] [2.07] [1.95] [2.05] Loan to Deposit

0.0458⁎⁎⁎ 0.0456⁎⁎⁎ 0.0460⁎⁎⁎ 0.0451⁎⁎⁎ 0.0543 0.0525⁎ 0.0548 0.0527⁎

[4.07] [4.01] [4.06] [3.75] [1.54] [1.88] [1.54] [1.82] Loan to deposit2

−0.0002⁎⁎⁎ −0.0002⁎⁎⁎ −0.0002⁎⁎⁎ −0.0002⁎⁎⁎ −0.0003 −0.000⁎3 −0.0003 −0.0002 [−3.53] [−3.44] [−3.48] [−3.07] [−1.53] [−1.72] [−1.50] [−1.58]

Commercial to

0.0024⁎ 0.0023 0.0021 0.0019 −0.0063 −0.0034 −0.0069 −0.0041

Home loan [1.72] [1.61] [1.46] [1.38] [−1.41] [−0.96] [−1.52] [−1.22] CD volatility

−0.2215⁎⁎⁎ −0.1797⁎⁎⁎ −0.2502⁎⁎⁎ −0.0681 −0.2407⁎⁎⁎ −0.1824⁎⁎⁎ −0.2610⁎⁎⁎ −0.0748 [−3.61] [−2.80] [−4.12] [−1.04] [−3.35] [−2.66] [−3.61] [−1.12]

Intercept 2.5578⁎⁎⁎ 30.103⁎⁎9 1.1709⁎ 62.8115⁎⁎⁎ 2.3239 33.9917⁎⁎ 0.9291 63.1114⁎⁎⁎

[3.31] [2.29] [1.68] [7.14] [1.18] [2.34] [0.47] [6.42] No. of obs 2137 2137 2137 2137 2137 2137 2137 2137 F/Wald tests

22.93 20.27 20.18 21.95 12.41 92.18 10.89 94.76

R2 0.075 0.076 0.067 0.088 0.055 0.070 0.061 0.081

Panel B. Analysis of Z-score with the ROAs for prior eight quarters

ln(Z8) OLS Fixed effects

Coef. t-Value Coef. t-Value Coef. t-Value Coef. t-Value

CR8 −0.1274 [−7.10]⁎⁎⁎ −0.1818 [−4.40]⁎⁎⁎ HHI −0.0106 [−5.16]⁎⁎⁎ −0.0136 [−2.80]⁎⁎⁎ ln(assets) −0.0018 [−0.06] −0.0089 [−0.31] 0.2777 [1.22] 0.0700 [0.31] Profit ratio 0.0057 [2.13]⁎⁎ 0.0060 [2.18]⁎⁎ 0.0052 [1.95]⁎ 0.0055 [2.03]⁎⁎

Loan to deposit

0.0539 [4.28]⁎⁎⁎ 0.0541 [4.23]⁎⁎⁎ 0.0658 [1.99]⁎ 0.0695 [2.10]⁎⁎

Loan to deposit2

−0.0002 [−3.59]⁎⁎⁎ −0.0002 [−3.49]⁎⁎⁎ −0.0003 [−1.78]⁎ −0.0003 [−1.80]⁎

Commercial to home loan

0.0005 [0.39] 0.0000 [0.03] −0.0054 [−1.45] −0.0065 [−1.59]

CD volatility −0.2159 [−3.48]⁎⁎⁎ −0.2527 [−4.11]⁎⁎⁎ −0.2346 [−3.28]⁎⁎⁎ −0.2609 [−3.59]⁎⁎⁎ Intercept 1.4092 [1.68]⁎ −0.2816 [−0.37] 0.4300 [0.24] −0.9431 [−0.52] No. of obs 1764 1764 1764 1764 F/Wald tests

22.58 18.24 10.17 8.42

R2 0.097 0.083 0.068 0.074

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Table 7 Effects of competition on stability in the commercial bank industry. OLS and panel regressions are estimated for the stability of commercial banks. The dependent variable is the Z-score of commercial banks using the ROAs and capital ratios for prior four or eight quarters. Year dummy is included but the coefficients are not reported. The t/z-statistics based on robust standard errors are reported in brackets below coefficient values. The symbols ***, **, and * represent statistical significance at the 1%, 5%, and 10% level, respectively. Variable definitions are provided in Appendix A.

Panel A. Analysis of Z-score with the ROAs and capital ratios for prior four quarters

ln(Z4) OLS Random effects

Coef. t-Value Coef. t-Value Coef. z-Value Coef. z-Value

Competition −0.5224 [−5.77]⁎⁎⁎ −0.3989 [−5.17]⁎⁎⁎ Inverse of Boone index −1.0715 [−3.43]⁎⁎⁎ −0.5305 [−2.45]⁎⁎ Inverse of Boone index2 0.5037 [1.79]⁎ 0.1764 [1.20] ln(assets) −0.1554 [−3.92]⁎⁎⁎ −0.1552 [−3.95]⁎⁎⁎ 0.3811 [2.28]⁎⁎ 0.5541 [2.98]⁎⁎⁎ Profit ratio 3.7195 [5.22]⁎⁎⁎ 3.8649 [5.48]⁎⁎⁎ 3.5188 [4.43]⁎⁎⁎ 3.6598 [4.85]⁎⁎⁎

Foreign 0.6560 [5.06]⁎⁎⁎ 0.6556 [5.05]⁎⁎⁎ 0.5303 [1.69]⁎ 0.4775 [1.33] Loan to deposit 0.0367 [8.18]⁎⁎⁎ 0.0352 [7.89]⁎⁎⁎ 0.0329 [2.44]⁎⁎ 0.0296 [2.25]⁎⁎

Loan to deposit2 −0.0001 [−6.91]⁎⁎⁎ −0.0001 [−6.64]⁎⁎⁎ −0.0001 [−2.29]⁎⁎ −0.0001 [−2.18]⁎⁎ Commercial to home loan 0.0004 [1.44] 0.0003 [1.21] 0.0007 [1.10] 0.0007 [1.06] CD volatility −0.1230 [−1.11] −0.2707 [−2.28]⁎⁎ −0.1994 [−1.34] −0.2971 [−1.97]⁎⁎ Intercept 3.6527 [6.44]⁎⁎⁎ 3.8079 [6.78]⁎⁎⁎ −5.3765 [−2.30]⁎⁎ −8.1030 [−3.03]⁎⁎⁎ No. of obs 738 738 738 738 F/Wald tests 32.16 29.52 443.36 473.39 R2 0.240 0.249 0.329 0.350

Panel B. Analysis of Z-score with the ROAs and capital ratios for prior eight quarters

ln(Z8) OLS Random effects

Coef. t-Value Coef. t-Value Coef. z-Value Coef. z-Value

Competition −0.5812 [−6.79]⁎⁎⁎ −0.4712 [−5.14]⁎⁎⁎ Inverse of Boone index −1.6732 [−6.29]⁎⁎⁎ −1.1938 [−5.27]⁎⁎⁎ Inverse of Boone index2 0.9888 [4.09]⁎⁎⁎ 0.6597 [3.74]⁎⁎⁎

ln(assets) −0.1555 [−4.17]⁎⁎⁎ −0.1559 [−4.25]⁎⁎⁎ 0.3199 [1.70]⁎ 0.3393 [1.77]⁎ Profit ratio 1.9727 [2.57]⁎⁎ 2.2290 [2.96]⁎⁎⁎ 1.7940 [2.06]⁎⁎ 2.0102 [2.41]⁎⁎

Foreign 0.5775 [4.65]⁎⁎⁎ 0.5796 [4.80]⁎⁎⁎ 0.4631 [1.48] 0.4515 [1.43] Loan to deposit 0.0303 [7.87]⁎⁎⁎ 0.0280 [7.36]⁎⁎⁎ 0.0279 [2.02]⁎⁎ 0.0250 [1.94]⁎

Loan to deposit2 −0.0001 [−6.41]⁎⁎⁎ −0.0001 [−5.74]⁎⁎⁎ −0.0001 [−1.82]⁎ −0.0001 [−1.74]⁎ Commercial to home loan 0.0001 [0.66] 0.0001 [0.26] 0.0005 [0.81] 0.0005 [0.72] CD volatility −0.2519 [−2.22]⁎⁎ −0.4390 [−3.65]⁎⁎⁎ −0.3248 [−3.98]⁎⁎⁎ −0.4664 [−6.26]⁎⁎⁎ Intercept 3.9713 [7.47]⁎⁎⁎ 4.2033 [8.07]⁎⁎⁎ −4.1093 [−1.57] −4.1711 [−1.54] No. of obs 682 682 682 682 F/Wald tests 30.08 29.83 443.48 713.57 R2 0.223 0.251 0.343 0.359

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4.3. Competition and stability in the commercial bank industry

In this section, we examine whether the relationship between competition and stability is different between MSBs and commercial banks. Table 7 reports the estimates of OLS and panel regressions for the Z-scores of commercial banks as a function of the measures for market competition. In the first regression of Panel A, where the key independent variable is Competition, a dummy for the presence of competitive pressure, the negative and significant coefficient implies that, consistent with the competition–fragility view, the presence of competition significantly increases the probability of bank defaults. In the second regression, the level of competition, measured by Inverse of Boone Index, is negatively correlated with the Z-score, while its squared term has a positive effect. That is, market competition significantly decreases the stability of commercial banks, but the effect is non-linear, consistent with the non-linear U-shaped relationship between competition and stability that is suggested by Tabak et al. (2012a). The results are

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qualitatively similar when panel regressions are run and when the Z-score with the ROAs for prior eight quarters is used in Panel B.10

Most of the coefficients of our control variables are signed as expected, except for those of the size of banks. The table shows the negative effect of ln(assets) on bank stability. Boyd et al. (2006) point out that competition and bank size are endogenously determined and thus one may need to employ instrumental variable estimations in this area. Without endogeneity corrections, they also report the negative effect of bank size. Tabak et al. (2012a) provide a possible explanation for this result in terms of the negative relationship between capital ratio and bank size. They argue that the larger a bank is, the more it benefits from competition, and that a greater capital ratio is advantageous for banks in the less competitive markets.

4.4. Additional analysis

Thus far, we have used market shares calculated based on total assets. In this section, we re-estimate the Boone indices using the market shares of total loans, in light of the fact that competitive pressure is more intensified in loan markets compared to deposit markets. We also re-calculate the market shares of commercial loans and household loans and see whether our results change.

In Table 8, we alternatively estimate the Boone indices using market shares based on total loans and re-examine the relationship between competition and stability in the MSB and commercial bank markets. The Z-score with the ROAs and capital ratios for the prior four quarters is used as the dependent variable. The results confirm our previous findings that the relationship between competition and stability varies depending on the type of bank involved. In Panel A, the coefficients for Competition and Inverse of Boone Index are positive and significant, suggesting that, consistent with Tables 5 and 6, competitive pressure enhances the stability of MSBs. Panel B reports that commercial banks are less vulnerable in less competitive markets, while the effect of competition is non-linear. The results are consistent with Table 7.

In Table 9, we re-estimate the Boone indices using market shares based on commercial loans for MSBs and on household loans for commercial banks, so as to reflect that MSBs concentrate their finance on SMEs while commercial banks compete to attract households. We then test whether our previous findings are sensitive to the definition of market shares. Panel A reports the determinants of the stability of MSBs as a function of competition re-estimated by using market shares of commercial loans. In both OLS and panel regressions, the coefficients for Competition and Inverse of Boone Index are still negative and statistically significant, confirming that MSBs tend to be more stable in more competitive markets. In Panel B, we examine the commercial bank industry and test the effects of competition based on its market shares of household loans. Again, the competition dummy is negatively correlated with commercial bank stability. Likewise, the Boon indices have a negative effect, but the relationship between Boon indices and stability is non-linear. Overall, our finding that the relationship between competition and stability varies depending on the characteristics of banks is robust to a variety of measures of competition and stability as well as model specifications.

4.5. Difference-in-differences analysis

Since the mid-2000s in accordance with drastic deregulations by the government in order to support microfinance and promote small loan markets, MSBs have aggressively expanded their microcredit loans and

10 We employ random effects models rather than fixed effects models for our panel analysis in order to estimate the effect of foreign banks, Foreign. Note that fixed effects regressions do not provide estimates for time-invariant variables such as Foreign. We also conduct fixed effects regressions, the results of which, as shown below, are similar to those of Table 7. More detailed results are available upon request.

ln(Z4) Inverse of Boone

Inverse of Boone2

ln(assets) Profit ratio

Loan to deposit

Loan to deposit2

Commercial to home loan

CD volatility

Intercept

Coef. −0.2920 0.2820 −2.0130 3.8959 0.0080 0.0000 0.0005 −0.2897 −31.7132 P 0.06⁎ 0.02⁎⁎ 0.00⁎⁎ 0.00⁎⁎ 0.58 0.22 0.32 0.08⁎ 0.00⁎⁎

No. of Obs: 738, F/Wald tests: 78.67, R2: 0.416. The symbols ***, **, and * represent statistical significance at the 1%, 5%, and 10% levels, respectively.

Table 8 Effects of competition based on total loans. In this table, the Boone index is re-estimated using the market share based on total loans. OLS and panel regressions are estimated for the stability of both MSBs and commercial banks. The dependent variable is the Z-score using the ROAs and capital ratios for prior four quarters. Year dummy is included but the coefficients are not reported. The t/z-statistics based on robust standard errors are reported in brackets below coefficient values. The symbols ***, **, and * represent statistical significance at the 1%, 5%, and 10% level, respectively. Variable definitions are provided in Appendix A.

Panel A. Competition and stability in the MSB industry

ln(Z4) OLS Fixed effects

Coef. t-Value Coef. t-Value Coef. t-Value Coef. t-Value

Competition 0.1461 [2.84]⁎⁎⁎ 0.1421 [2.49]⁎⁎

Inverse of Boone index 0.1120 [3.59]⁎⁎⁎ 0.1127 [3.65]⁎⁎⁎

ln(assets) −0.0769 [−3.51]⁎⁎⁎ −0.0789 [−3.61]⁎⁎⁎ −0.3346 [−2.62]⁎⁎ −0.3471 [−2.81]⁎⁎⁎ Profit ratio 0.0027 [4.11]⁎⁎⁎ 0.0026 [3.95]⁎⁎⁎ 0.0006 [0.66]⁎⁎ 0.0005 [0.58] Loan to deposit 0.0002 [2.48]⁎⁎ 0.0002 [2.46]⁎⁎ 0.0006 [2.21]⁎⁎ 0.0006 [2.31]⁎⁎

Loan to deposit2 0.0000 [−1.16] 0.0000 [−1.09] 0.0000 [−1.83] 0.0000 [−1.89]⁎ Commercial to home loan

0.0043 [3.08]⁎⁎⁎ 0.0043 [3.09]⁎⁎⁎ −0.0038 [−1.17] −0.0038 [−1.17]

CD volatility −0.2935 [−4.51]⁎⁎⁎ −0.2970 [−4.85]⁎⁎⁎ −0.2961 [−4.16]⁎⁎⁎ −0.2930 [−4.22]⁎⁎⁎ Intercept −0.2260 [−0.52] −0.2219 [−0.51] 0.2526 [0.15] 0.2345 [0.14] No. of obs 2164 2164 2164 2164 F/Wald tests 20.10 20.83 9.52 9.98 R2 0.058 0.063 0.083 0.864

Panel B. Competition and stability in the commercial bank industry

ln(Z4) OLS Random effects

Coef. t-Value Coef. t-Value Coef. z-Value Coef. z-Value

Competition −0.5479 [−5.40]⁎⁎⁎ −0.3068 [−3.00]⁎⁎⁎ Inverse of Boone index −1.4628 [−5.27]⁎⁎⁎ −1.1246 [−4.58]⁎⁎⁎ Inverse of Boone index2

0.8295 [4.10]⁎⁎⁎ 0.6944 [3.93]⁎⁎⁎

ln(assets) −0.1648 [−4.21]⁎⁎⁎ −0.1670 [−4.29]⁎⁎⁎ 0.5067 [2.79]⁎⁎⁎ 0.3463 [3.77]⁎⁎⁎ Profit ratio 4.8229 [6.92]⁎⁎⁎ 4.9438 [7.04]⁎⁎⁎ 4.2733 [5.87]⁎⁎⁎ 4.4288 [7.56]⁎⁎⁎

Foreign 0.6879 [5.23]⁎⁎⁎ 0.6929 [5.29]⁎⁎⁎ 0.5316 [1.53] 0.5862 [1.54] Loan to deposit 0.0388 [8.42]⁎⁎⁎ 0.0387 [8.33]⁎⁎⁎ 0.0349 [2.44]⁎⁎ 0.0373 [7.68]⁎⁎⁎

Loan to deposit2 −0.0001 [−6.88]⁎⁎⁎ −0.0001 [−6.77]⁎⁎⁎ −0.0001 [−2.22]⁎⁎ −0.0001 [−6.87]⁎⁎⁎ Commercial to home loan

0.0003 [0.94] 0.0002 [0.83] 0.0007 [0.89] 0.0006 [1.47]

CD volatility −0.3547 [−2.86]⁎⁎⁎ −0.3380 [−2.74]⁎⁎⁎ −0.3409 [−2.18]⁎⁎ −0.3294 [−3.14]⁎⁎⁎ Intercept 3.8635 [6.70]⁎⁎⁎ 3.9032 [6.80]⁎⁎⁎ −7.6638 [−2.99]⁎⁎⁎ −5.0618 [−3.42]⁎⁎⁎ No. of obs 738 738 738 738 F/Wald tests 30.80 28.12 518.10 287.27 R2 0.234 0.240 0.338 0.326

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real estate project financing loans. This aggressive loan policy by MSBs could expand their earnings during the real estate boom, while it became the main cause of increased risk during and after the global financial crisis from 2007. In this section, we examine whether MSBs have been riskier compared to commercial banks as well as how the magnitude of the effect of competition on stability has changed since 2007.

In order to answer these questions, we employ the Difference-in-Differences (DID) approach to identify the effects of the 2007 financial crisis, using MSBs as a treatment group and commercial banks as a control group. Specifically, we estimate the following panel regressions.

Stabilityit ¼ α þ β1MSBDit � 2007Dit þ β22007Dit þ β3Competition Measuresit þ XN k¼1

γkXk þ eit Stabilityit ¼ α þ β1MSBDit � 2007Dit � Competition Measuresit þ ρ22007Dit þρ3Compertition Measuresit þ

XN k¼1

φkXk þ uit

Table 9 Effects of competition based on commercial or house loans. In this table, the Boone index is re-estimated using the market share based on commercial loans for MSBs and on household loans for commercial banks. OLS and panel regressions are estimated for the stability of both MSBs and commercial banks. The dependent variable is the Z-score using the ROAs and capital ratios for prior four quarters. Year dummy is included but the coefficients are not reported. The t/z-statistics based on robust standard errors are reported in brackets below coefficient values. The symbols ***, **, and * represent statistical significance at the 1%, 5%, and 10% level, respectively. The variable definitions are provided in Appendix A.

Panel A. Competition based on commercial loans and stability of MSBs

ln(Z4) OLS Fixed effects

Coef. t-Value Coef. t-Value Coef. t-Value Coef. t-Value

Competition 0.2122 [4.63]⁎⁎⁎ 0.2200 [3.75]⁎⁎⁎

Inverse of Boone index 0.0974 [4.06]⁎⁎⁎ 0.0975 [3.20]⁎⁎⁎

ln(assets) −0.0723 [−3.31]⁎⁎⁎ −0.0755 [−3.46]⁎⁎⁎ −0.2748 [−2.19]⁎⁎ −0.3083 [−2.43]⁎⁎ Profit ratio 0.0027 [4.09]⁎⁎⁎ 0.0027 [4.18]⁎⁎⁎ 0.0006 [0.66] 0.0007 [0.71] Loan to deposit 0.0002 [2.56]⁎⁎ 0.0002 [2.45]⁎⁎ 0.0006 [2.22]⁎⁎ 0.0006 [2.23]⁎

Loan to deposit2 0.0000 [−1.29] 0.0000 [−1.13] 0.0000 [−1.87]⁎ 0.0000 [−1.85] Commercial to home loan

0.0046 [3.31]⁎⁎⁎ 0.0045 [3.19]⁎⁎⁎ −0.0034 [−1.06] −0.0036 [−1.13]

CD volatility −0.2854 [−4.77]⁎⁎⁎ −0.3413 [−6.02]⁎⁎⁎ −0.2986 [−4.11]⁎⁎⁎ −0.3484 [−4.83]⁎⁎⁎ Intercept −0.2881 [−0.66] −0.1637 [−0.38] −0.2078 [−0.12] 0.1397 [0.08] No. of obs 2164 2164 2164 2164 F/Wald tests 22.13 21.38 10.76 10.03 R2 0.067 0.062 0.090 0.876

Panel B. Competition based on house loans and stability of commercial banks

ln(Z4) OLS Random effects

Coef. t-Value Coef. t-Value Coef. z-Value Coef. z-Value

Competition −0.5402 [−4.76]⁎⁎⁎ −0.2509 [−1.86]⁎ Inverse of Boone index −1.2158 [−8.42]⁎⁎⁎ −1.0331 [−8.14]⁎⁎⁎ Inverse of Boone index2

0.4295 [8.94]⁎⁎⁎ 0.3784 [9.60]⁎⁎⁎

ln(assets) −0.1617 [−4.11]⁎⁎⁎ −0.1618 [−4.25]⁎⁎⁎ 0.6043 [3.13]⁎⁎⁎ 0.1956 [1.29] Profit ratio 4.9084 [7.01]⁎⁎⁎ 3.7364 [5.37]⁎⁎⁎ 4.2732 [6.06]⁎⁎⁎ 3.3183 [4.34]⁎⁎⁎

Foreign 0.6918 [5.16]⁎⁎⁎ 0.6694 [5.34]⁎⁎⁎ 0.5124 [1.37] 0.6069 [2.29] Loan to deposit 0.0396 [8.42]⁎⁎⁎ 0.0349 [7.61]⁎⁎⁎ 0.0347 [2.39]⁎⁎ 0.0350 [2.40]⁎⁎

Loan to deposit2 −0.0001 [−6.79]⁎⁎⁎ −0.0001 [−5.80]⁎⁎⁎ −0.0001 [−2.19]⁎⁎ −0.0001 [−2.01]⁎⁎ Commercial to home loan

0.0003 [0.94] 0.0000 [0.06] 0.0007 [0.96] 0.0004 [0.56]

CD volatility −0.1936 [−1.59] −0.0552 [−0.48] −0.2527 [−1.67]⁎ −0.1115 [−0.70] Intercept 3.7491 [6.54]⁎⁎⁎ 3.9344 [7.14]⁎⁎⁎ −9.3874 [−3.39]⁎⁎⁎ −2.3821 [−1.17] No. of obs 738 738 738 738 F/Wald tests 29.59 34.38 419.01 1069.54 R2 0.233 0.277 0.347 0.354

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where MSBD is a dummy variable equal to 1 for MSBs (treatment group) and 0 for commercial banks (control group) and 2007D is a dummy variable equal to 1 if an observation occurs after 2007 and 0, otherwise.

In the first regression, the panel estimate of the interaction term MSBD∙2007D, cβ1 is called the DID estimator which captures the mean difference in the stability between MSBs and commercial banks after 2007. Likewise, cρ1 in the second regression represents the difference in the effect of competition on stability between pre and after 2007 and between MSBs and commercial banks.11

Table 10 presents the estimation results of the DID model. The first regression reveals that the stability of MSBs experienced a significant decline after 2007. Specifically, the average stability of MSBs decreased

11 cρ1 can be called the difference-in-difference-in-differences (DDD) estimator (See Long et al. (2010) and a lecture note by Jeffrey Wooldridge entitled “What's New in Econometrics? Difference-in-Differences Estimation” at the NBER (National Bureau of Economic Research) Summer Institute 2007 (http://www.nber.org/WNE/Slides7-31-07/slides_10_diffindiffs.pdf). The estimator captures the time change in the average effect of competition for MSBs by netting out the change in the mean effect for commercial banks.

Table 10 Difference-in-differences approach for the stability of MSBs and commercial banks. The following panel regressions are estimated in this table:

Stabilityit ¼ α þ β1MSBDit � 2007Dit þ βz2007DitCompetition Measuresit þ XN k¼1

γkXk þ eit

Stabilityit ¼ α þ ρ1MSBDit � 2007Dit � Competition Measuresit þ ρz2007Dit þ ρzCompetition Measuresit þ XN k¼1

φkXk þ uit:

MSBD is a dummy variable equal to 1 for MSBs and 0 for commercial banks and 2007D is a dummy variable equal to 1 if the observation occurs after 2007 and 0, otherwise. The dependent variable is the Z-score using the ROAs and capital ratios for prior four quarters. The t-statistics based on robust standard errors are reported in brackets below coefficient values. The symbols ***, **, and * represent statistical significance at the 1%, 5%, and 10% level, respectively. Variable definitions are provided in Appendix A.

ln (Z4) Coef. t-Value Coef. t-Value Coef. t-Value

MSBD∙2007D∙competition 0.5141 [5.85]⁎⁎⁎ MSBD∙2007D∙inverse of Boone index 0.4416 [7.07]⁎⁎⁎ MSBD∙2007D −1.3411 [−16.50]⁎⁎⁎ −1.4952 [−17.60]⁎⁎⁎ −1.4813 [−17.98]⁎⁎⁎ 2007D 0.4737 [6.05]⁎⁎⁎ 0.3127 [3.79]⁎⁎⁎ 0.2630 [3.20]⁎⁎⁎

Competition −0.1036 [−2.32]⁎⁎ −0.3455 [−5.69]⁎⁎⁎ Inverse of Boone index −0.4111 [−7.98]⁎⁎⁎ ln(assets) 0.4761 [6.69]⁎⁎⁎ 0.5502 [7.65]⁎⁎⁎ 0.5223 [7.61]⁎⁎⁎

Profit ratio 0.0002 [0.39] 0.0002 [0.24] 0.0010 [0.16] Deposit to loan 0.0007 [4.18]⁎⁎⁎ 0.0006 [4.06]⁎⁎⁎ 0.0007 [4.16]⁎⁎⁎

Deposit to loan2 0.0000 [−3.24]⁎⁎⁎ 0.0000 [−3.20]⁎⁎⁎ 0.0000 [−3.23]⁎⁎⁎ Commercial to home loan −0.0006 [−1.89]⁎ −0.0006 [−1.89]⁎ −0.0006 [−2.01]⁎⁎ CD volatility −0.3612 [−7.78]⁎⁎⁎ −0.3218 [−6.89]⁎⁎⁎ −0.3542 [−7.76]⁎⁎⁎ Intercept −5.5825 [−6.15]⁎⁎⁎ −6.0271 [−6.65]⁎⁎⁎ −5.6586 [−6.20]⁎⁎⁎ No. of obs 2902 2902 2902 F/Wald tests 53.88 52.5 47.88 R2 0.413 0.499 55.210

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by 1.3411 compared to commercial banks after deregulation by the government and the global financial crisis. In the second and third regressions, we interact the competition measures with MSBD∙2007D. As discussed in Section 3.2, we use two proxies for competition; the opposite of the Boone index and Competition, a dummy variable which takes a value of 1 for a significantly negative Boone index and is otherwise 0. The table shows that the magnitude of the effect of competition on stability for MSBs is significantly greater after 2007 than that for commercial banks. When the market is competitive, the magnitude of an increase in the stability of MSBs after 2007 is 0.5141 greater than that of commercial banks. Overall, the results are consistent with our previous findings that MSBs with weak corporate governance tend to be more stable in competitive markets and this evidence is even stronger after the global financial crisis.

5. Conclusion

There are two alternative hypotheses that relate bank competition to stability. The conventional competition–fragility theory suggests that higher competition in financial industries causes financial institutions to lose their market power, leading to a decrease in profitability. In order to recover from financial losses, financial institutions are thus more likely to invest in riskier portfolios. Consequently, this risk-taking behavior will undermine the financial institutions' stability. On the other hand, competition– stability theory suggests that competition has a potentially positive effect on the stability of financial institutions. Accordingly, Boyd and De Nicolo (2005) show that banks with a greater market share in the loan market, which experience lower competition, tend to impose higher interest rates on their loans. A greater interest rate being charged by banks in a less competitive market may increase the risk-taking behavior of borrowing firms. Boyd and De Nicolo consequently argue that since the risk is ultimately shifted from borrowers to banks in this case, the default probability of banks increases in the riskiness of bank loans.

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A recent study by Martinez-Miera and Repullo (2010) forms partial support for Boyd and De Nicolo's (2005) risk-shifting effect in which the greater interest rates that exist in less competitive markets raise the risk of loans and thus make bank bankruptcy more likely. However, this work additionally takes into account the fact that greater interest rates also improve bank profitability, which is known as the interest effect, and suggests that there exists a U-shaped relationship between competition and the risk of bank failure. Thus far, empirical research has produced mixed results on the issue of the influence of bank competition on stability.

The purpose of this paper has been to investigate the relationship between competition and stability by using a sample comprising two different types of banks: MSBs and commercial banks in Korea. By doing so, we attempt to fill a gap in the current literature by examining whether differences in terms of governance structure, business models and regulatory treatments cause banks to interact with industry competition differently. We use quarterly panel data for MSBs and commercial banks from 1999, the end of Asian financial crisis, to 2011. We follow Boone (2008a, 2008b) who measures competitiveness using operating efficiency, which is known as the Boone index. The conventional concentration ratio and the Herfindahl–Hirschman Index are also used as supplementary measures for bank competition. We employ the Z-score as a measure of bank stability, which can capture how distant a certain bank is from insolvency. Finally, we perform pooling regressions as well as panel analysis to examine the relationship between competitive levels and stability for both MSBs and commercial banks. Section 3 described our measures and methodology in more detail.

Our empirical results generally support the hypothesis that the effect of bank competition on stability differs depending on the characteristics of banks involved. The results of multiple regressions show that bank competition has a significant and positive effect on the stability of MSBs with weak corporate governance. On the contrary, competition pressure significantly reduces the stability of commercial banks but, consistent with Tabak et al. (2012a), this relationship turns out to be non-linear. These results are robust across alternative proxies for competition and stability as well as various model specifications. Therefore, we can conclude that for commercial banks at least, higher competition creates a trade-off between the risk-shifting effect and interest effect, but the risk shifting effect overwhelms the interest effect for MSBs when the market is less competitive. In addition, our difference-in-differences analysis shows that the positive effect of competition on the stability of MSBs became even greater after deregulations by the Korean government and after the global financial crisis.

The main contribution of the paper is that this is one of the first works to provide evidence on the relationship between bank competition and stability, as conditional on different bank characteristics. Our new evidence that the influence of bank competition on stability differs depending on the characteristics of banks involved provides important implications on competition policy in the banking industry. According to our empirical results, competition policy should be applied differently to commercial banks and MSBs. In addition, our paper is the first paper to analyze Korean MSBs by using hand-collected data.

Appendix A. Variable descriptions

Name Descriptions

ln(Z) Natural logarithm of the Z-score, described in Section 3.2. ln(Z4) and ln(Z8) are calculated using the ROAs and capital ratios for prior 4 quarters and prior 8 quarters, respectively.

Competition A dummy which takes a value of 1 if Boone index is significantly negative, i.e., if competitive pressure exists, and 0, otherwise.

Inverse of Boone index The opposite of Boone index, described in Section 3.2. Inverse of Boone index2 Squared inverse of Boone index. CR Concentration ratio. CRN is defined as the market share held by top N banks.

CR4 and CR8 represent the concentration ratio by top four and eight banks, respectively. HHI Herfindahl–Hirschman Index, calculated by squaring the market share of each

bank and then summing the squares. ln(assets) Natural logarithm of total assets of banks. Profit ratio Total net income to total revenues ratio.

(continued on next page)

Appendix A (continued)

Name Descriptions

Foreign A dummy equal to 1 for a foreign bank and 0, otherwise. Loan to deposit Total loans to total deposits ratio. Loan to deposit2 Squared loan to deposit. Commercial to home loan Commercial loans to household loans ratio. CD volatility Monthly volatility of CD rates for prior one year. MSBD A dummy variable equal to 1 for MSBs and 0 for commercial banks. 2007D A dummy variable equal to 1 if the observation occurs after 2007 and otherwise 0.

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  • Bank competition and financial stability: A comparison of commercial banks and mutual savings banks in Korea
    • 1. Introduction
    • 2. Literature review
      • 2.1. Bank competition and stability
      • 2.2. Mutual savings banks and commercial banks in Korea
    • 3. Sample and methodology
      • 3.1. Sample
      • 3.2. Methodology
    • 4. Empirical results
      • 4.1. Analysis of market competition
      • 4.2. Competition and stability in the MSB industry
      • 4.3. Competition and stability in the commercial bank industry
      • 4.4. Additional analysis
      • 4.5. Difference-in-differences analysis
    • 5. Conclusion
    • Appendix A. Variable descriptions
    • References

Bank-competition-and-financial-stability-in-Asia-Pacific_2014_Journal-of-Banking-Finance.pdf

Journal of Banking & Finance 38 (2014) 64–77

Contents lists available at ScienceDirect

Journal of Banking & Finance

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / j b f

Bank competition and financial stability in Asia Pacific

0378-4266/$ - see front matter � 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jbankfin.2013.09.012

⇑ Corresponding author. Tel.: +44 1248382170. E-mail addresses: [email protected] (Xiaoqing (Maggie) Fu), [email protected]

(Yongjia (Rebecca) Lin), [email protected] (P. Molyneux). 1 The authors are grateful for funding from the University of Macau. We highly

appreciate the comments from the BOFIT Research Seminar, Australasian Finance & Banking Conference, Auckland Finance Meeting, and FMA Asian Conference Doctoral Student Consortium, especially those from Franklin Allen, Iikka Korhonen, Minghua Liu, Ronald Masulis and Yukihiro Yasuda. All errors are our responsibility.

2 Beck (2008) and Carletti (2008, 2010) provide excellent surveys of the l 3 Soedarmon et al. (2011) and Liu et al. (2012) estimate the competition

nexus for banks in 12 Asian economies and four South East Asian c respectively. In addition, a small number of cross-country empirical studie several Asia Pacific economies into their large sample sets in testing this rela See, for example, Beck et al. (2006a), Boyd et al. (2006), Evrensel (2008), Ber (2009), Schaeck et al. (2009), Behr et al. (2010), Turk Ariss (2010), and Angi (2012).

Xiaoqing (Maggie) Fu a, Yongjia (Rebecca) Lin a, Philip Molyneux b,⇑,1 a Faculty of Business Administration, University of Macau, Taipa, Macau b Bangor Business School, Bangor University, UK

a r t i c l e i n f o

Article history: Received 21 May 2013 Accepted 20 September 2013 Available online 1 October 2013

JEL classification: G21 G28

Keywords: Bank competition Financial stability Regulation Banks in Asia Pacific

a b s t r a c t

Analysis of the tradeoff between competition and financial stability has been at the center of academic and policy debate for over two decades and especially since the 2007–2008 global financial crises. Here we use information on 14 Asia Pacific economies from 2003 to 2010 to investigate the influence of bank competition, concentration, regulation and national institutions on individual bank fragility as measured by the probability of bankruptcy and the bank’s Z-score. The results suggest that greater concentration fosters financial fragility and that lower pricing power also induces bank risk exposure after controlling for a variety of macroeconomic, bank-specific, regulatory and institutional factors. In terms of regulations and institutions, the results show that tougher entry restrictions may benefit bank stability, whereas stronger deposit insurance schemes are associated with greater bank fragility.

� 2013 Elsevier B.V. All rights reserved.

1. Introduction

The impact of bank competition on financial stability has been a focus of academic and policy debate over the last two decades and particularly since the 2007–2008 global financial crises (Beck, 2008; Carletti, 2008; Careletti, 2010; Acharya and Richardson, 2009; Beck et al., 2010; OECD, 2011). Under the traditional compe- tition-fragility view, banks cannot earn monopoly rents in compet- itive markets and this results in lower profits, capital ratios and charter values. This makes banks less able to withstand demand- or supply-side shocks and encourages excessive risk-taking (Marcus, 1984; Keeley, 1990). Alternatively, the competition- stability view suggests that competition leads to greater stability. A less competitive banking market may lead to more risk-taking if the big banks are deemed too important to fail and as such obtain implicit (or explicit) subsidies via government safety nets (Mish- kin, 1999). In addition, banks with more market power tend to charge higher loan rates, which may induce borrowers to assume greater risk leading to greater default. In competitive banking

markets loan rates are lower, Too-Big-To-Fail issues and safety net subsidies are smaller, and this results in a positive link between bank competition and stability (Boyd and De Nicoló, 2005). It could also be the case, as noted by Martinez-Miera and Repullo (2010) that bank competition and stability are linked in a non-linear manner, and in a similar vein Berger et al. (2009) argue that com- petition and concentration may coexist and can simultaneously induce stability or fragility.

As noted above, recent studies on the causes of the credit crunch have highlighted deregulation and excessive competition as factors that led to financial sector meltdowns in the US and the UK (Llewellyn, 2007; Brunnermeier, 2009; Milne, 2009; OECD, 2011). Moreover, it is of interest to assess whether the relationship between banking competition and financial stability has been af- fected after the outbreak of the recent financial crisis. While a sub- stantial literature has emerged addressing this critical issue,2 to our knowledge, the problem has been inadequately covered for banks operating across the Asia Pacific region.3 Against this backdrop our paper investigates the impact of bank competition on financial

iterature. -stability ountries, s include tionship. ger et al. ner et al.

Xiaoqing (Maggie) Fu et al. / Journal of Banking & Finance 38 (2014) 64–77 65

stability for 14 Asia Pacific economies over the period from 2003 to 2010 and extends the previous empirical literature in several respects.4

First, previous studies have focused on using Z-scores or evi- dence of a real bank crisis as measures of banking sector risk/sta- bility. Here we extend the analysis by employing the probability of bankruptcy as an indicator of individual bank fragility.5 A real banking crisis can be an accurate indicator of banking sector stabil- ity, but its significance may be distorted for the following reasons: (1) banking crises are defined and announced differently across countries; (2) regulators may be less inclined to report bank insol- vencies because they may imply regulatory failure; and finally (3) regulators are reluctant to announce the failures of banks that play a key role within the system because they wish to avoid contagion effects (Uhde and Heimeshoff, 2009). The probability of bankruptcy, computed using the Black and Scholes (1973) and Merton (1974) contingent claims approaches provide a more appealing alternative. Compared to the use of accounting-based models (e.g., Z-score), this market-based measure of stability has the following advantages: (1) in efficient markets, stock prices reflect all available information; (2) market variables are unlikely to be influenced by firm’s accounting policies; and (3) market prices reflect future expected cash flows and thus should be more appropriate for use for prediction purposes.

Second, according to the structure-conduct-performance propo- sition, competition and concentration are inversely related; a more concentrated market will feature a lower degree of competition. However, criticisms of this view have led to a shift away from the presumption that structure is the most important determinant of the level of competition. Instead, proponents of what is now known as the New Industrial Organization (NIO) literature, such as Schmalensee (1982), argue that the strategies (conduct) of indi- vidual firms are equally, if not more, important than concentration, in explaining competitive conditions. Also, the related emergence of the theory of contestability (Baumol, 1982; Baumol et al., 1982) has spawned a variety of non-structural indicators of com- petition aimed at identifying firm conduct.6 In our study we include both structural and non-structural measures of competition to examine the concentration, competition and stability nexus in Asia Pacific banking.7

Thirdly, we incorporate both regulatory and institutional envi- ronmental factors in our models and also highlight the impact of the global turmoil on individual risk exposure in the region. Fol- lowing Berger et al. (2009), we adopt an instrumental variable technique with a Generalized Method of Moments (GMM) estima- tor to address potential endogeneity problems between bank com- petition and risk. We also include a series of sensitivity analyses using different model specifications.

4 See, for example, De Nicoló et al. (2003), Beck et al. (2006a), Boyd et al. (2006), Yeyati and Micco (2007), Berger et al. (2009), Schaeck and Cihak (2008), Schaeck et al. (2009), Uhde and Heimeshoff (2009), Behr et al. (2010), Turk Ariss (2010), Agoraki et al. (2011), Soedarmon et al. (2011), and Liu et al. (2012).

5 The Z-score is also used in this study to determine the robustness of our results. 6 These include measures of competition between oligopolists such as Iwata (1974)

and those that test for competitive behavior in contestable markets, Bresnahan (1982, 1989), Lau (1982) and Panzar and Rosse (1987). These indicators have been developed from (static) theory of the firm models under equilibrium conditions and mainly use some form of price mark-up over a competitive benchmark, such as price over marginal cost for the Lerner index and price over marginal revenue for the Bresnahan (1982) measure. The main exception is the Panzar and Rosse (1987) indicator that measures the relationship between changes in factor input prices and revenues earned by firms. See also Koetter et al. (2012) for recent studies using adjusted-Lerner indices to measure market power in banking.

7 The structural approach focuses on market structure measures such as market shares, concentration ratios for the largest sets of firms, and a Hirschman–Herfindahl index. Structural indicators measure actual market shares but do not allow inferences regarding the competitive behavior of banks. Non-structural measures are used to quantify bank pricing behavior. They include the Lerner index and the Panzar Rosse H-statistic (Berger et al., 2004).

Overall our results suggest that greater concentration fosters financial fragility, whereas lower pricing power also induces bank risk exposure after controlling for macroeconomic, bank-specific, regulatory and institutional factors. This finding supports the neutral view of the competition-stability relationship. It also implies that some banks in the region are able to attain greater discretion in price-setting to boost profits and reduce insolvency risk through channels other than increased concentration, such as product differentiation. Furthermore, there is evidence that larger banks are more likely to fail than their smaller counterparts. In addition, our results indicate that tougher entry restrictions may benefit bank stability, whereas stronger deposit insurance schemes appear to create greater bank fragility.

The remainder of the paper is organized as follows. Section 2 provides a review of the literature on competition and stability in banking. Section 3 introduces the econometric methodology. Section 4 describes the data used in the econometric tests. Sec- tion 5 presents the empirical results and Section 6 are the conclusions.

2. Literature review

Under the traditional competition-fragility hypothesis, com- petitive and/or less concentrated banking systems are more frag- ile. The ‘‘charter/franchise value’’ of banking, as modeled by Marcus (1984), and Keeley (1990), suggests that competition drives banks to undertake risk-taking strategies due to the con- traction of the latter’s franchise value. These models show that a higher charter or franchise value arising from increased market power may deter excessive risk-taking by the bank’s manage- ment. Because higher franchise value results in greater opportu- nity costs during bankruptcy, bank managers and shareholders may become more reluctant to engage in risky activities improv- ing bank asset quality.

Diamond (1984), Ramakrishnan and Thakor (1984), Boyd and Prescott (1986), Williamson (1986), and others show that more concentrated banking systems are composed of larger banks and that larger banks can capitalize on economies of scale and scope and better diversify their portfolios. Smith (1984) argues that banking relationships may endure for longer periods in less competitive environments if the information on the probability distribution of depositors’ liquidity needs is private. Hence, greater concentration and less competition could reduce liability risk and lead to greater stability in banking. Boot and Green- baum (1993) and Allen and Gale (2000, 2004) suggest that in a more competitive environment, banks earn less informational rent from their relationships with borrowers, which reduces their incentives to properly screen borrowers and increases the risk of fragility.

Competition can impact stability through contagion. Using a model of financial contagion in the interbank market Allen and Gale (2000) suggest that under perfect competition, all banks are price takers and none have an incentive to provide liquidity to troubled banks. As a result, troubled banks eventually fail with negative repercussions for the entire sector. Similarly, Saez and Shi (2004) argue that banks can cooperate, act strategically and help other banks to cope with temporary liquidity shortages in a market characterized by imperfect competition. Allen and Gale (2000) also find that a concentrated banking system with a small number of large institutions is more stable because banks are easier to monitor, less burdened by supervision, and therefore more resilient to shocks. Boot and Thakor (2000) suggest that larger banks tend to engage in ‘‘credit reputation/rating’’ because making fewer high-quality credit investments can increase the return of individual investments and thereby encourage financial

66 Xiaoqing (Maggie) Fu et al. / Journal of Banking & Finance 38 (2014) 64–77

soundness. Additionally, larger banks are assumed to enjoy com- parative advantages related to the provision of credit monitoring services.

Allen and Gale (2004) claim that financial crises are more likely to occur in less concentrated banking systems due to the absence of powerful providers of financial products that could reap benefits from the high profits that thus serve as a buffer against asset qual- ity deterioration. Similarly, Boyd et al. (2004) state that the pres- ence larger (monopolistic) banks in concentrated banking systems might enhance profits and thus reduce financial fragility by providing higher ‘‘capital buffers’’ that protect these systems against external macroeconomic and liquidity shocks.

A different argument among proponents of the competition-fra- gility hypothesis is that deposit insurance schemes can reduce fra- gility by preventing bank runs but also introduce moral hazard by providing incentives to banks to engage in riskier activities. Thus, in more competitive environments, more generous deposit insur- ance may undermine bank stability (Diamond and Dybvig, 1983; Matutes and Vives, 1996). In addition, Hellmann et al. (2000) sug- gest that deposit interest rate ceilings are still necessary to prevent banks from taking excessive risk in competitive markets, although minimum capital requirements can boost the charter value.

Under the alternative competition-stability hypothesis, more competitive and/or less concentrated banking systems are more stable. The ‘‘too big to fail’’ doctrine (Mishkin, 1999, 2006; Barth et al., 2012b) indicates that policymakers are more concerned about bank failures when the number of banks in a concentrated banking system is low. Thus, these large banks are often more likely to receive public guarantees or subsidies, which may gener- ate a moral hazard problem, encourage risk-taking behavior and intensify financial fragility (Kane, 2010; Rosenblum, 2011). More- over, contagion risk may increase in a concentrated banking sys- tem with larger banks.

Caminal and Matutes (2002) claim that lower competition can result in reduced credit rationing and larger loans, ultimately increasing the probability of bank failure. Boyd and De Nicoló (2005) argue that concentrated banking systems allow banks to charge higher loan rates, which may encourage borrowers to as- sume greater risk. Consequently, the volume of non-performing loans may increase, resulting in a higher probability of bank failure. However, Martinez-Miera and Repullo (2010) suggests that higher loan rates also produce higher interest revenues for banks. This dy- namic might generate a U-shaped relationship between bank com- petition and stability.

Beck et al. (2006a,b) suggest that bank size is positively corre- lated with organizational complexity; for example, monitoring a large bank is more difficult than monitoring a small bank. Accord- ingly, as firm size increases, transparency may decrease as a result of expansion across multiple geographic markets and business lines and the use of sophisticated financial instruments that facil- itate the establishment of complex corporate organizations. These developments may reduce managerial efficiency and internal cor- porate control and may increase operational risk. Increasing orga- nizational complexity can render both market discipline and regulatory action less effective in preventing excessive risk expo- sure (Cetorelli et al., 2007).

However, as indicated in Berger et al. (2009), the two strands of the literature do not necessarily produce opposing predictions regarding the relationship between bank competition and financial stability. The aforementioned authors argue that bank risks may not increase even if market power encourages riskier asset portfo- lios because banks may protect their charter values by using other methods to offset the greater risk exposure. Such methods may in- clude increasing equity capital, reducing interest rate risk, and sell- ing credit derivatives. As noted earlier, market structure measures may not be good measures of competition and this (to some ex-

tent) has been confirmed by Berger et al. (2004) and Beck (2008) who show that banking industry concentration can influence sta- bility through channels other than competition.

A substantial empirical literature has emerged testing for con- centration, competition and banking stability relationships across countries. Yeyati and Micco (2007), for instance, use a sample of commercial banks from eight Latin American countries over the period 1993–2002 and find a positive link between bank risk (as measured by the Z-score) and competition (as captured by the Panzar and Rosse 1987, H-statistic), whereas the coefficient for bank concentration is not significant. This result lends support to the competition-fragility paradigm. Schaeck and Cihak (2008) analyze the relationship between bank competition and soundness using a sample of more than 3600 banks from ten European countries and more than 8900 US banks for the period from 1995 to 2005. They suggest that competition as measured by the Boone indicator increases bank soundness by increasing efficiency and that more concentrated banking markets benefit from financial stability. Using data from 31 systemic banking crises in 45 coun- tries for the period from 1980 to 2005, Schaeck et al. (2009) show that competition (as captured by the Panzar Rosse H-statistic) re- duces the likelihood of a crisis and increases the time to crisis, even after they control for banking system concentration, which is neg- atively related to financial fragility.

In a similar study, Berger et al. (2009) use a sample of 8235 banks from 23 industrial countries over 1999–2005 and find that banks with market power (measured using the Lerner index) have less overall risk exposure, as captured by their Z-scores. These find- ings support the traditional competition-fragility view. On the other hand, they show that bank-level market power also results in riskier loan portfolios, as indicated by non-performing loan ra- tios. Berger et al. (2009) argue that banks can protect their charter value from higher loan risk by holding more equity capital. More recently, Anginer et al. (2012) examine the relationship between competition according to the Lerner index and systemic stability as captured by default risk under Merton’s (1974) contingent claim pricing framework. Using a sample of 1872 publicly traded banks from 63 countries between 1997 and 2009, they find a positive relationship between competition and systemic stability (and the results remain the same even when they conduct a robustness check using bank asset concentration as an alternative proxy for bank competition).

Liu et al. (2012) introduce a variety of bank-specific risk indica- tors (the ratio of loan-loss provisions to total loans, loan-loss re- serves to total loans, after-tax ROA volatility, and the natural logarithm of the Z-index) to investigate similar relationships for banks operating in South East Asia (Indonesia, Malaysia, the Philip- pines and Vietnam) between 1998 and 2008. They find that com- petition measured using the Panzar Rosse H-statistic is inversely and significantly related to most risk indicators except the natural logarithm of the Z-index, which suggests that competition does not erode bank stability. The researchers also find that concentration is negatively associated with bank risk, whereas regulatory restric- tions positively influence bank fragility.

Overall, cross-country evidence yields mixed results regarding the relationship between bank concentration, competition, and stability. Meanwhile, the findings do confirm that concentration and competition can coexist and may influence financial stability through different channels.

3. Methodology

We test whether bank concentration and competition influence bank stability employing bank-level data from 14 Asia Pacific econ- omies. To address potential endogeneity issues associated with

Table 1 Variable definitions and sources.

Variable Definition Data sources

Dependent variables Probability of bankruptcy The bank-level probability of bankruptcy based on method of Bharath and

Shumway (2008) Bankscope, Datastream

Z-score The bank-level Z-score; a larger value means less overall bank risk and higher bank stability

Bankscope

Independent variables CR3 A country-level structural indicator of bank concentration, measured by the

concentration of assets held by the three largest banks in each country, with higher value indicating greater market concentration

World Bank database on financial development structure and Bankscope

LERNER A bank-level non-structural indicator of bank competition, measured by the Lerner index using fixed-effects method, with higher values indicating less competition in the banking sector

Bankscope

E-LERNER A bank-level non-structural indicator of bank competition, measured by the efficiency-adjusted Lerner index using a stochastic frontier analysis approach, with higher values indicating less competition in the banking sector

Bankscope

SIZE The natural logarithm of total assets in thousands of USD BankScope LLP The ratio of loan loss provisions to total assets BankScope NIM Bank’s net interest income as a share of its interest-bearing (total earning) assets BankScope Entry restrictions Ratio of entry applications denied to applications received from domestic and

foreign banks World Bank Survey of Bank Regulation and Supervision (for details see Barth et al., 2008, 2012a,b)

Capital requirements Minimum regulatory capital-to-assets ratio per country World Bank Survey of Bank Regulation and Supervision (for details see Barth et al., 2008, 2012a,b)

Deposit insurance A dummy variable that takes a value of one if the country has deposit insurance, and zero otherwise

World Bank Survey of Bank Regulation and Supervision (for details see Barth et al., 2008, 2012a,b)

RGDP Rate of real GDP growth rate World Economic Outlook Database, IMF CRISIS A dummy variable that takes a value of one for the years 2008–2009, and zero

otherwise Compiled by the authors

Instrumental variables Activity restrictions Index measure that indicates whether bank activities in the

securities, insurance and real estate markets, ownership and control of non-financial firms are unrestricted, permitted, restricted or prohibited

World Bank Survey of Bank Regulation and Supervision (for details see Barth et al., 2008, 2012a,b)

The aggregate indicator ranges from 1 to 4. A higher value indicates greater activity restrictions arising from legal requirements

Financial freedom The indicator of the openness of the banking system is a composite index of whether government interference exists in the financial sector, such as regulation, financial products, allocation of credit, whether foreign banks are free to operate. Higher values indicate fewer restrictions on banking freedoms

Heritage Foundation (2010)

Property rights The Heritage Foundation property rights protection index. A higher value signifies weaker protection

Heritage Foundation (2010)

Xiaoqing (Maggie) Fu et al. / Journal of Banking & Finance 38 (2014) 64–77 67

measures of market power, we use an instrumental variable technique with a GMM estimator.8 Our panel data model has the following general form:

Bank Risk ¼ fðConcentration;Competition; Bank Controls; Regulatory and Institutional Controls;Macro ControlsÞ

ð1Þ

Notes on our dependent, explanatory and instrumental variables as well as data sources are presented in Table 1.

3.1. Market-based risk measure

Black and Scholes’s (1973) and Merton’s (1974) Distance to De- fault model is used to estimate the insolvency risk of listed banks. The model has been widely used in empirical research.9 However, there is only one paper employing this model in comparing the per- formance of market-based and accounting-based bankruptcy predic- tion models (Agarwal and Taffler, 2008). The Distance to Default

8 GMM is more efficient than 2SLS because it accounts for heteroskedasticity (Hall, 2005).

9 For example, see Hillegeist et al. (2004), Vassalou and Xing (2004), Gropp et al. (2004, 2006), Akhigbe et al. (2007), Chan-Lau and Sy (2007), Duffie et al. (2007), Bharath and Shumway (2008), and Campbell et al. (2008).

model views equity as a call option on the assets of a firm, with a strike price equal to the face value of the liabilities at time T when the liabilities mature. At time T, equity holders exercise their option and pay off the debt holders if the value of the firm’s assets is greater than the face value of its liabilities. Otherwise, if the value of the as- sets is insufficient to fully repay the firm’s debts, the call option be- comes worthless, and equity holders let it expire. In this scenario, the firm files for bankruptcy, and ownership is assumed to be trans- ferred to the debt holders at no cost, whereas the payoff for equity holders is zero. Estimates for the probability of bankruptcy are given by McDonald (2002). They are modified for dividends, and they re- flect the fact that the stream of dividends paid by the firm accrues to the equity holders:

P ¼ N � ln V AD � � þ u � d � r

2 A

2

� �� � T

rA ffiffiffi T p

0 B@

1 CA ð2Þ

where P is the probability of bankruptcy, N( ) is the cumulative nor- mal density function, VA is the value of assets, D is the face value of debts proxied by total liabilities, u is the expected return, d is the dividend rate estimated as total dividends/(total liabilities + market value of equity), rA is the standard deviation of assets (asset volatil- ity), and T is the time to expiration (taken to be 1-year).

68 Xiaoqing (Maggie) Fu et al. / Journal of Banking & Finance 38 (2014) 64–77

VA, u and rA are non-observable. This study uses the following method outlined by Bharath and Shumway (2008):

V A ¼ V E þ D ð3Þ

rA ¼ V E V A

rE þ D V A

rD ð4Þ

rD ¼ 0:05 þ 0:25 � rE: ð5Þ

u ¼ ri;t�1: ð6Þ

where VE is the market value of common equity, VA is the total value of assets, D is the face value of debts proxied by total liabilities, rA is the standard deviation of assets (asset volatility), rE is the standard deviation of daily stock returns multiplied by the square root of the average number of trading days in the year (set at 252 trading days), u is the expected return, and ri,t�1 is the bank’s stock returns over the previous year.10

3.2. Accounting-based risk measure

For our accounting based risk measure we use the Z-score which is widely used in the literature as a stability indicator (see, for instance, Boyd and Runkle, 1993; Lepetit et al., 2008; Laeven and Levine, 2009; Čihák and Hesse, 2010). Using accounting infor- mation on asset returns, its volatility and leverage, the Z-score is calculated as follows:

Zit ¼ ROAit þ Eit=TAit

rROAit ð7Þ

where ROA is the return on assets, E/TA is the equity to total assets ratio, and rROA is the standard deviation of return on assets.

The Z-score is inversely related to the probability of a bank’s insolvency. A bank becomes insolvent when its asset value drops below its debt and the Z-score shows the number of standard devi- ations that a bank’s return has to fall below its expected value to deplete equity and make the bank insolvent.

3.3. Concentration and competition measures

First, based on the structural approach, the degree of market concentration is used. Market concentration is measured as the ra- tio of the assets of the three largest banks to the total assets of the banking system in the country in question (CR3). Second, a non- structural indicator, the Lerner index (LERNER), is used to measure the degree of competition. This indicator has been widely used in recent bank research.11 The Lerner index captures the capacity of price power by calculating the difference between price and mar- ginal cost as a percentage of price.12 The degree of competition is gi- ven by the range 0 < Lerner index < 1. In the case of perfect

10 Hillegeist et al. (2004) use this approach to assess the probability of bankruptcy and they note that ‘‘since expected returns cannot be negative, we set the expected growth rate equal to the risk-free rate in these cases (p. 10)’’. Bharath and Shumway (2008) also use the risk-free rate to replace the expected return on assets as a robustness check (p. 1348). In our sample, the risk-free rates range between 0.20 and 5, whereas the expected returns are negative during the crisis period. Therefore, we follow these two studies and replace the expected return with the risk-free rate when the former is negative.

11 For example, see Claessens and Laeven (2004), Maudos and Fernández de Guevara (2004), Fernández de Guevara et al. (2005), Berger et al. (2009), and Maudos and Solís (2009).

12 The H-statistic, developed by Panzar and Rosse (1987), is an alternative tool for inferring the degree of competition in the banking industry. It is computed from reduced form revenue equations, and it measures the sum of the elasticities of a bank’s revenue with respect to the bank’s input prices (Claessens and Laeven, 2004). A critical feature of the Panzar Rosse H-statistic is that the test must be undertaken in long-run equilibrium.

competition, the Lerner index = 0; under a pure monopoly, the Lern- er index = 1. A Lerner index < 0 implies pricing below the marginal cost and could result, for example, from non-optimal bank behavior. Algebraically, the Lerner index is calculated as follows:

Lernerit ¼ðPTAit � MCTAitÞ=PTAit ð8Þ

where PTAit is the price of total assets proxied by the ratio of total revenues (interest and non-interest income) to total assets for bank i at time t, and MCTAit is the marginal cost of total assets for bank i at time t.

Following Fernández de Guevara et al. (2005) and Carbó-Valverde et al. (2009), we can calculate the output price (PTAit ) as the ratio of total revenues (interest and non-interest income) to total assets. Given the limited information on prices for loans and deposits,13 we use a single indicator of banking activity, namely total assets as a measure of bank output, as suggested by Shaffer (1993) and Berg and Kim (1994). Assuming that the heterogeneous flow of goods and services supplied by a bank is proportional to its total assets, the output price includes both inter- est income and non-interest income. Following Hasan and Marton (2003), Soedarmon et al. (2011), Sun and Chang (2011) and Jiang et al. (2013) we use a two input cost function specification that tends to be used in emerging market bank efficiency studies (due to data availability issues) to estimate marginal costs. We also cross check with a three-input cost function specification and also follow Koetter et al. (2008, and 2012) and estimate the efficiency-adjusted Lerner index using a stochastic frontier analysis approach for another robustness test.14

3.4. Other control variables

Following Schaeck and Cihak (2008), Laeven and Levine (2009) and Uhde and Heimeshoff (2009), we also include a range of bank- specific variables. A bank’s asset size (SIZE) is defined as the loga- rithm of its total assets. The ratio of loan-loss provisions to total as- sets (LLP) is used to measure output quality and the way in which managers invest in high risk assets. The net interest margin (NIM) is employed to track the profitability of a bank’s investing and lending activities.

Beck et al. (2006a) argue that there are two reasons why cross- country differences in bank regulatory policies and national insti- tutions should be considered in assessing the relationship between bank competition and financial stability. First, this approach pro- vides a simple robustness test for the competition-stability rela- tionship. Second, it presents additional information about the links between bank regulations, national institutions, and financial stability. Hence, following previous studies (Beck et al. 2006a; Lae- ven and Levine 2009; Delis et al. 2011, and Goddard et al. 2011), we also control for bank regulations and institutional environ- ments in investigating the effects of concentration and competition on bank stability.

Deposit insurance is a dummy variable that takes a value of one if a country has explicit deposit insurance and a value of zero otherwise.15 Credible deposit insurance can enhance financial stabil- ity by decreasing the likelihood of depositor runs. Conversely, if the capital positions and risk-taking of insured institutions are not super- vised carefully, insurers tend to accrue loss exposures that under- mine bank stability over the long-run. Capital requirement indicates the minimum capital requirement (capital-to-assets ratio) per coun-

13 Loan revenue data do not separate earned income from fixed income investments, and the financial costs of deposits are included with those of other liability products.

14 Appendix A presents the translog cost function used to estimate bank marginal cost.

15 In our sample, five countries (Australia, China, Sri Lanka, Pakistan, and Thailand) do not have deposit insurance.

Table 2 Descriptive statistics.

Variable Listed banks Listed and non-listed banks

Obs. Mean Std. dev. Min Max Obs. Mean Std. dev. Min Max

Probability of bankruptcy 1500 0.18 0.18 0.54 0 Z-score 1500 40.86 32.69 �2.69 196.64 4069 39.78 47.14 �40.28 681.92 CR3 1500 0.44 0.11 0.26 0.99 4069 0.46 0.13 0.26 0.99 Conventional Lerner index (LERNER) 1500 0.31 0.14 �1.26 0.68 4069 0.32 0.18 �2.75 0.82 Efficiency-adjusted Lerner index (E-LERNER) 1500 0.26 0.14 �1.32 0.65 4069 0.27 0.19 �2.79 0.81 Bank size (SIZE) 1500 16.22 1.60 10.11 21.05 4069 15.59 2 1.78 21.4 Loan loss provision% (LLP) 1500 1.80 4.27 0 149 4069 1.69 3.82 0 149 Net interest margin% (NIM) 1500 2.79 1.56 �1.49 11.04 4069 2.94 2.69 �60.57 39.36 Real GDP growth% (RGDP) 1500 3.96 3.88 �6.29 14.47 4069 5.37 4.23 �6.29 14.47 Global financial crisis (CRISIS) 1500 0.28 0.45 0 1 4069 0.27 0.44 0 1 Entry restrictions 1500 0.13 0.25 0 0.92 4069 0.09 0.22 0 0.92 Capital requirements% 1500 8.42 0.69 8 10 4069 8.32 0.63 8 10 Deposit insurance 1500 0.86 0.35 0 1 4069 0.75 0.43 0 1 Activity restrictions 1500 10.84 1.97 4 16 4069 11.28 2.59 4 16 Financial freedom 1500 44.85 14.43 30 90 4069 44 16.59 30 90 Property rights 1500 57.56 18.84 20 90 4069 52.45 22.03 20 90

The probability of bankruptcy is a market-based bank-level measure of financial fragility that is calculated using the method developed by Bharath and Shumway (2008). The Z-score is an accounting-based bank-level indicator of financial stability. The conventional Lerner index (LERNER) is a bank-level indicator of bank competition that is calculated as the difference between price and marginal cost as a percentage of price using fixed effect regression. The efficiency-adjusted Lerner index (ELERNER) is a bank-level efficiency-adjusted indicator of bank competition calculated as the difference between price and marginal cost as a percentage of price using a stochastic frontier analysis approach. CR3 is a country-level structural indicator of bank concentration calculated as the fraction of assets held by the three largest banks in each country. SIZE is the natural logarithm of total assets in thousands of USD. LLP is the ratio of loan loss provisions to total assets. NIM is the ratio of net interest income to interest-bearing (total earning) assets. RGDP is the rate of real GDP growth. Entry restrictions is the ratio of entry applications denied to applications received from domestic and foreign banks. Global financial crisis is a dummy variable that takes a value of one for the years 2008–2009 and zero otherwise. Activity restrictions is an aggregate index measure that indicate whether bank activities in the securities, insurance and real estate markets and the ownership and control of non-financial firms are unrestricted, permitted, restricted or prohibited. The capital requirement is the minimum regulatory capital-to-assets ratio per country. Financial freedom is an indicator of the openness of the banking system; it functions as a composite index of government interference in the financial sector, including regulations on financial products, allocation of credit, whether foreign banks are free to operate and other factors. Deposit insurance is a dummy variable that takes a value of one if the country has deposit insurance and zero otherwise. Property rights are measured using the Heritage Foundation property rights protection index.

Xiaoqing (Maggie) Fu et al. / Journal of Banking & Finance 38 (2014) 64–77 69

try, which is interpreted as another entry barrier indicator. In addi- tion, greater equity capital encourages prudent behavior. Hence, greater capital requirements are expected to indicate a more stable banking market. The variable entry restrictions is the ratio of the num- ber of banking licence applications denied to the number of applica- tions received from domestic and foreign entities. The effect of this control variable on bank stability is expected to be ambiguous be- cause restricted entry may reduce competitive pressure and thereby increase domestic bank profits, but it may also induce market ineffi- ciencies. The rate of real GDP growth (RGDP) is used as a proxy for the fluctuations in economic activity. CRISIS is a dummy variable that takes a value of one for the years 2008–2009 and zero otherwise.

To deal with the potential presence of endogeneity and heter- oskedasticity, following Berger et al. (2009), we employ a GMM pa- nel data estimator using activity restrictions, financial freedom, and property rights as instruments. Activity restrictions are a key deter- minant of the scope of a bank’s ability to provide fee-paying ser- vices. This measure reflects the level of regulatory restrictiveness for bank participation in securities market, insurance activities, real estate activities, and the ownership of non-financial firms. Financial freedom is an indicator of the openness of a financial sys- tem. This measure indicates the extent of government involvement in the financial sector, considering regulation, financial products, and the allocation of credit; the freedom of foreign banks to oper- ate; and the degree of regulation of financial market activities. Fi- nally, the protection of property rights is an important pre-requisite for a well-functioning financial system. A higher value of the Her- itage Foundation property rights protection index signifies weaker protection of property rights.

16 Please refer to Table 1 for details.

4. Data

The sample data focus on commercial banks in 14 Asia Pacific economies over 2003 and 2010. Financial information and stock market information, converted to US dollars, are obtained from the

Bankscope database by Bureau van Dijk and are supplemented by information from Datastream. Banking sector concentration ratios are obtained from the updated version of the World Bank database on financial development structures and supplemented by the Bankscope database; real GDP growth data are taken from the World Economic Outlook by the International Monetary Fund (IMF); and information on regulations and the institutional environ- ment come from several sources, including the World Bank database on ‘‘Bank Regulation and Supervision’’ (developed by Barth et al., 2001 and updated by Barth et al., 2006, 2008 and Barth et al., 2012a) and the 2010 index of Economic Freedom, which was pub- lished by The Wall Street Journal and The Heritage Foundation.16

After excluding banks with (1) missing, negative or zero values for the cost function needed to calculate the Lerner index, (2) miss- ing values for loan loss provisions, and (3) missing Z-score values, we obtain a final sample that includes unbalanced panel data for 14 Asia Pacific economies, with 4069 observations (see Appendix B). The subsample for listed banks includes 1500 observations (see Appendix C). All of the data are deflated by their correspond- ing year CPIs to the 2003 price level to control for inflation effects. Table 2 presents the descriptive statistics for all variables used in the study. All bank-level variables are averaged by bank for the period from 2003 to 2010, and the country-level variables are aver- aged by country for the same study period. Comparing listed banks with non-listed banks in the sample, Table 2 shows that on average listed sample banks enjoy a higher Z-score, lower loan loss provi- sion ratio, and larger in size, whereas non-listed banks have a high- er Lerner index and rely more on interest income. Moreover, markets with listed banks are less concentrated, subject to less activity restrictions, have a higher capital requirement ratio, and enjoy more financial freedom and better property rights protec- tion. Listed banks join the deposit insurance scheme in the markets with more entry restrictions.

Table 3 Concentration, Competition, and Stability measures.

Listed banks Listed and non-listed banks

Obs. CR3 LERNER E-LERNER Prob. of bankruptcy Obs. CR3 LERNER ELERNER Z-score

Panel A: mean by year 2003 153 0.4748 0.3191 0.2712 0.2004 423 0.4767 0.3154 0.2724 39.2417 2004 167 0.4305 0.3351 0.2885 0.0940 460 0.4596 0.3363 0.2943 42.0883 2005 185 0.4181 0.3273 0.2807 0.0834 519 0.4593 0.3173 0.2750 40.778 2006 188 0.4175 0.3116 0.2637 0.0799 549 0.4624 0.3076 0.2645 40.2366 2007 197 0.4297 0.3028 0.2529 0.1862 565 0.4644 0.3142 0.2695 39.448 2008 203 0.4472 0.2585 0.2056 0.2561 552 0.4661 0.2826 0.2351 37.3431 2009 211 0.4483 0.3151 0.2651 0.3369 534 0.4655 0.3206 0.2740 39.9518 2010 196 0.4550 0.3336 0.2828 0.1523 467 0.4647 0.3545 0.3072 39.4407

Panel B: mean by country Australia 48 0.6827 0.2954 0.2291 0.0820 111 0.6577 0.3206 0.2740 44.5649 China 35 0.5191 0.4343 0.3811 0.1295 700 0.5387 0.3914 0.3518 40.6025 Hong Kong 32 0.7064 0.4268 0.3823 0.0447 193 0.6971 0.3683 0.3281 41.2276 India 226 0.3409 0.3093 0.2598 0.1330 447 0.3389 0.3106 0.2663 42.8772 Indonesia 134 0.4562 0.2653 0.2270 0.1265 409 0.4583 0.2991 0.2661 47.6483 Japan 597 0.4089 0.3091 0.2538 0.2616 988 0.4077 0.3074 0.2521 39.9749 Korea 31 0.5057 0.3486 0.3009 0.1872 126 0.5033 0.3380 0.2866 28.4216 Malaysia 24 0.4563 0.4315 0.3842 0.0362 191 0.4571 0.3945 0.3547 47.0183 Pakistan 98 0.4376 0.2671 0.2316 0.0936 174 0.4404 0.2129 0.1766 17.5765 Philippines 78 0.5088 0.3175 0.2769 0.1145 184 0.4953 0.2448 0.2070 40.8976 Singapore 16 0.9156 0.4889 0.4410 0.0670 67 0.9145 0.3315 0.2856 62.3927 Sri Lanka 55 0.6165 0.2669 0.2365 0.1409 81 0.6171 0.2147 0.1857 34.9779 Taiwan 62 0.2719 0.2753 0.2236 0.1974 253 0.2712 0.3126 0.2646 28.714 Thailand 64 0.4550 0.3622 0.3147 0.1167 145 0.4539 0.2520 0.2070 34.2761

The probability of bankruptcy is a market-based bank-level measure of financial fragility that is calculated using the method developed by Bharath and Shumway (2008). The Z-score is an accounting-based bank-level indicator of financial stability. LERNER is a bank-level indicator of bank competition calculated as the difference between price and marginal cost as a percentage of price using fixed effect regression. ELERNER is a bank-level efficiency-adjusted indicator of bank competition calculated as the difference between price and marginal cost as a percentage of price using a stochastic frontier analysis approach. CR3 is a country-level structural indicator of bank concentration calculated as the fraction of assets held by the three largest banks in each country.

17 Yeyati and Micco (2007) use a sample of commercial banks from eight Latin American countries over the period 1993–2002 and find a positive link between bank risk (as measured by the Z-score) and competition (as captured by the Panzar and Rosse, 1987, H-statistic), whereas the coefficient of bank concentration is insignificant.

18 Using data from 31 systemic banking crises in 45 countries for the period from 1980 to 2005, Schaeck et al. (2009) show that competition reduces the likelihood of a crisis and increases the time to crisis and concentration is positively related to financial fragility. Anginer et al. (2012) use a sample of 1,872 publicly traded banks from 63 countries between 1997 and 2009 and find a positive relationship between competition and systemic stability and a negative relationship between concentration and systemic stability.

70 Xiaoqing (Maggie) Fu et al. / Journal of Banking & Finance 38 (2014) 64–77

Table 3 presents a summary of our concentration, competition and bank stability measures from 2003 to 2010 for 14 Asia Pacific countries by year (panel A) and by country (panel B). The pattern derived from the sample of listed banks is quite similar to that from the whole sample. Thus, we focus on the sample of listed banks. Based on the market measure of bank stability, bank risk in- creased overall from 2007 to 2009. The results imply that bank per- formance was most affected over 2009, a finding also confirmed by the IMF (2009). Bank risk decreased dramatically in 2010, which implies that this region was initially hit hard by the global crisis but has rapidly rebounded. Comparing bank risk by country using the market-based measure indicates that on average, banks operat- ing in Malaysia, Hong Kong, and Singapore are exposed to lower risk than those in other Asia Pacific economies. Meanwhile, Japa- nese, Taiwanese, and Korean banks are the most fragile.

When the findings regarding market concentration and compe- tition are compared by year, the structural and non-structural measures reveal different trends. The trend for the Lerner index (non-structural measure) is descending between 2005 and 2008 suggesting a decrease in pricing power, whereas industry concen- tration (structural measure) increases over the same period. The Lerner index exhibits varying degrees of market power across countries. Singapore has the highest efficiency-adjusted Lerner in- dex value (0.44), whereas Taiwan has the lowest value (0.22). Con- centration also varies across countries. The results suggest that concentration of assets held by the three largest banks in Singapore is 91.6%, indicating that the system is dominated by these banks. However, concentration in Taiwan is relatively low at 27.2%.

5. Empirical results

Table 4 presents the main results that indicate the impact of bank concentration and competition on financial stability. Two different risk exposure indicators are used as the dependent variables that proxy for financial stability: the probability of bankruptcy for listed banks (specifications 1–4) and the Z-score for both listed and non-

listed banks (specifications 5–8). We use the First Stage F-test and the Hansen’s J test to test for the relevance and validity of the instru- ments of the degree of market power, respectively. The Second Stage F-test is also used to test for goodness of fit for all regression models. The results support the use of the GMM panel data estimator.

Based on market measures, Table 4 indicates the significantly negative correlation for the Lerner index used in regression (1), suggesting that increases in the degree of bank pricing power are positively related to individual bank stability in Asia Pacific. Mean- while, the coefficient of bank concentration is significantly posi- tive, indicating that banks in more concentrated markets face greater risk. The robustness of the results is verified using regres- sion specifications (2)–(4). The findings lend support to the neutral view of the competition-stability nexus as both the competition- stability and competition-fragility views can be simultaneously va- lid. In this case, excessive concentration and lower pricing power simultaneously lead to bank fragility.

Our findings vary from those of most previous studies which fo- cus on banks operating in a specific geographic region such as Latin America,17 or a broader area.18 However, the results findings (we be- lieve) are not surprising for banks operating in Asia Pacific. On the one hand, most countries in this region (developing countries in par- ticular) have adopted ‘‘finance for growth’’ policies for a long period. The protected, larger banks in these concentrated banking systems

Table 4 Concentration, competition, and financial stability.

Dependent variable: prob. of bankruptcy Dependent variable: Z-score

(1) (2) (3) (4) (5) (6) (7) (8)

LERNER �1.3250** �1.0569** �1.4570*** �1.5704*** 53.4755*** 50.0600*** 50.5653*** 57.0264*** (0.5237) (0.5258) (0.5175) (0.5559) (18.1775) (18.4541) (17.0571) (20.4786)

CR3 2.2529*** 2.5413*** 2.2277*** 2.1215*** �46.3050*** �49.0417*** �46.6299*** �45.7716*** (0.5459) (0.5498) (0.5612) (0.5727) (9.0731) (9.5586) (8.8796) (9.4445)

SIZE 0.0700*** 0.0623** 0.0641** 0.0671** �3.0907*** �3.1619*** �3.0507*** �3.0621*** (0.0271) (0.0269) (0.0286) (0.0282) (0.9571) (0.9242) (0.9380) (0.9675)

LLP �0.5129 �0.2709 �0.9785 �0.7478 �23.1895 �28.1637 �27.5442 �20.3329 (0.8826) (0.8516) (0.9875) (0.9410) (33.3662) (33.6761) (34.3624) (33.4386)

NIM 0.0260 0.0195 0.0277 0.0296 0.2372 0.2588 0.2575* 0.2111 (0.0255) (0.0239) (0.0266) (0.0275) (0.1593) (0.1590) (0.1539) (0.1730)

RGDP �0.0040 �0.0015 �0.0038 �0.0052 �0.0475 �0.0707 �0.0409 �0.0456 (0.0031) (0.0031) (0.0032) (0.0033) (0.0826) (0.0813) (0.0807) (0.0841)

CRISIS 0.0642** 0.0822*** 0.0622** 0.0480* 1.1194 0.9282 1.0695 1.2774 (0.0250) (0.0252) (0.0257) (0.0272) (0.6965) (0.7283) (0.6760) (0.8057)

Entry restrictions �0.1791*** 3.8920 (0.0623) (3.1427)

Capital Requirement 0.0587 1.3472 (0.0435) (1.0743)

Deposit Insurance 0.0618** �0.9649 (0.0309) (1.5376)

First Stage F-test (LERNER) 8.25*** 8.01*** 8.72*** 7.78*** 10.36*** 9.91*** 11.23*** 9.68***

First Stage F-test (CR3) 117.24*** 134.54*** 120.29*** 108.27*** 234.57*** 226.27*** 236.17*** 233.67***

Hansen’s J v2 0.859 0.126 1.064 2.619 0.871 0.503 0.849 1.169 (P-value) (0.3541) (0.7226) (0.3023) (0.1056) (0.3508) (0.4783) (0.3569) (0.2797) Second Stage F-test 66.85*** 69.60*** 54.38*** 51.07*** 16.51*** 15.49*** 15.21*** 14.14***

No. of observations 1320 1320 1320 1320 3299 3299 3299 3299

Results from GMM panel data estimations to explain the impacts of bank concentration and competition on financial stability. The first dependent variable (specifications 1– 4) is the probability of bankruptcy, which is a market-based bank-level measure of financial fragility that is calculated using the method developed by Bharath and Shumway (2008). The second dependent variable (specifications 4–8) is Z-score, which is an accounting-based bank-level indicator of financial soundness. LERNER is a bank-level indicator of bank competition calculated as the difference between price and marginal cost as a percentage of price using the stochastic frontier analysis approach. CR3 is a country-level structural indicator of bank concentration calculated as the fraction of assets held by the three largest banks in each country. SIZE is the natural logarithm of total assets in thousands of USD. NIM is the ratio of net interest income to interest-bearing (total earning) assets. LLP is the ratio of loan loss provisions to total assets. RGDP is the rate of real GDP growth. Crisis is a dummy variable that takes a value of one for the years 2008–2009 and zero otherwise. Deposit insurance is a dummy variable that takes a value of one if the country has deposit insurance and zero otherwise. Capital requirement is the minimum regulatory capital-to-assets ratio for each country. Entry restrictions is the ratio of entry applications denied to applications received from domestic and foreign banks. The instrumental variables include activity restrictions, financial freedom, and property rights. *** Indicate significance at the 1% levels, respectively. Robust standard errors are in parentheses. ** Indicate significance at the 5% levels, respectively. Robust standard errors are in parentheses. * Indicate significance at the 10% levels, respectively. Robust standard errors are in parentheses.

Xiaoqing (Maggie) Fu et al. / Journal of Banking & Finance 38 (2014) 64–77 71

channel resources to ‘‘priority sectors’’. Their borrowers become ‘‘too large to fail’’, and hence, banks lose their incentive to develop an appropriate credit culture and may find themselves faced with rela- tively high levels of non-performing loans. In addition, banks are the most important source of public savings in the majority of Asia Paci- fic economies, which also makes them ‘‘too-big or too-systemically- important-to-fail’’ possibly leading to moral hazard problems (Sheng, 2009).

On the other hand, according to Elzinga and Mills (2011), the Lerner index is a ‘‘better indicator of a firm’s price-setting discre- tion than its ability to sustain monopoly prices’’ (p. 1). Thus, the re- sults may imply that banks in this region are able to obtain greater discretion in terms of price-setting to boost their profits and re- duce their insolvency risk through channels other than increased concentration (product differentiation).19 In other words, greater

19 For example, as indicated in a survey report provided by the IDC Financial Insights Asia/Pacific division, banks across the Asia Pacific region are considering the uniquely Asian opportunities for sustainable growth that have been generated by governments identifying new priority industries such as aerospace and defense in Singapore, green technology in China, and high technology in Taiwan and China. Banks in this region have identified two strategic technology initiatives that they can use to expand their reach and profitability – risk management and channel efficiency. The focus on risk management has mainly been generated by the growing availability and sophistica- tion of analytics technologies, whereas the emphasis on channel efficiency stems from the vast expansion of mobility across the region. As a result, there are a growing number of innovative strategic IT projects that drive business differentiation in Asia Pacific banks (IDC, 2012).

pricing power enhances the ability of banks to generate higher ‘‘cap- ital buffers’’ to protect them against external macroeconomic and liquidity shocks.

Among other control variables, the significantly positive coefficient for bank size suggests that larger banks face greater risk. Laeven and Levine (2009) also find the same result. The crisis dummy is positively and significantly related to bank risk, which implies that banks are more fragile during financial turmoil. In considering regulatory and institutional environments, we find that entry restrictions are significantly and negatively associated with the probability of bankruptcy, which suggests that a lower level of competitive pressure induces greater fragility for listed banks. This result is consistent with the empirical findings of Uhde and Heimeshoff (2009), who find that restricted market entry is likely to enhance bank stability in Western European banking. Deposit insurance is significantly associated with a higher proba- bility of bankruptcy, supporting the moral hazard argument regarding excessive risk-taking when a financial safety net is available. Again, the result is similar to the findings of Laeven and Levine (2009).

Table 4 also examines the impact of bank concentration and competition on the soundness of both listed and non-listed banks using the Z-score as a proxy for financial stability. The results of regressions (5)–(8) show that the Lerner index is positively and significantly related to the Z-score, whereas the coefficient of concentration is significantly negative. The finding confirms that lower pricing power and excessive concentration may simulta-

72 Xiaoqing (Maggie) Fu et al. / Journal of Banking & Finance 38 (2014) 64–77

neously lead to bank fragility. Meanwhile, the coefficient on bank size is significantly negative, which is also consistent with our pre- vious finding that larger banks face greater risk.

We undertake a variety of robustness tests on our main models. First, following Koetter et al. (2008, 2012) and Turk Ariss (2010), we use the efficiency-adjusted Lerner index to replace the conven- tional Lerner index as a measure of banking market competition. Our main results are similar (see Appendix D). Second, following Berger et al. (2009), we also use a quadratic term for the Lerner index (namely, LERNER2) to capture a possible non-linear relation- ship between competition and stability. The coefficient of the qua- dratic term is significantly negative for the probability of bankruptcy model and positive for the Z-score model. Based on the inflection points calculated, the results remain unchanged and are reported in Appendix E. Third, we use Tobit regression models to estimate the competition-stability nexus for listed banks, because the probability of bankruptcy is between zero and one. The main results are maintained (see Appendix F). Fourth, we employ the Lerner index estimated using a three-input cost function specification replacing the one estimated using the two- input specification. Overall, the key findings remain unchanged (see Appendix G).

6. Conclusions

This study investigates the competition-stability nexus using cross-country data from 14 Asia Pacific countries for the period from 2003 to 2010. Both market-based and accounting-based risk measures are employed to measure individual bank fragility for the first time. Meanwhile, both concentration and competition indicators are included in the models to determine their impacts on bank stability. The initial results show a substantial shift in the average risk exposure of banks over the entire sample period, accompanied by gradual increases in concentration and competi- tion. The main results not only highlight the significant negative association between the Lerner index and individual bank risk but also illustrate the significant positive relationship between the concentration ratio and bank fragility. In other words, the find- ings provide support for the neutral view of the competition- stability nexus, indicating that the competition-stability and com- petition-fragility theories can simultaneously apply to Asia Pacific banking markets. The results also confirm that bank concentration is an insufficient measure of bank competitiveness. Overall our findings hold when we control for an array of bank-specific, macroeconomic, regulatory and institutional factors.

In addition, our analyses indicate that smaller bank size may improve financial soundness. In terms of regulations and

Table B1 Number of both listed and non-listed banks in sample. Source: BankScope (Bureau Van Di

2003 2004 2005 2006

Australia 8 9 13 17 China 46 54 71 98 Hong Kong 11 25 30 29 India 58 57 56 58 Indonesia 48 51 56 55 Japan 130 129 126 124 Korea 17 17 19 18 Malaysia 22 23 25 24 Pakistan 16 17 22 24 Philippines 14 22 28 27 Singapore 4 6 9 10 Sri Lanka 9 10 10 11 Taiwan 24 24 36 35 Thailand 16 16 18 19

Total 423 460 519 549

institutions, the results show that tougher entry restrictions may enhance bank stability, whereas stronger deposit insurance schemes negatively influence financial soundness. Unsurprisingly, banks are found to be more fragile during the recent financial crisis.

The findings highlight several important issues for policymak- ers in Asia Pacific economies. First, to prevent excessive concentra- tion, regulators should adopt a more cautious approach to evaluating and approving merger and acquisitions at the national level. Policymakers should also seek to reduce policy lending by encouraging banks to develop stronger independent credit cul- tures. Second, to improve the efficiency of resource allocation within an economy, regulators should encourage financial innova- tion among banks based on the premise of effective risk manage- ment, which also enables banks to become more stable via product innovation. Third, a certain level of entry restriction is needed for both domestic and foreign entrants to maintain finan- cial soundness. This suggests there should be greater scrutiny of foreign banks that seek to make acquisitions in Asia Pacific coun- tries. Finally, deposit insurance schemes appear to foster moral hazard and risk shifting behavior so any policy moves to increase coverage should be treated with caution as this could have the unintended consequence of boosting risk as opposed to promoting stability.

Appendix A. Translog cost function for estimating bank marginal cost

To derive MCTAit , the following translog cost function is esti- mated while capturing bank specificities using bank fixed effects:

ln TCit ¼ a0 þ X2 j¼1

a1 ln w j it þ

1 2

X2 j¼1

X2 k¼1

ajk ln wkit þ b1 ln TAit

þ 1 2

b2ðlnTAitÞ 2 þ

X2 j¼1

b2j ln TAitlnw j it þ c1tT

1 2 c2t T

2

þ X2 j¼1

c3t T ln w j it þ c4tT ln TAit þ ei ð9Þ

MCTAit ¼ @TCit @TAit

¼ b1 þ b2 ln TAit þ X2 j¼1

b2j ln w j it þ c4t T

! TCit TAit

ð10Þ

where TCi is the bank’s total costs, TAi is the total assets, wi is the price of the factors of production, defined as follows: w1 is the price of purchased funds: interest expenses/total deposits and short-term funding, w2 is the price of labor and physical capital: non-interest

jk).

2007 2008 2009 2010 Total

19 18 14 13 111 117 110 107 97 700

28 26 24 20 193 58 56 54 50 447 56 55 52 36 409

127 127 122 103 988 16 15 15 9 126 24 24 25 24 191 25 26 23 21 174 23 24 24 22 184 10 9 10 9 67

9 9 11 12 81 33 33 34 34 253 20 20 19 17 145

565 552 534 467 4069

Table C1 Number of listed banks in sample. Source: BankScope (Bureau Van Dijk).

2003 2004 2005 2006 2007 2008 2009 2010 Total

Australia 6 6 6 6 6 6 6 6 48 China 2 2 3 3 4 5 9 7 35 Hong Kong 4 4 4 4 4 4 4 4 32 India 14 22 26 27 31 35 36 35 226 Indonesia 12 14 17 18 18 20 20 15 134 Japan 74 74 75 75 76 75 77 71 597 Korea 4 4 4 4 4 4 4 3 31 Malaysia 3 3 3 3 3 3 3 3 24 Pakistan 7 8 11 12 15 15 15 15 98 Philippines 8 10 10 10 10 10 10 10 78 Singapore 2 2 2 2 2 2 2 2 16 Sri Lanka 5 6 7 7 7 7 8 8 55 Taiwan 4 4 9 9 9 9 9 9 62 Thailand 8 8 8 8 8 8 8 8 64

Total 153 167 185 188 197 203 211 196 1500

Table D1 Concentration, competition, and financial stability (using efficiency-adjusted Lerner index).

Dependent variable: prob. of bankruptcy Dependent variable: Z-score

(1) (2) (3) (4) (5) (6) (7) (8)

E-LERNER �1.2413** �0.9908** �1.3632*** �1.4688*** 50.3293*** 47.1489*** 47.6084*** 53.4184*** (0.4886) (0.4899) (0.4824) (0.5183) (17.0441) (17.2802) (16.0116) (19.1364)

CR3 2.2323*** 2.5235*** 2.2064*** 2.1004*** �45.6700*** �48.4773*** �46.0288*** �45.1490*** (0.5494) (0.5531) (0.5641) (0.5761) (9.1422) (9.6322) (8.9477) (9.5238)

SIZE 0.0610** 0.0551** 0.0543** 0.0564** �2.6723*** �2.7750*** �2.6554*** �2.6184*** (0.0252) (0.0249) (0.0266) (0.0263) (0.9133) (0.8851) (0.9002) (0.9121)

LLP �0.4634 �0.2298 �0.9220 �0.6894 �24.1559 �29.0915 �28.4276 �21.5510 (0.8652) (0.8345) (0.9635) (0.9221) (33.1960) (33.4751) (34.1855) (33.2407)

NIM 0.0246 0.0184 0.0262 0.0280 0.2416 0.2626* 0.2615* 0.2178 (0.0249) (0.0233) (0.0260) (0.0268) (0.1571) (0.1570) (0.1521) (0.1702)

RGDP �0.0041 �0.0016 �0.0039 �0.0053 �0.0466 �0.0703 �0.0401 �0.0448 (0.0031) (0.0031) (0.0032) (0.0033) (0.0824) (0.0812) (0.0806) (0.0838)

CRISIS 0.0638** 0.0820*** 0.0618** 0.0476* 1.1244 0.9327 1.0746 1.2706 (0.0250) (0.0252) (0.0257) (0.0272) (0.6966) (0.7267) (0.6764) (0.8029)

Entry restrictions �0.1804*** 3.9543 (0.0618) (3.0849)

Capital requirement 0.0582 1.3380 (0.0428) (1.0507)

Deposit insurance 0.0619** �0.8995 (0.0308) (1.5083)

First Stage F-test (ELERNER) 8.52*** 8.29*** 9.02*** 8.05*** 10.78*** 10.34*** 11.66*** 10.15***

First Stage F-test (CR3) 117.24*** 134.54*** 120.29*** 108.27*** 234.57*** 226.27*** 236.17*** 233.67***

Hansen’s J v2 0.856 0.120 1.061 2.631 0.864 0.492 0.843 1.140 (P-value) (0.3547) (0.7291) (0.3029) (0.1048) (0.3526) (0.4828) (0.3586) (0.2857) Second Stage F-test 66.52*** 69.41*** 54.15*** 50.75*** 16.59*** 15.52*** 15.26*** 14.26***

No. of observations 1320 1320 1320 1320 3299 3299 3299 3299

Results from GMM panel data estimations to explain the impacts of bank concentration and competition on financial stability. The first dependent variable (specifications 1– 4) is the probability of bankruptcy, which is a market-based bank-level measure of financial fragility that is calculated using the method developed by Bharath and Shumway (2008). The second dependent variable (specifications 4–8) is Z-score, which is an accounting-based bank-level indicator of financial soundness. ELERNER is a bank-level efficiency-adjusted indicator of bank competition calculated as the difference between price and marginal cost as a percentage of price using the stochastic frontier analysis approach. CR3 is a country-level structural indicator of bank concentration calculated as the fraction of assets held by the three largest banks in each country. SIZE is the natural logarithm of total assets in thousands of USD. NIM is the ratio of net interest income to interest-bearing (total earning) assets. LLP is the ratio of loan loss provisions to total assets. RGDP is the rate of real GDP growth. Crisis is a dummy variable that takes a value of one for the years 2008–2009 and zero otherwise. Deposit insurance is a dummy variable that takes a value of one if the country has deposit insurance and zero otherwise. Capital requirement is the minimum regulatory capital-to-assets ratio for each country. Entry restrictions is the ratio of entry applications denied to applications received from domestic and foreign banks. The instrumental variables include activity restrictions, financial freedom, and property rights. *** Indicate significance at the 1% levels, respectively. Robust standard errors are in parentheses. ** Indicate significance at the 5% levels, respectively. Robust standard errors are in parentheses. * Indicate significance at the 10% levels, respectively. Robust standard errors are in parentheses.

Xiaoqing (Maggie) Fu et al. / Journal of Banking & Finance 38 (2014) 64–77 73

expenses/fixed assets,20 T is the time trend that captures the influ- ence of technological changes that lead to shifts in the cost function over time, and e is the error term.

Following Hasan and Marton (2003), Soedarmon et al. (2011), Sun and Chang (2011) and Jiang et al. (2013) we use a two input cost function specification (we also re-estimate the translog cost

20 Because of the lack of labor data, non-interest expenses are used as a proxy for labor and physical capital costs.

function with three inputs – purchased funds, labor and physical capital – as a further robustness test to investigate market power and risk issues, the sample size falls but results are in-line with the two input specification, (see Appendix G). As usual,21 symmetry restrictions apply to this function (i.e. ajk = akj). Meanwhile, the total cost and input price terms are normalized by w2. This imposes linear

21 See Claessens and Laeven (2004), Maudos and Fernández de Guevara (2004), Fernández de Guevara et al. (2005), Berger et al. (2009), and Maudos and Solís (2009).

Table E1 Test for non-linear relationship.

Dependent variable: probability of bankruptcy Dependent variable: Z-score

(1) (2) (3) (4)

LERNER �1.8018** 51.6104 (0.8726) (37.2177)

LERNER2 �4.3906* 143.7211*** (2.3836) (46.0858)

ELERNER �2.2149*** 62.2878* (0.7124) (33.1187)

ELERNER2 �4.0066* 140.1059*** (2.2827) (47.1578)

SIZE 0.1088*** 0.0771* �2.0614 �1.3844 (0.0402) (0.0419) (1.2560) (1.1248)

LLP 1.1718 0.8852 �60.9575 �60.9301 (2.2720) (2.2487) (54.9509) (55.7692)

NIM 0.0202 0.0236 �0.0187 �0.0437 (0.0600) (0.0600) (0.2700) (0.2759)

RGDP �0.0031 �0.0031 �0.1090 �0.1212 (0.0061) (0.0064) (0.1386) (0.1430)

CRISIS 0.0167 0.0145 2.3794** 2.4364**

(0.0449) (0.0465) (1.1293) (1.1467) Inflection point �0.205 �0.275 �0.180 �0.222 First Stage F-test (LERNER/ELERNER/CR3) 8.25*** 8.52*** 10.78*** 10.36***

First Stage F-test (LERNER2/ELERNER2/CR32) 2.35* 1.70 7.53*** 9.99***

Hansen’s J v2 0.044 0.151 0.058 0.137 (P-value) (0.8340) (0.6980) (0.8097) (0.7108) Second Stage F-test 22.25*** 20.92*** 6.76*** 6.37***

No. of observations 1320 1320 3299 3299

Results from GMM panel data estimations to explain the impacts of bank concentration and competition on financial stability. The first dependent variable (specifications 1– 2) is the probability of bankruptcy, which is a market-based bank-level measure of financial fragility that is calculated using the method developed by Bharath and Shumway (2008). The second dependent variable (specifications 3–4) is Z-score, which is an accounting-based bank-level indicator of financial soundness. LERNER is a bank-level indicator of bank competition calculated as the difference between price and marginal cost as a percentage of price using the stochastic frontier analysis approach. ELERNER is a bank-level efficiency-adjusted indicator of bank competition calculated as the difference between price and marginal cost as a percentage of price using the stochastic frontier analysis approach. CR3 is a country-level structural indicator of bank concentration calculated as the fraction of assets held by the three largest banks in each country. SIZE is the natural logarithm of total assets in thousands of USD. NIM is the ratio of net interest income to interest-bearing (total earning) assets. LLP is the ratio of loan loss provisions to total assets. RGDP is the rate of real GDP growth. Crisis is a dummy variable that takes a value of one for the years 2008–2009 and zero otherwise. *** Indicate significance at the 1% levels, respectively. Robust standard errors are in parentheses. ** Indicate significance at the 5% levels, respectively. Robust standard errors are in parentheses. * Indicate significance at the 10% levels, respectively. Robust standard errors are in parentheses.

Table F1 Concentration, competition, and probability of bankruptcy (Tobit regression).

(1) (2)

LERNER �0.1880*** (0.0331)

ELERNER �0.1799***

(0.0310) CR3 0.4596*** 0.4588***

(0.0748) (0.0748) SIZE 0.0025 0.0015

(0.0037) (0.0036) LLP 0.1758* 0.1773**

(0.0898) (0.0896) NIM �0.0090** �0.0090**

(0.0041) (0.0041) RGDP �0.0028 �0.0029

(0.0021) (0.0021) CRISIS 0.1454*** 0.1451***

(0.0122) (0.0122) Country effect yes yes Wald test 1051.70*** 1055.12***

No. of observations 1500 1500

This table presents the results of Tobit regressions. The dependent variable is the probability of bankruptcy, which is a market-based bank-level measure of financial fragility calculated using the method developed by Bharath and Shumway (2008). LERNER is a bank-level indicator of bank competition calculated as the difference between price and marginal cost as a percentage of price using fixed effect regression. ELERNER is a bank-level efficiency-adjusted indicator of bank competition calculated as the difference between price and marginal cost as a percentage of price using a stochastic frontier analysis approach. CR3 is a country-level structural indicator of bank concentration calculated as the fraction of assets held by the three largest banks in each country. SIZE is the natural logarithm of total assets in thousands of USD. LLP is the ratio of loan loss provisions to total assets. NIM is the ratio of net interest income to interest-bearing (total earning) assets. RGDP is the rate of real GDP growth. Crisis is a dummy variable that takes a value of one for the years 2008–2009 and zero otherwise. *** Indicate significance at the 1% levels, respectively. Robust standard errors are in parentheses. ** Indicate significance at the 5% levels, respectively. Robust standard errors are in parentheses. * Indicate significance at the 10% levels, respectively. Robust standard errors are in parentheses.

74 Xiaoqing (Maggie) Fu et al. / Journal of Banking & Finance 38 (2014) 64–77

Table G1 Concentration, competition, and financial stability – LERNER estimated using a 3-input specification (robustness check).

Dependent variable: Prob. of bankruptcy Dependent variable: Z-score

(1) (2) (3) (4)

LERNER �1.5303** 51.8100* (0.6683) (29.8397)

ELERNER �1.5496** 55.6842* (0.7050) (33.7837)

CR3 �0.2389 �0.2819 �99.7069** �105.8960** (0.9480) (0.9521) (40.3922) (45.1951)

SIZE 0.0059 0.0054 �3.7235* �3.9748* (0.0198) (0.0199) (1.9838) (2.2412)

LLP �1.6558 �1.6724 105.5617* 114.6456 (1.5140) (1.5641) (62.6924) (71.6090)

NIM 0.0415 0.0406 0.0438 0.0029 (0.0435) (0.0448) (0.3333) (0.3784)

CRISIS 0.1262*** 0.1260*** 2.0772 2.2418 (0.0195) (0.0198) (1.3357) (1.5081)

First Stage F-test 9.98*** 9.48*** 9.07*** 8.77***

First Stage F-test (CR3) 12.68*** 12.68*** 40.15*** 40.15***

Hansen’s J v2 1.897 2.266 1.193 0.699 Second Stage F-test 22.41*** 22.43*** 3.41*** 2.92***

No. of observations 786 786 2120 2120

Using the three-input specification to derive LERNER measures the number of observations reduces from 3299 to 2120 for listed and non-listed banks, and from 1320 to 786 for listed banks. We have to drop RGDP in our GMM model to avoid multicollineary problems, because the correlation coefficient between RGDP and CRISIS is �0.4548 for listed banks and �0.4222 for listed and non-listed banks. Results from GMM panel data estimations explain the impact of bank concentration and competition on financial stability. The first dependent variable (specifications 1–2) is the probability of bankruptcy, which is a market-based bank-level measure of financial fragility calculated using the method developed by Bharath and Shumway (2008). The second dependent variable (specifications 3–4) is Z-score, which is an accounting-based bank-level indicator of financial soundness. LERNER is a bank-level indicator of bank competition calculated as the difference between price and marginal cost as a percentage of price using the stochastic frontier analysis approach. In this case the LERNER is calculated using a three input specification (purchased funds, labor and physical capital). CR3 is a country- level structural indicator of bank concentration calculated as the fraction of assets held by the three largest banks in each country. SIZE is the natural logarithm of total assets in thousands of USD. NIM is the ratio of net interest income to interest-bearing (total earning) assets. LLP is the ratio of loan loss provisions to total assets. Crisis is a dummy variable that takes a value of one for the years 2008–2009 and zero otherwise. The instrumental variables include activity restrictions, financial freedom, and property rights. Overall, the key findings remain unchanged. *** Indicate significance at the 1% levels, respectively. Robust standard errors are in parentheses. ** Indicate significance at the 5% levels, respectively. Robust standard errors are in parentheses. * Indicate significance at the 10% levels, respectively. Robust standard errors are in parentheses.

Xiaoqing (Maggie) Fu et al. / Journal of Banking & Finance 38 (2014) 64–77 75

homogeneity to ensure that the cost minimizing bundle does not change if all of the input prices are multiplied by the same positive scalar. Thus, only changes in the ratios of the input prices affect the allocation of inputs. Following Lozano-Vivas and Pasiouras (2010), we also include ln(equity) in the efficiency model to control for the effect of risk. We then use the system GMM model to test the link between market power and financial stability. The key results are reported in Appendix G and remain the same.

In addition, following Koetter et al. (2008), we estimate Eq. (10) using a stochastic cost frontier approach and calculate marginal costs (MCSFATAit ).

Appendix B

See Table B1.

Appendix C

See Table C1.

Appendix D

See Table D1.

Appendix E

See Table E1.

Appendix F

See Table F1.

Appendix G

See Table G1.

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  • Bank competition and financial stability in Asia Pacific
    • 1 Introduction
    • 2 Literature review
    • 3 Methodology
      • 3.1 Market-based risk measure
      • 3.2 Accounting-based risk measure
      • 3.3 Concentration and competition measures
      • 3.4 Other control variables
    • 4 Data
    • 5 Empirical results
    • 6 Conclusions
    • Appendix A Translog cost function for estimating bank marginal cost
    • Appendix B
    • Appendix C
    • Appendix D
    • Appendix E
    • Appendix F
    • Appendix G
    • References

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Journal of Banking & Finance

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / j b f

Systemic risk, governance and global financial stability

http://dx.doi.org/10.1016/j.jbankfin.2014.04.012 0378-4266/� 2014 Elsevier B.V. All rights reserved.

⇑ Corresponding author. Tel.: +61 612 93855859; fax: +61 61293854763. E-mail address: [email protected] (F. Moshirian).

Luci Ellis a, Andy Haldane b, Fariborz Moshirian c,⇑ a Reserve Bank of Australia, Sydney, Australia b Bank of England, London, United Kingdom c Institute of Global Finance, University of New South Wales, Sydney, Australia

a r t i c l e i n f o

Article history: Received 31 May 2013 Accepted 9 April 2014 Available online 19 April 2014

JEL classification: G15 G25

Keywords: Systemic risk Bank governance Financial stability

a b s t r a c t

The paper argues that while attempts have been made to reform four of the five key pillars of banks’ oper- ation, (i.e. competition, resolution, supervisory, and auditing and valuation policies), less attention has been paid to the role of bank governance and systemic risk, despite a strong link between governance and risk-taking. The paper offers four solutions to strengthen bank governance. First, the regulatory cap- ital base of banks could be increased. Second, the compensation structure of managers could be reformed. Third, effort could be focussed on creating and implementing resolution regimes which offer the credible prospect of ‘‘bailing-in’’ creditors in the event of stress and fourth solution is to reform the structure of company law– for example, by extending control rights beyond shareholders. Furthermore, the paper argues that given the diversity of the whole financial system, it is expected that the risks individual finan- cial institutions face are also diverse. It cannot be assumed that the appropriate capitalisation is constant across all risks. While leverage ratios are a useful backstop measure and guard against potential gaming of risk-weights, their appropriate role is as a backstop. The diversity within the financial system also sup- ports the fact that a single measure of systemic risk is unlikely to be universally applicable, nor is a single instrument of financial stability policy.

� 2014 Elsevier B.V. All rights reserved.

1. Introduction

The recent global financial crisis highlighted the importance of addressing some of the incomplete financial reforms. One of these unaddressed reforms has been the contribution of systemic risk in destabilising financial markets. Recently, as reported by Bisias et al. (2012), international institutions such as the IMF, the BIS and the SFSB stated that systemic risk can be defined as a ‘‘risk of disrup- tion to financial services that is caused by an impairment of all or parts of the financial system and has the potential to have seri- ous negative consequences for the real economy.’’ One of the policy issues is how one might operationalize a policy to limit systemic risk given that a risk is inherently unobservable – only outcomes are observable. Unfortunately, there is no single operational defini- tion of systemic risk so far. Bisias et al. (2012) identified 31 differ- ent measures of systemic risk, emphasising a range of aspects of systemic risk and potential channels to financial distress. Indeed, as Bisias et al. (2012) point out, it is probably not desirable to have

a single measure of systemic risk as the focus of policy, as this may result in a ‘Maginot Line’ situation where vulnerabilities building in other parts of the financial system are missed.

The recent global financial crisis has encouraged both policy makers and researchers to find ways by which one could mitigate the impact of systemic risk. To this end, a number of attempts have been made to identify policies, including forward-looking policies, that could potentially address systemic risk.

As part of the efforts to address some of the underlying causes of the recent global financial crisis, the Financial Stability Board (‘FSB’) has been asked to act as the international standard-setter and attempt to address and coordinate issues underlying systemic risk. One of the key tasks of the FSB was to deal with the contribu- tions of large financial institutions to global systemic risk. To this end, the FSB has identified global systemically important financial institutions (‘G-SIFIs’), and more recently domestic systemically important banks (‘D-SIBs’). These multinational and large national financial institutions have attracted more policy attention, partly due to their size, complexity, interconnectedness and their contri- butions to national and global financial systems (Moshirian, 2011, 2012). As part of this process and excessive risk taking by some of these institutions, themes related to too-big-to-fail (‘TBTF’) have also emerged for debate and analysis.

176 L. Ellis et al. / Journal of Banking & Finance 45 (2014) 175–181

Another major development in recent years has been a focus on developing macroprudential frameworks to complement the tradi- tional microprudential approach to regulation. There is also a desire on the part of the policy makers and the market participants to see the financial system sharpen its ability to apply effective macroprudential policies without stifling economic growth and innovation. However, the identification and monitoring of systemic risk and optimal macroprudential policies are still in the early stage of development. In search for global financial stability, one area that has received less attention in recent years are the issues related to bank governance. More discussion and analysis of the role of governance within the operation of financial institutions and also the role of governance in contributing to global financial stability would be an essential part of any policy package aiming to address a number of the underlying causes of financial fragility over time. The paper argues that while there have been analyses of banks competition policies, resolution policies, supervisory poli- cies and auditing and valuation policies, less attention has been paid to the role of bank governance and systemic risk, despite a strong link between governance and risk-taking. This is puzzling (Jensen and Meckling, 1976).

Recent empirical evidence suggests this link may be especially strong for financial firms (Beltratti and Stulz, 2009; Ferreira et al., 2012; Laeven and Levine, 2009). Certainly, the global financial cri- sis unearthed bank governance failures operating at multiple lev- els: managers failing to control risk-takers, boards failing to control managers and investors failing to discipline either manag- ers or boards.

The purpose of this paper is to discuss some of the issues related to systemic risk, governance and financial stability. To this end, Section 2 of the paper discusses issues related to systemic risk, the identification of G-SFIS and D-SIBs and the recent policies related to macroprudential policies and the importance of imple- menting globally a number of regulatory policies that have been proposed by various international institutions; Section 3 analyses issues related to measurement of systemic risk. It argues that the diversity within the financial system also supports the fact that a single measure of systemic risk is unlikely to be universally appli- cable. Furthermore, this section discusses how, due to the diversity of the whole financial system, the risks individual financial institu- tions face are also diverse. The section also discusses why one may not assume that the appropriate capitalisation is constant across all risks. Section 4 discusses bank governance, including how this important aspect of global financial stability has been overlooked for vigorous debate and analysis in recent times and the impor- tance of having specific policies to address the current structural weaknesses of bank governance. This section offers solutions regarding how to strengthen bank governance, including the regu- latory capital base of banks, could be increased, the compensation structure of managers could be reformed and efforts could be made by putting resolution regimes which offer the credible prospect of ‘‘bailing-in’’ creditors in the event of stress, into place and Section 5 concludes.

1 This section of the paper benefited from a number of information and issues that can be found in the FSB publications and website.

2. Large financial institutions and propagation of systemic risk

2.1. Identification of systemically important financial institution

According to Financial Stability Board, a financial institution could be defined as a G-SIFI, if its failure or fragility could impact other financial institutions, the wider financial system and the domestic and international economies (FSB, 2011a, 2011b). Given the size and complex nature of these large financial institutions with other institutions, their failure or fragility could negatively impact the overall global financial system. A number of attributes

have been identified by the FSB that could qualify certain financial institutions to be part of G-SIFIS. These attributes include: size, lack of substitutability and interconnectedness. In addition, the Basel Committee for Banking Supervision (Basel Committee) identified cross-jurisdictional activity and complexity as other attributes of financial institutions which could fall into the category of G-SIFIs (see BIS, 2013).

As of November 2013, there are 29 designated G-SIFIs by the FSB. As part of an overall strategy to ensure more stable G-SIFs, these institutions are now required to maintain greater loss absor- bency capabilities under Basel III. As highlighted by the FSB’s var- ious publications, this additional requirement, which applies in addition to the Basel III capital requirements, is intended to reduce the ‘‘cross-border negative externalities’’ of the global financial system and to reduce systemic risk. Some regional and domestic banks could also pose systemic risk to the national and regional financial systems and national economies, (over and above those institutions identified as G-SIFIs). To this end, the Basel Committee has developed a framework and asked local authorities to identify whether a large domestic bank is systemically important from a domestic perspective (domestic systemically important bank (D-SIB). A number of national authorities have already identified their D-SIBs. One should also note that there are also some regional systemically important banks that could be operating in different continents whose operations could pose systemic risk. As part of an overall strategy to increase global financial stability, interna- tional institutions such as the FSB now have a strategy in place to ensure that national and international policy makers work together and ensure that the G-SIFIs and D-SIBs are better super- vised and these institutions, as state above, are required to put aside more capital as part of their operation. This is because the current strategy is to insulate the global financial system from sys- temic shocks on top of the Basel III capital and liquidity require- ments.1 As Moshirian (2012) stated, the Great Depression in the 1930s created incentive for authorities to collect national economic data so policy makers could measure the magnitude of economic downturn in the economy accurately. This attempt in the 1930s led to the emergence of relevant data that are used to generate what is now referred to as Gross Domestic Products (GDP). Similarly the recent global financial crisis has created a new impetus to policy makers and market participants to improve and also share some financial data, particularly when the financial market is becoming increasingly interconnected. The attempt by the FSB, in collaboration with central banks and supervisors, to create a mechanism that could facilitate the international sharing of firm-level data on sys- temically important financial institutions, is yet another step in the process of facilitating globalisation and another attempt to enhance the capacity of the global financial system to be more informed about the overall state of large financial institutions and the nature of their aggregate risk taking and business models.

2.2. Macroprudential policies and a global approach to financial stability

The FSB has been working on issues related to macroprudential policies over the last few years. While a number of proposals have emerged as part of the tools available to enhance the effectiveness of policy recommendations with respect to macroprudential strat- egies, this area of policy development is still in its infancy and requires more work (Arnold et al., 2012). Nevertheless, some good progress has been made in recent times including the work of the FSB itself. At the present time, there are four main areas of

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development proceeding under the supervision of the FSB. The first is developing measures to identify and monitor systemic risk, including both regulated banking and shadow banking. As the FSB (2011a) stated, despite some progress in collecting new data, the challenge remains to be able to identify tools and instruments that could guide forward policy making and ensure its credibility.

Therefore, to be able to identify and contain systemic risk through credible instruments and tools remains the second area of development. According to the FSB (2011a), there are currently a range of tools used by various countries to address systemic risk, and they fall roughly into three categories. These categories are: (1) tools to address financial stability risks arising from rapid credit expansion; (2) tools to address amplification mechanisms of systemic risk such as leverage and maturity mismatches and (3) tools to limit spillover effects from the failure of SIFIs.

To be able to develop institutional arrangements for macropru- dential regulators at both domestic and regional levels will be the third area of development.

Not surprisingly, the fourth area identified by the FSB is the importance of achieving regional and international cooperation that is essential to address challenges of systemic risk that often become supernational in their dimension. The increasing intercon- nectedness of global finance implies that some of the financial risk may well spill over to other parts of the world. One of the chal- lenges of our generation will remain how to ensure an effective global financial system that has the full support of national author- ities and the private sector, that in turn could minimise the oppor- tunity for regulatory arbitrage. Furthermore, consistency of action by all national authorities could also generate more confidence in the collective implementation of some of the current and future global agreements. Obviously, amongst the key protagonists of the financial market (i.e. policy makers, regulators, researchers and market participants) there should be constant dialogue, con- sultation, and reflection about some of these global agreements.

It is noteworthy that the BIS is also keen to see certain stan- dards globally accepted and implemented. For instance, Caruana (2013a) stated, if we do not control fragmentation tendency and also regulatory arbitrage, we could lose the benefits of globalisa- tion and the pace of process of financial globalisation that has accelerated due to technological changes and the increasing inter- dependence of national economies. Caruana (2013a) also stated that fragmentation could prevent the proper transmission of mon- etary policy, particularly in the Euro-zone area., Furthermore, in addition to a structured supervisory framework (e.g. Peer reviews), greater cooperation is needed between domestic and foreign policy makers, where each are cooperating in order to resolve issues. Fur- thermore, consistency must be achieved across the international stage. Rules and minimum standards should not be different based on the basis of a bank’s nationality. As a result, if some banks are held to a lower standard than the internationally agreed rules, this will result in an uneven playing field.

In this context, as the FSB (2011a) stated that there is a need to establish strong mechanisms to ensure consistent action by coun- tries to contain risk, and to ensure national regulatory frameworks are consistent.

With regard to regional cooperation, it should be noted that regional mechanisms, such as the college of supervisors, may work relatively well for some of the G-SIFs and also regional and domes- tic SIFS in the EU due to the high level of financial integration. However, such regional cooperation and information sharing may not necessarily work in other parts of the world such as Asia, where the political climate is different and the level of national autonomy much higher. One cannot dismiss a number of regional architectures that have been erected to bring the economies of the Asia Pacific region closer. For instance APEC is designed to promote free trade, while ASEAN and ASEAN plus 3 are more focused on free

trade and, economic and financial integration within the member countries of such blocks. Similarly ASEAN plus 6 and the East Asia Summit also have some economic, trade and finance objectives that are ultimately designed to bring a large number of countries economically and financially closer together. Given the importance of Asia and the massive amount of foreign capital flowing to this region and the increasing volume of trade, such cooperation and information sharing amongst policy makers with respect to the operations of some of the G-SIFs and regional and domestic SIFs will be crucial over time.

Regarding the supervision and coordination of G-SIFs and some of the regional and domestic SIFs, Caruana (2013b) has also stated ‘‘this kind of supervision requires not only a new and different kind of expertise but, even more importantly, the capacity and willing- ness to act under significant uncertainty. These include the ability to conduct group-wide consolidated supervision and to challenge banks’ business models, their corporate strategy, governance, risk profiles, ROE targets and capital plans. Also necessary will be the willingness to exercise judgment and to act pre-emptively under conditions of uncertainty’’.

3. Considerations for modelling policies to address systemic risk

3.1. Systemic risk measurement

Post-crisis, a large literature has started to build up, which attempts to incorporate macroprudential policy into canonical macro-models, generally of the micro-founded Dynamic Stochastic General Equilibrium modelling (DSGE) type or variants of these. These papers have therefore had to add some kind of financial sec- tor, so that the policy has something to operate upon. This exercise necessarily requires taking a position on asset and credit dynamics, but these relationships are generally less well understood than the inflation and real-side dynamics that are more relevant for analysis of monetary policy questions.

One of the problems with this line of research is that – unlike inflation – there is little welfare-theoretic basis for using typical measures of asset prices and credit as targets of macroprudential or any other policy. In the monetary policy literature, inflation (or inflation variability) maps into social welfare, either directly because agents are presumed to dislike inflation, or indirectly because price volatility is costly for output. For example, earlier generations of literature on monetary policy assumed an objective function for the policy maker that was a weighted average of infla- tion volatility and output volatility; this formulation, while not exactly corresponding to social welfare, turns out to bear close resemblance to it (Woodford, 2010).

In contrast, asset prices and credit do not enter into welfare functions in a way that suggests that their growth should be resisted by some arm of public policy. To the extent that wealth enters into welfare, one could argue that policies that increase asset prices are desirable. Indeed, some micro-founded models produce the result that under-borrowing, rather than over-borrowing, is the more pertinent issue, and that macroprudential policies could in fact be welfare-reducing (Benigno et al., 2013). More fundamen- tally, in the presence of credit constraints coming from information asymmetries, it is far from clear that ‘normal’ levels of credit are in fact optimal. This observation in no way contradicts the empirical observation that credit booms can be harmful for the subsequent evolution of output, because they can spark a financial crisis (Dell’Ariccia et al., 2012).

Even the internationally agreed prudential rules acknowledge this: rather than mandating a particular level of credit or asset prices or growth rate of these series, the Basel Committee’s guid- ance on application of the countercyclical capital buffer promoted

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Banking groups

Four major Australian banks

G-SIBs

Fig. 1. Trading assets and securities of the largest banking groups (includes derivative assets; sample of 100 banking groups; latest available ratios have been used where June 2013 data are unavailable). Sources: FSB; RBA; SNL Financial; The Banker; banks’ annual and interim reports.

178 L. Ellis et al. / Journal of Banking & Finance 45 (2014) 175–181

(but did not require) the use of a ‘buffer guide’ derived from devi- ation from trend of the ratio of credit to GDP (BCBS, 2010). There are many legitimate reservations about this guide and how it is for- mulated (see, for example Edge and Meisenzahl, 2011 and Box C in APRA and RBA, 2012), but the important point in this context is that it is understood even by the proponents of the buffer guide that there is no welfare-theoretic basis for a fixed level of the ratio of credit to GDP. This ratio can trend over long time horizons, and it can have different sustainable levels in different countries, depend- ing on such factors as the degree of financial development and demographic structure. Even substantial deviations from trend do not necessarily warrant a costly policy to prevent them. Dell’Ariccia et al. (2012) found that around one in three credit booms were followed by a banking crisis, which necessarily implies that around two in three were not.

This disconnect between these variables and welfare-theoretic measures indicates that there is a deeper goal being pursued. What financial stability policies – whether labelled ‘macroprudential’ or otherwise – are intended to do is limit systemic risk. Systemic risk is generally defined as the risk that the financial system might experience a generalised collapse or other form of distress. The rea- son for limiting this risk is to avoid the attendant harm on the real economy. The ultimate target is therefore output. In other words, asset prices and credit growth are useful information variables and possibly even intermediate targets of policy, much as mone- tary aggregates were in an earlier era. They are not, however, ulti- mate targets in same the way as inflation and output are targets of macroeconomic policy.

The above line of argument naturally raises the question of how one might operationalize a policy to limit systemic risk given that a risk is inherently unobservable – only outcomes are observable. Unfortunately, there is no single operational definition of systemic risk and possibly never will be. Bisias et al. (2012) identified 31 dif- ferent measures of systemic risk, emphasising a range of aspects of systemic risk and potential channels to financial distress. Among these are measures that capture deviations from trend in asset prices or credit, much as Dell’Ariccia et al. (2012) do, and measures that stress test the effects of large declines in these and related variables. Other measures focus on the propagation of distress from one financial institution to another, such that an idiosyncratic failure ends up causing a systemic problem. Dimensions of vulner- ability that are emphasised by the various measures include corre- lated exposures, network effects where the default of one party stresses its creditors, illiquidity and leverage.

Indeed, as Bisias et al. (2012) point out, it is probably not desir- able to have a single measure of systemic risk as the focus of policy, as this may result in a ‘Maginot Line’ situation where vulnerabili- ties building in some other part of the financial system are missed. This has obvious implications both for the approach to actual financial stability policy, and how that policy is captured in theo- retical models. In either situation, a single instrument, single target analogue to monetary policy is unlikely to be able to generate use- ful results.

3.2. Systemic risk and diversity

As is well known, the financial system performs a number of functions that are essential to the workings of a modern economy. As well as intermediating between savers and borrowers, the financial sector provides a conduit for payments and enables risk transfer.

These are a diverse set of functions and it should therefore be no surprise that the activities of the financial system give rise to a diverse set of risks. For example, banks and similar entities inter- mediate between borrowers and savers; fund managers also help savers deploy their funds, but without interposing their own

creditworthiness in place of that of borrowers or equity issuers, as is the case for banks. Insurance companies allow customers to transfer diversifiable risks to them, such as property losses from fire, flood and theft, as well as life insurance. Many of the insured risks that the financial system helps to transfer are themselves risks created within the financial system. This is consistent with the principle that financial losses are easier or more attractive to insure because they can be fully compensated by a financial pay- out. (An example of this is that people are more likely to insure their homes against loss from fire or flood than they are to insure their pets’ lives against the same risks: a financial payout may not be seen as a true compensation for such a loss.)

Even within the banking industry, banks serve different func- tions and therefore face different risks. Another way of saying this is that banks’ business models can vary. One way to think about bank business models is as a spectrum with investment banks at one end, commercial or retail banks at the other, and so-called ‘universal’ banks in the middle. Those labels correspond to differ- ent balance sheet structures: investment banks have larger hold- ings of securities and other trading assets, while commercial banks’ assets are mainly loans. As Fig. 1 shows, the importance of trading assets varies greatly within the largest global banking groups, and even within the 29 globally systemically important banks (G-SIBs) identified by the Basel Committee on Banking Supervision and the Financial Stability Board (FSB, 2013a, 2013b, 2013c), shown here as darker bars. While there are a cluster of banks with investment-banking business models in this group of G-SIBs, G-SIBs are represented across the whole range of observed balance sheet structures. In contrast, the four large Australian banks, shown as lighter bars in Fig. 1, for example, are all clearly commercial banks, with relatively little trading business.

Several conclusions can be reasonably drawn from these obser- vations. One is that if even the banking industry, let alone the whole financial system, is relatively diverse, it should be expected that the risks individual institutions face are also diverse. It cannot be assumed that the appropriate capitalisation is constant across all risks. This is a powerful argument against a single leverage ratio as the predominant instrument of prudential regulation. Even though there are concerns about the complexity of the current pru- dential framework, this suggests that risk-weighting is and should be here to stay. While leverage ratios are a useful backstop mea- sure and guard against potential gaming of risk-weights, their appropriate role is as a backstop.

The diversity within the financial system also supports the point made by Bisias et al. (2012) and mentioned earlier, that a single measure of systemic risk is unlikely to be universally

L. Ellis et al. / Journal of Banking & Finance 45 (2014) 175–181 179

applicable, and neither is a single instrument of financial stability policy. It will not be appropriate to conceive of macroprudential policy (or financial stability policy more generally) in the single instrument, single target framework that is generally used to ana- lyse monetary policy. Different aspects of prudential regulation will bear more effectively on particular risks and aspects of sys- temic risk than others. Rather, issues of macroprudential policy may be best thought of as a design problem in calibrating the entire prudential framework. Separating out particular aspects of prudential regulation into specific macroprudential ‘tools’, possibly with different governance from the prudential framework more broadly, may lead to coordination problems.

A more subtle issue arises in considering the niches that banks with different business models serve. Although there has been some debate about the social utility of some financial activity, and attempts such as the Volcker Rule to limit proprietary trading by banks, it is not the case that the optimal share of trading assets in bank balance sheets is zero. Neither is it the case that the opti- mal share of these assets would be the same across all banks. It depends on their business models and the particular risks they take on. Commercial banks are principally involved in intermedia- tion between savers and borrowers from the non-financial sectors. In doing so, though, they may incur risks that may be prudently laid off to other parts of the financial sector that may be able to diversify those risks more effectively. Among these risks are inter- est rate risk and FX risk: even quite plain-vanilla retail banking can involve an array of market risks depending on how diversified the bank’s funding base is and how global the business of the bank’s commercial customers. At least some of the trading assets of banks with more investment-banking business models arise through their interbank exposures built up as counterparties to commercial banks. These counterparty relationships can cross borders. Thus even in a national financial system like Australia’s, where the domestically owned banks are primarily commercial or retail ori- ented, the gamut of financial services can be provided, and risks transferred, because there are other, generally foreign banks, that can serve as counterparties to local banks’ hedging activity.

This implies that, although interconnectedness within the financial system can increase systemic risk, there is a limit below which this interconnectedness cannot prudently fall. In particular, although cross-border contagion is frequently identified as a risk to national financial stability, and inter-bank borrowing often a pre- cursor to financial crisis, a position of financial autarky is not the desirable equilibrium point, either. This has implications for mea- sures of systemic risk and crisis vulnerability, which should per- haps not be seen as linear indicators.

4. Bank governance and systemic risk

4.1. Five planks of effective reform

There are five key planks of any well-defined regulatory regime: entry – that is, competition policy; exit – that is resolution policy: regulation – that is, supervisory policy; accounting – that is, audit- ing and valuation policy; and governance. In the light of the crisis, there have been intense efforts to reform the first four of these pil- lars. For example, new regimes are being put in place for competi- tion (e.g. Parliamentary Commission on Banking Standards, 2013), regulation (e.g. Basel Committee on Banking Supervision, 2010a, 2010b), resolution (e.g. Financial Stability Board, 2011a, 2011b) and accounting (e.g. International Accounting Standards Board, 2013) within banking firms, nationally and internationally.

Yet the fifth pillar – governance – has been largely untouched. That is puzzling. Academic theory has long suggested a strong link between governance and risk-taking (Jensen and Meckling, 1976).

And recent empirical evidence suggests this link may be especially strong for financial firms (Beltratti and Stulz, 2009; Ferreira et al., 2012; Laeven and Levine, 2009). Certainly, the financial crisis unearthed bank governance failures operating at multiple levels: managers failing to control risk-takers, boards failing to control managers and investors failing to discipline either managers or boards.

Governance is also a potentially potent risk-mitigation tool. It has the potential to shape the risk-taking incentives of owners and managers. If reform can act on these incentives at source, it reduces the chances of regulators chasing risk around the system (Haldane, 2012). In other words, governance reform may be among the most effective ways of curbing risk-taking without engender- ing regulatory arbitrage.

4.2. Risk-taking incentives in banking

Although all firms face governance problems, there are three good reasons for believing these may be more acute in banking than in other sectors.

First, public companies have, for well over a century, benefitted from limited liability. This means that payoffs to equity-holders can mimic an out of money call option on its assets, with a strike price given by its debt liabilities (Merton, 1974). This payoff asym- metry shapes risk-taking incentives in potentially important ways. For example, the value of equity can then be boosted by increasing the variability of equity payoffs, either by investing in riskier assets or by leveraging those assets. This shifts risk up the capital struc- ture to the detriment of debt-holders (Jensen and Meckling, 1976). In other words, limited liability gives rise to a principal- agent problem between shareholders and debt-holders.

These incentive problems are likely to be more acute in banking than in other sectors: first, because risk is a deliberate choice var- iable for banks by dint of their choice of assets; and second, because their much greater leverage increases balance sheet risk and thereby the payoff asymmetry. These problems have long been recognised. They are the reason why limited liability came later to banking than other sectors (Crick and Wadsworth, 1936). And they are why double or even treble liability persisted in banking long after unlimited liability had been abolished (Haldane, 2012).

There is a second, well-known principal-agent problem within many firms, namely between shareholders and managers (Jensen and Meckling, 1976). This might arise because managers put their own objectives over those of shareholders. One means of aligning incentives between the two is to remunerate bank managers in equity. That has become a widespread practice during recent years, particularly within the financial sector. To take a striking example, in 2006 the typical bank CEO’s wealth rose by $1 million for every 1% increase in the value of their firm (Fahlenbrach and Stulz, 2011).

Compensating managers in equity is not, however, costless. By aligning managerial incentives with shareholders, the risk-shifting problem is potentially exacerbated. This includes incentives to ‘‘gamble for resurrection’’ when firms are nearing insolvency. Evi- dence during the crisis is revealing here. In 2006, the largest per- centage equity stakes by US bank CEOs were held in Lehman Brothers, Bear Stearns, Merrill Lynch, Morgan Stanley and Country- wide (Fahlenbrach and Stulz, op. cit.). In each case, this may have induced managerial incentives to gamble for resurrection – in each case unsuccessfully. In short, in solving one principal/agent prob- lem (between managers/shareholders), equity-based pay may have worsened another (between shareholders/debt-holders).

A third incentives issue is moral hazard. In principle, if risk is shifted to debt-holders they ought to seek compensation through higher yields. That, in turn, would impose a degree of discipline on shareholder/manager incentives to risk-shift (Haldane, 2012). In practice, evidence of debt-holders having exercised discipline

180 L. Ellis et al. / Journal of Banking & Finance 45 (2014) 175–181

consistently over bank risk-taking is scarce, including in the pre- crisis period (Flannery and Sorescu, 1996; Krishnan et al., 2005). One possible reason is that bank debt-holders’ risk senses tend to be dulled by insurance, either explicit in the case of bank deposi- tors or implicit in the case of bank bondholders. During this and previous crises, both classes of debt were effectively underwritten by the state to protect the wider economy from disorderly bank runs (Haldane, 2010).

But (implicit or explicit) insurance of bank debt has two unfor- tunate side-effects. First, it removes the market disciplining effect of debt which might otherwise damp risk-shifting incentives. Sec- ond, it shifts the burden of risk from debt-holders onto the state, and hence taxpayers, with a corresponding deadweight cost. In other words, a second principal-agent problem emerges – between bank investors and society as a whole (Haldane, 2010). This is not an incentive problem felt as acutely outside banking where the col- lateral costs of firm failure, and hence the probability of state insurance, are lower.

4.3. Reforming risk-taking incentives in banking

If these risk-taking incentives are potent, which crisis experi- ence suggests may have been the case, what might be done to counter them? There are various reform options on the table, which speak to each of the different dimensions of the incentive problem.

First, the regulatory capital base of banks could be increased. This reduces incentives to generate a leverage-induced rise in equity returns (Admati and Hellwig, 2013). Or, put differently, it reduces the amount of risk that will be shifted onto debt-holders. Regulatory reform initiatives, such as Basel III, augment regulatory capital standards and so would tend to reduce the principal-agent problem between shareholders and debt-holders. Nonetheless, there is an open debate about whether levels of capital in the sys- tem, even after Basel III, will be adequate to reshape fundamentally risk-shifting incentives (Admati and Hellwig, op.cit.).

Second, the compensation structure of managers could be reformed. For example, incentives to risk-shift to debt-holders would be diluted or internalised by paying managers in long-term debt, rather than cash or equity (Edmans and Liu, 2011). And incentives to gamble for resurrection could be reduced by having a lengthy period over which rewards are deferred (ex-ante) or clawed-back (ex-post) in the event of risks subsequently material- ising. Some progress has been made in enacting these reforms by regulators since the crisis, under the auspices of the Financial Stability Board (2009, 2012a, 2012b, 2012c). But, as with bank cap- ital, there is an open debate about whether existing international rules go sufficiently far in reshaping risk-taking incentives (Squam Lake Working Group on Financial Regulation, 2010; Parliamentary Commission on Banking Standards, 2013). The extent to which individual countries apply these rules consistently has also been queried (FSB, 2012a, 2012b, 2012c).

Third, efforts could be made to encourage debtor discipline, and hence discourage risk-shifting, by reducing the probability of gov- ernments needing to provide support to banks during crisis. One way this could be done is by putting in place resolution regimes which offer the credible prospect of ‘‘bailing-in’’ creditors in the event of stress (Coeuré, 2013). A number of countries have, or are in the process, of putting in place such regimes, again under the auspices of the Financial Stability Board (2011a, 2012a, 2012b, 2012c). As with the other initiatives, the jury remains out on the practical impact this will have on risk-taking incentives.

There is a final way in which incentives of bank stakeholders, broadly-defined, might be better aligned at source with societal preferences. That would be by reforming the structure of company law – for example, by extending control rights beyond

shareholders. The case for doing so seems especially strong in banking where the imbalance between shareholder control (100% of the balance sheet) and their stake (typically less than 5% of the balance sheet) is so significant. Some reform suggestions have been made in this direction (Parliamentary Commission on Banking Standards, 2013), but so far these have made relatively lit- tle headway.

5. Conclusion

The aim of this paper is to analyse some of the issues with respect to systemic risk, governance and global financial stability. The paper discusses the work of the Financial Stability Board (FSB) with respect to global, systemically important financial insti- tutions (G-SIFs) and domestic systemically important financial institutions (D-SIBs). Given the increasingly interconnected nature of the global financial system, this paper welcomes the attempt by the FSB and central banks and supervisors to facilitate the sharing of firm level data on systemically important financial institutions.

The paper argues that one of the issues, with respect to the glo- bal regulatory framework, is to ensure that certain standards are globally accepted and implemented. This is because, as Caruana (2013a) stated, if we do not control the tendency to fragmentation and also regulatory arbitrage, we could lose the benefits of global- isation and the pace of process of financial globalisation that has accelerated due to technological changes and the increasing inter- dependence of national economies. Furthermore, consistency must be achieved across the international stage. Rules and minimum standards should not be different based on a bank’s nationality. If some banks are held to a lower standard than those internationally agreed upon, this will result in an uneven playing field.

The paper also argues that given the diversity of the whole financial system, it should be expected that the risks individual financial institutions face are also diverse. It cannot be assumed that the appropriate capitalisation is constant across all risks. While leverage ratios are a useful backstop measure and guard against potential gaming of risk-weights, their appropriate role is as a backstop. The diversity within the financial system also sup- ports the fact that a single measure of systemic risk is unlikely to be universally applicable, and neither is a single instrument of financial stability policy.

The paper argues that while there have been analyses of banks competition policies, resolution policies, supervisory policies and auditing and valuation policies, less attention has been paid to the role of bank governance and systemic risk, despite a strong link between governance and risk-taking. The paper offers four solu- tions to strengthen bank governance. First, the regulatory capital base of banks could be increased. This reduces incentives to gener- ate a leverage-induced rise in equity returns. Second, the compen- sation structure of managers could be reformed. For example, incentives to risk-shift to debt-holders would be diluted or interna- lised by paying managers in long-term debt, rather than in cash or equity. Third, efforts could be made to put in place resolution regimes which offer the credible prospect of ‘‘bailing-in’’ creditors in the event of stress. There is a final way in which incentives of bank stakeholders, broadly-defined, might be better aligned at source with societal preferences. This would be by reforming the structure of company law – for example, by extending control rights beyond shareholders.

Disclaimer

The views expressed by Luci Ellis and Andy Haldane in this arti- cle are theirs and not necessarily those of the Reserve Bank of Australia and the Bank of England respectively. Luci Ellis would like to thank Elliott James for his valuable research assistance work.

L. Ellis et al. / Journal of Banking & Finance 45 (2014) 175–181 181

Fariborz Moshirian would like to acknowledge the support of the Australian Research Council (RG124356) for this project. Fariborz Moshirian would like to thank Christopher Wong and Jeffrey Chen for their research assistance work on this project. All errors remain the authors responsibility.

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  • Systemic risk, governance and global financial stability
    • 1 Introduction
    • 2 Large financial institutions and propagation of systemic risk
      • 2.1 Identification of systemically important financial institution
      • 2.2 Macroprudential policies and a global approach to financial stability
    • 3 Considerations for modelling policies to address systemic risk
      • 3.1 Systemic risk measurement
      • 3.2 Systemic risk and diversity
    • 4 Bank governance and systemic risk
      • 4.1 Five planks of effective reform
      • 4.2 Risk-taking incentives in banking
      • 4.3 Reforming risk-taking incentives in banking
    • 5 Conclusion
    • Disclaimer
    • References

Capital-regulation-bank-competition-and-financial-stability_2011_Economics-Letters.pdf

Economics Letters 113 (2011) 256–258

Contents lists available at SciVerse ScienceDirect

Economics Letters

journal homepage: www.elsevier.com/locate/ecolet

Capital regulation, bank competition, and financial stability Hendrik Hakenes a,b,∗, Isabel Schnabel b,c,d,1 a University of Bonn, Adenauerallee 24-42, 53113 Bonn, Germany b Max Planck Institute for Research on Collective Goods, Bonn, Germany c Gutenberg School of Management and Economics, Johannes Gutenberg University Mainz, 55099 Mainz, Germany d Centre for Economic Policy Research, London, United Kingdom

a r t i c l e i n f o

Article history: Received 18 November 2008 Received in revised form 2 July 2011 Accepted 14 July 2011 Available online 29 July 2011

JEL classification: G21 G28 D43

Keywords: Bank competition Capital regulation Risk-shifting Banking stability

a b s t r a c t

We analyze capital requirements if banks compete for loans and deposits. Banks and firms are subject to a risk-shifting problem. The ambiguous effect of competition on banks’ risk-taking translates into an ambiguous effect of capital requirements on financial stability.

© 2011 Elsevier B.V. All rights reserved.

1. Introduction

It is a widely held view that there is a trade-off between competition and stability in banking. The argument goes that competition erodes banks’ profit margins and charter values, which increases risk-taking incentives (Keeley, 1990; Allen and Gale, 2004). In an important paper, Boyd and De Nicolò (2005) have shown that this trade-off is not robust to the introduction of loan market competition. In their model, higher competition induces banks to lower loan rates, which mitigates the borrowers’ moral hazard problem, and hence risk-taking. Under this view, competition increases banking stability.

The diverging results are driven by the way that banks’ risk- taking is modeled. In the first type of models, banks solve a portfolio problem: they hold a portfolio of projects and choose the riskiness of these projects; given limited liability and deposit insurance, banks are subject to a risk-shifting problem. In the second type of

∗ Corresponding author at: University of Bonn, Adenauerallee 24-42, 53113 Bonn, Germany. Tel.: +49 228 73 9225; fax: +49 228 73 5048.

E-mail addresses: [email protected] (H. Hakenes), [email protected] (I. Schnabel). 1 Tel.: +49 6131 39 24191; fax: +49 6131 39 25588.

0165-1765/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.econlet.2011.07.008

models, banks solve an optimal contracting problem: they extend loans to entrepreneurs who determine the risk of their projects. Here the entrepreneurs are subject to a risk-shifting problem.

Our paper analyzes what these results imply for the effective- ness of bank capital regulation. For this purpose, we develop a model that encompasses both approaches. The optimal contract- ing problem looks exactly as in the paper by Boyd and De Nicolò (2005). We modify that model by adding a portfolio problem, al- lowing banks to choose the correlation of their loans. In such a setup, the relationship between banking competition and stabil- ity is ambiguous.2 We then introduce costly bank equity and capi- tal regulation and study the impact of capital requirements on the risk of individual loans, a bank’s correlation, and its probability of default.

Our model shows that capital regulation may destabilize the banking sector through its effect on banking competition. Stricter capital requirements attenuate competition for loans, implying higher loan rates, and hence higher risk-taking by firms. Therefore,

2 Martínez-Miera and Repullo (2010) present yet another channel. As in Boyd and De Nicolò (2005), bank competition reduces the moral hazard problem of entrepreneurs, but it also erodes banks’ capital buffers, leading to a U-shaped relationship between competition and stability.

H. Hakenes, I. Schnabel / Economics Letters 113 (2011) 256–258 257

the risk of single loans increases. Stricter capital requirements may also induce banks to choose a higher correlation of loans. Overall, these two effects may translate into an increase in the banks’ probability of default.

2. Model setup

Our setup follows the model by Boyd and De Nicolò (2005). Consider an economy with three dates, 0, 1, and 2. There are three types of agents: entrepreneurs, depositors, and banks. All agents are risk neutral. Entrepreneurs. There is a continuum of entrepreneurs who have no own resources, but have access to risky projects of fixed size, normalized to 1. Projects have constant returns to scale and yield, per invested unit, S with probability p(S), and 0 otherwise. p(S) satisfies p(0) = 1, p′(S) < 0, and p′′(S) ≤ 0 for all S ∈ [0, S̄]. The entrepreneurs’ choice of S (date 1) is unobservable by the bank and depositors. At date 2, the bank observes only whether the project has been successful. By assumption, financing contracts are simple debt contracts. The aggregate demand for loans L̄ is represented by a downward-sloping inverse demand curve rL(L̄), satisfying rL(0) > 0, r′L(L̄) < 0, and r

′′

L (L̄) ≤ 0. Depositors. The aggregate supply of deposits D̄ is represented by an upward-sloping inverse supply curve rD(D̄), satisfying rD(0) < rL(0), rD(0) ≥ 0, r′D(D̄) > 0, and r

′′

D(D̄) ≥ 0. Deposits are insured at a flat premium α. Banks. There are N banks. Each bank j extends loans Lj that are financed by deposits Dj and inside equity Ej, hence Lj = Ej + Dj. Aggregate deposits in the banking sector are equal to D̄ =∑N

j=1 Dj, aggregate loans are L̄ = ∑N

j=1 Lj. Each bank is run by a single owner-manager who provides the equity; the owners’ opportunity costs of capital are rE > p(0) rL(0), such that equity finance is expensive.3 Equity finance would be inefficient in the absence of moral hazard, but it helps to mitigate excessive risk- taking by banks. Assume that a regulator imposes a minimum capital requirement β, i. e., Ej ≥ β Lj. The introduction of capital requirements is the first major deviation from the setup introduced by Boyd and De Nicolò (2005).

Banks compete for deposits and loans in a Cournot fashion. When setting loan volumes, banks take into account the best responses of entrepreneurs. The second major innovation concerns banks’ asset side: We assume that a bank can influence the correlation of its loans, ρj ∈ [0, 1], which is unobservable by outsiders. More specifically, we assume that a bank’s projects have some ‘‘natural’’ correlation ρ0. The bank can increase or decrease the correlation at a (non-monetary) cost Cj, which is proportional to the size of its portfolio, i. e., Cj = C(Lj, ρj) = Lj c(ρj). The cost function c(ρj) satisfies the following conditions: c(ρ0) = 0 and c′(ρ0) = 0 for some ρ0 ∈ [0, 1], and c′′(ρj) > 0 for all ρj ∈ [0, 1]. Hence, the cost function is strictly convex with a minimum at ρ0; any deviation from the ‘‘natural’’ correlation is costly.

We make the following simplifying assumption about the correlation structure: For a given choice of ρ, all projects are perfectly correlated with probability ρ, and perfectly uncorrelated with probability 1 − ρ. In both cases, the expected portfolio payoff of the projects is equal to p(S) S. But the probability of default is equal to 1 − p(S) in the first case, and (due to the law of large numbers) zero in the second case. Hence, by raising ρ, a bank increases its default probability. ρ has a natural interpretation as the correlation between any two projects in the portfolio: with probability ρ, the correlation between any two projects is 1, with probability 1 − ρ, the correlation is 0. As a consequence, the ex- ante correlation between any two projects (and, hence, loans) is ρ · 1 + (1 − ρ) · 0 = ρ. Fig. 1 shows the time structure of the game.

3 This assumption is also used by Hellmann et al. (2000), Repullo (2004), and Repullo and Suarez (2004).

Fig. 1. Time structure.

3. Equilibrium

We solve the model by backward induction. We concentrate on symmetric equilibria and drop the index j when there is no danger of confusion. At date 1, the entrepreneurs choose project risk S to maximize expected profits for a given loan rate rL. An entrepreneur’s expected return is p(S) (S − rL). The first-order condition is p′(S) (S − rL) + p(S) = 0. Compared with the first best, S is too high. An increase in rL induces a further increase in risk.

Before the entrepreneurs’ choice of S, banks choose the loan correlation ρj for given deposit, loan, and equity volumes. The expected profit of bank j is

Πj = ρj p(S)[rL(L̄) Lj − (rD(D̄) + α) Dj]

+ (1 − ρj)[p(S) rL(L̄) Lj − (rD(D̄) + α) Dj] − rE Ej − Lj c(ρj)

= p(S) rL(L̄) Lj − [ρj p(S) + (1 − ρj)] (rD(D̄) + α) Dj − rE Ej − Lj c(ρj). (1)

We make use of the fact that all firms choose the same S in equi- librium. The first-order condition with respect to ρj is

∂Πj

∂ρj = (1 − p(S)) (rD(D̄) + α) Dj − Lj c

′ (ρj) = 0, (2)

where S depends on rL(L̄) because banks anticipate the en- trepreneurs’ risk choices. The first-best solution has Lj c′(ρj) = 0, implying that ρj = ρ0. Since c′′(ρj) > 0, ρj is higher than ρ0 for any p(S) < 1. Banks overspecialize due to limited liability and deposit insurance.

At the beginning of date 0, banks choose the profit-maximizing volumes of deposits, equity, and loans. Due to the balance sheet identity, Lj = Dj + Ej; hence, only two variables can be chosen independently. Since equity is expensive, a profit-maximizing bank takes no more equity than is required by regulation, Ej = β Lj and Dj = (1 − β) Lj. A bank’s optimization problem becomes

max Lj

{Lj[p(S)rL(L̄) − (1 − β)[ρjp(S) + (1 − ρj)](rD((1 − β)L̄)

+α) − βrE − c(ρj)]} s. t. S + p(S) p′(S)

= rL(L̄)

and (1 − p(S)) (rD((1 − β)L̄) + α) (1 − β) = c ′ (ρj). (3)

4. Capital regulation and bank risk

Now we analyze how capital regulation affects banks’ risk- taking. We consider the effect of capital regulation on the riskiness S of a single bank loan, a bank’s correlation ρ, and a bank’s probability of default. We will see that capital regulation affects banks’ risk not only by aligning the interests of banks and their creditors, but also through competition.

258 H. Hakenes, I. Schnabel / Economics Letters 113 (2011) 256–258

Consider an increase in the capital requirement β. This raises capital costs, which induces banks to choose lower deposit and loan volumes. The decrease in the aggregate loan volume L̄ translates into an increase in the loan rate rL(L̄) and into higher risk-taking by entrepreneurs. Hence, a tighter capital regulation increases the risk of individual loans because it attenuates the competition for loans and exacerbates the entrepreneurs’ moral hazard problem.

Proposition 1. Stricter capital requirements increase the risk of individual loans by raising the entrepreneurs’ risk-taking, dS/dβ > 0.

The effect on overall bank risk depends also on a bank’s correlation. A bank’s portfolio choice is governed by (2), which can be written as c′(ρ) = (1 − β) Φ, where Φ = [rD((1 − β) L̄) + α][1 − p(S(L̄))] > 0. Φ depends on β through rD and L̄. Totally differentiating (2), we derive the effect of capital regulation on a bank’s correlation:

dρ dβ

= 1

c′′(ρ)

[ −Φ + (1 − β)

 ∂Φ

∂L̄ ∂L̄ ∂β

+ ∂Φ

∂β

] , (4)

where ∂L̄/∂β < 0, ∂Φ/∂L̄ = (1 − β) r′D(D̄) (1 − p(S)) − (rD(D̄) + α) p′(S) S′(rL) r′L(L̄), and ∂Φ/∂β = −(1 − p(S)) r

D(D̄) L̄ < 0. The effect of capital regulation on a bank’s correlation is am-

biguous: First, a stricter regulation forces the banker to hold a higher equity share. As a consequence, the banker has a higher stake in the bank and chooses a lower correlation ρj. Second, an increase in β decreases the deposit rate through its negative effect on the aggregate deposit volume D̄. As a result, the bank’s margin increases, which also induces the bank to choose a lower ρj. This is the standard ‘‘charter value’’ effect found in the literature on the tradeoff between competition and stability. The third channel goes in the opposite direction. An increase in β raises the loan rate and thereby entrepreneurs’ risk-taking (Proposition 1), which trans- lates into a higher default probability of the bank’s loans, 1 − p(S). This makes gambling more attractive for the bank because default occurs more frequently; ρj increases. In this sense, the bank’s and the entrepreneurs’ risk-taking are complementary to each other. Proposition 2 gives the condition under which the third channel overcompensates the other two channels.

Proposition 2. Stricter capital requirements increase a bank’s port- folio correlation, dρ/dβ > 0, if and only if

(1 − β) 

∂Φ

∂L̄ ∂L̄ ∂β

+ ∂Φ

∂β

 > Φ. (5)

Finally, we discuss how capital regulation affects a bank’s default probability, PD = ρ (1 − p(S)). Taking the derivative with respect to β yields

d PD dβ

= dρ dβ

(1 − p(S)) − ρ p′(S) dS dβ

, (6)

leading to the following proposition.

Proposition 3. Stricter capital requirements increase a bank’s prob- ability of default, d PD/dβ > 0, if and only if

dρ dβ

> ρ p′(S)

1 − p(S) dS dβ

. (7)

Proposition 3 shows that tighter capital regulation may render a bank more risky. From Proposition 1, we know that a higher β makes every loan in a bank’s portfolio more risky. From Proposition 2, we know that a stricter capital regulation may also induce the bank to choose a more highly correlated portfolio. In such situations, it is obvious that the bank’s default probability must increase. Even if a higher β makes the bank take a less correlated portfolio, the effect on the entrepreneurs’ risk-taking may dominate, leading to a higher default probability of the bank.

5. Conclusion

Our paper has shown that capital regulation may destabilize the banking sector through its effect on banking competition. The ambiguous effect of competition on banks’ risk-taking translates into an ambiguous effect of capital regulation. A stabilizing effect of capital regulation tends to obtain in those situations where competition has a destabilizing effect (i. e., the ‘‘charter value effect’’ dominates), and vice versa. Given the prominence of capital requirements in today’s regulation, the empirical relationship between competition and stability should be of great interest to policy makers. The existing empirical evidence on the presumed trade-off between competition and stability in banking is rather mixed. In the light of our model, this suggests that capital regulation may not be suited in all circumstances to prevent excessive risk-taking in banking.

Acknowledgments

We thank John Boyd, Gianni De Nicolò, Jens Grunert, Robert Hauswald, Martin Hellwig, Eric Maskin (the editor), and an anony- mous referee for helpful suggestions. We also thank participants of the EFA in Ljubljana, the ESEM in Budapest, the Tor Vergata Confer- ence in Rome, the GEABA in Tübingen, as well as seminar partici- pants at the MPI in Bonn and the SFB/TR 15 in Berlin for comments. Financial support from Deutsche Forschungsgemeinschaft is grate- fully acknowledged.

References

Allen, F., Gale, D., 2004. Competition and financial stability. J. Money, Credit, Banking 36, 453–480.

Boyd, J.H., De Nicolò, G., 2005. The theory of bank risk taking and competition revisited. J. Finance 60, 1329–1343.

Hellmann, T., Murdock, K.C., Stiglitz, J.E., 2000. Liberalization, moral hazard in banking, and prudential regulation: are capital requirements enough? Amer. Econ. Rev. 90, 147–165.

Keeley, M.C., 1990. Deposit insurance, risk and market power in banking. Amer. Econ. Rev. 80, 1183–1200.

Martínez-Miera, D., Repullo, R., 2010. Does competition reduce the risk of bank failure? Rev. Finan. Stud. 23, 3638–3664.

Repullo, R., 2004. Capital requirements, market power, and risk-taking in banking. J. Finan. Intermediation 13, 156–182.

Repullo, R., Suarez, J., 2004. Loan pricing under basel capital requirements. J. Finan. Intermediation 13, 496–521.

  • Capital regulation, bank competition, and financial stability
    • Introduction
    • Model setup
    • Equilibrium
    • Capital regulation and bank risk
    • Conclusion
    • Acknowledgments
    • References

36.3ballen.pdf

Franklin Allen, Douglas Gale

Journal of Money, Credit, and Banking, Volume 36, Number 3 (Part 2), June 2004, pp. 453-480 (Article)

DOI: 10.1353/mcb.2004.0038

For additional information about this article

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FRANKLIN ALLEN

DOUGLAS GALE

Competition and Financial Stability

Competition policy in the banking sector is complicated by the necessity of maintaining financial stability. Greater competition may be good for (static) efficiency, but bad for financial stability. From the point of view of welfare economics, the relevant question is: what are the efficient levels of competi- tion and financial stability? We use a variety of models to address this question and find that different models provide different answers. The relationship between competition and stability is complex: sometimes com- petition increases stability. In addition, in a second-best world, concentration may be socially preferable to perfect competition and perfect stability may be socially undesirable.

JEL codes: D4, D5, D6, G2 Keywords: crises, banking concentration, dynamic, spatial,

and Schumpeterian competition.

In the banking sector, unlike other sectors of the economy, competition policy must take account of the interaction between competi- tion and financial stability. Greater competition may be good for (static) efficiency, but bad for financial stability.1 In this paper, we shall argue that the relationship between competition and financial stability is considerably more complex than this simple “trade-off” suggests, but understanding why increasing competition might reduce economic stability is a good starting point.

In an important paper, Keeley (1990) provided a theoretical framework and empirical evidence that deregulation of the banking sector in the U.S. in the

1. See Canoy et al. (2001) and Carletti and Hartmann (2003) for excellent surveys of the literature on financial stability and competition.

Prepared for the World Bank and Federal Reserve Bank of Cleveland project on Bank Concentration. Presented at the April 3–4, 2003 conference at the World Bank and the May 21–23, 2003 conference at the Federal Reserve Bank of Cleveland. We are grateful to an anonymous referee, to our discussants Stephen Haber and Charles Kahn, and to Elena Carletti, Qian Liu, Lemma Senbet, Andrew Winton, and participants in the conferences.

Franklin Allen is a professor of finance in the Department of Finance, Wharton School, University of Pennsylvania. E-mail: allenf�wharton.upenn.edu Douglas Gale is a professor in the Department of Economics, New York University. E-mail: douglas.gale�nyu.edu

Received May 29, 2003; and accepted in revised form May 29, 2003.

Journal of Money, Credit, and Banking, Vol. 36, No. 3 (June 2004, Part 2) Published in 2004 by The Ohio State University Press.

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1970s and 1980s had increased competition and led to a reduction in monopoly rents. This reduction in “charter value” magnified the agency problem between bank owners and the government deposit insurance fund. The bank owners or managers acting on their behalf had an increased incentive to take on extra risk, given the guaranteed funds available to them because of deposit insurance. As in the agency problem identified by Jensen and Meckling (1976), if the gamble was successful the equity owners would obtain the rewards while if it was unsuccessful the cost would be born by the deposit insurance fund. The extra risk that banks took on as a result of this agency problem caused a dramatic increase in bank failures during the 1980s. The US is not the only country where there appears to be an empirical relationship between increased competition and financial instability. Beck, Demir- guc-Kunt, and Levine (2003) find using data from 79 countries that crises are less likely in more concentrated banking systems.

Various empirical studies have found that the cost of financial instability is high. For example, Hoggarth and Saporta (2001) find that the average fiscal costs of banking resolution across countries are 16% of GDP. For emerging countries the figure is 17.5% and for developed countries it is 12%. As Table 1 shows, the costs of banking crises alone are estimated at 4.5% of GDP. Although these costs are substantial, they are much lower than the costs, estimated at 23% of GDP, of banking and currency crises occurring together. A proportion of the fiscal costs are transferred, so these figures do not represent the deadweight economic costs. A number of studies measure the cumulative output loss resulting from a financial crisis by using the deviation from trend output. Table 2 gives estimates for these costs. The average cumulative output loss for all crises is 16.9% of GDP. Here the costs of twin crises are again higher; the loss caused by twin banking and currency crises is 29.9% of GDP versus 5.6% for banking crises alone. However, in contrast to fiscal costs,

TABLE 1

Average Cumulative Fiscal Costs of Banking Crises in 24 Crises, 1977–2000

Non-performing loans Fiscal costs of banking resolution Number of crises (percentage of total loans) (percentage of GDP)

All countries 24 22 16 Emerging market countries 17 28 17.5 Developed countries 7 13.5 12 Banking crisis alone 9 18 4.5 Banking and currency crises of which 15 26 23 Emerging market countries 11 30 25 Developed countries 4 18 16 Banking and currency crises with 11 26 27.5

previous fixed exchange rate of which

Emerging market countries 8 30 32 Developed countries 3 18 16

Note: Source: Hoggarth and Saporta (2001, p. 150).

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TABLE 2

Output Losses Associated with Banking Crises, 1977–98

Average crisis length Average cumulative output losses Number of crises (years) (percentage of GDP)

All 43 3.7 16.9 Single banking crises 23 3.3 5.6 Twin banking and currency crises 20 4.2 29.9 Developed countries 13 4.6 23.8 Emerging market countries 30 3.3 13.9

Note: Source: Hoggarth and Saporta (2001, p. 155).

developed countries have a greater loss, 23.8% of GDP, than emerging countries, 13.9% of GDP.

The large literature on the efficiency of the banking industry (for a survey see, e.g., Berger and Humphrey, 1997) is mostly concerned with the cost- and profit- efficiency of retail banking. For example, Canoy et al. (2001) summarize the evidence as suggesting that the average bank operates at a cost level that is 10% or 20% above the best-practices level. This is just one (probably small) part of the total costs of deviations from perfect competition. Unfortunately, the total costs of a deviation from perfect competition have not been documented as carefully as the costs of financial instability.

Given the large and visible costs of financial instability, it is natural for policymak- ers to make the avoidance of financial crises a high priority. By contrast, the difficulty of measuring the efficiency costs of concentration may suggest that competition policy warrants a lower priority. In fact, the uncertainty about the costs of concentra- tion together with the perceived (negative) trade-off between competition and finan- cial stability may actually encourage policymakers to favor concentration at the expense of competition policy. This subordination of competition policy to financial stability may be unwise for a number of reasons, however. In the first place, the extent to which there is a negative trade-off between competition and financial stability may be questioned. The costs of financial crises are undoubtedly high, but it does not follow that it is necessary to reduce competition to avoid those costs. Secondly, the wide range of estimates of the efficiency costs from concentration is at least consistent with a high efficiency gain from greater competition. Thirdly, the costs of financial crises occur infrequently, perhaps every decade or few decades, whereas the inefficiency cost concentrations are born continuously.

The proper balance between competition and financial stability presupposes a framework in which we can identify the welfare costs and benefits of different levels of competition and financial stability. Our objective in this paper is to review a number of theoretical models as a prelude to the development of a theoretical framework in which the optimal policy can be identified. From the point of view of welfare economics, the relevant question is: What are the efficient levels of competition and financial stability? We use a variety of models to address this

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question and find that different models provide different answers. This should not be surprising. In a second-best world, concentration may be preferred to perfect competition (Schumpeter 1950) and perfect stability may be socially undesirable (Allen and Gale 1998). When we consider the relationship between competition and stability, we find that the idea of a simple negative trade-off is, again, too simple: sometimes competition decreases stability and sometimes perfect competition is compatible with the socially optimal level of stability.

We begin in Section 1 by describing the general equilibrium model of financial intermediaries and markets from Allen and Gale (2003a). They provide analogues of the classical theorems of welfare economics for a model of intermediation with asymmetric information. If financial markets are complete and contracts between intermediaries and their customers are complete, the perfectly competitive equilib- rium allocation is incentive-efficient. In this sense, perfect competition is socially optimal. There is no financial instability because contracts are completely contingent and hence there is no need to default. Similarly, if contracts are incomplete, the perfectly competitive equilibrium allocation is constrained-efficient, but now finan- cial instability is necessary for efficiency. If the banks cannot meet the fixed payments that they have promised, there is a financial crisis. A deviation from competition may increase financial stability, but cannot increase and is likely to reduce welfare. This result illustrates that, in general, there should be no presumption that reducing competition in order to increase financial stability is socially desirable.

In simple partial-equilibrium models, it is possible to generate a negative trade-off between competition and financial stability. However, even in this case, the nature of the trade-off between competition and stability is more complicated than was first thought. For example, Allen and Gale (2000a, chap. 8), Boyd and De Nicolo (2002), and Perotti and Suarez (2003) have identified a number of different effects of increased competition on financial stability. In some circumstances, increased competition can actually increase financial stability. These models are discussed in Section 2.

Introducing other kinds of frictions produces further complications to our picture of competition. On the one hand, as mentioned above, Allen and Gale (2000a, chap. 8) show that when search costs are introduced, competition among a large number of unitary banks may result in the monopoly price being charged. On the other hand, a system with two banks with branches at each location may result in the perfectly competitive price. A Hotelling-type model of spatial competition introduces a rich variety of effects concerning regional diversification and risk sharing. The profitability of banks is shown to be extremely sensitive to the precise form of local interactions.

Section 4 describes a model of Schumpeterian competition, in which firms compete by developing new products. The firm that makes the best innovation manages to capture the whole market. The equilibrium price equals the difference between the value of the successful firm’s product and the value of the second-best product. So the successful firm’s profit is equal to the social value of its innovation. This provides the firms with the right incentives to innovate efficiently. In this context, perfect competition is again desirable and can lead to efficiency. Clearly, this kind

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of innovation process is not consistent with financial stability. The successful innova- tor will survive while the unsuccessful will fail. Again, as in the benchmark model, efficiency requires a combination of perfect competition and financial instability. If the government is concerned with financial stability it may ensure that banks survive by regulating entry in different submarkets. We consider a setting where banks are assured of a monopoly in their region and consider the incentives to innovate. It is shown that this enforced stability leads to a welfare loss as might be expected. Less obvious is the result that there may be too little or too much investment in innovation. There can be too little because each bank only obtains profits from its own region. There can be too much because the bank is assured of a return no matter what happens.

Contagion is another important source of financial instability. It occurs when some shock, possibly small, spreads throughout the financial system and causes a systemic problem. Allen and Gale (2000b) developed a model of contagion with a perfectly competitive banking sector. It was shown that a shock that was arbitrarily small relative to the economy as a whole could cause all the banks in the financial system to go bankrupt. The contagion spreads through the interbank market. Section 5 extends the Allen and Gale (2000b) model of contagion to allow for imperfect competition in the banking sector. It is shown that in this case the economy is not as susceptible to contagion as it is with perfect competition. Each oligopolistic bank realizes that its actions affect the price of liquidity. By providing sufficient liquidity to the market they can ensure that contagion and their own bankruptcy are avoided. In this case there is a trade-off between competition and stability.

Concluding remarks are contained in Section 6.

1. COMPETITION AND CRISES

In the Arrow–Debreu model of general equilibrium, the fundamental theorems of welfare economics show that perfect competition is a necessary condition for efficiency. Allen and Gale (2003a) show that analogous results hold for a model of financial crises with complete markets. In this setting, perfect competition is compati- ble with the efficient level of financial stability. In this sense, there is no “trade- off” between competition and stability. We begin by describing the model of perfect competition in a financial system consisting of financial intermediaries and markets and then summarize the theoretical results in Allen and Gale (2003a).

There are three dates t � 0,1,2 and a single good at each date. The good is used for consumption and investment.

The economy is subject to two kinds of uncertainty. First, individual agents are subject to idiosyncratic preference shocks, which affect their demand for liquidity (these will be described later). Second, the entire economy is subject to aggregate shocks that affect asset returns and the cross-sectional distribution of preferences. The aggregate shocks are represented by a finite number of states of nature. All agents have a common prior probability density over the states of nature. All

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uncertainty is resolved at the beginning of date 1, when the aggregate state is revealed and each agent discovers his/her individual preference shock.

Each agent has an endowment of one unit of the good at date 0 and no endowment at dates 1 and 2. So, in order to provide consumption at dates 1 and 2, they need to invest.

There are two assets distinguished by their returns and liquidity structure. One is a short-term asset (the short asset), and the other is a long-term asset (the long asset). The short asset is represented by a storage technology: one unit invested in the short asset at date t � 0,1 yields a return of one unit at date t � 1. The long asset yields a return after two periods. One unit of the good invested in the long asset at date 0 yields a random return of more than one unit of the good that depends on the aggregate state at date 2.

Investors’ preferences are distinguished ex ante and ex post. At date 0 there is a finite number n of types of investors, indexed by i � 1,…,n. We call i an investor’s ex ante type. An investor’s ex ante type is common knowledge and hence contractible.

While investors of a given ex ante type are identical at date 0, they receive a private, idiosyncratic, preference shock at the beginning of date 1. The date 1 preference shock is denoted by θi � Θi, where Θi is a finite set. We call θi the investor’s ex post type. Because θi is private information, contracts cannot be explicitly contingent on θi.

Investors only value consumption at dates 1 and 2. An investor’s preferences are represented by a von Neumann–Morgenstern utility function, ui(c1, c2; θi), where ct denotes consumption at date t � 1,2. The utility function ui(·; θi) is assumed to be concave, increasing, and continuous for every type θi. Diamond and Dybvig (1983) assumed that consumers were one of two ex post types, either early diers who valued consumption at date 1 or late diers who valued consumption at date 2. This is a special case of the preference shock θi. The present framework allows for much more general preference uncertainty.

Allen and Gale (2003a) consider two different versions of the model, depending on the kind of contracts financial institutions offer to their customers. In the first version, contracts are completely contingent, subject only to incentive-compatibility constraints. More precisely, contracts are required to be incentive-compatible and are allowed to be contingent on the aggregate states η and individuals’ reports of their ex post types. Each intermediary offers a single contract and each ex ante type is attracted to a different intermediary.

One can, of course, imagine a world in which a single “universal” intermediary offers contracts to all ex ante type of investors. A universal intermediary could act as a central planner and implement the incentive-efficient allocation of risk. There would be no reason to resort to markets at all. Our world view is based on the assumption that transaction costs preclude this kind of centralized solution and that decentralized intermediaries are restricted in the number of different contracts they can offer. This assumption provides a role for financial markets in which financial intermediaries can share risk and obtain liquidity.

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At the same time, financial markets alone will not suffice to achieve optimal risk sharing. Because individual economic agents have private information, markets for individual risks are incomplete. The markets that are available will not achieve an incentive-efficient allocation of risk. Intermediaries, by contrast, can offer individuals incentive-compatible contracts and improve on the risk sharing provided by the market.

In the Diamond and Dybvig (1983) model, all investors are ex ante identical. Consequently, a single representative bank can provide complete risk sharing and there is no need for markets to provide cross-sectional risk sharing across banks. Allen and Gale (1994) showed that differences in risk and liquidity preferences can be crucial in explaining asset prices. This is another reason for allowing for ex ante heterogeneity.

In the context of intermediaries with complete markets and complete contingent incentive-compatible contracts, Allen and Gale (2003a) prove the following result.

Proposition 1: Under the maintained assumptions, the equilibrium allocation of the model with complete markets and incomplete contracts is constrained-efficient.

Proposition 1 assumes that intermediaries use complete, incentive-compatible contracts. In reality, we do not observe such complex contracts, for reasons that are well documented in the literature, including transaction costs, asymmetric infor- mation, and the nature of the legal system. These frictions can justify the use of debt and many other kinds of incomplete contracts that intermediaries use in practice. The second version of the model presented by Allen and Gale (2003a) assumes that intermediaries are restricted to using a set of incompletely contingent contracts. This framework allows for many special cases, including at one extreme the earlier model with completely contingent contracts and completely non-contingent debt contracts. Note that this framework allows for a wide variety of assumptions about what is feasible, but takes the set of feasible contracts as given. To endogenize the set of feasible contracts one would have to appeal to factors such as transaction costs, non-verifiable information, and so on.

When contracts are complete, there is no incentive for intermediaries to enter into commitments that they cannot carry out. When contracts are constrained to be incomplete, it may be (ex ante) optimal for the intermediary to plan to default in some states. In the event of default, it is assumed that the intermediary’s assets, including the Arrow securities it holds, are liquidated and the proceeds distrib- uted among the intermediary’s investors. For markets to be complete, which is an assumption we maintain here, the Arrow securities that the bank issues must be default free. Hence, we assume that these securities are collateralized and their holders have priority. Anything that is left after the Arrow security holders have been paid off is paid out pro rata to the depositors. Allen and Gale (2003a) demonstrate the following result for the case when contracts are incomplete and take the form of deposit contracts.

Proposition 2: Under maintained assumptions, the equilibrium allocation of the model with complete markets and incomplete contracts is constrained-efficient.

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This is an important result. It shows that, in the presence of complete markets and perfect competition, the incidence of default is optimal in a laisser-faire equilib- rium. There is no scope for welfare-improving government intervention to prevent financial crises. In fact competition and financial instability are both necessary for constrained efficiency.

This result demonstrates that in a standard framework achieving optimality does not require trading off competition and financial stability. As we will see below this result extends to a number of other circumstances.

It is important to stress that the results in this section are simply benchmarks to illustrate what may happen. The only costs modeled are the losses to consumers from inefficient risk sharing. Many features that may be important in practice, such as unemployment and bankruptcy costs to firms are excluded. When these are taken into account, there may be a role for government intervention to reduce the incidence of financial crises. What the results do show is that the operation of the financial system and the occurrence of crises when there are complete markets are not the problem. There must be some form of market failure for financial crises to be undesirable.

2. AGENCY COSTS

Keeley (1990) developed a simple model of risk taking by banks with two dates and two states when there is deposit insurance. He showed that as competition increased risk taking by banks also increased. In fact, deposit insurance is not necessary for this effect to be present although it does exacerbate it. Allen and Gale (2000a, chap. 8) developed a simple model of competition and risk taking to illustrate the agency problem.

When firms are debt-financed, managers acting in the shareholders’ interests have an incentive to take excessive risks, because the debtholders bear the downside risk while the shareholders benefit from the upside potential. This well known problem of risk shifting is particularly acute in the banking sector where a large proportion of the liabilities are in the form of debt (deposits). The risk-shifting problem is exacerbated by competition. Other things being equal, greater competition reduces the profits or quasi-rents available to managers and/or shareholders. As a result, the gains from taking excessive risks become relatively more attractive and this increases the incentive to exploit the non-convexity in the payoff function. Any analysis of the costs and benefits of competition has to weigh this effect against the supposed efficiency gains of greater competition.

To illustrate these ideas, consider the problem faced by a banking regulator who controls entry into the banking industry by granting charters to a limited number of banks. We use a model of Cournot competition, in which banks choose the volume of deposits they want, subject to an upward sloping supply of funds schedule. Having more banks will tend to raise the equilibrium deposit rate and increase the tendency to shift risks. What is the optimal number of charters?

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Should the regulator restrict competition by granting only a few charters or encourage competition by granting many?

2.1 A Static Model

Suppose that the regulator has chartered n banks, indexed i � 1,…,n. Each bank chooses a portfolio consisting of perfectly correlated risks. This as-

sumption is equivalent to assuming that the risk of each investment can be decom- posed into a common component and a purely idiosyncratic component. If there is a very large number of investments, the purely idiosyncratic components can be pooled perfectly. Then the idiosyncratic risks disappear from the analysis and we are left with a common component representing the systematic risks.

A portfolio is characterized by its size and rate of return. The bank’s investments have a two-point return structure: for each dollar invested, bank i will receive a return yi with probability p(yi); with probability (1 – p(yi)) they pay a return 0. The bank chooses the riskiness of its portfolio by choosing the target return yi on its investments. The function p(yi) is assumed to be twice continuously differentiable and satisfies

p(0) � 1, p(ȳ) � 0, and p′(yi) � 0, p″(yi) ≤ 0, ∀0 � yi � ȳ .

The higher the target return, the lower the probability of success and the more rapidly the probability of success falls. Because the investments have perfectly correlated returns, the portfolio return has the same distribution as the returns to the individual investments.

Let di ≥ 0 denote the total deposits of bank i, which is by definition the total number of dollars the bank has to invest. (For the moment, we ignore bank capital.) There is an upward sloping supply-of-funds curve. If the total demand for deposits is D � �idi , then the opportunity cost of funds is R(D), where R(D) is assumed to be a differentiable function satisfying

R′(D) � 0, R″(D) � 0, R(0) � 0 and R(∞) � ∞ .

We assume that all deposits are insured, so the supply of funds is independent of the riskiness of the banks’ portfolios. In the sequel, we consider the case where banks bear the cost of deposit insurance.

The payoff to bank i is a function of the riskiness of its own portfolio and the demand for deposits of all the banks

πi(y, d) � p(yi)[yidi � R(D)di] ,

where d � (d1,…,dn) and y � (y1,…,yn). Note that we have ignored the cost of deposit insurance to the bank in calculating its net return.

Since a bank can always ensure non-negative profits by choosing di � 0, it will always earn a non-negative expected return in equilibrium, that is, yidi � R(D)di ≥ 0. There is no need to introduce a separate limited-liability constraint.

In a Nash–Cournot equilibrium, each bank i chooses an ordered pair (yi, di) that is a best response to the strategies of all the other banks. Consider an equilibrium

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(y, d) in which each bank i chooses a strictly positive pair (yi, di)[0. As a necessary condition for a best response, this pair must satisfy the following first-order conditions

p(yi)[yi � R(D) � R′(D)di] � 0 ,

p′(yi)[yi � R(D)]di � p(yi)di � 0 .

Assuming that the equilibrium is symmetric, that is, (yi, di) � (y, d) for every i, the first-order conditions reduce to

y � R(nd) � R′(nd)d � 0 ,

p′(y)[y � R(nd)] � p(y) � 0 ,

and this implies that

� p(y) p′(y)

� y � R(nd) � R′(nd)d .

Given our assumptions on p(y), an increase in y reduces � p(y)�p′(y). Suppose that there are two symmetric equilibria, (y, d) and (y′, d′). Then y � y′ implies that R′(nd)d � R′(nd′)d′ , which, given our assumptions on R(D), implies that d � d′ and R(nd) � R(nd′). Then clearly y � R(nd) � y′ � R(nd′), contradicting the first equa- tion. So there is at most one solution to this set of equations, which determines both the size and the riskiness of the banks’ portfolio in a symmetric equilibrium.

Proposition 3: Under the maintained assumptions, there is at most one symmetric equilibrium (y*, d*)[0 which is completely characterized by the conditions

� p(y) p′(y)

� y � R(nd) � R′(nd)d .

What can we now say about the effect of competition on risk taking? Suppose that we identify the degree of competitiveness of the banking sector

with the degree of concentration. In other words, the larger the number of banks, the more competitive the banking sector is. So a first attempt at answering the question would involve increasing n ceteris paribus and observing how the riskiness of the banks’ behavior changes.

With a fixed supply-of-funds schedule R(·) it is most likely that the volume of deposits will remain bounded as n increases. More precisely, if we assume that R(D) → ∞ as D → ∞ then it is clear that D → ∞ is inconsistent with equilibrium. Then the equilibrium value of D is bounded above (uniformly in n) and this implies that d ≡ D�n → 0 as n → ∞. This in turn implies that R′(nd)d → 0 from which it immediately follows that y � R(nd) → 0 and p(y) → 0 , or in other words, that y and R(nd) both converge to ȳ.

Proposition 4: If R(D) → ∞ as D → ∞ then in any symmetric equilibrium, y � R(nd) → 0 and y → ȳ as n → ∞.

The effect of increasing competition is to make each bank much smaller relative to the market for funds and this in turn reduces the importance of the price effect

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(the R′(nd)d term) in the bank’s decision. As a result, banks behave more like perfect competitors and will increase their business as long as profits are positive. Equilibrium then requires that profits converge to zero, and this in turn implies that banks have extreme incentives for risk taking. In the limit as n → ∞, they will choose the riskiest investments possible in an attempt to earn a positive profit.

The effect of replicating the market. This exercise is enlightening but somewhat artificial since it assumes that we are dealing with a market of fixed size and increasing the number of banks without bound in order to achieve competition. Normally, one thinks of perfect competition as arising in the limit as the number of banks and consumers grows without bound. One way to do this is to replicate the market by shifting the supply-of-funds function as we increase the number of banks. Precisely, suppose that the rate of return on deposits is a function of the deposits per bank

R � R(D�n) .

In effect, we are assuming that, as the number of banks is increased, the number of depositors is increased proportionately, so that the supply of funds in relation to a particular bank is unchanged.

The effect of this change in the model is to make it more like the traditional model of a market in which, as the number of firms increases, the effect of any firm supply on the price of the product becomes vanishingly small. Here the effect of any bank’s demand for deposits on the equilibrium deposit rate becomes vanishingly small in the limit as the number of banks becomes unboundedly large. To see this, note that the first-order conditions become

y � R(d) � R′(d)dn�1 � 0

and

p′(y)[y � R(d)] � p(y) � 0 .

As before, we can ensure that d remains bounded as n → ∞ by assuming that R(d) → ∞ as n → ∞. Then the last term on the left hand side of the first equation will vanish as n → ∞ , leaving a limiting value of (y, d) that satisfies y � R(d). Substituting this in the second equation tells us that p(y) � 0. In other words, as the number of banks increases, the profit margins fall to zero, with the result that banks choose riskier and riskier investments.

Proposition 5: If R(D) → ∞ as D → ∞ then in any symmetric equilibrium, y � R(d) → 0 and y → ȳ as n → ∞.

This is a highly stylized model, so the results have to be taken with a grain of salt; nonetheless, they illustrate clearly the operative principle, which is that competi- tion, by reducing profits, encourages risk taking.

In this particular case, we have constant returns to scale in banking, so that in the limit, when there is a large number of individually insignificant banks, profits must converge to zero. In other words, banks will expand the volume of their

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deposits and loans until the deposit rate approaches the expected return on invest- ments. But this gives them an extreme incentive to shift risks to the depositors or the deposit insurance agency, since it is only by doing so that they can get positive profits at all.

With constant returns to scale, zero profit is always a necessary condition of equilibrium in a competitive industry. However, there are other ways of ensuring the same outcome even if constant returns to scale is not assumed. We replicated the market by increasing the number of banks and potential depositors in the same proportion. This is an interesting thought experiment, but it is not the same as the comparative static exercise the regulator is undertaking. Presumably, the regulator has to choose n optimally, taking as given the supply-of-funds schedule. Suppose that m is the number of depositors and n the number of banks. Then the market supply-of-funds schedule can be written as R(D/m) if R(·) is the individual supply- of-funds schedule. When m is very large, the supply of funds is elastic, other things being equal, so the banks will take the marginal cost of funds as being equal to the average cost R(D/m). However, increasing the number of banks n in relation to m will force profits down. If d remains bounded away from zero, the average cost of funds must increase to ∞ and if d goes to zero, profits will also go to zero. In this way, the regulator can achieve the effects of free entry, but there is no need to do this in order to ensure competition. Competition, in the sense of price-taking behavior, follows from having a large market, that is, a large value of m, independent of whether n is large or not. Clearly, the regulator does not want to drive profits to zero if it can be helped, because of the incentives for risk taking that that creates.

Cost of deposit insurance. The preceding analysis assumes that all deposits are insured and that the costs are not born by the banks. This is clearly unrealistic, so it makes sense to consider explicitly the cost of deposit insurance. We assume that the premium for deposit insurance is set before the banks choose their strategies and that it is the same for each bank, independently of the strategy chosen. In equilibrium, the premium accurately reflects the cost of deposit insurance provided by a risk neutral insurer.

Let π denote the premium per dollar of deposits. Then the objective function of bank i is p(yi)(yi � R(D) � p)di and the first-order conditions in a symmetric equilib- rium in which banks choose the strategy (y, d) will be

y � R(d) � π � R′(d)dn�1 � 0 ,

p′(y)[y � R(d) � π] � p(y) � 0 .

In equilibrium, the premium must be set so that the expected return on deposits is equal to the return demanded by depositors

R(d) � p(y)(R(d) � π) .

Substituting π � [(1 � p(y))�p(y)]R(d) into the first-order conditions yields

y � R(d)�p(y) � R′(d)dn�1 � 0 ,

p′(y)[y � R(d)�p(y)] � p(y) � 0 .

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Let (yn, dn) be a symmetric equilibrium when there are n banks and suppose that (yn, dn) → (y0, d0) as n → ∞. Then the first-order conditions imply that

lim n→∞

yn � R(dn)�p(yn) � 0 ,

which is only possible if yn → ȳ and p(yn) → 0, as before. Efficiency. Let us leave distributional questions on one side for the moment,

although historically they have been at the center of the arguments for competition in banking, and suppose that the regulator is only interested in maximizing surplus. Reverting to the constant-returns-to-scale case, two necessary conditions for Pareto optimality are that the average cost of funds be equal to the expected return on investments, and that the expected return on investments should be a maximum

R(D�m) � p(y)y , p(y)y ≥ p(y′)y′, ∀y′�[0, ȳ] .

Neither of these conditions will hold in equilibrium when m and n are very large. The first condition requires that the volume of deposits expand until the cost of funds equals the expected value of investments. However, when the market is highly competitive, we have y � R(D�m) ≅ 0 , so that p(y)y � R(D�m) � 0. As we also saw, the second condition cannot be satisfied in equilibrium, since as the market grows large (m, n → ∞), we have y → ȳ � ∞ and p(y) → 0, so p(y)y → 0. This is not only sub-optimal but the worst possible outcome because it minimizes the total surplus.

Suppose instead that we hold the value of m fixed and adjust n to maximize total surplus, taking the equilibrium values (y(n), d(n)) as given functions of n determined by the equilibrium conditions

y � R(nd�m) � R′(nd�m)dm�1 � 0 and

p′(y)[y � R(nd�m)] � p(y) � 0 . A “small change” in n will increase the expected revenue by

{p′(y(n))y(n) � p(y(n))}ny′(n) � p(y(n))y(n) ,

and the cost by

R(nd(n)�m){nd′(n) � d(n)} ,

so a necessary condition for an (interior) optimum is

{p′(y(n))y(n) � p(y(n))}ny′(n) � p(y(n))y(n) � R(nd(n)�m){nd′(n) � d(n)} .

This can be rewritten as

p(y(n))y(n) � R(nd(n)�m)d(n) � �{p′(y(n))y(n) � p(y(n))}ny′(n) � R(nd(n)�m)nd′(n) ,

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where the left hand side is the expected surplus generated by a single bank and the right hand side is the change in expected revenue per bank as n increases plus the change in the cost per bank of the funds borrowed. Since the left hand side is positive, the right hand side must be positive, too. But we know that the second term on the right must be negative since adding more banks reduces the volume of business each bank does, so the first term on the right is positive. We know that y′(n) is negative—increased competition leads to increased risk taking—so the term in braces must be positive. Assuming that p(y)y is concave in y, this tells us that n will be chosen so that y(n) is less than the value that maximizes expected revenue.

A fortiori, it will not, as we have seen, be as great as the value under free entry, since it is never optimal to let n → ∞. It may in fact, be optimal to let the number of banks remain quite small.

2.2 Loan Market Competition

The model in Allen and Gale (2000a) analyzes competition in the deposit market. The bank is assumed to invest deposits directly in a portfolio of assets with given risk characteristics. The bank directly determines the riskiness of its portfolio. As profits decline the bank’s preference for risk increases, so increasing competition leads to increasing risk and a decrease in stability. Boyd and De Nicolo (2002) point out that the assumption that banks invest directly in assets is crucial for the result. To show this, they extend the Allen and Gale model to include entrepreneurs. The entrepreneurs obtain loans from the banks and invest the money in risky ventures. Each entrepreneur chooses the riskiness of the venture he invests in. The entrepre- neurs, like the banks in the Allen and Gale model, have a greater incentive to take risk when profits are lower. However, the effect of competition among banks here is the opposite of what we observed in the Allen and Gale (2000a) model. Greater competition among banks reduces the interest rates that borrowers pay, increases the profitability of their ventures, and hence reduces the incentive to take risk. Thus, increased competition among banks leads to increased financial stability. The effect of competition in the deposit market is the same as before but Boyd and De Nicolo are able to show that the loan market effect dominates. The trade-off between competition and stability presented in the Allen and Gale model is reversed in the Boyd and De Nicolo model. As competition between banks increases the risks taken by borrowers is unambiguously reduced and financial stability is improved.

2.3 Dynamic Competition

The results in Section 2.1 demonstrate how the limited liability of managers and shareholders in a modern banking corporation can produce a convex objective function which in turn leads to risk-shifting behavior. This kind of behavior is most likely to occur when the bank is “close to the water line,” that is, when the risk of bankruptcy is imminent. For banks which are not in immediate danger of bankruptcy, the risk-shifting argument may be less relevant. However, even if a bank is not close to the water line, there may be other reasons for thinking that its objective

FRANKLIN ALLEN AND DOUGLAS GALE : 467

function is convex. Consider, for example, the winner-takes-all nature of competition. When banks compete for market share, the bank that ends up with the largest share may be able to exploit its market power to increase profitability. In this case, the profit function may be convex in market share, that is, doubling market share may more than double profits. Another reason is the presence of increasing returns to scale. If larger banks have lower average costs, then profits will be a convex function of the size of the bank. Either of these possibilities will give the bank an incentive to take riskier actions, even when the bank is not in immediate danger of bankruptcy.

These incentives for risk-taking behavior are naturally studied in a dynamic context. Suppose that a group of banks are competing over time. Their activities are constrained by the minimum capital ratio, so the only way to expand is to acquire more capital. Because of agency costs or the adverse signaling effects, it may be expensive for banks to raise capital from external sources, so they try to accumu- late capital by retaining earnings. The relative size of the bank matters, because it gives the bank a competitive edge over other banks. Because their reduced-form profit functions are convex and they are constrained by their capital, the game is a race to see who can accumulate capital or market share fastest.

When banks compete to capture greater market share or to reap economies of scale, they consider the effect of their actions not only on immediate profits but also on their future position in the market. How the bank’s current actions will affect its future position in the market depends on the nature of the risks involved and on the behavior of the other banks. Even if the profit function is only convex when the bank is close to the bankruptcy point, the bank’s objective function may be convex over a much wider region because the bank’s objective function incorporates or discounts future possibilities which are still far away. This may influence the shape of the bank’s objective function globally, through backward induction.

Allen and Gale (2000a, chap. 8) consider a variety of different models and show that risk taking can either be increased or decreased by competition in a dynamic setting. The first case analyzed involves a pair of duopolists who compete for market share. They play repeatedly for many periods. In each round market shares can go up or down a small amount. By taking a risky action instead of a safe action in any period they increase the variance of the change in market share. A crucial assumption is that there are “reflecting barriers” at the extreme values of market share. When a bank’s market share hits zero, it does not go out of business; at worst it will remain at zero for some period before bouncing back. This non-convexity is like having increasing returns in the neighborhood of zero. Similarly, when a bank’s market share hits 100% it must eventually bounce back, and this is like having locally decreasing returns. A simple numerical example is used to illustrate the effect of non-convexity on the bank’s behavior. When a bank’s market share is low, its objective function is convex and it has an incentive to take risk; when its market share is high, its objective function is concave and it has an incentive to avoid risk.

The second case analyzed assumes that there are “absorbing barriers.” Now when market share hits zero or one it stays the same with probability one. In this case it is shown that the incentive to take risks is eliminated. The reason is the assumption

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that the bank’s position can only change a little bit at a time. If the period length and the step size are made very small, the binomial process considered would approximate Brownian motion, which has continuous sample paths with probability one. It is the continuity of the movement of the bank’s market share over time which eliminates the usual incentive for risk taking. The bank becomes “bankrupt” as soon as its market share hits zero. It cannot go below the line and so it cannot shift risk to depositors or other creditors. Whether incentives to take risks are greater or less with reflecting barriers is ambiguous. While absorbing barriers eliminate the positive incentive for risk taking with reflecting barriers, they also eliminate the in- centive to avoid risk when market share is high that was found in the same model.

Perotti and Suarez (2002) consider the effect of dynamic competition in a model where there can be a banking duopoly or monopoly. If a bank fails when there is a duopoly the market structure switches temporarily to a monopoly. Banks can lend prudently in which case their portfolio of loans has a safe payoff. The alternative is to lend speculatively in which case there is some probability of a high payoff but the average payoff is low. Limited liability and deposit insurance mean lending speculatively can shift risks and be advantageous in the short run. However, a bank can be hit by a random solvency shock. If it lent prudently it will always survive this shock but if it lent speculatively it will fail unless loan returns are high. It is shown that this effect introduces an incentive for banks to lend prudently. A prudent bank will be less exposed to the risk of being driven out of business and will emerge as a monopolist if the other duopolist lent speculatively and is hit by a solvency shock. In their model this “last one standing effect” makes duopolistic banks unam- biguously more prudent and encourages stability.

The range of results obtained with these dynamic models illustrate how crucial the particular details of the model are in determining whether or not competition leads to more or less financial stability.

3. SPATIAL COMPETITION

So far, we have only considered competition in markets for homogeneous com- modities or services. Product differentiation occurs in the financial sector, just as it does in non-financial sectors, and it is important to consider the effect of product differentiation on the competitive process. Models of spatial competition are used to represent competition among firms with differentiated products and the same can be done for the banking sector. We can interpret the spatial dimension literally as representing banks with different locations or we can interpret it metaphorically as representing some other qualitative difference in the services provided. In either case, we find that the effects of concentration on competition in spatial models can be quite different from the results obtained for markets with undifferentiated products. In this section we consider two models of bank competition. The first model, from Allen and Gale (2000a), compares branch banking with unitary banking and shows that competition among a small number of banks (with many branches) may be

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more aggressive than competition among a large number of unitary banks. We then consider a Hotelling-type model of spatial competition and again compare competi- tion among a small number of banks (with many branches) with competition among a large number of unitary banks. The results concerning the trade-off between competition and diversification are very sensitive to the spatial arrangement of the branches.2

3.1 Unitary Banking versus Branch Banking

The model presented below exploits two types of imperfections arising from asymmetric information. The first is the presence of “lock-in” effects. Information is costly for both banks and their customers, whether borrowers or depositors, and once relationship-specific investments in information have been made, the parties may find themselves “locked-in” to the relationship. For example, a borrower having incurred a fixed cost of revealing its type to a bank will suffer a loss if it switches to another bank. In addition there is the well known “lemons effect,” that arises if the borrower leaves the bank with which it has been doing business for many years. These lock-in effects will be modeled by simply assuming that there is a fixed cost of switching banks. As is well known (see, e.g., Diamond 1971), switching costs give the bank a degree of monopoly power, even if the bank is not “large.”

There are other reasons why banks are monopolistic competitors. For locational reasons their services are not perfect substitutes. Differences in size and products and specialized knowledge also make them imperfect substitutes. Here, we focus on the lock-in effect.

The second essential imperfection arises from the fact that a bank’s customers have incomplete information about the services offered by a bank and the prices at which these services are offered, at the time when the relationship has begun. In fact, the smaller the bank is, the less likely it is that the bank’s reputation will be an adequate source of information about the quality and prices of the bank’s products.

A third important feature of this model is the fact that banks offer a variety of services. The simplest example of this is the case of a bank with a large number of branches. Since customers have different preferences over branch location, branches at different locations are offering different services. This means that a bank with many branches is offering a bundle of different services to their customers.

Location is not the only dimension along which banks differ, of course. They will offer different menus of accounts, or concentrate on different types of lending business; they will attract a different mix of retail or wholesale funds; they may diversify into non-bank products such as insurance or mutual funds. Location is a convenient metaphor for these different dimensions.

By exploiting these three features of the model, lock-in effects, limited information, and product diversity, we can reverse the usual presumption that greater concentra- tion leads to more efficient outcomes.

2. For other welfare analyses involving spatial competition and risk see Besanko and Thakor (1992) and Matutes and Vives (1996, 2000).

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The model. There is a finite set of locations indexed by l � 1,…,L and at each location there are two banking offices j � 1,2. Time is divided into an infinite number of discrete periods t � 1,2,…. There is a large number of individuals allo- cated exogenously to the different locations. Each period, these individuals have a demand for a unit of banking services which provides them with a surplus v. The value of banking services to individuals is distributed according to the distribution function F(v), that is, F(v) is the fraction of the population with valuation less than or equal to v.

At each date consumers are randomly assigned to a new location. They have an equal probability of arriving at any location and we assume that the number of locations is so large that the probability of returning to the same location is negligible and can be ignored. This is an extreme assumption, to be sure, but it serves to eliminate inconvenient and apparently unimportant complications. Each location is also assumed to receive a representative sample of the different types of individuals so, whatever the number of individuals at a given location, the distribution of types is F(v).

For simplicity, banks are assumed to have a zero marginal cost of providing banking services. Profit is thus identical to revenue.

At each date, the market is assumed to clear as follows. First, the individuals who have gathered at a particular location choose which bank to patronize. They do this before they know the price that the bank will charge. Next, the bank sets the price for its product (the interest rate on loans or deposit accounts, or the fees for other bank services). Finally, the consumers make one of three choices: to purchase the current bank’s services at the quoted price, to switch to the other bank, whose price by now has been fixed, or to do without the services of a bank. There is a fixed cost c � 0 of switching from one banking office to the other.

We consider two limiting cases of bank organization. In the first, which we call unitary banking, each bank has a single branch. In other words, each banking office represents an independent bank. In the second case, which we call branch banking, there are only two banks, each owning one branch in each location. That is, all the banking offices are organized into two large networks. Regardless of the form of organization, we use the term banking office to denote the smallest unit of the bank, whether it constitutes the entire bank or a branch of a larger bank network.

Individuals are assumed to observe only what happens at their own bank in each period and, since they move to a different location in each period, they have no knowledge of the previous behavior of the bank they are patronizing in the current period. The banks themselves are assumed to condition their behavior in each location on their experience at the same location. This maintains an informational symmetry between the unitary- and branch-banking forms of industrial organization, i.e., unitary banking and branch banking, since in each case only local information is being used to condition the (local) pricing decision.

Unitary banking. In this form of industrial organization each bank consists of a single office. A bank sets the price of its product in each period to maximize the present value of profits. In the static version of this model, it is well known that

FRANKLIN ALLEN AND DOUGLAS GALE : 471

the unique equilibrium involves each banking office choosing the monopoly price. More precisely, suppose that there is a unique price pM such that

pM(1 � F(pM)) ≥ p(1 � F(p)), ∀p .

A bank will clearly never want to charge more than pM. If the two banks at some location happen to charge prices p ≤ p′ ≤ pM , where p � pM, the bank charging p can always raise its price by � without losing any customers, because of the fixed cost of switching. This will clearly increase its profits, so the only equilibrium is for both banking offices to charge pM.

In a dynamic context things are generally more complicated, because of the possibility of supporting a different equilibrium by means of punishment strategies. Under the maintained assumptions, however, there is no possibility of using such strategies to increase the set of equilibria. Because individuals only observe what happens at their own locations and never return to the same location, nothing that a banking office does in the current period will be observed by individuals who will visit that location in the future. Further, since banks at other locations do not condition their behavior on what happens at this location, there is no possibility of the bank’s future customers being indirectly informed of a deviation through another bank’s reaction to this bank’s current deviation. Our informational assumptions have effectively severed any possible feedback from a current change in price to a future change in demand, so the argument used in the static model continues to apply. We conclude, then, that the unique, subgame perfect equilibrium of the unitary banking game consists of each bank, in each location, charging the monopoly price pM in every period. Consumers whose valuation v is greater than pM will purchase banking services; those whose valuation is less than pM will not. The equilibrium is inefficient for the usual reason: the monopoly price is too high and the monopoly quantity is too low.

Branch banking. Now suppose that banking offices are formed into two large networks. Each bank has one branch in each of the locations. Although consumers move from location to location they can stay with the same bank if they wish. The possibility of staying with the same bank generates a plethora of other equilibria. We describe one such equilibrium to illustrate the possibilities.

In each period, at each location, half the customers patronize each of the banks. Along the equilibrium path, the banks charge a price p � ε � 0 in every period. If, in any period, one of the banking offices has deviated from the equilibrium strategy, all the customers will leave the bank that last deviated and henceforth patronize branches belonging to the other bank. The banks continue to charge the same price. If no bank deviated, but some of the customers deviated in the past, the banks continue to charge the same price and the customers continue to patronize the two banks equally. The profits (for a single banking office) from deviating last one period and are less than or equal to (pM � ε)(1 � F(pM)); on the other hand, it loses the profits from these customers in each future period until they all disappear from the game. Customers only last a finite number of periods, but if the number of locations is large, they will be around on average for a very long time. Each period, a fraction

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l�1 of the customers dies and is replaced. So the equilibrium profits lost by a single banking office’s deviation are equal to [ε�(1 � l�1)(1 � δ)](1 � F(ε)). For δ suffi- ciently close to 1 and l sufficiently large, the profits from deviating are less than the profits of the equilibrium strategy. This shows that under branch banking, it is possible to support equilibria which are “more efficient” than the unique equilibrium in the case of unitary banking, where “more efficient” means that the sum of consumers’ and producers’ surplus is greater.

How should we interpret these results? The lock-in effects in the banking sector may be substantially greater than in most service industries and may be one of its distinguishing features. Small banks with a limited range of services and a limited geographical presence may have a greater incentive to exploit the lock-in effect than a large bank, because the large bank is always competing for the customer’s future business, in another product line or another location.

Empirical evidence. There is some empirical evidence in support of the view of competition presented above.3 Bordo, Rockoff, and Redish (1994) compare the Canadian and US banking systems from 1920 to 1980. During this period Canada had a few branch banks while the US had unit banking in many parts. It is found that the Canadian system outperformed the US system in a number of respects. First, in Canada the interest rates paid on deposits were generally higher and the income received by security holders was generally slightly higher than in the US. Second, the interest rates charged on loans were generally quite similar in the two countries. Finally, the returns on equity were generally higher in Canada. Taken together this evidence is consistent with the Canadian branch banking system being more competitive than the US unitary banking one.

Carlson and Mitchener (2003) also find evidence that branch banking is more competitive than unitary banking. Using data on national banks from the 1920s and 1930s, they compare states which just have unit banks with ones that have both branch banking and unitary banking. In the former states, many banks that charge high rates of interest on loans and pay low rates on deposits survive. However, in states with both types of banks this kind of local monopoly is eliminated. Both branch banks and unit banks price in exactly the same way.

3.2 Spatial Competition and Diversification

We next consider another model of spatial competition in the tradition of Hotelling. It is shown that competition can be consistent with diversification and hence stability. However, the precise way in which this works depends crucially on the particular assumptions made.

Let locations be denoted by n � 0, ±1, ±2,… and assume that there is a single bank at each location. Identical individuals are uniformly distributed on the real line. Each has one unit of a good that can be invested by the bank in a risky asset with return Rn. For simplicity assume that the returns Rn are i.i.d. with distribution

3. We are grateful to Stephen Haber for bringing this evidence to our attention.

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Rn � {R w.pr. π0 w.pr. 1 � π . In a symmetric equilibrium a bank will draw clients from the interval [n � 1�2, n � 1�2] and offer them a risk sharing contract that promises a payment D if the project is successful and nothing otherwise (the banker is risk neutral but has limited liability). The expected utility of the contract is πU(D) � (1 � π)U(0) � πU(D) if U(0) � 0 .

The bank chooses D to maximize profits taking as given the expected utility ū offered by the adjacent banks. Assuming linear transportation costs of one utility per unit distance, the marginal agent m � n who goes to bank n is determined by the condition that

πU(D) � (n � m) � ū � (m � n � 1)

or

2(n � m) � πU(D) � ū � 1 .

The profit per agent is π(R – D) so total profits are

π(R � D)2(n � m) � π(R � D)(πU(D) � ū � 1) .

The first-order condition is

πU′(D) � 1

R � D .

If we allow a bank to occupy several locations, it can pool several independent risks, which allows it to offer better risk sharing to its customers. However, it may face less competition. Whether it does depends on precisely which locations a bank is allowed to occupy. For example, if two banks occupy alternate locations, we get improved risk sharing with no loss of competition. Here there is no trade-off between competition and stability. If a bank occupies a sequence of adjacent loca- tions, it can extract a large amount of surplus from the consumers located in the middle. Here there is a trade-off between competition and stability.

4. SCHUMPETERIAN COMPETITION

Technological innovation is one of the major sources of growth in welfare. As Schumpeter (1950) famously pointed out, perfect competition undermines the incen- tive to innovate (when intellectual property rights are weak) and in that sense imperfect competition may be more “efficient” than perfect competition. Similar ideas apply in the financial sector. If banks innovate they may be able to capture the market and drive other banks out of business. Thus Schumpeterian competition may be associated with financial instability (creative destruction). We start by considering

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a “winner-takes-all” model of competition based on the work of Allen and Gale (2000c) which has this feature.

4.1 Winner-Takes-All Competition

There is a finite number of locations i � 1,…, n with a bank at each location. There are two dates, t � 0,1. At date 0, each bank i invests ki ≥ 0 in the development of a product. The banks’ opportunity cost of capital is R. The value of the product developed by bank i is given by Vi(ki, ω). There is symmetric information all agents know the function Vi(·) and the investment ki and have the same continuous probability distribution F(·) over the states of nature ω. We assume that V(0, ω ) � 0 for all ω so capital is essential to the development of a useful product. In general, the more the capital that is provided the greater is the probability that the value of the product is high. We assume that once the new product is developed it can be produced at constant marginal cost and, without essential loss of generality, we set the marginal cost equal to zero.

At date 1 identical consumers are uniformly distributed on the line interval [0, n]. Each consumer wants to consume at most one unit of a new product.

4.2 Equilibrium

At date 0 the banks jointly choose their investment strategies k � (k1,…, kn). At the beginning of date 1, the state of nature ω is realized and the banks observe the quality of the product they have developed

V(k, ω ) � V1(k1, ω ),…, Vn(kn, ω )) .

Then the banks engage in Bertrand competition. Since the qualities are assumed to be continuously distributed (for ki � 0) the probability of ties can be ignored. Then Bertrand competition will lead to an outcome in which the best product captures the entire market and the price charged for this product is equal to the difference between the value of the first- and second-best products. The price for every other product is zero. At this price, consumers are indifferent between the first- and second- best products, but they will demand only the first-best product in equilibrium (if a positive fraction of consumers were expected to choose the second-best product, the firm with the first-best product would have chosen a slightly lower price to capture the entire market). Formally, for any bank i let

V�i(k�i, ω ) � (V1(k1, ω ), …,Vi�1(ki�1, ω ),Vi�1(ki�1, ω ), …,Vn(kn, ω ))

denote the vector of the qualities of products j ≠ i; let

k�i � (k1, …, ki�1, ki�1, …, kn)

denote the allocation of investment in products j ≠ i; and let

V*�i(k�i, ω ) � maxj≠i{Vj(kj, ω )}

denote the highest value in the vector V–i(k–i, ω). Then, in the second-period equilib- rium, the price charged for the ith product is denoted by pi(k, ω) and satisfies

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pi(k, ω ) � max{Vi(k, ω ) � V * �i(k�i, ω),0}, ∀i .

Since the demand is equal to one for the best product and zero for the rest, the revenue of bank i is also equal to pi(k, ω).

At the first date, we look for a Nash equilibrium in the investment levels. The ith bank chooses ki to maximize E[pi(ki, k�i, ω )] � Rki , taking as given the invest- ment levels of the other banks, k–i. So a Nash equilibrium is a vector k

* such that

k*i �arg maxki≥0{E[pi(ki, k * �i, ω )] � Rki}

for each i.

4.3 Optimum

Since the cost of production at date 1 is zero, the surplus generated by consuming the ith product is Vi(ki, ω). Surplus is maximized by having all consumers consume the best product, so the total surplus at date 1 is

V*(k, ω ) ≡ maxi�1,…,n{Vi(ki, ω )} .

Assuming that the consumers are also risk neutral and that lump sum transfers are possible, the first-best efficient allocation is found by maximizing net surplus, that is, by solving the planner’s problem

maxk≥0V *(k) � R�

n

i�1 ki ,

where V*(k) ≡ E[V*(k, ω )] is the expected value of V*(k, ω). Define V*�i(k�i) ≡ E[V

* �i(k�i, ω )]. Then the objective function V

*(k) can be written equivalently as

V*(k) � E[max{Vi(ki, ω ) � V * �i(k�i, ω ), 0} � V

* �i(k�i, ω )]

� E[max{Vi(ki, ω ) � V * �i(k�i, ω ),0}] � V

* �i(k�i)

and the planner’s problem can be rewritten equivalently as

maxk≥0E[max{Vi(ki, ω ) � V * �i(k�i, ω ), 0} � V

* �i(k�i) � R�

n

i�1 ki

Suppose that k* is a solution to the planner’s problem above. A necessary condition is that ki

* maximizes

E[max{Vi(ki, ω ) � V * �i(k

* �i, ω ), 0}] � Rki � E[pi(ki, k

* �i, ω )] � Rki .

But this means that ki * satisfies the equilibrium condition for the ith bank’s choice

of ki. Hence, we have the following result. Proposition 6: If k* is a solution to the planner’s problem, then k* is a Nash

equilibrium of the banks’ investment “game.” In this case the allocation produced by the winner-takes-all competition is efficient.

The innovating banks have exactly the right incentives to invest. However, only

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one bank survives in each period. In other words there is considerable financial instability. In order to prevent this instability the government may wish to restrict competition and give each bank a monopoly in a particular region. We turn next to this restricted competition.

4.4 Restricted Competition

Next assume that the bank located at n has a legal monopoly of the region [i � 1�2, i � 1�2]. Then it maximizes E[V(ki,ω ) � Rki]. Clearly, surplus must be lower, both because we are not providing the best product to all consumers but also because we are not using the right investment level.

Will investment be too high or too low? Examples can be constructed to show that the answer is ambiguous. On the one hand losers are protected under the “competitive” arrangement. On the other hand winners suffer because they cannot capture the entire market.

To see this consider the following simple example. There is a continuum of identical consumers with measure one and two banks. Each bank can invest 0 or 0 � I � 1�2. If a bank invests zero the value of its good is zero; if it invests I the value of its good is 1. If the market is unified, there are three equilibria, two asymmetric equilibria in which only one bank invests and a mixed strategy equilib- rium in which the probability of investment is λ � 1 � I. Now suppose we divide the market between the two banks, each getting 1/2 of the market. Then each bank will invest for sure, since I � 1�2.

Comparing equilibria, we see that total investment is greater in the divided market. Also, comparing the unique pure strategy equilibrium of the divided market with the mixed strategy of the unified market, we can see that (the probability of) innovation is strictly higher in the divided market. In the mixed strategy equilibria of the divided market the probability of innovation is the same.

For the case I � 1�2 there will be no investment, and hence no innovation, in the segmented market. In this case investment and innovation are unambiguously lower than in winner-takes-all case.

These examples are enough to indicate that even in simple models it is hard to obtain robust comparative static results. As with the other models we have examined, the relationship between competition and stability is complex and nuanced.

5. CONTAGION

One source of instability in financial systems is the possibility of contagion, in which a small shock that initially affects one region or sector or perhaps even a few institutions, spreads from bank to bank throughout the rest of the system, and then affects the entire economy. There are a number of different types of contagion that have been suggested in the literature. The first is contagion through interlinkages between banks and financial institutions (see, e.g., Rochet and Tirole, 1996a, 1996b, Freixas and Parigi, 1998, Freixas, Parigi, and Rochet, 2000, and Allen and Gale,

FRANKLIN ALLEN AND DOUGLAS GALE : 477

2000b, for theoretical analyses and Van Rijckeghem and Weder, 2000, for empirical evidence). The second is contagion of currency crises (see, e.g., Masson, 1999, Eichengreen, Rose, and Wyplocz, 1996, and Glick and Rose, 1999). The third is contagion through financial markets (see, e.g., King and Wadwhani, 1990, Kyle and Xiong, 2001, and Kodres and Pritsker, 2002).

The notion of financial fragility is closely related to that of contagion. When a financial system is fragile a small shock can have a big effect. A financial crisis may rage out of control and bring down the entire economic edifice (see, e.g., Kiyotaki and Moore, 1997, Chari and Kehoe, 2000, Lagunoff and Schreft, 2001, and Allen and Gale, 2003b).

In this section we are interested in the relationship between contagion and financial fragility and competition. Allen and Gale (2000b) develop a model of financial contagion through the interbank market. They assumed perfect competition. In that case a small aggregate shock in liquidity demand in a particular region can lead to systemic risk. Although the shock is small it may cause a bank to go bankrupt and liquidate its assets. This in turn causes other banks which have deposits in it to also go bankrupt and so on. Eventually all banks are forced to liquidate their assets at a considerable loss. Perfect competition in the interbank market plays an important role in this contagion. Since each bank is small, acts as a price taker and assumes its actions have no effect on the equilibrium, no bank has an incentive to provide liquidity to the troubled bank.

Saez and Shi (2004) have argued that if banks are limited in number they may have an incentive to act strategically and provide liquidity to the bank that had the original problem. This will prevent the contagion and make the banks providing funds in this way better off. The formal model of this phenomenon that captures the role of imperfect competition is similar to the model in Bagnoli and Lipman (1989). They consider the problem of the provision of public goods through private contributions. There is a critical level of resources required to provide the public good. Each person becomes pivotal and in this case the public good can be provided. Similarly, here each bank would be pivotal in the provision of liquidity to pre- vent contagion.

Suppose there are two banks, each holding A units of an illiquid asset and M units of money. One unit of the asset is worth one unit if liquidated today and R units if liquidated tomorrow. Money is storable, so one unit of money today is worth one unit tomorrow. Each bank owes one unit of money to a depositor who withdraws today and the liquidation value of the portfolio to a depositor who withdraws tomorrow. In addition, bank 1 owes bank 2 B units today. Suppose that bank 1 has λ early consumers and 1 � λ late consumers. If λ � B � M � A � ((1 � λ)�R), then bank 1 cannot meet its commitments on its own. If it fails, however, bank 2 can claim only a fraction (B�(1 � B))(M � A) � B. Bank 2 has several actions it can take. It can forgive part of its debt. It can wait for payment at date 2. It can make a cash transfer to bank 1 at date 1. If R is big enough, this may be better for bank 2 than enforcing its debt now.

478 : MONEY, CREDIT, AND BANKING

The same argument obviously applies to any number of banks, though the coordi- nation problem will become more severe as the numbers increase. If there is a continuum of banks or asymmetric information, it may be impossible to sustain the cooperative equilibrium. Note that even in the case with a finite number of banks there may be a coordination failure; if every bank thinks the others will not contribute anything, it may be optimal not to contribute anything (this may depend on the extensive form, e.g., simultaneous moves rather than sequential moves).

Gradstein, Nitzan, and Slutsky (1993) show that Bagnoli and Lipman’s result is not very robust to the introduction of uncertainty. It remains to be seen whether a model of contagion and imperfectly competitive banks would also not be robust.

A model of an imperfectly competitive interbank market may therefore be more stable than the case where there is perfect competition. As in the original agency model of Keeley (1990) there is again a trade-off between competition and finan- cial stability.

6. CONCLUDING REMARKS

In this paper we have considered a variety of different models of competition and financial stability. These include general equilibrium models of financial interme- diaries and markets, agency models, models of spatial competition, Schumpeterian competition, and contagion. There is a very wide range of possibilities concerning the relationship between competition and financial stability. In some situations there is a trade-off as is conventionally supposed but in others there is not. For example, with general equilibrium and Schumpeterian models efficiency requires the combination of perfect competition and financial instability.

There is a large policy literature based on the conventional view that there is a trade-off between competition and stability. Since competition is generally viewed as being desirable because it leads to allocational efficiency, this perceived trade-off lead to calls for increased regulation of the banking sector to ensure the coexistence of competition and financial stability. The most popular instrument for achieving this end was the imposition of minimum capital requirements on banks. If the owners of banks were forced to put up significant amounts of capital, they would be unwilling to take risks because they would again stand to loose large amounts of funds. The Basel Agreement of 1988 imposed capital controls on banks so that the incentive to take risks would be reduced and they could compete on equal terms. A large literature has developed analyzing the effect of capital controls. For example, Hellman, Mur- dock, and Stiglitz (2000) showed in the context of a simple model of moral hazard that capital controls were not sufficient. In addition to capital controls, deposit rate controls were also necessary to achieve Pareto efficiency.

Our analysis suggests that the issue of regulation and its effect on competition and financial stability is complex and multi-faceted. Careful consideration of all the factors at work both at a theoretical and empirical level is required for sound policy.

FRANKLIN ALLEN AND DOUGLAS GALE : 479

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Branch Banking, Bank Competition, and Financial Stability.pdf

Branch Banking, Bank Competition, and Financial Stability Author(s): Mark Carlson and Kris James Mitchener Source: Journal of Money, Credit and Banking, Vol. 38, No. 5 (Aug., 2006), pp. 1293-1328 Published by: Ohio State University Press Stable URL: http://www.jstor.org/stable/3839007 . Accessed: 13/02/2015 16:11

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MARK CARLSON

KRIS JAMES MITCHENER

Branch Banking, Bank Competition, and

Financial Stability

It is often argued that branching stabilizes banking systems by facilitating diversification of bank portfolios; however, previous empirical research on the Great Depression offers mixed support for this view. Using data on national banks from the 1920s and 1930s, we show that branch banking raises the level of competition and increases exit from the banking system. This consolidation strengthens the system as a whole without necessaiiLly strengthening the branch banks themselves. Our empirical results suggest that the effects that branching had on competition were quantitatively more important than geographical diversification for bank stability in the 1920s and 1930s.

JEL codes: G21, N22, E44 Keywords: branch banking, bank consolidation, financial

stability, Great Depression.

ONE OF THE FOUNDATIONS of the theoretical literature on banking regulation is that branch banking leads to more stable banking systems by enabling banks to better diversify their assets and widen their depositor base (Gart, 1994, Hubbard, 1994). This conventional wisdom has been used to argue that historical banking crises in the United States, especially those ofthe 1930s, wouldhave been less severe had the U.S. permitted widespread branch banking (Friedman and Schwartz, 1963, Calomiris, 2000). The empirical literature examining U.S. banking instability during the Great Depression, however, has not universally confirmed this

We thank Waiyi Poon for valuable research assistance and Joe Mason, Bill Sundstrom, David Wheelock, Eugene White, and conference and seminar participants at the AEA Annual Meetings, the Economic History Society Annual Meetings, NBER-DAE, and Yale University for comments and suggestions. The views presented in this paper are solely those of the authors and do not necessarily represent those of the Federal Reserve System or its staff.

MARK CARLSON is an Economist at the Federal Reserve Board (E-mail: Mark.A. [email protected]). KRIS JAMES MITCHENER is an Assistant Professor from the Department of Economics at Santa Clara University and Faculty Research Fellow NBER (E-mail: kmitchener@ scu.edu).

Received December 16, 2003; and accepted in revised form April 3, 2005.

Journal of Money, Credit, and Banking, Vol. 38, No. 5 (August 2006) Copyright 2006 by The Ohio State University

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1294 : MONEY, CREDIT, AND BANKING

prediction. In fact, this research presents a paradox. Studies using aggregate bank failure data from the Depression find that states that allowed branch banking had lower failure rates than those that only allowed unit (or single-office) banking (Wheelock, 1995, Mitchener, 2000, 2005); although the result is consistent with the diversification hypothesis, these studies do not test the precise channel through which branching reduced failures. In contrast, studies using individual bank data from the same period cast doubt on the common view that the stabilizing benefits of branching operated via increased diversification opportunities. Calomiris and Mason (2000) and Carlson (2004) find that banks with branches were more likely to fail than unit banks, in part because they pursued strategies to reduce reserves rather than to diversify their portfolios.

In this paper, we resolve this empirical puzzle by focusing on an additional channel through which branch banking could affect financial stability: increased competition. Our hypothesis is that, faced with heightened competition, banks that are only marginally profitable are forced out of the banking system either through merger or voluntary liquidation. As these weaker banks close, the overall stability of a state's banking system improves through consolidation. Thus, in the 1920s and 1930s, states allowing branch banking experienced lower failure rates without the branch banks themselves necessarily being the strongest banks.

Our hypothesis draws on the theoretical and empirical literature that examines the removal of legal restrictions on competition, and then links it to the literature on bank failures. Although policymakers often debate whether there are tradeoffs between competition and stability, surprisingly little theoretical or empirical research has analyzed these linkages in depth (Allen and Gale 2000).l Consistent with the hypothesis posited here, Berger and Hannan (1998) find that banks not exposed to competition are able to exercise monopoly power and tend to be less efficient than banks subject to more competition. When laws restricting competition are relaxed, bank profits generally decline. This has been found both within the United States (Amel and Liang 1997) and internationally (Claessens, Demirguc-Kunt, and Huizinga, 2001, Levine, 1996). Moreover, the increase in competition resulting from the removal of branching restrictions has been linked to the weeding out of weak banks (Jayaratne and Strahan, 1998, Stiroh and Strahan, 2003). We similarly argue that the expansion of branching in the 1920s facilitated an increase in competition. To help clarify the theoretical debate over the effects of competition on financial stability, we directly test how the growth of branching influenced bank competition and how this in turn affected bank failures.

Since our hypothesis emphasizes changes in the competitive environment induced by the onset of branch banking, it is necessary to test our model using data from a period when branch banking was expanding in scope. Moreover, because we want

1. "On the one hand, there are many models of competition in the literature including models of bank regulation in a competitive environment. On the other hand, there is a well-developed literature on bank crises... But there is little on the impact of competition on stability." (Allen and Gale, 2000, p. 268). Important exceptions are Koskela and Stenbacka (2000) and Matutes and Vives (2000); however, this newer theoretical literature presents conflicting views on how competition affects financial stability.

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MARK CARLSON AND KRIS JAMES MITCHENER : 1295

uZ

m o

z

1900 1905 1910 1915 1920 1925 1930 Year

FIG. 1. Branches of U.S. Banks. Source: Federal Reserve Board of Governors (1931), Vol. 2. (Note: Home city indicates branches located in the same city as the bank's headquarters.)

to test how branching influences the stability of banking systems, we also need to examine a period when there were numerous failures. In this respect, the experience of the U.S. banking system from 1920 to 1930 is ideal since branching was expanding rapidly (Figure 1), and because the 1920s were characterized by a large number of bank failures (Figure 2).2 Finally, examining this period allows us to compare our

1 600

1400 * All Banks

e 1200 @ National Banks >

._ 1 000

L * L

O 800 | |

200 LIL LILILILILILII 1922 1923 1924 1925 1926 1927 1928 1929 1930

Year

FIG. 2. Bank Failures in the United States. Source: Federal Reserve Board of Governors (1943).

2. Chapman and Westerfield (1942) describe how the issue of branch banking gained national attention during the 1920s, in part because it was spreading rapidly in states such as California and prompting federal regulators to reconsider their longstanding prohibitions against it.

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1296 : MONEY, CREDIT, AND BANKING

results to existing research on the Great Depression and resolve the paradox that currently exists in the literature.

Our hypothesis has several testable propositions. First, the expansion of branching should change the competitive environment. If branch banking removes weaker banks from a banking system, then states permitting branch banking should experi- ence higher merger and voluntary liquidation rates and lower entry rates (by new banks) than states prohibiting it. Second, over time more competition in states permitting branch banking should result in lower profit levels. Finally, if the competitive shakeout induced by branching stabilizes banking systems by removing weak banks from the system, then in the long run failure rates should be lower in states where branch banking was expanding. The link between branching, competition, and stability ought to be present even after controlling for any benefits to stability coming from improved geographical diversification of bank portfolios. We draw on the bifurcated nature of the dual banking system that existed in the 1920s to design a statistical test to discriminate between the effects of geographical diversification and competition due to branching.

Our empirical results support the predictions of the competition hypothesis. States in which branching was more prevalent experienced more mergers and voluntary liquidations during the 1920s. We also find that although there was significant consolidation in the banking sector in states allowing widespread branch banking, profits were lower on average in these states, suggesting that branching led to increased competition rather than monopoly power. To test whether branching re- duced failures, we first confirm that our data produce the usual state-level result that states allowing branching or those with more branch offices had lower failure rates. We then construct proxies for the portfolio diversification and competition channels of branching and test whether their inclusion affects this result. Our econo- metric evidence shows that, at least for national banks, the consolidation effects were quantitatively more important than increased portfolio diversification opportunities for banking stability during this period. These results suggest that, at the onset of the Great Depression, there were still many weak banks in states prohibiting branch banking; the real shock of the 1930s caused many of these to fail. However, in states that permitted branching, weak banks had been pruned from the system, and failures were consequently lower at the system-wide level. Thus, we resolve the paradox in the existing literature by showing that the expansion of branching improved stability at the statewide level through the competitive shakeout process without necessarily improving individual banks' ability to diversify away risk during a large shock such as the Great Depression.

The paper proceeds as follows. Section 1 discusses the previous literature on branching and financial stability. In Section 2, we present our hypothesis for resolv- ing the existing puzzle in the literature. The next section tests the consolidation hypothesis and some of its implications for bank competition and financial stability. Section 4 provides concluding remarks.

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MARK CARLSON AND KRIS JAMES MITCHENER : 1297

1. THE EFFECTS OF BRANCH BANKING ON FINANCIAL STABILITY

An argument commonly articulated in the literature is that branch banking stabi- lizes banking systems by reducing their vulnerability to local economic shocks: branching enables banks to diversify their loans and deposits over a wider geo- graphical area or customer base.3 Restnctions on branching have been linked to the instability of banking systems. Calomins (2000) argues that bank failures were more prevalent in regions of the United States without branch banking as well as in countnes lacking it. Fnedman and Schwartz (1963) suggest that the absence of branching in the U.S. increased the seventy of the banking panics dunng the Great Depression. Moreover, they argue that the U.S. experience stands in contrast to Canada, which expenenced banking distress dunng the Depression but not wide- spread failures and a collapse of its banking system.4 The notion that branch banking stabilizes banking systems by increasing diversification opportunities is in fact an argument with old roots (Sprague 1903). In the 1920s, proponents of branch banking used this argument to encourage state legislatures to adopt laws legalizing branch banking (Preston, 1928, Southworth, 1928).

Research examining the effects of branching at the aggregate level generally supports the hypothesis that allowing branch banking increases systemic stability. Wheelock (1995) studies the eiTects of different state banking regulations on bank failures dunng the Depression and finds that states that allowed branch banking tended to have lower failure rates. Mitchener (2000, 2005) further examines state- and county-level bank failure rates. Controlling for economic fundamentals and differences in both state supervision and regulation, he also finds that states with legalized branching had lower failure rates dunng the Depression. Comparing 25 diiTerent countnes dunng the Great Depression, Grossman (1994) finds that countnes with large branching networks were less likely to expenence banking cnses.5 A1- though the studies that rely on aggregate data find a positive correlation between branch banking and financial stability, they do not establish the precise channel through which branching improved stability.6 It is therefore possible that the stability effects of branching are related to something besides or in addition to diversification.

3. Studies by Wacht (1968) and Lauch and Murphy (1970) find reduced variance in deposit flows for branch banks. Cherin and Melicher (1988) find that branching has moderating effects on asset returns.

4. Drummond (1991) and White (1983) make a similar argument. Kryzanowski and Roberts (1993), however, find that nationwide branch banking did not prevent banks in Canada from becoming techni- cally insolvent.

5. Many of these studies also include some measure of bank concentration as an explanatory variable. Although none of them provides an interaction term for branch banking and their measure of concentration, it is possible that these two effects worked together to influence failure rates. In Section 3, we relate our consolidation hypothesis to the issue of banking concentration. Our emphasis, however, differs in that we are testing whether changes in bank concentration are an outcome of branching laws and whether this in turn affected bank profitability.

6. Wheelock (1995) attributes the positive correlation between restrictions on branch banking and failure rates to limited diversification. Similarly Alston, Grove, and Wheelock (1994) also consider the impact of branching legislation on bank failures, and emphasize that branching may reduce a bank's susceptibility to distress in a particular area; however, they do not find that the ratio of (non-home- office) branches to total banks helps to explain the cross-state variation in failure rates during the 1920s.

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1298 : MONEY, CREDIT, AND BANKING

Studies using data on individual banks operating in the 1920s and 1930s paint a diiTerent picture of the effects that branching has on the survivorship of individual banks, and they cast doubt on the common view that the stabilizing benefits of branching operated via increased diversification opportunities. Calomins and Mason (2000) find that, dunng the Depression, Federal Reserve members that were branch banks tended to fail sooner than unit banks. Also, using data on individual state banks from this period, Carlson (2004) examines three states where branch banking was relatively widespread and finds that branched banks were more likely to fail than unit banks. Furthermore, he rejects some potential reasons for this phenomenon, including insufficient diversification and over-expansion on the part of banks. Instead, he finds that branch banks used diversification to reduce their reserves rather than to lower the risk of their portfolios a strategy that worked poorly during the global shock of the Great Depression.

Because it is difficult to reconcile the findings based on aggregate data (which report a negative relationship between bank failures and statewide branch banking) with the empirical results from studies using individual bank data (which are inconsis- tent with the view that the source of stability was improved opportunities for diversification), this article proposes an additional channel through which branching could have affected stability: competition. Although they do not construct a formal model, Berger, Demsetz, and Strahan (1999) argue that increased competition results in the purging of ineEcient banks from the banking system. Consistent with these ideas, Jayaratne and Strahan (1998) and Stiroh and Strahan (2003) find that the branch-banking reform that began in the United States in the 1980s resulted in the removal of weaker banks from the system. Additionally, Koskela and Stenbacka (2000) suggest that greater competition decreases interest rates and increases the likelihood that borrowers are able to remain solvent and repay their loans. These stud- ies suggest that the introduction of competition (in our case, driven by the growth of branch banking) may improve the stability of banking systems. On the other hand, Matutes and Vives (2000) argue that raising the level of competition causes an increase in failures as lower profits resulting from competition encourage banks to take on more risk.7 We are not aware of any previous studies that systematically test the effects of competition on the stability of banking systems in particular.8

2. BRANCH BANKING AND COMPETITION IN THE 1920S

As Figure 1 shows, the total number of branches operated in the United States nearly tripled between 1920 and 1930, rising from 1281 to 3518. Many of these branches were located in home-oice cities and the number of these branches

7. Demsetz and Strahan (1997) find that consolidation for bank holding companies (BHCs) enhanced diversification following regulatory reform in 1994, but that larger BHCs then operated with lower capital ratios and increased their risky lending.

8. Kaminsky and Schmukler (2002), however, compare broad financial systems of different countries between the early 1970s and the late l990s and find that, although reducing barriers to external competition initially results in some turmoil, the long run effect of deregulation is increased stability.

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MARK CARLSON AND KRIS JAMES MITCHENER : 1299

more than doubled, increasing from 508 in 1920 to 1131 in 1930 (Federal Reserve 1931, Vol. 2). We hypothesize that this expansion of branching networks increased the level of competition in states that allowed branching to occur. As a result of this dynamic process, banks that were only marginally profitable prior to the increase in competition would become unprofitable due to the increase in competition. In turn, these banks would likely merge with existing banks or voluntarily liquidate.9 Also, because it is less costly to open a branch than a new bank, it is likely that fewer new banks would be able to find an unexploited profitable niche and enter the market despite the fact that regulatory barriers to entry have been removed. With the exit of the weakest banks, the economic viability of the average bank would increase and the rate of failure for banks within that state would decline.l° The idea that the removal of bamers to competition would lead to a reduction in the number of banks in the banking system is consistent with the model by Economides, Hubbard, and Palia (1996).

Why should these competitive forces apply to the introduction of new branches (as a result of legal changes) and not simply to the emergence of new unit banks in the 1920s? First, banks with branches were more cost-effective, since some jobs at difFerent branches could be consolidated and performed at the head office, thus reducing employment costs (Federal Reserve 1931, Vol. 2, p. 224.) Also, start-up costs were lower, and in some states, regulators required less capital for new branches than for new unit banks (Southworth 1928). Second, new branches that were set up in previously restricted markets may have been more adept at realizing higher rates of return than comparable new unit banks since branches could transfer deposits out of the local market to regions where capital was in higher demand.ll The ability to obtain a cost advantage through branching and realize higher rates of return made entry into existing local markets easier for branch banks than new unit banks. Indeed, branch banking may have been instrumental in bringing banking and banking competition to small towns. Calomiris (2000, Chapter 1) makes a similar argument. In 1931 (the only year for which we have so far been able to locate the distribution of branches by town size), nearly half of all branches outside the home-office city were located in towns of less than 2500 people (Table 1). Figure 3 shows the locations of branches outside the home-office city.

Laws that permitted statewide branching applied only to state-chartered banks (which were regulated by state banking departments) while the data used below to test this hypothesis are for national banks (which were regulated by the C)ffice of the

9. Wheelock and Wilson (2000) find that, during the 1980s and 1990s, inefficiency reduced the likelihood that a bank would be acquired Carlson (2004), however, finds that during the early 1930s, acquired banks were generally weaker than other banks.

10. By limiting the development of secondary markets, entry barriers such as restrictions on branching could also prevent productive assets of weak banks from being digested or taken over by more efficient banks. Without the existence of local competition to absorb bank assets, weak banks may have been forced to sell productive assets in thin markets at fire sale prices or not at all, in turn increasing the likelihood of bank failures within the system.

11. Morgan, Rime, and Strahan (2003) provide another channel through which diversified banks improve stability shifting capital between regions to dampen economic shocks.

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TABLE 1

THE DISTRIBUTION OF BANK BRANCHES BY TOWN POPULATION (1931)

Branches Outside Branches Inside All Branches Home-Office City Home-Office City

Town Population Number Percent of Total Number Percent of Total Number Percent of Total

Under 500 189 16.32 2 0.09 191 5.73 500-1000 173 14.94 0 0.00 173 5.19 1000-2500 207 17.88 7 0.32 214 6.42 2500-5000 134 11.57 7 0.32 141 4.23 5000-10,000 107 9.24 9 0.41 116 3.48 10,000-25,000 91 7.86 27 1.24 118 3.54 25,000-50,000 46 3.97 63 2.90 109 3.27 50,000-100,000 60 5.18 132 6.07 192 5.76 100,000+ 151 13.04 1929 88.65 2080 62.39 Total 1158 100 2176 100 3334 100

Source: Federal Reserve Board (1931), Vol. 2.

---*- v@E

1300 : MONEY, CREDIT, AND BANKING

Comptroller of the Currency). The logic of our hypothesis, however, still applies to national banks since they would be subject to increased competition from the state banks that were allowed to establish branches. 12 Indeed, it may actually be better

otet }t "N

vY lAg6 e

WTAXW<" - azs : i - r4} YK o - Y o

FIG. 3. Branches of National and State Banks outside the City of the Home Office (December 31, 1931) Source: Federal Reserve Board of Governors (1931, Vol. 2, p. 17). (Note: In California there are numerous branches in the metropolitan areas centering around San Francisco and Los Angeles, but technically outside their city limits. On the map the dots extend considerably beyond the territory in which the branches are actually located around these cities.)

12. Additionally, state banks that converted into national banks were permitted to keep branches they had established while they were state banks, enabling branch banks to become national banks through a legal technicality.

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MARK CARLSON AND KRIS JAMES MITCHENER : 1301

for testing the competition hypothesis since the vast majority of national banks had no branches outside the home-office city over our period and therefore enjoyed no effective geographical diversification benefits. Excluding California, the country's nearly 8,000 nationally chartered banks operated 18 branches outside the home- office city in 1925. In 1930 this number was 27 (Federal Reserve Board of Governors 1943). Since our sample consists of national banks, and only a few of them branched outside the headquarter city, any observed or unobserved channels through which branching might affect both competition and diversification are likely to be less of a problem. Finally, using a sample consisting of national banks has the additional advantage that the regulatory environment and the attitudes of regulators regarding bank mergers were more uniform than those concerning state banks.

It has been shown by Calomiris and Mason (2000), among others, that less profitable banks were more likely to fail during the Great Depression. A key compo- nent of our hypothesis is that, in the long run, fewer of these banks would exist in states that allowed branch banking because the competitive pressures associated with the rise of branch banking networks prior to the Depression would have forced weak banks to exit from the banking system earlier in the decade. In states without the competitive pressures of branch banking, more weak banks would still exist at the start of the Depression and these states would therefore have more banks that would be likely to fail during the subsequent downturn. This interpretation is consistent with the findings in Mitchener (2000, 2005) and Wheelock (l99S): states allowing branch banking had lower failure rates than those prohibiting it. And it would also be true even though it was not necessarily the case that branch banks were the survivors.

The alternative hypothesis, that a banking system with branching is more diversi- fied and therefore more stable than a banking system with only unit banks, presup- poses that the two systems are in equilibrium. The banking system of the United States during the 1920s and 1930s, however, was in the process of transition. As noted above, branching was expanding rapidly in some states and the total number of banks was declining steadily from 29,715 in June 1920 to 23,855 by June 1930 (Federal Reserve 1943). The growth in branch banking during the 1920s was facilitated by a variety of legal and technological changes. In 1922, the Comptroller ruled that national banks could, "under the law, establish agencies, teller windows, or additional offices within the city of the parent bank provided state banks were permitted to operate branches in that state (Chapman and Westerfield, 1942, p. 97)," although these offices could not issue loans and were not full-fledged branches. Possibly seeing these offices as one step away from approval by the Comptroller of full-fledged branch banking for national banks, state banks may have responded by increasing their branching networks in order to compete with national banks. Relationships with correspondents were weakened due to amendments of the Federal Reserve Act in 1917 (which put check clearing in the hands of the Federal Reserve and required national banks to hold their entire reserve requirement at the Federal Reserve), possibly inducing banks to pursue the loss of deposits by buying banks and converting them to branches. Dramatic improvements in road networks and

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1302 : MONEY, CREDIT, AND BANKING

improvements in telephone networks likely improved the ability of managers to oversee branch networks. And rising urbanized populations in the newer cities of Los Angeles and Detroit (both augmented by the rise of the automobile, the former via the conversion of rails to roads and the latter which served as the industry's manufacturing hub) also led to an increase in demand for banking services. Both cities realized dramatic increases in the number of branch banking oices in the 1920s. It is also likely that more banks were willing to develop branch networks during the 1920s as they observed the success of branch banks in places such as California.l3 It is this structural change in the banking system wrought by these factors that motivates our hypothesis. The key role played by this change in the banking system suggests that our explanation is specific to the United States and may not apply to other economies during the 1920s, such as Canada and the United Kingdom, which had removed barriers to branching earlier and likely had completed the transition to a branch banking system. That is, as branching expanded rapidly in some U.S. states in the 1920s shakeout took place, initially causing exit and later reducing bank failures.

Because the growth in branching is attributable largely to shifts in the relationships of banks with each other, technological progress, and population/economic growth rather than changes in regulation specifically concerning branching, we use changes in actual branching activity over time, and across states, rather than changes in branching laws, to examine the effect of branching on a state's banking system. We argue that using actual branches provides a more complete picture of the effect that branching might have on the competitive environment because the laws regulating the establishment of branches varied substantially so that variables categorizing regu- lations capture quite different situations.l4

One notable change in the legal environment in the 1920s was the McFadden Act of 1927, which allowed national banks to establish local branches in the city of their home office if state law allowed branching. However, the Act imposed several restrictions: national banks could open no new branches in cities with fewer than 25,000 people, only two branches in cities with populations between 50,000 and 100,000, and at the discretion of the Office of the Comptroller of the Currency for cities of over 100,000 (Tippetts 1929).

It should be noted that the expansion of branching (and consequently the consolida- tion of the banking system) in the 1920s, which was driven by the establishment of the Federal Reserve, technological changes, population growth, and economic

13. California's branching network developed more quickly and extensively than any other state in the decade, in part due the financial entrepreneur, A.P. Giannini, who created an extensive branching network for the Bank of America. This spurred competing large banks in California to develop branching networks, especially in Los Angeles, to fight Bank of America's geographical expansion. Given the size of Bank of America's branching network in California, a change in its charter in 1927 (from state to national) could have a large impact on the results in our paper, so later we test whether our results on stability are sensitive to its inclusion.

14. For example: Massachusetts allowed trust companies to have one branch in the same city as the home office; New York allowed unlimited branching in the city of the bank's home office if the population of the city exceeded 50,000; Louisiana allowed banks to have up to two branches, which could be located in the parish of the home office; and California allowed statewide branching.

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MARK CARLSON AND KRIS JAMES MITCHENER : 1303

growth, is quite different from expansion of branching in the 1980s and 1990s, which appears to have been more strongly influenced by changes in regulation (Stiroh and Strahan, 2003, Kroszner and Strahan, 1998). Thus, focusing on a measure of branching activity is likely to better capture the effect of branching on the banking system during the 1920s than the shifts in regulation that have played a prominent role in dealing with the expansion of branching in recent periods.

Our hypothesis, which emphasizes how the expansion of branch banking within a state increases the competitive pressures on inefficient banks and can induce them to merge or voluntarily liquidate, is consistent with recent research examining the effects of bank deregulation (DeYoung, Hasan, and Kirchhoff, 1998, Berger, Demsetz, and Strahan, 1999, Jayaratne and Strahan, 1998, Stiroh and Strahan, 2003). However, it stands in contrast to one of the longstanding populist arguments lodged against branch banking. Opponents of branch banking have often complained that it was a form of cartelization that would result in consolidation of the industry and reduced competition, and that its growth would reduce the viability of businesses in small communities by siphoning funds to urbanized areas. Such sentiments were widely expressed in the first quarter of the 20th century when branching was spreading rapidly.ls

While the growth of branch banking may lead to consolidation, the effects on competition are not as clear as opponents of branching suggest. In fact, the economic theory or private-interest view of regulation argues that branching restrictions are used to protect inefficient, local monopolies and restrict competition.l6 The result of these intrastate regulations was less than full-scale competition in local deposit and loan markets. Chapman and Westerfield (1942, p. 233) described the situation in the 1920s and 1930s:

"Country bankers foresee danger to themselves in the possibility of inroads into their areas of operation, should the larger institutions of the cities be permitted to establish branches and compete with them in their area on equal terms. They know that such a policy would result in a reduction of interest rates in their towns and that their chances for the profitable use of their funds might be somewhat diminished unless they were prepared to go as far as their new nvals in serving customers cheaply. The alleged apprehension of unit bankers as to the monopolistic character of branch banking is, to say the least, selfish. What really motivates them is their desire to preserve their local monopolies and escape the competition of the more effective branch banks."

Our hypothesis is sympathetic to this view of regulatory impediments. It is also analogous to one that has been made in the context of the global financial services industry: just as foreign banks have brought necessary competition to inefficient domestic markets (Levine, 1996, Claessens, Demirguc-Kunt, and Huizinga, 2001, Folkerts-Landau and Lindgren, 1998), branch banking can introduce greater competi- tion to local markets, improving the cost and delivery of services to customers

15. See, for example, the discussion in Chapman and Westerfield (1942, p. 10) for examples of this view.

16. See Kroszner and Strahan (1998, 2000), Mitchener (2000), and Chapman and Westerfield (1942).

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1304 : MONEY, CREDIT, AND BANKING

and the safety of the banking system by forcing ineEcient banks to merge or go out of business.

3. TESTING THE COMPETITION/CONSOLIDATION HYPOTHESIS

This section tests several predictions of our hypothesis using data on national banks from the 1920s and the first two years of the Depression. First, we test whether the number of mergers and voluntary liquidations was higher (and the number of entries by new banks was lower) in states where there was more branch-banking activity. We then test whether other factors related to competition and consolidation, including the number of banks per capita and the profitability of different banks, are related to the extent of branch banking in a state. Finally, we test whether competition, induced by branching, is better at accounting for the variation in failure rates across states than the diversification argument.

3.1 Mergers, Voluntary Liquidations, and Entry in Branch Banking States Our first test examines industry consolidation whether there were more mergers

and voluntary liquidations for national banks in states that allowed branch banking- and whether there were also fewer new banks established in these states.l7 Table 2 summarizes the rate of entry of new national banks and rates of exits of national banks over the sample period of 1922-30 and groups by whether states permitted branch banking. (Appendix A describes the sources for our data. Detailed information on state branching laws is shown in Appendix B.) As the last column of the table shows, during the 1920s, states permitting some form of branching averaged somewhat more mergers and voluntary liquidations than states that prohib- ited branching.

The tabular results for the effects of branching on consolidation are tested more formally by regressing the number of exits and entries of national banks on a measure for branching activity in the state. Using observations on each state i, we estimate the following function:

COMPETITIONi = f l i l BRANCHi + :2BANKS/CAPi ( 1 )

+ GRYi + 4DEPINSi + :5BANKSi},

where COMPETITION is specified as: (1) the number of mergers, (2) voluntary liquidations, or (3) new branches for national banks.l8 BRANCH is a measure of

17. Wheelock (1993, p. 815) suggests that these changes may have occurred in the 1920s, but does not formally test this notion: "Like most Midwestern states, Kansas was a unit banking state during the 1 920s, with over 1,000 small banks in operation...Had branching restrictions been removed those counties might have experienced greater consolidation, through either mergers or failures."

18. It should be noted that entry of national banks is an imperfect measure of total entry, as one would also expect that the entry of state banks would be affected by branching. Additionally, some banks may prefer to enter the banking system as state banks in states allowing branching in order to establish branches, which might lead to measurement error in the dependent variable.

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TABLE 2

SUMMARY STATISTICS FOR NATIONAL BANKS

1922 1923 1924 1925 1926 1927 1928 1929 1930 Average

SOURCES AND NOTES: Rates are computed using data from the Annual Report of the Comptroller of the Currency (1922-1930). Rates are expressed as percentages of existing banks. Within-category rates are unweighted averages of state rates. The last column shows the average of the yearly observations over the entire sample period, 1922-30.

the extent of branching in the state; BANKS/CAP is the number of state and national banks per person, AGRY is the share of a state's income from agriculture in 1920, BANKS is the log number of national banks, and DEPINS is a dummy variable that equals one for states with deposit insurance systems.

Since exits and entries take on discrete integer values and are bounded below by zero, we analyze the number of exits and entries employing count data analysis, where the data are pooled across states. Count data analysis assumes that the dependent variable was generated through a Poisson process. Determining the effect of the independent variables shown in Equation (1) involves maximizing the log- likelihood function:

n

ln L(,) = , {yiXi ,B-exp(Xi 13)-ln Yi ! }, (2) i=l

MARK CARLSON AND KRIS JAMES MITCHENER : 1305

Unit Number of Banking states

Average number of banks

Average voluntary liquidation rate

Average merger rate

Average entry rate

Allows Number Some Form of states of Branching

Average number of banks

Average voluntary liquidation rate

Average merger rate

Average entry rate

29

157.9

2.2

2.1

1.9

19

169.1

2.6

2.4

1.9

30 30 28 28 28 29 30 29 28

175.8 173.2 165.8 165.6 155.4 147.6 143.3 142.3 138.2

1.4 1.7 1.8 2.4 1.7 2.0 2.2 3.5 4.7

1.2 2.0 1.8 2.2 1.9 2.3 2.2 3.5 4.0

2.4 1.8 1.5 2.3 1.9 1.2 1.3 2.3 2.2

18 18 20 20 20 19 18 19 20

168.6 169.1 171.6 172.9 167.3 172.8 176.7 165.9 157.5

1.8 1.5 2.6 1.6 3.4 2.0 2.8 4.0 4.9

1.4 1.6 2.6 1.2 2.8 2.2 3.1 4.4 3.8

2.6 1.9 2.5 2.5 1.8 1.2 1.5 2.0 0.8

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TABLE 3

EFFECT OF INITIAL LEVEL OF BRANCHING ON VOLUNTARY LIQUIDATIONS, MERGERS, AND ENTRY

Average Voluntary Liquidations Average Mergers Average New Banks

Coefficient Estimate S.E. Coefficient Estimate S.E. Coefficient Estimate S.E.

Intercept -6.80*** (1.17) -6.77*** (1.16) -4.63*** (1.09) Ratio of Branches to 5.02*** (1.56) 4.75*** (1-54) 1.47 (1.66)

Total Bank Offices

(1922 Value) Banks Per Capita 0.58** (0.23) 1.26*** (0.18) 1.15*** (0.17) Share of State Income 0.03** (0.02) 0.04** (0.02) 0.00 (0.02)

from Agriculture

Log Number of Banks 1.26*** (0.18) 1.26*** (0.18) 1.15*** (0.17) Deposit Insurance -0.46 (0.39) -0.49 (0.38) 0.83** (0.40) Dispersion 0.12 (0.08) 0.10 (0.07) 0.12 (0.10)

Observations 48 48 48 Log-likelihood - 96.9 -91.1 -96.1

NOTES: The symbols (***), (**), and (*) indicate statistical significance at the 1%, 5%, and 10% level, respectively. Standard errors are in parentheses. Data on voluntary liquidations, mergers, and entnes of national banks are from the Annual Report of the Comptroller of the Currency (1923-30). Population and income shares are from 1920 Census report. The log of the number of banks is the average number of national banks for 1923-30. Branch offices and total bank offices include both state and national banks. The dependent variable is the average count of voluntary liquidations, mergers, and entries of national banks in a state for the period 1923-30.

where y is the dependent variable vector, X is the matrix of independent variables, a is the coefficient vector, and n is the number of observations.l9 When analyzing counts, it is important to control for the size of the population from which the counts are produced. In the Poisson and negative binomial distributions the rate of "arrival" is constant so that the number of arrivals depends on the population size. Increasing the population by a given percentage should increase the number of arrivals by the same percentage. To correct for this, we include the log of the number of banks to account for the size of the "at-risk population" of banks.20

Count data analysis has several advantages over other estimation strategies: it treats observations with the value of zero as containing important information and takes into account the granularity in the data (the fact that the number of exits and entries can only take on whole numbers). Moreover, since entries and exits are truncated at zero rather than censored at zero, using an alternative estimator such as Tobit would produce biased estimates. When there is some evidence of overdispersion (the variance significantly exceeds the mean) in our sample, we employ a negative binomial distribution rather than the Poisson distribution. The coefficients can be interpreted as the percentage change in the dependent variable (number of entries or exits) due to a one-unit change in the independent variable.

Table 3 displays the results from cross-sectional regressions of the average number of mergers, voluntary liquidations, and entries from 1923 to 1930 on the ratio of branch oices for state and national banks to total branch and bank offices for state and national banks in 1922 (BRANCH) and the other explanatory variables described

l9. The likelihood function is based on a Poisson process, although this can be modified; it involves taking the factorial of the dependent variable (noting that the factorial of zero is one).

20. For a detailed treatment of count data analysis see Cameron and Trivedi (1998).

1306 : MONEY, CREDIT, AND BANKING

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TABLE 4

EFFECT OF AN INCREASE IN BRANCHING ON VOLUNTARY LIQUIDATIONS, MERGERS, AND ENTRY

Count

Average Voluntary Average Liquidations Average Mergers New Banks

(192S30) (192S30) (192S30)

CoeEcient Estimate S.E. Coefficient Estimate S.E. Coefficient Estimate S.E.

Intercept - 6.18*** (0.82) - 6.03*** (0.82) - 4.79*** (1.28) Change in the Ratio of 6.62*** (1.03) 6.66*** (1.03) 3.15 (2.48)

Branches to Total

Bank Of fices (1922-25) Banks Per Capita 0.32 (0.23) 1.19*** (0.13) 1.13*** (0.23) Shre of State Income 0.04*** (0.01) 0.04*** (0.01) - 0.01 (0.02)

from Agriculture

Log Number of Banks 1.21*** (0.13) 1.19*** (0.13) 1.13*** (0.23) Deposit Insurance - 0 50* (0.26) - 0 49* (0.26) 1.39*** (0.50) Dispersion N.A.A N.A.A 0.26 (0.17)

Observations 48 48 48 Log-likelihood - 165.9 - 150.3 - 48.4

NOTES: The symbols (***), (**), and (*) indicate statistical significance at the 1, 5, and 10 percent level, respectively. The symbol (^) indicates that we reject overdispersion in the data and estimate the regression using a Poisson distnbution. Standard errors are in parentheses. Data on voluntary liquidations, mergers, and entnes of national banks are from the Annual Report of the Comptroller of the Currency (1926-30). Population and income shares are from 1920 U.S. Census. Banks per capita is the 1920 value. The log of the number of banks is the average number of national banks for 192S30. Branch offices and total bank offices include both state and national banks. The dependent vanable is the average count of voluntary liquidations, mergers, and enties of national banks in a state for the penod 192S30.

in Equation (1). This specification enables us to examine whether cross-state variation is sufficient to identify a relationship between branching and our measures of exit. By holding our measure of branching at the 1922 level, we also alleviate concerns about the endogeneity of this variable. The estimated coefficients support the hypoth- esis that branch banking increases mergers and involuntary liquidations, leading to consolidation in a state's banking system. Testing these three coefficients jointly suggests that branching results in a net reduction of the number of banks (the Chi-squared test statistic has a p-value of 0.02). During the 1920s, states that had more extensive branch-banking networks had more voluntary liquidations than states that allowed only unit banking (Column 1). The point estimate suggests that an increase in the ratio branch offices to total offices by 0.1 (about one standard deviation) resulted in roughly a 50% increase in the number of voluntary liquidations. Column 2 also shows that states with more branching activity had significantly more mergers than unit banking states during the 1920s. The coefficient on the extent of branching suggests that its effect on the number of mergers is similar to its effect on voluntary liquidations. These results are consistent with White (1985), who describes the wave of bank mergers in the 1920s and suggests that the increase in mergers and the ability to branch may have been related. In contrast to the findings for voluntary liquidations and mergers, branching activity appears to have had little impact on entry by new national banks (Column 3).

We next examine whether there is a sufficient expansion of branching activity in the early 1920s to induce exit as our hypothesis suggests. As Appendix B indicates, some states had passed branching laws before 1920. So even though the evidence

MARK CARLSON AND KRIS JAMES MITCHENER : 1307

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TABLE 5

EFFECT OF BRANCHING ON VOLUNTARY LIQuIDATzoNs, MERGERS, AND ENTRY RJ8ING A POOLED SAMPLE

Voluntary Liquidations (1923-30) Mergers (1923-30) New sanks (1923-30)

Coeffscient Estimate s.E. Coefficient Estimate S.E. Coefficient Estimate S.E.

Intercept -5.2188* (0.39) -5.14*** (0.39) -4.08*** (0.44) Ratio of Branches to Total 2.41*** (0.39) 2.34*** (0.39) 0.51 (0.45)

Bank Offices (lagged) Banks Per Capita 0.66* (0.36) 0.67* (0.35) -0.99*** (0.07) Share of State Income 0.04*** (0.01) 0.04*** (0.01) ° °° (0.01)

from Agriculture Log Number of Banks 1.01*** (0.06) 1.02*** (0.06) 1.07*** (0.07) Deposit Insurance -0.72*** (0.18) -0.69*** (0.18) 0.81*** (0.20) 1924 0.20 (0.19) 0.01 (0.19) -0.21 (0.21) 1925 -0.02 (0.20) -0.25 (0.20) 0.13 (0.20) 1926 0.21 (0.19) 0.05 (0.19) -0.08 (0.21) 1927 0.25 (0.19) 0.18 (0.19) -0.38* (0.21) 1928 0.19 (0.19) 0.04 (0.19) -0.38* (0.22) 1929 O.59*** (0.19) 0.52*** (0.18) -0.02 (0.21) 1930 0.88*** (0.19) 0.55*** (0.18) -0.27 (0.22) Dispersion 0.39 (0.06) 0.39 (0.06) 0.54 (0.08)

Observations 384 384 384 Log-likelihood -1031 -964 -1086

NOTES: The symbols (***), (**) and (*) indicate statistical significance at the 1%, 5%, and 10% level, respectively. Standard elTors are in parentheses. Data on voluntary liquidations, mergers, and entnes of national banks are from the Annual Report of the Comptroller of the Currency (1922-30). Population and income shares are from 1920 Census report. The first year in each sample penod is the omitted year from the regression. Branch offices and total bank offices include both state and national banks. The dependent vanable is the count of voluntary liquidations, mergers, and entnes of national banks in a state per year.

from the tables and figures suggests that branching was expanding rapidly during the decade, in Table 4 we explicitly test whether the change in this branching ratio between 1922 and 1925 is associated with a greater average number of mergers and voluntary liquidations between 1926 and 1930. The statistically significant coeffi- cient on the change in the branching ratio shown in Columns 1 and 2 suggests that the expansion of branching in the early 1920s had a significant role in shaking out the banking system even though some branching laws had been passed earlier.

The results from Tables 3 and 4 are also consistent with Wheelock (1993), who showed that the overall percentage change in banks per capita in the 1920s was greatest in states where branching expanded the most.21 However, the decomposition presented here provides the further insight that branching encouraged banks to exit in a variety of ways. That voluntary liquidations increased suggests that the decline in banks per capita was not simply due to widespread purchases of banks by a few industry leaders. Rather the trends exhibited in the regressions are consistent with an overall increase in competition where branching was permitted.

We also estimated several alternative specifications in order to examine the ro- bustness of the results, including pooling the data and using the legal status of branching in a state rather than the extent of branching. Table 5 tests whether the

21. And they are also consistent with Berger, Kashyap, and Scalise (1995), who show that the introduction of nationwide banking beginning in the 1980s accelerated the reduction in the number and market share of small bank organizations for the period of recent regulatory liberalization.

1308 : MONEY, CREDIT, AND BANKING

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TABLE 6

EFFECT OF STATE BRANCHING LAWS ON VOLUNTARY LIQUIDATIONS, MERGERS, AND ENTRY USING

A POOLED SAMPLE

Voluntary Liquidations (1922-30) Mergers (1922-30) New Banks (1922-30)

Coefficient Estimate S.E. Coefficient Estimate S.E. Coefficient Estimate S.E.

lntercept -4.64*** (0 40) -4.79*** (0.41) -3.87*** (0.43) Law Allows Branching 0.24* (0.12) 0.21* (0.12) -0.10 (0.13) Banks Per Capita -0.12 (0.37) -0.04 (0.37) -1.61*** (0.07) Share of State Income 0.03*** (0.01) 0.038** (0.01) ° °° (0.01)

from Agriculture

Log Number of Banks 0.968** (0.06) 0.99*** (0.06) 1.08*** (0.07) Deposit Insurance -0.45** (0.19) -0.45** (0.19) 0.968** (0.19) 1923 0.19 (0.22) 0.31 (0.21) -0.06 (0.21) 1924 0.38* (0.22) 0.34 (0.21) -0.28 (0.22) 1925 0.18 (0.22) 0.09 (0.22) 0.05 (0.21) 1926 0.40* (0.22) 0.38* (0.21) -0.15 (0.22) 1927 0.46** (0.21) 0.55*** (0.21) -0.45** (0.22) 1928 0.43** (0.22) 0.43** (0.22) -0.45** (0.22) 1929 0.81*** (0.21) 0.89*** (0.21) -0.09 (0.22) 1930 1.07**8 (0.21) 0.89*** (0.21) -0.36 (0.22) Dispersion 0.56 (0.07) 0.55 (0.07) 0.60 (0.08)

Observations 480 480 480 Log-likelihood -1065 -968 -1603

NOTES: The symbols (***), (**), and (*) indicate statistical significance at the 1%, 5%, and 10% level, respectively. Standard errors are in parentheses. Data on voluntary liquidations, mergers, and entries of national banks are from the Annual Report of the Comptroller of the Currency (1922-30). Data on branching laws are from the Federal Reserve Board of Governors (1931) and population is from the 1920 Census. The first year in each sample penod is the omitted year from the regression. The dependent vanable is the count of voluntary liquidations, mergers, and entnes of national banks in a state per year.

annual number of mergers, voluntary liquidations, and entries from 1923 to 1930 are related to level of branching activity lagged one year, where the data are pooled over the entire sample period.22 The measure for the extent of branch banking in a state is lagged one year to reduce the potential for endogeneity.23 We include year dummies in Table S (and in other pooled specifications in the paper) to control for any time-specific effects. This allows us to account for the changes in the macroeconomic environment and any residual effects of the changes in the regulatory environment, such as the McFadden Act, not captured by our degree of branching measures. The coefficients on the extent of branching are again positive and statisti- cally significant at conventional levels for voluntary liquidations and mergers.

As a final robustness test regarding the exogeneity of our branching variable, in place of the measure based on the extent of branching, we use dummy variables indicating whether the state permitted branch banking. In a pooled regression, we find that states permitting statewide branching had more mergers and consolidations (Table 6). We note, however, that the rest of analysis in this article focuses on our

22. There is, however, substantial variation over time in our measure of the extent of branching. On average, it changes 75% between 1922 and 1930 and by 30% between 1926 and 1930.

23. As we indicated in the previous section, since technological and legal changes were driving the increase in branch banking in the 1920s, the lagged measure of the extent of branching seems plausibly exogenous.

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1310 : MONEY, CREDIT, AND BANKING

preferred measure, the extent of branching activity in a particular state, because it allows us to sidestep two definitional problems which complicate and potentially muddle the interpretation of regression results based on the legal status of branching in a state: (1) some states in the 1920s permitted limited branching, but what they permitted differed markedly and (2) some states differed in terms of what was practiced by regulators (de facto) and what was on the books (de jure). (See Appendix B for more details.)

3.2 Consolidation and Increased Competition versus Monopoly Power Although the results presented in the previous section support the view that

branch-banking activity increases mergers and exits, it is unclear whether this consol- idation led to a more competitive banking system or to a monopolistic banking system. For example, when banks are forbidden from establishing branches, it artificially segments the market and enables unit banks to develop monopolies within particular localities. The immediate impact of branching is that it introduces competition into these geographically segmented markets. If banks that were pre- viously insulated from the cool winds of competition are acquired or forced out of the market as a result of this increased competition, it is possible that the surviving banks in turn acquire monopoly power. This was one of the concerns of those who lobbied against more permissive branching laws in the 1920s: the end result of liberalization would simply be further concentration in the banking industry. To investigate further how statewide branching affects the competitive environment of a state's banking system, we examine the relationship between branching and two additional measures: banking sector concentration and bank profits.

We first test whether states allowing branch banking had more concentrated banking sectors by regressing measures of bank concentration on the extent of branch banking. The first measure of concentration is essentially an annual Herfindahl index of national banks. The Federal Reserve published data on the number of banks in diiTerent size categories with different levels of profitability for each state, for each of the years 1926-30 (Federal Reserve Board of Governors 1931).24 The index is constructed as follows: the number of banks is multiplied by the means of the size category; these groups are aggregated to get the total value of loans and investments in the state. The mean of each size category is divided by total state assets, squared, and multiplied by the number of banks in each size category. These fractions are then added together to obtain the index.25 Since the Federal

24. The size categories, based on loans and investments, are: category 1, under $150,000; category 2, $150,000-$250,000; category 3, $250,000-$500,000; category 4, $500,000-$750,000; category 5, $750,000-$1,000,000; category 6, $1,000,00(}$2,000,000; category 7, $2,000,000-$5,000,000; category 8, $5,()00,000-$10,000,000; category 9, $10,000,000-$50,000,000; category 10, $50,000,000 and over. We employ the profit information below.

25. Mathematically, we can write this is as:

p meanvalueofloans (i) 1

i=siEroup l [Li (mean value of loans(i))(banks in category(i))] | g P

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TABLE 7

BRANCHING AND INDUSTRY CONCENTRATION

Herfindahl Index 4-Firm Index Banks Per Capita

Coefficient Estimate S.E. Coefficient Estimate S.E. Coefficient Estimate S.E.

Intercept 1084*** (101) 0.21*** (0.02) 38.61*** (2.44) Ratio of Branches to 1088*** (301) 0.44*** (0.06) -69.65*** (6.34)

Total Bank Offices

(lagged)

1924 0.01 (0.03) -2.18 (3.39) 1925 0.02 (0.03) -3.07 (3.39) 1926 0.02 (0.03) -4.20 (3.40) 1927 60 (139) 0.03 (0.03) -5.31 (3.40) 1928 50 (139) 0.04 (0.03) -6.27* (3.40) 1929 101 (139) 0.04 (0.03) -6.97** (3.40) 1930 150 (139) 0.05 (0.03) -7.98** (3.40)

Observations 239 383 383 Adjusted R-square 0.04 0.14 0.25

NOTES: The symbols (***), (**), and (*) indicate statistical significance at the 1%, 5No, and 10% level, respectively. Estimated using a pooled sample. Data on the number of banks, size distnbution, and number of branches are from the Federal Reserve Board of Governors ( 1931 ) . Four-firm concentration index denved from Polk 's Bank Directory (various years) and the Comptoller of the Currency (various years) . Population is from the 1920 Census. The dummy for the first year in each time penod is the omitted year. Banks per capita and the four- firm index include state and national banks, whereas the Herfindahl index consists only of national banks. The dependent variables are concentration indexes.

Reserve data used to construct this index are only available for 1926-30, the results for the Herfindahl index are presented for this period. The second measure is a four-firm concentration ratio based on the deposits in the largest four commercial banks (state or national) in each state for each year of our sample. This measure allows us to examine a longer sample period (1923-30) as well as a measure based on the liabilities of banks. The third measure is simply the number of banks per capita (both state and national commercial banks), also calculated for 1923-30. The sample consists of annual observations on each state.

Analysis using the ratio of branch oEces to total bank offices of state and national banks in 1922 as our measure of branching indicates that states with more extensive branching had more concentrated banking sectors-higher Herfindahl scores, more deposits in the largest four banks, and fewer banks per capita.26 We find similar results if we pool the data and regress measures of bank concentration on the ratio of branch offices to total offices lagged one year (Table 7). Thus, regardless of the measure, states with more extensive branching tended to have more concentrated banking systems.

Next, we examine how branch banking affected bank profits. Since national banks were all regulated at the national level by the Office of the Comptroller of Currency and were largely restricted in their ability to have branches, differences in the level of competition that they faced would be a significant factor in creating differences

26. Our Herfindahl index is admittedly imperfect since it does not include state banks (comparable data do not exist), and state banks were on average smaller. The average assets (loans and investments) for state banks in 1926 was $1,272,183, whereas for national banks it was $2,403,295; state banks had 57% of total assets in the commercial banking system in this year. Nevertheless, since our other two measures of concentration also show that states with more branching activity had more concentrated banking systems, we are confident that our result is robust.

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1312 : MONEY, CREDIT, AND BANKING

in profit levels between states. Finding lower profits in states that witnessed a growth in branch banking would indicate that the national banks faced greater competition from state banks in these locations.27

To test how branching laws affect profitability, we use profit data on national banks that are available from 1926 to 1930 and that are decomposed by both size category (based on loans and investments) and by state. Rather than listing the actual profit rates, the Federal Reserve (1931) grouped the data into seven profit ranges, using the percentage return to capital as the measure of profitability.28 The data set thus has up to 70 observations per state, per year because there are 7 profit ranges for each of the 10 bank size categories.29 Because the ranges are ordered but the distance between them is not constant, an ordered logit is estimated using weights equal to the number of national barlks in each size category s, state i, and year t, earning a given level of profits. In Tables 8-10, we estimate variations on the following function:

PROFIT RANGEist = f { D l BVNCHit_ 1 + 52 STBANKSHAREit

+53 DEPINSi+SYsSIZEs +zYt YEARt), (3)

with observations weighted by the number of national banks in category ist. STBANKSHARE is the share of commercial banks that are state chartered, DEPINS is a dummy variable indicating whether a state had a deposit insurance system, and YEAR and SIZE are time and size dummies, respectively. BRANCH is one of our two measures of the extent of branching. In Table 8, BRANCH is the ratio of branch offices to total bank offices of state and national banks in 1922 and in Table 9 BRANCH is the change in this branching ratio between 1922 and 1925.

Tables 8 and 9 show that banks located in states with relatively more branching activity had declining profitability over the period 1926-30, suggesting that there was more competition in branch banking states.30 A higher initial level of branching or a more significant expansion of branching in the early 1920s is found to have reduced profitability as competitive forces unleashed by branching likely eliminated the geographic monopolies enjoyed by unit banks and state banking systems moved toward a new equilibrium. This is shown in Tables 8 and 9 by the shift in the coefficient on branching from positive in the beginning of the sample period to

27. Lower profitability may also result from higher merger costs; however, due to branching restrictions on nationally chartered banks, this interpretation is more likely to apply to state banks than the national banks used in our sample. Alternatively, national banks may have higher profits if branching leads to so much industry consolidation that surviving banks are able to generate oligopoly profits. Higher profits may also result if competition removes a sufficient number of inefficient national banks from the banking system.

28. These ranges are: deficit of 6% or more, deficit of 5.9%-0%, profit of less than 3%, profit of 3%-5.9%, profit of 6%-8.9%, profit of 9%-11.9%, and profit of 12% and over.

29. There may be less than 70 observations for a state within a year if none of the banks in a particular size category had profits within a particular profit range.

30. Calomiris and Ramirez (2002) also find that states that prohibited branching had higher profit levels. They attribute this partly to the ability of unit banks to exert monopoly power and partly to the higher level of risk that being an undiversified unit bank would entail.

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TABLE 8

EFFECT OF INITIAL LEVEL OF BRANCHING ON PROFITABILITY BY YEAR, 1926-30

1926 1927 1928 1929 1930

Coefficient Coefficient Coefficient Coefficient Coefficient Estimate S.E. Estimate S.E. Estimate S.E. Estimate S.E. Estimate S.E.

NOTES: The symbols (***), (**), and (*) indicate statistical significance at the 1%, 5%, and 10% level, respectively. Estimated using an ordered logit. Deposit insurance data are from Calomiris (1992) and White (1981). All other information is from the Federal Reserve Board of Governors (1931). The dependent variable is an ordered variable indicating the level of profitability weighted by the number of banks that reported earning that level of profits. Size category 6 is omitted.

1.2*** (0.2)

- 1.7*** (0.1)

-0.4*** (0.1) -1.6*** (0.1) -1.1*** (0.1) -0.6*** (0.1) -0.2*** (0.1) -0.1* (0.1)

0.2*** (0.1) 0.2 (0.1) 0.2* (0. 1) 0.2 (0.3)

-0.3*** (0.1) 0.7*** (0.1) 1.8*** (0.1) 2.7*** (0.1) 3.4*** (0.1) 4.4*** (0.1)

1752 - 13750

0.6** (0.2)

-2.5*8* (0.2)

-0.1** (0.1) -1.5*** (0.1) -o.g*** (0.1) -0.6*** (0.1) -0.2*** (0.1) -0.1 (0.1)

0.1 (0.1) 0.2* (0.1) 0.2 (0.1) 0.0 (0-3) 0.2* (0.1) 1.2*** (0.1) 2.3*** (0.1) 3.1*** (0.1) 3.8*** (0.1) 4.8*** (0.1)

1781 - 1 3634

0.1 (0.2)

- 1.9*** (0.2)

0.3*** (0.1) -1.6*** (0.1) -0.g*** (0.1) -o.5*** (0.1) -0.1** (0.1) -0.1 (0.1)

0.2*** (0.1) 0.3** (0.1) 0.3** (0.1) 0.7** (0.3)

-0.2** (0.1) 0.8*** (0. 1) 1.9*** (0.1) 2.8*** (0.1) 3.5*** (0.1) 4.4*** (0.1)

1737 - 1 3406

Ratio of Banks to Total Bank Offices (1922 value)

Ratio of State Banks to Total Banks

Deposit Insurance Size Category 1 Size Category 2 Size Category 3 Size Category 4 Size Category 5 Size Category 7 Size Category 8 Size Category 9 Size Category 10 Intercept 7 Intercept 6 Intercept 5 Intercept 4 Intercept 3 Intercept 2

Observations Log-likelihood

-0.4* (0.2)

-0.8*** (0.2)

0.2*** (0.1) -1.1*** (0.1) -0.7*** (0.1) -0.3*** (0.1) -0.1 (0.1) -0.2*** (0.1)

0.3*** (0.1) 0.3*** (0.1) 0.6*** (0.1) 1.1*** (0.3)

-1.1*** (0.1) -0.1 (0.1)

O.g*** (0.1) 1.8*** (0.1) 2.5*** (0.1) 3.6*** (0.1)

1715 - 1 3342

-1.1*** (0.2)

0.5*** (0.2)

0.3** (0.1) -0.8*** (0.1) -0.5*** (0.1) -0.1** (0.1)

o.o (0.1) o.o (0.1) 0.2** (0.1) 0.4*** (0.1) 0.6*** (0.1) 1.0*** (0-3)

-2.8*** (0.1) -1.9*** (0.1) -o.g*** (0.1)

0.2 (0.1) o.9*** (0.1) 2.1*** (0.1)

1697 - 12962

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TABLE 9

EFFECT OF GROWTH OF BRANCHING ON PROFITABILITY BY YEAR, 192S30

1926 1927 1928 1929 1930

Coefficient Coefficient Coefficient Coefficient Coefficient Estimate S.E. Estimate S.E. Estimate S.E. Estimate S.E. Estimate S.E.

NOTES: The symbols (***), (**), and (*) indicate statistical significance at the 1%, 5%, and 10% level, respectively. Estimated using an ordered logit. Deposit insurance data are from Calomiris (1992) and White (1981). All other information is from the Federal Reserve Board of Governors (1931). The dependent variable is an ordered variable indicating the level of profitability weighted by the number of banks that reported earning that level of profits. Size category 6 is omitted.

Change in Ratio of Branching to Total Bank Offices (1922-25)

Ratio of State Banks to Total Banks Deposit Insurance Size Category 1 Size Category 2 Size Category 3 Size Category 4 Size Category 5 Size Category 7 Size Category 8 Size Category 9 Size Category 10 Intercept 7 Intercept 6 Intercept 5 Intercept 4 Intercept 3 Intercept 2

Observations (weighted) Log-likelihood

2.5*** (0.6)

-1.8*** (0.1) -0.4*** (0.1) -1.6*** (0.1) -1.1*** (0.1) -0.6*** (0.1) -0.2*** (0.1) -0.1* (0.1)

0.2*** (0.1) 0.2 (0.1) 0.2* (0.1) 0.2 (0.3)

-0.3*** (0.1) 0.8*** (0.1) 1.8*** (0.1) 2.7*** (0.1) 3.4*** (0.1) 4.4*** (0.1)

1752 - 14369

-0.3 (0-7)

-2.6*** (0.2) -0.2*** (0.1) - 1 .5*** (0. 1) - 1 .0*** (O. 1 ) -0.6*** (0.1) -0.2*** (0.1) -0.1 (0.1)

0.1 (0.1) 0.2* (0.1) 0.2 (0.1) 0.1 (0.3) 0.3*** (0.1) 1.3*** (0.1) 2.4*** (0.1) 3.3*** (0.1) 4.0*** (0.1) 4.9*** (0.1)

1781 - 14101

-0.9 (0.7)

-2.0*** (0.2) 0.2*** (0.1)

- 1.6*** (0.1) -o.9*** (0.1) -0.5*** (0.1) -0.1** (0.1) -0.1 (0.1)

0.2*** (0.1) 0.3** (0.1) 0.3** (0.1) 0.7** (0-3)

-0.2 (0.1) 0.8*** (0. 1) 2.0*** (0.1) 2.8*** (0.1) 3.5*** (0.1) 4.5*** (0. 1)

1737 - 13758

-2.1*** (0.7)

-0.9*** (0.2) 0.2*** (0.1)

-1.1*** (0.1) -0.7*** (0.1) -0.3*** (0.1) -0.1 (0.1) -0.2*** (0.1)

0.3*** (0.1) 0.3*** (0.1) 0.6*** (0.1) 1.1 *** (0.3)

-1.0*** (0.1) -0.1 (0.1)

1.0*** (0.1) 1.9*** (0.1) 2.6*** (0.1) 3.7*** (0.1)

1715 - 135 1 1

-2.1*** (0.7)

o.s*** 0.3**

-0.8*** -0.5*** -0.1*

0.0 0.0 0.2*** 0.4*** 0.6*** 1.0***

-2.9*** -2.0*** -0.9***

0.1 0.9*** 2.1***

1697 - 1 3070

(0.2) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.3) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1)

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TABLE 10

BRANCHING AND PROFITABILITY, 1926-30

Coefficient Estimate S.E.

Ratio of Branches to -0.19* * * (0.07) Total Bank Offices (lagged)

Ratio of State Banks -1.29*** (0.06) to Total Banks

Deposit Insurance -0.16*** (0.03) Size Category 1 -1.36*** (0.05) Size Category 2 -0.84*** (0.04) Size Category 3 -0.45*** (0.03) Size Category 4 -0.15*** (0.03) Size Category 5 -0.12*** (0.04) Size Category 7 0.19*** (0.03) Size Category 8 0.27*** (0.05) Size Category 9 0.35*** (0.06) Size Categorz7 10 0.62*** (0.13) 1927 -0.02 (0.03) 1928 0.03 (0.03) 1929 -0.13*** (0.03) 1930 -0.93*** (0.03) Intercept 7 -0.59*8* (0.05) Intercept 6 0.38*** (0.05) Intercept 5 1.43*** (0.05) Intercept 4 2.34*** (0.05) Intercept 3 3.07*** (0.05) Intercept 2 4.12*** (0.06)

Observations 8640 Log-likelihood -69460

NOTES: The symbols (***), (**), and (*) indicate statistical significance at the 1%, 5%, and 10% level, respectively. Estimated using an ordered logit and pooled observations. Deposit insurance data are from Calomiris and White. All other information is from the Federal Reserve Board of Governors (1931). The dependent vaxiable is an ordered variable indicating the level of profitability weighted by the number of banks that reported earning that level of profits. Size category 6 and the year 1928 are also omitted.

negative by the end of the period. Depending on which measure of BRANCH we use, the effect on profits from branching turns negative in 1927 or 1928 and is significantly so starting in 1929. Using the 1929 regression, a one-standard-deviation increase in the ratio of branch offices to total offices or a one-standard-deviation increase in the growth of branching reduces the probability of being in a higher profit category by 4%-7%.

As a robustness check, we also considered a pooled regression specification (which includes year dummies) over the sample period 1926-30, and find that states with relatively more branching activity had lower profits (Table 10). We use our same measure of the extent of branch banking, but lag it one year. A one-standard- deviation increase in the ratio of branch offices to total bank offices reduces the probability of being in a higher profit category by about 3%. While this overall effect is not terribly large, it masks different effects for different size banks. A one- standard-deviation increase in the ratio of branch offices to total bank offices for small banks, which seem to have been most affected by branching activity, reduced the probability of being in a higher category by 20%. For the largest banks, the probability of being in a higher category was reduced 10%. There seems to have been little

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1316 : MONEY, CREDIT, AND BANKING

effect on the profitability of mid-size banks. The other coefficients are as expected. Larger banks were more profitable, and the years 1929 and 1930 were particularly bad years for bank profitability.

The shift from positive profits to negative profits in later years suggest that the banking system was being transformed by the consolidation that branch banking brought with it. Our findings are consistent with studies of recent banking deregula- tion such as Amel and Liang (1997) and Berger, Kashyap, and Scalise (1995), who view the removal of geographic restrictions as likely reducing the exercise of market power (by unleashing more actual or potential customers into local markets) and improving allocative eEciency (by enabling resources to flow more easily toward ac- tivities that yielded higher returns and more efficient producers).

3.3 Branch Banking and Financial Stability We now examine whether branch banking is responsible for lower national bank

failures in the 1920s and 1930s. We consider two ways that branching may have reduced the incidence of failures: (1) by improving diversification opportunities through geographical expansion and (2) by weeding out weak banks via the process of competition. We use the same data from the Federal Reserve that were used to examine bank profitability. Since the dependent variable we use is the number of failures, we estimate the regressions using count data analysis.

We first verify that we are able to replicate the state-level results of Wheelock (1995) and Mitchener (2000, 2005). We aggregate the size categories into state level observations and regress the number of failures of national banks in a state on variables indicating whether the state allowed branching, year dummies, and (log) number of banks in the state.31 (We also include a specification that uses the extent of branching as an independent variable.) The results shown in Table 11 match the previous literature and indicate that states permitting branch banking had fewer bank failures from 1927 to 30, the period where the profits regressions suggest that

. . .

competltlon was most lmportant. We now explore whether the lower number of failures from widespread branch

banking was due to increased competition, diversification, or both. To conduct this test, we develop proxies for the competition and diversification effects of branch banking by taking advantage of the bifurcated nature of the U.S. banking system. To proxy how branch banking could lower failures by increasing consolidation, we compute the ratio of the branches of state banks to total bank offices in the state (COMPETITION). In states where this ratio is larger, national banks will be competing with more bank offices that are the direct result of statewide branching. And since this variable is based on the expansion of state-chartered branching, it excludes any benefits to national banks from greater diversification opportunities.

31. We use a pooled sample. Following Bertrand, Duflo, and Mullainathan (2004), we adjust the standard errors for the fact that there are multiple observations for each state by clustering at the state level. In states where the de jure situation differed from the de facto situation (such as West Virginia, where the laws allowed branching but the state banking commissioner refused all applications for establishing branches) or where states do not have a law, the de facto situation is used.

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TABLE 11

THE EFFECTS OF BRANCHING LAWS ON NATIONAL BANK FAILURES, 1927-30

Legal Environment Actual Branches

Coefficient Estimate S.E. Coefficient Estimate S.E.

Intercept -2.84*** (0.89) -2.79*** (0.62) Branch Banking Permitted -0 79* * * (0.21) Ratio of Branches to -4.46*** (1.28)

Total Bank Offices (1922 value) Log Banks 0.65*** (0.11) 0.63*** (0.11) 1928 -0.17 (0.30) -0.16 (0.30) 1929 0.16 (0.29) 0.16 (0.29) 1930 1.07*** (0.26) 1.04 (0.27) Dispersion 0.87 (0.20) 0.91 (0.20)

Observations 192 192 Log-likelihood -8.2 -7.8

NOTES: The symbols (***), (**), and (*) indicate statistical significance at the lSo, 5%, and 10% level, respectively. Estimated using pooled data and count data analysis with a negative binomial distribution. Information on bank failures is from the Annual Report of the Comptroller. Information on the number of banks and branching laws is from the Federal Reserve Board of Governors (1931). The dummy for the year 1926 is omitted. The dependent variable is the number of failing banks in a state in a year.

To proxy for the diversification effects of branching on national banks, we compute both the percentage of national banks with branches (DIVERSIFYI) as well as the ratio of national bank branches to national banks (DIVERSIFY2). In states where DIVERSIFYI is larger, more national banks will have greater opportunities to diver- sify their portfolios across branch offices in the state. DIVERSIFY2 captures the extent to which national banks can diversify. Mathematically, these ratios are:

COMPETITION = (Branches of state banks)/ (4)

(Banks and branch offices of state and national banks)

DIVERSIFY1 = (Number of national banks with branches)/ (S)

(Number of national banks)

DIVERSIFY2 = (Branches of national banks)/ (6)

(Number of national banks)

Including these measures side-by-side in regressions will allow us to identify which channel was quantitatively more important in lowering national bank failures. We use the 1922 values for all three of theses ratios in order to ensure that they are plausibly exogenous.

For state i, year t, and size category s, we estimate the total national bank failures (FAILURE) by the following function:

FAILUREiSt = f {s1 COMPETITIONit_1 + 52DIVERSIFYit_1

+ 4DEPINSi + sBANKsit + 4BUSFAILit-l (7)

+ DsFARMFAILit_l + 6HERFt + S0iYEARt + SYsSIZEs} v

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1318 : MONEY, CREDIT, AND BANKING

1 800

5 6 Size Categories

FIG. 4. Annual Average Number of National Banks and Bank Failures by Size Category. Sources and Notes: Comptroller of the Currency (various years) and Federal Reserve Board (1931). Annual averages are constructed over the period 1926-30. Size categories are category 1, under $150,000; category 2, $150,00(}$250,000; category 3, $250,000-$500,000; category 4, $500,000-$750,000; category 5, $750,00s$1,000,000; category 6, $1,000,000- $2,000,000; category 7, $2,000,00>$5,000,000; category 8, $5,000,00s$ 10,000,000; category 9, $10,000,000- $50,000,000; category 10, $50,000,000 and over.

where COMPETITION and DIVERSIFY are lagged, and DIVERSIFY is one of the two measures of diversification defined above (DIVERSIFYI or DlVERSlFY2). Since our dependent variable is disaggregated by size, we are also able to include bank size indicators (SIZE) as additional conditioning variables a factor that has been associated with the probability of failure in previous studies (Calomiris and Mason, 2000, Carlson, 2004, White, 1984).32 Figure 4 shows the distribution of banks and failures by bank size category. To control for differences in real shocks across states, we include a measure of lagged business failures (BUSFAIL) and compute agricultural distress as the lagged farm failure rate (FARMFAIL). We control for additional state level factors: the (log) number of national banks (BANKS); the Herfindahl index (HERF), described above; and whether a state had a deposit insurance system (DEPlNs).33 All 48 states are used and we include year dummies

32. Larger banks may be more stable because of their greater ability to spread risks and coordinate emergency assistance with other banks and their reduced propensity to spread contagion when the banking sector is subjected to external shocks.

33. We include the deposit insurance indicator variable for two reasons. First, the presence of deposit insurance in eight states has been linked to higher failure rates in the 1920s in these states (Wheelock 1992, Wheelock and Wilson, 1995, Calomiris, 1990). Moreover, increased competition can potentially exacerbate the moral hazard problem associated with deposit insurance (Keeley, 1990). There is however, little overlap between states that allowed branching and state that had a deposit insurance program.

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MARK CARLSON AND KRIS JAMES MITCHENER : 1319

to capture any time-specific influences. Since each size category provides a unique observation, we have 10 observations for each state in each year.

Since our profit regressions suggest that the initial shakeout and consolidation process had taken place by the end of 1926, we pool our data over the period 1927-30, and estimate Equation (7) using count data analysis with a negative binomial distribution.34 However, because we observe each state multiple times, we cluster our standard errors as suggested by Bertrand, Duflo, and Mullainathan (2004).35

The relative effects of competition and diversification in producing lower failures rates in states allowing branch banking are shown in Table 12.36 The negative and statistically significant estimated coefficient on our ratio that proxies for competition supports the hypothesis that branch banking improves financial stability by weeding out inefficient banks through increased competition and consolidation. Increased competition stemming from branching significantly reduces failures. An increase in the competition ratio of 0.1 (about one standard deviation) results in a 35% decrease in the number of failures in a state. On the other hand, we find no evidence that either of our measures for diversification resulting from branching is associated with fewer national bank failures, where column 1 of Table 12 uses DIVERSIFY1 (the share of national banks with branches) and column 2 uses DIVERSIFY2 (average branches per national bank).

These results do not appear to be sensitive to choosing a diiTerent measure of the extent of branching. As a sensitivity test, we use one-year lagged values of COMPETITION, DIVERSIFYI, and DIVERSIFY2 (shown in Table 13). Here again, the sign on COMPETITION is negative and statistically significant. We find no evidence that either of our measures for diversification resulting from branching is associated with fewer national bank failures; indeed, the only statistically significant coefficient for either of the diversification measures has the wrong expected sign. It should be noted, however, that these results do not provide any indication about the importance of diversification through branching at state banks, where the bulk of the banking systems' branches were located.

The signs on the other explanatory variables in the failure regressions are largely as expected. Bank failures were significantly higher in 1929 and 1930 (as a result of the Depression). An increase in the banks at risk boosts failures. And consistent with the research using individual bank data from this period, larger banks were less

34. A few states have less than 10 observations per year because no banks exist in some size categories. 35. As an alternative, we estimated a panel regression with state-level fixed effects and found

similar effects. 36. Branching and bank size are generally correlated, and both allow a bank to diversify: size allows

the bank to make more total loans and branching permits the bank to make loans in different locations. In this analysis, we focus on the geographic diversification allowed by branching and control for bank size by including dummies for the size group of the bank. Another way of controlling for bank size is by repeating this regression using only banks of a particular size group. These regressions yield similar results to those shown here.

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TABLE 12

EFFECTS OF INITIAL LEVEL OF BRANCHING ON NATIONAL BANK FAILURES, 1927-30

Coefficient Estimate S.E. Coefficient Estimate S.E.

Ratio of Branches of -3.46* (2.09) -3.40* (2.08) State Banks to Total Bank Offices (1922 value)

Share of National Banks - 8.00 (14.59) with Branches (1922 value)

Ratio of National Branches -2.94 (4.78) to National Banks (1922 value)

Log Banks 0.57*** (0.11) 057**t (0.11) Herfindahl Index -0.15 (1.68) 0.10 (1.80) Business Fail Rate 0.00 (0.38) -0.03 (0.37) Farm Failure Rate 23.84 (102.09) 29.67 (100.39) Deposit Insurance 0.38 (0.35) 0.35 (0.33) Size Category 1 0.68* (0.38) 0.708 (0.38) Size Category 2 0.59* (0.30) 0.59** (0.30) Size Category 3 0.78*** (0.21) 0.78**t (0.22) Size Category 4 0.16 (0.25) 0.16 (0.25) Size Category 5 -0.51 (0.38) -0.51 (0.37) Size Category 7 -0.61 (0.38) -0.61 (0.38) Size Category 8 - 1.31 *** (0.51) - 1.30*** (0.51) Size Category 9 -1.75** (0.85) -1.74** (0.85) Size Category 10 -18.12*** (0.55) -18.09*** (0.55) 1928 -0.29 (0.27) -0.29 (0.27) 1929 0.05 (0.27) O.05 (0.28) 1930 1.06*** (0.26) 1.06*** (0.26) Constant -3.52*** (0.74) -0.61 (4.77)

Observations 1614 1613 Log-likelihood -665.7 -665.6

NOTES: The symbols (***), (**), and (*) indicate statistical significance at the 1%, 5So, and 10% level, respectively. Estimated using a negative binomial distribution. Standard errors are clustered to account for multiple observations from the same state. Data on bank failures are from the Annual Report of the Comptroller of the Currency ( 1932). Data on the number of bank, size distribution, branching laws, and number of branches is from the Federal Reserve Board of Governors (1931). Business failure rates are from the U.S. Department of Commerce and farm foreclosures from the Department of Agriculture ( 1936). Deposit insurance data are from Calomiris ( 1992) and White (1981). Size category 6 and the year 1927 are omitted. The dependent variable is the average number of failing banks in each group (where a group is a set of banks in a similar size category in a state).

1320 : MONEY, CREDIT, AND BANKING

likely to fail and smaller banks were more likely to fail.37 After controlling for other factors, farm failure rates and deposit insurance were statistically insignificant.

California had by far the most branches, about 660 in 1926. New York had the second most with approximately 489 branches. As a test of the robustness of our results, we eliminate California from the sample and repeat the estimation. As Table 14 shows, the results are similar in terms of the effects that the two channels had on failures. Interestingly, both the size of effect of competition and its significance are

37. We also tested an alternative hypothesis that branching decreases failures by facilitating a substitute to failure, namely merging with another bank. (We thank Joe Mason for suggesting this idea.) According to this view, branching reduces failures because it facilitates mergers by expanding the pool of possible merger partners from banks within the city to all state banks within the state. We tested this hypothesis by constructing an index of the ease with which banks might merge. This index is the number of mergers divided by the total number of bank exits (mergers, failures, and voluntary liquidations). However we do not find a significant relationship between this measure and the number of failures.

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TABLE 13

EFFECTS OF BRANCHING ON NATIONAL BANK FAILURES USING LAGGED BRANCHING, 1927-30

Coefficient Estimate S.E. Coefficient Estimate S.E.

Ratio of Branches of -3.64** (1.61) -3.92** (1.72) State Banks to Total Bank Oices

Share of National Banks 1.77 (2.93) with Branches

Ratio of National Branches 0.36 (0.24) to National Banks

Log Banks 057*** (0.11) 0.56*** (0.11) Herfindahl Index -0.18 (1.71) -0 40 (1.77) Business Fail Rate -0.08 (0.37) -0.10 (0.38) Farm Failure Rate 33.46 (98.64) 23.57 (98.17) Deposit Insurance 0.32 (0.33) 0.31 (0.33) Size Category 1 0.69* (0.38) 0.67* (0.38) Size Category 2 0.59* (0.31) 0.57* (0.31) Size Category 3 0.77*** (0.22) 0.77*** (0.22) Size Category 4 0.15 (0.25) 0.15 (0.25) Size Category 5 -0.52 (0.38) -0.52 (0.38) Size Category 7 -0.62 (0.38) -0.63* (0.38) Size Category 8 - 1.30*8 (0.51) - 1.32*** (0.51) Size Category 9 -1.76** (0.85) - 1.77** (0.85) Size Category 10 -17.60*** (0.56) -17.64*** (0.55) 1928 -0.28 (0.27) -0.30 (0.27) 1929 0.06 (0.28) 0.03 (0.28) 1930 1.08*** (0.26) 1.05*** (0.26) Constant -3.50*** (0.73) -3.71*** (0.71)

Observations 1613 1613 Log-likelihood -656.0 -655.5

NOTES: The symbols (***), (**), and (*) indicate statistical significance at the 1%, 5%, and 10% level, respectively. Estimated using a pooled sample and a negative binomial distribution. Standard errors are clustered to account for multiple observations from the same state. Data on bank failures are from the Annual Report of the Comptroller of the Currency (1932). Data on the number of bank, size distribution, branching laws, and number of branches is from the Federal Reserve Board of Governors (1931). Business failure rates are from the U.S. Department of Commerce and farm foreclosures from the Department of Agriculture (1936). Deposit insurance data are from Calomiris (1992) and White (1981). Size category 6 and the year 1927 are also omitted. The dependent variable is the Number of failing banks in each group (where a group is a set of banks in a similar size category in a state and by year).

MARK CARLSON AND KRIS JAMES MITCHENER : 1321

greater when California is excluded from the sample. The same size increase in the ratio of branches of state banks to total bank offices (again about one-tenth of a standard deviation) now reduces the number of failures by about 60%. The reported coefficients on the other variables are quite similar to those shown in the previous table.

To check the sensitivity of our results to the choice of stability measures, we also considered an alternative dependent variable the share of assets at national banks affected by failures. The results (not reported) are similar to what is displayed in Tables 12 and 13. States with more extensive state branching had a smaller share of assets located in failing national banks.

4. CONCLUSIONS

This paper revises our understanding of the role that branching played in improving the stability of banking systems during the 1920s and 1930s. Diversification was not

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TABLE 14

EFFECTS OF INITIAL LEVEL OF BRANCHING ON NATIONAL BANK FAILURES EXCLUDING CALIFORNIA,

1927-30

Coefficient Estimate S.E. Coefficient Estimate S.E.

Ratio of Branches of - 5.96*** (2.15) - 6.35*** (2.29) State Banks to

Total Bank Offices

(1922 value)

Share of National Banks - 6.76 (16.40) with Branches

(1922 value)

Ratio of National Branches - 0.40 (4.82) to National Banks

(1922 value)

Log Banks 0.54*** (0.11) 0.54*** (0.11)

Herfindahl Index - 0.21 (1.69) - 0.13 (1.80)

Business Fail Rate - 0.08 (0.38) - 0.10 (0.37)

Fann Failure Rate 6.69 (103.47) 13.17 (102.09) Deposit Insurance 0.34 (0.35) 0.31 (0.33)

Size Category 1 0.66* (0.39) 0.67* (0.39)

Size Category 2 0.53* (0.31) 0.54* (0.31)

Size Category 3 0.79*** (0.22) 0.79*** (0.22)

Size Category 4 0.15 (0.26) 0.15 (0.26)

Size Category 5 -0.49 (0.38) -0.49 (0.38)

Size Category 7 -0.65 (0.40) -0.65* (0.40)

Size Category 8 -1.30** (0.51) -1.30** (0.51)

Size Category 9 -1.75** (0.86) -1.74** (0.86)

Size Category 10 -17.83*** (0.58) -18.10*** (0.59)

1928 -0.29 (0.28) -0.28 (0.27)

1929 0.06 (0.28) 0.06 (0.29)

1930 1.06*** (0.27) 1.06*** (0.27)

Constant -3.27*** (0.75) -2.90 (4.80)

Observations 1573 1573

Log-likelihood - 635.4 - 635.6

NOTES: The symbols (***), (**), and (*) indicate statistical significance at the 1%, 5%, and 10% level, respectively. Estimated using a negative binomial distribution. Standard errors are clustered to account for multiple observations from the same state. Data on bank failures are from the Annual Report of the Comptroller of the Currency (1932). Data on the number of bank, size distribution, branching laws, and number of branches is from the Federal Reserve Board of Governors (1931). Business failure rates are from the U.S. Department of Commerce and farm foreclosures from the Department of Agriculture (1936). Deposit insurance data are from Calomiris (1992) and White (1981). Size category 6 and the year 1927 are omitted. The dependent variable is the average number of failing banks in each group (where a group is a set of banks in a similar size category in a state).

1322 : MONEY, CREDIT, AND BANKING

the primary channel through which branch banking made state banking systems more resistant to shocks. Instead, the expansion of statewide branch banking induced greater competition in states where it was permitted and improved the stability of their banking systems by removing weak and inefficient banks. Our results are largely consistent with recent literature that has examined the effects of deregulation in other settings. Like Amel and Liang (1997), who examine banks and branching in the 1980s, and Claessens, Demirguc-Kunt, and Huizinga (2001), who look at banks expanding internationally, we find that the growth of branch banking in the 1920s is associated with lower profits in the latter part of the decade. Our results put the well-documented response of unit bankers (particularly those located in rural areas) to the growth of branch banking in the 1920s in proper perspective. Because

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MARK CARLSON AND KRIS JAMES MITCHENER : 1323

the growth of statewide branching was eroding the monopoly profits that unit bankers had previously enjoyed, they responded (as predicted by the economic theory of regulation) by lobbying state and federal governments to legally limit it from spread- ing. As a result of their influence, many states consequently continued to prohibit branching during the 1920s.

Similar to what Jayaratne and Strahan (1998) and Stiroh and Strahan (2003) find for the branching deregulation of the 1980s, we hnd that this more competitive atmosphere is associated with a higher exit rate. This confirms what some economic historians have suggested, but not shown. As White (1985, p. 291) contends, "The number of small banks in rural areas needed to be reduced, and the mergers assisted the weaker institutions with less pain than the massive failures that followed. Unfortunately, this development was stifled by regulations in most states that forbade branch banking." The market upheaval and increase in competition resulting from the removal of legal barriers to entry, however, resulted in longer-run stability, with fewer failures in states where branching had spread. Our results also confirm the hypothesis of Berger, Demsetz, and Strahan (1999), that competition prompts weaker banks to leave the banking system, and parallels the findings of Kaminsky and Schmukler (2002), that international financial liberalization causes some initial turbu- lence in financial markets but over time results in reduced volatility.

APPENDIX A

Data Sources Two main sets of data are employed. The first consists of state-level aggregate

data on national banks, covering the period 1922-30. These data are used to compare the prevalence of entry and exits between states with different branching regimes. Information on number of banks, mergers, voluntary liquidations, and new banks are compiled using the Annual Report of the Comptroller of the Currency (1922-30).

The second contains additional information on national banks in each state between 1926 and 1930. These data are drawn from the Federal Reserve Board of Governors' (1931) Report on Branch, Chain and Group Banking, Volume 9: Bank Profts and further categorize banks by size (based on the sum of loans and investments). This source also includes the information on bank profits by size category. Data from this Federal Reserve report are also used for the construction of the Herfindahl index of banking concentration. The four-firm bank concentration index is calculated using data on the four largest national banks in each state from Polk's Bank Directory (various years).

Bank failures for 1926-30 are taken from the Annual Report of the Comptroller of the Currency (1932, Table 43, pp.208-228) and the Comptroller of the Currency's Statements of National Banks (1925-29), and are matched to the appropriate size category for the appropriate state using the information contained in Federal Reserve Board of Governors (1931).

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State bank data are matched to branching laws and to indicators of state economic activity. Information on the branching laws for each state are from the Federal Reserve's Report on Branch, Chain and Group Banking, Volume 2: Branch Banking in the United States. This reports the developments in each state's branch banking laws from 1909 until 1931. Detailed information on state branching laws (including whether the de jure rather than the de facto situation was used) is shown in Appendix B1. Information on Deposit Insurance follows Calomiris (1992) and White (1981). Business failures and population estimates are from the U.S. Department of Com- merce, Statistical Abstract of the United States (various years). Farm foreclosure (bankruptcies) rates are computed using data from U.S. Department of Agricul- ture (1936) while income shares are derived from the 1920 Census.

APPENDIX B

TABLE B1

BRANCH BANKING LAWS

State Type Year Law Passed Notes

Alabama Unit 1911 A few banks have branches Arizona Statewide 1901 Branching had been practiced before law came

into effect Arkansas Unit 1923 State commissioner authorizes a few "exceptions" California Statewide 1909 Branching had been practiced before law came

into effect Colorado Unit 1909 Connecticut Unit 1902 Delaware Statewide 1895 If charter allows Florida Unit 1913 Georgia Multiple 1929 Branching allowed until 1927, banned until 1929

when it is permitted in Savannah and Atlanta Idaho Unit 1919 Illinois Unit 1923 Prior to 1923, branches had been "not authorized" Indiana Unit 1921 Iowa Unit 1927 Prior to 1927, branches had been "not authorized" Kansas Unit* 1929 Law against it enacted in 1929. Previously there

was no law Kentucky Statewide* 1895 In 1902 the banking authority stated that law did not

authorize, but "was not construed as prohibitive." In 1909 the courts say the banks cannot have branches but can have "offices to receive deposits and pay checks or transact other necessary duties not requiring special discretion or business acumen." Observers at the time noted little difference between these agencies and branches.

Louisiana Limited 1902 Two branches in the same parish Maine Limited 1895 Only in the county of the home office or

contiguous counties Maryland Statewide 1910 Branching had been practiced before law

came into effect Massachusetts Limited 1928 Trusts can have branches in the same city, prior to

1928 were limited to one branch Michigan Limited* 1895 No law, but a lot of branches in the city

of the home office

(continue)

1324 : MONEY, CREDIT, AND BANKING

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TABLE B1

CONTINUED

State Type Year Law Passed Notes

NOTES: All types are as indicated by law (de jure) except were indicated by a * in which case the de facto type is used. Please consult the table for the reason the de jDsre is not used. Source: Federal Reserve Board of Governors (1931), lVeport of the Branchw, Chain, and Grosp Banking Comntittee, Volume 9.

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Allen, Franklin, and Douglas Gale (2000). Comparing Financial Systems. Cambridge, MA: MIT Press.

Alston, Lee, Wayne Grove, and David Wheelock (1994). "Why Do Banks Fail? Evidence from the 1920s." Explorations in Economic History 31, 409-431.

MARK CARLSON AND KRIS JAMES MITCHENER : 1325

Minnesota M. . . .

SSlSSlppl Missouri Montana Nebraska Nevada New Hampshire

New Jersey New Mexico

New York North Carolina

North Dakota

Ohio Oklahoma Oregon Pennsylvania

Unit Limited Unit Unit Unit Unit Unit*

Limited Unit

Limited Statewide

Unit*

Limited Unit* Unit Limited

1923 1924 1899 1927 1927 1909 1895

1895 1915

1919 1921

1895

1923 1895 1921 1927

Prior to 1924, the law had prohibited branches

No law prior to 1927 No law prior to 1927

Commissioner reported that he was not aware of any law prohibiting or allowing branching

Allows "mercantile corporation which maintains a banking department to continue operations at its branches." A clause in the law specifically for a particular corporation.

Branching within cities of 50,000 Branching had been practiced before law came

into effect Branches were not specifically mentioned, but

the law was construed as not permitting them In contiguous communities No law

Branches are only permitted in places where national banks have branches already. Prior to 1927, it had been in home office-city only.

No law per se, but instead what the capital requirements would be should the bank have branches

No law Law unclear, seems to be allowed in the county of

the home office

Went from "not authorized" to able to establish "agencies" which are branches in all but name

In 1922 authorized for anywhere, in 1928 restricted to cities of 50,000

Technically allowed by law from 1925 to 29, however the commissioner of banking did not permit them

Law of 1921 has banks articles of incorp. (AoI) state the "place or places where its offices be located," while the 1926 law has the AoI state the "place where its office..."

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South Dakota Tennessee

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Wisconsin Wyoming

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Multiple

Unit Unitt

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1895 1925

1905 1917 1929

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1909 1926

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  • Issue Table of Contents
    • Journal of Money, Credit and Banking, Vol. 38, No. 5 (Aug., 2006), pp. 1127-1403
      • Front Matter
      • The Predictive Content of the Output Gap for Inflation: Resolving In-Sample and Out-of-Sample Evidence [pp. 1127-1148]
      • Has U.S. Monetary Policy Changed? Evidence from Drifting Coefficients and Real-Time Data [pp. 1149-1173]
      • Taylor Rules and the Deutschmark: Dollar Real Exchange Rate [pp. 1175-1194]
      • Do Bank Loan Relationships Still Matter? [pp. 1195-1209]
      • Macroeconomic Dynamics and Credit Risk: A Global Perspective [pp. 1211-1261]
      • Stock Market Reaction to Financial Statement Certification by Bank Holding Company CEOs [pp. 1263-1291]
      • Branch Banking, Bank Competition, and Financial Stability [pp. 1293-1328]
      • Shorter Papers, Discussions, and Letters
        • Additional Evidence of Long-Run Purchasing Power Parity with Restricted Structural Change [pp. 1329-1349]
        • A Portfolio View of Banking with Interest and Noninterest Activities [pp. 1351-1361]
        • Multiple Regimes in U.S. Monetary Policy? A Nonparametric Approach [pp. 1363-1377]
        • Does Political Instability Lead to Higher Inflation? A Panel Data Analysis [pp. 1379-1389]
        • Euro-Illusion: A Natural Experiment [pp. 1391-1403]
      • Back Matter

CENTRAL BANK COMMUNICATION ON FINANCIAL.pdf

CENTRAL BANK COMMUNICATION ON FINANCIAL STABILITY*

Benjamin Born, Michael Ehrmann and Marcel Fratzscher

Central banks regularly communicate about financial stability issues. This article asks how such communications affect financial markets, based on a unique dataset covering more than 1,000 releases of Financial Stability Reports (FSRs) and speeches by 37 central banks over the past 14 years. The findings suggest that optimistic FSRs lead to significant and potentially long-lasting positive abnormal stock market returns, whereas no such effect is found for pessimistic FSRs. Speeches and interviews, in contrast, have smaller effects on market returns during tranquil times but have been influential during the 2007–10 global financial crisis.

The global financial crisis has triggered heated discussions on how best to achieve financial stability in the future. An important role in that regard has been assigned to central banks, many of which have already had or have been given explicit financial stability mandates. In light of this, a large number of central banks have communicated extensively on financial stability-related matters, e.g. through the publication of Financial Stability Reports (FSRs) and financial stability-related speeches and interviews.

Such communications have at times triggered substantial reactions in financial markets. To give a prominent example, on 5 December, 1996, Alan Greenspan, then chairman of the Federal Open Markets Committee (FOMC), said

Clearly, sustained low inflation implies less uncertainty about the future, and lower risk premiums imply higher prices of stocks and other earning assets. We can see that in the inverse relationship exhibited by price/earnings ratios and the rate of inflation in the past. But how do we know when irrational exuberance has unduly escalated asset values, which then become subject to unexpected and prolonged contractions as they have in Japan over the past decade?

The phrase ‘irrational exuberance’ was interpreted as a warning that markets were overvalued, leading to substantial declines in stock markets worldwide.

* Corresponding author: Michael Ehrmann, European Central Bank, Kaiserstrasse 29, 60311 Frankfurt am Main, Germany, Email: [email protected].

We thank for comments Refet G€urkaynak, our discussant Anja Baum, as well as participants at seminars at Bonn University, HEI Geneva, the BIS, FU Berlin, University of St Gallen, the ECB, University of Navarra, the Bank of England, the 2010 Konstanz Seminar on Monetary Theory and Policy, the 2010 Finlawmetrics conference, the University of M€unster/Viessmann European Research Centre/NBP conference ‘Heteroge- neous Nations and Globalized Financial Markets: New Challenges for Central Banks’, the CEPR/ESI 14th Annual Conference and the BoK-BIS Conference on Macroprudential Regulation and Policy. We are also grateful to a large number of colleagues in various central banks for their help in identifying the release dates of Financial Stability Reports. Earlier versions of this article have been circulated under the title ‘Macroprudential policy and central bank communication’. This article presents the authors’ personal opinions and does not necessarily reflect the views of the European Central Bank.

[ 701 ]

The Economic Journal, 124 (June), 701–734. Doi: 10.1111/ecoj.12039 © 2013 The Author(s). The Economic Journal © 2013 Royal Economic Society.

Published by John Wiley & Sons, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.

The aim of the current article is therefore to shed light on the potential effects of central bank communication about financial stability. Of course, the objective of macroprudential policies is rather broad (and generally not very precisely defined) – it can be described as countering the decline in measured risks during booms and its rise in busts (Brunnermeier et al., 2009). This is also reflected in the aims of the corresponding central bank communication. The ECB’s FSRs, for instance, aim to promote awareness in the financial industry and among the public at large of issues that are relevant for safeguarding the stability of the euro area financial system. By providing an overview of sources of risk and vulnerability for financial stability, the Review also seeks to play a role in preventing financial crises (European Central Bank, 2010, p. 7).1

Given the breadth of this definition, policy makers might want to achieve a multitude of aims, such as affecting asset prices, their volatility, the skewness or kurtosis of returns. They might also want to change market trends, burst bubbles, or reduce the co-movement of asset prices. Furthermore, the objectives are clearly time-varying and depend on market conditions – during financial crises, central banks typically take measures to support the financial system, whereas they would try to lean against financial booms and prevent crises in normal times. For tractability, our article has a relatively narrow focus; it takes a financial market perspective and studies how financial sector stock indices react to the release of such communication, given that the financial sector is one of its main addressees.

We are in particular interested in the effects on returns and on volatility. The analysis of returns allows us to understand to what extent the views that a central bank expresses in its communications get reflected in the markets. For instance, if the central bank expresses a rather pessimistic view about the prospects for financial stability and this view gets heard in financial markets, we would expect that stock prices for the financial sector decline. In that sense, these communications ‘create news’ (Blinder et al., 2008). With regard to volatility, central banks might either have the desire to reduce uncertainty in financial markets (which should lead to a reduction in volatility), or alternatively to generate a two-way risk for financial market participants or to instil greater uncertainty about a market development that the central bank views as undesirable, and thus to increase volatility.

But why and through what channels should central bank communications have an effect on financial markets at all? A number of factors could come into play here. First, the central bank is obviously an important player in financial markets. For instance, if it is ready to change its policy rates, it can affect asset prices directly. Its communication can therefore exert effects through what has been labelled the ‘signalling channel’ in the literature on foreign exchange interventions (Kaminsky and Lewis, 1996). Second, the analyses that feed into the communications are potentially of high quality and there are few other institutions communicating about financial stability, such that a central bank publication might indeed contain news. Thus, a coordination channel might be at play, whereby communication by the central bank works as a coordination

1 In a similar vein, the Bank of England’s FSRs aim ‘to identify the major downside risks to the UK financial system and thereby help financial firms, authorities and the wider public in managing and preparing for these risks’ See http://www.bankofengland.co.uk/publications/fsr/index.htm.

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device, thereby reducing heterogeneity in expectations and information and thus inducing asset prices to more closely reflect the underlying fundamentals, a channel that has also been found to be important to explain the effect of foreign exchange interventions (Sarno and Taylor, 2001; Fratzscher, 2008). This channel might imply that communications have longer-lasting effects, as they might change the dynamics in financial markets.

Importantly, the way central banks reach out to financial markets is most likely time-varying and depends on market conditions – we would expect them to have a much more direct effect during financial crises, when it comes to taking measures to support the financial system, whereas central banks might have a harder time being heard in normal times, when they try to lean against financial booms and prevent crises.

To conduct the empirical analysis, the article constructs a unique and novel database on communication comprising more than 1,000 releases of FSRs and speeches/ interviews by central bank governors from 37 central banks and over the past 14 years. We not only identify the precise timing of these communications but we also determine their content. We employ a computerised textual-analysis software (called Diction 5.0), which allows us to grade each of the central bank financial stability statements, based on different semantic features, according to the degree of optimism that is expressed.

The article’s findings suggest that communication about financial stability has important repercussions for financial sector stock prices. Moreover, there are clear differences between FSRs, on the one hand and speeches and interviews, on the other. FSRs clearly create news in the sense that the views expressed in FSRs move stock markets in the expected direction. This effect is quite sizeable as, on average, FSR releases move equity markets by more than 1% during the subsequent month. Another important finding is that FSRs also reduce market volatility. These effects are particularly strong if the FSR contains an optimistic assessment of the risks to financial stability, when FSRs are found to move equity markets upwards in up to two thirds of the cases. Speeches and interviews, in contrast, have only modest effects on stock market returns and tend not to reduce market volatility.

However, the effects of FSRs and speeches crucially depend on market conditions and other factors. Importantly, during the financial crisis, FSRs were moving financial markets less than before the crisis, while speeches by governors started to move financial markets. Finally, the results indicate that financial stability communication of central banks influences financial markets primarily via a coordination channel, i.e. it provides relevant information which exerts a significant and persistent effect on markets. Our results generalise to overall stock market indices, i.e. are not confined to financial sector stocks.

The article shows that while the release schedule of FSRs is pre-scheduled, speeches and interviews are a much more flexible communication tool. For instance, their number is clearly positively correlated with financial market volatility. Given their flexibility, speeches and interviews by definition carry some surprise element. Since it is often at the discretion of the central bank governors whether or not to make statements about financial stability, the fact that a governor feels compelled to raise financial stability issues in a speech or an interview can therefore be an important

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additional news component. In contrast, due to the fixed release schedule for FSRs, financial markets expect statements about financial stability issues on the release days. There might be surprising elements in their content, but the mere fact that the FSR is released does not come as a surprise. This difference might be at the heart of the different effects of the two instruments on market volatility.

The empirical findings of the article raise a number of policy issues. Communication on financial stability by a central bank has been watched closely and is likely to be watched even more closely in the future, and thus can potentially have an important influence on financial markets. Does this imply that central banks should limit transparency and their communication on financial stability, as argued by Cukierman (2009), or does this make the case for enhanced transparency and accountability, as argued by others (Born et al., 2011)? The findings of the article underline that communication by monetary authorities on financial stability issues can indeed influence financial market developments. Yet the findings also show that such communication may unsettle markets. Hence central bank communication on financial stability needs to be employed with utmost care, stressing the difficulty of designing a successful communication strategy on these matters.

The article proceeds in Section 1 by relating the current article to the existing literature. Section 2 explains the dataset underlying the empirical analysis. In particular, it reports how the measures for central bank communication have been extracted and quantified. It also presents the event study methodology that we employ. Section 3 discusses the empirical results and implications and presents robustness tests. Section 4 concludes.

1. Related Literature

This article relates to the recent literature on central bank communication (for a survey, see Blinder et al., 2008), which has identified three main objectives for monetary policy-related communication, namely

(i) to make central banks credible, (ii) to enhance the effectiveness of monetary policy and (iii) to make central banks accountable.

Interestingly, for all three objectives, there are clear parallels with financial stability- related communication (Born et al., 2012). At the same time, however, there are also important differences between these two types of communication. First, the opera- tional objective of monetary policy is generally much more precisely defined than that of financial stability, which often has multiple facets, instruments and, moreover, authorities that are in charge of it, thus contributing to the complexity of communication about financial stability.

Central banks have become much more transparent about their conduct of monetary policy over the last decades, along with an increasing importance given to communication. There is a debate on possible limits to central bank transparency (Morris and Shin, 2002; Mishkin, 2004; Svensson, 2006) but the arguments are much less contentious than in the case of financial stability-related communication. As demonstrated by Cukierman (2009), a clear case for limiting transparency can be made

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when the central bank has private information about problems within segments of the financial system. Release of such information may potentially be harmful, e.g. by triggering a run on the financial system. This suggests that policy makers need to be even more careful when designing a communication strategy with regard to their financial stability objectives.

While the literature on central bank communication for monetary policy purposes has been growing rapidly over the recent decade, the communication on financial stability has received less attention. Oosterloo and de Haan (2004) found that there is often a lack of accountability requirements for central banks’ financial stability objectives. Svensson (2003) argues that through the publication of indicators of financial stability in FSRs, central banks can issue early warnings to economic agents, thereby ideally preventing financial instability from materialising, ensuring that financial stability concerns do not impose a constraint on monetary policy. Cihak (2006, 2007) provides a systematic overview of FSRs as the main communication channel that central banks use for this purpose. He documents, on the one hand, that the reports have become considerably more sophisticated over time, with substantial improvements in the underlying analytical tools and on the other hand, that there has been a large increase in the number of central banks that publish FSRs. The frontrunners are the Bank of England, the Swedish Riksbank and Norges Bank (Norway’s central bank), all of which started publication in 1996/7. It is probably not a coincidence that these three central banks are typically also listed in the group of the most transparent central banks with regard to monetary policy issues (Eijffinger and Geraats, 2006; Dincer and Eichengreen, 2010). In the meantime, around 50 central banks are now releasing FSRs.

A first empirical analysis of FSRs has been conducted by Oosterloo et al. (2007), with the aim of understanding who publishes FSRs, for what motives and with what content. Their results indicate that there are mainly three motives for publication, namely to increase transparency, to contribute to financial stability and to strengthen cooper- ation between different authorities with financial stability tasks. They also find that the occurrence of a systemic banking crisis in the past is positively related to the likelihood that an FSR is published.

Even less work has been done with regard to the effects of financial stability-related communication. To our knowledge, the only exception is Allen et al. (2004), who conducted an external evaluation of the Riksbank’s work on financial stability issues and came up with a number of recommendations, such as making the objective of the Riksbank’s FSRs explicit, providing the underlying data, or expanding the scope of the FSR to, e.g., the insurance sector. The present article aims to fill this gap and analyses how central bank communications about financial stability are received in financial markets.

2. Measuring Communication and the Effects on Financial Markets

This Section introduces the dataset that we develop to study the effects of financial stability-related communication. We start by explaining the choice of data frequency, the sample of countries and time that we use and the choice of the financial sector stock market indices as our measure for financial markets. Subsequently, we describe

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the process for identifying the relevant communications, how their content is coded, and the econometric methodology.

2.1. Choice of Data Frequency, Data Sample and the Relevant Financial Markets

We are interested in the effects of financial stability-related communication on financial markets. A first choice that is required relates to the frequency of the analysis. Given the speed of reactions in financial markets, it is necessary to identify the timing of the events as precisely as possible. Identification of a precise time stamp will allow for an analysis in a very tight time window around the event, thereby ensuring that the market reaction is not distorted by other news. We opted for a daily frequency for two practical reasons. First, given the aim of providing a cross-country study over a relatively long horizon, financial market data are not consistently available at higher frequencies. Second, the identification of the precise days of the release of central bank communications has already not been trivial in many cases, whereas the identification of the exact time of the release within a day is largely impossible. While a higher frequency might have been desirable, it is important to note that daily frequency is commonly employed in the announcements effect literature – for instance, two classic references with regard to the effect of monetary policy on stock markets, Rigobon and Sack (2004) and Bernanke and Kuttner (2005) both use daily data.

The sample of countries and the time period of the study have been determined on the basis of the release of FSRs. We tried to identify the release dates of the FSRs or relevant speeches or interviews by central bank governors for all those central banks listed in Cihak (2006, 2007), i.e. for all central banks which release FSRs. We succeeded in identifying such release dates for 35 countries, 24 of which are advanced economies according to the IMF’s country classification. Additionally, we included the euro area, as well as the US as the only country that does not release an FSR, restricting ourselves to studying the effect of speeches and interviews in this case. In total, our sample therefore covers 37 central banks (see Table 1). Our sample starts in 1996, i.e. the year when the first FSR was released by the Bank of England. The data were extracted in October 2009, such that the sample ends on September 30, 2009. Importantly, this sample allows differentiating the effects during the global financial crisis from those before, as we would expect the central bank influence to differ substantially across these two periods.

As to the selection of a financial market that shall be subject of this study, we opted for stock market indices relating to the financial sector, as we expect that empirical effects of financial stability communication should be most easily detectable for this sector. Such data are available from Datastream back to 1996, i.e. to the start of our sample period, for all the countries in our sample. This choice is partially due to the large cross-country dimension and the need to get historical data for nearly one and a half decades, which limited the availability of less traditional market measures, such as implied volatilities or expected default frequencies (EDFs). While the link of these measures to financial stability would have been relatively direct, we hope that the financial sector stock indices (using Morgan Stanley Capital International (MSCI) indices) provide a measure that is reasonably closely related to financial stability issues, too. All stock indices are expressed in local currency, given that we are interested in the

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Table 1

Summary Statistics for FSRs and Speeches and Interviews

FSRs Speeches and interviews

By country Argentina 12 13 Australia 11 25 Austria 17 11 Belgium 7 3 Brazil 14 9 Canada 14 22 Chile 11 15 China 5 28 Czech Republic 5 11 Denmark 11 2 Euro Area 10 48 Finland 23 12 France 13 31 Germany 5 58 Greece 1 26 Hong Kong 12 44 Hungary 17 17 Indonesia 6 Ireland 4 2 Israel 6 7 Japan 8 32 Netherlands 8 17 New Zealand 10 18 Norway 20 3 Philippines 50 Poland 10 13 Portugal 5 8 Singapore 7 1 South Africa 11 20 South Korea 9 14 Spain 14 10 Sri Lanka 3 2 Sweden 24 18 Switzerland 7 16 Turkey 8 22 United Kingdom 25 23 United States 111

By year 1996 1 14 1997 3 39 1998 5 118 1999 7 56 2000 10 37 2001 14 17 2002 18 33 2003 25 32 2004 40 26 2005 53 17 2006 51 17 2007 54 68 2008 51 179 2009 35 115

Overall 367 768

Note: The Table shows the number of FSRs and speeches and interviews that are contained in the database, by country and by year.

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response of national financial markets to national communication. We will further- more show that our results are robust to using the overall stock market indices, rather than focusing on the financial sector stocks alone.

2.2. Choice and Identification of Communication Events

At the core of this article is a measure of communication events that quantifies their content. We focus on the two most important channels of communication about financial stability issues, namely FSRs and speeches and interviews. FSRs are typically relatively comprehensive documents that discuss various aspects of financial stability. They normally begin with an overall assessment of financial stability in the respective country, often including an international perspective. They usually contain an evaluation of current macroeconomic and financial market developments and the assessment of risks to banks and systemically relevant non-banking financial institutions. Cihak (2006) calls these sections the ‘core’ part of an FSR and differentiates them from the ‘non-core’ part that includes research articles on special issues, often written by outside experts. The weights attributed to these two parts vary considerably across central banks. The spectrum ranges from FSRs that only cover the core part (e.g. Norway) to FSRs which only consist of articles covering a special topic (e.g. France). Most central banks lie somewhere in between this range and are usually closer to the first type. Typically, FSRs are published twice a year, i.e. are relatively infrequent communications.

A second important channel for central banks to communicate about financial stability is to give speeches and interviews. By their very nature, these are much more flexible than FSRs. Their timing can often be chosen flexibly (Ehrmann and Fratzscher (2007) have shown this for monetary policy-related speeches), and their content can be much more focused. Of course, this is also due to the fact that they are much shorter than FSRs.

As we are interested in testing the response of financial markets to central bank communication, we need to identify the release dates as a first step (recall that we will conduct the analysis at a daily frequency, hence there is no need to identify the timing within a given day – as long as the release takes place before markets close). As to FSRs, we carefully ensured a proper identification of their release dates, mainly based on information provided on central banks’ websites and by central bank press offices and complemented with information from news reports about the release of FSRs as recorded in Factiva, a database that contains newspaper articles and newswire reports from 14,000 sources. As shown in Table 1, the dataset contains information on 367 FSRs. The increasing tendency of central banks to publish FSRs is reflected in this database. Starting from less than 10 FSRs per annum in the 1990s, we could identify around 50 FSRs each year in the mid-2000s (note that the drop in numbers in 2009 is entirely due to the fact that the sample ends in September, i.e. covers only three quarters of the year). As to the country coverage, the early publishers are obviously represented more frequently, with 20 and more reports, whereas ‘late movers’ have far fewer observations, down to one for the case of the Bank of Greece, which published its first FSR in June 2009 (for Indonesia and the Philippines, we could not identify the release dates; note that dropping these two countries from the sample does not affect our results in any substantive way).

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Identifying speeches and interviews is more difficult. Our objective is to extract all relevant public statements that relate to financial stability. For tractability, we restricted our search to speeches by the central bank governor – even in cases where a central bank has a member of its governing body who has an explicit assignment regarding financial stability. We used Factiva and extracted all database entries containing the name of the policy maker together with some keywords that appear regularly in the editorials of the FSRs.2 From all hits obtained, we extracted those containing statements by the relevant policy maker with a reference to financial stability issues. Since newswire reports typically record the precise time stamp, we were in a position to allocate the speeches and interviews to the appropriate trading days. Communications during weekends were allocated to the subsequent Monday, communications in the evening – such as dinner speeches – to the subsequent trading day. Furthermore, we very carefully chose only the first report about a given statement, which typically originated from a newswire service. This choice has the advantage that the reporting is very timely, usually coming within minutes of each statement, and that it is mostly descriptive without providing much analysis or interpretation. To avoid double counting, we discarded all subsequent reports or analysis of the same statement.

Several issues are worth noting about this data extraction exercise. First, the search was conducted only for English language items. We might therefore not have discovered all statements, if these were made and reported upon exclusively in other languages. However, this issue should not be very problematic as Factiva also contains newswire reports and the coverage of this topic by newswires is extensive.

Second, one can easily think of other keywords to use in the database search. We have experimented with larger sets, e.g. including also the terms ‘volatile’, ‘volatility’, ‘risk’, ‘adverse’ or ‘pressures’. However, the additional hits typically related to monetary policy communications (such as central bank governors talking about inflationary ‘pressures’, ‘risks’ to price stability etc.), such that the resulting dataset on financial stability communications was basically unaltered.

Third, the news sources might be selective in their reporting, thus possibly not covering all relevant statements. However, given the sensitivity of the topic and the importance that it has for financial markets, we are confident that the coverage is close to complete. Furthermore, as we are interested in testing the market response to communication, it makes sense to focus only on those statements that actually reach market participants and this is best achieved by looking at prominent newswire services.

Fourth, our news sources may wrongly report or misinterpret a statement by policy makers. Again, our objective is to assess communication from the perspective of financial markets and therefore we analyse the information market participants actually receive.

The resulting dataset contains 768 communications. The breakdown by year in Table 1 reveals large time variations, with a massive increase in speeches in 1998,

2 To be precise, we used the following search terms: ‘financial stability or systemic or systemically or crisis or instability or instabilities or unstable or fragile or fragility or fragilities or banking system or disruptive or imbalances or vulnerable or strains’.

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i.e. during the Asian and the Russian crisis, as well as during the financial crisis of 2007–9. This suggests that the occurrence of speeches and interviews is responsive to the prevailing circumstances, which is in stark contrast to FSRs, which are typically released at pre-specified dates. Speeches and interviews do therefore provide the central bank with a very flexible instrument for communicating financial stability concerns, as their timing can often be as the bank wishes.

2.3. Measuring the Content of Communications

Once we have identified the communication events, it is necessary to measure their content in order to make the data amenable to econometric analysis. In other words, we want to capture those dimensions and elements of FSRs and speeches/interviews that are relevant for financial market participants and thus will be reflected in asset prices.

A discussion of the various possibilities of achieving this is provided in Blinder et al. (2008). The simplest option consists of assigning a dummy variable that is equal to one on event days and to zero otherwise. While easily done, this approach limits the analysis severely, namely to a study whether communication affects volatility or absolute returns. If we are interested in the effect of the content of communication, a method for quantification of such content is required. The approach adopted in some parts of the literature on monetary policy-related communication, namely, reading the communications and coding them on various scales, was not feasible for our purposes, given the amount of text that needed to be quantified. We have therefore opted for an automated approach for the current article.3

We used the computerised textual-analysis software DICTION 5.0, which searches text for different semantic features by using a corpus of several thousand words and scores the text along an optimism dimension. This dimension may be important as it provides agents with information about the current state and the prospects of the financial system and underlying risks. The respective scores are computed by adding the standardised word frequencies of various subcategories labelled as optimistic and by subtracting the corresponding frequencies of pessimistic subcategories. In broad terms, optimism refers to ‘language endorsing some person, group, concept or event, or highlighting their positive entailments’.4

This software has been used extensively in communication sciences and in political sciences, e.g. for analysing speeches of politicians (Hart and Jarvis, 1997; Hart, 2000), but has also been applied in the context of central banks (Bligh and Hess, 2007; Armesto et al., 2009). Furthermore, Davis et al. (2012) have used it to measure the

3 An alternative approach is used by Lucca and Trebbi (2009), where FOMC statements are cut down into small segments of text, the semantic orientation of which is then calculated by checking how often these text segments appear in conjunction with the words dovish or hawkish in a large body of text.

4 The scores are computed using scores from six subcategories, by adding the standardised word frequencies of the subcategories labelled as optimism increasing by DICTION (praise, satisfaction and inspiration), while our pessimism score is computed by adding the standardised word frequencies of the subcategories labelled optimism decreasing by DICTION (blame, hardship and denial). The Praise score, for example, includes words that isolate social qualities (witty), physical qualities (strong), intellectual qualities (reasonable), entrepreneurial qualities (successful) and moral qualities (good). For more details, see the DICTION 5.0 manual http://www.dictionsoftware.com/files/dictionmanual.pdf.

© 2013 The Author(s). The Economic Journal © 2013 Royal Economic Society.

710 T H E E C O N O M I C J O U R N A L [ J U N E

reaction of financial markets to earnings announcements and find a significant incremental market response to optimistic and pessimistic language usage in earnings press releases.

There are several advantages of this approach over human coding of the text. First, the software creates a coding that is more mechanical and thus objective, compared to human coding which tends to be more judgmental. While some subjectivity could arise due to the choice of the content of the dictionaries against which a text is assessed, it is important to note that the corpus has been defined based on linguistic theory and without active participation by the authors of this article. Another advantage is the replicability of the coding, which is in stark contrast to human coding and also allows more text to be added without distorting the scoring process. Third, the automated approach allows a consistent coding of long passages of text and across a large number of communications. Human coding of long texts is rather difficult, as no part should in principle be given a larger weight in the assessment. Given the breadth of FSRs, this issue is particularly severe in the current application. At the same time, a drawback of the automated approach is that it does not consider the context of the text and thus cannot generate a ‘tailor-made’ coding for financial stability-related communication.

Based on this computerised textual-analysis software, we computed a score for each individual speech or interview (note that, effectively, we are coding the content of the related news reports, rather than the original source text) and for the overview part of each FSR.5 Subsequently, we transformed the resulting scores into a discrete variable, which takes the value of �1 for the lowest third of the distribution, a value of 0 for the middle part of the distribution, and the value of +1 for the upper third of the distribution. That is, a value of +1 denotes a relatively optimistic text, while a value of �1 corresponds to a relatively pessimistic statement. The discretisation of scores is required for the subsequent analysis, where we are interested in the market effects of optimistic versus pessimistic communications, rather than the effect of an incremental change in tone. This transformation was applied for the speeches as well as for the FSRs. Note that we test for robustness using a very different measurement approach, which also attempts to capture the surprise component contained in the respective communications, as well as (for the parts of the subsequent analysis where discretisation is not required) using the raw optimism scores given by the software.

It is important to note that this implies a relative coding, i.e. a given communication is scored in a comparative fashion against the other texts in the sample. However, due to the large sample, both across countries and along the time dimension, our communications cover periods of relative stability and tranquillity, as well as periods of financial market crises or turbulence. Accordingly, the overall sample of text should be relatively balanced, such that text which is coded with plus or minus one should indeed represent a corresponding opinion. We denote the resulting indicators by I

optimism;FSR it

and I optimism;speech it , respectively, where i denotes a given country and t stands for time.

5 While this overview carries different names across central banks, e.g. editorial, introductory chapter, executive summary, etc., it is rather similar in nature for all FSRs.

© 2013 The Author(s). The Economic Journal © 2013 Royal Economic Society.

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To get back to our initial example of Alan Greenspan’s irrational exuberance speech, both the speech as such and the corresponding Reuters article6 would be classified as pessimistic. In the Appendix, we provide more examples of speeches and interviews and of how they were coded.

2.4. The Event Study Methodology

What are the effects of FSRs and speeches/interviews on financial markets? The natural econometric approach to testing our hypotheses of interest is the event study methodology. We use this methodology because we are interested not only in the contemporaneous effect of financial stability statements, but we also want to know how persistent the effect is over time. We can define the release of an FSR, or the delivery of a speech or an interview as an event. The question we want to address is whether the event affects stock markets in a causal fashion. For that purpose, it is essential that we can compare the stock market evolution following the event to the counterfactual, i.e. a predicted value that we believe would have occurred had the event not happened. A crucial issue in any event study is therefore to find a benchmark model to calculate expected returns, which in turn allows calculation of abnormal returns.7 Most event studies look at the effect of events, such as earnings announcements or stock splits, on individual stocks and use some variant of a factor model, such as the Fama and French (1993) three-factor model or the Carhart (1997) four-factor model, which extends the previous model by a momentum factor.

Given that we are interested in the evolution of national stock market indices rather than of individual stocks, the book-to-market ratio and the size factor of the Fama– French model are not applicable. Following Edmans et al. (2007) and Pojarliev and Levich (2008), we start by defining normal returns as:

Rit ¼ c0i þ c1iRit�1 þ c2iRmt�1 þ c3iRmt þ c4iRmtþ1 þ c5iDt þ c6iTit�1 þ c7iSit�1 þ c8iMit�1 þ eit; ð1Þ

where Rit is the daily local currency return on the financial sector stock market index for country i on day t, Rmt is the daily US dollar return on Datastream’s global financial sector stock market index and Dt denotes dummy variables for Monday to Thursday. Tit�1 stands for the trend in stock markets over the 20 days prior to the event, Sit�1 for the standard deviation of daily stock market returns over the 20 days prior to the event and Mit�1 for the ‘misalignment’ of stock indices on the day preceding the event, measured as the percentage deviation of the stock indices from their national average over the entire sample period.

The first five factors follow Edmans et al. (2007). The lagged index return controls for possible first-order serial correlation. The global stock market index is meant to capture the effects of international stock market integration and since some indices

6 ‘Stock markets can become too exuberant – Greenspan. WASHINGTON, Dec 5 (Reuters) – The Federal Reserve must be wary when “irrational exuberance” infects stock and other asset markets because that could end up doing damage to the economy, Fed Chairman Alan Greenspan said on Thursday. […]’.

7 For overviews of the event study literature see, for example, MacKinlay (1997) or Kothari and Warner (2007).

© 2013 The Author(s). The Economic Journal © 2013 Royal Economic Society.

712 T H E E C O N O M I C J O U R N A L [ J U N E

might be lagging or leading the world index, Edmans et al. (2007) not only include the contemporaneous global returns, but furthermore a lead and a lag. By doing so, we assume exogeneity of Rm – if outcomes of central bank communications are correlated across countries, this biases the results against us. We test for the presence of international spillovers and find these to be very limited, such that the assumption of exogeneity might not be particularly stringent. The last three terms are due to earlier event studies on exchange rates such as Pojarliev and Levich (2008) or Fratzscher (2009). The trend factor attempts to allow for persistence in stock market movements and is therefore closely related to the momentum factor in the Carhart four-factor model. The inclusion of the standard deviation is an attempt to capture the effect of market volatility. Finally, the misalignment factor is based on the idea that there might be booms or busts in stock markets and that over a sufficiently long sample, there could be some mean reversion (albeit possibly allowing for a drift). We test for robustness to the exclusion of these last three terms, given that they are derived from the exchange rate literature rather than the stock market event studies and find our results to be qualitatively unaltered.

Model (1) is estimated country by country, only including days that were neither preceding nor preceded by communication events for 60 days (in each direction). Based on the estimated parameters (denoted by hats), it is then possible to calculate abnormal returns on event days as

êit ¼ Rit � ðĉ0i þ ĉ1iRit�1 þ ĉ2iRmt�1 þ ĉ3iRmt þ ĉ4iRmtþ1 þ ĉ5iDt þ ĉ6iTit�1 þ ĉ7iSit�1 þ ĉ8iMit�1Þ: ð2Þ

The hypothesis to be tested is whether communication leads to abnormal returns in the expected direction, i.e. whether

êit [ 0 if I optimism;c it ¼ 1 or êit\0 if I

optimism;c it ¼ �1; ð3Þ

where the superscript c stands for the two communication types, FSR and speeches or interviews. A more complex approach is required if we want to calculate the longer- term effects of communication beyond the event day. While we assume that world markets are exogenous to a communication in an individual country also over extended time windows, this is obviously not the case for the own lag, the recent trend, standard deviation and misalignment: as of the second day, it is necessary to calculate predicted returns for the preceding day and to substitute these into (2), thus yielding

êitþk ¼ Ritþk � ðĉ0i þ ĉ1iR̂itþk�1 þ ĉ2iRmtþk�1 þ ĉ3iRmtþk þ ĉ4iRmtþkþ1 þ ĉ5iDtþk þ ĉ6iT̂itþk�1 þ ĉ7iŜitþk�1 þ ĉ8iM̂itþk�1Þ: ð4Þ

Note that compared to (2), Rit�1, Tit�1, Sit�1 and Mit�1 have all been replaced by their predicted value in the absence of a communication event. For k ¼ 0, the two coincide, whereas for all days k > 0, it is important to calculate the appropriate predicted values. Tests for the effects of communication over longer time horizons with a time window of K days then amount to asking whether

XK

k¼0 êitþk [ 0 if I

optimism;c it ¼ 1 or

XK

k¼0 êitþk\0 if I

optimism;c it ¼ �1: ð5Þ

© 2013 The Author(s). The Economic Journal © 2013 Royal Economic Society.

2014] F I N A N C I A L S T A B I L I T Y C O M M U N I C A T I O N 713

Following common practice in the event study literature, we employ two types of tests for the effects of communications (both described in detail in MacKinlay, 1997). First, we apply a non-parametric sign test to study whether the above conditions hold in more than 50% of all cases. The underlying idea is that by construction – if the factor model is correct – abnormal returns and cumulated abnormal returns are on average zero and that it is equally probable that they are positive or negative. If the events systematically move stock markets in the expected direction, we should find that the abnormal returns are non-zero and of the expected sign, in significantly more than 50% of cases. The second (parametric) test checks the average size of the (cumulated) abnormal returns and tests these against the null hypothesis that they are zero.

In a similar vein, to test whether communications affect stock market volatility, we furthermore study whether

rêi;t=tþk \rêi;t�1=t�1�k if D c it ¼ 1; ð6Þ

where rêi;t=tþk is the standard deviation of daily abnormal returns in country i from time t to t + k, rêi;t�1=t�1�k , their standard deviation over the k days prior to the event and D

c it a

dummy variable that is equal to one on the days when a communication of type c is released in country i.8 Also here, we apply the non-parametric sign test whether the above conditions hold in more than 50% of all cases and the test whether the difference of the two standard deviations is equal to zero.

3. The Effects of Financial Stability-related Communication

This Section starts by identifying and testing for the effects of FSRs on financial markets (subsection 3.1). It proceeds by presenting a number of sample splits and robustness tests that also shed further light on the channels through which FSRs affect markets (subsection 3.2), before it turns to discussing the effect of speeches and interviews in subsection 3.3.

3.1. Effects of FSRs

We now turn to the question of the extent to which FSRs were affecting financial markets. A first test is provided in Figure 1, which compares the actual evolution of stock markets following FSRs to the predicted evolution on the basis of the benchmark model (1). The solid line plots the average actual cumulated returns over 60 days following the communication events. The dashed line, in contrast, shows the expected cumulated returns that would result from the benchmark model in the absence of a communication event. To combine pessimistic as well as optimistic FSRs in one chart, the cumulated returns are multiplied by �1 for pessimistic communications, whereas they are left unchanged for optimistic communications. Accordingly, we would expect the actual returns to lie above the predicted returns after FSRs if the markets follow the point of view expressed by the central bank (i.e. we observe negative abnormal returns

8 Excluding the daily abnormal returns on day t from calculating the post-event standard deviations does not alter our results. This implies that the results are not driven by the initial market reaction on the day of the announcement.

© 2013 The Author(s). The Economic Journal © 2013 Royal Economic Society.

714 T H E E C O N O M I C J O U R N A L [ J U N E

in response to pessimistic statements and positive ones in the case of optimistic communications).

Figure 1 provides a compelling picture of the effects of central bank communication. It shows that markets move in the direction of the central bank view, since the actual returns are substantially larger than the predicted returns. Moreover, the effect is quite sizeable economically: for several time windows, FSR releases move equity markets on average by more than 1% in the direction indicated by the FSRs.

Figure 1 also suggests that central bank communications are potentially affecting financial markets even at very long horizons, given that the gap between predicted and actual cumulated returns is present for the entire horizon of time windows we look at and begins to narrow only towards the end of the horizon.

The formal test results for the effects of FSRs are provided in Table 2. The first set of results relates to (5), i.e. tests whether optimistic statements yield positive abnormal returns, and pessimistic ones lead to negative abnormal returns. The first column shows the share of cases in which the condition was met, as well as the results of the non-parametric sign test. Shares above 0.5 would suggest that stock markets move in the direction of the content of communications. The statistical significance is assessed by asterisks (*** for 1%, ** for 5% and * for 10% significance) – whereas numbers that are significantly smaller than 0.5 would be characterised by apostrophes (‴ for 1%, ″ for 5% and ′ for 10% significance).

There is clear evidence that the views represented in FSRs get reflected in financial markets, in significantly more than 50% of all cases. In terms of magnitudes, which

–0.5

0

0.5

1.0

1.5

2.0

2.5

1 2 3 4 5 10 15 20 25 30 35 40 45 50 55 60

Predicted Actual

Fig. 1. Predicted Versus Actual Evolution of Stock Markets After FSRs Notes. The Figure compares the actual evolution of cumulated stock market returns (in %) following FSRs to the predicted evolution on the basis of the benchmark model (1). The solid line plots the average actual cumulated returns starting from day 1 after the communication event and up to day 60. The dashed line shows the expected cumulated returns that would result from the benchmark model in the absence of a communication event. The cumulated returns are multiplied by �1 for pessimistic communications, whereas they are left unchanged for optimistic communications.

© 2013 The Author(s). The Economic Journal © 2013 Royal Economic Society.

2014] F I N A N C I A L S T A B I L I T Y C O M M U N I C A T I O N 715

T a b le

2

E ff ec ts of

F S R s

N o o f d a ys

Jo in t m o d e l

P e ss im

is ti c F S R s

R e tu rn s

S D

R e tu rn s

S D

N o n -p a ra m e tr ic

P a ra m e tr ic

N o n -p a ra m e tr ic

P a ra m e tr ic

N o n -p a ra m e tr ic

P a ra m e tr ic

N o n -p a ra m e tr ic

P a ra m e tr ic

1 0 .5 4

0 .2 7 * * *

– –

0 .4 4 ′

�0 .3 3

– –

2 0 .5 4

0 .3 3 * *

– –

0 .4 6

�0 .5 4

– –

3 0 .5 8 * *

0 .4 6 * * *

– –

0 .4 0 ″

�0 .7 5

– –

4 0 .5 7 * *

0 .5 4 * * *

0 .5 1

�0 .0 8 *

0 .3 9 ‴

�0 .7 3

0 .5 0

�0 .1 0

5 0 .5 3

0 .4 4 * *

0 .5 3

�0 .0 7 *

0 .4 7

�0 .4 9

0 .5 1

�0 .0 5

1 0

0 .5 3

0 .6 3 * *

0 .5 5 * *

�0 .0 8 * *

0 .5 0

�0 .4 0

0 .4 8

�0 .0 4

1 5

0 .5 7 * *

0 .6 4 * *

0 .5 2

�0 .0 6 *

0 .4 4 ′

�0 .2 9

0 .5 1

�0 .0 8

2 0

0 .5 6 * *

0 .9 2 * *

0 .5 5 * *

�0 .0 5 *

0 .5 0

�0 .0 1

0 .5 6 *

�0 .0 7

2 5

0 .5 7 * *

1 .2 7 * * *

0 .5 6 * * *

�0 .0 7 * *

0 .4 8

�0 .2 8

0 .6 0 * *

�0 .1 3 * *

3 0

0 .5 8 * * *

1 .3 9 * * *

0 .5 6 * * *

�0 .0 5 *

0 .4 9

�0 .3 6

0 .5 7 * *

�0 .1 1 *

3 5

0 .5 7 * *

1 .2 7 * * *

0 .5 6 * * *

�0 .0 5 *

0 .5 1

0 .1 1

0 .5 6 *

�0 .1 0 *

4 0

0 .5 3

1 .2 1 * *

0 .5 5 * *

�0 .0 4

0 .5 4

0 .3 3

0 .5 2

�0 .0 7

4 5

0 .5 6 * *

1 .4 1 * * *

0 .5 5 * *

�0 .0 5 *

0 .5 2

0 .1 7

0 .5 6 *

�0 .1 2 * *

5 0

0 .5 6 * *

1 .6 0 * * *

0 .5 6 * * *

�0 .0 6 * *

0 .5 1

�0 .1 1

0 .5 6 *

�0 .1 2 * *

5 5

0 .5 6 * *

1 .4 7 * *

0 .5 6 * *

�0 .0 5 *

0 .5 5

0 .3 1

0 .5 8 * *

�0 .1 1 * *

6 0

0 .5 5 *

1 .2 1 * *

0 .5 5 * *

�0 .0 5 *

0 .5 4

0 .6 2

0 .5 8 * *

�0 .1 1 * *

© 2013 The Author(s). The Economic Journal © 2013 Royal Economic Society.

716 T H E E C O N O M I C J O U R N A L [ J U N E

N o o f d a ys

N e u tr a l F S R s

O p ti m is ti c F S R s

R e tu rn s

S D

R e tu rn s

S D

N o n -p a ra m e tr ic

P a ra m e tr ic

N o n -p a ra m e tr ic

P a ra m e tr ic

N o n -p a ra m e tr ic

P a ra m e tr ic

N o n -p a ra m e tr ic

P a ra m e tr ic

1 0 .4 9

�0 .0 9

– –

0 .5 3

0 .2 0 *

– –

2 0 .5 2

0 .1 0

– –

0 .5 5

0 .1 4

– –

3 0 .5 3

0 .1 8

– –

0 .5 5

0 .2 0

– –

4 0 .5 1

0 .1 2

0 .5 2

�0 .1 3 * *

0 .5 4

0 .3 7 * *

0 .5 1

�0 .0 2

5 0 .5 5

0 .2 1

0 .5 5

�0 .1 1 *

0 .5 4

0 .3 9 *

0 .5 3

�0 .0 5

1 0

0 .4 8

�0 .3 8

0 .5 5

�0 .0 8

0 .5 6 *

0 .8 4 * * *

0 .6 1 * * *

�0 .1 1 * *

1 5

0 .5 1

0 .0 2

0 .5 0

�0 .0 2

0 .5 8 * *

0 .9 5 * * *

0 .5 5 *

�0 .0 8 * *

2 0

0 .6 1 * *

0 .5 1

0 .5 5

�0 .0 2

0 .6 1 * * *

1 .7 5 * * *

0 .5 5

�0 .0 6 * *

2 5

0 .5 9 * *

0 .6 9

0 .5 5

�0 .0 4

0 .6 2 * * *

2 .1 8 * * *

0 .5 4

�0 .0 5

3 0

0 .5 9 * *

0 .9 0 *

0 .5 7 *

�0 .0 2

0 .6 5 * * *

2 .3 3 * * *

0 .5 5

�0 .0 3

3 5

0 .6 2 * * *

1 .5 0 * *

0 .5 5

�0 .0 4

0 .6 4 * * *

2 .5 3 * * *

0 .5 8 * *

�0 .0 1

4 0

0 .6 1 * *

1 .4 7 * *

0 .5 7 *

�0 .0 3

0 .5 9 * *

2 .6 3 * * *

0 .5 6 *

�0 .0 1

4 5

0 .5 7 *

1 .4 5 * *

0 .5 8 * *

�0 .0 4

0 .6 3 * * *

2 .8 6 * * *

0 .5 2

�0 .0 1

5 0

0 .5 9 * *

1 .4 6 *

0 .5 8 * *

�0 .0 5

0 .6 3 * * *

2 .9 7 * * *

0 .5 5

�0 .0 1

5 5

0 .6 1 * *

2 .1 2 * *

0 .5 7 *

�0 .0 4

0 .6 6 * * *

3 .0 9 * * *

0 .5 2

0 .0 0

6 0

0 .6 1 * *

2 .6 6 * * *

0 .5 5

�0 .0 5

0 .6 3 * * *

2 .8 7 * * *

0 .5 2

0 .0 0

N ot es : T h e

T a b le

sh o w s re su lt s o f th e

te st

fo r co

m m u n ic a ti o n

e ff e ct s.

T h e

fi rs t se t o f re su lt s (r e tu rn s,

n o n -p a ra m e tr ic ) te st s th e

sh a re

o f ca se s in

w h ic h

P K k ¼0

ê i tþ

k [

0 if

I o p ti m is m ;F S R

it ¼

1 o r P

K k ¼0

ê i tþ

k \ 0 if

I o p ti m is m ;F S R

it ¼

�1 , fo r d if fe re n t ti m e w in d o w s K

in th e ro w s o f th e T a b le . T h e se co

n d

co lu m n

(r e tu rn s,

p a ra m e tr ic ) sh o w s th e a ve ra g e si ze

o f th e cu

m u la te d a b n o rm

a l re tu rn s ð1 = N ÞP

N n ¼1

P K k ¼0

I o p ti m is m ;F S R

n t

ê n tþ

k a n d te st s w h e th e r th e se

a re

d if fe re n t fr o m

ze ro . T h e

co lu m n s fo r ‘S D ’ sh o w th e sh a re

o f ca se s in

w h ic h th e st a n d a rd

d e vi a ti o n o f a b n o rm

a l re tu rn s o ve r k d a ys

a ft e r th e re le as e o f a n F S R is sm

a ll e r th a n th e st a n d ar d

d e vi a ti o n

d u ri n g th e k d a ys

p ri o r to

th e re le as e , i. e . r ê i ;t = tþ

k \ r ê i ;t �1

= t�

1 �k

if D

c it ¼

1 (n

o n -p a ra m e tr ic ),

a n d

th e ir

a ve ra g e d if fe re n ce

(p a ra m e tr ic ) a n d

te st s th e se

a g a in st 0 .5

a n d 0 , re sp e ct iv e ly . T h e p a n e ls ‘P e ss im

is ti c F S R s’ , ‘N

e u tr a l F S R s’ a n d ‘O

p ti m is ti c F S R s’ o f th e T a b le

re p e a ts th e e x e rc is e fo r F S R s th a t h a ve

b e e n co

d e d a s

I o p ti m is m ;F S R

it ¼

�1 , I o p ti m is m ;F S R

it ¼

0 a n d

I o p ti m is m ;F S R

it ¼

1 , re sp e ct iv e ly . S ta n d a rd

d e vi a ti o n s a re

o n ly

ca lc u la te d

fo r ti m e w in d o w s e x ce e d in g th re e b u si n e ss

d a ys .

* * * , * * , a n d * in d ic a te

st a ti st ic a l si g n ifi ca n ce

a g a in st th e n u ll h yp o th e si s a t th e 1 % , 5 %

a n d 1 0 %

le ve ls , re sp e ct iv e ly . ‴,

″, a n d ′ in d ic a te

st a ti st ic a l si g n ifi ca n ce

a g a in st

th e a lt e rn a ti ve

h yp o th e si s a t th e 1 % , 5 %

a n d 1 0 %

le ve ls , re sp e ct iv e ly .

T a b le

2

(C on ti n u ed )

© 2013 The Author(s). The Economic Journal © 2013 Royal Economic Society.

2014] F I N A N C I A L S T A B I L I T Y C O M M U N I C A T I O N 717

are reported in the second column, FSRs generate abnormal returns on the day of the release of 0.27% on average and cumulated abnormal returns up to 1.6% in the longer run, with the largest effects found after 25–50 trading days, i.e. after 5–10 weeks. Such an effect is indeed sizeable and economically meaningful, in particular when considering that FSRs are generally released twice a year per country.

Are these effects equally generated by optimistic, neutral or pessimistic communi- cations? The existing announcement literature does not help in generating clear-cut hypotheses, because the various studies of differential responses of asset prices to positive and negative news have come to conflicting results: whereas Andersen et al. (2003), for instance, show that asset prices respond more strongly to negative news, Entorf et al. (2012) come to the opposite conclusion.

Table 2 provides a breakdown of our results according to the type of the FSR and reveals that optimistic FSRs affect financial markets particularly strongly. They typically generate positive abnormal returns, which are furthermore large in magnitude, thus leading to statistically significant estimates. The cumulated abnormal returns are largest after 55 days, amounting to more than 3%. This suggests that an optimistic assessment provided in FSRs leads to an improvement in stock market sentiment over a fairly long horizon, in a way that is not matched by pessimistic FSRs leading to a deterioration in sentiment. Importantly, this implies that FSRs tend not to be successful in conveying warning signals about systemic risks, which is of course one of their main intentions.

As we show, this result depends crucially on the market environment – prior to the global financial crisis, optimistic FSRs lead to increasing stock returns, whereas this effect disappears during the financial crisis. One possibility for rationalising these findings is therefore the existence of a confirmation bias (Lord et al., 1979; Hirshleifer, 2001), whereby stock market participants react more strongly to news that confirms their previous beliefs, which in this case boils down to a stronger reaction to positive news.

Table 2 also provides the results for tests of whether the release of FSRs lowers stock market volatility, i.e. tests of whether condition (6) holds, again using both the non- parametric sign test and the parametric test. There is compelling evidence that FSRs do indeed lead to a significant reduction in market volatility.

To summarise, these findings suggest, first, that communication about financial stability has the potential to affect financial markets. The views expressed in FSRs get reflected in stock market returns in a long-lasting fashion, in particular if the FSR contains an optimistic assessment of the risks to financial stability. FSRs also manage to reduce market volatility somewhat.

3.2. Sample Splits and Robustness

We have subjected our benchmark results to a number of sample splits and robustness tests and studied various extensions, which we now describe. All results are provided in Table 3. Given the large number of tests, we only show results for a time window of 25 business days.

© 2013 The Author(s). The Economic Journal © 2013 Royal Economic Society.

718 T H E E C O N O M I C J O U R N A L [ J U N E

T a b le

3

E ff ec ts of

F S R s – S a m p le S p li ts a n d R ob u st n es s

Jo in t m o d e l

P e ss im

is ti c F S R s

R e tu rn s

S D

R e tu rn s

S D

N o n -p a ra m e tr ic

P a ra m e tr ic

N o n -p a ra m e tr ic

P a ra m e tr ic

N o n -p a ra m e tr ic

P a ra m e tr ic

N o n -p a ra m e tr ic

P a ra m e tr ic

(A ) B en ch m a rk

0 .5 7 * *

1 .2 7 * * *

0 .5 6 * * *

�0 .0 7 * *

0 .4 8

�0 .2 8

0 .6 0 * *

�0 .1 3 * *

(B ) S a m p le sp li ts

1 . C o u n tr y g ro u p

A d va n ce d e co

n o m ie s

0 .5 6 *

0 .9 1 * *

0 .5 9 * * *

�0 .1 1 * * *

0 .4 6

�0 .3 1

0 .6 2 * * *

�0 .2 0 * * *

E m e rg in g e co

n o m ie s

0 .6 2 * *

2 .2 7 * *

0 .4 8

0 .0 5

0 .5 5

�0 .1 4

0 .5 0

0 .2 0

2 . C ri si s ve rs u s p re -c ri si s

P re -c ri si s

0 .6 3 * * *

2 .1 0 * * *

0 .5 5 *

�0 .0 5 * *

0 .3 9 ″

�1 .0 6

0 .6 1 * *

�0 .0 8 *

F in a n ci a l cr is is 2 0 0 7 – 1 0

0 .4 5

�0 .5 5

0 .6 0 * *

�0 .1 3 *

0 .5 8

0 .6 5

0 .5 8 *

�0 .2 0

3 . S u p e rv is o ry

ro le

C B is su p e rv is o r

0 .5 6

1 .4 7 * *

0 .5 5 *

�0 .0 9 *

0 .6 1

0 .9 6

0 .3 9

�0 .1 5

C B is n o t su p e rv is o r

0 .5 8 * *

1 .1 7 * *

0 .5 7 * *

�0 .0 6 *

0 .4 4

�0 .6 7

0 .6 6 * * *

�0 .1 3 *

(C ) R ob u st n es s

A ll st o ck s

0 .5 8 * * *

1 .1 6 * * *

0 .5 5 * *

�0 .0 2

0 .5 0

�0 .3 1

0 .5 8 * *

�0 .0 5

R is k -f re e ra te

a s p re d ic to r

0 .5 8 * * *

1 .4 2 * * *

0 .6 0 * * *

�0 .1 0 * *

0 .5 5

0 .0 3

0 .6 5 * * *

�0 .2 3 * * *

A lt e rn a ti ve

co d in g

0 .5 3

0 .7 2 *

0 .5 6 * * *

�0 .0 7 * *

0 .5 2

0 .4 3

0 .5 2

0 .0 1

R a w D ic ti o n sc o re s

– 0 .5 0 * * *

– –

– –

– –

(D ) T es ti n g fo r th e si gn a ll in g ch a n n el

S h o rt -t e rm

in te re st

ra te s

0 .5 4 *

0 .0 5

0 .5 5 * *

0 .0 1

0 .5 0

�0 .0 6

0 .5 9 * *

0 .0 0

L o n g -t e rm

in te re st

ra te s

0 .5 3

0 .0 2

0 .5 2

0 .0 0

0 .4 5

�0 .0 3

0 .5 1

0 .0 0

(E ) In te rn a ti on a l sp il lo v er s

U K F S R s

0 .4 7

�1 .0 1 ″

0 .5 4 * *

0 .0 3

0 .5 3

1 .4 3 * *

0 .5 7 * * *

0 .0 0

© 2013 The Author(s). The Economic Journal © 2013 Royal Economic Society.

2014] F I N A N C I A L S T A B I L I T Y C O M M U N I C A T I O N 719

N e u tr a l F S R s

O p ti m is ti c F S R s

R e tu rn s

S D

R e tu rn s

S D

N o n -p a ra m e tr ic

P a ra m e tr ic

N o n -p a ra m e tr ic

P a ra m e tr ic

N o n -p a ra m e tr ic

P a ra m e tr ic

N o n -p a ra m e tr ic

P a ra m e tr ic

(A ) B en ch m a rk

0 .5 9 * *

0 .6 9

0 .5 5

�0 .0 4

0 .6 2 * * *

2 .1 8 * * *

0 .5 4

�0 .0 5

(B ) S a m p le sp li ts

(1 ) C o u n tr y g ro u p

A d va n ce d e co

n o m ie s

0 .5 7 *

1 .1 0 * *

0 .5 9 * *

�0 .0 9 * *

0 .5 8 *

1 .6 2 * * *

0 .5 5

�0 .0 3

E m e rg in g e co

n o m ie s

0 .6 4

�0 .8 3

0 .4 0

0 .1 7

0 .6 9 * * *

3 .2 1 * * *

0 .5 1

�0 .0 7

(2 )C

ri si s ve rs u s p re -c ri si s

P re -c ri si s

0 .6 1 * *

0 .7 7 *

0 .5 5

�0 .0 1

0 .6 4 * * *

2 .7 3 * * *

0 .5 1

�0 .0 5 *

F in a n ci a l cr is is 2 0 0 7 – 1 0

0 .5 2

0 .4 7

0 .5 8

�0 .1 1

0 .5 2

�0 .3 2

0 .6 5 * *

�0 .0 1

(3 )S u p e rv is o ry

ro le

C B is su p e rv is o r

0 .6 3 *

0 .5 0

0 .5 6

�0 .0 5

0 .6 4 * *

2 .6 3 * * *

0 .6 3 * *

�0 .1 0 *

C B is n o t su p e rv is o r

0 .5 7

0 .7 9

0 .5 5

�0 .0 4

0 .5 9 *

1 .8 0 * * *

0 .4 6

0 .0 0

(C ) R ob u st n es s

A ll st o ck s

0 .5 4

0 .0 0

0 .4 8

0 .0 4

0 .6 6 * * *

1 .9 6 * * *

0 .6 0 * * *

�0 .0 6 * *

R is k -f re e ra te

a s p re d ic to r

0 .6 3 * * *

1 .1 1

0 .5 3

�0 .0 2

0 .7 0 * * *

2 .7 6 * * *

0 .6 1 * * *

�0 .0 5

A lt e rn a ti ve

co d in g

0 .5 7 *

0 .4 0

0 .6 3 * * *

�0 .1 2 * *

0 .5 9 * *

1 .8 6 * * *

0 .5 4

�0 .1 0 * *

R a w D ic ti o n sc o re s

– –

– –

– –

– –

(D ) T es ti n g fo r th e si gn a ll in g ch a n n el

S h o rt -t e rm

in te re st

ra te s

0 .5 8 *

0 .1 2 *

0 .5 1

0 .0 1 ‴

0 .5 8 * *

0 .0 4

0 .5 5

0 .0 0

L o n g -t e rm

in te re st

ra te s

0 .5 7 *

0 .0 3

0 .5 0

0 .0 0

0 .5 2

0 .0 0

0 .5 6 *

0 .0 0

(E ) In te rn a ti on a l sp il lo v er s

U K F S R s

0 .5 9 * * *

0 .6 5 *

0 .5 0

0 .0 6 ′

0 .4 8

0 .8 1

0 .7 0 * * *

�0 .0 9

N ot es : S e e n o te s to

T a b le

2 . A ll re su lt s re la te

to th e e ff e ct

o f F S R s a t a ti m e w in d o w o f 2 5 d a ys . R o w 1 re p o rt s th e b e n ch

m a rk

re su lt s, e a ch

su b se q u e n t ro w re p o rt s

re su lt s o f a sp e ci fi c sa m p le

sp li t o r ro b u st n e ss

te st . S a m p le

sp li ts fo r a d va n ce d / e m e rg in g e co

n o m ie s, p re -c ri si s/ fi n a n ci a l cr is is , C B a s su p e rv is o r o r n o t. R o b u st n e ss

te st s re la te

to u si n g o ve ra ll st o ck

in d ic e s ra th e r th a n fi n an

ci a l se ct o r st o ck s in d ic e s, to

re p la ci n g th e m o d e l o f p re d ic te d re tu rn s in

(1 ) b y th e ri sk

fr e e ra te , a s w e ll a s

to u si n g a n a lt e rn a ti ve

co d in g o f th e co

n te n t o f th e co

m m u n ic a ti o n s, o r u si n g th e ra w D ic ti o n o p ti m is m

sc o re s d ir e ct ly , ra th e r th a n th e ir d is cr e ti se d ve rs io n s. P a n e l

(D ) sh o w s th e e ff e ct s o n sh o rt

a n d lo n g -t e rm

in te re st

ra te s, a n d p a n e l (E ) te st s fo r th e in te rn a ti o n a l e ff e ct s o f U K F S R s.

T a b le

3

(C on ti n u ed )

© 2013 The Author(s). The Economic Journal © 2013 Royal Economic Society.

720 T H E E C O N O M I C J O U R N A L [ J U N E

3.2.1. Sample splits The first set of results relates to various sample splits. Given the large number of countries and the long time sample, it might be the case that there is substantial heterogeneity across countries or over time that we do not capture in the full sample. The first such split addresses possible cross-country heterogeneity, by re-running the estimation separately for all advanced and all emerging market economies (following the IMF’s country classification). Results are overall robust. The interesting insight, though, is that there is a reduction in volatility following FSRs by central banks in advanced countries, which is not found in emerging market economies.

The existing literature does not provide much guidance how to interpret these differences. Two studies explicitly compare central bank-related announcements and the corresponding market response in advanced and emerging market economies, namely Kuttner and Posen (2010) and Moser and Dreher (2010), both of which analyse the effect of appointments of new central bank governors. Moser and Dreher find for emerging market economies that such appointments can lead to a loss of credibility, whereas no such effect is found by Kuttner and Posen in advanced economies. If we apply these results to our case, a lower level of credibility could indeed also imply that FSRs are less capable of calming markets in emerging market economies, thus explaining the absence of a reduction in volatility in their case.

Also the second split along the time dimension reveals interesting patterns. Separate tests for the period prior to the financial crisis 2007–9 (defining the starting date in September 2007, i.e. with Northern Rock; defining the start of the crisis with Lehman does not affect our results) and the time of the crisis shows that FSRs have exerted no systematic effect on stock markets during the crisis.

The third sample split intends to identify whether the role of the central bank in financial supervision matters, by testing once for the effects of communication by central banks that do have a formal role in financial supervision and once for those central banks without such a task. The classification is based on the Central Bank as Financial Authority (CBFA) index developed in Masciandaro and Quintyn (2009).9

This differentiation does not seem to play an important role, given that the results are robust, and no major differences between the two groups emerge.

Further sample splits could be interesting to study. For instance, the style of FSRs has changed over time and differs across countries, such that a split into relatively more and less informative FSRs could be interesting. We leave this for future research.

3.2.2. Robustness The rows of panel (C) in Table 3 present several robustness tests. First, replacing the financial sector stock indices with the broad national stock market index, we can test whether our results apply more broadly, or are confined to the financial sector. In this

9 This index takes the value 1 if the central bank is not assigned the main responsibility for banking supervision; 2 if the central bank has the main (or sole) responsibility for banking supervision; 3 if the central bank has furthermore responsibility for either insurances or the securities markets; 4 if the central bank has responsibility in all three sectors. We allocate central banks to the group with supervisory functions if their index value is larger than one.

© 2013 The Author(s). The Economic Journal © 2013 Royal Economic Society.

2014] F I N A N C I A L S T A B I L I T Y C O M M U N I C A T I O N 721

case, the prediction model of (1) is re-estimated, with the global financial sector stock market index replaced by the overall global stock market index as provided by MSCI. The results are remarkably robust.

A second robustness test replaces the prediction model of (1) by a very simple alternative, namely the risk-free rate (proxied by the three-month US t-bill rate). Here also, results are robust qualitatively, and even strengthen quantitatively.10

Furthermore, results are also not sensitive to the precise way we had split the communications into optimistic and pessimistic content. To test for this, we take two routes: first, by defining an alternative approach to discretising the codes that attempts to control for the expected component contained in the communication and to construct a surprise measure instead. We do so by means of the following auxiliary regression:

C optimism;c it ¼ a0i þ a1q þ a2Tit�1 þ a3Sit�1 þ a4Mit�1 þ lit; ð7Þ

where C optimism;c it denotes the raw Diction coding of a given communication of type c

along the optimism dimension and a0i and a1q are country-fixed effects and time- fixed effects for each quarter of the sample, respectively. The country-fixed effects allow for the possibility that there is a different style in the reporting, thus leading to a different mean coding for each country. Such differences should be well known to observers and therefore not be a surprise. The time-fixed effects control for a common evolution across countries, given that often developments in financial markets are internationally determined. Such common time patterns should also not come as a surprise to financial markets. The last three explanatory factors are as described in benchmark model (1), i.e. they control for the trend, for stock market volatility and for a possible stock market misalignment. We retrieve the residuals l̂it from these regressions and define a communication to be optimistic if l̂it is above the 66th percentile in the distribution, as pessimistic if it is below the 33rd percentile and as neutral otherwise. Even though this classification is very different from the original, unconditional, one, it turns out that the results are remarkably robust.

Our second test for the role of our discretisation method reverts to the original, raw, scores generated by Diction. Higher scores denote more optimistic communications, such that we would expect stock returns to increase correspondingly. This is indeed what we find, consistently with our earlier results. With this measure, we are of course not able to separate out optimistic and pessimistic communications, such that we are neither able to conduct the non-parametric test, nor to fill the table where we break down the results by the content of the communication.

3.2.3. The importance of the signalling channel The next point we address here is through which channel communication affects financial markets. Is it that communication affects markets because it contains relevant information and thus coordinates markets and functions as a focal

10 We have also tested whether replacing the global financial sector stock index in (1) by the overall global stock index changes results and found this not to be the case (results available upon request).

© 2013 The Author(s). The Economic Journal © 2013 Royal Economic Society.

722 T H E E C O N O M I C J O U R N A L [ J U N E

point – akin to what is known as a coordination channel (Sarno and Taylor, 2001; Fratzscher, 2008)? Or is it that market participants believe that financial stability communication has a bearing on monetary policy decisions by central banks – or what is referred to as a signalling channel? The evidence discussed so far, in particular the persistence of the effects of communication, strongly points towards the coordination channel being at work (Sarno and Taylor, 2001). Yet a more direct test of these two channels is to ask whether financial market participants perceive that financial stability communication by central banks could be followed by monetary policy decisions, which should imply that market interest rates are reactive to such communications. As can be seen in panel (D) of Table 3, it is clear that there is no systematic reaction of short (three-month) or long (five to ten-year) interest rates. Thus, this is further evidence suggesting that there is very little role for a signalling channel and that it is rather the coordination channel that is at work – which is not too surprising, after all: most central banks in our sample have a clearly defined price stability objective and use their monetary policy tools to pursue this objective, such that there should be little scope for a signalling channel.

3.2.4. International spillovers Finally, we study spillover effects, i.e. whether the publication of an FSR in one country has effects on other countries. To implement this test, we took the country with the largest number of FSRs in our sample (namely the UK) and entered these communications into the model. Whenever a national communication has taken place 10 days before or after a given British FSR, we do not use these events. We find that the British FSRs tend to reduce volatility internationally but do not exert systematic effects on returns. The evidence points thus to some, albeit limited, international spillovers.

To summarise, the findings suggest that the effects of communication are not universal. Market conditions seem to matter, with different effects during the financial crisis. The origin of the communication also is important, with central banks in advanced economies exerting different effects from those in emerging economies. Finally, the evidence here further supports the conclusion that it is mainly a coordination channel that is at work – i.e. that communication provides relevant information about financial stability itself, rather than giving a signal about monetary policy, thereby affecting financial markets.

3.3. Effects of Speeches and Interviews

Having studied in great detail how financial markets react to central banks’ FSRs, we now turn to the effect of speeches and interviews by central bank governors. A first important difference between these two types of communications relates to the fact that FSRs typically have a predefined release schedule, whereas speeches and interviews are often much more flexible with regard to their timing. Figure 2 illustrates this flexibility by plotting the total number of speeches and interviews in all countries in a given quarter on the right-hand axis and the standard deviation of daily returns of the global financial stock index in each quarter on the left-hand

© 2013 The Author(s). The Economic Journal © 2013 Royal Economic Society.

2014] F I N A N C I A L S T A B I L I T Y C O M M U N I C A T I O N 723

axis. The evolution of the two lines is extremely close, clearly suggesting that communication intensifies in times of financial market turbulence.11

In the light of these findings, one might ask whether speeches and their content are predictable, such that financial markets might have priced in the effects already prior to the communication event. In such a case, our event study methodology would not be appropriate. Speeches tend to be given in times when stock market volatility is high and when stock markets are declining. While this is true, the mere fact that stock market volatility is high and/or stock markets are declining is not sufficient to predict the occurrence of a speech. There are many days when these conditions hold, yet no speeches are given. This might partially be due to the fact that often the date of a speech and its topic are decided upon long before delivery. Accordingly, their occurrence might not be as easy to predict based on market conditions. We will try to address the potential endogeneity in the robustness tests.

Turning to the effects on financial markets, Figure 3 repeats the analysis of Figure 1, by comparing the actual and the predicted evolution of stock markets after speeches and interviews. The findings are remarkably different from those shown in Figure 1.

0

20

40

60

80

100

0

1

2

3

4

1995q1 2000q1 2005q1 2010q1

Volatility (Left-axis) Speeches & Interviews (Right-axis)

Fig. 2. Stock Market Volatility and the Occurrence of Speeches and Interviews Notes. The Figure shows the total number of speeches and interviews in all countries in a given quarter on the right-hand axis (solid line) and the standard deviation of daily returns of the global financial stock index in each quarter on the left-hand axis (dashed line).

11 Results of a more formal test (available upon request) show that on days before an event, the standard deviation of daily stock market returns is substantially higher than on non-event days, which is in contrast to results for FSRs. Furthermore, speeches and interviews intensify during periods of stock market declines: whereas the average stock return prior to non-event days is typically positive, it is on average negative prior to speeches and interviews. No such pattern is visible for FSRs.

© 2013 The Author(s). The Economic Journal © 2013 Royal Economic Society.

724 T H E E C O N O M I C J O U R N A L [ J U N E

Speeches and interviews typically follow stock market declines and the model clearly predicts further declines subsequently (the dashed line in the Figure). As a matter of fact, actual returns do on average decline after a speech or an interview; however, comparing the expected with the actual evolution, it is also apparent that the stock markets decline by less than expected in the presence of central bank communications.

The results of the econometric tests are provided in Table 4, which is built in analogy to Table 2. The picture that emerges differs from the one for FSRs in several ways. With regard to the effects of speeches and interviews on returns, both the parametric and the non-parametric tests show results over longer horizons that are similar to those for FSRs – at the same time, for shorter horizons, we cannot detect any statistically significant effects. Furthermore, speeches do not lower stock market volatility – if anything, there is some tendency, especially of optimistic speeches, to somewhat increase it.

Of course, we have also subjected these results to the same sample splits and robustness tests as before. Again, given the large number of tests, we only show results for a time window of 25 business days in Table 5.

An interesting difference compared to FSRs arises when splitting the sample into the pre-crisis and the crisis period – whereas FSRs were found not to exert systematic effects on stock markets during the crisis, it is precisely then when speeches and interviews had their effects, underlining that speeches and interviews may be much more influential during periods of financial stress. Another difference is obtained for the robustness test using the risk-free rate as the prediction model. In contrast to the findings for FSRs, here, results weaken. This is not surprising, given that our prediction

–3.5

–30

–2.5

–2.0

–1.5

–1.0

–0.5

0

0.5

1 2 3 4 5 10 15 20 25 30 35 40 45 50 55 60

Predicted Actual

Fig. 3. Predicted Versus Actual Evolution of Stock Markets After Speeches and Interviews Notes. The Figure compares the actual evolution of cumulated stock market returns (in %) following speeches/interviews to the predicted evolution on the basis of the benchmark model (1). The solid line plots the average actual cumulated returns starting from day 1 after the communication event and up to day 60. The dashed line shows the expected cumulated returns that would result from the benchmark model in the absence of a communication event. The cumulated returns are multiplied by �1 for pessimistic communications, whereas they are left unchanged for optimistic communications.

© 2013 The Author(s). The Economic Journal © 2013 Royal Economic Society.

2014] F I N A N C I A L S T A B I L I T Y C O M M U N I C A T I O N 725

T a b le

4

E ff ec ts of

S p ee ch es

a n d In te rv ie w s

N o o f d a ys

Jo in t m o d e l

P e ss im

is ti c sp e e ch

e s a n d in te rv ie w s

R e tu rn s

S D

R e tu rn s

S D

N o n -p a ra m e tr ic

P a ra m e tr ic

N o n -p a ra m e tr ic

P a ra m e tr ic

N o n -p a ra m e tr ic

P a ra m e tr ic

N o n -p a ra m e tr ic

P a ra m e tr ic

1 0 .4 5 ″

�0 .0 9

– –

0 .5 4

0 .1 2

– –

2 0 .4 8

�0 .1 0

– –

0 .5 7 * *

0 .3 8 * *

– –

3 0 .4 9

�0 .1 0

– –

0 .5 2

0 .2 8

– –

4 0 .5 1

0 .1 1

0 .4 7 ′

0 .0 2

0 .5 1

0 .0 7

0 .5 0

�0 .1 8 *

5 0 .5 3 *

0 .2 6

0 .4 8

0 .0 1

0 .4 9

�0 .0 2

0 .5 3

�0 .1 9 * *

1 0

0 .5 5 * * *

0 .5 5 * *

0 .4 9

0 .0 0

0 .4 6

0 .0 5

0 .5 1

�0 .0 4

1 5

0 .5 4 *

0 .7 4 * *

0 .4 8

0 .0 4

0 .4 7

0 .0 6

0 .4 9

0 .0 5

2 0

0 .5 2

0 .7 3 * *

0 .4 9

0 .0 6 ″

0 .5 0

0 .1 7

0 .4 6

0 .0 6

2 5

0 .5 5 * *

1 .0 4 * *

0 .5 0

0 .0 6 ″

0 .4 7

0 .0 4

0 .4 8

0 .0 4

3 0

0 .5 4 * *

1 .0 4 * *

0 .5 1

0 .0 6 ″

0 .5 0

0 .6 3

0 .5 0

0 .0 4

3 5

0 .5 6 * * *

1 .0 6 * *

0 .5 0

0 .0 6 ′

0 .4 8

0 .7 0

0 .5 1

0 .0 4

4 0

0 .5 4 *

1 .0 1 *

0 .5 0

0 .0 5 ′

0 .5 1

0 .9 3

0 .5 2

0 .0 3

4 5

0 .5 2

0 .9 5 *

0 .5 0

0 .0 5 ′

0 .5 2

1 .1 3

0 .5 3

0 .0 1

5 0

0 .5 5 * * *

1 .2 4 * *

0 .5 1

0 .0 4 ′

0 .4 9

1 .0 6

0 .5 4 *

0 .0 0

5 5

0 .5 5 * *

1 .5 8 * *

0 .5 2

0 .0 4 ′

0 .5 0

0 .5 8

0 .5 3

0 .0 1

6 0

0 .5 5 * *

1 .6 3 * *

0 .5 0

0 .0 3

0 .5 0

0 .3 8

0 .5 1

0 .0 0

© 2013 The Author(s). The Economic Journal © 2013 Royal Economic Society.

726 T H E E C O N O M I C J O U R N A L [ J U N E

N o o f d a ys

N e u tr a l sp e e ch

e s a n d in te rv ie w s

O p ti m is ti c sp e e ch

e s a n d in te rv ie w s

R e tu rn s

S D

R e tu rn s

S D

N o n -p a ra m e tr ic

P a ra m e tr ic

N o n -p a ra m e tr ic

P a ra m e tr ic

N o n -p a ra m e tr ic

P a ra m e tr ic

N o n -p a ra m e tr ic

P a ra m e tr ic

1 0 .4 8

�0 .0 6

– –

0 .4 5 ″

�0 .0 5

– –

2 0 .4 4 ″

�0 .2 7

– –

0 .5 2

0 .1 7

– –

3 0 .4 6

�0 .5 9

– –

0 .5 0

0 .0 7

– –

4 0 .4 5 ′

�0 .4 9

0 .4 6

0 .0 8

0 .5 3

0 .2 8

0 .4 6

0 .1 5 ′

5 0 .4 5 ″

�0 .4 7

0 .4 5 ′

0 .1 0

0 .5 5 *

0 .4 8 *

0 .4 6

0 .1 1

1 0

0 .4 8

�0 .2 9

0 .4 9

0 .0 4

0 .5 7 * *

1 .1 1 * * *

0 .4 6

0 .0 1

1 5

0 .4 9

�0 .4 9

0 .5 0

0 .0 4

0 .5 4

1 .4 7 * * *

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0 .0 3

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0 .4 7

0 .0 7

2 5

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�0 .4 2

0 .5 6 * *

0 .0 2

0 .5 6 * *

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0 .4 8

0 .1 2 ″

3 0

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�0 .3 9

0 .5 3

0 .0 2

0 .5 7 * * *

2 .5 5 * * *

0 .4 9

0 .1 2 ″

3 5

0 .4 9

�0 .2 9

0 .5 2

0 .0 1

0 .6 0 * * *

2 .6 7 * * *

0 .4 9

0 .1 2 ″

4 0

0 .4 9

0 .0 7

0 .5 1

0 .0 1

0 .5 7 * * *

2 .7 8 * * *

0 .4 7

0 .1 1 ″

4 5

0 .5 0

0 .0 5

0 .5 1

0 .0 1

0 .5 6 * *

2 .8 3 * * *

0 .4 7

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5 0

0 .5 0

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0 .5 1

0 .0 2

0 .5 9 * * *

3 .3 3 * * *

0 .4 8

0 .1 1 ″

5 5

0 .5 0

0 .0 6

0 .5 3

0 .0 1

0 .5 9 * * *

3 .5 5 * * *

0 .5 0

0 .1 0 ″

6 0

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0 .5 2

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0 .4 8

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N ot es : S e e n o te s to

T a b le

2 b u t a ll re su lt s re la te

to sp e e ch

e s a n d in te rv ie w s ra th e r th a n F S R s.

T a b le

4

(C on ti n u ed )

© 2013 The Author(s). The Economic Journal © 2013 Royal Economic Society.

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T a b le

5

E ff ec ts of

S p ee ch es

a n d In te rv ie w s – S a m p le S p li ts a n d R ob u st n es s

Jo in t m o d e l

P e ss im

is ti c sp e e ch

e s a n d in te rv ie w s

R e tu rn s

S D

R e tu rn s

S D

N o n -p a ra m e tr ic

P a ra m e tr ic

N o n -p a ra m e tr ic

P a ra m e tr ic

N o n -p a ra m e tr ic

P a ra m e tr ic

N o n -p a ra m e tr ic

P a ra m e tr ic

(A ) B en ch m a rk

0 .5 5 * *

1 .0 4 * *

0 .5 0

0 .0 6 ″

0 .4 7

0 .0 4

0 .4 8

0 .0 4

(B ) S a m p le sp li ts

(1 ) C o u n tr y g ro u p

A d va n ce d e co

n o m ie s

0 .5 4 *

1 .0 2 * *

0 .4 8

0 .0 7 ″

0 .5 0

0 .5 6

0 .4 7

0 .0 4

E m e rg in g e co

n o m ie s

0 .5 7 *

1 .1 0

0 .5 6 * *

0 .0 4

0 .3 6 ″

�1 .7 4

0 .5 0

0 .0 3

(2 ) C ri si s ve rs u s p re -c ri si s

P re -c ri si s

0 .5 2

0 .4 8

0 .5 1

0 .0 2

0 .4 8

0 .4 7

0 .0 6

F in a n ci a l cr is is 2 0 0 7 -1 0

0 .5 9 * * *

1 .8 7 * *

0 .4 9

0 .1 1 ″

0 .4 5

0 .2 9

0 .4 8

0 .0 2

(3 ) S u p e rv is o ry

ro le

C B is su p e rv is o r

0 .5 4

0 .7 5

0 .5 0

0 .0 6

0 .5 0

0 .6 1

0 .4 9

�0 .0 3

C B is n o t su p e rv is o r

0 .5 6 * *

1 .3 7 * *

0 .5 1

0 .0 6

0 .4 4 ′

�0 .4 9

0 .4 7

0 .1 0 ′

(4 ) C lu st e ri n g

S p e e ch

e s a s p a rt

o f cl u st e r

0 .5 6 * *

1 .4 1 * *

0 .5 2

0 .0 2

0 .4 7

0 .1 8

0 .5 2

�0 .0 2

S p e e ch

e s o u ts id e cl u st e r

0 .5 3

0 .4 6

0 .4 7

0 .1 2 ″

0 .4 7

�0 .1 9

0 .4 1 ′

0 .1 5 ′

(C ) R ob u st n es s

A ll st o ck s

0 .5 1

0 .8 7 * * *

0 .4 8

0 .0 6 ″

0 .5 1

�0 .2 2

0 .4 8

0 .0 5

R is k -f re e ra te

a s p re d ic to r

0 .5 0

�0 .4 3

0 .5 3 * *

0 .0 0

0 .5 1

�0 .1 4

0 .5 1

0 .0 1

H e ck m a n se le ct io n m o d e l

0 .5 5 * *

0 .9 7 * *

0 .5 1

0 .0 6 ′

0 .4 7

0 .0 4

0 .4 8

0 .0 4

A lt e rn a ti ve

co d in g

0 .5 3

0 .7 5 *

0 .5 0

0 .0 6 ″

0 .4 8

0 .3 7

0 .4 7

0 .0 9 ′

R a w D ic ti o n sc o re s

– 0 .1 3 * *

– –

– –

– –

(D ) T es ti n g fo r th e si gn a ll in g ch a n n el

S h o rt -t e rm

in te re st

ra te s

0 .4 9

�0 .0 7

0 .5 3 *

�0 .0 3 *

0 .4 4 ″

�0 .0 1

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L o n g -t e rm

in te re st

ra te s

0 .4 9

�0 .0 6

0 .5 0

0 .0 0

0 .4 8

�0 .0 2

0 .5 0

�0 .0 1 * *

(E ) In te rn a ti on a l sp il lo v er s

U S sp e e ch

e s a n d in te rv ie w s

0 .4 9

0 .0 9

0 .5 1

0 .0 0

0 .5 4 * * *

0 .2 0

0 .5 0

0 .0 1

© 2013 The Author(s). The Economic Journal © 2013 Royal Economic Society.

728 T H E E C O N O M I C J O U R N A L [ J U N E

N e u tr a l sp e e ch

e s a n d in te rv ie w s

O p ti m is ti c sp e e ch

e s a n d in te rv ie w s

R e tu rn s

S D

R e tu rn s

S D

N o n -p a ra m e tr ic

P a ra m e tr ic

N o n -p a ra m e tr ic

P a ra m e tr ic

N o n -p a ra m e tr ic

P a ra m e tr ic

N o n -p a ra m e tr ic

P a ra m e tr ic

(A ) B en ch m a rk

0 .4 5 ′

�0 .4 2

0 .5 6 * *

0 .0 2

0 .5 6 * *

2 .0 2 * * *

0 .4 8

0 .1 2 ″

(B ) S a m p le sp li ts

(1 ) C o u n tr y g ro u p

A d va n ce d e co

n o m ie s

0 .4 5 ′

�0 .3 4

0 .5 2

0 .0 2

0 .5 8 * *

2 .5 3 * * *

0 .4 6

0 .1 5 ″

E m e rg in g e co

n o m ie s

0 .4 7

�0 .6 3

0 .6 5 * * *

0 .0 3

0 .5 2

0 .6 0

0 .5 2

0 .0 6

(2 ) C ri si s ve rs u s p re -c ri si s

P re -c ri si s

0 .4 8

�0 .8 8

0 .5 5 *

0 .0 1

0 .5 2

0 .8 4

0 .5 2

0 .0 1

F in a n ci a l cr is is 2 0 0 7 -1 0

0 .4 3 ″

0 .1 0

0 .5 6 *

0 .0 3

0 .6 2 * * *

3 .5 8 * * *

0 .4 3 ′

0 .2 7 ″

(3 ) S u p e rv is o ry

ro le

C B is su p e rv is o r

0 .4 8

0 .1 1

0 .5 0

0 .0 8

0 .5 6 *

1 .8 1 * *

0 .5 0

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0 .4 3 ′

�0 .9 7

0 .6 2 * * *

�0 .0 4

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2 .3 0 * * *

0 .4 5

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(4 ) C lu st e ri n g

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e s a s p a rt

o f cl u st e r

0 .4 7

�0 .0 8

0 .5 7 * *

0 .0 0

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S p e e ch

e s o u ts id e cl u st e r

0 .4 3 ′

�0 .9 6

0 .5 3

0 .0 5

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0 .1 6 ′

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0 .4 8

�0 .2 8

0 .5 3

0 .0 1

0 .5 2

1 .4 6 * * *

0 .4 4 ″

0 .1 3 ‴

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a s p re d ic to r

0 .5 1

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0 .6 0 * * *

�0 .0 8

0 .5 0

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0 .4 9

0 .0 8

H e ck m a n se le ct io n m o d e l

0 .4 5 ′

�0 .4 2

0 .5 6 * *

0 .0 2

0 .5 7 * *

1 .9 5 * * *

0 .4 9

0 .1 3 ″

A lt e rn a ti ve

co d in g

0 .4 8

�0 .5 1

0 .5 3

0 .0 1

0 .5 3

1 .8 4 * * *

0 .5 1

0 .0 8

R a w D ic ti o n sc o re s

– –

– –

– –

– –

(D ) T es ti n g fo r th e si gn a ll in g ch a n n el

S h o rt -t e rm

in te re st

ra te s

0 .4 1 ‴

�0 .1 7

0 .5 4 *

�0 .0 2

0 .4 2 ‴

�0 .1 5

0 .5 2

�0 .0 4 *

L o n g -t e rm

in te re st

ra te s

0 .4 2 ‴

�0 .0 4

0 .5 3

0 .0 0

0 .4 7

�0 .1 2

0 .4 8

0 .0 1

(E ) In te rn a ti on a l sp il lo v er s

U S sp e e ch

e s a n d in te rv ie w s

0 .5 5 * * *

0 .5 0 *

0 .5 3 * *

�0 .0 2

0 .5 3 *

0 .4 1

0 .4 9

0 .0 1

N ot es : S e e n o te s to

T a b le

3 b u t a ll re su lt s re la te

to sp e e ch

e s a n d in te rv ie w s ra th e r th a n F S R s. T h e T a b le

a ls o co

n ta in s te st re su lt s fo r sp e e ch

e s a n d in te rv ie w s th a t a re

p a rt

o f a cl u st e r o r n o t, a n d re su lt s fo r u n e x p e ct e d sp e e ch

e s b a se d o n e st im

a ti o n o f a H e ck m a n se le ct io n m o d e l.

T a b le

5

(C on ti n u ed )

© 2013 The Author(s). The Economic Journal © 2013 Royal Economic Society.

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model contained a momentum factor which was very important for speeches and interviews – they occur in times of declining stock markets and the prediction model predicts further declines. The choice of the prediction model is therefore important for speeches and interviews but less so for FSRs. The evidence regarding international spillovers reinforces our previous conclusion. In this estimation, we included the speeches and interviews by the chairman of the FOMC, as this provides us with the largest number of communications. We had seen before that FSRs had not generated large spillovers and we find here that speeches by the chairman of the FOMC generally do not affect international stock markets at all. This confirms that international spillovers tend to be rather limited.

Table 5 provides two additional results that we had not reported for FSRs. The first is an additional sample split and tests whether speeches and interviews exert different effects if they are clustered. We define a communication event to be part of a cluster if other speeches or interviews occur within 60 days after the event, or have occurred within 60 days before the event. As a matter of fact, these types exert very different effects. Speeches that are part of a cluster are influencing the market view, whereas isolated speeches do not and tend to increase market volatility.

The second additional test tries to tackle a possible endogeneity of speeches and interviews. We estimated a two-stage Heckman selection model, where the central bank in the first stage decides to deliver a speech or not and in the second stage, conditional on delivering a speech, decides whether the content is optimistic or pessimistic. We include the absolute value of the market trend, market volatility and the absolute value of our misalignment measure as predictors for the first stage and the market trend, market volatility and our misalignment measure as predictors for the second stage. Despite the fact that the model performs rather poorly, there are indeed a few speeches that get predicted by this model. Taking only the surprise component of our speeches, we can generate an alternative set of results, which is provided in Table 5. Results are remarkably robust to this variation. While this is due, in part, to the poor predictability of speeches, it also implies that the previous results were not driven by large effects of the predictable speeches.

To summarise, these findings suggest that speeches and interviews respond to economic conditions and are as such a rather flexible communication tool for central banks. Importantly, a sequence of speeches has a larger influence on financial markets than an isolated communication by the central bank governor. In contrast to FSRs, however, these communications affect markets only modestly in the short term and leave market volatility mainly unaffected; however, during the financial crisis they became more influential. An assessment of the effects of these tools therefore needs to clearly distinguish between the two.

4. Conclusions

This article has provided an empirical assessment of the effects of central bank communication about financial stability, a topic that has remained almost entirely unexplored in the literature to date. The article has studied the impact of central bank statements on financial markets, arguably one of the most important target groups of this type of communication. In more detail, it has constructed a unique dataset

© 2013 The Author(s). The Economic Journal © 2013 Royal Economic Society.

730 T H E E C O N O M I C J O U R N A L [ J U N E

covering over 1,000 communication events (a third of which being FSRs and two-thirds being speeches and interviews by central bank governors) by 37 central banks over a time period from 1996 to 2009, i.e. spanning nearly one and a half decades and analysed the reaction of financial sector stocks to these events.

The article’s findings suggest that communication about financial stability has important repercussions on financial sector stock prices. However, there are clear differences between FSRs on the one hand and speeches and interviews on the other. FSRs clearly create news in the sense that the views expressed in FSRs get reflected in stock market returns. These effects are furthermore long lasting. They also reduce market volatility. These effects are particularly strong if FSRs contain optimistic assessments of the risks to financial stability. Speeches and interviews, in contrast, move financial markets far less on average. In particular, while having only modest effects on stock market returns, they do not reduce market volatility. However, speeches and interviews were affecting market returns significantly more during the 2007–10 global financial crisis, indicating the potential importance of this communication tool during periods of financial stress. Importantly, our results generalise to overall stock market indices, i.e. are not confined to financial sector stocks and are robust to potential endogeneity of communications.

The mechanism by which the central bank affects financial markets seems to be related to the notion of a coordination channel, whereby communication by the central bank works as a coordination device, thereby reducing heterogeneity in expectations and information and thus inducing asset prices to more closely reflect the underlying fundamentals.

The article has also demonstrated how flexibly speeches and interviews can be used as a communication tool, with a higher frequency in times of heightened financial market volatility. In contrast to FSRs with their pre-defined release schedules, the mere occurrence of a speech or an interview can constitute news to financial markets in itself, a fundamental difference that might explain why the two communication channels have so different effects on market volatility. The findings of the article therefore underline that communication by monetary authorities on financial stability can influence financial market developments but that it needs to be employed with utmost care, stressing the difficulty of designing a successful communication strategy on financial stability.

Appendix A: Examples of Speeches and Interviews and Their Coding

5 March 1996: ‘Brazil Central Bk President Denies Bank Sector Instability’ ‘Central bank President Gustavo Loyola Tuesday denied rumors of instability in Brazil’s banking sector and said increasing bank investigations and encouragement for bank mergers have quelched any possibility of a crisis […]’. Source: Dow Jones International News Coded: Optimism = 1

27 October 1997: ‘China c. Banker Sees More Small Bank Bankruptcies’ ‘Some smaller Chinese banks and credit cooperatives could sink into bankruptcy due to bad loans, although a banking crisis was unlikely, central bank governor Dai Xianglong has said’. Source: Reuters News Coded: Optimism = �1

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28 January 1998: ‘UK BOE’s George Confident Asia Contagion Can Be Avoided’ ‘Governor of the Bank of England Eddie George said Wednesday he was ‘reasonably confident’ wider financial contagion from the Asia crisis could be avoided’. Source: Dow Jones International News Coded: Optimism = 1

9 November 2000: ‘Korea Markets Unstable as Worries Linger – c. Bank’ ‘South Korea’s financial markets continue to show signs of instability as the second phase of financial restructuring progresses, the governor of the central Bank of Korea said on Thursday’. Source: Reuters News Coded: Optimism = �1

19 September 2002: ‘Mboweni Confident of Financial Stability’ ‘SA’s financial regulators are highly optimistic about the stability of the country’s financial system, Tito Mboweni, the SA Reserve Bank governor, said yesterday […]’. Source: All Africa Coded: Optimism = 1

10 April 2003: ‘Fukui Says Should Consider Preemptive Move on Banks’ ‘Bank of Japan Governor Toshihiko Fukui said on Thursday that Japan should consider ways to provide ailing banks with capital as a preemptive measure before any financial crisis occurred’. Source: Reuters News Coded: Optimism = 0

24 September 2003: ‘Argentina’s Central Bank Downplays Big Bank Restructuring’ ‘Plans to restructure the Argentine financial sector in the wake of last year’s financial crisis do not entail a widespread shakeup of the country’s banks, top Argentine Central Bank officials said Tuesday’. Source: Dow Jones International News Coded: Optimism = 0

17 March 2004: ‘Greenspan says U.S. Banking System Healthy’ ‘Federal Reserve Chairman Alan Greenspan said on Wednesday the US banking system weathered the 2001 recession well, and was in good shape to help finance the economic recovery’. Source: Reuters News Coded: Optimism = 1

11 September 2007: ‘CREDIT WRAPUP 5-Trichet Sure Major Banks Sound, Bernanke Silent’ ‘Europe’s banks are sound despite the confidence blow from a US subprime crisis, said the head of the European Central Bank on Tuesday, while the […]’. Source: Dow Jones International News Coded: Optimism = 1

5 February 2008: ‘ECB’s Noyer: Global Fincl System In Crisis For More Than A Year’ ‘The global financial system has been in a crisis situation for over a year and the crisis isn’t over, Bank of France Governor Christian Noyer said Tuesday’. Source: Dow Jones International News Coded: Optimism = �1

24 September 2008: ‘Swedish c. Bank Head Repeats Financial System Stable’ ‘Swedish Riksbank Governor Stefan Ingves said on Wednesday Sweden was now feeling the effects of the recent market turmoil more strongly, but repeated reassurances that the financial system was stable’. Source: Reuters News Coded: Optimism = 1

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732 T H E E C O N O M I C J O U R N A L [ J U N E

3 October 2008: ‘Bernanke: Fed To Do All It Can To Combat Crisis’ ‘Federal Reserve Chairman Ben Bernanke said on Friday the U.S. central bank will do whatever it can to combat the credit crisis and help the economy’. Source: Reuters News Coded: Optimism = 0

6 October 2008: ‘Turkish Banks Face Narrower Credit Channels – c. Bank’ ‘Central Bank Governor Durmus Yilmaz said on Monday Turkish banks were facing narrower credit channels due to the global credit crisis, but said they faced no difficulty in renewing external loans’. Source: Reuters News Coded: Optimism = 0

University of Mannheim European Central Bank Deutsches Institut f€ur Wirtschaftsforschung

Submitted: 30 August 2011 Accepted: 7 November 2012

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Competition and Financial Stability Different Author 2004.pdf

Competition and Financial Stability Author(s): Franklin Allen and Douglas Gale Source: Journal of Money, Credit and Banking, Vol. 36, No. 3, Part 2: Bank Concentration and Competition: An Evolution in the Making A Conference Sponsored by the Federal Reserve Bank of Cleveland May 21-23, 2003 (Jun., 2004), pp. 453-480 Published by: Ohio State University Press Stable URL: http://www.jstor.org/stable/3838946 . Accessed: 06/02/2015 10:50

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FRANKLIN ALLEN

DOUGLAS GALE

Competition and Financial Stability

Competition policy in the banking sector is complicated by the necessity of maintaining financial stability. Greater competition may be good for (static) efficiency, but bad for financial stability. From the point of view of welfare economics, the relevant question is: what are the efficient levels of competi- tion and financial stability? We use a variety of models to address this question and find that different models provide different answers. The relationship between competition and stability is complex: sometimes com- petition increases stability. In addition, in a second-best world, concentration may be socially preferable to perfect competition and perfect stability may be socially undesirable.

JEL codes: D4, D5, D6, G2 Keywords: crises, banking concentration, dynamic, spatial,

and Schumpeterian competition.

IN THE BANKING SECTOR, unlike other sectors of the

economy, competition policy must take account of the interaction between competi- tion and financial stability. Greater competition may be good for (static) efficiency, but bad for financial stability.1 In this paper, we shall argue that the relationship between competition and financial stability is considerably more complex than this

simple "trade-off" suggests, but understanding why increasing competition might reduce economic stability is a good starting point.

In an important paper, Keeley (1990) provided a theoretical framework and

empirical evidence that deregulation of the banking sector in the U.S. in the

1. See Canoy et al. (2001) and Carletti and Hartmann (2003) for excellent surveys of the literature on financial stability and competition.

Prepared for the World Bank and Federal Reserve Bank of Cleveland project on Bank Concentration. Presented at the April 3-4, 2003 conference at the World Bank and the May 21-23, 2003 conference at the Federal Reserve Bank of Cleveland. We are grateful to an anonymous referee, to our discussants Stephen Haber and Charles Kahn, and to Elena Carletti, Qian Liu, Lemma Senbet, Andrew Winton, and participants in the conferences.

FRANKLIN ALLEN is a professor offinance in the Department of Finance, Wharton School, University of Pennsylvania. E-mail: [email protected] DOUGLAS GALE is a professor in the Department of Economics, New York University. E-mail: [email protected] Received May 29, 2003; and accepted in revised form May 29, 2003.

Journal of Money, Credit, and Banking, Vol. 36, No. 3 (June 2004, Part 2) Published in 2004 by The Ohio State University Press.

I

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454 : MONEY, CREDIT, AND BANKING

1970s and 1980s had increased competition and led to a reduction in monopoly rents. This reduction in "charter value" magnified the agency problem between bank owners and the government deposit insurance fund. The bank owners or managers acting on their behalf had an increased incentive to take on extra risk, given the guaranteed funds available to them because of deposit insurance. As in the agency problem identified by Jensen and Meckling (1976), if the gamble was successful the equity owners would obtain the rewards while if it was unsuccessful the cost would be born by the deposit insurance fund. The extra risk that banks took on as a result of this agency problem caused a dramatic increase in bank failures during the 1980s. The US is not the only country where there appears to be an empirical relationship between increased competition and financial instability. Beck, Demir- guc-Kunt, and Levine (2003) find using data from 79 countries that crises are less likely in more concentrated banking systems.

Various empirical studies have found that the cost of financial instability is high. For example, Hoggarth and Saporta (2001) find that the average fiscal costs of banking resolution across countries are 16% of GDP. For emerging countries the figure is 17.5% and for developed countries it is 12%. As Table 1 shows, the costs of banking crises alone are estimated at 4.5% of GDP. Although these costs are substantial, they are much lower than the costs, estimated at 23% of GDP, of banking and currency crises occurring together. A proportion of the fiscal costs are transferred, so these figures do not represent the deadweight economic costs. A number of studies measure the cumulative output loss resulting from a financial crisis by using the deviation from trend output. Table 2 gives estimates for these costs. The average cumulative output loss for all crises is 16.9% of GDP. Here the costs of twin crises are again higher; the loss caused by twin banking and currency crises is 29.9% of GDP versus 5.6% for banking crises alone. However, in contrast to fiscal costs,

TABLE 1

AVERAGE CUMULATIVE FISCAL COSTS OF BANKING CRISES IN 24 CRISES, 1977-2000

Non-performing loans Fiscal costs of banking resolution Number of crises (percentage of total loans) (percentage of GDP)

All countries 24 22 16 Emerging market countries 17 28 17.5 Developed countries 7 13.5 12 Banking crisis alone 9 18 4.5 Banking and currency crises of which 15 26 23 Emerging market countries 11 30 25 Developed countries 4 18 16 Banking and currency crises with 11 26 27.5

previous fixed exchange rate of which

Emerging market countries 8 30 32 Developed countries 3 18 16

NOTE: Source: Hoggarth and Saporta (2001, p. 150).

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FRANKLIN ALLEN AND DOUGLAS GALE : 455

TABLE 2

OUTPUT LOSSES ASSOCIATED WITH BANKING CRISES, 1977-98

Average crisis length Average cumulative output losses Number of crises (years) (percentage of GDP)

All 43 3.7 16.9 Single banking crises 23 3.3 5.6 Twin banking and currency crises 20 4.2 29.9 Developed countries 13 4.6 23.8 Emerging market countries 30 3.3 13.9

NOTE: Source: Hoggarth and Saporta (2001, p. 155).

developed countries have a greater loss, 23.8% of GDP, than emerging countries, 13.9% of GDP.

The large literature on the efficiency of the banking industry (for a survey see, e.g., Berger and Humphrey, 1997) is mostly concerned with the cost- and profit- efficiency of retail banking. For example, Canoy et al. (2001) summarize the evidence as suggesting that the average bank operates at a cost level that is 10% or 20% above the best-practices level. This is just one (probably small) part of the total costs of deviations from perfect competition. Unfortunately, the total costs of a deviation from perfect competition have not been documented as carefully as the costs of financial instability.

Given the large and visible costs of financial instability, it is natural for policymak- ers to make the avoidance of financial crises a high priority. By contrast, the difficulty of measuring the efficiency costs of concentration may suggest that competition policy warrants a lower priority. In fact, the uncertainty about the costs of concentra- tion together with the perceived (negative) trade-off between competition and finan- cial stability may actually encourage policymakers to favor concentration at the expense of competition policy. This subordination of competition policy to financial stability may be unwise for a number of reasons, however. In the first place, the extent to which there is a negative trade-off between competition and financial stability may be questioned. The costs of financial crises are undoubtedly high, but it does not follow that it is necessary to reduce competition to avoid those costs. Secondly, the wide range of estimates of the efficiency costs from concentration is at least consistent with a high efficiency gain from greater competition. Thirdly, the costs of financial crises occur infrequently, perhaps every decade or few decades, whereas the inefficiency cost concentrations are born continuously.

The proper balance between competition and financial stability presupposes a framework in which we can identify the welfare costs and benefits of different levels of competition and financial stability. Our objective in this paper is to review a number of theoretical models as a prelude to the development of a theoretical framework in which the optimal policy can be identified. From the point of view of welfare economics, the relevant question is: What are the efficient levels of competition and financial stability? We use a variety of models to address this

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456 : MONEY, CREDIT, AND BANKING

question and find that different models provide different answers. This should not be surprising. In a second-best world, concentration may be preferred to perfect competition (Schumpeter 1950) and perfect stability may be socially undesirable (Allen and Gale 1998). When we consider the relationship between competition and stability, we find that the idea of a simple negative trade-off is, again, too simple: sometimes competition decreases stability and sometimes perfect competition is compatible with the socially optimal level of stability.

We begin in Section 1 by describing the general equilibrium model of financial intermediaries and markets from Allen and Gale (2003a). They provide analogues of the classical theorems of welfare economics for a model of intermediation with asymmetric information. If financial markets are complete and contracts between intermediaries and their customers are complete, the perfectly competitive equilib- rium allocation is incentive-efficient. In this sense, perfect competition is socially optimal. There is no financial instability because contracts are completely contingent and hence there is no need to default. Similarly, if contracts are incomplete, the perfectly competitive equilibrium allocation is constrained-efficient, but now finan- cial instability is necessary for efficiency. If the banks cannot meet the fixed payments that they have promised, there is a financial crisis. A deviation from competition may increase financial stability, but cannot increase and is likely to reduce welfare. This result illustrates that, in general, there should be no presumption that reducing competition in order to increase financial stability is socially desirable.

In simple partial-equilibrium models, it is possible to generate a negative trade-off between competition and financial stability. However, even in this case, the nature of the trade-off between competition and stability is more complicated than was first thought. For example, Allen and Gale (2000a, chap. 8), Boyd and De Nicolo (2002), and Perotti and Suarez (2003) have identified a number of different effects of increased competition on financial stability. In some circumstances, increased competition can actually increase financial stability. These models are discussed in Section 2.

Introducing other kinds of frictions produces further complications to our picture of competition. On the one hand, as mentioned above, Allen and Gale (2000a, chap. 8) show that when search costs are introduced, competition among a large number of unitary banks may result in the monopoly price being charged. On the other hand, a system with two banks with branches at each location may result in the perfectly competitive price. A Hotelling-type model of spatial competition introduces a rich variety of effects concerning regional diversification and risk sharing. The profitability of banks is shown to be extremely sensitive to the precise form of local interactions.

Section 4 describes a model of Schumpeterian competition, in which firms compete by developing new products. The firm that makes the best innovation manages to capture the whole market. The equilibrium price equals the difference between the value of the successful firm's product and the value of the second-best product. So the successful firm's profit is equal to the social value of its innovation. This provides the firms with the right incentives to innovate efficiently. In this context, perfect competition is again desirable and can lead to efficiency. Clearly, this kind

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FRANKLIN ALLEN AND DOUGLAS GALE : 457

of innovation process is not consistent with financial stability. The successful innova- tor will survive while the unsuccessful will fail. Again, as in the benchmark model, efficiency requires a combination of perfect competition and financial instability. If the government is concerned with financial stability it may ensure that banks survive by regulating entry in different submarkets. We consider a setting where banks are assured of a monopoly in their region and consider the incentives to innovate. It is shown that this enforced stability leads to a welfare loss as might be expected. Less obvious is the result that there may be too little or too much investment in innovation. There can be too little because each bank only obtains profits from its own region. There can be too much because the bank is assured of a return no matter what happens.

Contagion is another important source of financial instability. It occurs when some shock, possibly small, spreads throughout the financial system and causes a systemic problem. Allen and Gale (2000b) developed a model of contagion with a perfectly competitive banking sector. It was shown that a shock that was arbitrarily small relative to the economy as a whole could cause all the banks in the financial system to go bankrupt. The contagion spreads through the interbank market. Section 5 extends the Allen and Gale (2000b) model of contagion to allow for imperfect competition in the banking sector. It is shown that in this case the economy is not as susceptible to contagion as it is with perfect competition. Each oligopolistic bank realizes that its actions affect the price of liquidity. By providing sufficient liquidity to the market they can ensure that contagion and their own bankruptcy are avoided. In this case there is a trade-off between competition and stability.

Concluding remarks are contained in Section 6.

1. COMPETITION AND CRISES

In the Arrow-Debreu model of general equilibrium, the fundamental theorems of welfare economics show that perfect competition is a necessary condition for efficiency. Allen and Gale (2003a) show that analogous results hold for a model of financial crises with complete markets. In this setting, perfect competition is compati- ble with the efficient level of financial stability. In this sense, there is no "trade- off" between competition and stability. We begin by describing the model of perfect competition in a financial system consisting of financial intermediaries and markets and then summarize the theoretical results in Allen and Gale (2003a).

There are three dates t = 0,1,2 and a single good at each date. The good is used for consumption and investment.

The economy is subject to two kinds of uncertainty. First, individual agents are subject to idiosyncratic preference shocks, which affect their demand for liquidity (these will be described later). Second, the entire economy is subject to aggregate shocks that affect asset returns and the cross-sectional distribution of preferences. The aggregate shocks are represented by a finite number of states of nature. All agents have a common prior probability density over the states of nature. All

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458 : MONEY, CREDIT, AND BANKING

uncertainty is resolved at the beginning of date 1, when the aggregate state is revealed and each agent discovers his/her individual preference shock.

Each agent has an endowment of one unit of the good at date 0 and no endowment at dates 1 and 2. So, in order to provide consumption at dates 1 and 2, they need to invest.

There are two assets distinguished by their returns and liquidity structure. One is a short-term asset (the short asset), and the other is a long-term asset (the long asset). The short asset is represented by a storage technology: one unit invested in the short asset at date t = 0,1 yields a return of one unit at date t + 1. The long asset yields a return after two periods. One unit of the good invested in the long asset at date 0 yields a random return of more than one unit of the good that depends on the aggregate state at date 2.

Investors' preferences are distinguished ex ante and ex post. At date 0 there is a finite number n of types of investors, indexed by i = 1,...,n. We call i an investor's ex ante type. An investor's ex ante type is common knowledge and hence contractible.

While investors of a given ex ante type are identical at date 0, they receive a

private, idiosyncratic, preference shock at the beginning of date 1. The date 1

preference shock is denoted by Oi C Oi, where 0i is a finite set. We call 0i the investor's ex post type. Because 0i is private information, contracts cannot be explicitly contingent on Oi.

Investors only value consumption at dates 1 and 2. An investor's preferences are

represented by a von Neumann-Morgenster utility function, ui(cl, c2; 0i), where ct denotes consumption at date t = 1,2. The utility function ui(.; Oi) is assumed to be concave, increasing, and continuous for every type Oi. Diamond and Dybvig (1983) assumed that consumers were one of two ex post types, either early diers who valued consumption at date 1 or late diers who valued consumption at date 2. This is a special case of the preference shock 0i. The present framework allows for much more general preference uncertainty.

Allen and Gale (2003a) consider two different versions of the model, depending on the kind of contracts financial institutions offer to their customers. In the first version, contracts are completely contingent, subject only to incentive-compatibility constraints. More precisely, contracts are required to be incentive-compatible and are allowed to be contingent on the aggregate states qr and individuals' reports of their ex post types. Each intermediary offers a single contract and each ex ante type is attracted to a different intermediary.

One can, of course, imagine a world in which a single "universal" intermediary offers contracts to all ex ante type of investors. A universal intermediary could act as a central planner and implement the incentive-efficient allocation of risk. There would be no reason to resort to markets at all. Our world view is based on the assumption that transaction costs preclude this kind of centralized solution and that decentralized intermediaries are restricted in the number of different contracts they can offer. This assumption provides a role for financial markets in which financial intermediaries can share risk and obtain liquidity.

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FRANKLIN ALLEN AND DOUGLAS GALE : 459

At the same time, financial markets alone will not suffice to achieve optimal risk sharing. Because individual economic agents have private information, markets for individual risks are incomplete. The markets that are available will not achieve an incentive-efficient allocation of risk. Intermediaries, by contrast, can offer individuals incentive-compatible contracts and improve on the risk sharing provided by the market.

In the Diamond and Dybvig (1983) model, all investors are ex ante identical. Consequently, a single representative bank can provide complete risk sharing and there is no need for markets to provide cross-sectional risk sharing across banks. Allen and Gale (1994) showed that differences in risk and liquidity preferences can be crucial in explaining asset prices. This is another reason for allowing for ex ante heterogeneity.

In the context of intermediaries with complete markets and complete contingent incentive-compatible contracts, Allen and Gale (2003a) prove the following result.

PROPOSITION 1: Under the maintained assumptions, the equilibrium allocation of the model with complete markets and incomplete contracts is constrained-efficient.

Proposition 1 assumes that intermediaries use complete, incentive-compatible contracts. In reality, we do not observe such complex contracts, for reasons that are well documented in the literature, including transaction costs, asymmetric infor- mation, and the nature of the legal system. These frictions can justify the use of debt and many other kinds of incomplete contracts that intermediaries use in practice. The second version of the model presented by Allen and Gale (2003a) assumes that intermediaries are restricted to using a set of incompletely contingent contracts. This framework allows for many special cases, including at one extreme the earlier model with completely contingent contracts and completely non-contingent debt contracts. Note that this framework allows for a wide variety of assumptions about what is feasible, but takes the set of feasible contracts as given. To endogenize the set of feasible contracts one would have to appeal to factors such as transaction costs, non-verifiable information, and so on.

When contracts are complete, there is no incentive for intermediaries to enter into commitments that they cannot carry out. When contracts are constrained to be incomplete, it may be (ex ante) optimal for the intermediary to plan to default in some states. In the event of default, it is assumed that the intermediary's assets, including the Arrow securities it holds, are liquidated and the proceeds distrib- uted among the intermediary's investors. For markets to be complete, which is an assumption we maintain here, the Arrow securities that the bank issues must be default free. Hence, we assume that these securities are collateralized and their holders have priority. Anything that is left after the Arrow security holders have been paid off is paid out pro rata to the depositors. Allen and Gale (2003a) demonstrate the following result for the case when contracts are incomplete and take the form of deposit contracts.

PROPOSITION 2: Under maintained assumptions, the equilibrium allocation of the model with complete markets and incomplete contracts is constrained-efficient.

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460 : MONEY, CREDIT, AND BANKING

This is an important result. It shows that, in the presence of complete markets and perfect competition, the incidence of default is optimal in a laisser-faire equilib- rium. There is no scope for welfare-improving government intervention to prevent financial crises. In fact competition and financial instability are both necessary for constrained efficiency.

This result demonstrates that in a standard framework achieving optimality does not require trading off competition and financial stability. As we will see below this result extends to a number of other circumstances.

It is important to stress that the results in this section are simply benchmarks to illustrate what may happen. The only costs modeled are the losses to consumers from inefficient risk sharing. Many features that may be important in practice, such as unemployment and bankruptcy costs to firms are excluded. When these are taken into account, there may be a role for government intervention to reduce the incidence of financial crises. What the results do show is that the operation of the financial system and the occurrence of crises when there are complete markets are not the problem. There must be some form of market failure for financial crises to be undesirable.

2. AGENCY COSTS

Keeley (1990) developed a simple model of risk taking by banks with two dates and two states when there is deposit insurance. He showed that as competition increased risk taking by banks also increased. In fact, deposit insurance is not necessary for this effect to be present although it does exacerbate it. Allen and Gale (2000a, chap. 8) developed a simple model of competition and risk taking to illustrate the agency problem.

When firms are debt-financed, managers acting in the shareholders' interests have an incentive to take excessive risks, because the debtholders bear the downside risk while the shareholders benefit from the upside potential. This well known problem of risk shifting is particularly acute in the banking sector where a large proportion of the liabilities are in the form of debt (deposits). The risk-shifting problem is exacerbated by competition. Other things being equal, greater competition reduces the profits or quasi-rents available to managers and/or shareholders. As a result, the gains from taking excessive risks become relatively more attractive and this increases the incentive to exploit the non-convexity in the payoff function. Any analysis of the costs and benefits of competition has to weigh this effect against the supposed efficiency gains of greater competition.

To illustrate these ideas, consider the problem faced by a banking regulator who controls entry into the banking industry by granting charters to a limited number of banks. We use a model of Cournot competition, in which banks choose the volume of deposits they want, subject to an upward sloping supply of funds schedule. Having more banks will tend to raise the equilibrium deposit rate and increase the tendency to shift risks. What is the optimal number of charters?

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FRANKLIN ALLEN AND DOUGLAS GALE : 461

Should the regulator restrict competition by granting only a few charters or encourage competition by granting many?

2.1 A Static Model

Suppose that the regulator has chartered n banks, indexed i = l,...,n. Each bank chooses a portfolio consisting of perfectly correlated risks. This as-

sumption is equivalent to assuming that the risk of each investment can be decom- posed into a common component and a purely idiosyncratic component. If there is a very large number of investments, the purely idiosyncratic components can be pooled perfectly. Then the idiosyncratic risks disappear from the analysis and we are left with a common component representing the systematic risks.

A portfolio is characterized by its size and rate of return. The bank's investments have a two-point return structure: for each dollar invested, bank i will receive a return Y, with probability p(yi); with probability (1 - p(yi)) they pay a return 0. The bank chooses the riskiness of its portfolio by choosing the target return Yi on its investments. The function P(Yi) is assumed to be twice continuously differentiable and satisfies

p(O) = 1, p(y) = 0, and p'(Yi) < 0, p"(Yi) < 0, VO < yi < .

The higher the target return, the lower the probability of success and the more rapidly the probability of success falls. Because the investments have perfectly correlated returns, the portfolio return has the same distribution as the returns to the individual investments.

Let di > 0 denote the total deposits of bank i, which is by definition the total number of dollars the bank has to invest. (For the moment, we ignore bank capital.) There is an upward sloping supply-of-funds curve. If the total demand for deposits is D = ,idi, then the opportunity cost of funds is R(D), where R(D) is assumed to be a differentiable function satisfying

R'(D) > 0, R"(D) > 0, R(0) = 0 and R(oo) = oo.

We assume that all deposits are insured, so the supply of funds is independent of the riskiness of the banks' portfolios. In the sequel, we consider the case where banks bear the cost of deposit insurance.

The payoff to bank i is a function of the riskiness of its own portfolio and the demand for deposits of all the banks

rri(y, d) = p(yi)[ydi - R(D)di] ,

where d = (dl,...,dn) and y = (l,... ,yn). Note that we have ignored the cost of deposit insurance to the bank in calculating its net return.

Since a bank can always ensure non-negative profits by choosing di = 0, it will always earn a non-negative expected return in equilibrium, that is, yidi - R(D)di > 0. There is no need to introduce a separate limited-liability constraint.

In a Nash-Courot equilibrium, each bank i chooses an ordered pair (Yi, di) that is a best response to the strategies of all the other banks. Consider an equilibrium

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462 MONEY, CREDIT, AND BANKING

(y, d) in which each bank i chooses a strictly positive pair (Yi, di) >> O. As a necessary condition for a best response, this pair must satisfy the following first-order conditions

p(Yi)[i - R(D) - R'(D)di] = O,

'(yi)[yi - R(D)]di + p(yi)di = 0.

Assuming that the equilibrium is symmetric, that is, (yi, di) = (y, d) for every i, the first-order conditions reduce to

y - R(nd) - R'(nd)d = 0,

p'(y)[y - R(nd)] + p(y) = 0,

and this implies that

_P - y - R(nd) = R'(nd)d. p'(y)

Given our assumptions on p(y), an increase in y reduces - p(y)/p'(y). Suppose that there are two symmetric equilibria, (y, d) and (y', d'). Then y > y' implies that R'(nd)d < R'(nd')d', which, given our assumptions on R(D), implies that d < d' and R(nd) < R(nd'). Then clearly y - R(nd) > y' - R(nd'), contradicting the first equa- tion. So there is at most one solution to this set of equations, which determines both the size and the riskiness of the banks' portfolio in a symmetric equilibrium.

PROPOSITION 3: Under the maintained assumptions, there is at most one symmetric equilibrium (y*, d*)>> 0 which is completely characterized by the conditions

p(y) -P,) = y - R(nd) = R'(nd)d .

What can we now say about the effect of competition on risk taking? Suppose that we identify the degree of competitiveness of the banking sector

with the degree of concentration. In other words, the larger the number of banks, the more competitive the banking sector is. So a first attempt at answering the question would involve increasing n ceteris paribus and observing how the riskiness of the banks' behavior changes.

With a fixed supply-of-funds schedule R(-) it is most likely that the volume of deposits will remain bounded as n increases. More precisely, if we assume that R(D) -- oo as D -- oo then it is clear that D -- oo is inconsistent with equilibrium. Then the equilibrium value of D is bounded above (uniformly in n) and this implies that d Din -> 0 as n -- oo. This in turn implies that R'(nd)d - 0 from which it

immediately follows that y - R(nd) - 0 and p(y) - 0, or in other words, that y and R(nd) both converge to y.

PROPOSITION 4: If R(D) -- oo as D - oo then in any symmetric equilibrium, y - R(nd) -> 0 and y -> y as n -> oo.

The effect of increasing competition is to make each bank much smaller relative to the market for funds and this in turn reduces the importance of the price effect

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FRANKLIN ALLEN AND DOUGLAS GALE : 463

(the R'(nd)d term) in the bank's decision. As a result, banks behave more like perfect competitors and will increase their business as long as profits are positive. Equilibrium then requires that profits converge to zero, and this in turn implies that banks have extreme incentives for risk taking. In the limit as n -> oo, they will choose the riskiest investments possible in an attempt to earn a positive profit.

The effect of replicating the market. This exercise is enlightening but somewhat artificial since it assumes that we are dealing with a market of fixed size and increasing the number of banks without bound in order to achieve competition. Normally, one thinks of perfect competition as arising in the limit as the number of banks and consumers grows without bound. One way to do this is to replicate the market by shifting the supply-of-funds function as we increase the number of banks. Precisely, suppose that the rate of return on deposits is a function of the deposits per bank

R = R(D/n) .

In effect, we are assuming that, as the number of banks is increased, the number of depositors is increased proportionately, so that the supply of funds in relation to a particular bank is unchanged.

The effect of this change in the model is to make it more like the traditional model of a market in which, as the number of firms increases, the effect of any firm supply on the price of the product becomes vanishingly small. Here the effect of any bank's demand for deposits on the equilibrium deposit rate becomes vanishingly small in the limit as the number of banks becomes unboundedly large. To see this, note that the first-order conditions become

y - R(d) - R'(d)dn-~ = 0

and

p'(y)[y - R(d)] + p(y) = 0.

As before, we can ensure that d remains bounded as n -> oo by assuming that R(d) -> oo as n -> oo. Then the last term on the left hand side of the first equation will vanish as n -> o , leaving a limiting value of (y, d) that satisfies y = R(d). Substituting this in the second equation tells us that p(y) = 0. In other words, as the number of banks increases, the profit margins fall to zero, with the result that banks choose riskier and riskier investments.

PROPOSITION 5: If R(D) -> oo as D -> oo then in any symmetric equilibrium, y - R(d) -> 0 and y -> y as n -> oo.

This is a highly stylized model, so the results have to be taken with a grain of salt; nonetheless, they illustrate clearly the operative principle, which is that competi- tion, by reducing profits, encourages risk taking.

In this particular case, we have constant returns to scale in banking, so that in the limit, when there is a large number of individually insignificant banks, profits must converge to zero. In other words, banks will expand the volume of their

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464 : MONEY, CREDIT, AND BANKING

deposits and loans until the deposit rate approaches the expected return on invest- ments. But this gives them an extreme incentive to shift risks to the depositors or the deposit insurance agency, since it is only by doing so that they can get positive profits at all.

With constant returns to scale, zero profit is always a necessary condition of equilibrium in a competitive industry. However, there are other ways of ensuring the same outcome even if constant returns to scale is not assumed. We replicated the market by increasing the number of banks and potential depositors in the same proportion. This is an interesting thought experiment, but it is not the same as the comparative static exercise the regulator is undertaking. Presumably, the regulator has to choose n optimally, taking as given the supply-of-funds schedule. Suppose that m is the number of depositors and n the number of banks. Then the market supply-of-funds schedule can be written as R(D/m) if R(.) is the individual supply- of-funds schedule. When m is very large, the supply of funds is elastic, other things being equal, so the banks will take the marginal cost of funds as being equal to the average cost R(D/m). However, increasing the number of banks n in relation to m will force profits down. If d remains bounded away from zero, the average cost of funds must increase to oo and if d goes to zero, profits will also go to zero. In this way, the regulator can achieve the effects of free entry, but there is no need to do this in order to ensure competition. Competition, in the sense of price-taking behavior, follows from having a large market, that is, a large value of m, independent of whether n is large or not. Clearly, the regulator does not want to drive profits to zero if it can be helped, because of the incentives for risk taking that that creates.

Cost of deposit insurance. The preceding analysis assumes that all deposits are insured and that the costs are not born by the banks. This is clearly unrealistic, so it makes sense to consider explicitly the cost of deposit insurance. We assume that the premium for deposit insurance is set before the banks choose their strategies and that it is the same for each bank, independently of the strategy chosen. In equilibrium, the premium accurately reflects the cost of deposit insurance provided by a risk neutral insurer.

Let x denote the premium per dollar of deposits. Then the objective function of bank i is p(yi)(yi - R(D) - p)di and the first-order conditions in a symmetric equilib- rium in which banks choose the strategy (y, d) will be

y - R(d) - - R'(d)dn-1 = 0,

p'(y)[y - R(d) - I] + p(y) = 0.

In equilibrium, the premium must be set so that the expected return on deposits is equal to the return demanded by depositors

R(d) = p(y)(R(d) + ) .

Substituting Xr = [(1 - p(y))/p(y)]R(d) into the first-order conditions yields

y - R(d)/p(y) - R'(d)dn- = 0,

p'(y)[y - R(d)/p(y)] + p(y) = 0.

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FRANKLIN ALLEN AND DOUGLAS GALE : 465

Let (yn, dn) be a symmetric equilibrium when there are n banks and suppose that (yn, dn) -> (y0, d0) as n -> oo. Then the first-order conditions imply that

lim yn - R(dn)/p(yn) = 0, n->oo

which is only possible if yn > y and p(yn) -> O, as before. Efficiency. Let us leave distributional questions on one side for the moment,

although historically they have been at the center of the arguments for competition in banking, and suppose that the regulator is only interested in maximizing surplus. Reverting to the constant-returs-to-scale case, two necessary conditions for Pareto optimality are that the average cost of funds be equal to the expected return on investments, and that the expected return on investments should be a maximum

R(D/m) = p(y)y ,

p(y)y > p(y)y', Vy'E[O, y] .

Neither of these conditions will hold in equilibrium when m and n are very large. The first condition requires that the volume of deposits expand until the cost of funds equals the expected value of investments. However, when the market is highly competitive, we have y - R(D/m) = O, so that p(y)y - R(D/m) < 0. As we also saw, the second condition cannot be satisfied in equilibrium, since as the market grows large (m, n -> oo), we have y -> y < oo and p(y) -> 0, so p(y)y -> O. This is not only sub-optimal but the worst possible outcome because it minimizes the total surplus.

Suppose instead that we hold the value of m fixed and adjust n to maximize total surplus, taking the equilibrium values (y(n), d(n)) as given functions of n determined by the equilibrium conditions

y - R(nd/m) - R'(nd/m)dm-1 = 0

and

p'(y)[y - R(nd/m)] + p(y) = O .

A "small change" in n will increase the expected revenue by

{p'(y(n))y(n) + p(y(n))}ny'(n) + p(y(n))y(n),

and the cost by

R(nd(n)/m){nd'(n) + d(n)},

so a necessary condition for an (interior) optimum is

{p'(y(n))y(n) + p(y(n))}ny'(n) + p(y(n))y(n) = R(nd(n)/m){nd'(n) + d(n)} .

This can be rewritten as

p(y(n))y(n) - R(nd(n)/m)d(n) = - {p'(y(n))y(n) + p(y(n))}ny'(n) + R(nd(n)/m)nd'(n),

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466 : MONEY, CREDIT, AND BANKING

where the left hand side is the expected surplus generated by a single bank and the right hand side is the change in expected revenue per bank as n increases plus the change in the cost per bank of the funds borrowed. Since the left hand side is positive, the right hand side must be positive, too. But we know that the second term on the right must be negative since adding more banks reduces the volume of business each bank does, so the first term on the right is positive. We know that y'(n) is negative-increased competition leads to increased risk taking-so the term in braces must be positive. Assuming that p(y)y is concave in y, this tells us that n will be chosen so that y(n) is less than the value that maximizes expected revenue.

A fortiori, it will not, as we have seen, be as great as the value under free entry, since it is never optimal to let n -> oo. It may in fact, be optimal to let the number of banks remain quite small.

2.2 Loan Market Competition The model in Allen and Gale (2000a) analyzes competition in the deposit market.

The bank is assumed to invest deposits directly in a portfolio of assets with given risk characteristics. The bank directly determines the riskiness of its portfolio. As profits decline the bank's preference for risk increases, so increasing competition leads to increasing risk and a decrease in stability. Boyd and De Nicolo (2002) point out that the assumption that banks invest directly in assets is crucial for the result. To show this, they extend the Allen and Gale model to include entrepreneurs. The entrepreneurs obtain loans from the banks and invest the money in risky ventures. Each entrepreneur chooses the riskiness of the venture he invests in. The entrepre- neurs, like the banks in the Allen and Gale model, have a greater incentive to take risk when profits are lower. However, the effect of competition among banks here is the opposite of what we observed in the Allen and Gale (2000a) model. Greater competition among banks reduces the interest rates that borrowers pay, increases the profitability of their ventures, and hence reduces the incentive to take risk. Thus, increased competition among banks leads to increased financial stability. The effect of competition in the deposit market is the same as before but Boyd and De Nicolo are able to show that the loan market effect dominates. The trade-off between competition and stability presented in the Allen and Gale model is reversed in the Boyd and De Nicolo model. As competition between banks increases the risks taken by borrowers is unambiguously reduced and financial stability is improved.

2.3 Dynamic Competition The results in Section 2.1 demonstrate how the limited liability of managers and

shareholders in a moder banking corporation can produce a convex objective function which in turn leads to risk-shifting behavior. This kind of behavior is most likely to occur when the bank is "close to the water line," that is, when the risk of bankruptcy is imminent. For banks which are not in immediate danger of bankruptcy, the risk-shifting argument may be less relevant. However, even if a bank is not close to the water line, there may be other reasons for thinking that its objective

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FRANKLIN ALLEN AND DOUGLAS GALE : 467

function is convex. Consider, for example, the winner-takes-all nature of competition. When banks compete for market share, the bank that ends up with the largest share may be able to exploit its market power to increase profitability. In this case, the profit function may be convex in market share, that is, doubling market share may more than double profits. Another reason is the presence of increasing returns to scale. If larger banks have lower average costs, then profits will be a convex function of the size of the bank. Either of these possibilities will give the bank an incentive to take riskier actions, even when the bank is not in immediate danger of bankruptcy.

These incentives for risk-taking behavior are naturally studied in a dynamic context. Suppose that a group of banks are competing over time. Their activities are constrained by the minimum capital ratio, so the only way to expand is to acquire more capital. Because of agency costs or the adverse signaling effects, it may be expensive for banks to raise capital from external sources, so they try to accumu- late capital by retaining earnings. The relative size of the bank matters, because it gives the bank a competitive edge over other banks. Because their reduced-form profit functions are convex and they are constrained by their capital, the game is a race to see who can accumulate capital or market share fastest.

When banks compete to capture greater market share or to reap economies of scale, they consider the effect of their actions not only on immediate profits but also on their future position in the market. How the bank's current actions will affect its future position in the market depends on the nature of the risks involved and on the behavior of the other banks. Even if the profit function is only convex when the bank is close to the bankruptcy point, the bank's objective function may be convex over a much wider region because the bank's objective function incorporates or discounts future possibilities which are still far away. This may influence the shape of the bank's objective function globally, through backward induction.

Allen and Gale (2000a, chap. 8) consider a variety of different models and show that risk taking can either be increased or decreased by competition in a dynamic setting. The first case analyzed involves a pair of duopolists who compete for market share. They play repeatedly for many periods. In each round market shares can go up or down a small amount. By taking a risky action instead of a safe action in any period they increase the variance of the change in market share. A crucial assumption is that there are "reflecting barriers" at the extreme values of market share. When a bank's market share hits zero, it does not go out of business; at worst it will remain at zero for some period before bouncing back. This non-convexity is like having increasing returns in the neighborhood of zero. Similarly, when a bank's market share hits 100% it must eventually bounce back, and this is like having locally decreasing returns. A simple numerical example is used to illustrate the effect of non-convexity on the bank's behavior. When a bank's market share is low, its objective function is convex and it has an incentive to take risk; when its market share is high, its objective function is concave and it has an incentive to avoid risk.

The second case analyzed assumes that there are "absorbing barriers." Now when market share hits zero or one it stays the same with probability one. In this case it is shown that the incentive to take risks is eliminated. The reason is the assumption

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468 : MONEY, CREDIT, AND BANKING

that the bank's position can only change a little bit at a time. If the period length and the step size are made very small, the binomial process considered would approximate Brownian motion, which has continuous sample paths with probability one. It is the continuity of the movement of the bank's market share over time which eliminates the usual incentive for risk taking. The bank becomes "bankrupt" as soon as its market share hits zero. It cannot go below the line and so it cannot shift risk to depositors or other creditors. Whether incentives to take risks are greater or less with reflecting barriers is ambiguous. While absorbing barriers eliminate the positive incentive for risk taking with reflecting barriers, they also eliminate the in- centive to avoid risk when market share is high that was found in the same model.

Perotti and Suarez (2002) consider the effect of dynamic competition in a model where there can be a banking duopoly or monopoly. If a bank fails when there is a duopoly the market structure switches temporarily to a monopoly. Banks can lend prudently in which case their portfolio of loans has a safe payoff. The alternative is to lend speculatively in which case there is some probability of a high payoff but the average payoff is low. Limited liability and deposit insurance mean lending speculatively can shift risks and be advantageous in the short run. However, a bank can be hit by a random solvency shock. If it lent prudently it will always survive this shock but if it lent speculatively it will fail unless loan returns are high. It is shown that this effect introduces an incentive for banks to lend prudently. A prudent bank will be less exposed to the risk of being driven out of business and will emerge as a monopolist if the other duopolist lent speculatively and is hit by a solvency shock. In their model this "last one standing effect" makes duopolistic banks unam- biguously more prudent and encourages stability.

The range of results obtained with these dynamic models illustrate how crucial the particular details of the model are in determining whether or not competition leads to more or less financial stability.

3. SPATIAL COMPETITION

So far, we have only considered competition in markets for homogeneous com- modities or services. Product differentiation occurs in the financial sector, just as it does in non-financial sectors, and it is important to consider the effect of product differentiation on the competitive process. Models of spatial competition are used to represent competition among firms with differentiated products and the same can be done for the banking sector. We can interpret the spatial dimension literally as representing banks with different locations or we can interpret it metaphorically as representing some other qualitative difference in the services provided. In either case, we find that the effects of concentration on competition in spatial models can be quite different from the results obtained for markets with undifferentiated products. In this section we consider two models of bank competition. The first model, from Allen and Gale (2000a), compares branch banking with unitary banking and shows that competition among a small number of banks (with many branches) may be

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FRANKLIN ALLEN AND DOUGLAS GALE : 469

more aggressive than competition among a large number of unitary banks. We then consider a Hotelling-type model of spatial competition and again compare competi- tion among a small number of banks (with many branches) with competition among a large number of unitary banks. The results concerning the trade-off between competition and diversification are very sensitive to the spatial arrangement of the branches.2

3.1 Unitary Banking versus Branch Banking The model presented below exploits two types of imperfections arising from

asymmetric information. The first is the presence of "lock-in" effects. Information is costly for both banks and their customers, whether borrowers or depositors, and once relationship-specific investments in information have been made, the parties may find themselves "locked-in" to the relationship. For example, a borrower having incurred a fixed cost of revealing its type to a bank will suffer a loss if it switches to another bank. In addition there is the well known "lemons effect," that arises if the borrower leaves the bank with which it has been doing business for many years. These lock-in effects will be modeled by simply assuming that there is a fixed cost of switching banks. As is well known (see, e.g., Diamond 1971), switching costs give the bank a degree of monopoly power, even if the bank is not "large."

There are other reasons why banks are monopolistic competitors. For locational reasons their services are not perfect substitutes. Differences in size and products and specialized knowledge also make them imperfect substitutes. Here, we focus on the lock-in effect.

The second essential imperfection arises from the fact that a bank's customers have incomplete information about the services offered by a bank and the prices at which these services are offered, at the time when the relationship has begun. In fact, the smaller the bank is, the less likely it is that the bank's reputation will be an adequate source of information about the quality and prices of the bank's products.

A third important feature of this model is the fact that banks offer a variety of services. The simplest example of this is the case of a bank with a large number of branches. Since customers have different preferences over branch location, branches at different locations are offering different services. This means that a bank with many branches is offering a bundle of different services to their customers.

Location is not the only dimension along which banks differ, of course. They will offer different menus of accounts, or concentrate on different types of lending business; they will attract a different mix of retail or wholesale funds; they may diversify into non-bank products such as insurance or mutual funds. Location is a convenient metaphor for these different dimensions.

By exploiting these three features of the model, lock-in effects, limited information, and product diversity, we can reverse the usual presumption that greater concentra- tion leads to more efficient outcomes.

2. For other welfare analyses involving spatial competition and risk see Besanko and Thakor (1992) and Matutes and Vives (1996, 2000).

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470 : MONEY, CREDIT, AND BANKING

The model. There is a finite set of locations indexed by I = 1,...,L and at each location there are two banking offices j = 1,2. Time is divided into an infinite number of discrete periods t = 1,2,.... There is a large number of individuals allo- cated exogenously to the different locations. Each period, these individuals have a demand for a unit of banking services which provides them with a surplus v. The value of banking services to individuals is distributed according to the distribution function F(v), that is, F(v) is the fraction of the population with valuation less than or equal to v.

At each date consumers are randomly assigned to a new location. They have an equal probability of arriving at any location and we assume that the number of locations is so large that the probability of returning to the same location is negligible and can be ignored. This is an extreme assumption, to be sure, but it serves to eliminate inconvenient and apparently unimportant complications. Each location is also assumed to receive a representative sample of the different types of individuals so, whatever the number of individuals at a given location, the distribution of types is F(v).

For simplicity, banks are assumed to have a zero marginal cost of providing banking services. Profit is thus identical to revenue.

At each date, the market is assumed to clear as follows. First, the individuals who have gathered at a particular location choose which bank to patronize. They do this before they know the price that the bank will charge. Next, the bank sets the price for its product (the interest rate on loans or deposit accounts, or the fees for other bank services). Finally, the consumers make one of three choices: to purchase the current bank's services at the quoted price, to switch to the other bank, whose price by now has been fixed, or to do without the services of a bank. There is a fixed cost c > 0 of switching from one banking office to the other.

We consider two limiting cases of bank organization. In the first, which we call unitary banking, each bank has a single branch. In other words, each banking office represents an independent bank. In the second case, which we call branch banking, there are only two banks, each owning one branch in each location. That is, all the banking offices are organized into two large networks. Regardless of the form of organization, we use the term banking office to denote the smallest unit of the bank, whether it constitutes the entire bank or a branch of a larger bank network.

Individuals are assumed to observe only what happens at their own bank in each period and, since they move to a different location in each period, they have no knowledge of the previous behavior of the bank they are patronizing in the current period. The banks themselves are assumed to condition their behavior in each location on their experience at the same location. This maintains an informational symmetry between the unitary- and branch-banking forms of industrial organization, i.e., unitary banking and branch banking, since in each case only local information is being used to condition the (local) pricing decision.

Unitary banking. In this form of industrial organization each bank consists of a single office. A bank sets the price of its product in each period to maximize the present value of profits. In the static version of this model, it is well known that

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FRANKLIN ALLEN AND DOUGLAS GALE : 471

the unique equilibrium involves each banking office choosing the monopoly price. More precisely, suppose that there is a unique price PM such that

PM(1 - F(pM)) > p(l - F(p)), Vp.

A bank will clearly never want to charge more than PM. If the two banks at some location happen to charge prices p < p' < pM, where p < PM, the bank charging p can always raise its price by e without losing any customers, because of the fixed cost of switching. This will clearly increase its profits, so the only equilibrium is for both banking offices to charge PM.

In a dynamic context things are generally more complicated, because of the possibility of supporting a different equilibrium by means of punishment strategies. Under the maintained assumptions, however, there is no possibility of using such strategies to increase the set of equilibria. Because individuals only observe what happens at their own locations and never return to the same location, nothing that a banking office does in the current period will be observed by individuals who will visit that location in the future. Further, since banks at other locations do not condition their behavior on what happens at this location, there is no possibility of the bank's future customers being indirectly informed of a deviation through another bank's reaction to this bank's current deviation. Our informational assumptions have effectively severed any possible feedback from a current change in price to a future change in demand, so the argument used in the static model continues to apply. We conclude, then, that the unique, subgame perfect equilibrium of the unitary banking game consists of each bank, in each location, charging the monopoly price PM in every period. Consumers whose valuation v is greater than PM will purchase banking services; those whose valuation is less than PM will not. The equilibrium is inefficient for the usual reason: the monopoly price is too high and the monopoly quantity is too low.

Branch banking. Now suppose that banking offices are formed into two large networks. Each bank has one branch in each of the locations. Although consumers move from location to location they can stay with the same bank if they wish. The possibility of staying with the same bank generates a plethora of other equilibria. We describe one such equilibrium to illustrate the possibilities.

In each period, at each location, half the customers patronize each of the banks. Along the equilibrium path, the banks charge a price p = ? > 0 in every period. If, in any period, one of the banking offices has deviated from the equilibrium strategy, all the customers will leave the bank that last deviated and henceforth patronize branches belonging to the other bank. The banks continue to charge the same price. If no bank deviated, but some of the customers deviated in the past, the banks continue to charge the same price and the customers continue to patronize the two banks equally. The profits (for a single banking office) from deviating last one period and are less than or equal to (PM - e)(1 - F(PM)); on the other hand, it loses the profits from these customers in each future period until they all disappear from the game. Customers only last a finite number of periods, but if the number of locations is large, they will be around on average for a very long time. Each period, a fraction

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472 : MONEY, CREDIT, AND BANKING

- 1 of the customers dies and is replaced. So the equilibrium profits lost by a single banking office's deviation are equal to [E/(1 - ?-1)(1 - 8)](1 - F(E)). For 8 suffi- ciently close to 1 and I sufficiently large, the profits from deviating are less than the profits of the equilibrium strategy. This shows that under branch banking, it is possible to support equilibria which are "more efficient" than the unique equilibrium in the case of unitary banking, where "more efficient" means that the sum of consumers' and producers' surplus is greater.

How should we interpret these results? The lock-in effects in the banking sector may be substantially greater than in most service industries and may be one of its distinguishing features. Small banks with a limited range of services and a limited geographical presence may have a greater incentive to exploit the lock-in effect than a large bank, because the large bank is always competing for the customer's future business, in another product line or another location.

Empirical evidence. There is some empirical evidence in support of the view of competition presented above.3 Bordo, Rockoff, and Redish (1994) compare the Canadian and US banking systems from 1920 to 1980. During this period Canada had a few branch banks while the US had unit banking in many parts. It is found that the Canadian system outperformed the US system in a number of respects. First, in Canada the interest rates paid on deposits were generally higher and the income received by security holders was generally slightly higher than in the US. Second, the interest rates charged on loans were generally quite similar in the two countries. Finally, the returns on equity were generally higher in Canada. Taken together this evidence is consistent with the Canadian branch banking system being more competitive than the US unitary banking one.

Carlson and Mitchener (2003) also find evidence that branch banking is more competitive than unitary banking. Using data on national banks from the 1920s and 1930s, they compare states which just have unit banks with ones that have both branch banking and unitary banking. In the former states, many banks that charge high rates of interest on loans and pay low rates on deposits survive. However, in states with both types of banks this kind of local monopoly is eliminated. Both branch banks and unit banks price in exactly the same way.

3.2 Spatial Competition and Diversification We next consider another model of spatial competition in the tradition of Hotelling.

It is shown that competition can be consistent with diversification and hence stability. However, the precise way in which this works depends crucially on the particular assumptions made.

Let locations be denoted by n = 0, ?1, +2,... and assume that there is a single bank at each location. Identical individuals are uniformly distributed on the real line. Each has one unit of a good that can be invested by the bank in a risky asset with return Rn. For simplicity assume that the returns Rn are i.i.d. with distribution

3. We are grateful to Stephen Haber for bringing this evidence to our attention.

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FRANKLIN ALLEN AND DOUGLAS GALE : 473

R -JR w.pr. n n- 0 w.pr. 1

- 7.

In a symmetric equilibrium a bank will draw clients from the interval [n - 1/2, n + 1/2] and offer them a risk sharing contract that promises a payment D if the project is successful and nothing otherwise (the banker is risk neutral but has limited liability). The expected utility of the contract is nU(D) + (1 - n)U(O) = ntU(D) if U(O) = 0.

The bank chooses D to maximize profits taking as given the expected utility a offered by the adjacent banks. Assuming linear transportation costs of one utility per unit distance, the marginal agent m < n who goes to bank n is determined by the condition that

rU(D) - (n - m) = - (m - n + 1)

or

2(n - m) = nU(D) - a + 1 .

The profit per agent is n(R - D) so total profits are

n(R - D)2(n - m) = n(R - D)(7cU(D) - u + 1).

The first-order condition is

7U'(D) = R R-D

If we allow a bank to occupy several locations, it can pool several independent risks, which allows it to offer better risk sharing to its customers. However, it may face less competition. Whether it does depends on precisely which locations a bank is allowed to occupy. For example, if two banks occupy alternate locations, we get improved risk sharing with no loss of competition. Here there is no trade-off between competition and stability. If a bank occupies a sequence of adjacent loca- tions, it can extract a large amount of surplus from the consumers located in the middle. Here there is a trade-off between competition and stability.

4. SCHUMPETERIAN COMPETlIION

Technological innovation is one of the major sources of growth in welfare. As Schumpeter (1950) famously pointed out, perfect competition undermines the incen- tive to innovate (when intellectual property rights are weak) and in that sense imperfect competition may be more "efficient" than perfect competition. Similar ideas apply in the financial sector. If banks innovate they may be able to capture the market and drive other banks out of business. Thus Schumpeterian competition may be associated with financial instability (creative destruction). We start by considering

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474 : MONEY, CREDIT, AND BANKING

a "winner-takes-all" model of competition based on the work of Allen and Gale (2000c) which has this feature.

4.1 Winner-Takes-All Competition There is a finite number of locations i = 1,..., n with a bank at each location.

There are two dates, t = 0,1. At date 0, each bank i invests ki > 0 in the development of a product. The banks' opportunity cost of capital is R. The value of the product developed by bank i is given by Vi(ki, o). There is symmetric information all agents know the function Vi(.) and the investment ki and have the same continuous probability distribution F(-) over the states of nature ca We assume that V(0, co) = 0 for all o so capital is essential to the development of a useful product. In general, the more the capital that is provided the greater is the probability that the value of the product is high. We assume that once the new product is developed it can be produced at constant marginal cost and, without essential loss of generality, we set the marginal cost equal to zero.

At date 1 identical consumers are uniformly distributed on the line interval [0, n]. Each consumer wants to consume at most one unit of a new product.

4.2 Equilibrium At date 0 the banks jointly choose their investment strategies k = (kl,..., kn). At

the beginning of date 1, the state of nature o is realized and the banks observe the quality of the product they have developed

V(k, co ) = Vl(kl, o ),... V(kn, )).

Then the banks engage in Bertrand competition. Since the qualities are assumed to be continuously distributed (for ki > 0) the probability of ties can be ignored. Then Bertrand competition will lead to an outcome in which the best product captures the entire market and the price charged for this product is equal to the difference between the value of the first- and second-best products. The price for every other product is zero. At this price, consumers are indifferent between the first- and second- best products, but they will demand only the first-best product in equilibrium (if a positive fraction of consumers were expected to choose the second-best product, the firm with the first-best product would have chosen a slightly lower price to capture the entire market). Formally, for any bank i let

V-i(k-i, co) = (Vl(kl, o ) ...,Vi_1(ki_I, Co ),Vi+l(ki+l, CO), ... Vn(kn, ))

denote the vector of the qualities of products j X i; let

k-i = (kl- ...ki-1, k-i+ , ..., kn)

denote the allocation of investment in products j ? i; and let

V*_i(k_i, w) = maxjti{Vj(kj, o )}

denote the highest value in the vector V_i(k_i, o). Then, in the second-period equilib- rium, the price charged for the ith product is denoted by pi(k, co) and satisfies

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FRANKLIN ALLEN AND DOUGLAS GALE 475

pi(k, co) = max{Vi(k, o) - V*i(k_i, co),0}, Vi.

Since the demand is equal to one for the best product and zero for the rest, the revenue of bank i is also equal to pi(k, o).

At the first date, we look for a Nash equilibrium in the investment levels. The ith bank chooses ki to maximize E[pi(ki, k_i, o )] - Rki, taking as given the invest- ment levels of the other banks, k_i. So a Nash equilibrium is a vector k* such that

kiEarg maxki>o{E[pi(ki, k_i, co)] - Rki}

for each i.

4.3 Optimum Since the cost of production at date 1 is zero, the surplus generated by consuming

the ith product is Vi(ki, o). Surplus is maximized by having all consumers consume the best product, so the total surplus at date 1 is

V*(k, co) _ maxi= ...{Vi(ki, co ) .

Assuming that the consumers are also risk neutral and that lump sum transfers are possible, the first-best efficient allocation is found by maximizing net surplus, that is, by solving the planner's problem

n

maxkV*(k) -

Rki, i=1

where V*(k) = E[V*(k, o )] is the expected value of V*(k, co). Define V*_i(k_i) = E[V*-i(k_i, o )]. Then the objective function V*(k) can be written

equivalently as

V*(k) = E[max{Vi((ki, o )- V*i(k-i, co), 0} + V*i(k-i, o )] = E[max{Vi(ki, co) = V*-i(k-i, o ),0}] + V*i(k-i)

and the planner's problem can be rewritten equivalently as n

maxk>oE[max{Vi(ki, co) - V*i(k, o), O} + V*i(ki)- REki i=1

Suppose that k* is a solution to the planner's problem above. A necessary condition is that ki maximizes

E[max{Vi(ki, co) -- Vi*(k*i, o ), 0}] - Rki = E[pi(ki, k* )] -Rki.

But this means that ki* satisfies the equilibrium condition for the ith bank's choice of ki. Hence, we have the following result.

PROPOSITION 6: If k* is a solution to the planner's problem, then k* is a Nash equilibrium of the banks' investment "game."

In this case the allocation produced by the winner-takes-all competition is efficient. The innovating banks have exactly the right incentives to invest. However, only

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476 : MONEY, CREDIT, AND BANKING

one bank survives in each period. In other words there is considerable financial instability. In order to prevent this instability the government may wish to restrict competition and give each bank a monopoly in a particular region. We turn next to this restricted competition.

4.4 Restricted Competition Next assume that the bank located at n has a legal monopoly of the region [i - 1/2,

i + 1/2]. Then it maximizes E[V(ki,o) - Rki]. Clearly, surplus must be lower, both because we are not providing the best product to all consumers but also because we are not using the right investment level.

Will investment be too high or too low? Examples can be constructed to show that the answer is ambiguous. On the one hand losers are protected under the "competitive" arrangement. On the other hand winners suffer because they cannot capture the entire market.

To see this consider the following simple example. There is a continuum of identical consumers with measure one and two banks. Each bank can invest 0 or 0 < I < 1/2. If a bank invests zero the value of its good is zero; if it invests I the value of its good is 1. If the market is unified, there are three equilibria, two asymmetric equilibria in which only one bank invests and a mixed strategy equilib- rium in which the probability of investment is X = 1 - I. Now suppose we divide the market between the two banks, each getting 1/2 of the market. Then each bank will invest for sure, since I < 1/2.

Comparing equilibria, we see that total investment is greater in the divided market. Also, comparing the unique pure strategy equilibrium of the divided market with the mixed strategy of the unified market, we can see that (the probability of) innovation is strictly higher in the divided market. In the mixed strategy equilibria of the divided market the probability of innovation is the same.

For the case I > 1/2 there will be no investment, and hence no innovation, in the segmented market. In this case investment and innovation are unambiguously lower than in winner-takes-all case.

These examples are enough to indicate that even in simple models it is hard to obtain robust comparative static results. As with the other models we have examined, the relationship between competition and stability is complex and nuanced.

5. CONTAGION

One source of instability in financial systems is the possibility of contagion, in which a small shock that initially affects one region or sector or perhaps even a few institutions, spreads from bank to bank throughout the rest of the system, and then affects the entire economy. There are a number of different types of contagion that have been suggested in the literature. The first is contagion through interlinkages between banks and financial institutions (see, e.g., Rochet and Tirole, 1996a, 1996b, Freixas and Parigi, 1998, Freixas, Parigi, and Rochet, 2000, and Allen and Gale,

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FRANKLIN ALLEN AND DOUGLAS GALE : 477

2000b, for theoretical analyses and Van Rijckeghem and Weder, 2000, for empirical evidence). The second is contagion of currency crises (see, e.g., Masson, 1999, Eichengreen, Rose, and Wyplocz, 1996, and Glick and Rose, 1999). The third is contagion through financial markets (see, e.g., King and Wadwhani, 1990, Kyle and Xiong, 2001, and Kodres and Pritsker, 2002).

The notion of financial fragility is closely related to that of contagion. When a financial system is fragile a small shock can have a big effect. A financial crisis may rage out of control and bring down the entire economic edifice (see, e.g., Kiyotaki and Moore, 1997, Chari and Kehoe, 2000, Lagunoff and Schreft, 2001, and Allen and Gale, 2003b).

In this section we are interested in the relationship between contagion and financial fragility and competition. Allen and Gale (2000b) develop a model of financial contagion through the interbank market. They assumed perfect competition. In that case a small aggregate shock in liquidity demand in a particular region can lead to systemic risk. Although the shock is small it may cause a bank to go bankrupt and liquidate its assets. This in turn causes other banks which have deposits in it to also go bankrupt and so on. Eventually all banks are forced to liquidate their assets at a considerable loss. Perfect competition in the interbank market plays an important role in this contagion. Since each bank is small, acts as a price taker and assumes its actions have no effect on the equilibrium, no bank has an incentive to provide liquidity to the troubled bank.

Saez and Shi (2004) have argued that if banks are limited in number they may have an incentive to act strategically and provide liquidity to the bank that had the original problem. This will prevent the contagion and make the banks providing funds in this way better off. The formal model of this phenomenon that captures the role of imperfect competition is similar to the model in Bagnoli and Lipman (1989). They consider the problem of the provision of public goods through private contributions. There is a critical level of resources required to provide the public good. Each person becomes pivotal and in this case the public good can be provided. Similarly, here each bank would be pivotal in the provision of liquidity to pre- vent contagion.

Suppose there are two banks, each holding A units of an illiquid asset and M units of money. One unit of the asset is worth one unit if liquidated today and R units if liquidated tomorrow. Money is storable, so one unit of money today is worth one unit tomorrow. Each bank owes one unit of money to a depositor who withdraws today and the liquidation value of the portfolio to a depositor who withdraws tomorrow. In addition, bank 1 owes bank 2 B units today. Suppose that bank 1 has K early consumers and 1 - X late consumers. If k + B > M + A - ((1 - X)/R), then bank 1 cannot meet its commitments on its own. If it fails, however, bank 2 can claim only a fraction (B/(1 + B))(M + A) < B. Bank 2 has several actions it can take. It can forgive part of its debt. It can wait for payment at date 2. It can make a cash transfer to bank 1 at date 1. If R is big enough, this may be better for bank 2 than

enforcing its debt now.

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478 : MONEY, CREDIT, AND BANKING

The same argument obviously applies to any number of banks, though the coordi- nation problem will become more severe as the numbers increase. If there is a continuum of banks or asymmetric information, it may be impossible to sustain the cooperative equilibrium. Note that even in the case with a finite number of banks there may be a coordination failure; if every bank thinks the others will not contribute anything, it may be optimal not to contribute anything (this may depend on the extensive form, e.g., simultaneous moves rather than sequential moves).

Gradstein, Nitzan, and Slutsky (1993) show that Bagnoli and Lipman's result is not very robust to the introduction of uncertainty. It remains to be seen whether a model of contagion and imperfectly competitive banks would also not be robust.

A model of an imperfectly competitive interbank market may therefore be more stable than the case where there is perfect competition. As in the original agency model of Keeley (1990) there is again a trade-off between competition and finan- cial stability.

6. CONCLUDING REMARKS

In this paper we have considered a variety of different models of competition and financial stability. These include general equilibrium models of financial interme- diaries and markets, agency models, models of spatial competition, Schumpeterian competition, and contagion. There is a very wide range of possibilities concerning the relationship between competition and financial stability. In some situations there is a trade-off as is conventionally supposed but in others there is not. For example, with general equilibrium and Schumpeterian models efficiency requires the combination of perfect competition and financial instability.

There is a large policy literature based on the conventional view that there is a trade-off between competition and stability. Since competition is generally viewed as being desirable because it leads to allocational efficiency, this perceived trade-off lead to calls for increased regulation of the banking sector to ensure the coexistence of competition and financial stability. The most popular instrument for achieving this end was the imposition of minimum capital requirements on banks. If the owners of banks were forced to put up significant amounts of capital, they would be unwilling to take risks because they would again stand to loose large amounts of funds. The Basel Agreement of 1988 imposed capital controls on banks so that the incentive to take risks would be reduced and they could compete on equal terms. A large literature has developed analyzing the effect of capital controls. For example, Hellman, Mur- dock, and Stiglitz (2000) showed in the context of a simple model of moral hazard that capital controls were not sufficient. In addition to capital controls, deposit rate controls were also necessary to achieve Pareto efficiency.

Our analysis suggests that the issue of regulation and its effect on competition and financial stability is complex and multi-faceted. Careful consideration of all the factors at work both at a theoretical and empirical level is required for sound policy.

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FRANKLIN ALLEN AND DOUGLAS GALE : 479

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Carlson, M., and K. Mitchener (2003). "Branch Banking, Bank Competition and Financial Stability." Working Paper, Department of Economics, Santa Clara University.

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Diamond, P. (1971). "A Model of Price Adjustment." Journal of Economic Theory 3, 156- 168.

Diamond, D., and P. Dybvig (1983). "Bank Runs, Deposit Insurance, and Liquidity." Journal of Political Economy 91, 401-419.

Eichengreen, B., A. Rose, and C. Wyplocz (1996). "Contagious Currency Crises: First Tests." Scandinavian Journal of Economics 98, 463-484.

Freixas, X., and B. Parigi (1998). "Contagion and Efficiency in Gross and Net Interbank Payment Systems." Journal of Financial Intermediation 7, 3-31.

Freixas, X., B. Parigi, and J. Rochet (2000). "Systemic Risk, Interbank Relations and Liquidity Provision by the Central Bank." Journal of Money, Credit, and Banking 32, 611-638.

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Glick, R., and A. Rose (1999). "Contagion and Trade: Why are Currency Crises Regional?" In The Asian Financial Crisis: Causes, Contagion and Consequences, edited by P. Agenor, M. Miller, D. Vines, and A. Weber, chap. 9. Cambridge, UK: Cambridge University Press.

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Keeley, M. (1990). "Deposit Insurance, Risk and Market Power in Banking." American Economic Review 80, 1183-1200.

King, M., and S. Wadhwani (1990). "Transmission of Volatility between Stock Markets." Review of Financial Studies 3, 5-33.

Kiyotaki, N., and J. Moore (1997). "Credit Chains." Journal of Political Economy 105, 211-248.

Kodres L., and M. Pritsker (2002). "A Rational Expectations Model of Financial Contagion." Journal of Finance 57, 768-799.

Kyle, A., and W. Xiong (2001). "Contagion as a Wealth Effect." Journal of Finance 56, 1401-1440.

Lagunoff, R., and S. Schreft (2001). "A Model of Financial Fragility." Journal of Economic Theory 99, 220-264.

Masson, P. (1999). "Contagion: Monsoonal Effects, Spillovers and Jumps between Multiple Equilibria." In The Asian Financial Crisis: Causes, Contagion and Consequences, edited by P. Agenor, M. Miller, D. Vines, and A. Weber, chap. 8. Cambridge, UK: Cambridge University Press.

Matutes, C., and X. Vives (1996). "Competition for Deposits, Fragility and Insurance." Journal of Financial Intermediation 5, 184-216.

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Perotti, E., and J. Suarez (2003). "Last Bank Standing: What Do I Gain if You Fail?" European Economic Review 46, 1599-1622.

Rochet, J., and J. Tirole (1996a). "Interbank Lending and Systemic Risk." Journal of Money, Credit, and Banking 28, 733-762.

Rochet, J., and J. Tirole (1996b). "Controlling Risk in Payment Systems." Journal of Money, Credit, and Banking 28, 832-862.

Saez, L., and X. Shi (2004). "Liquidity Pools, Risk Sharing and Financial Contagion." Journal of Financial Services Research 25, 5-23.

Schumpeter, J. (1950). Capitalism, Socialism and Democracy, 3rd edition. New York: Harper & Row.

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  • Article Contents
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  • Issue Table of Contents
    • Journal of Money, Credit and Banking, Vol. 36, No. 3, Part 2: Bank Concentration and Competition: An Evolution in the Making A Conference Sponsored by the Federal Reserve Bank of Cleveland May 21-23, 2003 (Jun., 2004), pp. 433-654
      • Front Matter
      • Bank Concentration and Competition: An Evolution in the Making [pp. 433-451]
      • Competition and Financial Stability [pp. 453-480]
      • Comment on "Competition and Financial Stability" by Franklin Allen and Douglas Gale [pp. 481-486]
      • Crises in Competitive versus Monopolistic Banking Systems [pp. 487-506]
      • Comment on "Crises in Competitive versus Monopolistic Banking Systems" by John H. Boyd, Gianni De Nicoló, and Bruce D. Smith [pp. 507-509]
      • How Foreign Participation and Market Concentration Impact Bank Spreads: Evidence from Latin America [pp. 511-537]
      • Comment on "How Foreign Participation and Market Concentration Impact Bank Spreads: Evidence from Latin America" by Maria Soledad Martinez Peria and Ashoka Mody [pp. 539-542]
      • Real Effects of Bank Competition [pp. 543-558]
      • Comment on "Real Effects of Bank Competition" by Nicola Cetorelli [pp. 559-562]
      • What Drives Bank Competition? Some International Evidence [pp. 563-583]
      • Comment on "What Drives Bank Competition? Some International Evidence" by Stijn Claessens and Luc Laeven [pp. 585-592]
      • Regulations, Market Structure, Institutions, and the Cost of Financial Intermediation [pp. 593-622]
      • Comment on "Regulations, Market Structure, Institutions, and the Cost of Financial Intermediation" by Asli Demirgüç-Kunt, Luc Laeven, and Ross Levine [pp. 623-626]
      • Bank Competition and Access to Finance: International Evidence [pp. 627-648]
      • Comment on "Bank Competition and Access to Finance: International Evidence" by Thorsten Beck, Asli Demirgüç-Kunt, and Vojislav Maksimovic [pp. 649-654]
      • Back Matter

Competition and Financial Stability.pdf

Comment on "Competition and Financial Stability" by Franklin Allen and Douglas Gale Author(s): Charles M. Kahn Source: Journal of Money, Credit and Banking, Vol. 36, No. 3, Part 2: Bank Concentration and Competition: An Evolution in the Making A Conference Sponsored by the Federal Reserve Bank of Cleveland May 21-23, 2003 (Jun., 2004), pp. 481-486 Published by: Ohio State University Press Stable URL: http://www.jstor.org/stable/3838947 . Accessed: 13/02/2015 16:16

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CHARLES M. KAHN

Comment on "Competition and Financial

Stability" by Franklin Allen and Douglas Gale JEL codes: G28, E44, L51

Keywords: financial contagion, banking regulation.

Allen and Gale (2004, this issue of JMCB) provide a thought-provoking tour d'horizon. I'd call it "ambitious," except that what they label a "prelude to the development of a theoretical framework" is too honest to be ambitious: they stead- fastly refuse to give a definitive answer to their fundamental question, "Does competi- tion encourage or discourage adequate financial stability?"-in other words, "Is there a tradeoff between competition and financial stability or are the two goals mutually reinforcing?"

In addressing this question, Allen and Gale employ several different interpretations of the two goals: In some sections, "competition" means price-taking behavior. In others, they interpret competition to be imperfect competition with some degree of market power. By "instability" Allen and Gale sometimes simply mean "the possibility of default." Often instability means the choice of overly risky or undiversi- fled investments. But in their most interesting case instability means a high degree of interdependence between bank defaults.

1. THE ARGUMENTS

I will also use this review as an opportunity to consider the often uncomfortable relationship between economic theory and policy making. Policy makers want, and need, definitive answers to questions like Allen and Gale's fundamental question. Theorists spin answers in every possible direction. When we are lucky, empiricists sort through those answers to determine their relative importance. Nonetheless this

CHARLES M. KAHN is a professor in the Departments of Economics and Finance at the University of Illinois Urbana-Champaign. E-mail: [email protected]

Received May 29, 2003; and accepted in revised form May 29, 2003.

Journal of Money, Credit, and Banking, Vol. 36, No. 3 (June 2004, Part 2) Published in 2004 by The Ohio State University Press.

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482 : MONEY, CREDIT, AND BANKING

process takes time and often fails to reach any definite conclusions. In the meanwhile, the theoretical arguments are all we have, and like Harry Truman, current policy makers sigh for a "one-handed economist."

Allen and Gale give them no comfort. They provide, not two, but many hands:1 1. The invisible hand. Allen and Gale (2003), following Prescott and Townsend

(1984), show conditions under which the welfare theorems hold in economies with financial intermediaries. In terms of this work "competition" means Walrasian price taking and "financial instability" means violating a contract by declaring bankruptcy. In this sense, financial instability is only present in an incomplete market setting; in a complete contract setting, there is no sense to the notion of violating a contract because every possible reaction and consequence is included in the terms of the contract itself. Allen and Gale (2003) specify a class of models in which incom- plete contracts are optimally chosen, provided the market for underlying Arrow securities is complete. Crucial to the structure is the assumption of no ex ante differential information; contracts are written and Arrow securities traded by symmet- rically informed agents. In such a world, there is no reason for a government to intervene to stabilize a financial market.

Although this is a natural starting point for a discussion, the argument seems to me to be a straw man. Even though bankruptcy procedures are expensive, sometimes particular banks ought to fail; few regulators or policy makers would argue otherwise. The real costs of financial instability must be found elsewhere.

2. A neighborly hand. Geographic diversification reduces risk. It may also reduce competition, depending on whether the geographically diverse banks have local monopolies or end up competing region by region. Nonetheless, since it is hard to see how consolidation could increase competition, score one for the tradeoff camp.

3. A hand in the cookie jar. Limited liability and high leverage, possibly exacer- bated by deposit insurance, by a winner-take-all objective function, or by some, though not all, market-share objective functions (or by options in executives' com- pensation packages?) tempt banks into overly risky positions. The lower the bank's charter value (for example due to increased competition), the greater the temptation. Allen and Gale's model of this is attractive in its simplicity and illustrates one classic form of the tradeoff between risk and competition. And yet, as they point out, the result is reversed if the potential for excessive risk taking is in the hands of the borrowers rather than the banks, or if the objective includes being the "last one standing."

4. The dead hand of the past. Lock-in of demand, through fixed costs of switching banks, can cause otherwise competitive firms to have monopoly power. Suppose nationwide banks are more likely to have continuing relationships with customers than are small banks. Then it is at least possible that such continuing relationships can lead to sustainable low prices through the operation of threats and counter threats in an infinitely repeated game.

1. And since they are old hands at this material, the fingerprints from these hands are often their own; several of the models they describe are found in greater detail in Allen and Gale (2000).

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DISCUSSION : 483

There is some empirical evidence that branch banks price more competitively than do unit banks. But for the story to be taken seriously we need better evidence that, for example, mobility causes nationwide banks to have more long-lived relations with customers than do local banks.

5. The hand of Shiva. Under Schumpeterian creative destruction, efficiency (that is, innovation) comes hand-in-hand with instability (that is, failure of unsuccessful enterprises). Allen and Gale present a model in which efficient innovation is achieved in an environment in which only one firm survives. The tradeoff between dynamic efficiency and static efficiency means that restriction of the competition may be desirable, but the correct remedy and the consequences for innovation and for stability are complex.

6. Dirty hands. Contagion-the spread of financial malady from one institution to another-is Allen and Gale's final interpretation of instability. The development of theories of financial fragility is currently a growth industry (in addition to the papers they cite, there are noteworthy recent contributions by Chang and Velasco, 2001, Cooper and Corbae, 2002, and Diamond and Rajan, 2001). Allen and Gale point out that competition, meaning price-taking behavior, increases the likelihood of instability, because financial system stability is a public good, one subject to free riding.

2. IMPLICATIONS: POLICY AND RESEARCH

What are we to make of all of this? Despite the high-minded even-handedness of the piece, I detect a mildly anti-interventionist undertone. Many clever arguments for intervention have been thought up over the years. Each requires a precise calculation of type and degree of intervention. Offsetting one argument against another implies a preference for inaction.

The political economy of the relationship between economists and policy makers ought to reinforce this bias. There is a great temptation for policy advocates to use interventionist theories as cover for rent-seeking behavior: "We need to limit entry into these financial activities in order to increase the stability of the financial system; we need charter value to encourage us to keep to the straight-and-narrow." Since the benefits of these interventions are tightly focused, and the benefits of nonintervention are widespread and dispersed, caution dictates skepticism when facing advocates of intervention.

Note that most of the arguments Allen and Gale adapt are not particular to the financial industry. The problems of excessive risk taking apply to all limited liability corporations. Many of the models they employ were originally developed for examin- ing manufacturing firms and industrial organization (spatial competition and patent races, for example). Their message seems to be that these models should make no more of a case for intervention in the financial industry than in other industries of the economy.

The exception, the one argument for intervention which is distinctively focused on financial intermediation, is the fear of contagion. It is also by far the most

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important argument in the arsenal, the justification that financial regulators consider most significant and the one that economists must take most seriously.

The issue is not new. "Financial contagion" is merely the latest version of a traditional theme in the literature on intermediation. Financial intermediaries pro- vide so many different, nebulous services that it is a recurring rhetorical device to say, "Banks are on the one hand focused on doing A, but meanwhile they also (possibly inadvertently) do B." In various incarnations of the argument, a variety of activities have played the role of "A" and "B." By providing credit, banks inadvertently expand the money supply. By cutting back on their lending activi- ties, banks inadvertently cause feedbacks throughout the macroeconomy. The most recent twists go through the payments system and liquidity channels: "Contagion" and "financial fragility" are just the latest buzzwords. (I too am guilty of packing old wine in new skins; see Kahn and Santos, 2003).

So what to do? As I said Allen and Gale are too wise and too honest to provide an easy answer. I am either less honest or more foolhardy. Is there a tradeoff between financial efficiency and financial stability? Perhaps. Is financial instability expensive? Probably, although not all of the things Allen and Gale call "instability" are equally expensive. But inefficiency is always with us, and instability is (by definition, I suppose) intermittent. Rather than sacrifice efficiency, it is better be prepared to mitigate crises when they do arise.

Indeed, it is hopeless to try to approach regulation any other way: many times the "benefits" from instability are in the form of discipline on agents in stable times. But such discipline is a myth if crises are handled badly. If in the midst of a crisis the regulator bails out the directors and shareholders as well as the depositors, then allowing the crisis is not efficient after all.

Try as it might, a regulator can no more commit not to intervene in an LTCM or September 11th crisis than it could commit to its own abolition. Crisis interventions can be carried out well or badly, but some sort of intervention is inevitable. The best that can be hoped for is that the regulator has plans and procedures in place to serve, in effect, as the template-the commitment device-for the techniques to be used in that intervention.

It is important to rethink several aspects of banking regulation in this light: deposit insurance, closure policy, and payment system regulations. Regulatory policies can have long-run effects on a financial system's efficiency and stability. But the effect of announced policies will inevitably be filtered through participants' perceptions of their credibility. To do so requires modeling the activities of a regulator with its own bureaucratic objectives. Relevant studies of financial regulation are Boot and Thakor (1993), Campbell, Chan, and Marino (1992), Goodhart et al. (1998), Kahn and Santos (2001), Kane (1990), Mailath and Mester (1994), and Repullo (2000); more generally, see Alesina and Tabellini (2003) and the literature on political business cycles.

This perspective also provides renewed significance for a once-common type of study within economics: the regulatory history. Examinations of regulators' re- sponses to particular crises can help us understand the effects and limitations of

This content downloaded from 147.143.2.5 on Fri, 13 Feb 2015 16:16:11 PM All use subject to JSTOR Terms and Conditions

DISCUSSION : 485

having particular frameworks in place. (The classic example of this sort of study is of course Friedman and Schwartz, 1971. For other examples relevant to central

banking, see Fleming and Garbade, 2002, Lowenstein, 2001, McAndrews and Potter, 2002, Meltzer, 2002, and Shapiro, 1980).

3. CONCLUSION

While Allen and Gale have refused to take an explicit stand on whether competi- tion and financial stability are conflicting objectives, they have, by balancing their

arguments so finely, implicitly argued against competition-reducing interventions. But the most powerful argument in favor of intervention is the one they treat most

briefly: the fear that financial contagion will cause systemic crisis. If we wish to

prevent regulators from fighting financial crises by reducing competition in normal

times, we need to ensure that they have in place effective procedures to mitigate crises when they do arise.

LITERATURE CIT'ED

Alesina, Alberto, and Guido Tabellini (2003). "Bureaucrats or Politicians?" Working Paper No. 238, Innocenzo Gasparini Institute for Economic Research (June 2003).

Allen, Franklin, and Douglas Gale (2000). Comparing Financial Systems. Cambridge, MA: MIT Press.

Allen, Franklin, and Douglas Gale (2003). "Financial Intermediaries and Markets." Working Paper No. 00-44-C, Wharton Financial Institutions Center, Econometrica, forthcoming.

Allen, Franklin, and Douglas Gale (2004). "Competition and Financial Stability." Journal of Money, Credit, and Banking 36, 453-480. (this issue of JMCB)

Boot, Amoud W.A., and Anjan V. Thakor (1993). "Self-interested Bank Regulation."American Economic Review 83, 206-212.

Campbell, T.S., Y.S. Chan, and A.M. Marino (1992). "An Incentive-Based Theory of Bank Regulation." Journal of Financial Intermediation 2, 255-276.

Chang, Roberto, and Andres Velasco (2001). "A Model of Financial Crises in Emerging Markets." Quarterly Journal of Economics 116, 489-517.

Cooper, Russell, and Dean Corbae (2002). "Financial Collapse: A Lesson from the Great Depression." Journal of Economic Theory 107, 159-190.

Diamond, Douglas, and Raghuram Rajan (2001). "Liquidity Risk, Liquidity Creation and Financial Fragility: A Theory of Banking." Journal of Political Economy 109, 287-327.

Fleming, Michael J., and Kenneth D. Garbade (2002). "When the Back Office Moved to the Front Burner: Settlement Fails in the Treasury Market after 9/11." Federal Reserve Bank of New York Economic Policy Review 8, 35-57.

Friedman, Milton, and Anna Jacobson Schwartz (1971). Monetary History of the United States, 1867-1960. Princeton, NJ: Princeton University Press.

Goodhart, C., P. Hartmann, D. Llewellyn, L. Rojas-Suarez, and S. Weisbrod (1998). Financial Regulation: Why, How, and Where Now? New York: Routledge.

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486 : MONEY, CREDIT, AND BANKING

Kahn, Charles M., and Joao A.C. Santos (2001). "Allocating Bank Regulatory Powers: Lender of Last Resort, Deposit Insurance and Supervision." Federal Reserve Bank of New York Working Paper (November 2001).

Kahn, Charles M., and Joao A.C. Santos (2003). "Endogenous Financial Systems and Prudential Regulation." Federal Reserve Bank of New York (June 2003), unpublished manuscript.

Kane, E.J. (1990). "Principal-Agent Problems in S&L Salvage." Journal of Finance 45, 755-764.

Lowenstein, Roger (2001). When Genius Failed: The Rise and Fall of Long-Term Capital Management. New York: Random House.

Mailath, G.J., and L.J. Mester (1994). "A Positive Analysis of Bank Closure." Journal of Financial Intermediation 3, 272-299.

McAndrews, James J., and Simon M. Potter (2002). "Liquidity Effects of the Events of September 11,2001." Federal Reserve Bank of New York Economic Policy Review 8, 59-79.

Meltzer, Allan H. (2002). A History of the Federal Reserve: 1913-1951, Vol. 1. Chicago: University of Chicago Press.

Prescott, E., and R. Townsend (1984). "Pareto Optima and Competitive Equilibria with Adverse Selection and Moral Hazard." Econometrica 52, 21-45.

Repullo, R. (2000). "Who Should Act as a Lender of Last Resort? An Incomplete Contracts Model." Journal of Money Credit and Banking 32, 580-605.

Shapiro, Max (1980). The Penniless Billionaires. New York: Times Books.

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  • Article Contents
    • p. [481]
    • p. 482
    • p. 483
    • p. 484
    • p. 485
    • p. 486
  • Issue Table of Contents
    • Journal of Money, Credit and Banking, Vol. 36, No. 3, Part 2: Bank Concentration and Competition: An Evolution in the Making A Conference Sponsored by the Federal Reserve Bank of Cleveland May 21-23, 2003 (Jun., 2004), pp. 433-654
      • Front Matter
      • Bank Concentration and Competition: An Evolution in the Making [pp. 433-451]
      • Competition and Financial Stability [pp. 453-480]
      • Comment on "Competition and Financial Stability" by Franklin Allen and Douglas Gale [pp. 481-486]
      • Crises in Competitive versus Monopolistic Banking Systems [pp. 487-506]
      • Comment on "Crises in Competitive versus Monopolistic Banking Systems" by John H. Boyd, Gianni De Nicoló, and Bruce D. Smith [pp. 507-509]
      • How Foreign Participation and Market Concentration Impact Bank Spreads: Evidence from Latin America [pp. 511-537]
      • Comment on "How Foreign Participation and Market Concentration Impact Bank Spreads: Evidence from Latin America" by Maria Soledad Martinez Peria and Ashoka Mody [pp. 539-542]
      • Real Effects of Bank Competition [pp. 543-558]
      • Comment on "Real Effects of Bank Competition" by Nicola Cetorelli [pp. 559-562]
      • What Drives Bank Competition? Some International Evidence [pp. 563-583]
      • Comment on "What Drives Bank Competition? Some International Evidence" by Stijn Claessens and Luc Laeven [pp. 585-592]
      • Regulations, Market Structure, Institutions, and the Cost of Financial Intermediation [pp. 593-622]
      • Comment on "Regulations, Market Structure, Institutions, and the Cost of Financial Intermediation" by Asli Demirgüç-Kunt, Luc Laeven, and Ross Levine [pp. 623-626]
      • Bank Competition and Access to Finance: International Evidence [pp. 627-648]
      • Comment on "Bank Competition and Access to Finance: International Evidence" by Thorsten Beck, Asli Demirgüç-Kunt, and Vojislav Maksimovic [pp. 649-654]
      • Back Matter

Defining Financial Stability.pdf

WP/04/187

Defining Financial Stability

Garry J. Schinasi

© 2004 International Monetary Fund WP/04/187

IMF Working Paper

International Capital Markets Department

Defining Financial Stability1

Prepared by Garry J. Schinasi

October 2004

Abstract

This Working Paper should not be reported as representing the views of the IMF. The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those of the IMF or IMF policy. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate.

The main objective of this paper is to propose a definition of financial stability that has some practical and operational relevance. Financial stability is defined in terms of its ability to facilitate and enhance economic processes, manage risks, and absorb shocks. Moreover, financial stability is considered a continuum: changeable over time and consistent with multiple combinations of the constituent elements of finance. The paper also discusses several practical implications of the definition that should be considered when using it for policy analysis or developing an analytical framework. JEL Classification Numbers: E60, G00, H00 Keywords: Finance, stability, fragility, crises Author(s) E-Mail Address: [email protected]

1 This paper was written while on sabbatical from the IMF and is part of a manuscript on financial stability issues. I gratefully acknowledge the IMF’s financial support under its Independent Study Leave Program. I also gratefully acknowledge the support and encouragement of De Nederlandsche Bank (DNB) and the European Central Bank (ECB) while visiting them in 2003 and 2004, especially Tommaso Padoa-Schioppa, Mauro Grande, and John Fell at the ECB and Henk Brouwer, Jan Brockmeijer, Aerdt Houben, and Jan Kakes at the DNB. I am grateful to Tommaso Padoa-Schioppa, John Fell, Mauro Grande, Aerdt Houben, Jan Kakes, and Jukka Vesala for extensive discussions on this topic and for comments on earlier drafts of this paper.

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Table of Contents

Page

I. Introduction....................................................................................................................3

II. Prior Concepts of Finance and Finance’s Strengths and Weaknesses ...........................4

III. Key Principles for Defining Financial Stability.............................................................6

IV. Definition of Financial System Stability........................................................................8

V. Some Practical Implications of the Definition.............................................................11

Annex: Alternative Definitions of Financial Stability .............................................................13 References................................................................................................................................17

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I. INTRODUCTION

Does financial stability require the soundness of institutions, the stability of markets, the absence of turbulence, low volatility, or something more fundamental? Can it be achieved and maintained through individual private actions and unfettered market forces alone? If not, what is the role of the public sector in fostering financial stability, as opposed to private- collective action: is it just to make way for the private sector to achieve an optimum on its own, or is a more proactive role necessary for achieving the full private and social benefits of finance? Is there a consensus on how to achieve and maintain financial stability?

The last three questions are not likely to have clear answers without a useful answer to the first question. Likewise, without a good working definition, the growing financial stability profession will continue to find it difficult to develop useful analytical frameworks for examining policy issues. Unfortunately, there is no single, widely accepted and used definition of financial stability. There have been recent attempts to define financial stability, but most of them seem to fit into a particular theme of a paper or speech. In addition, most authors prefer to define financial instability or systemic risk (see the attached Annex starting on page 13).

The approach taken here is to define financial stability rather than its absence, in part because this is likely to be the more useful “policy” objective. A policy objective of avoiding financial instability or crisis—or of managing systemic risk—could bias policy decisions, analyses, and analytical frameworks towards sacrificing both private and social benefits of finance. A more positive or constructive approach—such as the one proposed in this paper— may serve additional practical purposes, including leaving open the possibility of assessing whether the private and social benefits of finance can be increased further. This would be particularly useful in countries that have relatively undeveloped financial systems.

As anyone who has tried to define financial stability knows, there is as yet no widely accepted model or analytical framework for assessing financial system stability and for examining policies as there is for economic systems and in other disciplines.2 This is because the analysis of financial stability is still in its infant stage of development and practice, as compared with—for example—the analysis of monetary and/or macroeconomic stability. In the rare cases in which financial systems are expressed rigorously, they constitute one or two equations in a much larger macroeconomic model possessing most of the usual macro- equilibrium and macro-stability conditions. In addition, there are reasons to believe that a single target variable cannot be found for defining and achieving financial stability—as there is believed to be for defining and achieving monetary stability—although many doubt that a single target variable approach accurately represents actual practice in monetary policymaking.

2 See the paper by Houben, Kakes, and Schinasi (2004), which proposes a framework for financial stability (and also draws on concepts developed here). The IMF’s bilateral and multilateral financial market and system surveillance, and the IMF’s and World Bank’s Financial Sector Assessment Program are also making progress in this direction.

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Lacking a framework, a set of models, or even a concept of equilibrium, it is difficult to envision a definition of financial stability akin to that which economists normally demand and use. Nevertheless, it would be useful to have one that allows for the development of policy frameworks and analytical tools. The definition proposed in this paper is one step in this direction, and is offered for wider debate.

The paper is organized as follows: Section II briefly presents some prior concepts of finance and its strengths (benefits) and weaknesses (fragilities), drawing on analysis in a companion study. These concepts serve as both practical and analytical focal points for developing a concept of financial stability in the absence of a widely accepted concept of equilibrium and analytical framework. For simplicity, Section III identifies five principles that a useful definition could encompass. It also makes a case for seeing financial stability as occurring along a changeable continuum or range of conditions of the constituent parts of the financial system, as opposed to a single configuration or state of these parts as is most often used in microeconomic and macroeconomic models. Section IV proposes a broad definition and discusses the meaning of some of its language. The final section identifies several practical implications of the definition that should be carried over into any policy or analytical framework that utilizes it.

II. PRIOR CONCEPTS OF FINANCE AND FINANCE’S STRENGTHS AND WEAKNESSES

Before developing a working definition of financial stability, it would be useful to consider the following understandings as prerequisites or as relevant concepts and ideas.3

First, a barter economy is less effective and efficient in allocating scarce resources than is an economy with the ability to use financial claims on future real resources. A discussion of financial stability must necessarily take place within the context of a monetary economy in which there exists a money (now usually fiat money) that is universally accepted as the economy’s unit of account and means of payment.

Second, money is not necessarily the most desirable store of value—except in the very short run or during episodes of financial distress and dysfunctions. Throughout recorded history, human ingenuity has driven an evolutionary process of finance to overcome this persistent deficiency. Modern finance provides substitutes for money that provide temporary and reversible intertemporal means-of-payment and store-of-value services. These substitutes are promises to pay money in the future and are designed in part to facilitate intertemporal resource allocations.

Third, many of the services provided by money and finance are both private and public goods. They are private goods in providing benefits to individuals in their private affairs, benefits that convey only to the counterparts engaged in specific transactions. They separately and jointly provide public goods as well, because they allow multilateral trade and exchange to be more efficient, in part by eliminating the need for Jevons’ “double coincidence of wants,” both sectorally at moments in time and intertemporally. In addition, 3 See Schinasi (2004) for a more detailed discussion and analysis of many of these points.

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finance provides public goods beyond those of fiat money: by enhancing and distributing the public-good characteristics of fiat money, finance enlarges society’s opportunities for—and efficiency in—intertemporal economic processes such as trade, production, wealth accumulation, economic development and growth, and ultimately social prosperity. In sum, the universal acceptability of money and the existence of an effective process of finance together create an environment that provides collective benefits to all members of society.

Fourth, an alternative and useful way of seeing finance is to bring to the surface one of its defining characteristics. Unlike fiat money—which eliminates the element of human trust in trade and exchange—finance involves human promises to pay back specific amounts of fiat money in the future. In this way, finance existentially embodies uncertainty (about human trust). Modern financial systems have evolved to provide beneficial and necessarily imperfect ways of transforming this fundamental uncertainty into quantifiable and “priceable” risks, such as default risk, and, through social arrangements (both markets and financial institutions), also market risk, liquidity risk, and so on. In less traditional but no less appropriate terms, modern finance provides societies with effective, albeit imperfect, mechanisms for transforming, pricing, and allocating economic and financial uncertainties and risks.

Finally, because finance existentially embodies uncertainty, there are both potential benefits and costs associated with it.4 On the one hand, finance enhances the private and social benefits of fiat money: in part by enlarging the pool of liquidity available for production, consumption, and exchange; and in part by facilitating and enhancing the efficiency of intertemporal economic processes. In effect, the willingness to engage in finance (i.e., to take the leap of faith) and accept the uncertainty of trust has created social welfare gains far beyond what fiat money alone could provide.

On the other hand, trust is fragile: it can, and often enough does, become a source of potential financial instability, which in the wrong circumstances can affect both individual and social welfare. To the extent that doubts about human trust are transformed by the financial system into market and other financial risks, they too can become companion sources of instability—even more so if a society’s financial-market mechanisms are impaired and unable to effectively reallocate and price such doubts. How such doubts propagate through the financial system is an important determinant of whether they either self-correct and remain isolated and harmless or become widespread, harmful, and perhaps even systemic. Because finance supports and facilitates real economic processes, these potential instabilities may well extend to the real economy.

4 Diamond and Dybvig (1983) and Diamond and Rajan (2000) explore this in the context of bank intermediation.

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III. KEY PRINCIPLES FOR DEFINING FINANCIAL STABILITY

While there is scope for being more comprehensive and inclusive, a small number of key principles can be identified for developing a working definition of financial stability.5 One that requires more elaboration than the others is that it is useful to consider financial stability as occurring along a continuum—rather than as a static condition.

The first principle is that financial stability is a broad concept, encompassing the different aspects of finance (and the financial system)—infrastructure, institutions, and markets. Both private and public persons participate in markets and in vital components of the financial infrastructure (including the legal system and official frameworks for financial regulation, supervision, and surveillance). Governments borrow in markets, hedge risks, operate through markets to conduct monetary policy and maintain monetary stability, and own and operate payments and settlement systems. Accordingly, the term “financial system” can be seen as encompassing both the monetary system with its official understandings, agreements, conventions, and institutions as well as the processes, institutions, and conventions of private financial activities.6 Given the tight interlinkages between all of these components of the financial system, (expectations of) disturbances in any of the individual components can undermine the overall stability, requiring a systemic perspective. At any given time, stability or instability could be the result of either private institutions and actions, or official institutions and actions, or both simultaneously and/or iteratively.

A second useful principle is that financial stability not only implies that finance adequately fulfills its role in allocating resources and risks, mobilizing savings, and facilitating wealth accumulation, development, and growth; it should also imply that the systems of payment throughout the economy function smoothly (across official and private, retail and wholesale, and formal and informal payments mechanisms). This requires that fiat (or central bank) money—and its close-substitute, derivative monies (such as demand deposits and other bank accounts)—can adequately fulfill its role as the universally accepted means of payment and unit of account and, when appropriate, as a (short-term) store of value. In other words, financial stability and what is usually regarded as a vital part of monetary stability overlap to a large extent.

A third principle is that the concept of financial stability relates not only to the absence of actual financial crises but also to the ability of the financial system to limit, contain, and deal with the emergence of imbalances before they constitute a threat to itself or economic processes. In a well-functioning and stable financial system, this occurs in part through self-corrective, market-disciplining mechanisms that create resilience and prevent

5 There are also many prerequisites for establishing a sound and stable financial system, such as: macroeconomic stability and a policy framework for maintaining it; an adequate—if not effective—framework for financial regulation, supervision, and surveillance (implicitly mentioned in the text as infrastructure; well- established codes, standards, and business practices), and more generally private incentive structures; and an enforceable legal system that supports productive private financial contracts.

6 This is adapted from the definition of “international financial system” in Truman (2003).

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problems from festering and growing into system-wide risks. In this respect, there may be a policy-related trade-off entailing the choice between allowing market mechanisms to work to resolve potential difficulties and intervening quickly and effectively—through liquidity injections via markets, for example—to restore risk-taking and/or to restore stability. Thus, financial stability entails both preventive and remedial dimensions.

A fourth important principle is that financial stability be couched in terms of the potential consequences for the real economy. Disturbances in financial markets or at individual financial institutions need not be considered threats to financial stability if they are not expected to damage economic activity at large. In fact, the incidental closing of a financial institution, a rise in asset-price volatility, and sharp and even turbulent corrections in financial markets may be the result of competitive forces, the efficient incorporation of new information, and the economic system’s self-correcting and self-disciplining mechanisms. By implication, in the absence of contagion and the high likelihood of systemic effects, such developments may be viewed as welcome—if not healthy—from a financial stability perspective.

A fifth principle—consistent with those already discussed and the actual dynamism of finance—is that financial stability be thought of as occurring along a continuum. An example that is more transparent is the health of an organism, which also occurs along a continuum. A healthy organism can usually reach for a greater level of health and well being, and the range of what is normal is broad and multi-dimensional. In addition, not all states of un- health (or illness) are significant, systemic, or life threatening. And some illnesses, even temporarily serious ones, allow the organism to continue to function productively and can have a cleansing effect, leading to greater health. One implication of seeing financial stability in this way is that maintaining financial stability does not necessarily require that each part of the financial system operate persistently at peak performance; it is consistent with the financial system operating on a “spare tire” from time to time.7

The concept of a continuum is relevant because finance fundamentally involves uncertainty, is dynamic (meaning both inter-temporal and innovative), and is composed of many interlinked and evolutionary elements (infrastructure, institutions, markets). Accordingly, financial stability is expectations-based, dynamic, and dependent on many parts of the system working reasonably well. What might represent stability at one point in time, might be more stable or less stable at some other time, depending on other aspects of the economic system—such as technological, political, and social developments. Moreover, financial stability can been seen as being consistent with various combinations of the conditions of its constituent parts, such as the soundness of financial institutions, financial markets conditions, and effectiveness of the various components of the financial infrastructure.

7 See Greenspan (1999).

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IV. DEFINITION OF FINANCIAL SYSTEM STABILITY

Broadly, financial stability can be thought of in terms of the financial system’s ability: (a) to facilitate both an efficient allocation of economic resources—both spatially and especially intertemporally—and the effectiveness of other economic processes (such as wealth accumulation, economic growth, and ultimately social prosperity); (b) to assess, price, allocate, and manage financial risks; and (c) to maintain its ability to perform these key functions—even when affected by external shocks or by a build up of imbalances—primarily through self-corrective mechanisms.

A definition consistent with this broad view is as follows:

A financial system is in a range of stability whenever it is capable of facilitating (rather than impeding) the performance of an economy, and of dissipating financial imbalances that arise endogenously or as a result of significant adverse and unanticipated events.

The meanings of several phrases need to be explained.

First, the concept of a range of stability represents the concept of a continuum as a key building block. The continuum for financial stability can be thought of as multidimensional and occurring across a multitude of observable and measurable variables. The set of variables should encompass a subset that tries to quantify, albeit imperfectly, how well finance is facilitating economic and financial processes such as savings and investment, lending and borrowing, liquidity creation and distribution, asset pricing, and ultimately wealth accumulation and growth.

As a continuum, financial stability can be seen practically as somewhat broader and less precise than the ability to return to a single and sustainable position or time path after a shock or perturbation, as with other (Newtonian) concepts of equilibrium and stability in some disciplines (including economics). The proposed definition is consistent with a financial system being in a perpetual state of flux and transformation while its ability to perform its key functions remains well within a set of tolerable boundaries—defined over a set of measurable variables—that are consistent with it successfully playing its important facilitative and efficiency-enhancing roles. Observable states approaching these boundaries would indicate that the financial system is losing some of its ability to perform; observations outside these boundaries would indicate that the system is no longer effectively facilitating economic processes, perhaps because aggregate production is substantially below its potential on account of funds not being channeled to profitable activities, risks not being managed, and shocks not being absorbed. In such cases, remedial action would be required, which in the extreme would mean crisis resolution and restoration.

To illustrate the multidimensional nature of the definition, consider a very simplistic two-dimensional example. In assessing the joint stability of financial markets and financial institutions, one might be able to identify combinations of interest rate spread volatility (as a possible market source of instability) and banking system capital (as an institutional source of shock-absorptive capacity) that are consistent with the financial system continuing

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effectively to facilitate efficient resource allocation. Likewise, other combinations could be identified that would not be consistent with stability. The former would constitute the range of stability and the latter would fall outside this range.

A more comprehensive sets of factors could be envisioned for determining a grid over which a continuum is defined. Statistical tools could be utilized to select such factors by considering historical episodes of both stability and instability, in part by using forward- looking, market-determined expectations of future outcomes and matching them with actual outcomes. This methodology could, in principle, also help to establish estimates of boundaries or zones separating stability from potential instability.8

A second phrase that needs some explanation is facilitating (rather than impeding) the performance of an economy. This phrase means, among other things, that finance is contributing to (rather than impeding) the efficient allocation of real resources, the rate of growth of output, and the processes of saving, investment, and wealth creation—and may also entail and include other observable and measurable aspects of economic performance.

Third, the term, “dissipate financial imbalances,” means a movement along the continuum in the direction of stability (away from boundaries) and not instability, for example in asset prices and portfolio flows, implicitly through self-corrective mechanisms. Such adjustments would include the exit and entry of market participants (financial institutions or nonfinancial entities acting on behalf of others or individuals acting directly in the markets).

There are other aspects of the definition worth noting. The proposed definition leaves open the possibility that the financial system could become capable of impeding the performance of the economy endogenously, even in the absence of unanticipated events (shocks), for example through the accumulation of imbalances caused by asset mispricing and/or other market “imperfections.” This is consistent with the ample historical evidence that financial systems, particularly banking systems, are prone to the build up of imbalances (credit-risk concentrations or illiquidity, for example) and even instability. Banks internalize the fragilities associated with the properties of liquidity, and are therefore prone to instability themselves.9 Banks, other financial institutions, and even markets can be seen as social arrangements—or as clearing houses—for assessing, pricing, and trading human promises necessarily involving uncertainty and risk, including uncertainty about the fundamental element of trust in financial contracts. Social arrangements and institutional features of economic systems try to internalize the potential adverse consequences of negative externalities associated with the frailties of human trust. A tangible example of this is that banks internalize the potential adverse consequences of failures of trust by economizing on

8 In principle, this approach could be generalized and made amenable to theoretical and empirical model building. For example, one could define a set of n variables that encompass all relevant measures of aspects of financial stability. The range of stability could be defined as a subset of n-tuples bounded by n functions (most likely nonlinear) defining the limits of stability in terms of n variables.

9 See Diamond and Rajan (2000 and 2002).

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information about large pools of debtors and their ability to pay future claims or promissory notes. In internalizing these elements of financial risk and uncertainty, financial institutions and markets themselves embody the potential for financial fragility, which ultimately finds its source in a failure of human trust in some meaningful way (for example, a default).

The definition also presupposes that there are aspects of finance that embody either negative or positive externalities. In this sense, improvements in the ability of finance to facilitate rather than impede economic processes—including providing greater financial stability—is welfare improving in terms of enhancing the efficiency of resource allocation (and pricing), especially inter-temporally.10 Some points along the continuum of financial stability are more welfare-improving (and efficiency-enhancing) than others, and some points along the continuum of instability are to be avoided, seemingly at all costs.11 Thus, in moving from a condition of stability to instability, the contribution of the financial system to aggregate economic welfare is being reduced.

A stable financial system is one that enhances economic performance in many dimensions, whereas an unstable financial system is one that detracts from economic performance. In this sense the definition is “normative.” Ultimately—and unlike physical instabilities such as earthquakes, floods, and sunspots—financial instability can be dealt with through massive intervention by authorities, including redefining the rules of the market place. But these measures would be “last resort” reforms to prevent the economic system from collapsing—as, for example, during the world-wide depression in the 1930s and more recently in Asia during 1997–98.

To illustrate the broad nature of this definition of financial stability, two corollary definitions are useful:

(i) A financial system is entering a range of instability whenever it is threatening to impede the performance of an economy.

(ii) A financial system is in a range of instability when it is impeding performance and threatening to continue to do so.

A more general definition that does not require the specification of what constitutes a “financial system” is:

Financial stability is a condition in which an economy’s mechanisms for pricing, allocating, and managing financial risks (credit, liquidity, counterparty, market, etc.) are functioning well enough to contribute to the performance of the economy (as defined above).

10 See Schinasi (2004).

11 There would seem to be a trade-off in financial systems between financial stability and efficiency, but this is difficult to analyze given that there are different concepts of both stability and efficiency. There is some work on this in the theoretical banking literature, but none could be found at the financial-system level.

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V. SOME PRACTICAL IMPLICATIONS OF THE DEFINITION

Looking at financial stability in this way allows for the delineation of financial conditions and potential difficulties according to their intensity, scope, and potential threat to systemic stability. One could, for example, think of potential financial difficulties as falling into one of the following fairly broad categories:

• difficulties in a single institution or market not likely to have system-wide consequences for either the banking or financial system;

• difficulties that involve several relatively important institutions involved in market activities with some nontrivial probability of spillovers and contagion to other institutions and markets; and

• problems likely to spread to a significant number and types of financial institutions and across usually unrelated markets for managing liquidity needs, such as forward, interbank, and even equity markets.

Problems occurring within each of these categories would require different diagnostic tools and policy responses, ranging from doing nothing to intensifying supervision or surveillance of a specific institution or market, to liquidity injections into the markets to dissipate strains, to interventions into particular institutions.

The financial stability definition also involves several complexities that have practical significance in terms of assessing risks to the well-functioning of the financial system and the contribution public policy can make to ensuring financial stability.

• Developments in financial stability cannot be summarized in a single quantitative indicator. In contrast with price stability, for instance, there is as yet no unequivocal unit of measurement for financial stability.12 This reflects the multifaceted nature of financial stability as it relates to both the stability and resilience of financial institutions, and to the smooth functioning of financial markets and settlement systems. Moreover, diverse factors need to be weighed in terms of their potential ultimate influence on real economic activity.

• Developments in financial stability are inherently difficult to forecast. Assessing the state of financial stability should not only take stock of disturbances as they emerge, but also indicate the risks and vulnerabilities that could lead to such disturbances occurring in the future. A forward-looking approach is therefore needed in order to establish the build-up of risks and imbalances and to take account of the transmission lags in policy instruments. The challenge is that financial crises are inherently difficult to predict because of many factors, for example, contagion effects and nonlinearities in the relationships between the constituent parts of finance. In

12 See Andy Haldane (2004) for an attempt to set out how this might be done for financial stability.

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addition, risks to financial stability often reflect the far-reaching consequences of unlikely events. This implies that the focus of attention should not be the mean, median, or mode of possible outcomes or states but the entire distribution of them, and particularly the left “tail.” Beyond this, the distribution of possible outcomes may be subject to greater fundamental uncertainty (in the sense of Knight, 1921) than traditional macroeconomic projections, reflecting lack of knowledge about the actual shape of the probability distribution. This would imply that forecasts of financial stability might be inherently less reliable than forecasts of monetary or macroeconomic stability, for which there are well-worked and more reliable models and more timely and useful data. Thus, in the large sets of financial indicators that are now being used by central banks and international financial institutions, the relationships between indicators and financial stability conditions may not be strong or robust enough to be reliable for assessments and prediction. Nevertheless, in looking at a broader array of indicators, in developing better frameworks, and in utilizing sophisticated statistical tools, there may be scope for improving the ability to monitor and assess financial stability in the future.

• Developments in financial stability are only partly controllable. The policy instruments that can be used to safeguard financial stability generally have other primary objectives, such as protecting the interests of deposit holders (in the case of prudential instruments), fostering price stability (in the case of monetary policy), or promoting a swift settlement of financial transactions (in the case of policies governing payment and settlement systems). Besides timing lags, the impact of these policy instruments on financial stability is thus often indirect; in some cases there may even be friction with the instrument’s initial objective. Moreover, developments in financial stability are highly susceptible to exogenous shocks—ranging from natural catastrophes to abrupt swings in market sentiment—further limiting their controllability.

• Policies aimed at financial stability often involve a trade-off between resilience and efficiency. Measures to enhance financial stability often involve weighing the pursuit of an efficient allocation of financial resources against the ability to exclude or absorb shocks to the financial system. This implies a risk/return judgment that is difficult to make in a fully objective manner. For instance, in the sphere of prudential policies, higher solvency requirements will reduce the risk of a bank not being able to absorb an adverse shock but will also imply capital costs and foregone investment opportunities. Similarly, exchange restrictions may reduce or exclude certain risks related to international capital flows but may also limit the efficiency of the domestic financial market.

• Policy requirements for financial stability may be time inconsistent. Since the use of some public policy instruments to safeguard financial stability circumvents market forces, the short-term stability gain may come at the cost of a longer-term stability loss. In particular, measures such as the provision of lender-of-last-resort finance or deposit guarantee may undermine market discipline, thereby creating moral hazard or adverse selection. This intertemporal trade off is a fundamental issue in financial system policymaking.

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ALTERNATIVE DEFINITIONS OF FINANCIAL STABILITY

This appendix provides an overview of definitions or descriptions of financial stability by a selected group of officials, central banks, and academics.13

John Chant and others (Bank of Canada)14

“Financial instability refers to conditions in financial markets that harm, or threaten to harm, an economy’s performance through their impact on the working of the financial system.... Such instability harms the working of the economy in various ways. It can impair the financial condition of non-financial units such as households, enterprises, and governments to the degree that the flow of finance to them becomes restricted. It can also disrupt the operations of particular financial institutions and markets so that they are less able to continue financing the rest of the economy.... It differs from time to time and from place to place according to its initiating impulse, the parts of the financial system affected, and its consequences. Threats to financial stability have come from such diverse sources as the default on the bonds of a distant government; the insolvency of a small, specialized, foreign exchange bank; computer breakdown at a major bank; and the lending activities of a little- known bank in the U.S. Midwest (pp. 3–4).

Andrew Crockett (Bank for International Settlements and Financial Stability Forum)15

“...define financial stability as an absence of instability...a situation in which economic performance is potentially impaired by fluctuations in the price of financial assets or by an inability of financial institutions to meet their contractual obligations. I would like to focus on four aspects of this definition.

“Firstly, there should be real economic costs.... Secondly, it is the potential for damage rather than actual damage which matters.... Thirdly, my definition refers...not just to banks but to nonbanks, and to markets as well as to institutions.... Fourth, my definition allows me to address the question of whether banks are special...all institutions that have large exposures—all institutions that are largely interconnected whether or not they are themselves directly involved in the payments system—have the capacity, if they fail, to cause much widespread damage in the system.”

13 Some authors choose not to define financial stability and instead use the concept of systemic risk. See Oosterloo and Haan (2003) for a discussion of this concept.

14 See Chant (2003).

15 See Crockett (1997).

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Deutsche Bundesbank16

“The term financial stability broadly describes a steady state in which the financial system efficiently performs its key economic functions, such as allocating resources and spreading risk as well as settling payments, and is able to do so even in the event of shocks, stress situations, and periods of profound structural change.”

Wim Duisenberg (European Central Bank)17

“...monetary stability is defined as stability in the general level of prices, or as an absence of inflation or deflation. Financial stability does not have as easy or universally accepted a definition. Nevertheless, there seems to be a broad consensus that financial stability refers to the smooth functioning of the key elements that make up the financial system.”

Roger Ferguson (Board of Governors of the U.S. Federal Reserve System)18

“It seems useful...to define financial stability...by defining its opposite: financial instability. In my view, the most useful concept of financial instability for central banks and other authorities involves some notion of market failure or externalities that can potentially impinge on real economic activity.

“Thus, for the purposes of this paper, I’ll define financial instability as a situation characterized by these three basic criteria: (i) some important set of financial asset prices seem to have diverged sharply from fundamentals; and/or (ii) market functioning and credit availability, domestically and perhaps internationally, have been significantly distorted; with the result that (iii) aggregate spending deviates (or is likely to deviate) significantly, either above or below, from the economy’s ability to produce.

Michael Foot (U.K. Financial Services Authority)19

“...we have financial stability where there is: (a) monetary stability; (b) employment levels close to the economy’s natural rate; (c) confidence in the operation of the generality of key financial institutions and markets in the economy; and (d) where there are no relative price movements of either real or financial assets within the economy that will undermine (a) or (b).

“The first three elements of this definition are, I hope, noncontentious. In respect of (a) and (b), it seems implausible to define financial stability as occurring in a period of rapid inflation, or in a mid-1930s style period of low inflation but high unemployment.

16 Deutsche Bundesbank (2003).

17 See Duisenberg (2001).

18 See Ferguson (2003).

19 See Foot (2003).

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“Similarly in respect of (c), it would be strange to argue that there was financial stability in a period when banks were failing, or when normal conduits for long-term savings and borrowing in either the personal or corporate sectors were seriously malfunctioning. Such circumstances would mean the participants had lost confidence in financial intermediaries. It would mean, almost certainly, that economic growth was being damaged by the unavailability or relatively high cost of financial intermediation.

“This leaves us with (d).... I would say that there are four main channels by which changes in asset prices might affect the real economy: by changing household wealth and thereby consumption…by a change in equity prices...by their impact on firms’ balance sheets which can then affect corporate spending...[and] by their impact on capital flows, with for example inflows of capital—as during the dot.com boom in the US—strengthening the domestic currency.”

Sir Andrew Large20

“In a broad sense.....think of financial stability in terms of maintaining confidence in the financial system. Threats to that stability can come from shocks of one sort or another. These can spread through contagion, so that liquidity or the honoring of contracts becomes questioned. And symptoms of financial instability can include volatile and unpredictable changes in prices. Preventing this from happening is the real challenge.”

Frederick Mishkin (Columbia University)21

...Financial instability “occurs when shocks to the financial system interfere with information flow so that the financial system can no longer do its job of channeling funds to those with productive investment opportunities.”

Norges Bank22

“Financial stability means that the financial system is robust to disturbances in the economy, so that it is able to mediate financing, carry out payments, and redistribute risk in a satisfactory manner.”

Tommaso Padoa-Schioppa (European Central Bank)23

“...[financial stability is] a condition where the financial system is able to withstand shocks without giving way to cumulative processes, which impair the allocation of savings to investment opportunities and the processing of payments in the economy. 20 See Large (2003).

21 See Mishkin (1999).

22 See Norwegian Central Bank (2003).

23 See Padoa-Schioppa (2003).

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“The definition immediately raises the related question of defining the financial system...[which] consists of all financial intermediaries, organized and informal markets, payments and settlement circuits, technical infrastructures supporting financial activity, legal and regulatory provisions, and supervisory agencies. This definition permits a complete view of the ways in which savings are channeled towards investment opportunities, information is disseminated and processed, risk is shared among economic agents, and payments are facilitated across the economy.”

Anna Schwartz (National Bureau of Economic Research)24

“A financial crisis is fueled by fears that the means of payment will be unobtainable at any price and, in a fractional reserve banking system, leads to a scramble for high-powered money. It is precipitated by actions of the public that suddenly squeeze the reserves of the banking system.... The essence of a financial crisis is that it is short-lived, ending with a slackening of the public’s demand for additional currency.”

Nout Wellink (De Nederlandsche Bank)25

“According to our own definition at the Nederlandsche Bank, a stable financial system is capable of efficiently allocating resources and absorbing shocks, preventing these from having a disruptive effect on the real economy or on other financial systems. Also, the system itself should not be a source of shocks. Our definition thus implies that that money can properly carry out its functions as a means of payment and as a unit of account, while the financial system as a whole can adequately perform its role of mobilizing savings, diversifying risks, and allocating resources. Financial stability is a vital condition for economic growth, as most transactions in the real economy are settled through the financial system. The importance of financial stability is perhaps most visible in situations of financial instability. For example, banks may be reluctant to finance profitable projects, asset prices may deviate excessively from their underlying intrinsic values, or payments may not be settled in time. In extreme cases, financial instability may even lead to bank runs, hyperinflation, or a stock market crash.”

24 Schwartz (1986).

25 See Wellink (2002).

References

Chant, John, 2003, “Financial Stability As a Policy Goal,” in Essays on Financial Stability, by John Chant, Alexandra Lai, Mark Illing, and Fred Daniel, Bank of Canada Technical Report No. 95 (Ottawa: Bank of Canada), September, pp. 3–4.

Crockett, Andrew, 1997, “The Theory and Practice of Financial Stability,” GEI Newsletter Issue No. 6 (United Kingdom: Gonville and Caius College Cambridge), 11–12 July.

Davis, Philip, 2002, “A Typology of Financial Instability,” Financial Stability Report 2 (Vienna: Österreichische Nationalbibliothek).

Deutsche Bundesbank (2003), “Report on the Stability of the German Financial System,” Monthly Report, Frankfurt, December.

Diamond, Douglas W., and Raghuram G. Rajan, 2001, “Liquidity Risk, Liquidity Creation, and Financial Fragility: A Theory of Banking,” Journal of Political Economy, Vol. 109, 2, pp. 287–327.

——— , 2001, “Banks, Short Term Debt, and Financial Crises: Theory, Policy Implications, and Applications,” Proceedings of Carnegie Rochester Series on Public Policy (Amsterdam: Elsevier Science),Vol. 54, No. 1, pp. 37–71.

Duisenberg, Wim F., 2001, “The Contribution of the Euro to Financial Stability,” in Globalization of Financial Markets and Financial Stability—Challenges for Europe (Baden-Baden: Nomos Verlagsgesellschaft), pp. 37–51.

Ferguson, Roger, 2002, “Should Financial Stability Be An Explicit Central Bank Objective?” (Washington: Federal Reserve Board).

Foot, Michael, 2003, “What Is ‘Financial Stability’ and How Do We Get It?” The Roy Bridge Memorial Lecture (United Kingdom: Financial Services Authority), April 3.

Greenspan, Alan, 1999, “Do Efficient Markets Mitigate Financial Crises?” speech delivered before the 1999 Financial Markets Conference of the Federal Reserve Bank of Atlanta.

Haldane, Andrew, 2004, “Defining Monetary and Financial Stability” (unpublished; London: Bank of England), February.

Houben, Aerdt C.F.J., Jan Kakes, and Garry Schinasi, 2004, “Toward a Framework for Safeguarding Financial Stability,” IMF Working Paper 04/101 (Washington: International Monetary Fund) and DNB Occasional Paper (Amsterdam, Forthcoming).

Large, Sir Andrew, 2003, “Financial Stability: Maintaining Confidence in a Complex World,” in Financial Stability Review (London: Bank of England), December, pp. 170–74.

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Mishkin, Frederick, 1999, “Global Financial Instability: Framework, Events, Issues,” Journal of Economic Perspectives, Vol. 13 (Fall), pp. 3–20.

Norwegian Central Bank, 2003, Financial Stability Review, February.

Padoa-Schioppa, Tomasso, 2003, “Central Banks and Financial Stability: Exploring the Land In Between,” in The Transformation of the European Financial System, ed. by Vitor Gaspar and others (Frankfurt: European Central Bank), pp. 269–310.

Schinasi, Garry J., 2003, “Responsibility of Central Banks for Stability in Financial Markets,” in Current Developments in Monetary and Financial Law—Volume 2 (Washington: International Monetary Fund), Chapter 17.

——— , 2004, “Private Finance and Public Policy,” IMF Working Paper 04/120 (Washington: International Monetary Fund).

Schwartz, Anna J., 1986, “Real and Pseudo-Financial Crises” in Financial Crises and the World Banking System, ed. by Forrest Capie and Geoffrey E. Woods (New York: St. Martin’s Press).

Truman, Edwin, 2003, Inflation Targeting in the World Economy (Washington: Institute for International Economics).

Wellink, Nout, 2002, “Current Issues in Central Banking”, (Oranjestad: Central Bank of Aruba), November 14.

proposal msc.docx

Proposal

· Introduction

(a) Must capture the interest of the reader

(b) Get the readers interest and convince them of the significance of the problem

· Define a question

a) Does competition affect financial stability?

· Reference 15 different academic journals (Harvard referencing)

(a) Which I have provided

(b) The more recent the better not older than 6 years old.

· Why do you think the topic is important?

· Research method is an important section

(a) I will be collecting data from bank scope

(b) I will use different ratios to define the state of competition (capital on asset, liquidity, equities, deposits on loans etc.)

(c) I will use regressions and different scatters, histograms and other graphs to represent my data

· How will my proposal add value to the business problem

· Literature review (important)

(a) What have other scholars suggested about this topic and what are the gaps.

(b) Group various scholars together on their point of view and suggestion do not list them. For good flow of writing.

(c) Whats known and whats unknown show and state

(d) Find and describe the theories and the studies which support and also oppose your approach to the problem

· Hypothesis

(a) Clearly state your expectation for the results of your study ( I do believe that competition has an effect on financial stability)

· Purpose

(a) the goal must be clear and it must be like an investigation activity

(b) put attention and show the benefits of the study

· Methodology

(a) Describe research perspective and past, present and possible viewpoints

· Problem statement

· Proposals are written in present and future tense

· Words

a) Proposal – 1,600 words

b) Literature review – 1,300 words

c) Research methods – 500 words

· Summary

· Problem statement and research questions

· Aims & objectives

· Rationale/justification

· Brief literature review

· Research methods

(at least 500 words)

· Resources/costs

· Any other factors

· A detailed statement of

· What you intend to do?

· How you intend to get the information you need?

· When you intend to do it by? (during the next 3-6 months)

· Indicates your ability to conduct the study

· Summarize the main literature – theory, evidence

Research questions

· What is happening?

· (descriptive, survey/case study)

· How many?

· (descriptive, measurement, quantitative)

· Why?

· (explanatory/predictive, quantitative/qualitative)

· How can we improve?

· (action research)

literature review

· Evaluation/implementation

· E.g. to critically evaluate relevant research as a basis for improving organisational practices

· What are the ‘gaps’ in the literature – the key objective

· Conducting research

· to provide a rationale for your study

· to put the study into the context of what is already known about the topic

· to discuss the conceptual/theoretical basis for your study

A critique of literature

· Developing a ‘critical’ approach

· To what extent does it answer your research question?

· Look for weaknesses in the work you are reading e.g. in the design, how findings are reported, limitations, areas for further research

· How does this study compare with others?

· What is the current state of knowledge?

Literature

· Literature = published material

· Value of publication depends on type & quality of information

· credibility, objectivity, reliability (verifiable)

· E.g. journals, textbooks, theses, newspapers, company documents, internet, personal communications

· Primary sources

· original data collected by you

· Secondary sources

· reports on original work written by other people (or data collected by other people)

Data and methodology

· Which data will be collected to answer the question? (I will use both quantitative and qualitative but more towards quantitative)

· Quantitative

· structured data collection methods & tools

· Qualitative

· fieldwork = case study, ethnography, survey

· flexible data collection methods & tools

· Employ a suitable methodology you studied in Management Research or Research Methods or Financial Econometrics

· Remember, simple statistical methods can be very effective