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Firm bribery and credit access: evidence from Indian SMEs
Nirosha Hewa Wellalage &Stuart Locke &
Helen Samujh
Accepted: 11 March 2019
#Springer Science+Business Media, LLC, part of Springer Nature 2019
Abstract This study investigates the effects of paying
bribes on access to credit for small and medium enter-
prises (SMEs). Bribery is variously portrayed, in the
literature, as greasing the wheel (helping) or sand in
the wheel (impeding) applications for credit. Studies
supporting both perspectives leave the issue unresolved,
encouraging further analysis, using reliable data and
robust analytic methods. An examination of The World
Bank Enterprise Surveys of SME data for India, using
an instrumental variable probit model, provides a more
definitive answer. SME bribery is detrimental to
accessing credit and more so for firms that have been
in business for many years and operating on a small
scale. Involvement of supply and demand side forces
increases the need for multiple control variables. From a
supply side perspective, high corruption increases diffi-
culties for financial institutions to control borrower risk
and recover loans. Accordingly, financial institutions
reduce their lending to SMEs, which mostly belong to
a high-risk category. Unlike large firms, SMEs paying
bribes to grease the wheel are drawn to the informal
sector, avoiding attention from officials. Where SMEs
pay bribes in the formal sector, it is noticed and likely to
increase the probability that other parties will also de-
mand payments. The demand side argument regards
bribes as tax, increasing loan costs to SMEs. Conse-
quently, making significant bribes decreases SMEs’
profitability. Less profitable SMEs may not obtain ac-
cess to credit. From a policy perspective, anti-corruption
measures, in emerging and low-income economies, are
vital for developing SMEs and stimulating significant
welfare gains.
Keywords Corruption .Bribes .SME .Credit access .
India .IV probit regression .Endogeneity
JEL classification D73 .E5 .G21 .L25 .L26
1 Introduction
This paper examines how corruption influences external
credit access for small- and medium-sized enterprises
(SMEs)
1
in India. There is a growing body of literature
concerning the implications of corruption and, in partic-
ular, the significance of bribery in the business world.
An economic framework, adopting the ‘grease the
wheel’or the ‘sand in the wheel’concept, considers
whether corruption increases access to finance (by
greasing the wheels) or reduces access through raising
the cost to the borrower (through sand in the wheel).
The quality of the institutional environment may
significantly impact the development of credit market
Small Bus Econ
https://doi.org/10.1007/s11187-019-00161-w
1
The Enterprise Surveys undertaken by the World Bank in 2014
(www.enterprisesurveys.org) categorise Indian firms having up to 99
employees as SMEs, and that is the definition used in this study. Firms
with 5 to 19 employees are categorised as small firms, and those with
20 to 99 employees are defined as medium-sized.
N. Hewa Wellalage (*):S. Locke :H. Samujh
School of Accounting, Finance and Economics, Waikato
Management School, The University of Waikato, Gate 1 Knighton
Road, Private Bag 3105, Hamilton 3240, New Zealand
e-mail: nirosha@waikato.ac.nz
in many regards (Galli et al. 2017). As an example, the
enforcement of legal rights (La Porta et al. 1997;Galli
et al. 2017) and the competitiveness of the bank market
(Cavalluzzo et al. 2002) play a role in the credit market,
thereby affecting SMEs’access to external credit. Al-
though corruption is universally recognised as an im-
portant dimension of the quality of institutions, scant
literature reports the effects of corruption on SMEs’
credit access. The credit market, for SMEs, provides a
fertile domain for plentiful corruption. There is stringent
vetting of SMEs’applications for credit, and financial
institutions face potentially greater risks in serving
SMEs, creating an environment where additional moti-
vating forces might be utilised to purchase ‘obliging-
ness’from a credit supplier. Additionally, particular
elements and structures of the SME/bank relationship,
for example large amounts of casualness, obscure lines
amongst business and SME proprietors, further increas-
ing the potential risk of corruption.
Although making informal payments (e.g. bribes) is
detrimental to all types of businesses, it poses a major
problem for the development of SMEs (Gbetnkom
2012;Şeker and Yang 2014). When corruption is high,
financial institutions will find it harder to control bor-
rower risk and recover loans. Unlike large firms, where
SMEs pay bribes, they are drawn to the informal sector
to avoid being noticed by officials. When an SME pays
bribes in securing credit, it is noticed and potentially
increases the number of other parties who will demand
payments. In that case, financial institutions reduce their
lending to SMEs, which mostly belong to a high-risk
category.
There are both supply and demand side forces in-
volved. From the supply side perspective, bank em-
ployees extracting bribes increase the risk to the credit
supplier, and consequently, the cost of lending to the
SME portfolio increases. Corruption can bring greater
ambiguity to banks’claims and their enforcement ac-
tions against corrupt firms in the case of loan non-
payment. Therefore, this reduced enforcement power
against defaulting SMEs moderates banks’passion to
lend to bribing firms (Qi and Ongena 2018). The de-
mand side argument regards bribes as a tax that in-
creases the cost of loans to the borrowers (Fungáčová
et al. 2015), i.e. the SME and, more specifically, the
SME owner. As a consequence of the need to make
significant bribes, SMEs’profitability is decreased. If
bribery leads to an erosion of the efficient allocation of
resources, then these bribery costs are likely to lower the
profits of the SMEs. Financial institutions, as they per-
ceive SMEs as risky because of low profitability ven-
tures, will respond from the supply side by denying
them credit. Both these arguments tend to curb the
availability of external credit, which in turn increases
the level of credit constraints faced by the SMEs.
The full extent of bribery is unknown but a 2004
estimate placed it at US$1000 billion per annum, or 3%
of the world’s gross domestic product (GDP), of which
SMEs pay a large portion (UNIDO and UNODOC
2007). For SMEs, corruption may reduce access to
resources and impose informal ‘fees’for services, de-
priving them of advancement opportunities. There is
likely to be distorted pricing and signals through the
economy. Clarity on whether growth is stymied by
credit constraints resulting from corruption is essential.
This paper contributes to extant knowledge in four
important ways. First, to the best of our knowledge, our
paper is the first investigation of credit constraints in
SMEs from the perspective of corruption, using micro-
data, which are country-specific, rich and recent. We
extend the current literature by measuring corruption
perception, using a dummy variable, and corruption
intensity with a continuous variable of firm’s credit
constraint. Previous research often measures bribery as
a dummy variable and fails to capture adequately the
intensity of bribery. Second, we investigate the firm
characteristics and owner–manager’s interactions using
bribery in credit access. Firm and owner–manager char-
acteristics can influence the impact of bribery on firms’
credit access by easing or tightening indebtedness. Thus,
this study provides significant insights into the impact of
bribery on SMEs in emerging economies. Third, we use
an objective measure of financial constraint, which is
strongly correlated with macroeconomic indicators,
rather than indirect proxies. Recent studies
(Gorodnichenko and Schnitzer 2013;Hansenand
Rand 2014; Wellalage and Locke 2016,2017) use self-
reported measures to capture credit constraints, but such
self-reporting metrics are subject to potential measure-
ment errors. Fourth, the micro-econometric robustness
of our analysis is an advance, as we deal directly with
the issue thata firm’s explanatory variables are frequent-
ly related endogenously
2
with dependent variables. Our
2
An endogeneity problem occurs when an explanatory variable is
correlated with the error term (Wooldridge 2002). Endogeneity can
arise from the following sources: unobservable heterogeneity, simulta-
neity, omitted variable biases and reverse causality.
N. Hewa Wellalage et al.
use of an instrumental variable approach with a two-step
probit model controls for endogeneity bias, which is a
recurring econometric problem in credit constraint and
corruption studies. When omitted variable(s) cannot be
measured, the robust remedy for controlling
endogeneity is an instrumental variable regression,
which is the method we follow.
Our examination of the relationship between credit
constraints and corruption uses firm-level data for 7153
SMEs in India. We apply a direct approach to measuring
credit-constrained firms, using specific credit questions
in the World Bank Enterprise Survey (World Bank
Enterprise Surveys 2014). An instrumental probit model
analysis finds SMEs paying bribes are on average
68.2% more likely to be credit constrained than their
counterparts that do not pay bribes. Also, a bribe inten-
sity variableis significantly positively related with credit
constraints. Specifically, when bribe intensity increases
1%, the SME’s credit constraint increases 3.4%, when
all other factors are constant. This result supports the
‘sand in the wheel’concept.
Section 2of this paper reviews relevant research in
the literature and hypotheses development. Section 3
covers a discussion of data, variables, methods and
procedures used in the empirical study. Section 4con-
tains the results. Section 5explains the robustness tests
and Section 6concludes this paper.
2 Literature review
2.1 Indian SMEs and institutional environment
The International Monetary Fund (IMF) classifies India
as part of the Emerging and Developing Asia Group and
the World Bank categorises it as a lower middle-income
nation. SMEs play a significant role in India’seconomic
development through innovation, diversification and
employment generation. An estimated 29.8 million
SMEs in India (Improving Access to Finance for
Women-owned Businesses in India 2014) contribute
11.5% of GDP, 45% of industrial output and 40% of
exports and employ nearly 69 million people (Asian
Development Bank 2015).
Corruption and access to finance are the biggest
barriers to growth and development for SMEs in India
(Fungáčová et al. 2015; Honorati and Mengistae 2007;
Kato and Sato 2015). Despite their enormous economic
contribution and the likelihood they experience the
impact of bribery and corruption more acutely than
larger companies, there is surprisingly little research
focusing on SMEs in India. With fewer resources at
their disposal than larger enterprises, SMEs are particu-
larly vulnerable to bribery, and proportionately, more of
them may be affected.
Incidences of corruption and tax evasion in India
regularly attract media attention and fuel debate. The
problem of non-performing loans in Indian, especially
public sector, banks is indicative of the issue. When a
quota is established for the minimum percentage of
loans made that must go to SMEs, and also to agricul-
ture, it is likely SME owners will want to be in the quota
and potentially to be willing to pay an entry fee to get in
the door. Corruption, tax evasion and crimes have re-
sulted in an estimated US$462 billion in gross illicit
assets leaving India (Kar 2011). Further, Debroy and
Bhandari (2011) consider public officials in India might
be consuming 1.26% of the country’s GDP through
corruption. According to the International Property
Rights Index (IPRI) (2015),
3
India, with a score of
5.25/10, ranks 10th in Asia and Oceania and 62nd in
the world. The IPRI index value is indicative of a
complicated environment for both economic freedom
and the efficient operation of markets.
2.2 Hypotheses development
Corruption is a major concern in emerging economies,
because it influences growth and productivity (Mauro
1995;MéonandWeill2010;Wei2000). Since external
credit is a driving force for growth (Levine et al. 2000),
it is important to ascertain whether corruption affects
economic development indirectly via external credit
provision for firms. Theoretically, the effect of corrup-
tion on firms’access to credit follows two strands:
positive or negative. One line of thought in the literature
argues that corruption is likely to have beneficial effects
in emerging economies for firms suffering from restric-
tive private monopolies and government practices
(Voskanyan 2000). Corruption is seen as beneficial in
countries with poorly functioning institutions and
3
The IPRI is an annual comparative study that aims to quantify the
strength of property rights—both physical and intellectual—and to
rank countries accordingly. The IPRI scores and ranks each country
based on ten factors reflecting the state of its legal and political
environment, physical property rights and intellectual property rights.
The higherthe score of IPRI, the higher the strength of property rights.
Finland received the highest score of 8.3/10.
Firm bribery and credit access: evidence from Indian SMEs
defective bureaucracy (Wei 2000; Weill 2011). When
accessing external finance, such as bank loans, corrup-
tion allows firms to overcome bureaucratic processes
and unclear or complex regulations (Agrawal and
Knoeber 2001; Khwaja and Mian 2005), exemplifying
the grease the wheel concept.
The alternative view (Mauro 1995; Reinikka and
Svensson 2005) espouses a sand in the wheel concept
where corruption in lending contributes to a reduction in
afirm’s bank debt due to increasing cost to borrowers.A
bribe amounts to a tax on borrowers and constitutes an
obstacle to credit (Fungáčová et al. 2015). The core of
the debate between grease the wheel and sand in the
wheel lies in the combination of corruption with low-
quality governance. Poor definition and enforcement of
property rights, in the emerging economies, permits
corruption to grease the wheels of access to external
finance for SMEs. Ineffective legal enforcement of con-
tracts, weakregulations and difficulty in enforcing prop-
erty rights encourage private entrepreneurs to rely on
informal forms of security (Ahlstrom et al. 2000)while
actively seeking alternative governance structures and
contractual arrangements (Peng 2001). Consequently,
informal ties and relational governance tend to fill the
‘institutional void’. Research by Nguyen and Van Dijk
(2012), one of the limited number of country-specific
studies that drills down to the firm level, examines
corruption, growth and governance in private- versus
state-owned firms in Vietnam. They observe corruption
impacts private enterprise more adversely than state-
owned businesses. SMEs collectively lack established
lobby groups and political connections, and compared
to large enterprises, they may become exposed to heavy
bribe requests and extortion. Bribes reduce SMEs’in-
vestment and impact on their long-term survival
(Jovanovic 2002). Also, corruption favours a particular
class of people and creates inequality in opportunities
when accessing bank credit and other external sources
of finance (Mo 2001). Corrupt lending processes are
detrimental to the development of SMEs, as they restrict
their long-term credit access that in turn culminates in
restricting private sector activities and slow growth for
SMEs. The restriction of opportunity and productivity
leads to frustration and socio-political instability where
there is an inadequate formal institutional infrastructure
(Khanna and Palepu 1997).
In an efficient financial system, all capital should
earn very similar risk-adjusted returns. Distortions in
the intermediation system, such as corruption, rent
seeking and lobbying, will bias the capital-allocation
process (Khwaja and Mian 2005). SMEs choose to act
opaquely to mitigate the risk of governmental expropri-
ation or to lower the costs that they may face from
predatory governments forcing them to pay bribes. This
complicates how firms access external financing and
makes it harder to write enforceable contracts with
suppliers, because of moral hazards and adverse selec-
tion problems. When the risk of expropriation is high,
firms in greater need of transparency allocate capital less
efficiently and grow more slowly than those that func-
tion well at minimum levels of transparency (Durnev
et al. 2009). At the country level, King and Levine
(1993), Levine (1998) and Beck et al. (2000)showthat
differences in legal origins and efficiency explain dif-
ferences in financial access. The certainty of the law and
the opportunity to enforce legal rights in court impact
upon bank lending decisions and, thus, on the SMEs’
access to credit. However, SMEs’ability to access ex-
ternal credit is affected by the limited quantity and
accuracy of information available (Berger and Frame
2007; Mason and Brown 2013), which impedes the
assessment of their creditworthiness and access to credit
(Moro et al. 2014; Petersen and Rajan 1997). When
there is an environment with a weak law enforcement
system or corrupt legal system, financial institutions will
find it difficult to control borrower risk and recover the
loan in the event of default. As a consequence, financial
institutions will be more restrictive in lending ex ante,
increasing credit constraints.
Although recent research using micro-level data em-
phasises the relationship between corruption and access
to finance, the findings are inconclusive (Fungáčová
et al. 2015; Kato and Sato 2015). This may be due to a
causal relationship between corruption and access to
finance. On one hand, in a highly corrupt environment,
bribes act as a vehicle for access to limited resources,
such as bank credit, and financial institutions may allo-
cate funds for firms who are willing to pay bribes. On
the other hand, corruption fosters credit constraints,
especially for small firms. This is because in a corrupt
lending process, bank officials choose firms who are
willing to pay bribes instead of credible SMEs. This is
burdensome for SMEs relying on limited financial re-
sources. However, it has not been established whether
the relationship is causal and, if it were to be so, what the
driving factors might be. Does a lack of access to formal
finance encourage entrepreneurs to pay bribes to gain
access to the formal finance or does corruption prevent
N. Hewa Wellalage et al.
them from accessing formal finance? While keeping in
mind the causality effect we hypothesise:
H
1
:There is a negative relationship between pay-
ing bribes and level of credit constraints faced.
Although corruption is pervasive, most studies measure
bribery as a dummy variable, which may not sufficiently
capture bribe intensity (Van Vu et al. 2017; Zhou and Peng
2012). Few studies measure bribery as a continuous vari-
able, which may be due to a lack of availability of corrup-
tion data (Chen et al. 2013;VuandVanLe2016). Never-
theless, it appears that offering/paying higher bribes can
have an effect on credit access, with consequent costs and
benefits to the firm. As an example, Chen et al. (2013),
adopting a country-level study of access to bank credit,
observe a positive link between a proxy for the amount of
bribes the firm provides and its smooth operations, and
increased access to external credit. As only well-
performing firms can afford to pay bribes, Bgood [com-
mercial] principles are often complied with because they
are compatible with the incentives of individual bankers
through the grease the wheel mechanism^(Chen et al.
2013, p. 2544). This, of course, ignores the distribution of
who receives the gains and the effective pricing of risk.
The common insight is that firms in emerging mar-
kets are more likely to pay bribes to bend or overcome
regulatory constraints (Harstad and Svensson 2011).
The average bribe, as a percentage of annual sales (bribe
intensity), may be higher for SMEs when compared to
larger firms. If all firms operate in the same institutional
environment, then the extent of a bribe depends on the
bargaining power of firms (Francisco and Pontara
2007). In a bargaining model where the size of the bribe
depends upon negotiation between the firm and a
bureaucrat, Svensson (2003) suggests the firm’sbribery
amount depends on its Bability to pay.^Firms with high
profits, or firms expecting high profits in the near future,
have weak bargaining power to resist the demand for
bribe payments. Lenders wishing to extract a bribe see
profitable firms as capable of paying. Clarke and Xu
(2004) find that firms with relatively higher profitability
can afford to pay higher bribes to get things done.
Where firms have high operating costs, Rand and Tarp
(2012) find bribery amounts available to ease access to
finance are significantly reduced.
A visibility argument proposed by Vu and Van Le
(2016) suggests a firm’s visibility affects its bribe inten-
sity. Small firms may need to pay less to the bureaucrat
to bend the rules to get credit access because their
visibility is low vis à vis their larger counterparts. Nev-
ertheless, proponents of grease the wheel claim that high
bribes lead to an efficient process for allocating credit,
since the most efficient firms will be able to afford to
pay the highest bribes (Lui 1985). On the other hand,
sand in the wheel suggests high bribe payments can
contribute to a reduction in a firm’sbankdebtdueto
increasing the cost of the loan. In these cases, a bribe
amounts to a tax on SME owners and constitutes an
obstacle to credit (Fungáčová et al. 2015).
The issue of whether the size of bribes enhances
credit access is primarily an empirical question. It is
important to determine the direction of causality. While
keeping in mind the causality effect we hypothesise:
H
2
:There is a negative relationship between the
amount of bribe offered by SMEs and credit
constraints.
2.3 Moderation effect of f irm characteristics
and owner–manager characteristics on bribes and credit
constraints
In the absence of effective economic institutions, bribes
may be channels to access credit for SMEs. However, the
impact of bribery on a firm’s credit access may be driven
by firm-specific characteristics and owner/manager char-
acteristics. Therefore, the variation across firms in their
credit access may be explained by (i) the variation across
firms in bribery payments and (ii) firm-specific charac-
teristics and owner/manager characteristics.
SME ownership can moderate the bribe and credit
access relationship. Female ownership, foreign
ownership and government ownership may have a
mediation effect on the bribes and credit constraints
relationship. Bellucci et al. (2010) suggest the informa-
tion from businesses owned by females is limited and
less reliable in weak institutional environments due to
information asymmetry. In this environment, an adverse
selection problem works against loan applications from
creditworthy female-owned SMEs. A bribe, in such
instances, may act as a vehicle for obtaining access to
external credit for the SME. From a financial institu-
tion’s perspective, to bend the rules for a creditworthy
female-owned SME is more comfortable than allocating
credit to a less creditworthy male-owned SME for the
same size bribe (Wellalage and Locke 2016).
Firm bribery and credit access: evidence from Indian SMEs
Foreign ownership may not influence access to credit
in the same way as government ownership. A lax or
undeveloped institutional framework that does not deter
corruption is generally observed in public administra-
tion (Fungáčová et al. 2015). Consequently, government
ownership may positively moderate the bribe and credit
access relationship when compared to foreign owner-
ship. Hence, having foreign ownership may weaken the
relation between bribery and SMEs’credit access.
Firm characteristics, such as firm size, firm age and
firm level innovation, may also moderate the bribe and
credit constraints relationship in SMEs. Additionally,
we argue that large firms that have grown over the years
have more political capital than young nascent firms;
accordingly, firm size and age may moderate bribes and
the credit access relationship. Additionally, large firms
that have been around longer than the recently started
firms are more likely to know the loop-holes and have
learned to either avoid them or find an alternative way to
deal with cumbersome situations.
Innovating SMEs experience greater difficulty
accessing external finance than their large counterparts
(Hutton and Lee 2012;MasonandBrown2013;Mina
et al. 2013), which is likely to be attributable to high
levels of uncertainty, risk and extremely skewed profits
(Scherer and Harhoff 2000). Additional opaqueness
emerges where innovative firms intentionally maintain
information asymmetries to avoid revealing information
to competitors (Hall and Lerner 2010; Mancusi and
Vezzu l l i 2010). According to the above arguments, we
expect firm-level innovation may positively moderate
the bribe and credit access relationship in SMEs.
3 Data, method and analysis
3.1 Data
Data drawn from the World Bank, 2014 Enterprise
Surveys,
4
uses information from registered firms. The
surveys include representative random samples of firms
across the world, using the same core questionnaire and
the same sampling method. The World Bank, 2014
Enterprise Surveys incorporate interviews with Indian
business owners and top managers from 9281 small,
medium and large enterprises from June 2013 through
December 2014. After excluding large firms, there are
7800 SME observations. We also exclude private for-
eign individuals, company-owned or organisation-
owned firms, government or state-owned firms and
firm-owned firms.
5
These exclusions, following Chen
et al. (2013), remove a problem where different owner-
ship arrangements may impact the SME arrangements
for access to credit. There are 7153 firm-level observa-
tions remaining.
The Enterprise Surveys collect both qualitative and
quantitative information at the firm level. Details regard-
ing each business include business location and business
type, owner/manager demographics and characteristics
(owner/manager gender, top manager experience), busi-
ness financial information (net profit reinvestment pro-
portion, total expenses) and information relating to firm
accessibility to external financing sources. In addition,
the survey provides responses to questions concerning
security of property rights, reliability of legal systems
and the level of corruption.
Dependent variable Our dependent variable (Con-
straints) is derived from the following survey questions:
(i) At this time, does this establishment have an over-
draft facility
6
?
(ii) At this time, does this establishment have a line of
credit or loan from a financial institution?
From these two questions we calculate Constraints as
value 1 if the firm does not have (i) an overdraft facility
or (ii) a line of credit or loan from a financial institution:
otherwise 0, following the taxonomy of Love and
4
The World Bank’s Enterprise Surveys offer an expansive array of
economic data on 130,000 firms in 135 countries. The World Bank
Enterprise Survey website provides details as to how the surveys are
conducted (http://www.enterprisesurveys.org). An Enterprise survey is
a firm-level survey of a representative sample of an economy’sprivate
sector. The surveys covera broad range of business environment topics
including access to finance, corruption, infrastructure, crime, competi-
tion and performance measures.
5
Creating a dichotomous variable for private foreign individuals,
company- or organisation-owned firms, government or state-owned
firms and other firm-owned firms groups (denoted as group 1) and
other firms (denoted as group 2), we check the credit accessibility
differences using ANOVA, which confirms that different ownership
forms do not exhibit the same impact on credit accessibility. Hence, we
have excluded group 1.
6
Overdraft facility is a short-term credit agreement with the bank. This
facilitates an account holder to use or withdraw more than they have in
their account, without exceeding a specified maximum amount. An
overdraft facility can be offered on a secured (assets are pledged as
security) or unsecured (no assets are pledged as security) basis.
N. Hewa Wellalage et al.
Martínez Pería (2014). However, this Constraints proxy
has two limitations. First, some SMEs which do not
need borrowings are coded as credit constrained. Sec-
ond, SMEs may have overdraft facility or other borrow-
ing facility, that is close to being fully utilised and the
SMEs need more credit to run their operations smoothly,
but are classified as not credit constrained. Nevertheless,
this objective metric captures credit constrained firms in
a non-subjective manner; thus, it increases the reliability
of the Constraints variable. Given the limitation of the
data, our objective proxy of credit Constraints is
appropriate.
7
Independent variables The main independent variables
(Bribe_D and Bribe_Intensity) are derived from the
following two survey questions:
(i) Inyourexperienceisittruethatestablishmentsare
sometimes required to make gifts or informal pay-
ments to public officials to ‘get things done’with
regard to customs, taxes, licences, regulations, ser-
vices, etc.?
(ii) On average, what percentage of total annual sales,
or estimated total annual value, do establishments
like this one pay in informal payments or gifts to
public officials for this purpose?
In line with best practice in corruption studies, survey
question (i) refers to ‘establishments like this’to help
elicit truthful responses (Billon and Gillanders 2016).
However, this self-reported measure is subject to poten-
tial measurement errors, arising from business owners’
reticence in stating the correct value of bribes.
Our two metrics are:
1) Bribe_D: captures paying a bribe or not, taking
value 1 if the firm makes gifts or informal payments
to public officials to ‘get things done’with regard to
customs, taxes, licences, regulations, services, etc.;
otherwise 0.
2) Bribe_Intensity: a continuous variable, indicating
percentage of total annual sales forming informal
payments or gifts to public officials. A calculation
of the percentage of annual sales occurs where the
firm provides only estimates of total annual value.
Control variables In line with prior studies, we control
for firm characteristics and firm ownership factors that
may affect credit constraints. The firm characteristic
variables are firm size, firm core activities, firm location,
firm legal status, age, exporting ability and firm-level
innovation.
Prior studies note that the firm size is an important
determinant of firm credit constraints. The most fre-
quently cited factor that exacerbates credit constraints
of small firms is ‘opaqueness’(Berger and Frame 2007;
Berger and Udell 1998). Large firms with more tangible
assets tend to have greater access to long-term debt
(Burkart and Ellingsen 2004) compared to small firms
with fewer assets to use as collateral. Similarly, young
SMEs face greater difficulties in accessing external fi-
nance and incur higher costs, due largely to information
asymmetry between the banks and the SMEs
(Chakravarty and Xiang 2011). Using World Bank data,
Chakravarty and Xiang (2011) report that older and
larger firms enjoy easier access to external financing
when compared to younger and smaller firms. There-
fore, controlling for firm size and firm age is likely to be
important to our study.
The legal form ofa business can also affect the access
to external finance and the capital structure decisions of
SMEs (Yildirim et al. 2013). As an example, Coleman
(2000) reports that incorporation is positively associated
with greater use of bank financing compared to sole
proprietorship. Moreover, engaging in international
trade will make SMEs less vulnerable to fluctuations
in domestic demand, improving the export-oriented
firm’s financial stability and profitability (Yildirim
et al. 2013). Therefore, export-oriented SMEs may have
higher access to external financing compared to non-
exporting SMEs. Hence, we include firm legal status
and export status as control variables in this study.
Firms in the manufacturing industry face different
environmental and economic conditions and, thus,
tend to have unique variance of earnings and sales
compared to other industries. Barbosa and Moraes
(2004) argue that the relationship between industry
classification and financial leverage is based on an
assumption that industry classification is a proxy for
business risk. Therefore, controlling for industry ef-
fect is important in our study. Also, firm-level inno-
vation has a significant positive impact on external
financing (Hall and Ziedonis 2001;Baumand
Silverman 2004). We include firm-level innovation
as a control variable.
7
In Section 5—Robustness, we used alternative proxies of credit
constraints and report the robustness of results.
Firm bribery and credit access: evidence from Indian SMEs
Capital_City is a categorical variable. It is included
in the model to capture the effect of the geographical
location of firms. The literature suggests that firms
located in capital cities vis à vis firms located in non-
capital cities have easier access to markets for both
inputs and outputs, reducing their marginal costs of
production (Kumarasamy and Singh 2018). Availability
of a superior infrastructure reduces costs and so in-
creases capital city firms’profitability (Elbadawi et al.
2001), leading to easier access to external finance.
In a weak institutional environment, the characteris-
tics of a SME owner–manager play a significant role in
accessing credit (N. Wellalage and Locke 2016). We
control owner–manager characteristics in our analysis.
Various studies show that gender of the owner–manager
plays an important role in accessing credit (De Mel et al.
2009; Wellalage and Locke 2016). Prior results suggest
discrimination against female entrepreneurs exists in
African countries (Richardson et al. 2004; Cavalluzzo
et al. 2002). Anecdotal evidence suggests various char-
acteristics of female-owned businesses lead financial
institutions to refuse credit to female-owned SMEs.
These characteristics include things such as women’s
entrepreneurship in smaller firms and riskier ventures
(Coleman 2000), young firms (Riding and Swift 1990)
and being home-based or operating in the informal
sector (Dollar et al. 2005).
Nofsinger and Wang (2011) argue the experience of
the owner–manager plays a crucial role in overcoming
some of the problems that hinder SME access to external
finance, including information asymmetry and moral
hazard. From the external finance providers’perspec-
tive, experienced owner–managers are better performers
than less-experienced owner–managers. It is rational to
factor experience into the process of evaluating the
creditworthiness of SMEs. Therefore, we control for
owner-manager characteristics to obtain a better under-
standing of the relationship between bribes and credit
constraints.
Tab le 1provides the definition and measurement of
variables included in the models.
3.2 Model
This study applies a discrete choice probit model for
binary choice (yes: no) responses to the credit con-
straints question. In the binary probit model, credit
constraint is shown as 1, while access to credit is 0.
The probit model is:
Tabl e 1 Definition and measurement of variables included in the
models
Variable Definition Measurement
Dependent variable
Constraints Variable captures
constraints when a firm:
does not have (i) over-
draft facility and (ii) a
line of credit or loan
from a financial institu-
tion
1 = yes; 0 =
otherwise
Independent variable
Bribe_D Variable determines the
existence of a bribe.
1 = yes; 0 =
otherwise
Bribe_Intensity Variable indicates
percentage total annual
sales, paid in informal
payments or gifts to
public officials.
Percentage
Bribe_D ×
Small
Interactive variable
captures the interaction
between existence of
bribe and firm size
(measured by Small).
Dichotomous
variable
Bribe_Intensity
×Small
Interactive variable
captures the interaction
between value of bribe
and firm size (measured
by Small).
Continuous
variable
Bribe_D ×
Female
Interactive variable
captures the interaction
between existence of
bribe and existence of
female ownership of
firm (measured by
Female).
Dichotomous
variable
Bribe_Intensity
× Female
Interactive variable
captures the interaction
between value of bribe
and existence of female
ownership of firm
(measured by Female).
Continuous
variable
Bribe_D ×
Inno
Interactive variable
captures the interaction
between existence of
bribe and innovation
status of the firm.
Dichotomous
variable
Bribe_Intensity
× Inno
Interactive variable
captures the interaction
between value of bribe
and innovation status of
the firm.
Continuous
variable
Bribe_D ×
Foreign_O-
wner
Interactive variable
captures the interaction
between existence of
bribe and foreign
ownership of the firm
Dichotomous
variable
N. Hewa Wellalage et al.
pY¼1jX1;X2;…Xk
ðÞ¼ΦzðÞ
¼Φβ
0þβ1:X1þβ2:X2þ…þβκXk
ðÞ
where pis the outcome of the dummy (0–1) variable for
the k
th
observation, Φis the standard cumulative normal,
X
k
is the vector of explanatory variables for observation
kand βis the vector of coefficients to be estimated. The
probit coefficients are not directly interpretable, but
marginal effects for continuous variables are available
as:
∂∅xβ
∂xk
j¼∅xβ
βk
Tabl e 1 (continued)
Variable Definition Measurement
(measured by
Foreign_Owner).
Bribe_Intensity
×
Foreign_O-
wner
Interactive variable
captures the interaction
between value of bribe
and existence of foreign
ownership of the firm
(measured by
Foreign_Owner).
Continuous
variable
Bribe_D ×
Governmen-
t_Owner
Interactive variable
captures the interaction
between existence of
bribe and government
ownership of the firm
(measured by
Government_Owner).
Dichotomous
variable
Bribe_Intensity
×
Governmen-
t_Owner
Interactive variable
captures the interaction
between value of bribe
and existence of
government ownership
of the firm (measured by
Government_Owner).
Continuous
variable
Bribe_D × Age Interactive variable
captures the interaction
between existence of
bribe and firm age.
Dichotomous
variable
Bribe_Intensity
×Age
Interactive variable
captures the interaction
between value of bribe
and firm age.
Continuous
variable
Control variable
Capital_City Firm from capital city 1 = yes; 0 =
otherwise
Manufacturing Firm from manufacturing
industry
1 = yes; 0 =
otherwise
Small Firm has 5 to 19
employees.
1 = yes; 0 =
otherwise
Medium Firm has 20 to 99
employees.
1 = yes; 0 =
otherwise
Sole-Prop Firm legal status is sole
proprietorship.
1 = yes; 0 =
otherwise
Female Firm has female owner(s). 1 = yes; 0 =
otherwise
lnage Log number of years since
firm has been
established
Year (s )
lntop_exp Log number of years’
experience of the firm’s
manager
Year (s )
Exporter Firm exported 10% or
more sales directly or
indirectly.
1 = yes; 0 =
otherwise
Firm has at least 1% of
foreign ownership.
1 = yes; 0 =
otherwise
Tabl e 1 (continued)
Variable Definition Measurement
Foreign_O-
wner
Governmen-
t_Owner
Firm has at least 1% of
government ownership.
1 = yes; 0 =
otherwise
Inno Firm has either (i) intro-
duced new or signifi-
cantly improved prod-
ucts or services; or (ii)
introduced new or sig-
nificantly improved
methods of manufactur-
ing products or offering
services.
1 = yes; 0 =
otherwise
Personal_Loan Firm owner(s) have
outstanding personal
loans that are used in the
business.
1 = yes; 0 =
otherwise
Instrument variable
Weak_Judici-
ary
The level of judiciary
system corruption, based
on the answer to the
question: Does SME
owner strongly disagree
or tends to disagree that
the court system is fair,
impartial and
uncorrupted?
1 = yes; 0 =
otherwise
Sec_Avg_Bribe For each individual SME,
firm bribery is averaged
across all other firms
within the same locality
and the same sector but
excludes the firm itself.
Percentage
Note: All variables are sourced from the World Bank, 2014 Enter-
prise Surveys (http://www.enterprisesurveys.org)
Firm bribery and credit access: evidence from Indian SMEs
where X
k
is a vector of independent variables (kis the
number of independent variables), βis the vector of
estimated coefficients and ϕis the normal density func-
tion. For dummy variables, the discrete change in prob-
ability when the dummy variable switches from 0 to 1 is
calculated as Φ(X
1
β)−Φ(xβ), where X
1
=X
0
=X
k
ex-
cept that the i
th
elements of X
1
and X
0
are set to 1 and 0,
respectively.
3.3 Analysis
Empirical findings in prior research show mixed results,
which may, in part, be due endogeneity, arising from
measurement errors, auto-correlated error terms, simulta-
neous causality and omitted variables. Improved
econometric methods, over time, led studies using panel
data as a solution for endogeneity, e.g. the analysis by Van
Vu et al. (2017) of the relationship between corruption,
types of corruption and financial performance in Vietnam-
ese SMEs. Fixed-effect models and models in differences
provide efficient solutions when the unobservable charac-
teristics are time-invariant (Wooldridge 2015).
In our sample, the firm’s unobserved characteristics
may be time-varying and firm-fixed effects are insuffi-
cient to eliminate spurious relationships between cor-
ruption and credit constraints. The instrumental variable
(IV) method controls for possible endogeneity and a
Smith and Blundell (1986) test provides a simple check
for endogeneity in limited information contexts simul-
taneous with limited dependent variable models. This is
appropriate for considering endogeneity between the
credit constraint proxy and Bribe_D and Bribe_Intensity
variables. The null hypothesis of the Smith and Blundell
(1986) test specifies Bribe_D and Bribe_Intensity ex-
planatory variables. The null hypothesis specifies all
explanatory variables as being exogenous to the credit
constraint proxy. To avoid the pernicious effects of weak
instruments,
8
the explanatory power and the exclusion
restrictions of the instruments are tested. The test rejects
the null hypothesis that Bribe_D and Bribe_Intensity
variables are exogenous at a 1% significance level.
Valid instrumental variables need to satisfy two con-
ditions: (i) they are uncorrelated with error term (u), and
(ii) they are moderately associated with the suspect
endogenous variables, once the other independent vari-
ables are controlled. Since (u) is unobservable, the most
common guideline for determining whether the selected
instruments are relevant in the first stage equation is the
F-test for joint significance. If the F-test exceeds the
value 10, then the selected instrument is accepted as
relevant.
In a judiciary system, fairness and how it could affect
business is an instrument for Bribe_D (Judiciary).
Fernandez and Kraay (2005) and Honorati and
Mengistae (2007) link corruption as a proxy for the
quality institutions supporting property rights, while
Cull and Xu (2005) and Johnson et al. (2000) use the
judiciary system as a proxy for the quality of property
rights. Accordingly, consistent with the expectation that
access to finance depends on the level of judiciary
system fairness, we use Judiciary as a proxy for the
Bribe_D. It has a value of 1 if the SME owner strongly
disagrees or tends to disagree with the statement in the
World Bank survey, Bthat the court system is fair, im-
partial and uncorrupted,^otherwise it takes a value 0.
The F-test for instrument relevance for our instrument
variable Judiciary is 21.54, enhancing confidence that
the instrument is appropriate. Although given the con-
straints, the Judiciary proxy addresses the endogeneity
problem in relation to error terms, and reverse causality
is not fully ruled out.
Following Fisman and Svensson (2007), Qi and
Ongena (2018) and Wellalage et al. (2018), this study uses
locality-sector average of bribery (Local_Sec_Avg_Bribe)
as an instrument for Bribe_Intensity. Following the Qi and
Ongena (2018) technique for each individual SME, firm
bribery is averaged across all other firms within the same
locality and the same sector but excludes the firm itself.
Local_Sec_Avg_Bribe is exogenous to the firm and is
likely determined by (i) the business mode of the sector
and (ii) the rent-seeking ability of the bureaucrats, which
are exogenous to the firm. So, instrumenting
Bribe_Intensity by Local_Sec_Avg_Bribe can minimise
omitted unobservable errors that are correlated with bribe
intensity at the firm, but not the locality-sector level. The
F-statistic is distant from the rejection cut-off of 10 (F-test
is 28.29), enhancing confidence that the instrument set is
appropriate.
On the basis of the diagnostic test values, the pro-
posed instruments are well-specified and the economet-
ric findings are robust. It is suitable to infer that the
instrumental variable probit model (i.e., IV probit) is
appropriate.
8
The smaller the correlation between the instrument and the endoge-
nous variable, the larger the standard errors of the instrumental variable
estimatorwill be. Furthermore, lowcorrelation between the instrument
and the endogenous variable can drive towards asymptotic-biased
estimators (Wooldridge 2002).
N. Hewa Wellalage et al.
4Results
Table 2provides a list of the variables included in the
regression analysis and their respective descriptive sta-
tistics. We find that approximately 38% of Indian SMEs
are credit constrained. This is consistent with Beck and
Demirguc-Kunt (2006), who in their multi-country
study, state that 39% of small firms and 36% of
medium-sized firms report credit accessibility as a major
constraint. A minimum (0) and maximum (1), combined
with a median of 0 and mean of approximately .3792,
for Constraints suggests significant variability and
skewness of credit constraints in our sample. The vari-
able Bribe_D has a mean (median) of .2357 (0), indi-
cating that approximately 24% of SMEs make informal
payments to get things done in India. Bribe_Intensity
shows a mean (median) value of .0007 (0). This aligns
with Vu and Van Le (2016), noting low mean values for
paying bribes and bribe intensity variables in Vietnam-
ese SMEs. A plausible reason for the low values of
Bribe_D and Bribe_Intensity is that smaller firms are
home-based and do not need any registration or other
activities that require government or other third-party
authorisation. Nevertheless, the number of observations
for Bribe_D and Bribe_Intensity suggest that SME
owners are reluctant to provide expropriation details
concerning their businesses. Only 6% of SMEs are
located in the state or union territory capitals of India,
9
and 77% of SMEs belong to the manufacturing industry.
Our sample consists of approximately 43% small
firms and 57% medium-sized firms. Fifty-four percent
of firms are sole proprietorships and only 13% of SMEs
have female owners. The mean (median) value of the
firm age is 19 (16) years. The results for the variable
Manager_exp show that the mean (median) value of
experience is approximately 13 (10) years. However,
the values for manager experience range from less than
1 to 65 years. Approximately 10% of the sample firms
export sales directly or indirectly. Foreign ownership
and government ownership are rare in this sample.
Specifically, only .6 and .11% of the sample firms have
at least 1% foreign ownership and government owner-
ship, respectively.
The Inno variable indicates almost 56% of the sample
SMEs are innovators, as they had introduced new or
significantly improved products or services or methods
of manufacturing products in the previous triennium. The
mean value of Personal_Loan indicates that approximate-
ly 15% of SMEs have outstanding personal loans as a
source of funds for the firm. These descriptive character-
istics provide additional dimensions and a potential for
richer analysis when examining the relationship between
credit constraints and corruption in India. The non-
parametric distributions of the variables are important.
The variance inflation factor (VIF), checking for the
multicollinearity for all explanatory and dependent vari-
ables, is 1.62, indicating that our results are not affected
by a multicollinearity issue.
10
As a robustness test, the
Spearman rank correlation matrix
11
is computed and it
shows a significant correlation between credit constraints
and bribe proxies,
12
supporting a claim that corruption
interacts with firm credit constraints in SMEs.
Tab le 3, panel A, presents probit estimations of credit
constraints and corruption relationship considering an
existence of bribes (Bribe_D). Panel B provides probit
estimations of credit constraints and corruption relation-
ship, considering bribe intensity as a continuous vari-
able (Bribe_Intensity). A Smith and Blundell (1986)test
for exogeneity suggests correlation in the unobserved
covariates that determine both the corruption variables
(i.e. Bribe_D and Bribe_Intensity) and credit con-
straints, pointing to the advantages of using IV probit
and IV probit marginal effects.
The following discussion of results consider the IV
probit and IV probit marginal effect. The instrument
variable of Bribe_D (i.e. Weak_Judiciary)ispositively
correlated with SMEs’credit constraints and are signif-
icant at the 1% level. In particular, column III in panel A
reports that a Bribe_D variable change from 0 to 1 (i.e.
not paying bribes to paying bribes) increases the
standardised IV probit index by 4.8 standard deviations.
Further, considering the marginal effect, as shown in
panel A column IV, paying bribes increases the proba-
bility of SMEs’credit constraints by approximately
9
India consists of 29 states and 7 union territories. The National
Capital Territory of Delhi is the administrative capital territory of India
and Mumbai is the financial, commercial and entertainment capital of
India.
10
Gujarati and Porter (2003) suggest that there is no evidence of
multicollinearity if the VIF (variance inflation factor) value is below
the critical level of 10.
11
If both variables are dichotomous, Pearson correlation = Spearman
correlation = Kendall’s tau. Most of our model variables are
dichotomous.
12
The results are not reported to save space but are available from the
authors upon request.
Firm bribery and credit access: evidence from Indian SMEs
68.24%. The instrument variable of Bribe_Intensity (i.e.
Sec-Avg-Bribe) is positively correlated with SMEs’
credit constraints and significant at the 1% level. Col-
umn VII in panel B reports a 1% increase in Bribe
Intensity results in a 2.720 standard deviation increase
in the predicted probit index. Further, the marginal
effect, in column VIII, indicates that a 1% increase in
Bribe_ Intensity leads to a 3.43% increase in credit
constraints for SMEs paying a bribe vis à vis SMEs
not paying bribes. Further, the IV coefficient of paying
bribes (Bribe_D) is greater than the IV coefficient of
proxy for the size of the bribe (Bribe_Intensity). This
indicates that the impact of the existence of a bribe is
higher than the bribe intensity for SMEs in credit-
constrained conditions.
These findings fit with both the supply side and the
demand side arguments concerning credit constraints.
The supply side argument espoused by La Porta et al.
(1997) claims that in the case of default, corruption
reduces law enforcement by the courts in cases of bor-
rowers defaulting. When corruption is high, financial
institutions find it harder to control borrower risk and
recover loans. Therefore, financial institutions reduce
their lending to SMEs, which are mostly in a high-risk
category. The demand side argument regards bribes
acting as a tax that increases the cost of a loan to the
borrower, that is, the SME and, more specifically, the
SME owner. Also, if bribery leads to an erosion of the
efficient allocation of resources, then these, as bribery
costs, are likely to lower SME profits. Financial institu-
tions, as they perceive SMEs as risky because of low
profitability ventures, will deny them credit.
Our results reject both hypothesis 1 (H
1
:There is a
negative relationship between paying bribes and level of
credit constraints faced) and hypothesis 2 (H
2
:There is
a negative relationship between the amount of bribe
offered by SMEs and credit constraints).Corruption
imposes an additional barrier for SMEs in India when
accessing external finance, which is consistent with the
sand in the wheel concept.
Tab le 3presents a summary of the effect of interac-
tive variables and control variables on credit constraints.
Panels A and B indicate that the mean credit constraint
for small firms (Small) is higher than for medium-sized
Tabl e 2 Descriptive statistics
Variable Obs Mean Median Min Max
Dependent variable
Constraints 7153 .3792 0 0 1
Independent variable
Bribe_D 7153 .2357 0 0 1
Bribe_Intensity 6158 .0007 0 0 .4057
Control variable
Capital_City 7153 .0597 0 0 1
Manufacturing 7153 .7664 1 0 1
Small 7153 .4285 0 0 1
Medium 7153 .5716 1 0 1
Sole_Prop 7153 .5405 1 0 1
Female 7153 .1319 0 0 1
Firm_age 7141 18.97 16 1 141
lnage 7141 2.691 2.772 0 4.94
Manager_exp 7130 13.12 10 1 65
lntop_exp 7130 2.33 2.302 0 4.17
Exporter 7153 .1004 0 0 1
Foreign_Owner 7153 .0060 0 0 1
Government_Owner 7153 .0011 0 0 1
Inno 7153 .5581 1 0 1
Personal_Loan 7153 .1469 0 0 1
N. Hewa Wellalage et al.
Tab le 3 Probit estimation results of credit constraints and corruption relationship in SMEs in India
Variable Panel A Panel B
Probit Marginal effect IV probit IV marginal effect Probit Marginal effect IV probit IV marginal effect
(I) (II) (III) (IV) (V) (VI) (VII) (VIII)
Bribe_D .0666*** (.0836) .0228*** (.0286) ––
Bribe_Intensity –––3.185** (.8945) 1.095 (3.076)
±Weak_Judiciary –4.825*** (.3412) .6824*** (.2571) ––
±Sec_Avg_Bribe –––2.720*** (.0300) .0343*** (.0109)
Bribe_D × Small −7.052*** (.1875) −.0720*** (.0260) −1.934*** (.1190) −.2108** (.0763). ––––
Bribe_D × Female −.2417** (.1037) −.0825** (.0354) −.8788*** (.0880) −.0826** (.0444) ––––
Bribe_D × Inno −.0252 (.0756) −.0086 (.0258) −2.174*** (.1674) −.0022*** (.1208) ––––
Bribe_D × Foreign_Owner .4657 (.6062) −.0823** (.0353) −1.247*** (.4810) .1371 (.2344) ––––
Bribe_D × Government_Owner −.2116 (2.910) .1590 (.2069) 2.169 (2.450) −.0177 (1.008) ––––
Bribe_D × Age .0049** (.0028) .0017** (.0009) −.0911*** (.0075) .0013 (.0054) ––––
Bribe_Intensity × Small –––– 7.140*** (1.226) 1.401 (.0459) 1.153*** (.0320) .0574* (.0095)
Bribe_Intensity × Female –––– −5.673 (.2535) −2.478 (.1775) 2.125* (1.211) .0774 (.0192)
Bribe_Intensity × Inno –––– −3.857** (3.990) −1.041** (6.705) −6.332*** (.8495) −.2354*** (.0078)
Bribe_Intensity ×
Foreign_Owner
++
––––
Bribe_Intensity × Government_Owner
++
––––
Bribe_Intensity × Age –––– .0053** (.0025) .0018** (.0008) .0050** (.0026) .0042*** (.0030)
Capital_City .4388*** (.0663) .1499*** (.0224) .1914** (.0794) .1651*** (.0405) .5007*** (.0719) .1722*** (.0244) .1142 (.1682) .0288*** (.2281)
Manufacturing .3001*** (.0377) .1025*** (.0127) .2428*** (.0436) .0943*** (.0189) −.3069*** (.0397) −.1056*** (.0135) .0055 (.0823) .058 (.0600)
Small
+
.3980*** (.0367) .1359*** (.0122) .6433*** (.0349) .1352*** (.0129) .3651*** (.0350) .1255*** (.0117) −.0949 (.1231) .0426*** (.0109)
Sole-Prop .3304*** (.0342) .1129*** (.0114) .1909*** (.0487) .1089*** (.0234) .3358*** (.0368) .1154*** (.0124) .1248 (.1112) .0120*** (.0113)
Female .0052 (.0610) .0018 (.0208) −.2002*** (.0511) −.0088*** (.0229) −.0405 (.0566) −.0140 (.0194) −.2819*** (.0586) −.0061* (.0258)
lnage .0374 (.0252) .0128 (.0086) .3463*** (.0281) .0109 (.0178) .0449** (.0253) .0153* (.0087) −.0413 (.0338) .0073 (.2687)
lntop_exp −.1041*** (.0235 −.0355*** (.0080) .0169 (.0267) −.0356** (.0135) −.1143*** (.0254) −.0393*** (.0087) −.0289 (.0573) −.0186** (.5614)
Exporter −.3561*** (.0591) −.1216*** (.0200) −.1603** (.0630) −.1147*** (.0308) −.2969*** (.0659) −.1021*** (.0226) −.1296 (.1069) −.2120 (.6074)
Foreign_Owner −.3982* (.2374) −.1360* (.0811) −.1703 (.1833) −.1369 (.0859) −.5323** (.2590) −.1831** (.0900) −.1321 (.2627) −.2584 (.4061)
Government_Owner .5608 (.4882) .1915 (.1667) .5355 (.4093) .1921 (.1683) .2523 (.5499) .0867 (.1891) .6469 (.3827) .1286 (.1013)
Inno −.3777*** (.0364) −.1290*** (.0121) .3525*** (.0903) −.1300** (.0559) −.3831*** (.0349) −.1317*** (.0116) −.2351** (.1124) −.1869*** (.5614)
Personal_Loan −.5781*** (.0490) −.1974*** (.0163) −.2443*** (.0822) −.1918*** (.0429) −.5306*** (.0527) −.1825*** (.0176) −.1595 (.1841) −.0407*** (.2255)
Pseudo-R
2
.0961 .0950
Log-likelihood −4271.09 −2295.74 −3702.61 −6478.87
Sample size 7118 7118 6913 6913 6131 6131 6131 6131
Wald test of exogeneity (p_value) 21.02*** 7.180***
Firm bribery and credit access: evidence from Indian SMEs
firms. Panel A and panel B results align with Beck and
Demirguc-Kunt (2006) and Schiffer and Weder (2001),
who find that small firms face greater constraints than
medium and large counterparts. It may be that in emerg-
ing markets moral hazard problems are so rampant that
smaller firms cannot persuade external finance pro-
viders to finance them (Wellalage and Locke 2017).
Further, greater opacity is associated with greater con-
straints on small firms. Although the interactive variable
Bribe_D × Small indicates that bribe-paying small firms
experience slightly less credit constraints than non-
bribe-paying small firms, when measuring bribes as a
continuous variable, the interactive variable Bribe In-
tensity × Small reveals that the credit constraints of the
bribe-paying small firms is higher than for non-bribe-
paying small firms.For firms, a bribe is an additional
payment to government, analogous to a tax, suggesting
these bribery costs are likely to lower profits of SMEs.
Financial institutions, as they perceive small firms as
risky because of low profitability ventures, will deny
them credit.
Tab le 3panels A and B indicate that the mean credit
constraint for female-owned SMEs is lower than that for
male-owned SMEs. This may be due to preferential
treatment of female entrepreneurs in India’smicro-
credit markets (Wellalage and Locke 2017) where fe-
male owners have greater access to external financing.
The interactive variable Bribe_D × Female in panel A
demonstrates that the mean credit constraint of bribe-
paying female-owned SMEs is approximately 9% lower
than for non-bribe-paying female-owned firms (Sum of
coefficients of Bribe_D × Female β−.0826 and Fe-
male β−.0061).
The one possible reason is that mainly female-owned
businesses are concentrated in industry sectors where
firms are smaller in size and in which cash sales pre-
dominate (Wilson and Tagg 2010), in contrast to
growth-oriented male-owned SMEs that reinvested their
profit. In such a scenario, the female-owned businesses
appear to have higher discretionary cash flow and bal-
ances. Lenders prefer to offer credit for higher profit
margin SMEs when evaluating the borrowers’riskiness.
In this situation, bribes may work as greasing the wheels
for high profit margin female-owned SMEs to eliminate
bureaucratic red tape.
It is also noted that women typically try to access
smaller amounts of funding because the so-called fem-
inine occupations are less capital intensive (D’espallier
et al. 2011). The perception of higher risk amongst loan
Note: Probit regressions. The dependent variable is a dummy variable taking 1 if the SME faces credit constraints and 0 otherwise. Panel A reports first-stage probit, marginal effect, IV probit
and IV marginal effect regression results, respectively, for credit constraints when measuring bribes as dummy variable (Bribe_D). Panel B reports first stage probit, marginal effect, IV probit
and IV marginal effect regression results, respectively, for credit constraints when measuring bribes as continuous variable (Bribe_Intensity). These models provide standard errors, which are
in parentheses. The Wald test of exogeneity is reported in the last row as a chi-squared statistic with 1 degree of freedom
* Significant at 10% level; ** significant at 5% level; *** significant at 1% level
+
This study sample consists of small and medium enterprises. Medium variable is omitted (base variable) and small variable used in the regression. Capital City,Manufacturing,Small,Sole
Prop,Female,Exporter,Foreign_Owner,Government_Owner,Inno and Personal_Loan are dichotomous variables. lnage and lntop_exp are continuous variables. Bribe_D* Size,
Bribe_Intensity* Size,Bribe_D* Age,Bribe_Intensity* Age,Bribe_D* Female,Bribe_Intensity* Female,Bribe_D* Inno,Bribe_Intensity* Inno,Bribe_D ×Foreign_Owner,
Bribe_Intensity ×Foreign_Owner,Bribe_D ×Government_Owner and Bribe_Intensity ×Government_Owner are interactive variables. ±Weak_Judiciary is an instrumental variable for
Bribe_D dependent variable and Sec_Avg_Bribe is an instrumental variable for Bribe_Intensity variable
++
Bribe_Intensity ×Foreign_Owner and Bribe_Intensity ×Government_Owner variables are omitted in regression due to limited observations
N. Hewa Wellalage et al.
officers causes them to prefer smaller-sized loans
(Muravyev et al. 2009). The perception of risk increas-
ing with the size of the loan may encourage approval of
small loans. However, the higher administrative cost of
approving multiple small loans will result in smaller
returns to the lender. In this situation, bribes may work
as greasing the wheels for high profit margin female-
owned SMEs to eliminate bureaucratic red tape.
Panel A column IV indicates that innovative SMEs
are less credit constrained than non-innovative SMEs,
whichisconsistentwithgovernmentinterventionbe-
coming a common practice to support innovative SMEs
in India over recent years. It may be that innovation
signals the quality of firm activities, embodying evi-
dence that the firm is well-managed, leading to an
increase in lenders’confidence about the prospective
borrower and easing access to credit. Additionally, in-
novation may suggest that the firm is associated with
having clever people. Therefore, lending to people per-
ceived as clever is less risky than lending to non-
innovative firms. Hall and Ziedonis (2001), discussing
the US semi-conductor industry, observe that patent
rights have a significant positive impact in attracting
venture capital funding. Baum and Silverman (2004)
record similar evidence. This may be that a patent indi-
cates future cash flows. Nevertheless, when innovative
SMEs pay a bribe, they have a further reduction in credit
constraints. The bribe increases the ease of access as it
greases the wheel in obtaining external finance.
There is a location effect, with SMEs in a capital city
facing higher credit constraints than SMEs in other
locations. In particular, the marginal effect indicates that
firms from a capital city face approximately 16.51%
(panel A using Bribe_D variable) and 2.88% (panel B
using Bribe_Intensity variable) higher credit constraints
vis à vis SMEs from other locations. In high firm density
settings, whereby financial institutions may be more
sophisticated in filtering applications and deemed high
risk, SMEs are not succeeding in obtaining external
financing. On the other hand, most firms in metropolitan
cities trying to expand to survive could be already
highly geared.
There is an industry effect, with manufacturing firms
facing more credit constraints than firms from other
sectors. This is consistent with the view that indicates
industry as a proxy of risk. Panel A shows the
manufacturing industry firms are faced with 9.43%
higher credit constraints than those in the non-
manufacturing sectors. It is plausible the banks tend to
show a bias towards certain industries (Rajan and
Zingales 1998) and the manufacturing sector incurs
higher up-front costs and has higher fixed costs, greater
operating leverage and generally higher financial lever-
age, which may make for a higher risk profile.
A statistically significant negative correlation exists
between top managers’experience and credit constraints
(panel A lntop_exp coefficient is −.0356 and panel B
lntop_exp coefficient is −.0186). This is similar to the
results from a South Asian SMEs study by Wellalage
and Locke (2017). We find greater manager experience
assists in reducing SME credit constraints.
Exporting SMEs are less credit constrained than
non-exporting SMEs. Panel A reports that export
firms are approximately 11.47% less credit
constrained than non-exporters (see panel A),
when measuring the existence of bribes as a dum-
my variable. Further, panel B shows exporters are
21.20% less credit constrained than non-exporting
SMEs, when measuring bribes as a continuous
variable. Firms that are more productive are more
likely to export and earn higher profits (Manova
2012) and financial institutions are more
favourably disposed to offering credit to export
firms. Also, government incentives to build an
export economy strengthen that the signalling po-
sition of exporting SMEs is important.
Sole proprietors are more likely to face higher credit
constraints than non-sole proprietor and SME proprie-
tors. Panel A shows that sole proprietors are approxi-
mately 10.89% more credit constrained than their multi-
owner SME counterparts when measuring existence of
bribes as a dummy variable. Panel B reports that sole
proprietors are approximately 1.20% more credit
constrained than their multi-owner counterparts when
measuring bribe as a continuous variable. Further, our
study indicates that when SME owners have outstand-
ing personal loans used in their businesses, they face
fewer credit constraints than those without outstanding
personal loans. When bribes are measured as a dummy
variable, the marginal effects indicate that SMEs with
personal loans face approximately 19.18% lower credit
constraint (panel A column III) and 4.07% lower credit
constraint compared to SMEs without personal loans in
the business enterprise. The line between business and
personal lives of SME owners is often blurred. There-
fore, SME owners having outstanding personal loans
can indicate their creditworthiness, which could posi-
tively affect credit access.
Firm bribery and credit access: evidence from Indian SMEs
Figure 1presents the marginal effect plots for the
interaction term with bribery (Bribe_D).
13
These plots
demonstrate important differences between SMEs pay-
ing and not paying bribes.
Figure 1a plots the relationship between firm size
(small firm or not) and firm-level credit constraints.
Small-sized firms face higher credit constraints than
medium-sized firms regardless of whether or not they
pay bribes. Figure 1shows that as firm size decreases
from medium to small, credit constraints increase. Small
firms paying bribes are less credit constrained than small
firms not paying bribes. However, within the medium-
13
Due to the low number of observations, some interactions variables
with Bribe_Intensity are dropped. Therefore, we onlyhave plots for the
interaction terms of the variables with Bribe_D.
.3 .35 .4 .45 .5
Pr(Constrints3)
0 1
small
bribe_d=0 bribe_d=1
Adjusted Predictive Margins with 95% CIs
.2 .25 .3 .35 .4
Pr(Constrints3)
0 1
female
bribe_d=0 bribe_d=1
Adjusted Predictive Margins with 95% CIs
.25 .3 .35 .4 .45 .5
Pr(Constrints3)
0 1
Inno1
bribe_d=0 bribe_d=1
Adjusted Predictive Margins with 95% CIs
0.2 .4 .6
Pr(Constrints3)
0 1
foreign
bribe_d=0 bribe_d=1
Adjusted Predictive Margins with 95% CIs
-.2 0.2 .4 .6 .8
Pr(Constrints3)
0 1
government
bribe_d=0 bribe_d=1
Adjusted Predictive Margins with 95% CIs
.2 .3 .4 .5
Pr(Constrints3)
020 40 60 80 100
age
bribe_d=0 bribe_d=1
Adjusted Predictions of bribe_d with 95% CIs
bribe_d=0
bribe_d=1
ab
cd
ef
Fig. 1 a–fProbit marginal effect plots for the interactive variables
N. Hewa Wellalage et al.
Tab le 4 Robustness probit regression results of credit constraints and corruption relationship in SMEs in India using alternative proxy for credit constraints
Variable Probit Marginal effect IV probit IV marginal effect Probit Marginal effect IV probit IV marginal effect
(I) (II) (III) (IV) (V) (VI) (VII) (VIII)
Panel A: dependent variable—cost of external finance
Bribe_D .0641*** (.0402) .0179*** (.0112) –
Bribe_Intensity –– 1.989*** (2.153) .5720*** (.6191)
±Weak_Judiciary –1.709*** (.2563) .6019*** (.0816)
±Sec_Avg_Bribe –– .4173*** (1.947) .2298*** (.2555)
Control variables Ye s Yes Yes Ye s Yes Ye s Yes Ye s
Pseudo-R
2
.0179 .0191
Log-likelihood −3544.27 −7799.42 −3138.89 −6478.87
Sample size 7118 7118 6913 6913 6131 6131 6131 6131
Wald test of exogeneity (p_value) 17.06*** 15.46**
Panel B: dependent variable—difficulties in accessing external finance
Bribe_D .0672*** (.0755) .0061*** (.0130) –
Bribe_Intensity –– .0567*** (.2678) .0195*** (.7813)
±Weak_Judiciary –1.414*** (.2011) .4136*** (.0906)
±Sec_Avg_Bribe –– 5.587*** (1.715) .1042*** (.0300)
Control variables Ye s Yes Yes Ye s Yes Ye s Yes Ye s
Pseudo-R
2
.0195 .0194
Log-likelihood −4279.08 −7799.42 −3702.00 −6478.87
Sample size 7000 7000 6899 6899 6131 6131 6131 6131
Wald test of exogeneity (p_value) 26.84*** 5.91**
Panel A reports the results using costs of external finance as proxy for credit constraints. Constraints proxy takes value 1 if SME owner manager responds that BAccess to Finance is an
obstacle to the current operations of this establishment^as a moderate, a major or a very severe obstacle; otherwise, Constraints proxy takes value 0. Panel B reports the results using
difficulties in accessing external finance as proxy of credit constraints. Constraints proxy takes value 1 if either SME (1) applies for and is denied credit (applicant), or (2) does not apply for
credit because application procedures are too complex, or collateral requirements are too high/does not have guarantor or the size of loan and maturity is insufficient or interest rate is too high
(non-applicant). Columns I to IV report probit regression results for credit constraints when measuring bribes as dummy variable (Bribe_D). Columns V to VIII report probit regression
results for credit constraints when measuring bribes as continuous variable (Bribe_Intensity).
Firm bribery and credit access: evidence from Indian SMEs
sized firm category, those firms paying bribes are slight-
ly more credit constrained than those not paying bribes.
Figure 1b plots the relationship between the
owner’s gender and firm-level credit constraints.
Male-owned SMEs are more highly credit
constrained than female-owned SMEs, whether or
not they pay bribes. Paying bribes has significantly
greater impact on female-owned SMEs’level of
credit constraints than on male-owned SMEs.
When female-owned SMEs pay bribes, they have
lower credit constraints compared to female-owned
SMEs that not pay bribes.
Figure 1c plots the relationship between firm-
level innovation and firm-level credit constraints.
Regardless of paying bribes or not, innovative
SMEs have less credit constraints than non-
innovative firms, and innovative SMEs paying
bribes are less credit constrained than innovative
SMEs not paying bribes.
Figure 1d plots the relationship between foreign
ownership (at least 1% foreign ownership) and
firm-level credit constraints, showing that non-
foreign-owned firms are always more highly credit
constrained than SMEs with some foreign owner-
ship. When non-foreign-owned firms pay bribes,
they are slightly less credit constrained than non-
foreign firms not paying bribes. However, when
foreign-owned SMEs pay bribes, they are more
credit constrained than foreign-owned SMEs not
paying bribes.
Figure 1e plots the relationship between govern-
ment ownership (at least 1% government owner-
ship) and firm-level credit constraints. It shows
that regardless of government ownership, bribe-
paying firms face slightly more credit constraints
than non-bribe-paying firms.
Figure 1f plots the relationship between firm age
(age as a continuous variable) and firm-level credit
constraints. For newer firms, there is virtually no
relationship between credit constraint and bribes.
As firm age increases, those SMEs paying bribes
face considerably lower credit constraints.
The expected moderating influence expected,
from the discussion of control variables in
Section 2.3 above, is not strong. As reflected in
Tab le 3many of the variables are statistically
significant but not all have negative signs. Robust-
ness checking is necessary.
5 Robustness
To assess the robustness of our findings, two tests were
performed.
14
First, we checked whether our results hold
when different proxies for credit constraints are used.
Credit constraints can occur due to difficulties in
accessing external credit or high costs associated with
external credit.
(i) Following Gorodnichenko and Schnitzer (2013),
we re-analysed our study sample using costs of
external finance as proxy for credit constraints.
The World Bank Enterprises Surveys measure the
cost of external financing on a scale ranging from 1
(BNo obstacle^)to4(BVery Severe obstacle^). We
found that results are substantially the same as for the
main dependent variable (Constraints), so overall inter-
pretation of the results does not alter.
(ii) Following Wellalage et al. (2018), we re-analysed
our study sample using difficulties in accessing
external finance as proxy for credit constraints.
This study develops a Constraints variable as fol-
lows: when an SME (1) applies for and is denied credit
(applicant), or (2) does not apply for credit because
application procedures are too complex, or collateral
requirements are too high/does not have guarantor or
the size of loan and maturity is insufficient or interest
rate is too high (non-applicant) (Table 4).
In a second set of robustness testing, we re-estimated
the regression results using logit and IV logit regression
techniques. Although coefficients changed slightly, the
overall interpretation of the results are similar to the
baseline results.
6 Conclusion
This paper explores the relationship between corruption
and access to credit for SMEs in India. Micro-
econometric modelling provides a robust framework of
analysis as it recognises and minimises the endogeneity
of corruption in credit constraints and reverses causality
issues. The sand in the wheel concept aligns with the
14
Second robustness test results are not reported in order to save space
and are available from the authors upon request.
N. Hewa Wellalage et al.
evidence indicating that paying bribes and bribe inten-
sity increase credit constraints for SMEs when
endogeneity is controlled, using instrumental variables.
We ascertain that approximately 38% of Indian
SMEs are credit constrained. However, only approxi-
mately 24% of SMEs make informal payments to get
things done in India. Our results reject our hypotheses
that ‘there is a negative correlation between bribes and
level of credit constraints faced’and that ‘the amount of
bribe offered by SMEs is negatively correlated with the
level of credit constraints with which they are
confronted.’Specifically, our findings from our Indian
SMEs’firm-level data indicate that:
&SMEs paying bribes are, on average, 68.2% more
likely to be credit constrained than their counterparts
that do not pay bribes.
&Bribe intensity increases the probability of SMEs’
credit constraints by approximately 3.4%.
&The impact of the existence of a bribe is higher than
the bribe intensity for SMEs (in credit constraints).
Overall, we find that corruption imposes an addition-
al barrier for SMEs in India when accessing external
finance and endorses the sand in the wheel concept. We
also find that to access credit, it is advantageous for the
SME to be innovative, with experienced managers, who
have taken a personal loan.
SMEs may not always be able to avoid paying bribes
or other unofficial payments, because refusing to engage
in corrupt practices jeopardises the survival of the en-
terprise. In general, bribe payments avoid ideological
discrimination for SMEs and indicate that bribes be-
come a tax on SME borrowers and so are obstacles to
gaining credit. We recommend bribe payments be
analysed in ways that provide for useful policy implica-
tions. Extending this research to other countries in South
Asia that are recipients of funding from the World Bank,
Asian Development Bank and other agencies will be
particularly helpful and important for policy making in
those countries.
Our analysis suggests enhanced SME access to fi-
nance relies mainly on the development of the gover-
nance mechanisms such as those that provide a strength-
ened legal environment. For an SME choosing to oper-
ate in the formal sector, consistency with controls at
national, state/territory and local levels may result in
lengthy postponements that add to costs. Such expenses
may be relatively higher than those borne by large
enterprises. As an example, the introduction of GST in
India by the central Government from 1 July 2017, to
replace multiple State and local taxes and charges, may
serve to reduce red tape: it may also promote the infor-
mal economy as SMEs prefer to stay under the radar.
In such scenarios, SMEs may employ less savoury
commercial expediencies to accelerate progress and re-
duce the cost of delays. Hence, a fundamental issue to
address is the enactment of regulations that strengthen
property rights and foster improvements in the develop-
ment of SMEs in India. Policymakers, through improv-
ing the quality of the judiciary and implementing poli-
cies that lead to reduction in corruption levels, are likely
to achieve the largest gains in national productivity. Our
research indicates that anti-corruption measures are vital
for the development of SMEs in India.
The delimitation between legitimate and degenerate
practice is not simple to characterise, and SMEs gener-
ally may not have the ability to recognise or delineate
the subtleties and nuances of appropriateness in trans-
actions. For example, giving endowments, keeping in
mind the end goal is to maintain great business connec-
tions, is a common practice and permitted, while offer-
ings to influence choice are entirely taboo. There are
numerous hazy areas, and the mix of customary prac-
tice, varying between regions, makes for a murky pic-
ture. Acceptance that it is not clear-cut as to what are
legitimate and what are illicit practices points to the need
for a clearly articulated set of principles and more de-
velopment of public officials.
Further, our results indicate that the implementation
of successful anti-corruption programmes will produce
beneficial outcomes. Agency costs are high, from the
perspective of national development, where officials
rent-seek through their office. Monitoring costs are high
when there isa substantial cash economy and laundering
rents is a simple matter of course. Bonding options
require a civil service salary which is recognised as
reasonable, and this in many emerging and low-
income economies may only be achievable with fewer
civil servants.
Our study has limitations, some of which may be
fruitful avenues for future research. The possibility of
extending the research to other countries is important for
policy making in countries that are recipients of SME
development funding from funding agencies. The time
period for this analysis relates to 2013–2014 for 7153
SMEs. However, this 1-year slice of data offers the
opportunity only for cross-sectional analysis of the latest
Firm bribery and credit access: evidence from Indian SMEs
available data. The analysis and findings suggest poten-
tial advice for policymakers based on the extant situa-
tion. Future research may be able to use time-series data,
should it become available, allowing analyses of bribery
impact on credit access for SMEs during pre-crisis and
post-crisis periods, and could shed light on changes if
any, in credit access behaviours.
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