Post on 01-Jun-2020
DOES CORRUPTION IMPACT THE PROBABILITY FIRMS APPLY FOR LOANS?
AN EMPIRICAL RESEARCH ON DEVELOPING COUNTRIES IN
EAST ASIA & PACIFIC AND SOUTH ASIA
Current version: December 2018
Jean-Christophe Statnik
jcstatnik@univ-lille2.fr
Thi-Le-Giang Vu 1
thilegiang.vu.etu@univ-lille.fr
Abstract: Corruption is a big problem for development and gets the attention of many scholars. Nevertheless, the impact of corruption on the ability firms apply for loans for developing countries is still a question. Using Enterprise Survey from World Bank and other data from Transparency International and The World Economic Forum, we find corruption increases the ability firms apply for loans in developing countries. Moreover, the effect of corruption depends on the level of country development. In upper-middle income countries, corruption discourages firms from applying loans, while in lower-middle income countries, corruption encourages firms to apply loans. We also find burden of government regulation is a channel to explain for this effect. To get over burdens in regulation, firms find corruption as one way to reach their targets at lower costs.
1 Corresponding author
1. INTRODUCTION
Capital plays a vital role in the development of enterprises. Therefore, access to finance is a
topic getting the attention of not only entrepreneurs but also many researchers in the world.
Most of researches about this topic focus on applicants to find constraints hampering them
from getting loans (Cavalluzzo, Cavalluzzo, & Wolken, 2002; Cole, 1998, 2008; Petersen &
Rajan, 1994; Stiglitz & Weiss, 1981). Although non-applicants account for a large proportion,
researches about them are still less. Studies about this field pay more attention to developed
countries while developing countries receive much less attention. Therefore, we choose to
study about accessing to finance for non-applicants in developing countries to field this gap.
More detail, we concentrate our study to corruption and find how corruption impact the non-
applicants and discourage them from applying loans.
Prior researches found corruption may have both positive and negative impacts on applicants
for bank credit. As a negative effect, high corruption reduces the amount of loans which banks
grant to borrowers (Anaere, 2014; Weill, 2011)
and mitigate firm
Weill, 2015; Weill, 2011). For non-applicants, as our best knowledge, only Galli (2017) find
corruption constrains small and medium enterprises in developed countries from applying for
loans. From the findings in literature, especially the results of Galli (2017), we question about
the effect of corruption on non-applicants in developing countries. Is this effect similar or
different from developed countries?
To exploit this question, we use the Enterprise Surveys (ES) conducted by World Bank from
2012 to 2016 for upper-middle income countries and lower-middle income countries2 in (1)
East Asia & Pacific and (2) South Asia. The survey collected objective data based on the
experience and the perception of enterprises about their operation environment. The data
includes 14 countries Cambodia, China, Indonesia, Lao, Malaysia, Myanmar, Papua New
Guinea, Philippines, Solomon Island, Timor-Leste, Thailand, Vietnam, India and Pakistan. Of
these, China, Malaysia and Thailand are upper-middle income countries and the rest are lower-
middle income countries.
Classification is based on the criteria of World Bank. Before July 01, 2017, upper-middle income countries will have GNI/capita range from USD4036 12475; lower-middle income countries will have GNI/capita range from USD1026 4035 and low-income countries are smaller than USD1026. Since July 01, 2017, new threshold for upper-middle income countries is USD 3956-12235; lower-middle income countries are USD 1006-3955 and low-income countries are less than USD1006.
Our research is important in the following issues: First, in access to finance field, compare to
researches about applicants who applied for finance, study about non-applicants is a new field
of research. The importance of this field is gradually confirmed when recent researches find
the number of discouraged borrowers3 is twice or even three times as the number of borrowers
who are rejected from applying for loans (Freel, Carter, Tagg, & Mason, 2012; Gama, Duarte,
. Besides, most of predecessor researches about
discouraged borrowers are conducted in developed areas or developed countries such as Europe,
United Kingdom, United Stated, Australia and Canada. The percentage of discouraged
borrowers compare to total loan seekers4 in these countries range from 8% to 23%. Cross
country researches, especially researches for developing countries, are rare. As our best
knowledge, there are three studies of Chakravarty and Xiang (2009), Chakravarty and Xiang
(2013) for ten developing countries around the world and Gama et al. (2017) for less developed
countries in Eastern Europe and Central Asia. As our opinion, researches about discouraged
borrowers are especially important for developing countries because in these countries, funds
to finance for development are vital while the percentage of discouraged borrowers are very
high, but studies to understand them are less. Contributing to research of discouraged borrowers
in developing countries, our study focuses on upper-middle income countries and lower-middle
income countries in (1) East Asia & Pacific and (2) South Asia. These two regions are the
fastest growing regions in the world. Therefore, capital for development is must be very large.
However, the incidence of discouraged borrowers in these regions is very high around 67% of
loan seekers. Finding determinants constraining firms from applying for loans in these regions
may have significant meanings for authorities in reducing the rate of discouraged borrowers
and contributes to the literature understandings about non-applicants in developing countries.
Second, there are abundant researches about the threats of corruption to economy and society.
Nevertheless, studies investigating the impact of corruption on discouraged borrowers are rare.
Studies of Chakravarty and Xiang (2009), Martin Brown (2011) and Chakravarty and Xiang
(2013) mention about this effect. However, they find a significant impact of corruption
on discouraged borrowers. Only Galli (2017) concludes that small and medium enterprises in
European are more likely refrain from applying for bank loans if corruption is high. In our
study, we investigate developing countries and we find the effect of corruption on discouraged
borrowers is significant. Moreover, we prove that the impacts of corruption differ according to
Discouraged borrowers are non-applicants who need capital but apply for loans because of fearing to be rejected. 4 Loan seekers are enterprises who need credit. They are both borrowers who applied for credit and discouraged borrowers who need credit but apply for credit.
level of country development. In detail, in higher development countries, higher level of
corruption will discourage firms from applying for loans. However, in lower development
countries, if level of corruption increases, firms are more willing to apply for credit. This is a
new point contributing to the literature about the impact of corruption on discouraged
borrowers. We explain for this result by the burden of regulation. More precisely, the burden
of regulation is a channel for the negative effect of corruption on discouraged borrowers. More
burden of regulation will push the firms into using corruption as one way to get over these
burdensome.
In the next section, we review researches related to discouragement. The third part proposes
hypothesis. The fourth part mentions about data and methodology. The final parts show the
results and conclusion.
2. LITERATURE REVIEW
2.1 Discouraged borrowers and elements affect discouragement
Although empirical researches about discouraged borrowers was conducted since 1990s by
Jappelli (1990) for households in United State, by Mayank Raturi (1999) in Zimbabwe and by
Levenson and Willard (2000) in United State, the first theory was built later by Kon and Storey
(2003). In the research, Kon and Storey (2003) define discouraged borrowers are good
borrowers apply for loans because they think they will be rejected. They take into
account the application costs and those prevent them from applying for loans. Application costs
can be financial costs such as costs to gather information required by banks, costs for time to
travel to banks, time to wait for application process, time to meet bank officers. Application
costs can also be psychic costs such as reluctance to provide information to outsiders, lack of
knowledge and skills to complete application documents, reluctance to enter the bank.
Kon and Storey (2003) explain that asymmetric information is a reason for discouragement. If
banks have clear information about borrowers, their mistakes in deciding for financing will be
reduced and when borrowers know that banks make good decisions, their applications will
increase. Therefore, discouraged borrowers decrease when there is less asymmetric
information. Kon and Storey (2003) conclude three elements impacting the scale of
discouragement are screening error of the banks, application costs and the different between
interest rate of bank and interest rate of money lender. Reducing screening error of the banks
will mitigate the discouragement. While discouragement will increase if application costs are
high and sources of funding are less.
Following the theory of Kon and Storey (2003), empirical researches find the rate of collateral,
relationship lending, entrepreneur characteristics, firm characteristics and distance are factors
impacting on discouraged borrowers. These researches prove that the above elements impact
asymmetric information, hence affect the incidence of discouraged borrowers. Besides,
literature also find perceptions about operating environment and economic conditions are
significant effects on discouragement.
Rate of collateral
Rate of collateral impacts not only applicants who apply for loans but also non-applicants who
may be discouraged from applying. For those non-applicants, they are good enough to apply
nk. Therefore, they are
ateral does increase
the incidence of discouraged borrowers (Kon & Storey, 2003).
Lending relationship
Lending relationship play an important role in reducing asymmetric information. Close
relationship between lenders and borrowers can mitigate credit restriction (Berger & Udell,
Rajan, 1994). Researches about discouragement find two opposite impacts of lending
relationship on discouraged borrowers. Chakravarty and Xiang (2009), Freel et al. (2012) and
Chakravarty and Xiang (2013) find stronger relationship between a lender and a firm can
reduce the likelihood of firm being discouraged. This conclusion is also get in the research of
Chakravarty and Yilmazer (2009) when they find that if firms have relationship with more
credit sources, the probability of applying for loans will increase. However, Han, Fraser, and
Storey (2009) conclude that risky borrowers are more likely be discouraged when they make
longer relationship with banks. In the line with Han et al. (2009), Vincent (2010) also confirms
discouraged borrowers have stronger relationships with financial institution. During the time
building relationship, they understand both financial institution and themselves. Therefore,
they know their ability to success applying for loans and that discourages them from applying
for credit.
Entrepreneur characteristics
Next factor influencing discouraged borrowers is entrepreneur characteristics. Characteristics
of managers have significant impacts on the development and prospect of firms. These
characteristics also impact on the tendency to apply for credit. Entrepreneur characteristics
include gender, educational level, professional experience, credit quality and wealth.
Chakravarty and Xiang (2009) and Gama et al. (2017) suggested that if top manager is male,
that firm will be less self-rationing. Explain for this reason, researches of Treichel and Scott
2006; Watson 2006; Coleman 2000 cited in Freel et al. (2012) find that women are less self-
confidence, more risk averse and want to keep control of their business. They have the feeling
that applying for loans will create more risks. Therefore, they are less likely apply for credit
than men.
About the educational level of owner, most of research found the positive relationship between
educational level and credit availability (Parker and Van Praag 2006, Krasniqi 2010 cited in
Bellier, Sayeh, and Serve (2012)). Chakravarty and Xiang (2009) conduct research for
developing countries and report that if top managers of the firm have high education (university
or higher level), that firm will less discourage themselve from applying for loan. In the line
with Chakravarty and Xiang (2009), Fraser (2014) applies both quantitative and qualitative
methods on SMEs in UK and confirms that owners of discouraged firms are lower levels of
education.
In addition, owners of discouraged firms are also less wealthy and less professional experience
(Fraser, 2014). Fastenbauer and Robson (2014) implement an qualitative research in UK and
Austria and they conclude that experience of owners impact on the probability to apply for loan.
Experience owners understand the development of their firm. They also can evaluate more
accurate the benefit they get if they apply for loans. Hence, they are less self-discouraged.
Recent research of Gama et al. (2017) confirms the effect of owner professional experience on
discouragement.
Firm characteristics
It is widely agreed that firm size impact on discouragement and small firms are less likely apply
for credit (Cole, 2008; Ferrando & Mulier, 2015; Fraser, 2014; Freel et al., 2012; Han et al.,
2009; Mac an Bhaird, Vidal, & Lucey, 2016; Xiang, Worthington, & Higgs, 2015). This is
reasonable because small firms usually have opaque information. Therefore, they are hard to
prove their creditworthiness with lenders. In addition, small firms usually face with high
probability of application rejection (Holton, Lawless, & McCann, 2014) and the feeling that
they will be turned down again discourages them from applying for new loans (Fraser, 2014;
Xiang et al., 2015).
Besides firm size, researchers find other characteristics affecting to discouraged borrowers.
Cole (2008) stated in the results that discouraged firms are younger than other firms in the
sample. These firms also have worse credit quality and more likely locate in urban areas. In the
line with Cole (2008), Ferrando and Mulier (2015) studies 9 countries in Europe and confirms
that discouraged borrowers are younger and riskier than applying firms. In addition, if trade
credit is more available for firms, they will be likely to discourage themselves. Trade credit is
another alternative source of fund with lower cost. Hence if trade credit is available, firms have
tendency to use more trade credit than applying for loans. In contrast, Biais and Gollier (1997)
and Voordeckers and Steijvers (2006) proved that trade credit can work as a signaling
instrument to reduce asymmetric information. When firms use trade credit, their suppliers will
get private information from them and the private information can convey to banks. Based on
this argument, Gama et al. (2017) find trade credit has negative impact on discouragement.
Mention other firms characteristics, research of Han et al. (2009) in US small business find
firms using external finance such as business credit cards or business credit lines seem less to
be discouraged because they have signal that they are creditworthiness. About the ownership
structure, Martin Brown (2011) and Xiang et al. (2015) state that foreign ownership increases
discouragement because these firms reply more on internal funds from parent firms and less
apply for external finance sources. Martin Brown (2011) also find government-owned firms
are less seeking for credit. Besides, exporting firms are more likely apply for credit because
they need capital to finance for their operation.
Firm perception about the operating environment
Firm perception about the operating environment has a significant impact on the probability
firms apply for bank loans. These perceptions can be firm perception about access to finance
or level of corruption in the economy. Mac an Bhaird et al. (2016) find firms will discourage
themselves from applying for loans if they feel access to finance is a severe problem. Impact
of corruption on discouragement will be discussed in detail in the following part.
Distance between lenders and borrowers
Closer distance from a bank office to a firm can make it easier for that firm to get financing
(Agarwal & Hauswald, 2010) because bank can collect soft information and get benefit from
private information. Kon and Storey (2003) also explain the distance between lenders and
borrowers can reduce application costs for bank and helps lenders make better decisions.
Hence, the incidence of discouraged borrowers decreases. Confirm theory of Kon and Storey
(2003), Gama et al. (2017)
lenders and they find firms located in the big city are less likely become discouraged borrowers.
Economic conditions and legal environment
Chakravarty and Xiang (2009); Chakravarty and Xiang (2013) use the average GDP growth
rate during research period to proxy for country characteristic. They conclude that there is a
negative correlate between growth rate and discouragement. Higher growth rate will reduce the
discouragement. Gama et al. (2017) find quite similar result that firms are less probability of
being discouraged in countries which GDP per capita is high. Because in these countries, firms
face with lower financing obstacle -Kunt, Laeven, & Maksimovic, 2006).
About the legal environment, -Kunt and Maksimovic (1998) conclude that the
incidence of firms financing their growth by external sources is higher in countries which legal
systems is efficient. This result implies that in inefficiency legal system countries, firms will
use more internal sources to finance for their growth and limit their applications for external
sources. Besides, - r-Kant (2011) find bank loan maturity is 5. Firms are more likely get long-term
In addition,
in countries which have better institutional environments, banks are more willing to provide
long-term debts to SMEs. Findings of - -Kant (2011) are
important to authorities because their policies to improve legal and institutional environment
have direct impact on In short, findings from studies of
-Kunt and Maksimovic (1998); - -Kant (2011) imply
that the efficiency of legal environment has a positive effect on the ability firms apply for loans.
In the line with previous studies, Gama et al. (2017) also expect the strength of the legal
protections of creditors and borrowers and the number of discouraged borrowers have negative
correlation. However, the results they find is opposite with their expectation. Increasing legal
protections will increase the number of discouraged borrowers. To explain for this result, they
find in their research the strength of legal protection has positive relation with collateral.
Stronger legal protections increase the collateral requirements and hence increase the incidence
of discouraged borrowers. Mac an Bhaird et al. (2016) also find greater regulatory quality will
increase the number of discouraged borrowers and they
- and -Kant (2011) use economic freedom index as a proxy for institutional environment. The index includes 10 institutional factors: trade policy, fiscal burden of government, government intervention in the economy, monetary policy, capital flows and foreign investment, banking and finance, wages and prices, property rights, regulation, and black market activity. It is measured by the Heritage Economic Freedom Index.
2.2 Corruption and discouragement
Corruption and its measures
Corruption is a complicated issue and it still exists arguments about corruption definitions.
However, there is a consensus in the literature that corruption is the misuse of public office for
private gains (Nye, 1967; Philp, 2016; Treisman, 2000). Based on the measurement of
corruption from internal, external or hybrid. Asiedu and Freeman (2009) classify three ways to
capture corruption. The first way measures firms perception/experiences about corruption in
the environment in which they operate. This way captures internal information. It reflects the
perception/experiences about level of corruption in a country. However, the accurateness
depends on the way to collect samples and answers of firms because corruption is a sensitive
issue. In addition, each country has its own culture, economic and political environment so firm
perception about corruption in each country is different. The same answer severe corruption
mean that two countries have the same level of corruption. Hence,
comparing level of corruption across countries may not be easily. The second way is the
assessment of analysts who are outside a country. This way overcomes the shortcomings of the
first method that there is the same criterion to assess countries, so the result can compare across
countries. Nevertheless, the assessment is usually based on media reports, so it may not
accurately reflect the country level of corruption. The third way is a combination of many
sources about corruption into an index, for example Corruption Perceptions Index. Therefore,
this measurement mitigates the shortcomings of both above methods.
Using a different way of classification, Gutmann, Padovano, and Voigt (2015) analyze two
proxies for corruption that are perception versus experience with bribery. They conclude
that two proxies may not highly correlated because respondent characteristics and country
.
Corruption and discouragement
Theories and empirical researches have proved vigorous evidences that corruption harms
economic development. Nevertheless, at another side, literature also find that in case of
cumbersome regulations, corruption can improve the efficiency and help growth (Huntington,
1968; Leff, 1964; Leys, 1965). This efficiency is shown by corruption can help things to be
done faster (Leys, 1965; Lui, 1985) or like competitive bidding, corruption can allocate
contracts to lowest cost firms (P. J. Beck & Maher, 1986; Lien, 1986).
In access to finance field, corruption is also found having both positive and negative impacts.
In the positive side, find higher level of corruption can increase bank
debt ratio. Chen et al. (2013)
decisions to grant loans are not affected by bribery, but bribery enables better economic
performance firms to receive larger amount of loans. In the negative side, Anaere (2014) shows
that the banks less likely grant loan to firms and households in private sector if the level of
corruption in that country is high and Weill (2011) concludes corruption constrains bank credit
in both developed and developing countries. However, most of researches in access to finance
field focus on applicants, while researches about impacts of corruption on discouraged
borrowers are rare.
Researches about discouraged borrowers of Chakravarty and Xiang (2009), Martin Brown
(2011) and Chakravarty and Xiang (2013) added corruption as an independent variable.
Nevertheless, they find no statistical evidence that corruption impact on discouragement.
Recently, Galli (2017) conducts the research on micro, small, and medium-sized enterprises in
Europe. They find that high level of corruption impedes firms from applying for bank loans
and behaviors of small firms are more effected by corruption than larger firms. In addition, the
percentage of small firms refraining from applying for credit is higher in countries which have
high level of corruption. To proxy for corruption, Galli (2017) use perception corruption at
country level such as Corruption Perceptions Index launched by Transparency International
and Control of Corruption from Worldwide Governance Indicators published by World Bank.
3. HYPOTHESIS DEVELOPMENT
The study about the impact of corruption on discouraged borrowers of Galli (2017) concludes
that high corruption increase the probability of discouragement in small firms and this research
investigates developed countries where corruption is under the control and low. Our research
is conducted in developing countries where corruption is quite high. The average of Corruption
Perceptions Index in the study of Galli (2017) is 68, while in our data is 366. This value
confirms the pervasion of corruption in developing countries. Axelrod (1986) stated that a
behavior will become a norm when it spreads in a population. In the context of corruption,
when corruption is pervasive in the society, it will become a normal behavior. The feelings of
guilt and the loss of reputation will decrease as the number of persons violating the rules
increase (Andvig, 1991). In addition, the ability to be detected and punished is more difficult
Corruption Perceptions Index is scaled from 0 to 100 with 0 is very severe perception of level of corruption and 100 is the lowest perception of corruption
because of the compromise of the community for this action. In that case, Della Porta (2017)
confirms that
Conducting the research for developing countries and based on these above arguments, we
expect corruption may reduce the discouraged borrowers in developing countries, because
corruption can reduce application cost and increase the chance firms receive the loans.
Hypothesis 1: Corruption has a negative impact to discourage borrowers.
Besides concluding that corruption has a positive impact on discouraged borrowers, further
research about the impact of corruption, Galli (2017) divide the data into two parts based on
the level of corruption and author finds firms is more self-discouraged in high corruption
countries than in low corruption countries. Moreover, according to Treisman (2000) and
Pellegrini (2011), there is a correlation between the level of country development and
corruption. More develop countries have a lower level of corruption. The above results suggest
two ideas which lead to the second hypothesis: (1) it may exist different impacts of corruption
on discouraged borrowers in different groups of countries depending on their level of
development and (2) the same result Galli (2017) got on developed countries, we expect the
effect of corruption on discouraged borrowers may get positive sign for countries which are
higher level of development.
Hypothesis 2: The effect of corruption on discouraged borrowers depends on the level of
country development and in higher development countries, corruption hamper firms from
applying loans.
4. DATA AND METHODOLOGY
4.1 Data
We use The Enterprises Surveys (ES) conducted by World Bank. This is a project of World
Bank to gather data based on experiences and perception of the operating
environment. The project has collected data from about 155,000 firms in 148 countries.
Standard methodology to collect information has been applied in 139 countries to better
compare different countries and different time. The survey use stratified random sample
method to collect data. Follow this method, population is divided into homogeneous sub groups
and simple random samples are selected from each group with sample sizes proportional to
strata sizes. This method ensures representation of samples across the population. Firm size,
business sector and geographic region are strata of the survey.
The ES is face to face interview with business owners and top managers. Private contractors
have been hired by World Bank to conduct the surveys. These contractors Government
agencies or organizations associated with Government because of sensitive questions related
to Government. To collect the data, at first interviewers use a screener questionnaire over the
phone to check if enterprises are eligible and make appointment. After that, interview is
conducted by face to face with the owner or managers of each enterprise.
The target regions of our research are (1) East Asia and Pacific and (2) South Asia. We choose
these areas because they are two fastest growing regions in the world so capital for development
is vital. Discouragement will be an important issue if it happens in these regions. The research
period is from 2012 to 2016. During this 5-year period, the economy have much
fluctuation, so there is less bias between countries surveyed in 2012 and countries surveyed in
2016. In addition, there is just a slightly difference between the survey version in 2012-2013
and the version in 2014-2016. Therefore, we can collect as much common information as we
need to build the research model. Based on these criteria, we get 17 countries classified by
upper-middle income countries, lower-middle income countries and low-income countries.
Rioja and Valev (2004) find financial development and growth have a positive relationship in
middle region and high region (countries with middle and high levels of financial
development). This effect is even stronger in middle region and insignificant in low region.
The low or insignificant impact of financial development on growth in low income countries
is also found in other research of Deidda and Fattouh (2002), Rousseau and Wachtel (2002).
Based on the above researches, we could expect that discouragement phenomenon is an
important constraint for the growth of middle-income countries. Therefore, our target focuses
on middle income countries which include upper-middle income countries and lower-middle
income countries.
Finally, we got 14 countries in both regions. They are Cambodia, China, Indonesia, Laos,
Malaysia, Myanmar, Papua New Guinea, Philippines, Solomon Island, Timor-Leste, Thailand,
Vietnam, India and Pakistan. Of these, China, Malaysia and Thailand are upper-middle income
countries and the rests are lower-middle income countries. To ensure that the results are
reliable, we take only responses rated by interviewers are truthful and somewhat truthful. If
using only truthful responses, we will lose almost half of observations. Therefore, truthful
responses are used for the robustness check to confirm our main findings.
In addition, we also get data from Transparency International and World Economic Forum for
country-level variables. From Transparency International, we extract Corruption Perceptions
Index. This index measures level of corruption in public sector through perceptions of business
people and country experts. The index is averaged from different sources in a country. Selected
sources must meet certain criteria to ensure the reliability, the comparableness and the
development of the index in the future. At least three sources are needed to calculate Corruption
Perceptions Index for one country. This index is scaled from 0 to 100 with 0 is very severe
perception of level of corruption and 100 is the lowest perception of corruption.
Besides, we use data from Global Competitive Index (GCI) issued by World Economic Forum.
The GCI includes the set of institutions, policies, and factors collected from internationally
recognized agencies to determine the level of productivity of a country. From the GCI, we
extract burden of government regulation This indicator measures the level of
burden for business to comply with governmental administrative requirements (e.g. permits,
regulations, reporting). The indicator is collected from answers of leading business executives
and ranks from 1 to 7 where 1 is extremely burdensome and 7 is not burdensome at all.
4.2 Methodology
In order to test our first hypothesis dealing with the impact of corruption on discouraged
borrowers we use the following equation.
DISCOURAGED BORROWERSi,k = 1*CORRUPTIONi,k + 2*LENDING RELATIONSHIPi,k +
3*ENTREPRENEUR CHARACTERISTICSi,k + 4*FIRM CHARACTERISTICSi,k + 5*PERCEPTION
OPERATING ENVIRONMENTi,k + 6*DISTANCEi,k + 1*COUNTRYk 2*INDUSTRYn 3*YEARt i,k
(1)
Dependent variable
Discouraged borrowers is observed by the answers of firms to the question What was the main
reason why this establishment did not apply for any line of credit or loan? . If answer
for the question is No need for a loan - establishment had sufficient capital , they are not
discouraged borrowers. The firms will be considered as discouraged borrowers if their reasons
for not applying are application procedures were complex, interest rates were not favorable,
collateral requirements were too high, size of loan and maturity were insufficient, or they did
think the application would be approved.
Dependent variable got value 1 if firm i in country k is considered discourage borrowers based
on its answers and got value 0 if firm applies for loans. This measure of discouragement is
applied by many previous researches (Chakravarty & Xiang, 2009, 2013; Galli, 2017; Gama et
al., 2017).
Independent variables
CORRUPTION
We use some proxies for corruption. The first proxy is at firm level and based on the survey.
Firms are required to choose the degree in which corruption is an obstacle to their operation.
The levels of corruption range from 0 to 4
We define corruption as an
corruption is major obstacle or very severe obstacle and 0 for no obstacle, minor obstacle or
moderate obstacle. In the Enterprise Survey, perception about corruption is the most general
question capturing the level of corruption in a country and this question also got the highest
rate of answers. Therefore, choosing it as a main variable, we get the biggest number of
observations. In addition, perception corruption is also widely used in researches about
corruption (Mo, 2001; Podobnik, Shao, Njavro, Ivanov, & Stanley, 2008; Treisman, 2000;
Weill, 2011).
Nevertheless, this proxy may have problems when compare across countries. The tolerant
towards corruption can be various in different countries (Cameron, Chaudhuri, Erkal, &
Gangadharan, 2005), so two countries having the same value for perception corruption may
have different levels of corruption. To overcome this disadvantage, we use another proxy in
the survey at firm level to robustness for the main result. This variable is calculated by the
number of times firms make gifts or informal payments to public officials divided by the
number of times they apply or use public services. Doing this way, we measure experience
about corruption instead of perception corruption, so this variable look corruption at another
edge. Besides, we also use the Corruption Perceptions Index issued by Transparency
International as one proxy for corruption at country level.
Other variables in the model are control variable. We control for lending relationship,
entrepreneur characteristics, firm characteristics, distance, firm perception about the operating
environment and economic conditions.
LENDING RELATIONSHIP: One way to measure lender borrower relationship is scope .
If a borrower buys most of it financial products such as checking account, saving
the asymmetric information reduces (Steijvers & Voordeckers, 2009). Norden and Weber
(2010) finds that information on credit line usage and account activity will be used to manage
the relationship between banks and their customers. Therefore, we use financial products which
banks offer borrowers such as checking or saving account, overdraft facility as proxies for
lending relationship. When firms buy these services, the banks can get more precise
information about the firms by observing the changes in checking or saving accounts (Petersen
& Rajan, 1994). This increases the understanding of the banks with the firms. Financial services
are dummy variables got value 1 if firms either have a checking, saving account with banks or
an overdraft facility. Negative impacts of these variables on the incidence of discourage
borrowers is expected.
ENTREPRENEUR CHARACTERISTICS are measured by gender and experience of top
managers.
Gender is a dummy variable received value 1 if top managers are female and 0 for other. If top
managers are female, the probability of discouraged borrowers is expected increase.
Experience of top managers is measured by number of years working in the sector. Experience
of top managers may mitigate discouragement.
FIRM CHARACTERISTICS include age of firm, size of firm, ownership and other
characteristics.
Firm age: as long as firms operate in the market, possible for credit institutions to get
sufficient information from older firms (Berger & Udell, 1995; Kysucky & Norden, 2015;
Petersen & Rajan, 1994). Therefore, asymmetric information reduces, and older firms are
expected less discouragement.
Firm size is measured by the number of employees. There are three sizes: small (firms have 5
to 19 employees), medium (firms have 20 to 99 employees) and large (firms have 100
employees or more). Larger firm is expected less discouragement.
Foreign owned firm is a dummy variable received value 1 if the percentage of firm owned by
foreign individuals, companies, organizations is greater than 50% and 0 for others. As Martin
Brown (2011) and Xiang et al. (2015), we expect foreign ownership increases self-rationing
because these firms reply more on internal funds from parent firms and less apply for external
finance sources.
Direct export is captured by a percentage of sale. Martin Brown (2011) finds exporting firms
are more likely apply for credit because they need capital to finance for their operation. We
expect direct export has negative impact on the probability of discouraged borrowers.
Trade credit is measured by a proportion of total annual purchases of material inputs bought
on credit. Biais and Gollier (1997) and Voordeckers and Steijvers (2006) proved that trade
credit can work as a signaling instrument to reduce asymmetric information so we expect that
trade credit has negative impact on discouragement.
Innovation is a dummy variable received value 1 if firms spend on formal research and
development activities. Although, R&D is highly related to information opacity, this activity
also requires financing. Therefore, a negative impact between innovation and discouragement
is expected (Gama et al., 2017).
Internationally-recognized quality certification is a dummy variable received value 1 if firms
got internationally-recognized quality certification. This variable is expected to have a negative
effect on discouragement because quality certificate can reduce asymmetric information.
Annual financial statements checked and certified by an external auditor is a dummy variable
received value 1 if annual financial statements were checked and certified by an external
auditor. This variable is also expected to have a negative relationship to the probability of
discouraged borrowers.
Credit line is a dummy variable got value 1 if firms have a line of credit or loan from a financial
institution. Petersen and Rajan (1994) measure information transparency of firm by credit line
so we expect firms having credit line will be less self-rationing.
DISTANCE BETWEEN LENDERS AND BORROWERS.
Two proxies for distance are main business city and city with population over one million.
Financial institutions are usually located in business and crowed cities. Hence, firms located in
these areas may close to their lenders. Therefore, lenders would have better information about
them and can reduce screening errors, so discouragement can be reduced.
FIRMS PERCEPTION ABOUT THE OPERATING ENVIRONMENT
Access to finance is a dummy variable got value 1 if firm feels access to finance is a major
obstacle or very severe obstacle to their operation. We expect that firms which feel access to
finance is a problem to their operation are less likely apply for credit.
Besides the above control variables, we also control for country, industry and year as fixed
effects. Model cluster standard errors by country.
For the second hypothesis testing the impact of corruption on discouraged borrowers at
different level of country development, we add in the equation (1) an interaction term between
the corruption variable and GDP per capita.
DISCOURAGED BORROWERSi,k 1*CORRUPTIONi,k*GDPk,t 2*LENDING RELATIONSHIPi,k
3*ENTREPRENEUR CHARACTERISTICSi,k 4*FIRM CHARACTERISTICSi,k 5*PERCEPTION
OPERATING ENVIRONMENTi,k + 6*DISTANCEi,k 1*COUNTRYk 2*INDUSTRYn 3*YEARt i,k
(2)
In addition, to confirm the result, we divide the sample into two groups based on the GDP per
capita. The first group has GDP per capita higher than 5800USD, and this group is also called
upper-middle income countries based on the classification of World Bank. The second group
has GDP per capita lower than 3400USD and belongs to lower-middle income countries group
of World Bank. Using equation (1), we retest the impact of corruption on discouraged
borrowers in two groups.
5. RESULTS
5.1 Univariate analysis
- Discouraged borrowers in East Asia & Pacific and South Asia
In our sample very high (the average is 85.1%).
Reasons for not applying is stated in the Table A2. About 53% of non-applying firms reveal
that they have sufficient capital, so they s and they are not
application procedures were complex, interest rate were not favorable, collateral were too high,
size of loan and maturity were insufficient, or they fear to be rejected. These firms are called
discouraged borrowers.
The percentage of discouraged borrowers accounts for 67% of loan seekers (Table A1). This
rate is extremely high in East Asia & Pacific and South Asia. Compare to Eastern and Western
Europe in the research of Martin Brown (2011), the incidence of discouraged borrowers is 22.6%
or Eastern Europe and Central Asia in the research of Gama et al. (2017), the percentage of
discouraged borrowers is 34%. These numbers confirm the discouraged borrowers in
developing countries is an important issue and should be got the attention.
- Discouraged borrowers versus applicants
Statistics in table A4 show that discouraged borrowers and applicants are significant
differences. Corruption perception is more severe for discouraged borrowers than applicants.
However, applicants pay more gifts/unformal payments to officials than discouraged borrowers.
Applicants also seem have more relationship with banks when they usually use financial
services from banks. They have more women being top managers and their top managers have
longer years of experience. About firm characteristics, in the line with literature review,
applicants are older and bigger. Their export as percentage of sales is higher. They more invest
in innovation and use more trade credit. I applicants got internationally-
recognized quality certifications more than discouraged borrowers and their financial
statements are checked frequently by an external auditor. In addition, applicants have more line
of credit than discouraged borrowers, and they usually located in crowded cities. However,
statistic result reveals that applicants are owned by foreign individuals, companies or
organizations more than discouraged borrowers.
5.2 Findings from the main model
Regarding our equation (1), the column 1 in Table B3 shows that corruption has a negative
impact on discouraged borrowers as expected. The more level of corruption which firms feel,
the more they apply for loans. This result opposites with the finding of Galli (2017). However,
an important difference between our research and Galli (2017) is that we focus on developing
countries while Galli (2017) conducts the research on developed countries. In developing
countries, corruption is more prevalent (Olken & Pande, 2012). Hence, corruption behaviors
are perceived by firms as less serious as it would be. Officials who take part in corruption
activities less feel loss of their reputation when their actions are detected (Andvig, 1991). In
that case, they will more likely break the rule for their own benefit (Akerlof, 1980) and firms
may use corruption as a way to achieve their target
that they will pay higher Della Porta (2017). Therefore, our
result may reasonable that high level of corruption can make firms more likely apply for loans
because corruption increases their probability receiving the loans and reduces the application
costs.
Interestingly, the second hypothesis is also confirmed when the effect of corruption on
discouraged borrowers depends on the level of country development. The column 2 in Table
B3 reveals that when GDP per capita equal to 0, the effect of corruption on discouragement is
negative. However, this negative impact is reducing when GDP per capital increases, and it
turns to a positive impact when GDP per capital are high enough. These results are also stable
when we split the sample into two groups: upper-middle income countries and lower-middle
income countries. The coefficient of the variable orrupt is positive and highly significant in
upper-middle income countries (column 3 Table B3). On the contrary, it is negative and highly
significant in lower-middle income countries (column 4 Table B3).
Further investigating, we calculate the marginal effect of the interaction variable on
discouragement. We find that when GDP per capita of a country is less than about USD2200,
the effect of corruption on discouragement is negative (Table B4). That means increasing level
of corruption will reduce the incidence of discouraged borrowers and this result is significant
at 1% level. The negative effect is still significant at 10% level until the GDP per capita is
around USD2600. In contrast, in higher development countries, the impact of corruption on
discouragement is positive. At GDP per capita higher than about USD6800, the more
corruption is, the more firms discourage themselves from applying for loans and this result is
significant at 10% level (Table B4).
In summary, for developing countries in our study, corruption grease the wheel and increase
the ability firms apply for loans. However, the impact of corruption on discouraged borrowers
depends on the level of country development. In higher development countries, increasing
corruption will increase the probability firms discourage themselves from applying loans. In
contrast, in lower development countries, higher corruption may encourage firms to apply more
for loans.
For other control variables in the model, our findings are confirmed by literature. Firms using
more financial services will less discouragement. This result is also found by Gama et al. (2017).
Like Fastenbauer and Robson (2014), we find experienced top managers are likely more apply
for loans. Besides, firm characteristics proving the creditworthiness such as firm size (size);
firms getting internationally-recognized quality certification (certificate); firms having
financial statements checked and certified by an external auditor (ExterAudit) or firms having
a line of credit from a financial institution (creditline) have significant negative impacts on
discouraged borrowers. These are reasonable because creditworthy firms are less opaque
information and their chances to receive the loans are also higher. Therefore, they less
discourage themselves. These results get consensus by Martin Brown (2011), Chakravarty and
Xiang (2013) et Gama et al. (2017). In the same line with Martin Brown (2011) and Xiang et
al. (2015), we also find foreign ownership (Ffirm) increase discouragement. Finally, firm
characteristic variables related to expense such as export firms (diexport); innovation
(innovation), are negative impacts on discouraged borrowers. These firms need more capital to
finance for their operation, so they are more apply for loans (Gama et al., 2017; Martin Brown,
2011).
5.3 Explanation for the impact of corruption on discouraged borrowers
There is a consensus in literature that corruption is highly correlated with regulation. Poor
enforcement of legal institutions leads to higher corruption (Tonoyan, Strohmeyer, Habib, &
Perlitz, 2010). In case of poor enforcement of law, bureaucrats may create a lot of red tape to
make difficulties for enterprises and force them to pay bribes (Banerjee, 1997). Enterprises
involve in corruption because they want to overcome cumbersome regulations (Shleifer &
Vishny, 1993). Follow Wei and Kaufmann (1999),
benchmark to explain for the impact of corruption on discouraged borrowers. More precisely
we test the following equation:
DISCOURAGED BORROWERSi,k 1*CORRUPTIONi,k*BURDENk,t 2*LENDING
RELATIONSHIPi,k 3*ENTREPRENEUR CHARACTERISTICSi,k 4*FIRM CHARACTERISTICSi,k +
5*PERCEPTION OPERATING ENVIRONMENTi,k + 6*DISTANCEi,k 1*COUNTRYk 2*INDUSTRYn
3*YEARt i,k (3)
The interaction between corruption variable and burden of government regulation reveals that
corruption has a negative impact on discouraged borrowers when government regulation is
extremely burdensome (Table B5). Nevertheless, when government regulation is less
burdensome, the negative effect reduces and may turn to positive effect. These results are very
significant at 1% level. In detail, marginal effects in Table B6 state that if the value of
BURDEN variable is lower than 3.5, the higher corruption reduces discouragement.
However, when this benchmark is higher than 3.9, the higher corruption increases the
probability of discouragement.
The descriptive statistic shows that upper-middle income countries have BURDEN variable
higher than 4, except Thailand (average 3.5) and lower-middle income countries have
BURDEN variable lower than 3.5, except Laos (3.9) and Indonesia (3.8). The above findings,
one more time, confirm the negative effect of corruption on discouraged borrowers in lower-
middle income countries and the positive effect in upper-middle income countries. More
important, these effects are explained by burden of government regulation. In lower-middle
income countries where regulation is less efficient, to overcome burdensome regulations, firms
find corruption as a solution. This way may save their time and give them more chance to reach
their target with lower costs. In addition, the ability to be caught and punished is low for both
of giving and receiving corruption because of the pervasion of corruption in the social.
(Leys, 1965; Lui, 1985). In contrast, in upper-
middle income countries, the enforcement of regulations is more efficient, high corruption
discourages firms from applying for loans. This result is supported by
and Galli (2017).
5.4 Robustness check
- Applying other measurements for corruption
To robustness check for our model, we apply other ways to measure corruption. First, we
capture corruption by the number of times firms give gifts or pay informal payments to officials
against the number of times they apply or use public services. The higher this rate is, the more
corruption firms suffer. Second, we use Corruption Perceptions Index (CPI) published by
Transparency International. This index measures perception corruption in public sector through
experts, business people and uses a scale of 0 to 100. In contrast with the above measurements,
higher value of CPI means lower level of corruption and CPI capture perception corruption at
country level.
Columns (1) and (4) in Table B7 reveal that for the full data, corruption have negative effects
on discouragement. These results confirm our main finding that in developing countries,
corruption has a negative impact on discouraged borrowers. Testing the effect of corruption on
different groups of country, we get robustness that higher level of corruption reduces the
number of discouraged borrowers in lower-middle income countries (Columns (3), (6) in
Table7). For upper-middle income countries, only Corruption Perceptions Index confirm that
corruption hampers firms from applying for loans (Column (5) Table B7). In summarize,
corruption may reduce the application costs, so it alleviates discouraged borrowers.
Nevertheless, this impact is more significant in countries which their level of development is
low. For higher development countries, the effect slightly turns to positive and firms less apply
for loans if they feel corruption is high.
- Applying another measurement for discouraged borrowers
In further, we measure dependent variable using a stricter method. Kon and Storey (2003)
d
they will be rejected. Applying this definition, we capture discouraged borrowers by: firms
have a line of credit from a financial institution and for loans not because they
have sufficient capital, but because application procedures were complex, interest rates were
not favorable, collateral requirements were too high, size of loan and maturity were insufficient,
would be approved.
When a firm has a line of credit from a financial institution, it could prove its credit worthiness
with that financial institution. Petersen and Rajan (1994) also measured information
transparency of firm by credit line. Therefore, we classify firms having line of credits as good
borrowers and use the measurement of discouraged borrowers as the above discussion. This
way of measure captures the full definition of Kon and Storey (2003). Dependent variable got
value 1 if firms are considered discourage borrowers and got value 0 if firms apply for loans.
Result in column (1) Table B8 reveals that corruption have a negative effect on discouraged
borrowers. Although this result is insignificant, it is in the line with the main result. Hypothesis
2 is confirmed when the results of other columns in Table B8 show the effect of corruption on
discouraged borrowers depends on the level of countries development. In countries which GDP
per capita are lower than 2600USD, high corruption decreases the level of discouragement,
while in countries which GDP per capita are higher than 4000USD, high corruption increases
the probability of discouragement (Table B9). The negative effect in lower-middle income
countries and the positive impact in upper-middle income countries are still robustness at 1%
level of significant when we split the sample. These results confirm our main findings.
- Robustness check by using only truthful responses in the survey
To ensure the reliability of the results, we use only truthful responses in the survey to re-test
our main findings. We lost 2847 observations in the model. However, the results we get are the
same with the main results and all hypothesis are confirmed. In developing countries,
corruption has negative impact on discouraged borrowers and the impact of corruption depends
on the level of country development. All results are shown in Table B10.
- Robustness check by excluding China and India from the sample
China belongs to group of upper-middle income countries and India belongs to group of lower-
middle income countries. They are two big countries comparing to the rests and the number of
observations in these two countries dominate the sample. Therefore, to avoid the bias, we retest
our work by excluding China and India out of the sample. We again find high corruption
prevents applying for loans in upper-middle income countries but encourages applying for
loans in lower-middle income countries. Our findings are robustness when we interact
corruption with GDP per capita and when we split the sample (Table B11). These results
.
6. CONCLUSIONS
Using Enterprise Survey conducted by World Bank in the period from 2012 to 2016 for upper-
middle income countries and lower-middle income countries in East Asia & Pacific and South
Asia, we find that in developing countries, firms less discourage themselves when they feel
high corruption. Moreover, the effect of corruption on discouragement depending on the level
. High corruption can encourage firms in lower-middle income
countries while discourage firms in upper-middle income countries from applying for loans.
For upper-middle income countries, our finding is in the same line with research of Galli (2017).
For lower-middle income countries, although the negative effect of corruption on
in the literature. In this case, corruption can work as a lubricant to grease the wheel and mitigate
obstacles in accessing to finance . We explain this
result by the fact that burden of government regulation is one of explanations for the different
impacts of corruption on discouragement.
Our findings are robust when we measure corruption by different ways both at firm level and
country level. These results still hold when we use stricter way to capture discouraged
borrowers and we also prove that our findings
dominate the research sample.
Finally, although we find higher corruption can encourage firms in lower-middle income
countries to apply for loans, good effect for the economy.
Borrowers are less hesitance to apply for loans because corruption can reduce their application
costs and they feel that they have more chance to receive the loans. They can be both good and
bad borrowers. In the robustness check, our results state that in lower-middle income countries,
higher corruption can encourage good discouraged borrowers to apply for loans (Table B8).
(Table B3). Therefore, higher corruption more benefit for good borrowers or bad
borrowers is still a question for the future researches.
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APPENDIX
Appendix A Descriptive Statistic
Table A1 Discourage borrowers in East Asia & Pacific and South Asia
Country Total
(1) = (2)+(3)
Don't apply
(2)
Apply
(3)
Discouraged borrowers
(4)
Loan seekers
(5) = (3)+(4)
DBs/Loan seeker
(6) = (4) / (5)
China 2,556 1,913 643 771 1,414 54.5%
Malaysia 911 673 238 200 438 45.7%
Thailand 767 706 61 349 410 85.1%
Cambodia 316 276 40 99 139 71.2%
Indonesia 1,255 1,092 163 546 709 77.0%
Laos 358 306 52 100 152 65.8%
Myanma 579 502 77 156 233 67.0%
Papua New Guinea 65 49 16 11 27 40.7%
Philippines 986 804 182 66 248 26.6%
Solomon 146 118 28 18 46 39.1%
Timor Leste 109 92 17 35 52 67.3%
Vietnam 963 542 421 91 512 17.8%
India 8,777 8,010 767 3,050 3,817 79.9%
Pakistan 1,087 999 88 315 403 78.2%
Total 18,875 16,082 2,793 5,807 8,600 67.5%
Table A2 Main reasons for not applying loans Countries Main reasons for not applying loans/line of credit
No need a loan
sufficient capital
Complex application procedure
Not favorable interest
rates
High collateral
Insufficient size/
maturity
Did not think it
would be approved
Other Total
China 57.7% 11.2% 10.0% 8.6% 6.7% 4.3% 1.6% 100.0%
Malaysia 54.1% 5.9% 14.8% 6.1% 2.0% 1.5% 15.6% 100.0%
Thailand 49.1% 14.4% 18.3% 5.8% 4.7% 6.6% 1.1% 100.0%
Cambodia 61.6% 17.3% 10.7% 5.2% 2.2% 1.1% 1.8% 100.0%
Indonesia 41.9% 5.2% 25.6% 9.6% 6.0% 4.2% 7.6% 100.0%
Lao 54.3% 13.6% 8.3% 5.0% 1.0% 5.3% 12.6% 100.0%
Myanma 64.7% 17.9% 5.0% 2.8% 2.6% 3.0% 4.0% 100.0%
Papua New Guine 69.4% 6.1% 8.2% 2.0% 0.0% 6.1% 8.2% 100.0%
Philippines 88.5% 1.8% 3.8% 1.8% 0.4% 0.6% 3.1% 100.0%
Solomon Island 82.2% 5.9% 1.7% 6.8% 0.8% 2.5% 100.0%
Timor Leste 60.9% 14.1% 9.8% 7.6% 3.3% 3.3% 1.1% 100.0%
Vietnam 80.1% 5.6% 4.6% 4.1% 1.1% 1.5% 3.0% 100.0%
India 45.6% 7.7% 11.9% 9.9% 6.1% 3.1% 15.7% 100.0%
Pakistan 62.2% 5.9% 16.8% 6.5% 1.2% 3.0% 4.4% 100.0%
All sample 53.1% 8.3% 12.3% 8.1% 4.8% 3.2% 10.2% 100.0%
Tab
le A
3 M
ain
vari
able
s at
fir
m le
vel a
nd o
ther
var
iabl
es a
t cou
ntry
leve
l
Table A4 Summary statistics of variables
Variable Obs Mean Std. Dev. Min Max
Discouraged borrowers Applicants
Mean (SD)
Mean (SD)
DBs 8600 0.68 0.47 0 1 1
(0) 0
(0)
corrupt 19388 0.28 0.45 0 1 0.29 (0.45)
0.22*** (0.42)
corrupt1 11270 0.16 0.34 0 1 0.17 (0.35)
0.19** (0.36)
CPI 20212 36.26 4.76 21 50 36.62 (4.28)
36.58 (5.43)
relation1 20093 0.87 0.33 0 1 0.86 (0.35)
0.86 (0.34)
relation2 19577 0.42 0.49 0 1 0.4 (0.49)
0.47*** (0.50)
gender 20170 0.17 0.37 0 1 0.15 (0.36)
0.19*** (0.39)
experience 19738 15.11 9.56 1 70 14.53 (9.11)
16.71*** (9.52)
age 19981 18.35 13.07 0 162 17.6 (12.32)
18.03 (12.60)
size 20140 1.91 0.77 1 3 1.82 (0.76)
2.13*** (0.76)
Ffirm 20226 0.03 0.18 0 1 0.02 (0.12)
0.03*** (0.18)
diexport 20168 7.99 23.02 0 100 6.17 (20.44)
11.59*** (26.39)
tradecr 18920 45.11 35.23 0 100 41.97 (34.13)
49.44*** (33.46)
innovation 20099 0.25 0.44 0 1 0.23 (0.42)
0.33*** (0.47)
certificate 19914 0.37 0.48 0 1 0.34 (0.47)
0.43*** (0.50)
ExterAudit 19713 0.65 0.48 0 1 0.60 (0.49)
0.66*** (0.47)
creditline 19262 0.29 0.45 0 1 0.26 (0.44)
0.76*** (0.43)
access 20037 0.12 0.32 0 1 0.16 (0.36)
0.15 (0.36)
city1m 20308 0.50 0.50 0 1 0.49 (0.50)
0.60*** (0.49)
maincity 20307 0.70 0.46 0 1 0.69 (0.46)
0.63*** (0.48)
GDP 20308 2983.40 2302.54 1196 9649 2905.47 (2220.71)
3701.76*** (2630.67)
BURDEN 19391 3.58 0.48 3 5 3.63 (0.41)
3.69*** (0.57)
Significant levels 1% (***), 5% (**), 10% (*) for the difference in the means between discouraged borrowers and applicants
Appendix B Econometric models
Table B1 Variables definitions
Symbol Explain Sources
DEPENDENT VARIABLE DBs Dummy =1 for discouraged borrowers and 0 for applicants ES
DBs1 Dummy=1 for discouraged borrowers who have credit lines and 0 for applicants ES
INDEPENDENT VARIABLES
Corruption
corrupt Dummy =1 if corruption is major obstacle and very severe obstacle and 0 for otherwise ES
corrupt1 Number of times paying gifts (informal payments) divided by number of times applying public services ES
CPI Corruption perception index range from 0-100 with 100 is clear of corruption TI7
Lending relationship
relation1 Dummy =1 if firms have a checking or savings account and 0 for otherwise ES
relation2 Dummy =1 if firms have a overdraft facilitiy and 0 for otherwise ES
Entrepreneur characteristics
gender Dummy=1 if top managers are female and 0 for otherwise ES
experience Number of years of experience top managers work in the sector ES
Firm characteristics
age Firm age measured by number of year establishment operates ES
size Firm size measured by logarith number of employees for firms having more than 1 employee ES
Ffirm Dummy =1 if percentage of firm owned by foreigners is greater than 50% and 0 for otherwise ES
diexport Direct export as a percentage of sale ES
tradecr Proportion of total annual purchases of material inputs purchased on credit ES
innovation Dummy =1 if firms spent on formal research and development activities and 0 for otherwise ES
certificate Dummy =1 if firm got internationally-recognized quality certification ES
ExterAudit Dummy =1 if firms' annual financial statements are checked and certified by an external auditor ES
creditline Dummy = 1 if firms have a line of credit or loan from a financial institution ES
Firms perception about the operating environment
access Dummy =1 if access to finance is major obstacle and very severe obstacle and 0 for otherwise ES
Distance between lenders and borrowers
city1m Dummy = 1 if firm located in city with population over 1 million ES
maincity Dummy = 1 if firm located in main business city ES
Economic conditions GDP GDP per capita World Bank
BURDEN Burden of government regulation WEF8
7 Transparency International 8 The World Economic Forum
Tab
le B
2 -
Cor
rela
tion
Table B3 Main models (Equation 1 and 2)D d apply for loans because of complex procedures, high interest rate, high collateral, insufficient size of loan & maturity, fearing to be rejected and got value 0 if firms applied for loans. Column (1) tests the effect of corruption on discouraged borrowers for full data; Column (2) interact corruption and GDP per capita; Columns (3) & (4) split the sample into 2 groups (Upper-middle income countries and lower-middle income countries).
Regression of columns (1), (3) and (4) use Equation 1: Discouraged borrowersi,k 1*corrupti,k 2*relation1i,k + 3*relation2i,k + 4*genderi,k 5*experiencei,k 6*agei,k 7*sizei,k 8*Ffirmi,k 9*diexporti,k 10*tradecri,k 11*innovationi,k 12*certificatei,k + 13*ExterAuditi,k 14*creditlinei,k 15*accessi,k 16*city1mi,k 17*maincity + 1*COUNTRYk 2*INDUSTRYn 3*YEARt i,k
Regression of column (2) uses Equation 2: Discouraged borrowersi,k 1*corrupti,k*GDPk,t 2*relation1i,k + 3*relation2i,k 4*genderi,k 5*experiencei,k 6*agei,k 7*sizei,k 8*Ffirmi,k 9*diexporti,k 10*tradecri,k 11*innovationi,k 12*certificatei,k 13*ExterAuditi,k 14*creditlinei,k 15*accessi,k 16*city1mi,k 17*maincity 2*INDUSTRYn 3*YEARt i,k
(1) (2) (3) (4) VARIABLES Full data Interact GDP Upper Lower GDP 0.00003 (0.00008) corrupt -0.270** -0.66555*** 2.217*** -0.337*** (0.118) (0.19299) (0.177) (0.050) corrupt*GDP 0.00019** (0.00008) relation1 0.157 0.51565** -0.031 0.089 (0.150) (0.20377) (0.115) (0.175) relation2 -0.190** -0.05926 -0.125 -0.111** (0.094) (0.13490) (0.216) (0.045) gender -0.018 0.05300 -0.014 0.070 (0.121) (0.15194) (0.218) (0.125) experience -0.017*** -0.01938** -0.004 -0.019*** (0.005) (0.00806) (0.034) (0.004) age -0.002 0.00450 -0.025 -0.002 (0.003) (0.00725) (0.020) (0.004) size -0.239*** -0.21666** -0.123 -0.228** (0.087) (0.09339) (0.099) (0.101) Ffirm 0.393** 0.25885 0.575*** 0.620*** (0.192) (0.16729) (0.191) (0.224) diexport -0.002** -0.00351*** -0.004* -0.003** (0.001) (0.00134) (0.002) (0.001) tradecr -0.006 -0.00546 0.003 -0.009*** (0.004) (0.00378) (0.004) (0.002) innovation -0.132*** -0.33363*** -0.049 -0.116*** (0.035) (0.11183) (0.097) (0.043) certificate -0.175*** -0.17400*** -0.093 -0.175*** (0.055) (0.06360) (0.171) (0.049) ExterAudit -0.412* -0.49437* -1.021*** -0.253 (0.243) (0.26598) (0.381) (0.177) creditline -1.908*** -2.03057*** -3.278*** -1.431*** (0.563) (0.55916) (0.620) (0.346) access 0.136 0.15930 -0.061 0.209 (0.196) (0.20247) (0.567) (0.216) city1m 0.001 -0.09851 0.499 -0.032 (0.148) (0.24564) (1.034) (0.110) maincity -0.052 0.25003 0.115 -0.165 (0.162) (0.30218) (0.451) (0.129) Constant 2.774*** 2.18681*** 2.495*** 2.287*** (0.322) (0.64248) (0.835) (0.246) Observations 7,370 7,370 1,848 5,522 Correctly classified 81.94% 80.73% 85.50% 81.06% Country FE YES YES YES Industry FE YES YES YES YES Year FE YES YES YES YES Cluster country YES YES YES YES Method LOGIT LOGIT LOGIT LOGIT
Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
Table B4 Marginal effect of corruption on the probability of discouragement at different level of GDP per capita
margins, dydx(corrupt) at (GDP=(1000 (200) 9000)) Average marginal effects Number of obs = 7370 Model VCE : Robust Expression : Pr(DBs), predict() dy/dx w.r.t. : 1.corrupt
1.corrupt _at GDP dy/dx
Delta-method Std. Err. z P>z [95% Conf. Interval]
1000 -0.0749 0.0198 -3.78 0.000 -0.1138 -0.0360
1200 -0.0686 0.0183 -3.76 0.000 -0.1044 -0.0328
1400 -0.0624 0.0169 -3.70 0.000 -0.0955 -0.0293
1600 -0.0562 0.0157 -3.58 0.000 -0.0870 -0.0254
1800 -0.0501 0.0148 -3.38 0.001 -0.0791 -0.0211
2000 -0.0440 0.0142 -3.10 0.002 -0.0719 -0.0162
2200 -0.0380 0.0139 -2.73 0.006 -0.0653 -0.0107
2400 -0.0320 0.0140 -2.28 0.023 -0.0595 -0.0045
2600 -0.0261 0.0145 -1.80 0.071 -0.0545 0.0023
2800 -0.0202 0.0152 -1.33 0.184 -0.0501 0.0096
6600 0.0787 0.0481 1.64 0.102 -0.0155 0.1729
6800 0.0832 0.0500 1.66 0.096 -0.0147 0.1811
7000 0.0876 0.0519 1.69 0.091 -0.0141 0.1892
7200 0.0919 0.0538 1.71 0.087 -0.0135 0.1972
7400 0.0961 0.0556 1.73 0.084 -0.0129 0.2052
7600 0.1003 0.0575 1.74 0.081 -0.0124 0.2130
7800 0.1044 0.0594 1.76 0.079 -0.0120 0.2208
8000 0.1084 0.0613 1.77 0.077 -0.0117 0.2284
8200 0.1123 0.0631 1.78 0.075 -0.0114 0.2360
8400 0.1161 0.0650 1.79 0.074 -0.0112 0.2434
8600 0.1199 0.0668 1.79 0.073 -0.0111 0.2508
8800 0.1235 0.0686 1.80 0.072 -0.0110 0.2581
9000 0.1271 0.0705 1.80 0.071 -0.0110 0.2652
Note: dy/dx for factor levels is the discrete change from the base level.
Graph B1 Average marginal effects of corruption on the probability of discouragement at different level of GDP per capita
0 2000 4000 6000 8000 10000GDP per capita in $
Average Marginal Effects of 1.corrupt with 95% CIs
Table B5 Interaction between corruption and burden of government regulation (Equation 3)
D apply for loans because of complex procedures, high interest rate, high collateral, insufficient size of loan & maturity, fearing to be rejected and got value 0 if firms applied for loans.
Regression of the model : Discouraged borrowersi,k 1*corrupti,k*BURDENk,t 2*relation1i,k + 3*relation2i,k 4*genderi,k + 5*experiencei,k 6*agei,k 7*sizei,k 8*Ffirmi,k 9*diexporti,k 10*tradecri,k 11*innovationi,k 12*certificatei,k 13*ExterAuditi,k
14*creditlinei,k + 15*accessi,k 16*city1mi,k 17*maincity 2*INDUSTRYn 3*YEARt i,k
VARIABLES Interact BURDEN BURDEN 0.304 (0.573) corrupt -4.842*** (1.084) corrupt*BURDEN 1.325*** (0.312) relation1 0.645*** (0.176) relation2 -0.078 (0.137) gender 0.072 (0.170) experience -0.016** (0.006) age 0.004 (0.007) size -0.218** (0.091) Ffirm 0.299 (0.232) diexport -0.003** (0.001) tradecr -0.005 (0.004) innovation -0.354*** (0.111) certificate -0.170*** (0.066) ExterAudit -0.490* (0.263) creditline -2.009*** (0.580) access 0.135 (0.211) city1m -0.038 (0.215) maincity 0.207 (0.278) Constant 0.917 (2.448) Observations 7,063 Correctly classified 80.29% Industry FE YES Year FE YES Cluster country YES Method LOGIT
Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
Table B6 Marginal effect of corruption on the probability of discouragement at different level of burden of government regulation
margins, dydx(corrupt) at (BURDEN=(2.7 (.1) 5.0))
Average marginal effects Number of obs = 7063 Model VCE : Robust
Expression : Pr(DBs), predict() dy/dx w.r.t. : 1.corrupt
1.corrupt
_at BURDEN dy/dx Delta-method
Std. Err. z P>z [95% Conf. Interval]
2.7 -0.2148 0.0327 -6.58 0.000 -0.2788 -0.1507
2.8 -0.1912 0.0287 -6.66 0.000 -0.2474 -0.1349
2.9 -0.1675 0.0251 -6.67 0.000 -0.2168 -0.1183
3.0 -0.1439 0.0219 -6.56 0.000 -0.1870 -0.1009
3.1 -0.1205 0.0193 -6.24 0.000 -0.1584 -0.0827
3.2 -0.0975 0.0173 -5.64 0.000 -0.1313 -0.0636
3.3 -0.0748 0.0160 -4.69 0.000 -0.1061 -0.0435
3.4 -0.0528 0.0154 -3.43 0.001 -0.0829 -0.0226
3.5 -0.0314 0.0155 -2.03 0.043 -0.0617 -0.0010
3.6 -0.0107 0.0161 -0.67 0.505 -0.0423 0.0208
3.7 0.0090 0.0171 0.53 0.598 -0.0245 0.0426
3.8 0.0279 0.0185 1.51 0.131 -0.0083 0.0641
3.9 0.0458 0.0201 2.28 0.023 0.0064 0.0852
4.0 0.0627 0.0220 2.84 0.004 0.0195 0.1059
4.1 0.0785 0.0243 3.23 0.001 0.0308 0.1261
4.2 0.0932 0.0270 3.45 0.001 0.0402 0.1461
4.3 0.1068 0.0302 3.54 0.000 0.0476 0.1659
4.4 0.1192 0.0339 3.52 0.000 0.0529 0.1856
4.5 0.1306 0.0381 3.43 0.001 0.0559 0.2052
4.6 0.1408 0.0429 3.28 0.001 0.0568 0.2248
4.7 0.1499 0.0482 3.11 0.002 0.0555 0.2443
4.8 0.1579 0.0540 2.93 0.003 0.0521 0.2637
4.9 0.1649 0.0602 2.74 0.006 0.0469 0.2829
5.0 0.1709 0.0668 2.56 0.011 0.0399 0.3019
Note: dy/dx for factor levels is the discrete change from the base level.
Table B7 Robustness check: replace other proxies for corruption (Equation1)D apply for loans because of complex procedures, high interest rate, high collateral, insufficient size of loan & maturity, fearing to be rejected and got value 0 if firms applied for loans.
Columns (1), (2), (3) test the impact of number of times firms pay gifts (corrupt1) on discouraged borrowers. Column (1) conducts the test on full data, column (2) for group of upper-middle income countries and column (3) for group of lower-middle income countries.
Regression of columns (1), (2) and (3) : Discouraged borrowersi,k 1*corrupt1i,k 2*relation1i,k 3*relation2i,k 4*genderi,k + 5*experiencei,k 6*agei,k 7*sizei,k 8*Ffirmi,k 9*diexporti,k 10*tradecri,k 11*innovationi,k 12*certificatei,k 13*ExterAuditi,k
14*creditlinei,k 15*accessi,k 16*city1mi,k 17*maincity + 1*COUNTRYk 2*INDUSTRYn 3*YEARt i,k
Columns (4), (5), (6) test the impact of Corruption Perception Index (CPI) on discouraged borrowers. Colume (4) conducts the test on full data, column (5) for group of upper-middle income countries and column (6) for group of lower-middle income countries.
Regression of columns (4), (5) and (6) : Discouraged borrowersi,k 1*CPIk,t 2*relation1i,k + 3*relation2i,k 4*genderi,k + 5*experiencei,k 6*agei,k 7*sizei,k 8*Ffirmi,k 9*diexporti,k 10*tradecri,k 11*innovationi,k 12*certificatei,k 13*ExterAuditi,k
14*creditlinei,k 15*accessi,k 16*city1mi,k 17*maincity 2*INDUSTRYn 3*YEARt i,k
(1) (2) (3) (4) (5) (6) VARIABLES Full data Upper Lower CPI-Full data CPI-Upper CPI-Lower corrupt1 -0.201** 0.045 -0.258*** (0.078) (0.139) (0.070) CPI 0.036 -0.121*** 0.124** (0.037) (0.035) (0.059) relation1 -0.228 0.473* -0.420** 0.398** 0.103 0.093 (0.205) (0.251) (0.208) (0.161) (0.136) (0.178) relation2 -0.212*** -0.041 -0.153*** -0.050 -0.134 0.053 (0.059) (0.077) (0.048) (0.128) (0.168) (0.102) gender 0.086 0.059 0.132 0.039 -0.156 0.063 (0.096) (0.279) (0.131) (0.168) (0.212) (0.089) experience -0.019*** -0.002 -0.022*** -0.018** -0.003 -0.027*** (0.004) (0.026) (0.004) (0.007) (0.030) (0.005) age -0.001 -0.029 0.001 0.004 -0.027 0.005 (0.004) (0.021) (0.005) (0.007) (0.020) (0.007) size -0.315*** -0.119 -0.334*** -0.220** -0.136** -0.229** (0.052) (0.084) (0.065) (0.098) (0.069) (0.112) Ffirm 0.337 -0.090 0.635** 0.192 0.522*** 0.337 (0.266) (0.093) (0.314) (0.182) (0.192) (0.294) diexport -0.003** -0.009** -0.004** -0.004*** -0.004 -0.004*** (0.001) (0.004) (0.001) (0.001) (0.003) (0.001) tradecr -0.006 0.006 -0.010*** -0.006 0.004 -0.011*** (0.004) (0.004) (0.002) (0.004) (0.004) (0.002) innovation -0.045 -0.146*** 0.017 -0.339*** -0.023 -0.301*** (0.053) (0.050) (0.039) (0.112) (0.182) (0.108) certificate -0.234*** 0.005 -0.267*** -0.181*** -0.123 -0.141** (0.079) (0.191) (0.067) (0.065) (0.141) (0.060) ExterAudit -0.438 -1.208*** -0.102 -0.438* -0.849* -0.312 (0.315) (0.457) (0.146) (0.265) (0.463) (0.281) creditline -1.795*** -3.079*** -1.357*** -2.091*** -3.205*** -1.624*** (0.611) (0.617) (0.443) (0.568) (0.648) (0.441) access 0.008 0.233 0.025 0.166 0.201 0.251 (0.257) (0.710) (0.290) (0.234) (0.700) (0.251) city1m 0.183* 0.400 0.134 -0.047 0.210 -0.135 (0.103) (0.840) (0.084) (0.224) (1.185) (0.163) maincity -0.137 -0.152 -0.080 0.239 0.245 0.241 (0.134) (0.550) (0.122) (0.285) (0.550) (0.315) Constant 3.087*** 2.048*** 2.502*** 1.031 7.149*** -1.195 (0.433) (0.342) (0.458) (1.464) (2.091) (2.263) Observations 4,729 1,128 3,601 7,657 1,937 5,720 Correctly classified 80.69% 82.36% 79.76% 80.72% 85.23% 79.98% Country FE YES YES YES Industry FE YES YES YES YES YES YES Year FE YES YES YES YES YES YES Cluster country YES YES YES YES YES YES Method LOGIT LOGIT LOGIT LOGIT LOGIT LOGIT
Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
Table B8 Robustness check: changing the way to measure discouraged borrowers (Equation 1 and 2) have credit line and they need credit, but they
of complex procedures, high interest rate, high collateral, insufficient size of loan & maturity, fearing to be rejected. Column (1) tests the effect of corruption on discouraged borrowers for full data; Column (2) interact corruption and GDP per capita; Columns (3) & (4) split the sample into 2 groups (Upper-middle income countries and lower-middle income countries).
Regression of columns (1), (3) and (4) use Equation 1 : Discouraged borrowersi,k 1*corrupti,k 2*relation1i,k + 3*relation2i,k + 4*genderi,k 5*experiencei,k 6*agei,k 7*sizei,k 8*Ffirmi,k 9*diexporti,k 10*tradecri,k 11*innovationi,k 12*certificatei,k + 13*ExterAuditi,k 14*creditlinei,k 15*accessi,k 16*city1mi,k 17*maincity + 1*COUNTRYk 2*INDUSTRYn 3*YEARt i,k
Regression of column (2) uses Equation 2: Discouraged borrowersi,k 1*corrupti,k*GDPk,t 2*relation1i,k + 3*relation2i,k 4*genderi,k 5*experiencei,k 6*agei,k 7*sizei,k 8*Ffirmi,k 9*diexporti,k 10*tradecri,k 11*innovationi,k 12*certificatei,k 13*ExterAuditi,k 14*creditlinei,k 15*accessi,k 16*city1mi,k 17*maincity 2*INDUSTRYn 3*YEARt i,k
(1) (2) (3) (4) VARIABLES Strict Strict-Interract GDP Strict-Upper Strict-Lower GDP 0.00001 (0.00006) corrupt -0.419 -0.98928*** 2.019*** -0.579*** (0.265) (0.22171) (0.149) (0.155) corrupt*GDP 0.00030*** (0.00005) relation1 -0.108 0.45220 0.019 -0.253 (0.176) (0.29939) (0.180) (0.196) relation2 -0.010 0.17559 0.107 -0.067 (0.125) (0.16797) (0.117) (0.132) gender -0.051 -0.12815 -0.219 0.099 (0.221) (0.18534) (0.210) (0.270) experience -0.031*** -0.04068*** 0.001 -0.035*** (0.005) (0.00591) (0.028) (0.002) age -0.003 0.00633 -0.032 -0.000 (0.004) (0.00789) (0.025) (0.006) size -0.125 -0.15527 -0.022 -0.129 (0.137) (0.12932) (0.220) (0.151) Ffirm 0.537* 0.64286*** 0.898*** 0.440 (0.286) (0.21869) (0.105) (0.489) diexport -0.006*** -0.00761*** -0.002 -0.006*** (0.002) (0.00142) (0.001) (0.002) tradecr -0.008*** -0.00880*** -0.003 -0.010*** (0.003) (0.00302) (0.003) (0.003) innovation -0.103** -0.31436*** -0.199 -0.094 (0.046) (0.10290) (0.205) (0.062) certificate -0.316*** -0.29040** -0.068 -0.398*** (0.107) (0.11834) (0.166) (0.089) ExterAudit -0.528 -0.56706 -1.069* -0.401*** (0.333) (0.36326) (0.628) (0.119) access 0.333 0.45922* 0.213 0.349 (0.225) (0.25539) (0.260) (0.289) city1m -0.166* -0.46418 -0.303 -0.117 (0.093) (0.29262) (1.001) (0.071) maincity -0.222 0.36086 0.138 -0.310 (0.224) (0.44476) (0.530) (0.200) Constant 0.855 0.25070 -0.104 2.205** (0.920) (0.84723) (0.940) (0.879) Observations 3,177 3,177 782 2,395 Correctly classified 77.02% 74.41% 80.18% 76.08% Country FE YES YES YES Industry FE YES YES YES YES Year FE YES YES YES YES Cluster country YES YES YES YES Method LOGIT LOGIT LOGIT LOGIT
Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
Table B9 Marginal effect of corruption on the probability of discouragement at different level of GDP per capita. Discouraged borrowers is measured by strict method.
margins, dydx(corrupt) at (GDP=(1000 (200) 9000))
Average marginal effects Number of obs = 3177 Model VCE : Robust Expression : Pr(DBs2), predict() dy/dx w.r.t. : 1.corrupt
1.corrupt _at GDP dy/dx
Delta-method Std. Err. z P>z [95% Conf. Interval]
1000 -0.1151 0.0293 -3.93 0.000 -0.1725 -0.0577
1200 -0.1055 0.0276 -3.82 0.000 -0.1595 -0.0514
1400 -0.0957 0.0260 -3.68 0.000 -0.1466 -0.0448
1600 -0.0859 0.0245 -3.51 0.000 -0.1339 -0.0379
1800 -0.0760 0.0231 -3.29 0.001 -0.1212 -0.0307
2000 -0.0660 0.0218 -3.02 0.002 -0.1089 -0.0232
2200 -0.0561 0.0208 -2.70 0.007 -0.0967 -0.0154
2400 -0.0460 0.0199 -2.31 0.021 -0.0850 -0.0071
2600 -0.0359 0.0192 -1.87 0.061 -0.0736 0.0017
2800 -0.0258 0.0188 -1.37 0.169 -0.0626 0.0110
4000 0.0352 0.0211 1.67 0.096 -0.0062 0.0766
4200 0.0454 0.0222 2.05 0.041 0.0020 0.0888
4400 0.0556 0.0233 2.39 0.017 0.0099 0.1012
4600 0.0657 0.0245 2.68 0.007 0.0177 0.1137
4800 0.0758 0.0258 2.94 0.003 0.0253 0.1263
5000 0.0859 0.0271 3.17 0.002 0.0328 0.1390
5200 0.0960 0.0284 3.37 0.001 0.0402 0.1517
5400 0.1060 0.0298 3.56 0.000 0.0476 0.1644
5600 0.1159 0.0312 3.72 0.000 0.0549 0.1770
5800 0.1258 0.0325 3.87 0.000 0.0621 0.1896
6000 0.1357 0.0339 4.01 0.000 0.0693 0.2021
6200 0.1455 0.0352 4.13 0.000 0.0765 0.2145
6400 0.1552 0.0365 4.25 0.000 0.0837 0.2268
6600 0.1649 0.0378 4.36 0.000 0.0908 0.2389
6800 0.1744 0.0390 4.47 0.000 0.0979 0.2509
7000 0.1839 0.0402 4.57 0.000 0.1050 0.2628
7200 0.1933 0.0414 4.67 0.000 0.1121 0.2745
7400 0.2026 0.0426 4.76 0.000 0.1191 0.2860
7600 0.2117 0.0437 4.85 0.000 0.1262 0.2973
7800 0.2208 0.0447 4.94 0.000 0.1331 0.3085
8000 0.2298 0.0458 5.02 0.000 0.1401 0.3195
8200 0.2386 0.0468 5.10 0.000 0.1469 0.3303
8400 0.2473 0.0477 5.18 0.000 0.1538 0.3409
8600 0.2559 0.0487 5.26 0.000 0.1605 0.3513
8800 0.2643 0.0496 5.33 0.000 0.1672 0.3615
9000 0.2726 0.0504 5.40 0.000 0.1738 0.3715 Note: dy/dx for factor levels is the discrete change from the base level.
Table B10 Robustness check: using only truthful answers and re-test the main model (Equation 1 and 2)
D interest rate, high collateral, insufficient size of loan & maturity, fearing to be rejected and got value 0 if firms applied for loans. Column (1) tests the effect of corruption on discouraged borrowers for full data; Column (2) interact corruption and GDP per capita; Columns (3) & (4) split the sample into 2 groups (Upper-middle income countries and lower-middle income countries). Data includes only truthful answers. Regression of columns (1), (3) and (4) use Equation 1: Discouraged borrowersi,k 1*corrupti,k 2*relation1i,k + 3*relation2i,k +
4*genderi,k 5*experiencei,k 6*agei,k 7*sizei,k 8*Ffirmi,k 9*diexporti,k 10*tradecri,k 11*innovationi,k 12*certificatei,k + 13*ExterAuditi,k 14*creditlinei,k 15*accessi,k 16*city1mi,k 17*maincity + 1*COUNTRYk 2*INDUSTRYn 3*YEARt i,k
Regression of column (2) uses Equation 2 : Discouraged borrowersi,k 1*corrupti,k*GDPk,t 2*relation1i,k + 3*relation2i,k 4*genderi,k 5*experiencei,k 6*agei,k 7*sizei,k 8*Ffirmi,k 9*diexporti,k 10*tradecri,k 11*innovationi,k 12*certificatei,k 13*ExterAuditi,k 14*creditlinei,k 15*accessi,k 16*city1mi,k 17*maincity 2*INDUSTRYn 3*YEARt i,k
(1) (2) (3) (4) VARIABLES Full data Interact GDP Upper Lower GDP 0.00014* (0.00007) corrupt -0.512*** -0.95441*** 1.570* -0.527*** (0.139) (0.24256) (0.858) (0.047) corruptGDP 0.00023* (0.00012) relation1 -0.476*** 0.10904 0.009 -0.646*** (0.139) (0.20695) (0.416) (0.147) relation2 -0.175 -0.02242 -0.073 -0.099 (0.172) (0.20633) (0.249) (0.119) gender -0.060 -0.01266 -0.373*** 0.070 (0.161) (0.14114) (0.144) (0.168) experience -0.019** -0.02273** -0.029 -0.017*** (0.008) (0.00933) (0.041) (0.004) age -0.004 0.00181 -0.023 -0.004 (0.003) (0.00594) (0.016) (0.004) size -0.232*** -0.23436*** -0.127 -0.208** (0.077) (0.07994) (0.097) (0.095) Ffirm 0.077 -0.00585 0.250* 0.408 (0.176) (0.13921) (0.151) (0.257) diexport -0.003*** -0.00383*** -0.004 -0.003** (0.001) (0.00131) (0.003) (0.001) tradecr -0.005 -0.00510 0.006*** -0.009*** (0.004) (0.00389) (0.002) (0.002) innovation -0.036 -0.19598 -0.343*** 0.096 (0.117) (0.16300) (0.086) (0.065) certificate -0.335*** -0.32744*** -0.190*** -0.415*** (0.073) (0.07888) (0.049) (0.062) ExterAudit -0.348*** -0.39160** -0.434** -0.497*** (0.133) (0.16940) (0.214) (0.100) creditline -1.809*** -1.92255*** -3.428*** -1.222*** (0.691) (0.68086) (0.489) (0.412) access -0.033 -0.06280 -0.265 0.060 (0.194) (0.18247) (0.257) (0.194) city1m 0.167 -0.03415 0.610 0.125 (0.169) (0.24200) (1.820) (0.129) maincity 0.050 0.39781 0.281 -0.116 (0.239) (0.38706) (0.570) (0.180) Constant 3.061*** 1.62536** 2.207 2.855*** (0.533) (0.65359) (1.461) (0.540) Observations 4,523 4,523 1,278 3,245 Correctly classified Country FE YES YES YES Industry FE YES YES YES YES Year FE YES YES YES YES Cluster country YES YES YES YES Method LOGIT LOGIT LOGIT LOGIT
Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
Table B11 Robustness check: exclude China and India from the sample (Equation 1 and 2) apply for loans because of complex procedures, high interest rate,
high collateral, insufficient size of loan & maturity, fearing to be rejected and got value 0 if firms applied for loans. Column (1) tests the impact of corruption on discouraged borrowers for the data excluding China and India; Column (2) interacts corruption and GDP per capita; Column (3) & (4) split the sample into 2 groups (Upper-middle income countries excluding China and lower-middle income countries excluding India).
Regression of columns (1), (3) and (4) use Equation 1: Discouraged borrowersi,k 1*corrupti,k 2*relation1i,k + 3*relation2i,k + 4*genderi,k 5*experiencei,k 6*agei,k 7*sizei,k 8*Ffirmi,k 9*diexporti,k 10*tradecri,k 11*innovationi,k 12*certificatei,k + 13*ExterAuditi,k 14*creditlinei,k 15*accessi,k 16*city1mi,k 17*maincity + 1*COUNTRYk 2*INDUSTRYn 3*YEARt i,k
Regression of column (2) use Equation 2 : Discouraged borrowersi,k 1*corrupti,k*GDPk,t 2*relation1i,k + 3*relation2i,k 4*genderi,k 5*experiencei,k 6*agei,k 7*sizei,k 8*Ffirmi,k 9*diexporti,k 10*tradecri,k 11*innovationi,k 12*certificatei,k 13*ExterAuditi,k 14*creditlinei,k 15*accessi,k 16*city1mi,k 17*maincity 2*INDUSTRYn 3*YEARt i,k
(1) (2) (3) (4) VARIABLES Exclude Chi&Ind Exclude Chi&Ind Upper-Exclude Chi Lower-Exclude Ind GDP 0.00004 (0.00008) corrupt -0.051 -0.91541*** 2.037*** -0.410* (0.353) (0.29824) (0.152) (0.220) corruptGDP 0.00027*** (0.00006) relation1 -0.033 0.21874 -0.496 -0.073 (0.116) (0.16580) (0.446) (0.146) relation2 -0.026 0.02541 0.117 -0.146 (0.174) (0.16491) (0.522) (0.169) gender -0.016 0.18216 0.148 -0.054 (0.162) (0.20044) (0.179) (0.180) experience -0.017 -0.01784 0.094*** -0.033*** (0.015) (0.01723) (0.005) (0.010) age 0.004 0.02045* -0.067*** 0.017** (0.011) (0.01221) (0.006) (0.007) size -0.077 -0.04085 -0.238* -0.034 (0.154) (0.14020) (0.137) (0.189) Ffirm 0.544** 0.50977** 1.252 0.634*** (0.265) (0.21605) (1.317) (0.242) diexport -0.001 -0.00200 0.003 -0.001 (0.002) (0.00245) (0.006) (0.003) tradecr -0.003* -0.00357** -0.008*** -0.002 (0.002) (0.00144) (0.001) (0.002) innovation -0.046 -0.65345*** 0.565*** -0.117 (0.181) (0.21899) (0.190) (0.191) certificate -0.337** -0.34008* -0.453*** -0.217 (0.162) (0.19134) (0.124) (0.260) ExterAudit -0.471* -0.68716*** -0.285 -0.647** (0.252) (0.23135) (0.245) (0.299) creditline -2.493*** -2.63835*** -2.756** -2.516*** (0.359) (0.33339) (1.221) (0.356) access 0.683*** 0.72412*** 1.126** 0.688*** (0.111) (0.12038) (0.444) (0.112) city1m 0.186 0.28839 0.095 0.293** (0.276) (0.30504) (0.766) (0.120) maincity 0.353* 0.68513*** 0.888*** 0.172 (0.200) (0.25985) (0.003) (0.190) Constant 3.481*** 2.74352*** 2.133*** 2.964*** (0.526) (0.49801) (0.538) (0.463) Observations 2,543 2,543 597 1,946 Correctly classified 81.71% 80.30% 85.76% 81.55% Country FE YES YES YES Industry FE YES YES YES YES Year FE YES YES YES YES Cluster country YES YES YES YES Method LOGIT LOGIT LOGIT LOGIT
Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1