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Banking and credit extension: does religious diversity matter?

Saibal Ghosh Centre for Advanced Financial Research and Learning, Mumbai, India

Abstract Purpose – While several facets of credit extension by banks have been extensively studied, one aspect which has largely bypassed the attention of researchers is the intrinsic attitude towards risk. To investigate this in detail the purpose of this paper is to employ data on India for an extended time period to understand whether religious diversity affects bank credit and other (flow) outcome variables, such as profitability, costs and returns. Design/methodology/approach – Given the longitudinal nature of the data, the author’s employ fixed effects regression methodology which enables us to control for unobserved characteristics that might affect the dependent variable. Findings – The analysis indicates that religious diversity lowers credit off-take by lowering the number of accounts, although the number of deposit accounts improves. The behaviour however, differs across high- and low-income states and during the pre- and post-crisis periods. In addition, the evidence supports the fact that the overall negative credit response arises from the behaviour of national banks. Practical implications – The analysis explores an important and hitherto unidentified aspect driving banking outcomes in the Indian context. This would suggest that any policy intervention that seeks to influence bank behaviour would need to take on board the intrinsic risk-appetite of key stakeholders. Originality/value – To the best of the author’s knowledge, this is one of the earliest studies for India to carefully examine the interface between religious diversity and bank behaviour. Keywords India, Religion, Banking, Credit Paper type Research paper

1. Introduction In the wake of the crisis, a significant volume of research has been undertaken towards understanding the various facets of bank behaviour. For example, several papers have explored the role of competition in impacting bank lending (Schaeck et al., 2010; Jimenez et al., 2013). Other studies have focussed on the role of bank capital (Gambacorta and Mistrulli, 2004) or even for that matter, the role of banking lending standards (Dell Ariccia et al., 2012). Yet others have analysed the role of monetary policy in influencing bank lending strategy (Degryse et al., 2009; Jimenez et al., 2012; De Santis and Surico, 2013).

One aspect which has largely bypassed the attention of researchers is people’s intrinsic attitude towards risk. As observed by Akerlof (2007), there is a pressing need in macroeconomics to incorporate norms which capture how decision makers should or should not behave, instead of merely presuming the constrained maximisation outcomes. He goes on to remark that “religious identity gives us a good example of such norms” (Akerlof, 2007, p. 8). Advancing this argument further, it has been contended that norms tied to religious identities can significantly influence economic outcomes (La Porta et al., 1999; Barro and McCleary, 2003; Guiso et al., 2003, 2006). According to the social identity theory, the process of self- categorisation which forms an individual’s identity is derived to a large extent from such membership in a social group as one’s religious denomination (Tajfel and Turner, 1979; Benjamin et al., 2010). This embeddedness exerts a significant influence on people’s behaviour, since they internalise the multiple social identities as well as the behavioural norms of their group (Stets and Burke, 2000). As a result, by providing moral and ethical teachings for their adherents to encourage them to behave in a specific way, religion might directly influence individual economic behaviour (Barro and McCleary, 2003).

In this paper, following Akerlof (2007), we capture the intrinsic attitude towards risk by religious identity. Accordingly, we employ decadal data for India to examine the link

International Journal of Social Economics

Vol. 44 No. 12, 2017 pp. 2287-2301

DOI 10.1108/IJSE-06-2016-0176

Received 5 July 2016 Revised 22 November 2016 Accepted 10 January 2017

The current issue and full text archive of this journal is available on Emerald Insight at: www.emeraldinsight.com/0306-8293.htm

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between religious diversity and banking outcome. More specifically, we address two questions: first, does religious diversity matter for banking-related outcomes at the state level, after holding constant other relevant factors? Second, does the state-level evidence also resonate in bank-level data? We proxy banking outcomes by both physical (e.g. bank offices and deposit and credit accounts) and financial (e.g. credit, profitability and cost) variables. While cost-related variables have been earlier employed as proxy for bank risk (Hadad et al., 2011; Cubillas et al., 2012), recent research has also employed financial variables as indicators of bank risk (Berger and Turk-Ariss, 2014). The role of credit as a risk indicator needs no gainsaying: numerous studies both at the academic (Borio and Lowe, 2002; Borio and Drehmann, 2009; Jimenez and Saurina, 2006; Saurina et al., 2014) as well as at the policy level (International Monetary Fund, 2004; Basel Committee for Banking Supervision, 2010) have persuasively documented its relevance in fomenting bank risk. We construct an index of religious diversity and examine its proximate association with these banking outcomes, both at the state-banking as well as at the bank level. Our analysis appears to suggest that religious diversity matters for banking outcomes and the effect is, in fact, significant.

We proxy religion by the religious demography of the population. This is consistent with research which captures religious diversity by the share of religious adherents in total population (Adhikari and Agrawal, 2016a). Kumar et al. (2011) and Benjamin et al. (2010) find that Protestants are more risk averse or make safer financial investments than Catholics, while Dohmen et al. (2011) observe the opposite. Earlier, Barsky et al. (1997) had demonstrated that risk tolerance varies significantly by religion. Likewise, Halek and Eisenhauer (2001) also find that the religious leaning of the population affects their attitude towards risk. These findings are confirmed in Renneboog and Spaenjers (2012) who show that as compared with more direct measures such as attendance, religious demography is a much reliable indicator of religious diversity.

The Indian case presents a compelling laboratory to examine this issue for several reasons. For one, banks have a pan-India presence and have the flexibility to locate across states depending on their business philosophy, customer orientation and risk appetite, subject to the overall guidelines prescribed by the Indian central bank. Second, India is a federal polity with democratically elected government across states. State governments are the fulcrum at which public policy decisions are made. As a result, political parties compete intensely for the right to govern at the state level (Chhibber and Nooruddin, 2004). The states differ widely in terms of their locational advantages, business environment, economic development and religious demography, providing considerably leeway to banks in the credit extension decisions. Finally, India is one of the emerging economies for which a comprehensive and reliable state-level data are available, both at the state level as well as for religious demography. This time-series, cross-sectional variation in the data for an elongated time span makes it amenable to rigorous statistical analysis.

The rest of the analysis proceeds as follows. In Section 2, we discuss the literature and set out the testable hypothesis. Section 3 describes the database and presents some summary statistics. The results and a discussion are set out in Section 4, while the final section concludes.

2. Literature and hypothesis development Bergan and McConatha (2000) define religious diversity as a number of dimensions associated with religious beliefs and involvement. They argue that reliance on religious attendance alone as a measure of religious diversity could lead to incorrect conclusions, especially in cases where older adults are involved, since attendance might be physical challenging for them. Other recent studies have emphasised the need for multi-dimensional focus encompassing varied concepts as the subjective, cognitive, behavioural social, and cultural dimensions (Chumbler, 1996; Ellison, 1991; Ellison et al., 1989).

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The importance of religious diversity, in both the social and educational realms, has increasingly been emphasised in recent research. For instance, Bergan and McConatha (2000) uncover a positive relation between religious diversity and happiness across three broad age groups (adolescents, young adults and adults). Similarly, Walker (2003) also analyses several aspects of religion and morality and conclude that the religious experience is important in moral functioning. Subsequently, Roccas (2005) also highlights a high degree of correlation between religious diversity and moral values.

This argument is reinforced by the social norm theory. The theory postulates that the religious norms of the local population in which the organisation is established influences the management perspective towards risk, irrespective of whether management is itself religious, since the local population is an important element of the environment in which managers live and operate (Cialdini and Goldstein, 2004; McGuire et al., 2011). In addition, such influence on the management is amplified by the need of organisations to maintain organisational legitimacy.

Recent studies indicate that firms operating in different social environments exhibit different behaviour. This literature links religious adherence to lower risk taking (Hilary and Hu, 2009; Adhikari and Agrawal, 2016a), lower incidences of financial reporting irregularities and lower earnings management (Kanagaretnam et al., 2015; Lievenbruck and Schmid, 2014). However, to the best of our knowledge, no prior study has analysed the link between religious demography and bank outcomes, especially in the context of an emerging economy.

Despite scant academic research, it seems generally accepted that human elements, such as the traits and preferences of managers and investors, play a role in bank risk-taking (Lo, 2008). The high level of geographic dispersion of banks across states with differing religious mix makes it imperative for them to balance the expectations of stakeholders with their appetite for risk. The fact that local religious composition can influence firm innovative activity has also been highlighted in recent research (Adhikari and Agrawal, 2016b).

Theoretically, religious diversity could influence bank behaviour in three ways. First, from the demand side, religious people are risk-averse (Miller and Hoffman, 1995; Diaz, 2000; Miller, 2000) and as a result, banks with more religious customers are likely to have less risky borrowers. This, in turn, is likely to ensure greater likelihood of loan repayments, lowering the proclivity of the bank to assume greater risk. Second, in order to promote the business interests of their organisation while respecting the local requirements, managers will seek to conform to the social norms of the particular geographical region in which they operate. As a result, not only will the religiosity of customers influence banks’ attitude towards risk, but additionally, the bank will also need to rebalance its social values in order to ensure congruence between its values and those of its customers. This in turn, could lower the inclination of the bank to “search for yield”, thereby dampening risk. Finally, the religious norms of the local population in which the bank operates is likely to influence the overall risk profile of the bank, irrespective of whether the bank employees are themselves religious or not (Dyreng et al., 2012). The effect of this on bank risk is not obvious, a priori. We refer to the latter two as the supply motive of religious diversity on bank risk behaviour. Therefore, the relationship between religious diversity and bank behaviour at the state level is an issue that remains to be empirically addressed. Consequently, we postulate the following hypothesis:

H1. State religious diversity is expected to exert a negative impact on bank risk.

We contribute to the extant literature in three-distinct ways. First, we contribute to the literature on financial economics by linking religion with banking outcomes. Previous studies have focussed either on non-financial firms (Hilary and Hu, 2009) or even if they have looked at banks, are based on the experience of developed economies (Adhikari and Agrawal, 2016a). To illustrate, by combining firm-level with information on religious

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diversity, Hilary and Hu (2009) show that firms headquartered in US counties with high religious diversity exhibited lower risk exposure. Similarly, using data on USA, Adhikari and Agrawal (2016a) find that banks headquartered in counties with higher religious diversity assume lower risk. More recently, Chircop et al. (2017) use branch level data and show that it is negatively related to bank risk-taking. Since the social, economic and institutional characteristics differ significantly across country groupings, it remains a moot issue whether the impact of religious diversity on banking outcomes as manifest for developed markets also reverberate in emerging economies. Relatedly, our paper finds echo in the work of Ellul and Yerramilli (2013) who show that US bank holding companies with stronger risk management prior to the crisis experienced lower negative stock returns and has a smaller quantum of delinquent loans. Unlike comparable studies, we focus on bank lending and related outcomes such as deposit and credit accounts, including costs and returns. To the best of our knowledge, this is one of the earliest studies for an emerging economy and most certainly for India to correlate religion with banking outcomes.

Second, we contribute to the literature on regional economics by examining the interlinkage between banking outcomes and religious diversity at the state level. Since religious beliefs are likely to be slow moving, we employ decadal data for an extended time span which allows these influences to play themselves out. Employing variation in the timing of Ramadan, Schofield (2014) exploits crop-year-district level data for India to show a negative effect of Ramadan fasting on agricultural output. At the cross-national level, Campante and Yanagizawa-Drott (2015) show that longer Ramadan fasting hours exert a negative impact on output growth in Muslim countries. Unlike these studies which focus on real outcomes, our focus is primarily on banking outcomes.

And finally, we augment the literature on financial inclusion by exploring the role of religion in influencing banking outcomes. Using cross-country survey data on Central and Eastern European economies including Turkey, Beck and Brown (2011) show that religious adherence influences the opening of bank accounts. These findings are echoed in subsequent research, wherein employing the FINDEX database, the authors show that religious beliefs influence the opening of accounts with a formal financial institution, after controlling for individual and country-level characteristics (Demirguc Kunt et al., 2013). Recent findings using supply side financial inclusion data are however, less compelling: the link between religious adherence and financial inclusion appears to be valid only for certain (and, not all) measures of inclusiveness (Naceur et al., 2015). The Reserve Bank of India has also recently advocated the need for due consideration to interest-free banking as part of its medium-term path to enhance financial inclusion (Reserve Bank of India, 2015). The present paper complements these studies by examining the impact of religious diversity on banking outcomes and the role played by the financial crisis in this regard.

3. The database and variables For our analysis, we employ two sets of data, one at the state-banking level and the other at the bank level. In case of the former, we construct a data set of both financial (e.g. total credit) as well as physical (e.g. number of bank offices and deposit as well as credit accounts) variables for 14 major states, including observations every ten years such as 1960-1961, 1970-1971, 1980-1981, 1990-1991 and 2000-2001 and 2010-2011. The choice of states is consistent with the practice that compares the performance of non-special category states (Ahluwalia, 2002; Arulampalam et al., 2009; Ghosh, 2013)[1].

The benefit of employing decadal data is twofold. First, it helps to ascertain the impact of religious diversity on banking outcomes for an extended time period that is otherwise not possible for data with short time-series. Second, consistent data on several state-level variables, particularly for religious diversity, is available only at decadal intervals, coinciding with the Census.

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Second and consistent with our state-level analysis, we use decadal data on domestic commercial banks (comprising public and private banks) for the years 1981, 1991, 2001 and 2011. We are able to extract data on a total of 49 domestic commercial banks, after accounting for mergers and consolidations, which on aggregate, accounted for over 85 per cent of the banking assets in each of these years. To moderate the influence of extreme observations for the relevant variables, we average the bank-level data for the five-year window around the given year. Accordingly, the bank-specific data for 1981 is the average of the relevant variables for the years 1979-1983 and likewise, for the other years.

Following from the spirit of the empirical framework in Hilary and Hu (2009), we make a distinction between local (i.e. present primarily in a single state) and national (i.e. having multi-state presence) banks. To do this, we consider the share of total bank branches in the state where the bank is headquartered. We designate banks as local if at least 65 per cent of its branches during these years are in the headquarter state (the median value of the share of bank branches in the headquarter state across years is much lower, ranging from a minimum of 31 per cent in 2011 to a maximum of 61 per cent in 1991); otherwise, it is designated as national. Using this criterion, we have a total of 21 local banks; the remaining are classified as national banks[2].

Consistent with our state-banking analysis, we employ credit as the major outcome variable of interest. This stands in contrast to studies which focus on bank risk (Adhikari and Agrawal, 2016a). In addition, we also examine its sub-components, such as cash credit and private sector credit. Besides, as mentioned earlier, we also consider other outcome (flow) variables, such as profitability, costs and returns.

We rely on several data sources. The bank-specific data are sourced from the Reserve Bank of India (Statistical Tables relating to Banks), whereas the state-banking data are obtained from the Basic Statistical Returns. Both of these are annual publications, the former provides bank-specific balance sheet and profit and loss information, while the latter provides state-level banking outcomes, including number of accounts (deposits as well as credit), bank offices as well as total credit.

In addition, for state-level variables, we also employ several other data sources, such as the Handbook of Statistics on Indian Economy ( for data on state income, SDP) and Economic Survey ( for information on decadal literacy, population and working age population).

The key variable of interest is Religiosity. In their analysis of religious demography of India, Joshi et al. (2003) have provided the state-wise decadal information on religious diversity classified under three heads: Indian Religionists, Muslims and Christians. In their study, the former includes, in addition to Hindus, other religious groups such as Sikhs, Buddhists and Jains and several smaller groups, such as Parsis and Jews. Using this information, we compute an index of Religiosity so that higher values of the index indicate higher levels of religious diversity.

In Table I, we provide a description of the variables, including data sources and summary statistics. We have two set of outcome variables, one at the state-banking level and the other at the bank level. In case of the former, we find that total credit equals 4.04; the number of deposit account per capita is roughly 25 per cent higher as compared with the corresponding per capita credit accounts.

At the bank level, which includes a shorter period (1981-2011, with data at decadal frequency), the average credit is quite high, corresponding with the expansion in credit, post-nationalisation (we pre-fix the bank-level credit outcome by B_ and the state-banking credit outcome by S_). Average bank profitability has hovered around 0.5 per cent. Mention needs to be made that we do not take into account current deposits while calculating the cost of funds, based on the rationale that these accounts do not pay interest.

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Variable Definition Data source No. of Obs. Mean (SD) p. 75 (p. 25)

Outcome: State level S_Credit Log [1 + (state-level

credit/WPI)] Data on both credit and WPI are from Reserve Bank of India (RBI)

83 4.044 (0.754) 4.520 (3.600)

Bank office Bank offices/100,000 persons

Numerator is from RBI and denominator is from Census

83 5.688 (3.243) 7.780 (3.160)

Deposit account

Deposit accounts/100,000 persons

Numerator is from RBI 70 4.419 (0.440) 4.720 (4.110)

Credit account Deposit accounts/100,000 persons

Numerator is from RBI 70 3.529 (0.593) 3.920 (3.350)

Outcome: Bank level B_Credit Log(1 + (credit/WPI)] RBI 179 5.532 (1.363) 6.260 (5.180) B_Cash credit Log(1 + (cash credit/WPI)] RBI 179 5.324 (1.282) 5.960 (4.990) B_Pvt. credit Log(1 + (private credit/

WPI)] RBI 139 4.526 (2.224) 6.132 (3.445)

RoA Net profit/Asset RBI 159 0.005 (0.004) 0.008 (0.001) Lending rate Interest earned of credit/

Total credit RBI 118 0.109 (0.023) 0.126 (0.092)

Cost of fund Interest expended on deposits plus borrowings/ (Total savings and time deposits plus borrowings)

RBI 91 0.067 (0.016) 0.082 (0.052)

Independent: State level Religious diversity (RDIV)

1 – H, where H is the Herfindahl index of religion based on three major categories: Indian Religionists, Muslims and Christians. The H is calculated as

P js 2 j where

s is the share of religious population of type j

Joshi et al. (2003) 84 0.210 (0.132) 0.260 (0.115)

PCNSDP Log (per capita income, at constant prices)

Economic and Political Weekly Research Foundation and Handbook of Statistics on Indian Economy, Reserve Bank of India (RBI)

84 4.310 (0.343) 4.545 (4.050)

Literacy Log (literacy rate) Statistical Abstract, Government of India

82 3.849 (0.432) 4.220 (3.540)

WAS Working age population (age 15-59)/Total population

Indiastat.com 84 0.557 (0.082) 0.600 (0.530)

DENS Log (state population/ 1,000 sq. kms)

Census of India, various years

84 0.451 (0.2882) 0.675 (0.235)

S_MERGER Dummy ¼ 1 for the bifurcated state in the year of merger, else zero

Wikipedia 196 0.005 (0.071) ..

(continued)

Table I. Variable definitions, data source and summary statistics

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The major variable of interest is religious diversity, labelled RDIV. The average value of the variable during the period was 0.21; the values at the 25th and 75th percentile were 0.12 and 0.26, respectively, signifying moderately high diversity.

4. Empirical strategy 4.1 State-banking level To examine the impact of religious diversity on banking outcome in state s at time t, we undertake regressions of the following form:

ys;t ¼ ao þa1 RDIVð Þt þg0Xs;t þms þZt þes;t (1) In (1), y is one of the outcome variables as indicated above. The coefficient of interest is α1, indicating the impact of religious diversity on banking outcomes.

X is a vector of state-specific variables, such as per capita income (proxy for demand conditions), logarithm of literacy (proxy for human capital); share of working age population (proxy for potential labour supply) and population density. μ and η are state- and year-fixed effects that takes into account all unobservable state – (e.g. changes in state tax laws) and year – (e.g. changes in monetary policy) factors that are not directly taken on board in the regression. With data on 14 states for an average of 5.9 years, we have a maximum of 82 state-years.

4.2 Bank level At the bank level, the regression specification for bank b headquartered in state s at time t takes the following form:

Ybs;t ¼ aþb RDIVð Þs;t þg01Mbs;t þg02Ns;t þms þyb þZt þebs;t (2) In (2), Y is the outcome variable and the coefficient of interest is b, signifying the impact of religious diversity on bank behaviour. Following Micco et al. (2007), Beck et al. (2012) and Berger et al. (2014), M is a vector of bank-specific variables, such as log asset (proxy for scale economies), demand deposits to asset (proxy for funding structure) and the share of non-interest income to asset (proxy for business model). The vector N includes state-level variables such as per capita income and logarithm of population; μ, θ and η are state-, bank- and year-fixed effects that takes into account all unobservable state-, bank- (e.g. changes in governance structure) and year- factors that are not directly taken on board in the regression.

Both these models are estimated using fixed effects with double-clustered standard errors (Cameron et al., 2011). With information on 49 banks for an average of 3.2 years, we have a maximum of 159 bank-years.

Variable Definition Data source No. of Obs. Mean (SD) p. 75 (p. 25)

Independent: Bank level LTA Log (Asset) RBI 172 6.076 (0.763) 6.576 (5.607) DDEP Demand deposits/Asset RBI 172 0.114 (0.051) 0.143 (0.077) NINT Non-interest income/Asset RBI 159 0.010 (0.005) 0.012 (0.007) NATIONAL Dummy ¼ 1 if a bank is

national, else zero Calculated from RBI data

196 0.571 (0.496) 1 (0)

B_MERGER Dummy ¼ 1 for the five- year window around the merger, else zero

RBI 196 0.092 (0.289) ..

Table I.

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5. Results and discussion 5.1 State-banking level Estimation results are reported in Table II. The coefficient of interest is RDIV. In columns 2 and 6, which includes the full set of control variables, the coefficient is statistically significant. In column 2 for example, the point estimate is −2.8. In other words, greater religious diversity lowers credit, presumably by dampening the number of credit accounts (column 7). The magnitude is quite substantial: a one standard deviation increase in religious diversity lowers credit by 37 per cent. On the other hand, religious diversity improves financial inclusion by improving the number of deposit accounts per capita: a 1 per cent increase in religious diversity increases the number of deposit accounts by 1.6 per cent points. With the average number of deposit account per 100,000 persons being 4.4, this is not so compelling a difference.

A natural question to ask is: does the response differ across high- and low-income states? To do this, we classify states based on their median per capita income and re-estimate Equation (1) separately for these two categories.

The results in Table III show that the negative effect on credit is driven primarily by the response of low-income states and the positive effect on deposit account is more a phenomenon of the high income states. In other words, low income states with higher religious diversity are more likely to witness a decline in credit, whereas high income states with higher religious diversity are more likely to experience higher deposit account openings, per capita. It is possible that religious reasons play a role in the choice of banking

Variable S_Credit Bank office Deposit accounts Credit accounts (1) (2) (3) (4) (5) (6) (7) (8)

RDIV −0.41 (0.35)

−2.83* (1.62)

0.09 (2.61)

−16.27 (10.84)

0.01 (0.28)

1.61* (0.89)

−0.48** (0.23)

3.90 (2.52)

Controls Yes Yes Yes Yes Yes Yes Yes Yes State FE No Yes No Yes No Yes No Yes Year FE Yes Yes Yes Yes Yes Yes Yes Yes State; No. of Obs. 14; 82 14; 82 14; 82 14; 82 14; 70 14; 70 14; 70 14; 70 Period 1961-2011 1961-2011 1961-2011 1961-2011 1961-2011 1961-2011 1961-2011 1961-2011 R2 0.91 0.97 0.83 0.94 0.89 0.97 0.85 0.92 Notes: Standard errors (clustered by state and year) in parentheses. *,**,***Significant at 10, 5 and 1 per cent levels, respectively

Table II. Religious diversity and bank behaviour

Variable S_Credit Bank office Deposit Accounts Credit Accounts State category

High income

Low Income

High income

Low income

High income

Low Income

High income

Low income

(1) (2) (3) (4) (5) (6) (7) (8)

RDIV −1.98 (4.85)

−4.76* (2.82)

23.66 (36.29)

−14.07 (22.31)

5.61*** (1.91)

0.33 (0.98)

−0.47 (0.54)

−11.55*** (4.02)

Controls Yes Yes Yes Yes Yes Yes Yes Yes State FE Yes Yes Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Yes Yes No. of Obs. 39 43 39 43 39 31 39 31 R2 0.98 0.98 0.96 0.94 0.98 0.95 0.97 0.98 Notes: Standard errors (clustered by state and year) in parentheses. *,**,***Significant at 10, 5 and 1 per cent levels, respectively

Table III. Religious diversity and bank behaviour – High- vs Low-income states

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habits, particularly with regard to credit, in these low-income states. To exemplify, an IFC survey revealed that over 40 per cent of respondents in Syria who had never applied for a loan cited religious reason as the major factor (International Finance Corporation, 2007). Likewise, a 2006 survey in Algeria revealed that 20 per cent of micro enterprise owners do not apply for a loan owing to religious considerations (Bankakademie International, 2006). In contrast, it is possible to envisage that greater religious diversity in states with high levels of per capita income impels greater account opening (e.g. current accounts), which could be driving these results.

A related issue of interest is: how did this response evolve in the post-crisis period? To investigate this empirically, we run regressions as earlier and include an interaction term of RDIV × Post crisis. Provided greater religious diversity leads to an improvement in the outcome variables in the post-crisis regime, the coefficient on the interaction term would be positive.

As observed from Table IV, the coefficient on the interaction term is statistically significant in columns (2) and (6). In other words, greater religious diversity appears to have dampened credit off-take (column 2) by lowering the number of credit accounts, especially in the post-crisis period (column 8) as also lowered the number of deposit accounts, particularly in low-income states (column 6). It is of interest to note that, notwithstanding the dampening effect of religious diversity on credit accounts post-crisis, the net effect was an increase in the number of credit accounts by roughly 5 per cent points. It is possible to envisage that the global economic headwinds exerted a stronger negative effect on low-income states by chilling credit appetite in the form of a reduction in the number of credit accounts.

5.2 Bank level Akin to the state-level analysis, we conduct similar estimation utilising bank level data. The results are set out in Table IV. As discussed earlier, we make a distinction between local and national banks and present the estimation results separately for these two categories. All specifications include the control variables as earlier in Equation (2), but only the coefficients of interest are reported for brevity.

In Table V, the coefficient on RDIV is negative and statistically significant in column (1), but positive and statistically negative in column (2). In other words, the overall negative response of bank credit to religious diversity is primarily driven by national banks. Combining this with our earlier discussion on state-banking outcome, it is possible (keeping in

Variable S_Credit Bank office Deposit accounts Credit accounts

State category High income

Low Income

High income

Low income

High income

Low income

High income

Low income

(1) (2) (3) (4) (5) (6) (7) (8)

RDIV −2.31 (5.21)

1.39 (2.85)

20.72 (39.65)

−6.76 (38.37)

5.49*** (2.06)

4.57 (3.19)

−0.18 (3.55)

23.65*** (4.97)

RDIV × Post- crisis

−7.81 (8.34)

−9.44*** (3.59)

−68.77 (59.19)

−11.23 (39.72)

−2.76 (5.62)

−6.64** (3.27)

6.64 (10.03)

−18.92*** (6.77)

Controls Yes Yes Yes Yes Yes Yes Yes Yes State FE Yes Yes Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Yes Yes No. of Obs. 39 43 39 43 39 31 39 31 R2 0.983 0.984 0.964 0.948 0.990 0.996 0.980 0.986 Notes: Standard errors (clustered by state and year) in parentheses. *,**,***Significant at 10, 5 and 1 per cent levels, respectively

Table IV. Religious diversity

and bank behaviour – post-crisis

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view the different time horizons) to infer that the overall negative effect on credit at the state-level is essentially driven by the national banks.

At the disaggregate level, it appears that the negative response of religious diversity on bank credit essentially pertains to cash credit and much less so for private credit. Looking at profitability and price-based outcomes, the evidence indicates that greater religious diversity impels local banks to charge lower lending rates; there does not appear to exist any perceptible effect on the cost of funds.

As earlier, we investigate the response during the post-crisis period. The evidence indicates that, national banks experienced a sharper decline in credit in the post-crisis period, of the order of 2.9 per cent points (column 1) and that this decline was primarily manifest in case of private credit (column 5) (Table VI).

Among others, although there was no response at the aggregate level, local banks appear to have lowered their cost of funds in response to religious diversity post-crisis (column 12), although overall, their cost of funds were actually higher by roughly ten basis points, entailing a net effect of 9.2 ( ¼ 9.62 − 0.43) basis points (a difference of 5 per cent compared to the overall period).

To summarise, religious diversity appears to exert a discernible impact on bank credit extension and costs, an impact that differs across bank categories (national vs local) and across pre- and post-crisis regimes.

6. Concluding remarks In the wake of the crisis, a significant volume of research has focussed on the risk-taking behaviour of banks. In this context, using data for an elongated period, the paper focusses on a specific intrinsic aspect of risk and correlates it with physical and financial outcomes for banks. The findings suggest that religious diversity lowers credit off-take by lowering the number of accounts, although the number of deposit accounts improves. The behaviour however, differs across high- and low-income states and during the pre- and post-crisis periods.

Subsequently, we drill down this aggregate behaviour by examining the response of national vs local banks. The evidence is consistent with the aggregate response and supports the fact that the overall negative credit response arises from the behaviour of national banks. Local banks in contrast, lower lending rates.

From a policy perspective, the analysis contributes to the literature by analysing an important and hitherto unidentified aspect driving banking outcomes in the Indian context. This would suggest that any policy intervention that seeks to influence bank behaviour would need to take on board this previously unaddressed risk-appetite of key stakeholders.

B_Credit B_cash credit B_Private credit RoA Lending rate Cost of funds National banks

Local banks

National banks

Local banks

National banks

Local banks

National banks

Local banks

National banks

Local banks

National banks

Local banks

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

RDIV −1.773* (1.019)

0.065* (0.037)

−4.382** (2.063)

0.187 (0.159)

−4.601 (5.435)

1.337 (0.924)

−0.074 (0.071)

−0.004 (0.008)

−0.232 (0.427)

−0.457* (0.236)

1.519 (1.039)

0.062 (0.609)

Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Bank FE

Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

State FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No. of Obs. 92 67 92 67 73 59 92 67 72 46 53 38 R2 0.903 0.967 0.979 0.971 0.889 0.949 0.722 0.905 0.927 0.954 0.949 0.985

Notes: Standard errors (clustered by bank and year) in parentheses. *,**,***Significant at 10, 5 and 1 per cent levels, respectively

Table V. Religious diversity and bank behaviour – National vs local banks

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B _C

re di t

B _c as h cr ed it

B _P

ri va te

cr ed it

R oA

L en di ng

ra te

C os t of

fu nd

s N at io na l

ba nk

s L oc al

ba nk

s N at io na l

ba nk

s L oc al

ba nk

s N at io na l

ba nk

s L oc al

ba nk

s N at io na l

ba nk

s L oc al

ba nk

s N at io na l

ba nk

s L oc al

ba nk

s N at io na l

ba nk

s L oc al

ba nk

s (1 )

(2 )

(3 )

(4 )

(5 )

(6 )

(7 )

(8 )

(9 )

(1 0)

(1 1)

(1 2)

R D IV

− 2. 66 3* *

(1 .1 83 )

1. 16 3* *

(0 .4 90 )

− 5. 05 6* *

(2 .1 45 )

0. 56 7

(1 .3 32 )

− 56 .3 50

(5 9. 83 0)

40 .4 05 ** *

(1 3. 30 4)

− 0. 10 4

(0 .0 95 )

− 0. 01 2

(0 .0 13 )

− 0. 07 0

(0 .5 97 )

− 0. 48 8*

(0 .2 73 )

1. 76 3

(1 .2 21 )

9. 61 8* **

(2 .5 49 )

R D IV

× P os t

cr is is

− 0. 29 4* **

(0 .1 19 )

0. 16 4*

(0 .0 93 )

− 0. 18 2

(0 .1 83 )

0. 43 5

(0 .4 08 )

− 2. 38 8* **

(0 .8 90 )

− 0. 51 3

(1 .9 90 )

− 0. 00 8*

(0 .0 04 )

0. 00 6

(0 .0 05 )

0. 03 9

(0 .0 52 )

0. 01 7

(0 .0 16 )

0. 01 1

(0 .0 24 )

− 0. 42 5* **

(0 .0 96 )

B an k F E

Y es

St at e F E

Y es

Y ea r F E

Y es

N o. of

O bs .

92 66

73 58

92 66

72 46

53 38

R 2

0. 94 7

0. 90 5

0. 97 9

0. 97 2

0. 89 0

0. 96 1

0. 72 4

0. 90 7

0. 92 9

0. 95 6

0. 95 0

0. 95 6

N o te s:

St an da rd

er ro rs

(c lu st er ed

by ba nk

an d ye ar ) in

pa re nt he se s. *, ** ,* ** Si gn

if ic an t at

10 ,5

an d 1 pe r ce nt

le ve ls ,r es pe ct iv el y

Table VI. Religious diversity

and bank behaviour – Post-crisis

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Notes

1. These states are: Andhra Pradesh (AP), Karnataka (KAR), Kerala (KER) and Tamil Nadu (TN) in Southern region, Haryana (HAR), Punjab (PUN), Rajasthan (RAJ) and Uttar Pradesh (UP) in the Northern region, Bihar (BH), Odisha (ORS), and West Bengal (WB) in the Eastern region and Gujarat (GUJ) Maharashtra (MAH) and Madhya Pradesh (MP) in the Western region.

2. The average share of local bank branches in the headquarter state is 0.787 (standard deviation of 0.105), whereas for national banks, it equals 0.259 (standard deviation of 0.145). The difference is statistically significant at the 0.01 level.

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