ARE GOOD OR BAD BORROWERS DISCOURAGED FROM
APPLYING FOR LOANS? EVIDENCE FROM US
SMALL BUSINESS CREDIT MARKETS
Working Paper No. 95
March 2008
Liang Han, Stuart Fraser and David J Storey
Warwick Business School’s Small and Medium Sized Enterprise Centre
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1
ARE GOOD OR BAD BORROWERS DISCOURAGED FROM APPLYING FOR
LOANS? EVIDENCE FROM US SMALL BUSINESS CREDIT MARKETS
Liang Han (Hull University Business School)†
Stuart Fraser (Warwick Business School)
David J. Storey (Warwick Business School)
Abstract
This paper conducts an empirical analysis on a U.S small business finance data-set to
investigate which factors affect the likelihood of being a discouraged borrower. We find that
riskier borrowers have higher probabilities of being discouraged. The results suggest that, in
the US, discouragement is an efficient self-rationing mechanism, in that high risk borrowers
are more likely to be discouraged than low risk borrowers, and the efficiency of this
mechanism increases as information asymmetries are resolved. We also report that low risk
borrowers are less likely to be discouraged in concentrated markets than in competitive
markets; and, in concentrated markets, high risk borrowers are more likely to be discouraged
the longer their financial relationships. These results suggest that discouragement is more
efficient in concentrated markets than in competitive markets. There is little evidence to
suggest that application costs discourage small businesses from borrowing.
JEL: G14 G21
Keywords:
†
Discouraged Borrowers; Self-rationing;
Information Problem
Corresponding author: L.Han@hull.ac.uk.
2
ARE GOOD OR BAD BORROWERS DISCOURAGED FROM APPLYING FOR
LOANS? EVIDENCE FROM US SMALL BUSINESS CREDIT MARKETS
1. Introduction
The problem of imperfect and asymmetric information lies at the heart of financing small
businesses (e.g. Berger and Udell, 1998). This is because small firms are recognised as
more informationally opaque than large firms, and the collection of private information,
such as the risk type of small business borrowers, is costly (Ang, 1991). In providing
finance for small firms, bank lenders are generally assumed to have poorer information
about the individual small business than the borrower 1 . Theoretically, faced by
asymmetric information, banks could either ration credit (Stiglitz and Weiss, 1981) or
offer a menu of contracts which act as a self-selection mechanism to distinguish good,
from bad, borrowers (Bester, 1985). However, in conditions of imperfect information,
amongst potential small business borrowers, some do not apply for bank loans even if they
need capital. This is because they think their application will be rejected. Such borrowers
are called ‘Discouraged Borrowers’.
Kon and Storey (2003) define discouraged borrowers as those creditworthy (good)
borrowers who do not apply because they feel they will be rejected. In this paper, we
extend the scope of discouraged borrowers by including all types of borrowers (both good
and bad) who experience discouragement. There are two principal reasons for doing this.
Firstly, in purely practical terms, it is difficult to make a clear empirical distinction
between good and bad borrowers since the definition of a creditworthy borrower may vary
from lender to lender depending on risk tolerance levels.
Secondly, and relating to the
1
Jovanovic (1982), Storey (1994) and De Meza and Southey (1996) argue that the information advantage of
borrowers is created through a learning-by-doing process. New entrepreneurs are likely to be less well
informed than established entrepreneurs. External lenders, e.g. banks, however have considerable
experiential knowledge of new firms.
3
principal aim of the paper, in order to test whether discouragement is an efficient selfrationing mechanism, a sample which includes both good and bad borrowers in the
discouraged group is required.
According to the theory developed by Kon and Storey (2003) the reason discouraged
borrowers exist is because of information asymmetries and positive application costs.
They argue that banks do not know borrower types - either good (low risk) or bad (high
risk). If they did, then they would charge an appropriate premium. But discouragement can
also be viewed as a self-rationing mechanism by which potential borrowers make an
application decision. This paper acknowledges that bad borrowers can also be discouraged
so that discouragement is a good thing where the ‘bad’ are discouraged. Banks do not care
if bad borrowers are discouraged; but they do care if good borrowers are discouraged
and/or if bad borrowers get into the loan pool. So, in terms of discouraged borrowers, we
define self-rationing as efficient when it discourages bad borrowers but not good
borrowers. Hence, with efficient self-rationing, good borrowers will be less likely to be
discouraged than bad ones. Furthermore, in a fully informed market, all bad borrowers are
discouraged from applying.
This paper conducts both univariate and multivariate analysis on data from the 1998 U.S
Survey of Small Business Finances (SSBF) and finds that, after controlling for the
characteristics of both the business and entrepreneur, riskier borrowers are more likely to
be discouraged. With the improvement of information quality, as financial relationships
become longer, riskier borrowers have an increasing likelihood of being discouraged.
These results suggest that discouragement is an efficient self-rationing mechanism. We
also report that low risk borrowers are less likely to be discouraged in concentrated
markets than in competitive markets and in concentrated market, high risk borrowers are
4
more likely to be discouraged over longer relationships, suggesting that discouragement is
more efficient in concentrated markets than in competitive markets. However, we find
little evidence that application costs are a key determinant of discouragement amongst
small businesses. The rest of the paper is structured as follows. Section 2 reviews both
theoretical and empirical literature related to discouraged borrowers. Section 3 introduces
the data and the methodology employed in this paper. Section 4 reports the empirical
findings and Section 5 concludes.
2. Discouraged Borrowers – Theories and Empirics
Discouraged borrowers have drawn insufficient attention from researchers until very
recently. This is partly because they do not influence risk levels in banks’ loan portfolios
and partly because information on them is difficult for banks to obtain. However, their
importance is now increasingly recognised in both theoretical and empirical work for three
reasons. Firstly, it has been found that discouragement leads to financial constraints for
small businesses as they are more likely to report discouragement than rejection
(Levenson and Willard, 2002). Secondly, discouragement may point to discrimination in
the market for small business finance. In this context, it has been reported that the
likelihood of being discouraged varies with the ethnic background of the entrepreneur both
in the US (Cavalluzzo et al. 2002) and in the UK (Fraser, 2007) with discouragement
being more likely amongst ethnic minority groups than white owned businesses. Thirdly,
the examination of discouraged borrowers is actually a test of the lending efficiency of
financial institutions in financing small businesses in terms of screening errors and
application costs (Kon and Storey, 2003).
5
According to the theory proposed by Kon and Storey (2003), one of the most important
determinants of discouragement, which is of crucial concern to lenders, is the
unobservable quality of borrowers. Ideally, lenders would like to encourage good
borrowers and discourage bad borrowers, but they do not know, or do not know exactly,
the borrower’s quality because of information asymmetries. In the empirical literature,
borrower quality is measured in several ways. For example it is proxied by a Dun and
Bradstreet Score (Cavalluzzo et al. 2002), or by the variance of returns to equity (Booth
and Booth, 2006).
Imperfect information therefore lies at the heart of the concept of discouraged borrowers
and the acquisition of reliable information from informationally opaque small business
borrowers is a concern to lenders. Petersen and Rajan (2002) use business credit card and
credit lines to measure the information transparency of the business. Business credit card
holders and lines of credit users are argued to be informationally transparent because their
creditworthiness has been assessed in the external credit market. Relationship lending
plays an important role in alleviating the adverse consequences of information
asymmetries for small business borrowers. Indeed, relationships are argued to improve
the availability (Petersen and Rajan, 1994) and reduce the cost (Berger and Udell, 1995) of
small business finance. However, on the downside for borrowers, relationships may yield
a rent to banks and as a result, loan rates may increase with the length of the relationship
(Degryse and Ongena, 2005). Apart from the effects of the length of relationships on small
business finance, the type of financial service supplied (e.g. Petersen and Rajan, 1994;
Carey et al. 1998; Cowling, 1999) and the distance between banks and their customers
(e.g. Petersen and Rajan, 2002; Degryse and Ongena, 2005) have previously been
identified as influencing credit access and price.
6
Another important determinant in Kon and Storey’s model of discouraged borrowers are
screening errors made by lenders which again arise due to information asymmetries. In
this regard, some types of lenders have an advantage in terms of private information
acquisition. Banks, for instance, may collect information by monitoring transactions on
current accounts held by borrowers; whilst, non-financial lenders and venture capitalists
do not provide such services to their small business customers. Banks are the most
important and comprehensive provider of financial services for small businesses (Bilter et
al. 2001). Consequently, banks may have higher acceptance rates on loan applications
from small businesses because of access to information on the businesses use of other
financial services provided by the bank2. Another possible reason is that small firms have
lower application costs when they apply for credit from banks than from other types of
institution. Thus, small firms are less likely to be discouraged if they expect to apply for
finance from a bank.
Physical distance between lenders and borrowers is another key factor in financing small
businesses. On the one hand, for information reasons, external financiers prefer small
business customers close to them as monitoring costs are lower and moral hazard
problems are more easily identifiable. On the other hand, small businesses are discouraged
partly because of higher application costs incurred by the physical distance to lenders
(Kon and Storey, 2003). However, it is now well documented that small business
financing behaviour has been significantly altered with the development of new
information technologies. For example, Petersen and Rajan (2002) reported that in the
U.S., the probability that a bank communicates with its small business borrowers in person,
instead of using telephone or mail, declined from 59% in 1973 to 36% in 1993.
2
We find that 71% of applicants who were always approved borrowed from banks compared with only 29%
of those that borrowed from non-bank institutions. Unfortunately, we don’t know whether banks have a
higher or a lower rejection rate on loan applications.
7
Meanwhile, the average physical distance from a bank to its typical small business
borrowers had increased from 16 miles, in the period 1973-1979, to 68 miles in the period
1990-1993. Indeed, online banking now allows small businesses to lower or eliminate
some transaction costs, such as those of transport. It also facilitates access to multiple
lenders, located at a distance from the business, by lowering search costs. Han (2008)
provides empirical support for this using a UK SME finance dataset. Equally, advanced
information technologies also enable banks to develop electronic lending and credit
decision making systems which substantially lower the costs of lending (Allen et al, 2002).
As a result, both banks and their customers benefit from improvements in lending
technologies and the quality and variety of banking services (Berger, 2003) to the extent
that large banks have increased their share of small business loans (Frame et al, 2001).
Overall, the evidence suggests that online banking has had a favourable impact on both the
availability and cost of finance for small business borrowers.
Another factor which may affect the likelihood of discouragement, and relates to both
information issues and the cost of lending, is the extent to which financial relationships are
concentrated with a single lender. Small business borrowers may self-select to borrow
from a single lender or disperse their borrowing across multiple lenders by comparing the
benefits and costs between these two financing strategies. Concentrated relationships may
signal higher borrower quality (Bris and Welch, 2005) and help to overcome the free-rider
problem (Holmstrom, 1982; Márquez, 2002). Concentration also reduces transaction and
negotiation costs. On the other hand, multiple borrowing relationships provide the
opportunity for competition between finance providers and reduce the problem of rent
extraction which arises where a single lender alone has access to private information about
the borrower (von Thadden, 1992). Indeed, it has been found that lower borrowing
concentration has a significantly negative effect on the cost of borrowing (Repetto et al,
8
2004). This is because a single lender may charge above the market price on small
business loans through a “lock-in” mechanism (Degryse and Cayseele, 2000). By
borrowing from multiple sources, businesses can also insure themselves against liquidity
shocks that hit banks so that liquidity risks are reduced (Detragiache et al, 2000).
In summary, discouragement can be associated with the demographic profile of the
entrepreneur, the quality of the borrower, information issues (including screening errors)
and application costs.
3. Data and Methodology
3.1 Data
This paper examines empirically the effects of demographic profiles, borrower quality,
information issues and application costs on the likelihood of financial discouragement.
The data used here is the 1998 U.S. Survey of Small Business Finances (SSBF98). The
survey collects information on the use of credit by small firms and creates a generalpurpose database on the finances of such firms, with a target population of all for-profit,
non-financial, non-farm, non-subsidiary businesses with fewer than 500 employees. The
dataset contains 3,561 sample firms, representing 5.3 million small businesses operating in
1998 in the U.S. In this sample: 2,099 firms did not apply for external finance, in the three
years before the survey, because they had no need for it; 962 firms applied for external
finance; and 500 did not apply because of fear of rejection. The latter group are defined as
discouraged borrowers. That is, around one third of the sample firms with demand for
external finance were discouraged borrowers. Hence, as discussed earlier, our definition of
discouraged borrowers encompasses all businesses (both high and low risk), with capital
demands, but which did not apply because of fear of rejection. In contrast, recall that Kon
9
and Storey (2003) limit their definition of discouraged borrowers to ‘good’ (low risk)
potential borrowers who are discouraged from borrowing by information asymmetries
and/or application costs.
3.2 Variables
In order to estimate the effects of the determinants of discouragement, we use a logistic
estimation procedure3 on the sample firms with capital demands. The dependent variable
is coded as one if the sample venture was discouraged from applying for external funds;
zero otherwise. For the independent variables – and following on from the previous
section - we group these into four categories: (1) characteristics of the principal and
venture; (2) borrower quality; (3) application costs; and (4) information issues and the
nature of the primary lender, along with control variables representing industry and market
concentration. The definitions of the variables, and summary statistics, are reported in
Table 1.
Table 1: here
On average, a ‘typical’ firm in the sample was family-owned, 11 years old and has 6
employees. It was owned by a 49 years old male owner who has a college degree, 16 years
of experience in business and a total personal wealth of half million dollars. 41% of the
sample had demand for external capital and 34% of them (14% of the total sample) were
discouraged borrowers. One of the key determinants in examining discouraged borrowers
is the quality or risk levels of the borrower. Borrower quality can be measured by a Dun
and Bradstreet (D&B) Score (Cavalluzzo et al. 2002), or by the variance of returns to
3
Another possibility is to conduct a nested logistic model with the upper level modeling the demands for
capital and the lower level modeling the choice between applying and being discouraged. The authors
attempted to construct such a model but it failed to converge due to non-concavity in the likelihood
function. Our interpretation is that this suggests the simpler models are more appropriate for the data.
10
equity (Booth and Booth, 2006). In this paper, we use instrumented D&B scores to
measure the risk levels of borrowers in order to overcome the problem of endogeneity
where the credit score is correlated with the error term or unobservables in the
discouragement equation. Indeed, the instrumented measure is more appropriate than the
real scores because unobserved entrepreneurial talent may affect both discouragement and
credit scores, leading to endogeneity.
Regarding information issues, we follow Petersen and Rajan (2002) and use business
credit card and credit lines to measure the information transparency of the business. We
also measure the severity of the information problem by the length of relationship with the
primary financial service provider; this measure has been widely used in the existing
literature (e.g. Berger and Udell, 1995). In addition, we measure the concentration of
creditors by the number of financial service suppliers used by the sample firm. We expect
that, as information improves, low risk small businesses are less likely to be discouraged
and high risk borrowers are more likely to be discouraged. The third factor influencing
discouraged borrowers are application costs. These are examined using a continuous
variable, namely the physical distance to the primary lender, and a binary variable,
whether the business applied for a loan online. We expect that discouragement is less
likely when application costs are low.
In Kon and Storey (2003), lenders’ screening errors are an important determinant of
discouragement. However, we cannot directly measure this error because the information
used in the lending decision is not available in the survey so we cannot test whether a loan
application has been mistakenly rejected or mis-priced. Instead, we use a dummy variable
for whether the primary lender is a bank to represent the nature of the institution. As
argued earlier, banks may make fewer screening errors because of their advantage in
11
information collection resulting from their ability to monitor other services used by
borrowers such as bank accounts. Therefore, we expect that small businesses are less
likely to be discouraged when the primary lender is a bank due to the likelihood of fewer
screening errors. We also include the demographics of the entrepreneur and business
characteristics in regressions. These variables include, for example, the age, education
background and personal wealth of the principal owner and the size, capital structure and
use of financial products of the business. Finally, we include control variables such as
industry and market concentration.
Panels A and B (Table 2) report simple continuous and bivariate correlations on the
independent variables, respectively. Table 2 shows that none is above 0.70 for continuous
variables (Panel A) and apart from the high correlations between the binary instrumented
D&B scores, all of others are under 0.30 (Panel B). As a result, we use continuous
instrumented D&B scores (INST_DB2) in most of the following regressions and binary
ones in a robustness check. This suggests that any problems in the regression models due
to multicollinearity are minimal.
Table 2: Here
4. Empirical Results
4.1 Univariate Tests
We begin the analysis with univariate comparisons of the key determinants of
discouragement between small businesses, which applied for loans, and those which were
discouraged from borrowing despite having capital demands. The results are shown in
Table 3.
The first important finding is that discouraged borrowers are riskier than
12
applicants: discouraged borrowers have higher actual and instrumented D&B scores than
applicants (p<0.01). The table also indicates that, on average, discouraged borrowers are
younger and smaller (measured by the total number of employees) than applicants and are
less likely to be incorporated. However, the capital structures of these two groups do not
differ significantly in terms of the ratios of total liabilities to total assets.
Regarding the demographic profile of the entrepreneur, discouraged borrowers are: lessexperienced (measured by the number of years of experience in business and
management); poorer (measured by the total value of the owner’s home equity and the net
worth of other assets); less likely to have a college degree; and more likely to belong to an
ethnic-minority group. Discouraged borrowers also have more concentrated financing
relationships than applicants. However there may be an issue of causality here because
concentrated relationships may be caused by being discouraged from approaching other
lenders. Unfortunately, the analysis cannot fundamentally solve this causal problem
because of the limited information available from the data.
Table 3 also indicates that: the proportion applying for finance online amongst applicants
is more than twice that amongst discouraged borrowers; and applicants are more likely to
have a bank as their primary financial service provider. Finally, Table 3 shows that
discouraged borrowers do not differ from applicants, at conventional levels of statistical
significance, either in terms of the length of their financial relationships or the distances to
their primary lender.
Table 3: here
13
4.2 Business/Entrepreneur Characteristics and Application Costs
We now run four logistic models to examine the determinants of discouraged borrowers
and the results are shown in Table 4. In Model 1, we estimate the probability of being
discouraged in terms of the characteristics of the entrepreneur and business. We add
differently instrumented proxies for borrower’s quality in Models 2 and 3 and application
costs variables are added in Model 4. Examination of the ‘percent concordant’ statistic, in
the final row of the table, suggests that the percentage of correctly predicted cases of
discouragement increases as explanatory variables are added to the model. In each model,
dummy variables representing industry and the concentration of local banking markets are
included. The effects of market concentration on discouragement are discussed at the end
of this section.
Table 4: here
In Model 1, we include only the characteristics of the business and entrepreneur (in
addition to industry and market concentration dummies). The results, reported in the first
column, indicate that discouragement is less likely in larger firms and in those businesses
which used external finance, such as business credit cards and business credit lines. The
existing literature (e.g. Berger and Udell, 1998) suggests that larger businesses are less
risky and more informationally transparent. In contrast, smaller firms are relatively
informationally opaque which may increase the screening and monitoring costs of external
lenders. These costs may make lending to small firms less profitable and hence increase
discouragement amongst small business borrowers. As mentioned earlier, the use of some
types of financial products, such as business credit cards and loans, may signal a good
quality borrower as its creditworthiness has already been examined by existing lenders.
Indeed, businesses which lack these good signals are more likely to be discouraged. The
14
negative coefficients on the use of external loans, supports this signalling hypothesis.
Other business characteristics, such as age and capital structure/debt level of the business,
do not have significant effects on the likelihood of discouragement.
Model 1 also shows that the probability of being discouraged is positively associated with
ethnic minority, older and poorer groups of entrepreneurs. The coefficient of the ethnic
minority variable is positive and statistically significant at the 10% level in Model 1. It
becomes insignificant in the following models in which we add risk variables. Indeed,
inspection of the correlation between ‘ethnic-minority’ and ‘risk’ (see Table 2) shows a
positive correlation which is significant at the 1% level suggesting that ethnic-minority
owned businesses are riskier. Therefore, the ethnic variable affects the likelihood of
discouragement through the risk variables. Lower confidence, amongst older
entrepreneurs, may explain their higher rates of discouragement. We also find that an
increase in the personal wealth of the entrepreneur significantly reduces the probability of
being discouraged. This is because wealthier entrepreneurs are less likely to be
discouraged by ‘hard’ financial reasons, such as collateral/personal guaranty requirements
or application costs. This highlights the importance of the personal wealth of the
entrepreneur as a primary source of collateral and again emphasises that, for small
businesses, the personal wealth of the owner and the assets of the business are difficult to
separate (Ang et al. 1995).
Models 2 and 3 are run using differently instrumented risk measures. The first measure is
a probability weighted average of the risk levels (expected risk level) and is thus a
continuous variable (INST_DB2 in Model 2). The other set of measures is a group of four
dummy variables representing moderate (base group), average, significant and high risk
levels respectively. The use of these measures addresses the endogeneity issue relating to
15
firms’ risk levels. While we cannot completely eliminate the problem of endogeneity, all
our results are nevertheless robust to these alternative specifications.
The principal result of these two models is that it is the riskier borrowers that are most
likely to be discouraged, implying that discouragement is an efficient self-rationing
mechanism. As emphasised earlier the definition of ‘discouraged borrowers’ formulated
by Kon and Storey (2003)’ is extended to include both good and bad borrowers so as to
examine the efficiency of the self-rationing role of discouragement. Our result points to a
low, but nevertheless positive, level of market imperfection in US small business financial
markets. Only when there is no discouragement amongst good borrowers is the market
deemed to be perfect4. Even so, the opportunities for further improvements seem modest.
Application costs are another important potential determinant of discouragement. These
costs are proxied by the physical distance to the primary lender and whether loan
applications were made online. Model 3 indicates that neither of these two variables have
a statistically significant impact on the likelihood of being discouraged.
This suggests
that application costs are not a key reason for discouragement.
4.3 Information Issues and the Nature of the Primary Lender
Table 5 presents the results of three further logit models which include variables
representing information issues and the nature of the primary lender/financial service
supplier. Apart from the independent variables examined earlier (Table 4), Model 5 (Table
5) considers the effects of the length of relationship between borrowers and the primary
lender (LOGRELATION_PI), the concentration of creditors (N_FS) and the nature of the
primary lender, i.e. whether or not it is a bank (PITYPE_BANK). Model 6 also includes
4
Kon and Storey also note that good (and bad) borrowers are not discouraged when “lottery conditions”
exist. However this polar extreme is not relevant in an informed credit market such as the US in 1998.
16
an interaction term between risk and relationship lengths (DB2LNR) to examine how the
effects of relationships vary over borrowers with different levels of risk. We also report a
parsimonious version of Model 6 (Model 7) because only a subset of the explanatory
variables in the full equation are statistically significant. For Model 7, marginal effects are
also presented.
Table 5: here
Model 1 shows that small businesses are less likely to be discouraged where the primary
lender is a bank. This is compatible with the view that banks have an advantage in private
information collection compared to other lenders, due to their provision of other services
to borrowers. It is this information advantage which reduces their screening errors. The
significantly negative coefficient on the number of sources of financial services suggests
that borrowers with dispersed financial relationships are less likely to be discouraged5.
This result is reasonable because a borrower can make multiple or repeated applications
from different lenders. Another possible reason is that multiple financing relationships are
positively associated with higher quality borrowers. This interpretation is compatible with
existing theoretical models (e.g. Bolton and Scarfstein, 1996) and empirical analysis (e.g.
Han et al. 2008).
The existing literature has highlighted the importance of relationship lending in private
information acquisition (e.g. Berger and Udell, 1995) and thus, we expect that borrower’s
quality would be more easily identified, and banks’ screening errors would be reduced, by
developing a longer relationship. In other words, as relationship lengths increase, high
5
A causal problem may exist here because one can argue that small businesses may have dispersed creditors
if they are not discouraged. The information collected in the data does not allow us to examine this causal
relationship.
17
(low) quality borrowers would be less (more) likely to be discouraged. Indeed, the results
of Models 6 and 7 support our expectation. The most significant difference between
Models 5 and 6, in Table 5, is that after adding an interaction term between borrower’s
risk and relationship lengths, the coefficient of borrower’s quality (INST_DB2) is no
longer statistically significant; whilst the coefficients of the other independent variables
change little. This result, along with the significance of the coefficient of the interaction
term, implies that the ‘relationship effects’ may depend on the borrower’s level of risk.
Based on the result of the parsimonious model (Model 7), we plot the estimated
probabilities of being discouraged (Figure 1), for five ‘hypothetical’ businesses, over the
length of their relationships with the primary lender (holding other variables at either their
mean or median). They differ in risk levels only and the curves plotted in Figure 1 are the
estimated probabilities of being discouraged over length of relationship, for, from bottom
to top, businesses with low risk (DB_SCORE=1), moderate risk (DB_SCORE=2), average
risk (DB_SCORE=3), high risk (DB_SCORE=4) and significant risk (DB_SCORE=5),
respectively. Figure 1 shows that high quality borrowers, i.e. those of low risk and
moderate risks, are less likely to be discouraged by developing a longer relationship with
the primary lender. In contrast, low quality borrowers, i.e. those of high and significant
risks, are more likely to be discouraged from borrowing as the length of their relationships
increase. Figure 1 illustrates that the relationship effects on discouragement depend on
borrower risk levels and a longer relationship, between borrower and lender, improves the
efficiency of the self-rationing mechanism by discouraging bad borrowers and
encouraging good ones.
Figure 1: here
18
4.4 Market Concentration
Earlier results (Tables 4 and 5) also shows that the concentration of the banking market,
where the borrower’s headquarters are located, has a strong impact on the likelihood of
discouragement. For example, the likelihood of discouragement of a borrower, which has
its headquarters located in a moderately concentrated banking market, is nearly 17
percentage point lower than where the headquarters are located in a competitive market
(see Model 7, Table 5). This result suggests that discouragement is affected by the degree
of concentration in local banking markets. This is consistent with the existing literature
which has identified the effects of market concentration on small business finance (see e.g.,
Cavalluzzo et al, 2002; and Petersen and Rajan, 1995) due essentially to the impact of
competition on the market power of lenders (e.g. Degryse and Ongena, 2005; Berger
2004).
In particular, Petersen and Rajan (1995) argued that the ‘market condition’ effects work
through relationship lending because the extent of market competition is important in
determining the value of lending relationships – indeed they found evidence to support the
view that it is easier for lenders to internalise the benefits of lending in a more
concentrated market. Following this idea, we further examine the effects of market
conditions on discouragement by including interaction terms between borrower’s quality,
relationships and market concentration. This final set of results is reported in Table 6.
Table 6: here
Table 6 suggests that the likelihood of discouragement is jointly determined by borrower’s
quality, length of relationship and market concentration. Here, we would expect the
probability of discouragement to decrease (resp. increase) amongst good (resp. bad)
19
borrowers as the length of their relationships increase (as shown in Figure 1). However,
market concentration will affect the bank’s returns from investing in relationships: in a
competitive market there is less incentive to invest in relationships which would be
expected to reduce the magnitude of the impact of relationships on discouragement.
Accordingly, the joint effects of borrower’s quality, length of relationship and market
concentration are shown in Figures 2-1 to 2-5.
These graphs plot the estimated
probabilities of being discouraged, over the length of relationships, in competitive,
moderately concentrated and highly concentrated banking markets, for borrowers of
increasing risk levels.
Figures 2-1 and 2-2 show three important results. Firstly, the likelihood of discouragement
amongst good borrowers (with risk levels below average) reduces over the length of the
relationship regardless of the degree of concentration in the local banking market. Hence,
this result is consistent with our earlier findings as shown in Figure 1, suggesting that
relationships improve information quality and reduce the likelihood of discouragement for
good borrowers. Secondly, borrowers in competitive markets are more likely to be
discouraged than in concentrated markets. Thirdly, the figures show that there is no big
difference in ‘market condition effects’ between moderate concentration and high
concentration and the effects are not significant in magnitude in concentrated markets.
The fact that relationships reduce discouragement amongst good borrowers in competitive
markets suggests that banks are able to identify good borrowers, over the course of
relationships, even where there is little investment in the relationship. However, the lower
likelihood of discouragement, in concentrated banking markets, points to the higher value
of relationships to lenders (and hence higher investment) in these markets and supports
Petersen and Rajan (1995)’s hypothesis of the beneficial effects of market concentration
20
for lenders.
The similarity of the findings in moderately concentrated and highly
concentrated markets suggests that the value of relationships is similar in both types of
market.
In other words, a moderately concentrated banking market is sufficient for
lenders to invest enough in relationships such that good borrowers are unlikely to feel
discouraged from applying for loans.
Figure 2-1: here
Figure 2-2: here
Figure 2-3 shows how the estimated probability of being discouraged of an average risk
borrower varies over the length of relationships.
This graph indicates that, in a
competitive market, relationships reduce the likelihood of discouragement but that
relationships increase the likelihood of discouragement in concentrated markets. This
suggests that banks in competitive markets invest an insufficient amount in relationships
to be able to distinguish average/marginal borrowers from good borrowers (compare with
Figures 2-1 and 2-2). However banks in concentrated markets have greater incentives to
invest in relationships so that riskier marginal borrowers are increasingly likely to be
identified and are discouraged by this.
Figure 2-3: here
Figures 2-4 and 2-5 present the estimated probabilities of bad borrowers (with risk levels
above average). In competitive markets, the likelihood does not change significantly over
relationships; while in concentrated markets, the likelihood increases significantly. These
results suggest that concentrated markets are more efficient than competitive markets in
21
terms of discouraging bad borrowers from borrowing. Again this points to the higher
value of relationships, and superior information production, in concentrated markets as an
explanation for these findings.
Figure 2-4: here
Figure 2-5: here
5. Conclusions and Implications
The importance of discouraged borrowers has been recognised in both the theoretical and
empirical literature. Kon and Storey (2003) theory predicts that application costs,
screening errors due to information problems and market rates will affect the likelihood of
discouragement amongst ‘good’ borrowers. This paper has conducted an empirical
analysis, based on United States data, which encompasses both ‘good’ and ‘bad’
discouraged borrowers. In this manner we have been able to examine the determinants of
discouragement and test whether discouragement is an efficient self-rationing mechanism6.
We find that both the demographics of the entrepreneur and business characteristics have a
strong influence on discouragement. These characteristics include business size, the use of
financial products, age and the personal wealth of the entrepreneur. Having controlled for
these characteristics, we then find that riskier borrowers are more likely to be discouraged.
Indeed, amongst high risk borrowers, this likelihood increases as information quality
improves with longer financial relationships; while, the likelihood for low risk borrowers
decreases.
6
One limitation of this paper is that we cannot examine the effects of interest rate identified in Kon and
Storey’s model because of the cross-sectional nature of the data.
22
The empirical results derived in this paper have three important implications. Firstly, they
imply that discouragement is an efficient self-rationing mechanism because bad borrowers
are more likely to be discouraged than good borrowers. Discouragement can therefore
reduce banks’ costs in dealing with loan applications from risky borrowers. It can also
help to overcome the problem of ‘overinvestment’ resulting from the mistaken approval of
applications from risky borrowers due to screening errors. Secondly, improvements in
information quality increase the efficiency of discouragement as a self-rationing
mechanism. With longer relationships, for instance, risky borrowers become more and
more pessimistic about the outcome of their loan applications and therefore become
increasingly likely to be discouraged from applying in the first place; while, less risky
ones become less likely to be discouraged. Thirdly, our results suggest that the extent of
market concentration has a strong impact on the discouragement of small business
borrowers. Discouragement, as a self-rationing mechanism, is more efficient in
concentrated banking markets because, in these markets, risky borrowers are more likely,
and less risky ones are less likely, to be discouraged.
It would, however, be unwise to assume these implications apply in small business
financing markets in all countries. The US data, upon which these conclusions were
derived, reflect a relatively sophisticated small business financing marketplace – and yet
even here we find some good borrowers are discouraged and some bad ones did apply.
What we would expect is that in less sophisticated markets the nature and scale of
discouragement would change and that discouragement itself could become a “litmus test”
of market imperfections. Testing this view requires more empirical analysis of
discouragement in the context of a diverse range of countries.
23
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27
Table 1: Descriptive Statistics
Total number of observations is 3561. Not reported here, but available upon request from the authors, are the descriptive statistics
of nine dummies representing the industry based on two-digit SIC code.
Min
Max
Mean
Median
Std.Dev.
MSA
Definition
Business is in a Metropolitan Statistical Area (0,1)
0.0000
1.0000
0.7812
1.0000
0.4135
HHI3_B1
Headquartered in a competitive banking market (0,1) 1
0.0000
1.000
0.0514
0.000
0.2208
HHI3_B2
Headquartered in a moderately concentrated banking market (0,1) 1
0.0000
1.0000
0.4249
0.0000
0.4944
HHI3_B3
Headquartered in a highly concentrated banking market (0,1) 1
0.0000
1.0000
0.5234
1.0000
0.4995
Variable
Characteristics of the business and entrepreneur
Demands for external capital (0,1)
CAPNEEDS
0.0000
1.0000
0.4106
0.0000
0.4920
DB
Discouraged borrower (0,1)
0.0000
1.0000
0.1404
0.0000
0.3475
C_CORPORATION
Business is incorporated (0,1)
0.0000
1.0000
0.2491
0.0000
0.4325
FAMILY_OWNED
Family owned (0,1)
0.0000
1.0000
0.8531
1.0000
0.3540
2
LOGFAGE
Firm age (years)
0.0000
4.6540
2.4494
2.4849
0.7858
LOGTOTEMP
Total employment number2
0.0000
6.1779
1.8999
1.6094
1.5544
LGOGROWTH2
Sales growth: sale (year t) / sales (year t-1) 2
0.0000
5.3458
0.7913
0.7238
0.3582
LOGTLBTA
Capital structure: total liability/ total business assets2
0.0000
16.3805
0.5278
0.3499
0.9288
F4_BCC
Business credit card (0,1)
0.0000
1.0000
0.3993
0.0000
0.4898
F7_CRL
Credit lines (0,1)
0.0000
1.0000
0.3606
0.0000
0.4802
DEGREE
Owner has a college degree or above (0,1)
0.0000
1.0000
0.5218
1.0000
0.4996
MALE
Male owner (0,1)
0.0000
1.0000
0.7352
1.0000
0.4413
MINOR
Minority owner (0,1)
0.0000
1.0000
0.1435
0.0000
0.3506
LOGOAGE
Owner’s age (years) 2
2.9957
4.5643
3.9216
3.9318
0.2221
0.0000
4.2905
2.8036
2.9444
0.6953
0.0000
4.7536
0.4296
0.2429
0.5130
1.0000
5.0000
2.9700
3.0000
1.0402
LOGEXP
LNPW2
Experience of owner in business (years)
Personal assets of owner (million $)2, 3
2
Quality of borrowers
DB_SCORE
Dun and Bradstreet score: categorical4
28
Variable
INST_DB12
Definition
Instrumented D&B score: moderate risk (0,1) 5
5
Min
Max
Mean
Median
Std.Dev.
0.0000
1.0000
0.2064
0.0000
0.4048
INST_DB13
Instrumented D&B score: average risk (0,1)
0.0000
1.0000
0.6810
1.0000
0.4662
INST_DB14
Instrumented D&B score: high risk (0,1) 5
0.0000
1.0000
0.1098
0.0000
0.3127
INST_DB15
Instrumented D&B score: significant risk (0,1) 5
0.0000
1.0000
0.0028
0.0000
0.0529
INST_DB2
Instrumented D&B score: weighted average5
1.4538
4.1491
2.6984
2.6503
0.4814
Distance to the primary institution (miles) 2
Apply loans online (0,1)
0.0000
8.0876
1.3444
1.0986
1.1432
0.0000
1.0000
0.0410
0.0000
0.1983
0.0000
8.0000
2.3002
2.0000
1.5238
0.0000
1.0000
0.8750
1.0000
0.3307
0.0000
6.6606
4.1339
4.1109
1.0143
Application costs
LOGDIST_PI
ONLINE
Information issues and nature of primary lender
N_FS
Number of sources of financial services
PITYPE_BANK
Primary institution is a bank (0,1)
LOGRELATION_PI
Length of relationship with primary institution (months)
2
Interaction terms
DB2LNR
INST_DB2 * LOGRELATION_PI
0.0000
6.5806
0.9549
0.0000
1.8995
DB2LNRHB1
INST_DB2 * LOGRELATION_PI * HHI3_B1
0.0000
19.7290
0.5691
0.0000
2.5154
DB2LNRHB2
INST_DB2 * LOGRELATION_PI * HHI3_B2
0.0000
20.3723
4.6802
0.0000
5.8072
DB2LNRHB3
INST_DB2 * LOGRELATION_PI * HHI3_B3
0.0000
22.4352
5.7374
6.2213
5.8583
Note:
1.
2.
3.
4.
5.
Concentration of banking market is measured by the Herfindahl Index which is a categorical variable and measures the degree of competition within the local financial market. It is
equal to one when the index locates between 0 and 1000, meaning the financial market is less concentrated. It equals two when the index is between 1000 and 1800, suggesting a
moderate concentrated financial market. It equals three when the index is larger than 1800, meaning that the financial market is highly concentrated.
These variables are transformed into nature log value of one plus the real value.
Personal wealth is defined as the total value of the owner’s home equity and the net worth of other assets.
Dun and Bradstreet score on risk is categorical with a range from 1 to 5, where 1 is ‘low risk’, 2 is ‘moderate risk’, 3 is ‘average risk’, 4 is ‘high risk’ and 5 is ‘significant risk’.
We estimate the instrumented D&B scores by conducting ordered logistic models on the business and entrepreneur variables and their credit history. One instrumented D&B score
(INST_DB1) follows the categorical nature of the score, ranging from 2 (moderate risk) to 5 (high risk). The score is recoded to a specific category where the sample firm has the
highest probability of falling into this category. For comparable purpose, the other instrumented D&B score (INST_DB2) is continuous and a weighted average of the possible
categories where the weight is the probability of falling into a specific category estimated from an ordered logistic model. Detailed instrumentation process is available from the
authors on request.
29
Table 2: Correlation Matrix
Panel A
2
3
4
5
6
7
8
9
10
LOGFAGE
1.0000
2
LOGTOTEMP
0.2553*
3
LOGGROWTH2
-0.2377*
-0.0261*
1.0000
4
LOGOAGE
0.4825*
0.1472*
-0.1242*
1.0000
5
LOGEXP
0.6582*
0.2907*
-0.1950*
0.6053*
6
LNPW2
0.3029*
0.4883*
-0.0719*
0.2907*
0.3341*
1.0000
7
LOGTLBTA
-0.0849*
0.0335*
0.0344*
-0.0534*
-0.0223
-0.0301
8
INST_DB2
-0.5523*
-0.2441*
0.1048*
-0.4586*
-0.4562*
-0.3355*
0.1911*
1.0000
9
LOGRELATION_PI
0.3918*
0.0950*
-0.0963*
0.1908*
0.30253*
0.1124*
-0.0415*
-0.2347*
1.0000
10
LOGDIST_PI
-0.0150
0.0642*
-0.0019
0.0027
0.0125
0.0650*
0.0309
0.0295
-0.0908*
1
2
3
Panel B
1.0000
1
C_CORPORATION
1.0000
2
FAMILY_OWNED
-0.1646*
1.0000
3
OWNER_MANAGED
-0.1131*
0.0776*
4
5
6
DEGREE
MALE
MINOR
*
0.1094
*
0.0484
-0.0616
*
*
4
1.0000
5
1.0000
6
7
8
9
-0.1114
-0.0968*
1.0000
-0.0854
*
-0.0370*
0.0816*
1.0000
0.0314
0.0134*
*
0.0386
*
-0.0539*
1.0000
*
0.0919
0.0735*
-0.0622*
1.0000
-0.1315
8
F7_CRL
0.1598*
-0.1246*
-0.0901*
0.0855*
0.1180*
-0.0888*
0.2965*
*
-0.0806
*
-0.0523
*
*
*
*
*
0.2113*
1.0000
-0.0997
*
-0.0421
*
*
0.1493*
0.0971*
-0.0746
*
-0.0952
*
*
*
10
11
12
13
F39_LFS
INST_DB12
INST_DB13
INST_DB14
*
0.2136
*
0.1202
-0.0920
-0.0195
*
*
0.0547
0.0138
-0.0483
*
0.1761
0.1136
11
12
13
14
15
16
1.0000
*
F4_BCC
F33_EQL
1.0000
10
7
9
*
1
1
*
0.0796
0.0074
0.0413
0.0781
*
0.1235
-0.0722
0.0134
-0.0130
*
0.0313
0.1111
-0.0003
*
-0.0378
*
-0.0831
-0.0193
-0.1830
*
*
0.0585
*
0.1330
*
1.0000
0.1625
0.1506
*
0.2260
-0.1567
*
-0.0534
*
1.0000
-0.0018*
0.1748
-0.1222
*
-0.0339
-0.0374
*
*
*
*
1.0000
0.0021
-0.7451*
1.0000
-0.1791*
-0.5131*
*
0.0564
-0.0209
-0.1188*
0.1410
*
*
*
1.0000
14
INST_DB15
0.0062
0.0070
-0.0168
-0.0129
-0.0042
0.0994
-0.0324
-0.0398
0.0574
-0.0271
-0.0775
-0.0186
1.0000
15
ONLINE
0.0021
-0.0102
-0.0133
0.0477*
0.0150
0.0002
0.0714*
0.0453*
0.0347*
0.0416*
-0.0390*
-0.0074
0.0633*
-0.0110
1.0000
16
PITYPE_BANK
0.1136*
-0.0584*
-0.0477*
0.0904*
0.0715*
-0.010
0.1261*
0.1052*
0.0295
0.0643*
0.0941*
-0.0491*
-0.0493*
0.0040
0.0010
1.0000
denotes statistical significant level of 5% or lower.
30
Table 3: Univariate Tests on Mean and Median
t test is conducted for continuous variables on mean and z test for binary variables on median.
Applicants
(N=962)
Discouraged
Borrowers
(N=500)
p value
Firm age (years)
13.1487
11.5000
0.0045
Business is incorporated (0,1)
0.2963
0.2160
0.0010
Total employment number
35.9023
9.2640
<.0001
Sales growth: salest/ salest-1
1.8739
1.6651
0.6172
2848.3310
583.7350
0.3998
Owner’s age (years)
48.8773
49.0280
0.7931
Experience of owner in business (years)
18.4470
16.3360
0.0003
Personal Assets of owner (million $)
1.2156
0.3275
0.0012
Owner has a College degree or above (0, 1)
0.5395
0.4400
0.0003
Minority owner (0, 1)
0.1414
0.2420
<.0001
Dun and Bradstreet score: categorical*
3.0624
3.346
<.0001
Instrumented D&B score: weighted average
2.7664
3.0008
<.0001
Instrumented D&B score: moderate risk (0,1)
0.1892
0.0700
<.0001
Instrumented D&B score: average risk (0,1)
0.6518
0.6720
0.4389
Instrumented D&B score: high risk (0,1)
0.1549
0.2480
<.0001
Instrumented D&B score: significant risk (0,1)
0.0042
0.0100
0.1755
Online loan applications (0,1)
0.0676
0.0320
0.0048
Distance to the primary institution (miles)
25.7837
19.6797
0.5185
Primary lender is a bank (0,1)
0.8909
0.8260
0.0005
Number of sources of financial services
3.2536
1.9600
<.0001
Length of Relationship with primary institution
(months)
85.7010
81.8779
0.4458
variable
Characteristics of the business and entrepreneur
Capital structure: total liability/ total business assets
Quality of borrowers
Application costs
Information issues and nature of primary lender
Note: * Dun and Bradstreet score on risk is categorical with a range from 1 to 5, where 1 is ‘low
risk’, 2 is ‘moderate risk’, 3 is ‘average risk’, 4 is ‘high risk’ and 5 is ‘significant risk’.
31
Table 4: Determinants of Discouraged Borrowers: Characteristics and Application Costs
The probabilities modelled by Logit are DB=1, i.e. a business is discouraged from borrowing. Not reported here, but available upon request from the authors, are the results
of eight dummies representing the industry of the business based on two-digit SIC code. The base group of market concentration is HHI3_B1=1. ***, **, and * denote
statistical significant level of 1%, 5% and 10% respectively.
Model 1
Estimate
Std. Err.
INTERCEPT
-4.3083***
1.4164
MSA
0.4298**
0.1696
0.3044
HHI3_B2
-0.5525*
HHI3_B3
-0.3999
0.3059
Characteristics of the business and entrepreneur
C_CORPORATION
0.1634
0.1659
FAMILY_OWNED
0.1890
0.2090
LOGFAGE
0.1815
0.1300
LOGTOTEMP
-0.3272***
0.0630
LOGGROWTH2
-0.0897
0.1647
LOGTLBTA
0.0158
0.0544
0.1415
F4_BCC
-0.3926***
F7_CRL
-0.8259***
0.1558
DEGREE
-0.1716
0.1453
MALE
-0.1868
0.1466
0.1658
MINOR
0.3155*
LOGOAGE
1.3420***
0.3809
LOGEXP
-0.1708
0.1518
0.2417
LNPW2
-1.0251***
Quality of borrowers
INST_DB2
INST_DB13
INST_DB14
INST_DB15
Application costs
LOGDIST_PI
ONLINE
Number of observations
1345
-2Log
1420.860
Likelihood Ratio X2
286.7048
Percent Concordant
77.2
Estimate
-7.8041***
0.3729**
-0.5786*
-0.4053
Model 2
Std. Err.
1.6609
0.1714
0.3080
0.3093
0.1543
0.1791
0.3087**
-0.3489***
-0.0532
-0.0241
-0.2804*
-0.8282***
-0.1774
-0.1591
0.1789
1.6860***
-0.1754
-0.8713***
0.1677
0.2109
0.1351
0.0641
0.1655
0.0557
0.1451
0.1573
0.1463
0.1483
0.1702
0.3933
0.1529
0.2412
0.6629***
0.1577
1345
1403.083
304.4822
77.8
Model 3
Estimate
Std. Err.
-6.2774***
1.5393
0.3884**
0.1716
-0.5807*
0.3077
-0.4132
0.3091
Estimate
-8.6960***
0.3647**
-0.5661*
-0.3563
0.1624
0.1958
0.2809**
-0.3275***
-0.0716
-0.00771
-0.3116**
-0.8592***
-0.1990
-0.1474
0.2670
1.6124***
-0.1767
-0.9333***
0.1674
0.2110
0.1348
0.0638
0.1660
0.0551
0.1443
0.1575
0.1467
0.1480
0.1689
0.3922
0.1529
0.2421
0.1980
0.3570
0.3079**
-0.3156***
-0.0354
0.0009
-0.3479**
-0.8185***
-0.1670
-0.2193
0.1912
1.7912***
-0.1877
-0.8663***
0.1792
0.2293
0.1484
0.0694
0.1739
0.0557
0.1574
0.1702
0.1585
0.1590
0.1839
0.4311
0.1621
0.2557
0.7813***
0.1698
0.7351***
1.0584***
-0.0707
0.2421
0.2838
0.9622
-0.0623
-0.4961
0.0631
0.3376
1345
1463.173
302.3918
77.8
Model 4
Std. Err.
1.8154
0.1847
0.3325
0.3323
1183
1216.309
282.1819
78.6
32
Table 5: Determinants of Discouraged Borrowers: Information Issues
The probabilities modelled by Logit are DB=1, i.e. a business is discouraged from borrowing. Not reported here, but available upon request from the authors, are
the results of eight dummies representing the industry of the business based on two-digit SIC code. The base group of market concentration is HHI3_B1=1. ***, **,
and * denote statistical significant level of 1%, 5% and 10% respectively.
Model 5
Estimate
Std. Err.
INTERCEPT
-8.3628***
1.9106
MSA
0.3609*
0.1927
0.3469
HHI3_B2
-0.6868**
HHI3_B3
-0.4260
0.3466
Characteristics of the business and entrepreneur
C_CORPORATION
0.2787
0.1880
FAMILY_OWNED
0.4579*
0.2395
0.1563
LOGFAGE
0.3539**
LOGTOTEMP
-0.1896**
0.0741
LOGGROWTH2
0.0227
0.1775
LOGTLBTA
0.0200
0.0554
F4_BCC
-0.2256
0.1635
0.1781
F7_CRL
-0.6718***
DEGREE
-0.0619
0.1654
MALE
-0.1676
0.1658
MINOR
0.1675
0.1922
0.4424
LOGOAGE
1.6860***
LOGEXP
-0.1171
0.1696
0.2595
LNPW2
-0.7699***
Quality of borrowers
0.1814
INST_DB2
1.0253***
Application costs
LOGDIST_PI
-0.0504
0.0713
ONLINE
-0.3427
0.3533
Information Issues and the nature of primary lender
0.0640
N_FS
-0.5009***
PITYPE_BANK
-0.5981*
0.3150
LOGRELATION_PI
0.0840
0.0812
DB2LNR
Number of observations
1183
-2Log
1140.730
Likelihood Ratio X2
357.7606
Percent Concordant
81.9
Model 6
Std. Err.
2.5362
0.1935
0.3494
0.3493
Estimate
-4.5046*
0.3478*
-0.6594*
-0.3866
0.3038
0.4826**
0.3427**
-0.1768**
0.0128
0.0269
-0.2437
-0.6871***
-0.0823
-0.1490
0.1915
1.7053***
-0.1160
-0.7742***
0.1888
0.2407
0.1562
0.0743
0.1786
0.0559
0.1644
0.1791
0.1659
0.1664
0.1931
0.4432
0.1697
0.2595
-0.3603
0.6286
-0.0458
-0.3235
0.0711
0.3539
-0.5021***
-0.5922*
-0.8881**
0.3419**
0.0641
0.3166
0.4312
0.1491
1183
1135.382
363.1089
82.3
Estimate
-5.8793***
0.2483
-0.6746**
-0.4259
Model 7
Std. Err.
1.4952
0.1795
0.3271
0.3268
Marginal Effect
0.0616
-0.1674
-0.1057
0.3742**
0.4802**
0.1254
-0.2025***
0.1742
0.2149
0.1145
0.0678
0.0929
0.1192
0.0311
-0.0503
-0.7458***
0.1658
-0.1851
1.7761***
0.3748
0.4407
-0.6497***
0.2320
-0.1612
-0.5067***
-0.5091*
-0.6896***
0.2737***
0.0604
0.2794
0.1335
0.0394
1280
1269.847
369.6071
81.2
-0.1257
-0.1263
-0.1711
0.0679
33
Table 6: Determinants of Discouraged Borrowers: Market Conditions
The probabilities modelled by Logit are DB=1, i.e. a business is discouraged from borrowing. The base
group of market concentration is HHI3_B1=1. ***, **, and * denote statistical significant level of 1%,
5% and 10% respectively.
INTERCEPT
Estimate
-4.3594**
Std. Err.
1.7017
Marginal effects
Characteristics of the business and entrepreneur
C_CORPORATION
0.3774**
0.1740
0.0922
**
FAMILY_OWNED
0.4807
0.2145
0.1175
LOGFAGE
0.1157
0.1145
0.0283
LOGTOTEMP
-0.2050***
0.0678
-0.0501
***
0.1655
-0.1821
0.3736
0.4213
0.2319
-0.1591
F7_CRL
LOGOAGE
LNPW2
-0.7452
***
1.7239
***
-0.6509
Information issues and the nature of primary lender
N_FS
PITYPE_BANK
LOGRELATION_PI
-0.5043***
0.0601
-0.1232
*
0.2801
-0.1309
***
-0.6456
0.1320
-0.1578
0.2409
-0.5357
Market conditions
MSA
HHI3_B2
HHI3_B3
DB2LNRHB1
DB2LNRHB2
DB2LNRHB3
Number of observations
-2Log
Likelihood Ratio X2
Percent Concordant
0.1796
0.0589
**
1.0303
-0.4952
*
-1.7763
1.0118
-0.4341
*
0.0841
0.0363
***
0.0465
0.0650
***
0.0443
0.0649
-2.0265
0.1486
0.2660
0.2655
1280
1272.973
366.4807
81.0
34
Figure 1: Estimated Probability of being Discouraged
1
Prob (significan risk)
0.9
Prob (high risk)
Estimated probability
0.8
0.7
Prob (average risk)
0.6
0.5
0.4
Prob (moderate risk)
0.3
0.2
Prob (low risk)
0.1
0
0
50
100
150
Length of relationship in months
Figure 2-1: Estimated Probability of being Discouraged: low risk borrowers
0.8
0.7
estimated probability
0.6
0.5
0.4
0.3
P ro b (c o m pe titive m a rke t)
0.2
P ro b (highly c o nc e ntra te d m a rke t)
0.1
P ro b (m o de ra te ly c o nc e ntra te d m a rke t)
0
0
20
40
60
80
100
120
140
160
180
length of relationship in months
35
Figure 2-2: Estimated Probability of being Discouraged: moderate risk
borrowers
0.85
0.75
estimated probability
0.65
0.55
0.45
P ro b (c o m pe titive m a rke t)
0.35
P ro b (highly c o nc e ntra te d m a rke t)
0.25
P ro b (m o de ra te ly c o nc e ntra te d m a rke t)
0.15
0
20
40
60
80
100
120
length of relationship in months
140
160
180
Figure 2-3: Estimated Probability of being Discouraged: average risk borrowers
0.85
estimated probability
0.75
0.65
P ro b (c o m pe titive m a rke t)
0.55
P ro b (highly c o nc e ntra te d m a rke t)
0.45
0.35
P ro b (m o de ra te ly c o nc e ntra te d m a rke t)
0.25
0
20
40
60
80
100
120
length of relationship in months
140
160
180
36
Figure 2-4: Estimated Probability of being Discouraged: high risk borrowers
0.85
P ro b (highly c o nc e ntra te d m a rke t)
0.75
P ro b (m o de ra te ly c o nc e ntra te d m a rke t)
estimated probability
P ro b (c o m pe titive m a rke t)
0.65
0.55
0.45
0.35
0.25
0
20
40
60
80
100
120
140
160
180
length of relationship in months
estimated probability
Figure 2-5: Estimated Probability of being Discouraged: significant risk
borrowers
0.95
P ro b (highly c o nc e ntra te d m a rke t)
0.85
P ro b (m o de ra te ly c o nc e ntra te d m a rke t)
0.75
P ro b (c o m pe titive m a rke t)
0.65
0.55
0.45
0.35
0.25
0
20
40
60
80
100
120
length of relationship in months
140
160
180
37