Financial Report

The cost of growth: small firms and the pricing of bank loans

Anoosheh Rostamkalaei . Mark Freel

Accepted: 28 September 2015 / Published online: 14 October 2015

� Springer Science+Business Media New York 2015

Abstract Drawing upon data from the 2007 UK

Survey of SME Finance, the current analysis is

concerned with the extent to which growth firms are

discriminated on price in loan markets, or, more

simply, the extent to which growth firms pay more for

credit. Given relatively small turndown rates histor-

ically (Vos et al. in J Bank Finance 31(9):2648–2672,

2007), higher credit prices may be a more substantial

growth constraint than the access to finance issues that

have dominated the academic literature to date. To this

end, we observe, inter alia, that firms who have

recorded recent high growth are more likely to pay

higher interest rates for the loan they obtained.

Moreover, small-sized firms who intend to grow

through the introduction of new products exhibit a

higher probability of paying more for credit than their

peers. Finally, acknowledging that banks are not risk

funders, we discuss the potential policy implications

of these findings.

Keywords Growth firms � Entrepreneurial financing � Bank loans � Interest rate � Innovative firms

JEL Classifications L21 � L26 � G32

1 Introduction

It has long been recognised that a small group of high

growth firms create the bulk of the net new jobs in an

economy. These are Storey’s (1998) ‘‘ten percenters’’

or Birch’s (1990) ‘‘gazelles’’. Unsurprisingly, these

firms have been the focus of considerable academic

research (Henrekson and Johansson 2010) and policy

attention (Hoffman 2007). Indeed, informed recent

debate has focused on the merits of further shifting the

emphasis of entrepreneurship policy away from the

creation of new ventures to the support of high growth

firms (cf. Shane 2009; Mason and Brown 2011). This

view is consistent with recent evidence that suggests

that the presence of ‘‘ambitious entrepreneurship’’ is a

stronger predictor of macroeconomic growth than

entrepreneurial activity in general (Stam et al. 2007).

In this light, identifying and supporting growth firms

are key priorities.

Much of the extant academic research has been

concerned with the characteristics of growing firms

(Barringer et al. 2005; Baum et al. 2001) or with the

(often institutional) determinants of growth (Davids-

son and Henrekson 2002; Barkham et al. 2012). Less

A. Rostamkalaei (&) � M. Freel ESI, Lancaster University Management School,

Lancaster, UK

e-mail: [email protected]

M. Freel

Telfer School of Management, University of Ottawa,

Ottawa, Canada

e-mail: [email protected]

123

Small Bus Econ (2016) 46:255–272

DOI 10.1007/s11187-015-9681-x

attention has been paid to the issue of barriers to

growth, that is to the obstacles faced by firms, as they

expand rapidly (Lee 2013). However, an important

subset of barriers that has received attention relates to

finance (Becchetti and Trovato 2002; Beck et al. 2005;

Beck and Demirguc-Kunt 2006). In general, this line

of research has explored the extent to which limits to

access to various forms of external finance constrain

the growth of smaller firms. A prominent finding in

this literature is that growth firms are likely to be less

successful loan applicants (e.g. Freel 2007). Failure,

from this perspective, is typically defined in terms of

simple loan turndowns or loan scaling such that

growth firms are more likely to receive either no loan

or a smaller amount than applied for. These firms are

credit-rationed: that is, assuming that these growth

firms are otherwise observationally indistinct from

successful applicants, banks are rationing credit on

some basis other than price.

However, whilst growth firms may disproportion-

ately face turndowns or loan scaling, it still remains

that the majority receive the loans they apply for (Vos

et al. 2007). In these cases, it is the terms of the loans

which are of interest. In particular, if growing firms are

shown to pay systematically higher prices for debt,

then this may be of greater concern than the smaller

numbers who are credit-rationed. Whilst higher price

may reflect higher risk, higher loan prices may also

hinder firm development, as the resources required to

invest in growth are diverted to the loan provider. This

question is the focus of the current study. Drawing on

data from the 2007 UK Survey of SME Finance (Cosh

et al. 2008), we model the price firms paid for variable

rate loans. Our models contain information both on

past growth and on future growth intentions, including

the proposed growth strategies. We find evidence that

both past growth and future growth intention, condi-

tional on strategy, associate with higher loan prices.

The manuscript is structured as follows: Section 2

briefly reviews the literature on bank financing of

small firms, with particular emphasis on growth firms,

and develops three hypotheses that link loan pricing

and firm growth. Section 3 describes our data. Sec-

tion 4 elaborates on our models and modelling

choices. Section 5 presents our empirical results.

And Sect. 6 offers concluding remarks, drawing out

initial implications for entrepreneurs and

policymakers.

2 Literature review

In accessing bank finance, compared to large and

established companies, small firms are disadvantaged

by their information opacity, the relative scarcity of

collateralisable assets, and disproportionately high

monitoring costs (Beck and Demirguc-Kunt 2006;

Berger and Udell 1998). For start-ups, lack of credit

history and high rates of failure also contribute to their

unfavourable situations. In consequence, the small

firm sector has long been thought to be subject to credit

rationing (Parker 2002; Stiglitz and Weiss 1981; Vos

et al. 2007, among many): a situation in which some

borrowers are denied credit or receive a lower amount

of credit than they applied for. An important condition

holds that these firms are, in all other respects,

indistinguishable from those who have received (full)

credit (Parker 2002). In such a situation, a firm is

known as credit-rationed. It does not receive the

money it requested despite being willing to pay a

higher interest rate (de Meza 2002). In short, banks are

seen to ration credit on some basis other than price.

In practice, credit institutions use a variety of

techniques to distinguish between good and bad

borrowers, employing different contract terms such

as higher pricing, collateralisation, and sub-optimal

loan sizes (Parker 2002). If banks were to use similar

contract terms, employing a pooled interest rate for all

types of borrowers, good borrowers would likely

either exit the loan market (Parker 2002; Stiglitz and

Weiss 1981) or subsidise lower-quality borrowers (de

Meza 2002). Using different contract terms is a means

to reveal the types of borrowers (Parker 2002) and to

recognise varying risks of default. For example,

collateral is perceived as a sign of entrepreneurs’

commitment and confidence in their success. The

willingness to secure a loan with collateral, frequently

through personal asset, acts as a positive signal to

banks about the qualities of the entrepreneur as a good

borrower (Berger and Udell 1998; Binks and Ennew

1996). In the presence of such instruments, and

accounting for borrower heterogeneity, there is limited

evidence of broad-based credit rationing in the small

firms’ literature (Freel 2007). However, the absence of

credit rationing does not necessarily entail the absence

of discrimination. Indeed, given differing risk profiles

attendant upon varying firm characteristics and strate-

gies, banks must inevitably discriminate one firm from

256 A. Rostamkalaei, M. Freel

123

another in terms of contracts they offer for credit. In

this case, banks seek to ration credit on the basis of

price and price-related characteristics.

Firm strategy and performance are principal sources

of borrower heterogeneity that may bear upon risk. As

noted above, only a small proportion of small firms

make much of a contribution to net job creation,

innovation, or increased productivity (Shane 2009;

OECD 2013, 60). Due to their importance, small

growing firms have been the subject of numerous

studies aiming to describe the growth cycle and to

identify the factors supporting or impeding growth

(Dobbs and Hamilton 2007). Financial structure and

access to finance at the time of growth are common

themes in these studies. Of course, access to finance

does not directly cause growth, but credit constraints

may affect growth by suppressing it (Binks and Ennew

1996; Vickery 2008), or forcing managers to rely on

internal funds as a source of growth investment

(Rahaman 2011). Internal sources of financing, often

personal wealth or retained earnings, are typically the

first option of an entrepreneur (Vos et al. 2007; Berger

and Udell 1998). However, internal sources are likely to

be limited and this limitation may act to constrain the

growth of the firm (Beck and Demirguc-Kunt 2006).

Indeed,Rahaman(2011)showsthatasexternalfinancial

constraints lessen, firms switch from internal to external

funds as a means to finance growth. Moreover, these

patterns of transition from internal to external funding

are most pronounced in small unquoted companies

(Rahaman 2011). These firms are more likely to be

financially constrained and to face information prob-

lems. However, there is likely to be an important

complementarity between internal and external finance:

‘‘Access to internal sources of finance may play the twin

roles of proxying for internal financial capacity as well

as providing a signal about the quality of future growth

opportunities. Such signals, in turn, reduce the external

financial constraint’’ (Rahaman 2011, p. 723). In short,

small growing firms are eventually likely to view

external sources of finance as a complement to internal

sources and to increasingly use external sources to fund

growth. Crucially, of these external sources, banks are

consistently identified as the primary provider of

external funds for small firms (Robb and Robinson

2014).

In this vein, for instance, Beck et al. (2005), based

on data from a firm-level survey conducted by the

World Bank, find that financial obstacles are perceived

as the most important barriers to growth. The identi-

fied barriers largely revolve around bank finance and

include: the provision of collateral, the bureaucratic

procedures of banks, the social networks of borrowing,

and the price of finance. In other studies, perceived

financing constraints are also shown to have a positive

association with growth intention (Binks and Ennew

1996; Nitani and Riding 2013). Firms intending to

grow expect to encounter more problems than firms

which actually experienced growth. That is, growing

firms (who are often smaller and younger firms)

anticipate that lack of credit history and an established

relationship with banks will result in tighter credit

availability (Binks and Ennew 1996).

Consistent with the perception of finance as a

barrier to growth, recent empirical research has

provided evidence that growth firms are more likely

to have their loan applications refused (Riding et al.

2012), face loan scaling (Freel 2007), and identify

themselves as discouraged borrowers (Freel et al.

2010). Typical rationalisation of these findings

focuses on the higher risk associated with growth

firms. However, despite this risk, most loan applicants

go on to successfully borrow all or some of the money

they sought. For instance, using data from the US

National Survey of Small Business Finance, Levenson

and Willard (2000) estimated that only 6 % of firms

‘‘had an unfulfilled desire for credit’’, of which 2 %

were actually denied funding and 4 % were discour-

aged from applying. More specifically, Vos et al.

(2007) observed that fast growing small firms in the

UK and USA, respectively, applied for and obtained

more sources of financing than non-growth firms. It

follows that, if most applicants are successful, the

focus of the discussion should shift from credit access

to terms of credit. Central to credit terms are the prices

firms pay for their loans.

To the extent that higher loan prices reflect higher

borrower risk (Berger and Udell 2003; Berger et al.

2005), one would anticipate growth firms facing

higher loan rates. Firm growth implies change: change

in, inter alia, employment, sales, market share, or

assets. Rapid growth implies rapid change. These

changes occur over a specific period of time (Dobbs

and Hamilton 2007), and research has shown small

firm growth to be episodic (Brush et al. 2009). In other

words, growth is a temporary and dynamic phase that

many firms experience (Nightingale and Coad 2014),

and growing firms undertake several alterations in

The cost of growth: small firms and the pricing of bank loans 257

123

their business processes and products. Not only is the

outcome of these changes uncertain, but the pace of

change makes it more difficult for banks and credit

institutions to monitor growing firms and evaluate

their performance (Binks and Ennew 1996). Past

research has shown that the price of obtaining funds

rises as the valuation of the firm becomes less

straightforward for its investors (Strahan 1999). In

this way, the increased levels of information asym-

metry attached to growing firms increase their risk and

consequently the financial constraints they face (Beck

and Demirguc-Kunt 2006; Beck et al. 2005; Binks and

Ennew 1996; Nitani and Riding 2013). Higher loan

price, reflecting higher risk (Strahan 1999), may be a

key manifestation of financial barriers for growth-

oriented entrepreneurs.

The foregoing leads us to two linked hypotheses:

H1 Firm which experienced growth in the near past

pay higher interest rates on loans.

H2 Firms which intend to grow in near future pay

higher interest rates on loans.

Small firms may take a variety of paths to growth

(Garnsey et al. 2006). The variety in paths is likely to

be underpinned by variety in strategy. Importantly, the

various growth strategies that entrepreneurs take

impose different levels of additional risk to their

firms. For example, Wiklund and Shepherd (2005)

report research that suggests that ‘‘tried-and-true’’

strategies lead to higher mean performance, whilst

risky strategies—with higher performance variety—

may lead to both greater individual successes and

more frequent failures. This is consistent with the view

that innovation only spurs growth in a ‘‘handful of

‘superstar’ fast growth firms’’ (Coad and Rao 2008),

whilst for the bulk of firms innovative investments

lead to zero or negative returns.

To the extent that banks primarily provide non-

syndicated commercial loans to small businesses

(Berger and Udell 2003), banks are not providers of

risk capital. That is, banks do not share in the upside

gain of spectacular growth. Accordingly, the greater

risk of failure is likely to bear on the lending decision

and on the price of the loan, more than the prospect of

dramatic success. In this vein, Freel (2007) provides

evidence that innovators were less likely to get access

to all of the funds they seek from their banks (i.e. to

face loan scaling). Similarly, Nitani and Riding (2013)

find that costs of borrowing are higher for R&D

intensive firms. In short, the foregoing leads us to

anticipate that firms seeking to grow through innova-

tion will face higher borrowing costs than firms

seeking to expand by simply doing ‘‘more of the

same’’.

H3 Loan pricing is related to growth modes such

that more aggressive growth strategies will associate

with higher interest rates and safer strategies will be

associated with lower interest rates.

3 Data and methodology

The data used in this study are a sub-sample drawn

from the 2007 UK Survey of SME Finance (Cosh et al.

2008). Since the data were collected in autumn 2007,

we anticipate that our results are not greatly influenced

by the major changes in banking environment starting

from December 2008 in the USA. However, we reflect

upon the implications of the timing of the study in our

concluding remarks. Respondents to the survey were

owners or managers of firms, excluding public and

not-for-profit organisations, with less than 250

employees or/and £35 million turnover. The initial

sample was provided by Dun and Bradstreet with more

than 82,000 firms. However, after considering the

survey criteria, survey quota, and accessibility, around

25,000 firms were contacted. The response rate was

10 %. This response rate might increase the risk of

sampling bias; however, the proportion of responses is

the same across all sizes of companies (Cosh et al.

2008). Testing for non-response bias was not possible.

In addition, weighting the respondents based on size,

sector, and region and comparing them with break-

down of 4.3 million businesses in the UK show that

firms with zero employees represent relatively less

than population statistics. We bear these limitations in

mind for interpretation of our results. The survey

collected information on a variety of financial tools

firms had been using (within the 3 years prior to the

survey date) for business purposes including largest

single outstanding loan. For these loans, data on

interest rate and other terms of contract were collected.

The survey includes 2500 firms; however, for the

purpose of this study, 247 firms are the focus. These

are the firms which use banks’ commercial loans and

mortgages, with variable interest rate, at the time of

258 A. Rostamkalaei, M. Freel

123

data collection. Interest rates incorporate elements of

both the prevailing riskiness of the economic envi-

ronment and the perceived (or measured) riskiness of

the individual borrower. By focusing only on variable

rates loans, we hope to control for the former and

address only the latter. Variable interest rates comprise

of a base rate plus some premium above base. 1 The

former may be thought to capture the economic

conditions at any given time, whilst the latter

addresses the riskiness of the entrepreneur or firms.

By focusing on the premium paid over the base rate,

variations in absolute rates that may reflect different

underlying economic conditions at the time of loan

granting are largely controlled for. Crucially, whilst

our loans were all outstanding on the survey date, they

were not all awarded contemporaneously. The survey

collected data on the premium paid over the base rate,

rather than the final interest rate. We hold that changes

in rate premiums largely reflect the dynamics of the

lending environment and firm-level characteristics,

and much less the underlying economic conditions.

Unlike several studies (Binks and Ennew 1996; Beck

and Demirguc-Kunt 2006; Beck et al. 2005; Vos et al.

2007) regarding financial constraints or loan pricing,

our research deals with an objective measure of higher

or lower price.

In contrast to variable rates, and to the extent that

they do not vary over time, fixed rates are likely to

reflect borrower riskiness and economic conditions

only at the time at which they were awarded.

Accordingly, fixed loan rates for loans awarded at

different times are not directly comparable. We set

them aside in the current analyses. 2

3.1 Dependent variable

In constructing our dependent variable, we use a

survey question that asks respondents the rate they

paid for their largest outstanding bank loan. The

questions were only directed at those firms who

reported using bank loan and mortgage facilities at the

time of data collection (around 25 % of sample firms).

Of these, 41 % provided information on the variable

interest rate. The remainder held fixed rate loans.

Firms holding variable rate loans were offered a

categorical response variable, which expressed the

rate in percentage points above base. Specifically,

firms could indicate the rate they paid in one of the

seven rate ranges. The lowest range was 0–2 %;

thereafter, the next four categories increased by 2 %

points at a time. The two final categories indicated

variable interest rate in the ranges of 10–15 % and

more than 15 % over the prevailing base rate.

However, no firms reported paying more than 10 %

over base rate.

Figure 1 represents the distribution of contracted

rate premiums in the sample. The majority of loans

falls in the first category of 0–2 % premium rate

(57 %), followed by the second category of 2–4 %

(33 %). Because of the small number of observations

for premium rates of more than 4 %, we recoded all

these categories into one category. Accordingly, our

final dependent variable has three orderings: 0–2 %,

2.01–4 %, and [4 %. The ordered nature of our dependent variable is reflected in our choice of

analytical method—ordered probit—which we outline

below.

3.2 Independent variable

Our independent variables are constructed to allow us

to test hypotheses 1–3. Accordingly, they are con-

cerned with growth and growth strategies. To this end,

0.00-2.00%, 57.14%

2.01-4.00%, 33.20%

4.01-6.00%, 3.09%

6.01-8.00%, 6.18%

8.01-10.00%, 0.39%

Fig. 1 Frequency of premium rates in the sub-sample of firms using loan and mortgages with variable interest rate

1 In the UK, this is typically the Bank of England base rate plus

some premium determined by individual banks. 2 To confirm our intuition, we performed a similar suite of

analyses on fixed rate loans. As expected, these models were

poor predictors of loan rate, with few significant variables. The

results are available on request.

The cost of growth: small firms and the pricing of bank loans 259

123

the data allow us to construct three measures of

growth. In the first instance, and in line with H1, we

focus on the growth history. Firms are considered to

experience past growth if respondents declared they

experienced 30 % increase in sales turnover for each

of the 3 years preceding the survey date. 3 This is a

fairly high threshold, and these growth firms may

reasonably be thought of as ‘‘supergrowth’’ firms

(Delmar et al. 2003). In practical terms, these high

growth firms were coded 1, with all other firms coded

as 0.

To address H2, our second independent variable

focuses on growth aspirations. The relevant survey

question captures the owner-managers’ growth inten-

tion over the 3 years subsequent to 2007. Owner-

managers’ growth intentions are not trivial in distin-

guishing between actual growers and non-growers.

Indeed, there is a longstanding view that ‘‘one of the

most important factors [in influencing growth] is the

commitment of the leader of the company to achieving

growth’’ (Smallbone et al. 1995, p. 59). In this

instance, respondents were asked whether they

planned for their firm to ‘‘grow substantially’’, ‘‘grow

moderately’’, ‘‘stay the same’’, or ‘‘become smaller’’.

We coded firms intending to grow substantially or

moderately as 1. Respondents who indicated that they

wished their firms to stay the same size or to become

smaller were coded as 0. 4

However, since questions relating to growth inten-

tions are likely to be prone to both a normative bias

and the over-optimism of the entrepreneurs, we also

focus on specific growth strategies. By this means, we

investigate our third hypothesis. To this end, the

survey included a question on how firms intended to

grow (directed only to those firms indicating a growth

intention). Specifically, the question identifies four

possible growth strategies: ‘‘move into new markets’’,

‘‘introduce new products or services’’, ‘‘increase sale

with existing products and services’’, and ‘‘hire more

employees’’. These strategies are not mutually exclu-

sive and firms could indicate all, some, or none. In line

with our stated hypothesis, we consider ‘‘new market’’

and ‘‘new products or services’’ to be higher-risk,

more aggressive strategies, whilst ‘‘sales of existing

product’’ and ‘‘hiring more employees’’ are lower-

risk, less aggressive strategies. In each case, firms

indicating the intention to follow one of the strategies

were coded 1; otherwise, firms were coded 0. This

results in 4 binary dummy variables that are entered

into the models. Respondents had the option to add to

these strategies, but because of small number of

observations those responses are excluded from the

analyses.

In addition to the variables that allow us to directly

test our hypotheses, we also estimate models incor-

porating a ‘‘supergrowth’’ variable. This variable was

defined by the survey investigators (Cosh et al. 2008)

such that firms characterised as ‘‘supergrowth’’ expe-

rienced more than 30 % increase in turnover each of

the 3 years prior to the survey and intend to sustain the

growth moderately or substantially over the 3 years

subsequent to the survey. This measure reflects the

past and future orientation of the firms, excluding

start-ups (firms in business for\2 years). In essence, this variable is an interaction term between realised

past growth and future growth intentions.

3.3 Control variables

In modelling small firm loan prices as a function of our

independent variables, it is important to control for

other influences on price. These are likely to be factors

which lower or raise perceived risk. Two factors, in

particular, are commonly considered in the empirical

literature: the role of collateral and relational lending.

Credit institutions consider collateral as a positive

signal that alleviates lending constraints by reducing

information asymmetries or default risks (Berger and

Udell 1998; Parker 2002). The information asymmetry

between banks and entrepreneurs retards banks’

ability to distinguish between good and bad entrepre-

neurs. However, the entrepreneur, aware of their

situation, and trying to avoid imperilling their assets,

increases their effort to succeed. Strahan (1999) argues

that collateral makes post-investment monitoring

activities easier but does not affect the price, and the

riskiness of a firm is reflected in the price it pays.

Whilst pledging collateral may not necessarily lower

the risk (price) for growth firms, it is not an

unambiguous merit (Binks and Ennew 1996). That

is, as the risks of these firms increase, the gap between

the banks’ valuation of the assets (at the time of

probable default) and the costs of obtaining those

3 The survey question specified the 30 % threshold.

4 As a robustness check, we coded only those firms declaring an

intention to grow substantially as 1, otherwise 0. The results

were unchanged.

260 A. Rostamkalaei, M. Freel

123

assets from the firm rises. Hence, growing small firms,

comparing to other small firms, are more prone to

under-evaluation of their assets or ‘‘inadequate col-

lateral’’. To mitigate this problem and respond to

growing firms’ increasing demands for funds, banks

may rely on relationship lending (Binks and Ennew

1996).

The severity of information opacity can be miti-

gated by relational lending. Relationships allow banks

to gather information about the firm and entrepreneur

over time and to shift the emphasis of lending

decisions from hard to soft criteria (Beck and Demir-

guc-Kunt 2006). This reduced problem of information

asymmetry may translate into greater access to bank

finance at lower prices (Binks and Ennew 1996).

However, there is no general consensus about the

effect of relationship banking. Sharpe (1990) suggests

that banks, relying on the fact that firms are locked in,

internalise the benefits of the relationship. Peterson

and Rajan (1994) conclude that there is no significant

association between length of lending relationship and

lower interest rate, excepting an insignificant effect

where the bank also provides other financial services

to the firm. Moreover, loan pricing may also exhibit a

cyclical pattern. That is, when firms switch to new

banks, interest rate decreases in order to lock in the

new customers. However, after a while, firms are

charged the same price that they should have paid if

they had stayed with their initial bank (Ioannidou and

Ongena 2010) or an even higher price to compensate

the early subsidies (Kim et al. 2012). Finally, when

banks collect enough information about the firm’s

performance, the interest rate decreases again (Kim

et al. 2012).

Yet, despite the equivocal literature, the provision

of collateral and the existence of longer-term rela-

tionships are likely to be important control variables in

loan pricing models. In our model, these two variables

are part of a set of controls intended to capture

important aspects of the loan contract. Collateral is

measured as a simple dummy variable taking the value

1 if the firm was asked to provide collateral in securing

the loan, and 0 otherwise. Relationship banking is

proxied by the length of relationship with the firm’s

primary bank. This information was captured categor-

ically, with the smallest category indicating a banking

relationship of 0–3 years. Firms in this category were

coded 0, indicating no relationship banking; other-

wise, firms were coded 1.

In addition to these 2 variables, we also include

indicators of the purpose of the loan and of the source

of the loan. In the first instance, we are able to observe

whether the intended use of the loan was for working

capital or for the purchase of assets. Physical asset,

purchased with a loan, can have a similar function as

collateral (Berger and Udell 1998) and imply lower

risk. We code loans sought for the purchase of physical

assets as 1; otherwise, we code them as 0. In terms of

loan source, this describes the relationship between the

banks and the firm further. Specifically, firms were

asked whether their main bank was the only provider of

the loan, one of the providers, or whether the loan was

provided by a bank other than the firm’s primary bank.

In the last instance, we would anticipate that the

‘‘external’’ bank would have had less information

about the quality of the firm and the entrepreneur. In

general, we anticipate that working with a new bank or

securing a loan from multiple sources may impact the

price of loan (Kim et al. 2012; Peterson and Rajan

1994; Vos et al. 2007). In addition, we controlled for

the access of the entrepreneur to other sources of

external finance. Entrepreneurs may use more than one

source of external finance to fund their company, and

the various forms available may be more or less

sensitive to information asymmetries and require more

or less information disclosure or firm monitoring. To

this end, the pecking order hypothesis (Myers 1984)

posits that firms exhibit a preference hierarchy in

seeking sources of finance, starting from internal

sources to debt and then equity financing. To the

extent that external equity is rare and that other forms

of debt instrument (e.g. leases and overdrafts) entail

lower agency costs, term loans may be at the bottom of

the hierarchy. In this case, firms may view term loans

from banks as funding of last resort. Those who

approach banks later, having exhausted all other

avenues of funding, may be viewed as more risky than

those who approach banks early, confident in their

ability to repay principal and interest and to satisfy

monitoring requirements. 5 Alternatively, using multi-

ple sources may signal to banks good management and

lessen the risk. Regardless, it is clear that the financing

decisions of the entrepreneur prior to or at the time of

the loan request may affect the perceived riskiness of

the business. The issue is one of the sequencings (i.e.

5 We are grateful to an anonymous reviewer for raising this

possibility.

The cost of growth: small firms and the pricing of bank loans 261

123

when the bank was approached in relation to other

sources of finance). Unfortunately, our data do not

allow us to directly address this issue. Rather, to reflect

the idea that the entrepreneur has exhausted less costly

sources of financing, and those which entail a lower

agency burden, we build a proxy based upon the

number of sources of external finance the firms had

used during the 3 years prior to, or were using at the

time of, the survey. Ideally, we would like detail on the

financing of the firm before the loan request, but the

data did not provide any information to shed light on

the historical financing activities. The index is a simple

count of identified use of loans from the owner, loans

from family and friends, leasing and higher purchase

agreements, credit cards, and overdraft funding.

Our second set of control variables is intended to

capture firm heterogeneity. The first of these variables

is a ‘‘usual suspect’’ in empirical studies of small

firms—viz. size. Size has been shown to affect both

access to and price of credit (Aterido et al. 2011; Beck

et al. 2005; Binks and Ennew 1996; Freel 2007; Vos

et al. 2007). Even within small firm samples, larger

firms are less likely to suffer (or to suffer less) from

information opacity and their performance may be

more easily evaluated (Berger and Udell 1998). In this

study, size is measured by the number of employees

and coded into four size-bands: zero employees, 1–9,

10–49, and more than 50 employees. The zero size-

band provides our reference category. We also control

for broad sectoral variation at the SIC division level.

Here, agriculture acts as our reference category.

Finally, we also include the age of the business as a

control variable. As the firm grow older, one expects

that the credit history and reputation of the firm act as

risk-mitigating factors. Due to the structure of the

questionnaire and number of observations, we defined

age of the business as 1 if it is older than 10 years and

zero otherwise.

Beyond these structural characteristics, banks also

rely on information they have on the quality of the

owner of the business (as a borrower) (Berger and

Frame 2007). To this end, we were able to incorporate

in our models measures of entrepreneurial experience,

and owner-manager’s age and gender. However, when

these are included with business age in our models,

collinearity becomes a concern. In the final analyses,

we use age of the entrepreneur in preference to

entrepreneurial experience. Importantly, our key

findings are robust to this choice. Lastly, we control

for gender of the principal owner. This is measured as

a simple binary variable taking the value 1 if the

principal owner was male, and 0 otherwise. 6

3.4 Descriptive statistics

Table 1 presents descriptive statistics for the variables

used in our analyses. As the data in panel A illustrate,

most of the firms are active in the service sector, have

between 10 and 49 employees, and are older firms. For

almost two-thirds of firms, their main bank is the only

provider of the loan. From the data in panel B, 82 % of

firms are principally owned and managed by men; the

remaining 18 % of firms are run by women or jointly.

Seventy-seven percentage of sample firms had a

banking relationship extending more than 3 years,

and 76 % of firms were required to collateralise the

loan of interest.

Panel B also records the distribution of firms across

our key independent variables, such that 17 % of firms

were classed as ‘‘supergrowth’’ firms (i.e. firms

experiencing growth more than 30 % in each of the

3 years preceding the survey and intending to growth

in the three subsequent years). This figure is largely

constrained by the 19 % of sample firms that were

recorded as having experienced growth in the previous

3 years. Perhaps unsurprisingly, 71 % of firms

reported an intention to pursue growth in the coming

years. This large figure may speak to normative biases

or over-optimism. However, only around 25 % of

firms indicate an intention to ‘‘grow substantially’’,

which is closer to the number of past growers. In terms

of growth strategies, 22 % of firms indicated their

intention to seek growth through penetrating into new

markets, 30 % expect growth through new product or

services, 55 % expect to increase the sale of existing

product in the same market, and 34 % plan to recruit

more employees. As noted, these strategies were not

mutually exclusive and firms could select more than

one strategy for growth.

4 Methods

To examine the relationship between firm growth and

the price of loans, and given the ordered nature of our

6 This would include cases where the principal ownership was

female or shared.

262 A. Rostamkalaei, M. Freel

123

dependent variable, we estimate a series of ordered

probit models (Greene and Hensher 2009). However,

only a proportion of the sample report loan rates, since

only a proportion of our sample have outstanding

loans. Focusing only on these firms may result in

sample selection bias. This bias may result from two

selection issues: firstly, we only deal with firms that

applied for and were offered loans and, secondly,

among those firms, we opt to consider only those that

received variable rated loans. To control for potential

issues of selection bias, we estimate a two-stage model

(Heckman 1979). For completeness, we present the

results of both the simple ordered probit and the two-

stage ordered probit in Tables 3 and 4 (the details of

the selection model used in the two-stage Heckman

model is detailed in the following section). Ordered

probits, along with other forms of regression, are

sensitive to collinearity among the independent vari-

ables. For this purpose, Table 2 also displays variance

inflation factors (VIFs) for all the explanatory vari-

ables—calculated in regressions excluding and

including the inverse Mills ratio (IMR). In no cases

is there evidence of multicollinearity.

5 Results

Table 3 presents the result of the simple ordered probit

models. All the models are statistically significant at

99 %. In the first instance, our base model considers

the control variables that are intended to proxy firm

heterogeneity. Here, we note that firm size associates

with loan pricing. That is, as the size of the firm

increases, loan price decreases. This is consistent with

our expectations. Beyond this, we observe that older

firms, the use of funds to purchase assets, and the

provision of collateral are significantly negatively

related to the probability of paying higher interest

rates. To restate, if a firm used the loan to purchase

fixed assets and/or provided collateral for the loan,

then the probability of paying a higher price for the

loan falls. In contrast, there is tentative evidence that

Table 1 Frequency table of the characteristics of firms, owners, and loan for firms using loan and mortgages with variable rate

A. Freq Percentage Cum. B. Mean Std. Dev

Sector Business older than 10 years 65 %

Agriculture, etc. 21 8.11 8.11 Age of the owner (years) 50.88 10.36

Manufacturing 20 7.72 15.83 Male ownership 82 %

Construction 30 11.58 27.41 Purchased asset with loan 53 %

Wholesale/retail 32 12.36 39.77 More than 3 years relationship with bank 77 %

Service sectors 156 60.23 100 Collateral 76 %

Size

0 employee 16 6.18 6.18 Supergrowth 17 %

1–9 employees 66 25.48 31.66 Past growth 19 %

10–49 employees 105 40.54 72.2 Growth intention 71 %

50–249 employees 72 27.8 100 New market 25 %

Loan provider New product 31 %

Only main bank 168 64.86 64.86 More sale 55 %

Main bank one of the providers 57 22.01 86.87 More employees 34 %

Main bank not a provider 34 13.13 100

Number of sources of finance used a

0 12 4.63 4.63

1 34 13.13 17.76

2 78 30.12 47.88

3 80 30.89 78.76

4 45 17.37 96.14

5 10 3.86 100

a Including leasing, loan from owner, loan from family and friends, credit cards, and overdraft

The cost of growth: small firms and the pricing of bank loans 263

123

the probability of paying a higher loan rate rises with

the age of the entrepreneur.

The second model includes all our control variables

along with past growth. The significant variables from

our base model continue to associate with loan prices,

except for the age of the business. However, we also

now note a negative relationship between loan syndi-

cation and the probability of paying higher interest

rates. Importantly, and in line with hypothesis 1, firms

that experienced rapid growth in the past have a higher

probability of paying more interest.

Model 3 is concerned with growth intentions. In this,

our control variables largely act in the same manner.

However, we do not find any support for our second

hypothesis. There is no evidence that firms declaring an

intention to grow in the future pay higher rates of

interest. Our initial intuition was that this was likely to

relate to the high proportion of firms reporting a growth

intention. Over 70 % of firms in the sub-sample

declared an intention to grow over the 3 years following

the survey. However, recoding the variable to indicate

only those firms planning to grow ‘‘substantially’’ does

not change this finding. It would seem that banks pay

little regard to broad growth intentions in pricing loans.

However, it may also reflect the countervailing effects

of different intended strategies (see below).

As a supplementary analysis, we introduce an

interaction term to model 4. It indicates that, when

coupled with past growth, growth intentions do

associate with higher loan prices. That is, firms

enjoying growth in the past and planning to grow in

the future are more likely to have paid higher rates of

interest on their loans. The survey team termed such

firms ‘‘supergrowth’’, but one may also think of them

as sustainable growers. Regardless, this result pro-

vides further evidence in support of the global

hypothesis that growing firms are discriminated on

price in loan markets. These firms differ from ‘‘future

growth’’ firms to the extent that, despite having proven

past success, they continue to pay more for loans than

their non- and less growing peers.

The final model in Table 3 is concerned with the

relationship between different growth strategies and

loan prices. Here, the results are broadly in line with

H3. Of our four growth modes, growth through new

product introduction is positively associated with loan

rates, whilst growth through sales of existing products

is negatively associated with growth rates. In other

words, firms pursuing a ‘‘more of the same’’ strategy

appear to pay less for loans than those pursuing more

aggressive, innovative strategies. In our analysis,

penetrating into new market and hiring more employ-

ees are not significant explanatory variables in

predicting the probability of higher or lower interest

rates. These results support our speculations about the

associations between modes of growth and loan

pricing, whereby riskier strategies are associated with

more expensive bank financing. Conversely, firms

Table 2 Variance inflation factor

Variables VIF a

VIF b

Age of the owner 1.40 1.45

Male ownership 1.23 1.35

Sector-Ref: agriculture

Manufacturing 1.98 2.12

Construction 2.42 2.59

Wholesale/retail 2.40 2.51

Service sectors 3.61 3.77

Size-Ref: zero

1–9 employees 4.59 5.87

10–49 employees 5.90 8.38

50–249 employees 5.40 8.68

Business older than 10 years 1.55 1.77

Asset purchased with loan 1.15 1.21

Loan provider-Ref: only main bank

Main bank one of the providers 1.17 1.22

Main bank not a provider 1.20 1.27

Relationship with bank ([3 years = 1) 1.20 1.19 Collateral (yes = 1) 1.13 1.19

Count of financial resource c -Ref: 0

1 Type 3.77 4.35

2 Types 6.07 7.87

3 Types 6.09 7.93

4 Types 4.36 5.61

5 Types 2.02 2.48

Past growth 4.98 6.49

Future growth 2.54 2.64

Supergrowth 5.81 7.05

New market 1.48 1.46

New product 1.64 1.58

More sale 1.80 1.79

More employees 1.79 1.85

IMR – 1.71

a Matrix of variables excluding IMR,

b Matrix of variables

including IMR, c use of credit card, overdraft, leasing, loan

from the owner, and loan family and friends

264 A. Rostamkalaei, M. Freel

123

Table 3 Results of one-stage ordered probit model

One-stage models Base model Past growth Future growth Supergrowth Modes of growth

Coef. Std.

Err.

Coef. Std.

Err.

Coef. Std.

Err.

Coef. Std.

Err.

Coef. Std.

Err.

Age of the owner 0.0158* 0.008 0.0205** 0.009 0.0162* 0.008 0.0235** 0.009 0.0121 0.009

Male ownership -0.162 0.219 -0.192 0.220 -0.171 0.220 -0.338 0.240 -0.186 0.220

Sector-Ref: agriculture

Manufacturing 0.674 0.414 0.682 0.416 0.671 0.414 0.699 0.445 0.764* 0.425

Construction 0.526 0.395 0.504 0.396 0.519 0.395 0.44 0.409 0.65 0.413

Wholesale/retail 0.389 0.383 0.34 0.385 0.394 0.383 0.291 0.391 0.413 0.387

Service sectors 0.108 0.333 0.0657 0.335 0.108 0.333 0.0936 0.341 0.118 0.335

Size-Ref: zero

1–9 -0.0831 0.350 -0.0595 0.350 -0.0808 0.350 -0.0742 0.392 -0.00772 0.356

10–49 -0.714** 0.357 -0.721** 0.357 -0.710** 0.357 -0.703* 0.398 -0.648* 0.369

50–249 -0.776** 0.377 -0.809** 0.378 -0.784** 0.378 -0.772* 0.411 -0.798** 0.393

Business older than

10 years

-0.346* 0.206 -0.281 0.210 -0.334 0.209 -0.18 0.235 -0.316 0.213

Asset purchased with

loan

-0.432** 0.172 -0.433** 0.172 -0.425** 0.173 -0.404** 0.184 -0.475*** 0.176

Loan provider-Ref: only main bank

Main bank one of the

providers

-0.342 0.213 -0.402* 0.218 -0.342 0.214 -0.393* 0.224 -0.302 0.218

Main bank not a

provider

-0.0392 0.259 -0.0102 0.260 -0.032 0.260 -0.065 0.293 -0.0366 0.267

Relationship with bank

([3 years = 1) -0.2 0.210 -0.226 0.211 -0.191 0.212 -0.323 0.238 -0.207 0.213

Collateral (yes = 1) -0.540*** 0.185 -0.513*** 0.186 -0.541*** 0.186 -0.425** 0.199 -0.498*** 0.189

Sources of finance-Ref: 0

1 Type 0.683 0.452 0.737 0.453 0.691 0.453 0.848* 0.507 0.623 0.456

2 Types 0.375 0.416 0.383 0.416 0.385 0.417 0.487 0.468 0.368 0.419

3 Types 0.637 0.427 0.627 0.427 0.636 0.427 0.775 0.482 0.615 0.431

4 Types 0.633 0.435 0.635 0.434 0.629 0.435 0.597 0.498 0.568 0.440

5 Types 0.017 0.617 -0.16 0.633 0.00391 0.620 -0.0449 0.677 -0.0165 0.632

Past growth 0.420* 0.219

Future growth 0.0449 0.128

Supergrowth 0.496* 0.261

New market -0.0117 0.221

New product 0.504** 0.216

More sale -0.389** 0.192

More employees 0.0358 0.212

/cut1 -0.763 0.828 -0.336 0.860 -0.667 0.872 0.0943 0.990 -0.878 0.852

/cut2 0.55 0.828 0.99 0.862 0.646 0.872 1.378 0.994 0.478 0.850

Number of observation 230 230 230 206 230

Prob [ v2 0.0004 0.0002 .0006 0.0058 0.0002

Pseudo R 2

.1131 .1216 .1133 .1085 .1315

Dependent variable is contracted premium rate on variable rate loans (1 = 0–2 %, 2 = 2–4 %, 3 = More than 4 %)

*** p \ 0.01, ** p \ 0.05, * p \ 0.1

The cost of growth: small firms and the pricing of bank loans 265

123

intending to sell more of ‘‘tested-and-tried’’ products

are associated with lower cost of financing. The

countervailing effects of aggressive and conservative

intended strategies may also help explain the lack of a

significant finding in support of H2.

As noted earlier in the paper, the foregoing analyses

may be susceptible to selection biases arising from our

focus only on those firms who held variable rate loans.

To control for the potential sample selection bias, the

Heckman (1979) two-stage model has been used. In the

first stage, we estimate a probit model of the probability

of accessing loans for all the observations in the

sample. To calculate the probability of having loans in

firms, we introduce the following selection equation:

pðaccessing to loanÞ ¼ f export; innovation; capitalð expenditure; size; assets; legal

status; age of the businessÞ

In our selection equation, we try to consider not

only the variables that ease access to loans (e.g. firm

size, asset base, and legal status) (Beck and Demirguc-

Kunt 2006; Berger and Udell 1998, 2006; Freel 2007),

but also variables that may affect the demand for loans

(e.g. export activity, innovation, and recent capital

expenditure). In this way, we see loan utilisation as a

function of both firms’ demand and banks’ willingness

to supply. Exporting, innovation, and capital expen-

diture are reported by the owners or managers. From

this equation, we calculate the inverse Mills ratio

(IMR) which is subsequently used as an additional

explanatory variable in the second-stage model. As

Table 4 records, the coefficient of the IMR is statis-

tically significant in four models out of five. 7 This

suggests the presence of selection bias (Jones 2007,

36–37), although our data do not support the existence

of selection bias in one of the models. 8

Turning to our two-stage ordered probit, with

Heckman correction; Table 4 takes a similar approach

to Table 3, but all models include the IMR calculated

from the probit selection equation. Although the thrust

of these results is broadly in line with regard to our

independent variables, there is one intriguing differ-

ence with respect to our control variables. Firm size,

measured by the number of employees, was a negative

and significant explanatory factor in the probability of

paying higher loan prices in the absence of our control

for potential selection bias. However, when selection

is controlled for, size is no longer significant. It would

seem that, whilst size may associate with holding a

loan, it has no robust influence on loan pricing.

However, syndication, collateralisation, and loan use

continue to be significantly negatively associated with

the probability of paying higher interest rates.

In all but our ancillary ‘‘supergrowth’’ model, the

existence of sample selection bias is indicated. How-

ever, where this is controlled for, we continue to find

evidence to support hypotheses 1 and 3—though not

hypothesis 2. In other words, firms which have

recorded past growth or who intend to grow through

innovation are likely to have paid higher rates of

interest on their loans. Our sustainable, or ‘‘super-

growers’’, are also likely to have paid a higher price for

credit. These firms, whilst not denied credit, are

discriminated on the basis of price. In the next section,

we turn to the implications of these findings.

6 Discussion and concluding remarks

Based on UK survey of SME Finance (2007), we find

that growth firms pay higher interest rates on bank

loans. This result holds after controlling for the effects

of size, owner’s experience, industry sector, loan

purpose, collateral, and relationship banking. In

simple terms, firms that have successfully grown their

businesses in the recent past paid higher interest rates.

Even where these firms anticipated sustaining their

growth, they exhibited a higher probability of paying

more. That is, despite evidence of success and

ambition, interest rates are higher.

Moreover, although intention to grow does not, on its

own, show any association with higher price, we note

that intended growth strategy associates with loan price.

Specifically, more risky strategies, involving the intro-

duction of new products and services, associated with

higherinterestrate;whilst,moreconservativestrategies,

associated with increased sales of the same products in

existing markets, associate with lower loan prices.

Crucially, none of the foregoing need imply a

criticism of banks. Growth and innovation are likely to

7 The results of first-stage probit regression are available on

demand. 8 Another model considering the use of variable rate loans as

the dependent variable of the probit (selection) model was also

estimated. The results were broadly in line with the reported

approach.

266 A. Rostamkalaei, M. Freel

123

Table 4 Result of second-stage ordered probit model

Two-stage models Base model Past growth Future growth Super growth Modes of growth

Coef. Std.

Err.

Coef. Std.

Err.

Coef. Std.

Err.

Coef. Std.

Err.

Coef. Std.

Err.

Age of the owner 0.0175* 0.009 0.0231** 0.010 0.0185** 0.009 0.0292*** 0.010 0.0138 0.009

Male ownership -0.115 0.240 -0.15 0.242 -0.148 0.243 -0.349 0.271 -0.149 0.242

Sector-Ref: agriculture

Manufacturing 0.626 0.444 0.632 0.446 0.624 0.443 0.619 0.470 0.698 0.450

Construction 0.369 0.430 0.367 0.432 0.344 0.429 0.3 0.441 0.522 0.441

Wholesale/retail 0.108 0.425 0.0697 0.426 0.119 0.424 0.0662 0.433 0.0882 0.425

Service sectors 0.0937 0.362 0.0608 0.363 0.0862 0.361 0.0368 0.369 0.0887 0.359

Size-Ref: zero

1–9 0.462 0.420 0.506 0.421 0.474 0.422 0.331 0.473 0.59 0.431

10–49 -0.102 0.442 -0.109 0.444 -0.0965 0.444 -0.232 0.501 0.0351 0.465

50–249 -0.00638 0.488 -0.0495 0.490 -0.0195 0.489 -0.177 0.541 0.0396 0.511

Business older than

10 years

-0.465* 0.241 -0.373 0.246 -0.420* 0.247 -0.311 0.269 -0.441* 0.251

Asset purchased with loan -0.395** 0.190 -0.394** 0.192 -0.374* 0.192 -0.426** 0.204 -0.430** 0.195

Loan provider-Ref: only main bank

Main bank one of the

providers

-0.471** 0.230 -0.555** 0.236 -0.478** 0.231 -0.552** 0.239 -0.454* 0.235

Main bank not a provider -0.149 0.280 -0.132 0.280 -0.121 0.282 -0.302 0.324 -0.115 0.290

Relationship with bank

([3 years = 1) -0.138 0.230 -0.171 0.231 -0.118 0.232 -0.282 0.258 -0.16 0.234

Collateral (yes = 1) -0.415** 0.200 -0.377* 0.201 -0.424** 0.200 -0.316 0.211 -0.363* 0.203

Sources of finance-Ref: 0

1 Type 0.716 0.508 0.739 0.508 0.754 0.510 1.002* 0.570 0.671 0.514

2 Types 0.259 0.464 0.239 0.464 0.293 0.466 0.393 0.519 0.245 0.470

3 Types 0.545 0.481 0.515 0.481 0.546 0.481 0.675 0.538 0.515 0.486

4 Types 0.447 0.479 0.407 0.479 0.443 0.479 0.318 0.548 0.377 0.486

5 Types -0.143 0.646 -0.369 0.664 -0.169 0.651 -0.266 0.716 -0.173 0.663

Past growth 0.499** 0.243

Future growth 0.121 0.141

Supergrowth 0.646** 0.275

New market -0.0482 0.228

New product 0.603*** 0.225

More sale -0.25 0.206

More employees -0.0164 0.232

IMR 0.955** 0.417 0.888** 0.421 0.983** 0.418 0.71 0.444 1.031** 0.422

/cut1 0.58 1.041 1.041 1.070 0.901 1.108 1.079 1.195 0.647 1.072

/cut2 1.905* 1.048 2.385** 1.079 2.226** 1.114 2.408** 1.204 2.018* 1.078

Number of observation 201 201 201 182 201

Prob [ v2 0.0024 0.0010 0.0029 0.0054 0.0012

Pseudo R 2

.1168 .1280 .1188 .1267 .1381

Dependent variable is contracted premium rate on variable rate loans (1 = 0–2 %, 2 = 2–4 %, 3 = More than 4 %)

*** p \ 0.01, ** p \ 0.05, * p \ 0.1

The cost of growth: small firms and the pricing of bank loans 267

123

entail additional risks to small businesses. Although

banks are the primary sources of financing when

entrepreneurs decide to seek external financing (Robb

and Robinson 2014), banks are not risk funders.

Rather, in assessing loan applications, banks are

interested in the ‘‘serviceability’’ of the firms not the

value of the business: the ability to generate enough

cash flow to pay the debt (Cowling et al. 2012; Lee

et al. 2014). In this sense, growing small firms may be

perceived as less attractive to risk-averse banks. Other

sources of external financing such as venture capital

funds are presumed to be better suited to the financing

of viable high-risk projects. However, for reasons of

both supply and demand, venture capital is used by

only a small proportion of firms. In our sample, only

four out of 2500 firms sought venture capital financing

in the 3 years prior to 2007 and only 11 % reported

that they may consider equity financing in future. If the

risk of a project is too high that banks cannot offer any

interest rate to hedge the risk, the project may be

declined or the loan downsized. More often, however,

the interest rate rises (Parker 2002) and valuable

capital is diverted to the loan provider in the form of a

risk premium. This might open a door for interventions

designed to ameliorate the apparent risk of growth

firms.

Academic commentary has recently argued that

interventions in the process of establishment or growth

of SMEs are justified if targeted to growing and

innovative firms (Shane 2009; Mason and Brown

2011; Nightingale and Coad 2014). If programmes do

not recognise the differences among the firms, their

implementation will favour lower-quality firms at the

expenses of higher-quality ones (Nightingale and

Coad 2014). Supporting lower-quality firms would

decrease the investment rate of return and conse-

quently would increase the price of capital for all type

of firms (Nightingale and Coad 2014). Alas, it seems

easier to call for support targeted to high growth firms

than to provide practical guidance on how this may be

achieved. In large part, this is because ‘‘[high growth

firms] are found across all sectors of the economy, a

heterogeneity that is also reflected in their age, size,

origin, and ownership’’ (Mason and Brown 2011,

p. 222).

We believe that focusing on the riskiness of growth

firms may be a useful starting point for practical

intervention. This rests on an appreciation of growth

risk as both objective and perceived. To the extent that

growth firms are objectively riskier, there is little

policy that can do other than offering to bear risk. This

is what loan guarantee schemes (LGS) currently do.

Whilst belief in the existence of credit rationing is the

fundamental rationale for loan guarantee schemes

(Cowling 2010), in practice they encourage incremen-

tality or additionality in lending (Riding et al. 2007).

That is, they encourage lending to firms that would

have received turndowns otherwise due to their higher

risk of default (Zecchini and Ventura 2009). Crucially,

guarantors typically apply a fee to cover defaults and

protect the integrity of the scheme, thus raising loan

price. Regardless, our concern is not with firms that

would otherwise be turned down for a loan. Rather,

ours is with those [growth] firms who pay a higher

price for loans. To this end, whilst Riding and Haines

(2001) observes that the objective of LGS is to assist

small firms, not to subsidize risky ones, one might

wonder if there was a role for a targeted schemes

whose objective was to subsidise risk. Of course, the

broader provision of grants to growing firms would be

a more direct form of subsidy—providing some funds

and signalling firm quality in the event of a loan

application.

Regardless, in the absence of further evidence, we

are agnostic on the desirability of interventions aimed

at addressing the objective riskiness of growth firms—

at least, beyond that which already exists. However,

we are more convinced of the merits of potential

interventions aimed at reducing perceived riskiness.

Much of the banks assessment of small firm risk is

likely to result from the greater information opacity

attendant upon small firms generally and growing

small firms specifically. Past evidence has suggested

that, for SMEs, relationship banking may provide

access to finance at lower costs (Binks and Ennew

1996). Relationships reduce information asymmetry.

However, there may be other, more timely, ways of

reducing information asymmetries. In this, an analogy

may be drawn with the growing number of Investment

Readiness Programmes across Europe (Mason and

Kwok 2010). Mason and Kwok (2010) note that the

primary reason that businesses are not ‘‘investment

ready’’ is one of the information failures. In large part,

this involves presentational shortcomings: ‘‘Even if

the underlying proposition is sound a business may

still fail to raise finance if the business plan is poorly

constructed and presented’’ (Mason and Kwok 2010,

p. 272). The parallels to ‘‘debt readiness’’ are clear.

268 A. Rostamkalaei, M. Freel

123

Given the relative use of debt and equity even among

growing small firms, interventions designed to

improve the ‘‘debt readiness’’ of growing firms may

be well suited. In line with ‘‘investment readiness’’

(Mason and Harrison 2001), the main goal of such

assistant to growth firms should be increasing the

quality of loan application and also providing infor-

mation on the different banking product and services,

and their associate costs, potentially available for

those firms.

In conclusion, based on the 2007 UK survey of

SME Financing and information on the variable rate

loans, we find that growing firms hold more expensive

loans. Similarly, those whose future growth plans

revolve around innovation are also more likely to hold

higher priced loans. We interpret these findings to

indicate a relationship between firm risk (both objec-

tive and perceived) and loan pricing. However, there

are inevitably limitations to our research. In the first

instance, higher loan rates may simply reflect the

willingness of growth firms to accept poorer contract

terms. Busy entrepreneurs must allocate precious time

and resources to apply for a loan. In consequence, they

are more willing to meet the higher loan price because

of the higher opportunity/transaction costs they

incur—relative to non-growth firms. 9 Secondly, for

the firms that had grown in the three previous years,

our data do not provide any information on whether

the premium rate was contracted before, after or

coincidental to growth. Still, the significant partial

relationship between the modes of future growth and

interest rate suggests that even if the loan is granted

before initiating growth process, it captures the higher

risk profile. Moreover, the modal number of years

firms had held loans in the sample was between 1 and

3 years. Thirdly, from all the bank facilities available

to SMEs, our study was only concerned with term

loans and only variable rate term loans. Overdrafts or

lines of credit, which are likely to be important sources

of working capital, are only a minor component in our

financial ‘‘bundling’’ explanatory variable. Future

research might investigate the relationship among

different risk profiles, the propensity to use broader

bank facilities, and the price those facilities obtain.

Although we loosely proxy the capital structure of the

firm in terms of number of sources an entrepreneur

uses, this sheds limited light on the perceived riskiness

of the business prior to contracting loan terms and

conditions. Further research, where data are available,

may consider the riskiness of the business due to its

proximate financing decisions.

Fourthly, we concentrated on variable rate loans

and the premium above base rates. Our expectation

was that base rates control for macro-fluctuations.

Nonetheless, our results may be context specific. The

UK banking system is relatively concentrated on

supply side (Competition Commission Report 2002).

As reported by Competition Commission (2002), SME

owners mainly work with one bank for all their

required services and rarely change their banks for

better prices. Owners of course have the option to seek

quotes from different banks, but the associate costs

and the perceived importance of banking relationship

to the owners make bargaining difficult (Competition

Commission Report 2002). Moreover, the scope for

‘‘shopping around’’ is more limited than would be the

case in a more fragmented banking market. In the UK

SME loan market, lending relationship becomes

important for banks and SMEs: banks try to lock in

their customers, as SMEs are less likely to switch to

new banks, and SMEs use relationship banking to

access finance more easily or on better terms (Berger

and Udell 2006). Concentration in banking markets

has been shown to associate with the extent of

relational lending (Ashton and Keasey 2005). More-

over, establishing a long-term relationship would aid

banks to assess SMEs activities with lower degree of

information opacity (Ashton and Keasey 2005). In

such a market, growing and innovative firms may be

more likely to accept higher fees in order to keep their

relationship with their banks and ensure their access to

finance at the time of cash flow difficulties. However,

increasing competition among banks may increase

customers’ bargaining power and lower the price of

loans (Rice and Strahan 2010).

The final consideration is the pertinence of our

findings given the current situation in the UK loan

market following the financial crisis. Small firms’

access to bank facilities experienced a sharp decline

from 2008. Whilst SMEs decreased their demand for

finance, the supply side was marked by a ‘‘U-shaped

pattern’’, with an initial decline and subsequent

recovery to the levels experienced before December

2009 (Cowling et al. 2012). Small businesses, in the

early part of this period, experienced higher rejection

9 We are grateful to anonymous reviewer for raising this

possibility.

The cost of growth: small firms and the pricing of bank loans 269

123

rates comparing to previous years. But the situation

eased considerably after 2009 (Financing SMEs and

Entrepreneurs 2014: An OECD Scoreboard 2014).

These patterns held for all types of SMEs. Intrigu-

ingly, in the case of growing and innovative firms,

firms intending to grow reduced their demands, but

firms who had achieved growth before the crisis

maintained the same level of debt demand (Cowling

et al. 2012). Nonetheless, Lee et al. (2014) show that

access to bank finance for innovative firms became

more difficult after the financial crisis (based on

2007–2012 loan applications) and that these firms

were more likely to be unable to secure debt financing

from any bank. Yet, the average credit scoring of

innovative and non-innovative firms did not differ

significantly during this period, suggesting that

assessments of objective risk remained at the same

level (Lee et al. 2014). One possible explanation of

higher rates of loan refusal for innovative firm might

be banks’ increased perceived risk about their activ-

ities. In the recessionary period, the most significant

factor affecting the loan appraisal decision was the

size of businesses, with growth orientation apparently

ignored in the process of decision-making (Cowling

et al. 2012). Regardless, given the recovery of loan

approval rates to before-crisis levels, we anticipate

that banks are likely to rely upon the same criteria to

appraise loan applications as prevailed in the pre-

recession period. In short, bank assessment of risk and

subsequent pricing are likely to follow similar logics

today as when our data were collected. 10

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