Credit Rationing in Small Business Bank Relationships

8/2/2019 Credit Rationing in Small Business Bank Relationships

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Credit rationing in small business bank relationships

November 2010

Abstract

This paper studies how credit rationing develops over bank relationships. I analyze a uniquedataset of matched loan application and loan contract information which allows me to

establish the actual degree of credit rationing by relating borrowers’ demand to the bank’ssupply. The results show that credit rationing due to informational asymmetries isconsiderable. In line with theoretical predictions I find that, in the outset of bank relationships, credit rationing is significantly higher for opaque than for transparent borrowerswhile it decreases over bank relationships for both groups. Relating the observed degree of rationing to the dynamics in demand and supply over time I find that the decrease in rationingstems from a more pronounced increase in granted than in requested loan amounts.Furthermore, I find that borrowers who were credit rationed at their previous loan increasetheir demand more moderately than previously non-rationed borrowers while the increase ingranted loan amounts does not differ between previously rationed and non-rationedborrowers. Together with the finding that rationed borrowers perform worse ex-post, this

suggests that credit rationing has real effects on firms’ economic prosperity.

Keywords: Credit rationing, relationship lending, small business lending, asymmetricinformation, loan applications

JEL classification: D82, G20, G21, G30



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1 Introduction

A large theoretical literature models credit rationing in the presence of informational

asymmetries (e.g. Stiglitz and Weiss (1981)). Yet, establishing credit rationing empirically is

difficult and very little is known about its actual extent and importance. This paper uses

demand information from loan applications to establish the degree of credit rationing as well

as its evolution when informational asymmetries resolve over bank-borrower relationships

and tests whether credit rationing affects loan repayment. Understanding the extent and the

effects of credit rationing is important to design adequate policies to overcome, for instance,

the obstacles that impede opaque firms in their access to credit.

The theoretical literature on credit rationing shows that in the presence of asymmetric

information both adverse selection and moral hazard may lead to credit rationing (e.g. Jaffee

and Russell (1976), Stiglitz and Weiss (1981)). Over bank-borrower relationships, the ability

of banks to produce information (e.g. Diamond (1984), Ramakrishan and Thakor (1984),

Boyd and Prescott (1986)) decreases the risk of adverse selection and moral hazard, and

therefore the need to ration credit. So far, the empirical evidence on credit rationing is scarce

particularly due to a lack of demand data. Cheng and Degryse (2010) analyze requested and

granted amounts and study how a reduction in informational asymmetries due to the

introduction of a public credit registry influences the degree of credit rationing in the credit

card market. Yet, missing from the literature is empirical evidence on the actual degree of

rationing for small firms’ investment loans and its evolution when information problems are

resolved over bank relationships.

Thus, in this paper I analyze a unique panel dataset of matched loan applications and loan

contracts that includes both requested and granted loan terms as well as borrower and

relationship characteristics at the time of loan origination. The dataset consists of nearly

99,000 loans to small enterprises extended by one bank in Bulgaria over the period April 2003

to September 2007. It allows me to provide evidence on the degree of credit rationing by



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relating borrowers’ demand to the bank’s supply. Since the wedge between demand and

supply is informative about the resolution of informational asymmetries over time, I

investigate not only how it relates to firm characteristics but also how it evolves over

sequential loan contracts. Analyzing chains of short-term investment loans complements

studies that focus on credit lines to assess how banks use the information they gather from

multiple interactions with their borrowers (e.g. Berger and Udell (1995) and Norden and

Weber (2010)). Furthermore, by establishing the dynamic patterns of requested and granted

loan amounts that arise when bank and borrower interact repeatedly, I am able to connect the

degree of observed credit rationing to the evolution of its components over time. Finally, I

complement the analysis by studying differences in the ex-post loan performance of rationed

vs. non-rationed borrowers to assess whether and how credit rationing may affect borrowers

and the bank.

The results show that credit rationing due to informational asymmetries is present as well

as considerable in lending to small business borrowers. In line with predictions from the

credit rationing theory, I find that opaque firms (i.e. firms that are comparatively young or

small when starting to borrow from the bank) are significantly more rationed than more

transparent firms and that the degree of credit rationing decreases significantly over loan

sequences. Furthermore, loan officer changes lead to higher credit rationing, which seems to

be driven rather by the loss of private information than by a possible collusion between

borrowers and their long-time loan officers. Finally, besides the extent of asymmetric

information, the observable credit risk of firms determines the degree of credit rationing.

Taking the analysis one step further and studying the dynamics behind the observed

reduction of credit rationing over bank-borrower relationships I find that granted loan

amounts increase significantly over time. I also find requested loan amounts to increase over

relationships but on a lower level. The resolution of information problems over bank

relationships therefore leads to lower credit rationing because the bank is willing to increase



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its stakes. 1 Furthermore, I find that borrowers who were credit rationed at their previous loan

increase their demand more moderately than previously non-rationed borrowers while the

increase in granted loan amounts does not differ between previously rationed and non-

rationed borrowers. Together with the finding that rationed borrowers perform worse ex-post,

this suggests that credit rationing may have real effects on firms’ economic prosperity.

The remainder of the paper is organized as follows. Section 2 derives testable hypotheses

from the banking theories on credit rationing and then reviews the related empirical literature.

Section 3 provides institutional details on the loan granting process and describes the data

while section 4 presents the findings from the empirical analyses. Section 5 concludes.

2 Related literature

2.1 Theory

Modern theories of credit rationing rationalize why banks may rather set an interest rate

below the market-clearing rate and ration credit than increase interest rates when facing an

increased demand. Stiglitz and Weiss (1981) show that credit rationing occurs if borrowers

are heterogeneous with respect to their default risks and banks are unable to distinguish

between borrowers with different characteristics. A rise in interest rates then does not only

influence demand. Instead, it may affect the quality of demand as the least risky borrowers

drop out of the market and the average risk of the borrower pool increases (adverse selection)

and its behavior because some borrowers may opt for the riskier project after having received

funding (moral hazard). Both effects, in turn, impact negatively on banks’ expected profits

and therefore induce banks to ration borrowers. 2 While banks do accumulate private

1 The concept of “starting small” is also established in the corporate finance literature (e.g. Tirole (2006)) tomodel so-called staged financing, in the industrial organization literature to explain the development of businesspartnerships in states of uncertainty (e.g. Rauch and Watson (2003)) and in the venture capital literature whenventure projects are financed under uncertainty and the threat of moral hazard (e.g. Bergemann and Hege (1998)and Wang and Zhou (2004)).2 Rationing can take the two forms of (i) loan size rationing, which means that, at the current interest rate, allborrowers are served but demand a larger loan amount than they finally receive from the bank and (ii) borrowerrationing, which means that some borrowers get no loan at all although they may have profitable investment



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information about their borrowers, there nevertheless remain informational asymmetries, e.g.

concerning borrower heterogeneity in entrepreneurial ability, especially in the case of very

young and small firms.

In a multi-period setting (see Sobel (1985), Ghosh and Ray (2001) and the experimental

evidence by Brown and Serra-Garcia (2010)), credit rationing is expected to decrease over

bank-borrower relationships. In the case of adverse selection, on the one hand, informational

asymmetries are reduced and a pooling of heterogeneous borrowers does no longer occur

reducing the need for credit rationing. On the other hand, “bad” borrowers reveal themselves

over time and are not granted a further loan. Adverse selection thus impacts on the degree of

credit rationing over loan sequences along the intensive and extensive margins. In the case of

moral hazard, credit rationing should also decrease over bank relationships as borrowers

become more transparent and firm owners’ stakes in their companies increase over time so

that they can bear a larger share of the risk decreasing the risk of moral hazard.

In sum, this leads to the following testable hypotheses:

H1: Credit rationing is tighter for opaque borrowers (e.g. young and small firms) than for

more transparent borrowers in the beginning of bank relationships.

H2: Credit rationing decreases over bank-borrower relationships.

2.2 Related empirical studies

An extent set of papers studies the influence of bank relationships on credit availability

and generally finds a positive relation between various measures of relationship strength and

credit availability (Petersen and Rajan (1994, 1995), Harhoff and Körting (1998), Machauer

projects and are indistinguishable from those borrowers who receive loans (Keeton (1979)). Jaffee and Russell(1976) establish loan size rationing in a model in which borrowers differ in their probability of default in thesense that some borrowers are honest and repay whenever they are able to while other borrowers are dishonestexperiencing a utility increase from defaulting and do so whenever the costs of default are lower than thecontracted repayment. In the model of Parker (2003) adverse selection and thus borrower rationing occursbecause borrowers differ in their ability. In a moral hazard model, Watson (1984) shows that borrower rationingmay occur because borrowers reduce their effort as a response to an increased interest rate. Ghosh, Mookherjeeand Ray (2000) derive both loan size and borrower rationing in the presence of moral hazard.



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and Weber (1998)). Cole (1998) and Angelini, Di Salvo and Ferri (1998) establish that the

valuable private information seems to be gathered very early in the relationship, while Elsas

and Krahnen (1998) find that especially risky borrowers benefit from bank relationships.

Scott (2006) shows that loan officer turnover, which is connected with a loss of soft

information, is positively related to the probability that banks deny credit. Finally, increasing

contracted loan amounts have empirically been documented in developing and transition

countries (e.g. Armendariz and Murdoch (2000)) as well as in developed countries (Ioannidou

and Ongena (2010)).

While these papers have established the value of close bank relationships on the

availability of credit for small firms, they have not been able to directly observe borrowers’

requests, relate them to the actual loan terms granted and establish the extent of credit

rationing. Furthermore, many of these studies are confined to using cross-sectional data. This

paper uses a more comprehensive and direct measure of credit rationing which incorporates

the demand side: the wedge between requested and granted loan amounts for those borrowers

receiving credit. The panel structure of the employed dataset then allows to explicitly follow

borrowers over their bank relationships and to analyze the evolution of credit rationing over

individual loan sequences. 3

Several studies examine borrower (type I) rationing providing evidence on the firm

characteristics that determine the probability of being denied credit (e.g. Brown, Ongena,

Popov and Yesin (2010), Cole (2010)). Their results show that younger, smaller and

observationally riskier firms are more likely to be denied credit. This study, on the contrary, is

concerned with loan size (type II) rationing for those borrowers who receive credit and thus

complements these findings. My results show that the comparatively young and small firms

3 One caveat to this approach is that it assumes requested and granted loan amounts to mirror ‘real’ demand andsupply although both may be driven by strategic considerations. Whereas the dataset at hand does not allow meto fully resolve this issue, it takes the analysis of credit availability one step further by incorporating loanapplications and shedding some first light on the degree and the dynamic evolution of credit rationing.



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are also more loan size rationed than the older and larger firms once they receive credit, with

this effect being especially pronounced in the beginning of bank relationships.

Finally, this study is related to the literature that aims at disentangling demand and supply

effects on credit availability by using information from both loan applications and loan

contracts. Cheng and Degryse (2010) observe requested and granted loan amounts and study

the impact of the introduction of a public credit registry on credit rationing in the Chinese

credit card market. Furthermore, Jimenez, Ongena, Peydro and Saurina (2010) examine the

impact of macroeconomic and financial shocks on the probability that a loan request results in

a loan granted and Puri, Rocholl and Steffen (2010a) examine how the US financial crisis

affected retail bank lending at German savings banks. This study extends the existing

evidence on demand and supply effects in bank lending by analyzing requested and granted

loan amounts, i.e. the dynamic patterns that arise on both the demand and the supply side, in a

sequence of interactions between borrowers and a bank.

3 Data and methodology

3.1 The data and the Bank’s loan granting process

The dataset used in this study comprises all small annuity loans, credit lines and

overdrafts (loans with amounts up to 50,000 EUR) to firms extended by one Bulgarian bank

(henceforth called the “Bank”) between April 2003 and September 2007. For each loan the

dataset includes information from the borrowers’ loan applications on the loan terms that

were requested. I match this information with data on the actually granted loan terms as stated

in the loan contracts as well as with borrower characteristics and relationship indicators at the

time of loan origination. Definitions of all variables are provided in Table 1.

[Insert Table 1 here]



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All observations with missing loan or firm-level data are excluded. Since the following

empirical analysis focuses on the evolution of requested and granted loan sizes and their

relation over a loan sequence, all loans after the ninth are excluded due to very few

observations in these categories. Based on the fact that interest rate and collateral

requirements are fixed for small loans whereas they are individually negotiated in the loan

granting process for medium loans (loans with amounts of more than 50,000 EUR),

eventually all medium loans are excluded from the main analysis. This leads to the final

sample of 98,888 loans to 53,305 firms among which are 20,355 repeat clients with loan

sequences of up to nine loans. The subsample of loans to repeat clients will be important

throughout my empirical exercise as it allows me to control for unobserved (time-invariant)

firm heterogeneity.

As of September 2007, 30 commercial banks and branches of foreign banks operate in

the Bulgarian banking system. Total sector assets amount to 26.5 billion EUR (an increase of

34% over the preceding twelve months mainly funded by an increase in deposits and equity)

with the five largest banks accounting for 56% of total banking assets (Bulgarian National

Bank (2007)). The Bank that provided me with the data is a commercial, full-service bank

with a nationwide branch network. As with the majority of banks in Bulgaria and the region,

it is owned by foreign strategic and commercial investors 4 who implemented the Bank’s

structure, its lending technology, risk management, management information system and staff

training and accomplish regular supervision. The Bank provides a wide range of financial

services to private and business clients but has a special focus on and expertise in lending to

small enterprises. Compared to the aggregate banking system, where only 41% of assets are

loans to enterprises, 70% of the assets of the Bank are enterprise loans.

At the heart of the Bank’s lending technology is a personnel-intensive analysis of the

borrower’s debt capacity. Approaching the Bank, a prospective borrower first of all meets a4 In 2007, 82% of bank assets in Bulgaria were in the hands of institutions with majority foreign ownership. InCentral and Eastern Europe the average share of foreign bank assets in 2007 was 80%.



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client advisor who assesses whether the borrower meets the Bank’s basic requirements. If this

is the case, the client fills in a loan application form. On this form the client indicates her

preferred loan amount, maturity and currency as well as the purpose of the loan. The client

also has to provide information about the firm ownership, other bank relations and the free

cash flow available for the repayment of the loan. In a next step, the Bank’s credit

administration prepares information on the borrower’s credit history with this Bank and other

banks. 5 At the same time, the loan officer conducts the financial analysis which includes a

personal visit to the borrower’s site. Eventually, the loan officer presents the client’s demand

and the suggested loan terms together with the information gathered during the financial

analysis to the Bank’s credit committee which makes the final decision on the granted loan

terms. Collateral requirements and interest rates are largely standardized and play a minor role

in the individual loan contracting process for my sample of small loans. Therefore, I will not

explicitly consider these loan terms throughout the empirical analysis.

Concentrating the analysis on small loans from one bank in an emerging market provides

an ideal ground for studying credit rationing because informational asymmetries are

presumably severe, especially among young and small firms and in the beginning of bank

relationships. The Bank’s largely standardized loan contracts for small loans leave mainly

loan amount (and maturity 6) as means for the Bank to deal with borrowers’ credit risks and

the risks arising from informational asymmetries. Finally, since the loan granting process is

the same for all observed loans possible heterogeneity is reduced at this level.

3.2 The degree of credit rationing

5 Enterprise loans in Bulgaria are covered both by the public credit registry and, since 2005, a private creditbureau. At the same time, contract enforcement remains problematic as indicated by the 2010 Doing BusinessIndicators which rank Bulgaria 87 th among 183 countries in this category (see www.doingbusiness.org).6 Since I find amount and maturity to be complementary loan contract terms, the analysis focuses on requestedand granted loan amounts.



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Since I observe requested and granted loan amounts I am able to establish credit

rationing by the extent to which borrowers receive a smaller loan amount than they requested.

I denote this as the Share granted and measure it as the ratio of granted to requested loan

amounts. Note that this is an inverse measure of credit rationing with smaller values

indicating tighter rationing. Figure 1 contains a scatterplot of borrowers’ requested loan

amounts against their respective granted loan amounts (both measured in log EUR) and

provides an indication of the importance of credit rationing in the sample. All observations

above the 45 degree line are loans that are granted with a lower than requested loan amount,

i.e. are credit rationed ( Share granted < 1). In total, these loans make up 26.0% of the sample.

For loans on the 45 degree line requested and granted loan amounts are identical, while 4.6%

of the loans are granted with larger than requested loan amounts ( Share granted > 1).

[Insert Figure 1 here]

Figure 2 displays the distribution of the degree of credit rationing ( Share granted ) for all

loans and the subsample of first loans. In both samples, the median (indicated by the triangle)

is one and coincides with the 75 th percentile. This matches the above illustration in Figure 1

that the majority of loans are granted at their requested amount and only very few loans show

larger requested than granted amounts. Interestingly, the dispersion of values is considerably

larger for first loans than for the full sample which is a first indication that over multiple

interactions between borrowers and the Bank the Share granted increases towards one. The

following analysis explores this in more detail.

[Insert Figure 2 here]



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Theory predicts that the degree of credit rationing depends on the extent of asymmetric

information. Table 2 therefore displays the degree of credit rationing ( Share granted ) for

subsamples of opaque vs. more transparent borrowers. To capture the effect of different levels

of asymmetric information between borrowers and to separate it from the effect of repeated

interactions over time the table provides evidence for the subsample of first loans as well as

the subsample of all later loans. Two proxies for firm opaqueness widely used in the banking

literature are firm age (e.g. Berger, Klapper and Udell (2001)) and firm size (e.g. Berger and

Udell (1995) and Petersen and Rajan (1995)). I define Initially young firms as those with firm

age of up to two years at their first loan because such firms have not had the time to establish

a public track record (see Petersen and Rajan (1994)). To define Initially small firms, I follow

Petersen and Rajan (1995) and split the sample at the median value of firm size at the first

loan.


Table 2 shows that loan size rationing is significantly larger for the Initially young than

for the initially old firms and that this result holds for the subsamples of first and later loans.

The economic difference, however, is very small in the subsample of later loans while it is

considerably larger for first loans. In line with this, the last column of Table 2 shows that the

decrease in loan size rationing between first and later loans is more pronounced on average

for the Initially young firms than for the initially old firms. The difference-in-difference

estimate (in bold) indicates that this difference in the decrease of rationing is indeed

significant. Findings for the Initially small vs. initially large firms are very similar with

differences between the two groups being somewhat more pronounced. Thus, Table 2 clearly

indicates that these measures of asymmetric information play an important role in the Bank’s

decision to grant a lower than requested amount. Confirming hypotheses H1 and H2, the



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group of opaque borrowers is found to be more credit rationed than the group of transparent

borrowers and first loans are more rationed than later loans in a loan sequence.

To measure relationship strength I use the Loan number which indicates how many

interactions between the borrower and the Bank have taken place providing the Bank with the

opportunity to monitor borrowers and to observe their repayment behavior. Table 3 provides

summary statistics on the evolution of the Share granted over loan sequences. Panel A shows

that the observed degree of credit rationing decreases considerably over an average loan

sequence with this effect being most pronounced in the beginning of bank relationships. The

Share granted increases significantly in the beginning of loan sequences from 0.90 to 0.97

between the first and the fifth loan. 7 While borrowers start, on average, with receiving 90% of

their requested loan amount, they almost receive their requested amount after several

interactions with the Bank. At the same time, Requested amount and Granted amount increase

over a bank-borrower relationship nearly doubling on average between the first and the ninth

loan. This indicates that the observed reduction in credit constraints over bank relationships is

due to a disproportionate increase in granted loan amounts and not driven by decreased

demand because, for instance, borrowers establish lending relationships with other banks.

Panel B of Table 3 reports the evolution of the degree of credit rationing over loan

sequences of different lengths. It shows that the Share granted increases similarly for all loan

sequences no matter how many loans they comprise. Furthermore, the initial degree of credit

rationing hardly varies for sequences of different lengths. These results provide further

evidence for hypothesis H2. In particular, they show that the decrease in credit rationing over

bank relationships does not only occur because risky borrowers drop out of the sample but

most notably because repeat borrowers receive increasing loan amounts over multiple

interactions with the Bank.

7 To rule out that the observed pattern is driven by changes in the Bank policy over years, I also investigate loansequences that start in different years and find similar patterns no matter in which year bank relationships begin.



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Using this more comprehensive measure of credit rationing thus confirms findings from

previous studies that employ indirect or equilibrium outcome measures for credit availability

(e.g. Petersen and Rajan (1994) and Ioannidou and Ongena (2010)). Yet, this paper is, to the

best of my knowledge, the first being able to provide evidence on the actual degree of

rationing and its evolution over bank-borrower relationships. A crucial part of the following

empirical analysis will be concerned with the determinants of credit rationing and the

underlying dynamics on the borrower and Bank side over the course of bank relationships.

3.3 Determinants of the degree of credit rationing

I study the factors that influence the degree of loan size rationing in the sample in two

steps. First, I estimate an OLS model for the full sample with Share granted i,k,t (the ratio of

the granted loan amount to the requested loan amount for loan k taken out by firm i in month

t ) as the dependent variable:

Share granted i,k,t = a + β1A i,t + β2Fi,t + β3Lk + β4Bt + β5Tt + e i,k,t (1)

A i,t is a vector of indicators measuring the level of asymmetric information, while F i,t and

Lk are vectors of firm and loan characteristics. B t and T t are vectors of branch and year-month

dummies accounting for the branch-specific (such as local competition) and general (such as

macroeconomic and monetary conditions, the Bank’s refinancing situation and the Bank’s

prevailing interest rate and collateral requirements for small loans) environment at the time of

loan disbursement.

In a second step, I estimate outcome equation (1) as a panel model with firm fixed effects

to control for any unobserved time-invariant borrower heterogeneity that may influence the



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Share granted . The fixed effects estimator concentrates the analysis on the factors that

determine differences in the degree of credit rationing over the course of individual bank-

borrower relationships.

Indicators of asymmetric information

The variable Loan number indicates the number of the current loan and measures the

intensity of the bank-borrower relationship. 8 Most importantly, it captures the dynamic

patterns that arise along a chain of interactions between borrowers and the Bank. To allow for

non-linear effects I include the dummy variables Loan number_2, …, Loan number_5 (which

pools interactions number five to nine because of the fewer observations in these categories

and because the descriptive analysis has displayed that most of the action happens in the

beginning of the relationship) and use Loan number_1 as the reference category. As outlined

in section 3.2, I use the dummy variables Initially young and Initially small to identify the

groups of opaque borrowers that might face tighter credit rationing. To study whether credit

rationing evolves differently over bank relationships for opaque vs. transparent firms I assess

the interaction effects Loan number_2*Initially young, …, Loan number_5*Initially young

and Loan number_2 * Initially small,…, Loan number_5*Initially small .

When a borrower applies for a loan, it is the loan officer with whom interaction takes

place and who collects all the borrower-specific data necessary for the subsequent decision on

whether to grant a loan and under which conditions. 9 If the information gathered by the loan

officer cannot fully be transmitted within the bank, which is likely for qualitative soft

information, part of it is lost in case a loan officer change takes place and some of the effects

of a close bank-relationship on the reduction of credit rationing may be tempered. The

8 I do not include the duration of a Bank relationship to measure the level of asymmetric information because itis highly correlated with Loan number . However, rerunning all regressions with Bank relationship instead of

Loan number yields qualitatively and quantitatively very similar results.9 See Berger and Udell (2002), Stein (2002) and for empirical papers using loan officer information e.g. Liberti(2005), Scott (2004, 2006), Uchida, Udell and Yamori (2006), Beck, Behr and Güttler (2009) and Liberti andMian (2009).



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variable Loan officer change therefore indicates whether the loan officer has changed during

the duration of the previous loan.

Firm and loan characteristics

The included firm characteristics are further indicators of asymmetric information and

control for borrower risk. Sole proprietorships are more opaque than incorporated firms

because they do not have to provide certified annual reports according to Bulgarian law,

hence the dummy variable Sole proprietorship equals one if the firm is a sole proprietorship

and zero otherwise. Borrowers that are highly indebted face a higher risk of default in case of

external shocks to their income so that I introduce Leverage , the firm’s total debt as share of

its total assets at the disbursement date of the loan. A firm with little financial scope

(Ln( Disposable income )) to react to unforeseen cuts to its income is more vulnerable to

external shocks and thus more risky because the repayment of the loan may be endangered

more easily. I also include an indicator whether the firm has loans from Other banks . Having

access to other funding sources may reduce the firm’s need for funds at this Bank reducing

observed credit rationing due to lower demand. At the same time, firms borrowing from

multiple sources may be the more productive and successful firms and therefore less credit

rationed. Finally, whether a borrower faced repayment problems at her previous loan may

influence the degree of rationing at the next loan so that I include the dummy variable

Previous arrears which is one if the borrower was past-due on interest or principal payments

more than 30 days at her previous loan, and zero otherwise. To account for all remaining

differences in firm characteristics the regressions contain seven Industry dummies .

One loan characteristic which raises little concern to be endogenous to the determination

of all other loan contract terms is the Fixed capital loan variable. It indicates whether a loan is

for fixed capital financing or working capital otherwise, which is induced by the purpose of

the loan and thus predetermined (exogenous) to the decision on other loan terms. If a loan is



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intended for fixed capital financing, the underlying asset may be sold in case of default

lowering the risk associated with such loans. Similarly, an Annuity loan (dummy variable

which is one if the loan is an annuity loan and zero if it is a credit line or overdraft) may be

considered less risky because of its regular repayment schedule. To account for the fact that

new loans in a sequence may be granted before the previous loan is fully repaid, I include the

dummy variable Add-on loan .

Finally, loan maturity is possibly endogenous to the determination of loan amount and its

inclusion in the regressions would bias the estimates. Studying requested and granted loan

amounts and maturities reveals that both loan terms are complements because for 67% of all

loans they are adjusted into the same direction, i.e. requests for both loan terms are either

higher, lower or equal to both granted loan terms. The Spearman rank correlation between the

Share granted and the ratio of granted to requested maturity is 0.44 and significant (p-value

<0.01) which means that the two variables are not independent and I therefore concentrate the

analysis on requested and granted loan amounts.

3.4 Summary statistics

Table 4 compares the observable loan and firm characteristics for the two subsamples of

non-rationed vs. rationed loans. Column (1) shows that those firms which receive the same as

or a larger than requested loan amount exhibit only a slight difference in requested and

granted maturities. On the contrary, column (2) shows that firms which are credit rationed are

granted equivalently shorter than requested maturities. This is a further indication that loan

amount and maturity are complementary contract terms rather than substitutes. The credit

rationed firms in column (2) are more likely to be young and small at their first loan, are

younger in general and have shorter bank relationships than the non-rationed firms (column

(1)). They are also smaller in terms of total assets and disposable income so that they are

clearly the less transparent firms, which is in line with predictions from the credit rationing



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theory. Interestingly, credit rationed loans are twice as likely to be in Arrears for over 30 days

as non-rationed loans. A t-test confirms that these differences in firm characteristics are

statistically significant at the 0.01-level when comparing the two groups.


4 Results

4.1 Determinants of the degree of credit rationing

Table 5 displays the regression results on the determinants of the Share granted based on

estimations for both the full sample and the panel of repeat clients. Regressions for the full

sample include industry, branch and year-month dummies, but they do not include the

variables Loan officer change , Previous arrears and Add-on loan because they are not defined

for all first loans. The regressions for the subsample of repeat clients include firm fixed

effects to account for unobserved time-invariant firm heterogeneity and year-month dummies.

The branch and industry dummies as well as the variables Initially young , Initially small and

Sole proprietorship are excluded from these regressions due to (almost) no within-variation.

Standard errors are reported in parentheses and are adjusted for clustering at the firm level.

Effects of asymmetric information indicators and firm and loan variables

Column (1) of Table 5 presents OLS estimates for the cross-sectional variation of

variables in the full sample. The results confirm that firms with more intense bank

relationships and more transparent and less risky firms are less credit rationed.

The variables Loan number_2, …, Loan number_5 capture the effect which the intensity

of the bank-borrower relationship has on observed loan size rationing for the initially older

and larger firms. The more often such firms borrow from the Bank, the less credit rationed

they are with the degree of credit rationing decreasing significantly between the first two





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funds or from increased supply because firms with several bank relationships are the more

profitable and vital ones. I will explore supply and demand issues in section 4.2.

These results provide information on the criterions that matter for credit rationing. While

the economic impact of most of the additional firm characteristics is relatively small, being an

opaque (i.e. young or small) firm when starting the bank relationship is an important factor of

receiving smaller than requested loan amounts. The considerably higher constraints for

Initially young and Initially small firms indicate that the extent of (publicly) available

information matters for being credit rationed. At the same time, repeated interactions with the

same lender reduce credit rationing over time because the Bank is able to collect (private)

information on a borrower’s risk and repayment behavior. Thus, the results confirm

hypotheses H1 and H2: credit rationing is higher for the group of opaque firms, and later

loans are less rationed than first loans. To test whether H2 is also confirmed along individual

borrower relationships, I introduce firm fixed effects in a next step and analyze the sample of

repeat clients.


Repeat clients

The results from the repeat client analysis presented in column (2) are very similar to

those from the full sample in column (1) with the economic impact of the Loan number

dummies being stronger than in the pooled analysis. Thus, when focusing on the variation of

variables over borrowers’ loan sequences and controlling for unobserved borrower

heterogeneity I also find that repeated interactions with the Bank and thus decreased

informational asymmetries are an important determinant of the reduction of credit rationing.

Consequently, hypothesis H2 is also confirmed for individual borrower relationships. This

implies that the decrease in the degree of credit rationing observed over time is not only due



20

to a sample effect because “bad” borrowers drop out (the extensive margin effect) but also

because individual borrowers experience less credit rationing in later stages of their bank

relationships (the intensive margin effect). Besides, I find confirmation for the earlier result

that borrowers who are observably less risky are less credit rationed ( Leverage ,

Ln( Disposable income ), Fixed capital loan ).

Column (3) includes those explanatory variables which are not defined for all first loans

and therefore uses the second loan as the reference category. The results show that a previous

Loan officer change leads to higher loan size rationing, but the economic effect is relatively

small (1.4 percentage points). 10 This confirms the reasoning in Berger and Udell (2002) that

not all of the soft information gathered by loan officers can be transformed into common

knowledge within the Bank. An alternative explanation would be that the borrower and the

loan officer were colluding leading to better loan terms than the borrower risk would justify.

Although there are a few relationships between borrowers and loan officers for which

collusion might be a possible explanation because they last up to nine interactions, the

average number of interactions with a loan officer is 1.7 for repeat clients leaving little room

for collusion. Therefore, it seems as if the partial loss of the acquired information during a

loan officer change is the main driver of the observed increase in credit rationing after a loan

officer change.

When a borrower was in arrears for more than 30 days at her previous loan ( Previous

arrears ) the degree of rationing at the next loan is significantly higher again confirming that

the Bank is also concerned with the observable credit risk of a firm when rationing credit.

Finally, add-on loans are less rationed although they increase the overall exposure of the

client at the Bank. This implies that the Bank only grants new loans before the full repayment

10 The loan officer changes observed in the dataset mostly occur because loan officers are promoted within theBank or because they leave the Bank. The Bank does not follow a policy to regularly rotate its loan officersinternally to avoid too close relationships between clients and loan officers that might lead to decisions ratherbased upon personal considerations than objective judgements (see Hertzberg, Liberti and Paravisini (2010) forpositive effects of loan officer rotation).



21

of the previous loan if it has collected sufficient positive information about the borrower. Not

surprisingly, the interaction effects between the Loan number dummies and Initially young or

Initially small do not play any significant role in this specification since most of the within-

firm variation for these variables is between the first and the second loan.

4.2 Requested and granted loan amounts over loan sequences

The analysis so far has established the existence of credit rationing and provided

evidence for its magnitude as well as its evolution over bank-borrower relationships. The

observed decrease in credit rationing over bank relationships may be interpreted as

confirmation for the positive relation between close bank relationships and credit availability

shown by previous studies that use indirect measures of credit constraints (e.g. Petersen and

Rajan (1994, 1995)). However, these studies had to assume that increased credit availability

stems from banks’ willingness to provide more funds to more transparent borrowers. The

structure of my dataset enables me to take the analysis one step further and to disentangle

whether the observed reduction in credit rationing is due to increasing stakes in bank lending

or whether it is driven by processes on the demand side. 11

Table 6 therefore reports results from a panel model with firm fixed effects for the

determinants of granted and requested loan amounts, respectively. All specifications include

year-month effects to account for the macroeconomic environment as well as the Bank’s

prevailing loan contract terms for small loans at the time of loan disbursement. Standard

errors are reported in parentheses and are adjusted for clustering at the firm level.


11 The analysis cannot incorporate whether borrowers are credit rationed at other banks nor whether borrowerswould not prefer to realize a larger loan amount if it was possible. However, the structure of the dataset allowsme to observe the evolution of both bank’s granted loan contract terms as well as borrowers’ requests overmultiple interactions and to draw conclusions on their relation from the results.



22

The regression results in column (1) show that granted loan amounts indeed increase

over loan sequences. A Wald test for differences in adjacent loan numbers indicates that this

increase is significant up to loan number four. For instance, granted loan amounts for the

second loan are, on average, by 23.0% higher than for the first loan. The other firm and loan

level variables show that Ln( Granted amount ) is furthermore determined by the firm’s

observable credit risk. If firms are more indebted ( Leverage ) they are granted smaller

amounts, while loans that finance a fixed asset ( Fixed capital loan ) which may be sold in case

of default and Annuity loan s with regular repayment schedules and thus lower risk show

higher granted amounts. Finally, the existence of lending relationships with Other banks leads

to higher granted loan amounts at this Bank indicating that firms with several lending

relationships seem indeed to be the more successful ones.

In column (2) the explanatory variables that are not defined for first loans are added and

thus loan sequences with the second loan as the reference category are examined. It turns out

that the fact whether a borrower’s previous loan was credit rationed ( Previously rationed )12

does not influence the Bank’s decision about the granted loan amount at the current

interaction. The further firm level variables reveal that granted loan amounts are smaller after

a Loan officer change (18.7% on average). As shown in section 4.1 this may be explained by

the loss of proprietary information when loan officers are assigned new portfolios or leave the

Bank. Finally, a borrower’s previous repayment performance has a considerable impact on the

amount granted for the current loan. A borrower who was in arrears of more than 30 days

during the maturity of her previous loan ( Previous arrears ) receives a 31.5% lower loan

amount at the current interaction.

12 This adds a dynamic component to the model because the extent of the previous rationing includes the grantedamount of the previous loan (i.e. the lagged dependent variable). However, I have to refrain from estimating adynamic panel model since in my setting the time intervals (between adjacent loan numbers) differ betweensequences of observations. Using the dummy variable Previously rationed reduces the bias that would arise fromthe direct introduction of the lagged Share granted in a fixed effects regression.



23

Turning to the evolution of Ln( Requested amount ) in column (3) reveals that requested

amounts also increase significantly over multiple interactions with the Bank (as indicated by a

Wald test for differences in adjacent loan numbers) but on a smaller level than the granted

amounts. Together with the significantly positive but again smaller coefficient for Other

banks , this provides clear evidence that the observed decrease in credit rationing over loan

sequences does not stem from borrowers’ reduced demand at this Bank due to their access to

debt capital from other sources. Interestingly, column (4) shows that, in contrast to the Bank

side, the fact of being Previously rationed seems to influence borrowers’ requested amounts.

The significantly negative interaction terms between the Loan number dummies and

Previously rationed indicate that those borrowers experiencing loan size rationing at their

previous loan increase their demand more moderately than the non-rationed borrowers.

While the credit rationing models do not incorporate dynamic patterns on the demand

side, this result may hint at borrower learning. Agarwal, Driscoll, Gabaix and Laibson (2008)

study borrower learning in the credit card market and find that borrowers seem to learn to

avoid paying future fees through negative feedback, i.e. the experience of past fees. Similarly,

borrowers may learn from the negative feedback they receive from previous credit constraints

how much they may reasonably request from this Bank and adapt their requested loan

amounts accordingly. An alternative explanation is that credit rationing has real effects on

borrowers’ economic activities so that rationed borrowers prosper less than non-rationed

borrowers and therefore increase their demand more moderately. This would also explain why

the Bank does not increase granted loan amounts relatively more for previously credit

rationed borrowers – a result one would expect to find in the absence of real effects if the

group of rationed borrowers was a pool of “good” and “bad” firms due to adverse selection.

In this case, the Bank should be expected to make up for the previous rationing of the then

revealed good firms.



24

Summarizing, being able to measure credit rationing as the ratio of supply to demand and

to disentangle the dynamic patterns that arise when borrower and bank interact repeatedly

reveals several interesting results. First, credit rationing is considerable and most pronounced

for opaque borrowers and in the beginning of bank relationships but is resolved with

decreasing informational asymmetries over bank relationships. Second, the decrease in the

degree of credit rationing over bank-borrower relationships is driven by an increase in granted

loan amounts as documented by Armendariz and Murdoch (2000) and Ioannidou and Ongena

(2010). Third, borrowers’ demand for credit also increases over multiple interactions with the

Bank and, since granted loan amounts increase on a larger level, borrowers are granted an

increasing fraction of their demand. Taking these results together provides evidence in line

with arguments that bank relationships are valuable because banks are able to collect and

assess information in due course and benefit borrowers by better loan terms over time (see

Boot (2000) and Ongena and Smith (2000) for overviews). However, the findings on

borrowers’ and the Bank’s reaction to previous credit rationing suggest that credit rationing

may also have real effects on firms. To shed some more light on the effects of credit

rationing, the next section will examine the factors that influence the ex-post performance of

loans.

4.3 Ex-post loan performance

Table 7 reports marginal effects from Probit regressions on the determinants of Arrears

for the full sample of loans (columns (1) and (2)) and the subsample of repaid loans (columns

(3) and (4)). All specifications include industry-, branch- and year-month fixed effects to

account for the industry- and location-specific as well as the general environment at the time

of loan disbursement to capture external economic shocks that might impact on borrowers’

loan repayment performance. The main interest lies in examining whether the group of credit

rationed borrowers performs differently from the group of non-rationed borrowers.



25


The results in column (1) show that the group of credit rationed borrowers ( Share

granted < 1) is significantly more likely to be past-due more than 30 days on interest or

principal repayments during the maturity of their loans than the group of non-rationed

borrowers (borrowers receiving the amount they requested or a larger than requested amount).

The difference of 1.7 percentage points is economically large given that the mean of Arrears

amounts to only 4%. This supports the above reasoning that credit rationing may have real

effects on firms not only curbing their demand if they take out another loan but also impacting

on their repayment performance and thus their perceived riskiness at the next interaction with

the Bank.

Another explanation, why credit rationed borrowers may perform worse than non-

rationed borrowers, is the presence of adverse selection problems. This would imply that the

group of credit rationed borrowers is a pool of “good” and “bad” opaque borrowers whereas

the group of non-rationed borrowers is more transparent to the lender and thus consists of less

risky borrowers on average who are expected to perform better ex-post. Given that Bulgaria

has a public credit registry and a private credit bureau which cover all extended loans, adverse

selection problems should be of limited importance since banks can easily obtain information

on borrowers’ total credit as well as their repayment performance. Yet, 45.5% of first-time

borrowers at the Bank had not taken out any debt before, which explains why asymmetric

information is nevertheless severe and adverse selection may play a role in lending to opaque

borrowers. Finally, moral hazard problems may also explain the observed relation between

credit rationing and ex-post loan performance. Having to face credit rationing might induce

some borrowers to value the relationship with the Bank less and thus to default on their loans.

Importantly, these explanations are not mutually exclusive but further disentangling them



26

would need distinct information which may, for instance, be obtained from specifically

designed field experiments (see Karlan and Zinman (2009) who use randomized direct mail

offers that permit identification of the different information problems).

Column (2) further distinguishes between loans granted with a higher than requested

amount ( Share granted > 1) and loans granted with their requested amount (the reference

category). It turns out that loans granted with larger than requested loan amounts are also

more likely to display arrears but the effect is economically much smaller than the effect for

the rationed group vs. the reference group. In contrast to Puri, Rocholl and Steffen (2010b), I

do not find evidence that the length of the bank relationship or the existence of a previous

loan ( Loan number ) reduces the likelihood to default. The additional firm and loan controls

show that older and larger borrowers are less likely to repay late, while sole proprietorships

and more indebted borrowers ( Leverage ) are more likely to exhibit arrears of more than 30

days. Interestingly, a Loan officer change during the duration of the loan leads to a

significantly higher probability of arrears. One reason for this may be that the loss of the

personal link between the borrower and the loan officer provides an incentive for the

borrower to perform worse which hints at the presence of moral hazard problems. Studying

only the subsample of loans with a maturity date before the end of my observation period (i.e.

loans for which the variable Arrears cannot change any more) in columns (3) and (4)

confirms the main conclusions from the full sample analysis.

5 Conclusions

This paper studies credit rationing in a comprehensive framework using loan application

as well as loan contract information from nearly 99,000 small loans granted by one bank in

Bulgaria between April 2003 and September 2007. Unlike previous studies I observe the

firm’s requested and the Bank’s granted loan terms and measure credit rationing by the ratio

of granted to requested loan amounts. This allows me to provide direct evidence on the



27

presence of credit rationing as well as its degree and evolution over repeated interactions

between borrowers and the Bank. It also enables me to study the underlying dynamic demand

and supply patterns and to link them to the observed degree of credit rationing. To assess

whether credit rationing affects borrowers I complement the analysis by examining

differences in the ex-post loan performance between rationed and non-rationed borrowers.

In line with predictions from the credit rationing theory the results show that opaque

firms (i.e. firms that are comparatively young or small when they first borrow from the Bank)

are significantly more rationed than more transparent firms and that the degree of credit

rationing decreases over loan sequences. Furthermore, loan officer changes lead to higher

credit rationing, which seems to be driven rather by the loss of private information than by a

possible collusion between borrowers and their long-time loan officers. Finally, besides the

extent of asymmetric information, the observable credit risk of firms determines the degree of

credit rationing.

When studying the dynamics behind the observed reduction of credit rationing over

bank-borrower relationships I find that granted loan amounts increase significantly over time.

The same holds for requested loan amounts but they increase on a lower level. The resolution

of information problems over bank relationships therefore leads to lower credit rationing

because the Bank is willing to increase its stakes disproportionately. Furthermore, I find that

borrowers who were credit rationed at their previous loan increase their demand more

moderately than previously non-rationed borrowers while the increase in granted loan

amounts does not differ between previously rationed and non-rationed borrowers. Together

with the finding that rationed borrowers perform worse ex-post, this suggests that credit

rationing may have real effects on firms’ economic prosperity.

One question that arises concerns the transferability of my results. On the one hand,

concentrating the analysis on small loans from one bank in an emerging market provides an

ideal ground for studying credit rationing because informational asymmetries are presumably



28

severe. Furthermore, the loan granting process is the same for all observed loans reducing

possible heterogeneity at this level. On the other hand, the bank and its loan contracts that are

largely standardized with respect to interest rates and collateral requirements may seem

special. However, standardizing interest rates is not uncommon in other loan categories like

overdraft facilities and the Bank’s lending technology is not singular in the context of

emerging countries. So the data provide a natural setting to gain insights into the dynamics of

requested and granted loan amounts in multiple interactions between borrowers and banks.

The dynamic patterns found in this study complement and connect key elements of the

literatures on asymmetric information and credit rationing as well as relationship lending and

credit availability of small firms. However, the analysis of the demand side has shown that

factors like borrower learning or possibly borrower bargaining power may play a role in the

loan contracting process. Future research may address these issues by tracing out the actual

demand curve from shocks to the supply side (since the granted loan amount is essentially the

equilibrium outcome) and comparing the estimated demand to the observed requested loan

amounts. Disentangling these demand and supply effects as well as disentangling the

information problems underlying observed credit rationing empirically with more

comprehensive data (e.g. on borrowers’ different sources of credit - in markets with and

without information sharing among lenders) seem fruitful areas of future research.



29

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33

Table 1. Variable definitions

Variable Definition

Dependent variablesShare granted Granted loan amount as a share of requested loan amount (%)Granted amount Loan amount as stated in the loan contract (EUR)Requested amount Requested loan amount (EUR)Arrears Interest or principal payments are past-due more than 30 days at date of

repayment or end of observation period (1=yes, 0=no)

Loan characteristicsPreviously rationed Indicates if the previous loan was credit rationed (1=yes, 0=no)Requested maturity Requested loan maturity (months)Granted maturity Loan maturity as stated in loan contract (months)Fixed capital loan Loan is for fixed capital financing (1=yes, 0=no)Annuity loan Loan is an annuity loan vs. credit line or overdraft (1=yes, 0=no)

Add-on loanCurrent loan was granted before full repayment of previous loan (1=yes,0=no)

Branch Branch dummies which are one for the branch that granted the loan

Asymmetric informationindicatorsLoan number Indicates the number of the loan in a sequence of loans a borrower takes out

from the Bank (1 to 9)Bank relationship Months since first contact between bank and client at disbursement dateLoan officer change Firm experienced a loan officer change during duration of previous loan

(1=yes, 0=no)

Firm characteristicsAge Firm age at disbursement date (years)Initially young Firm age was below or equal to two years when first borrowing from bank

(1=yes, 0=no)Assets Total assets of firm at disbursement date (EUR)Initially small Firm size (total assets) was below median firm size when first borrowing

from the bank (1=yes, 0=no)Asset growth Difference between total assets at current and last loan disbursement as a

share of total assets at last loan disbursementSole proprietorship Firm is sole proprietorship (1=yes, 0=no)Leverage Total debt as share of total assets of firm at disbursement dateDisposable income Total disposable income per month at disbursement date (EUR)Other banks Borrower has other bank relationships (1=yes, 0=no)Previous arrears Borrower was past-due on interest or principal payments more than 30 days

at the previous loan (1=yes, 0=no)Industry Industry dummies which are one if firm belongs to one of the following

sectors: Agriculture, Construction, Manufacturing, Trade, Transport,Tourism, Other services



34

Figure 1. Scatterplot of requested against granted loan amounts

This figure plots Ln( Requested amount ) which is the natural logarithm of the requested loan amount in EURagainst the respective LN( Granted amount ) which is the natural logarithm of the granted loan amount in EURfor all loans. On the solid 45 degree line lie all loans for which the granted loan amount equals the requested loanamount. Above the solid line are all credit rationed loans with a smaller granted than requested loan amount(Share granted < 1) and below the solid line are all loans for which the granted loan amount is larger than therequested loan amount ( Share granted > 1). See Table 1 for definitions of all variables.



35

Figure 2. Boxplot of the degree of credit rationing for all loans and for first loans

This figure displays the distribution of the degree of credit rationing ( Share granted ) for all loans and for thesubsample of first loans. The triangle indicates the median, while the box ranges from the 25 th to the 75 th percentile.



36

Table 2. Asymmetric information and the degree of credit rationing for first vs. laterloans

This table reports the average Share granted for First loans and Later loans in loan sequences, for differentsubsamples based on the extent of asymmetric information. Initially young (old) firms have a firm age below orequal to (above) two years when first borrowing from the Bank. Initially small (large) firms are of firm size below(equal to or above) the median firm size based on total assets when first borrowing from the Bank. The table alsoprovides T-tests for differences between groups ( difference ) and F-tests for differences between pairs of groups(difference-in-difference ). ***, **, * denote significance at the 0.01-, 0.05- and 0.1-level.

First loansN = 53,305

Later loansN = 45,583 Diff / Diff-in-diff

Share granted

Initially young firms, N = 17,998 0.86 0.94 -0.08***

Initially old firms, N = 80,890 0.91 0.95 -0.05***

Diff / Diff-in-diff -0.05*** -0.02*** -0.03***

Initially small firms, N = 49,588 0.86 0.94 -0.08***Initially large firms, N = 49,300 0.93 0.97 -0.04***Diff / Diff-in-diff -0.08*** -0.03*** -0.05***



Table 3. The degree of credit rationing over bank relationships

Panel A. Sample means by loan sequence

This table displays sample means for the degree of credit rationing as well as the requested and granted loan amounts by loan number for the full sa

definitions of all variables.

Loan number 1 2 3 4 5 6

Share granted 0.90 0.94 0.96 0.96 0.97 0.97 Requested amount 5,350 5,452 6,008 6,651 7,368 8,191 9Granted amount 4,647 4,979 5,543 6,135 6,941 7,574 8N 53,305 24,055 11,577 5,419 2,475 1,179



Panel B. Sample means over loan sequences of different lengths

This table displays sample means for the degree of credit rationing ( Share granted ) for each loan number inloan sequences of different lenghts. See Table 1 for definitions of all variables.

Total number of loans in the sequence 1 2 3 4 5 6 7 8 9Loan number Share granted 1 0.90 0.89 0.89 0.89 0.90 0.91 0.91 0.95 0.892 0.94 0.95 0.95 0.95 0.93 0.95 0.97 0.933 0.96 0.95 0.96 0.96 0.95 0.96 0.954 0.96 0.97 0.96 0.97 0.94 0.985 0.98 0.97 0.98 0.97 0.966 0.97 0.95 0.98 0.987 0.95 0.99 0.978 1.00 0.989 1.00



39

Table 4. Sample means for subsamples of non-rationed vs. rationed loans

***, **, * denote that variables are significantly different from each other at the 0.01-, 0.05- and 0.1-level usinga two-sided T-test.

Non-rationed Rationed(1) (2) (1) - (2)N = 73,174 N = 25,714

Loan characteristicsShare granted 1.02 0.64 0.38***Requested amount 5,051 7,328 -2,277***Granted amount 5,163 4,701 462***Requested maturity 29.26 38.01 -8.75***Granted maturity 27.57 26.71 0.85***Fixed capital loan 0.52 0.50 0.02***Annuity loan 0.76 0.73 0.03***

Add-on loan 0.73 0.76 -0.03***Arrears 0.03 0.06 -0.02***

Asymmetric information indicatorsLoan number 1.94 1.64 0.30***Bank relationship 10.41 6.85 3.56***Loan officer change 0.25 0.27 -0.03***

Firm characteristicsAge 8.60 7.79 0.81***Initially young 0.17 0.23 -0.06***Assets 35,208 25,464 9,744***Initially small 0.47 0.60 -0.13***Asset growth 0.50 0.48 0.03Sole proprietorship 0.92 0.91 0.01Leverage 0.14 0.15 -0.01***Disposable income 505 385 120***Other banks 0.67 0.64 0.02***



40

Table 5. Determinants of credit rationing

Column (1) includes results for the full sample from an OLS regression and columns (2) and (3) reportresults from fixed effects regressions for the subsample of Repeat clients (loans disbursed to firms that takeout more than one loan from the Bank during the observation period). Standard errors are reported inparentheses and account for clustering at the firm level. The dependent variable Share granted is the grantedloan amount as a share of the requested loan amount and indicates the degree of credit rationing. Allexplanatory variables are defined in Table 1. ***, **, * denote significance at the 0.01-, 0.05- and 0.1-level.

(1) (2) (3)Full sample Repeat clients Repeat clients

Dependent variable Share grantedLoan number_2 0.027*** 0.041***

(0.002) (0.004)Loan number_3 0.034*** 0.057*** 0.015***

(0.003) (0.006) (0.005)Loan number_4 0.032*** 0.062*** 0.017**

(0.004) (0.008) (0.008)Loan number_5 0.043*** 0.088*** 0.039***(0.004) (0.010) (0.011)

Initially young -0.042***(0.003)

Loan number_2*Initially young 0.027*** 0.020***(0.005) (0.006)

Loan number_3*Initially young 0.037*** 0.017** -0.003(0.006) (0.008) (0.008)

Loan number_4*Initially young 0.033*** 0.020* 0.004(0.009) (0.012) (0.013)

Loan number_5*Initially young 0.046*** 0.033* 0.013

(0.010) (0.017) (0.017)Initially small -0.063***

(0.002)Loan number_2*Initially small 0.033*** 0.032***

(0.003) (0.004)Loan number_3*Initially small 0.037*** 0.035*** -0.000

(0.004) (0.006) (0.006)Loan number_4*Initially small 0.057*** 0.056*** 0.019**

(0.006) (0.007) (0.008)Loan number_5*Initially small 0.050*** 0.041*** 0.005

(0.006) (0.009) (0.010)Loan officer change -0.014***

(0.004)Sole proprietorship 0.013***

(0.003)Leverage -0.060*** -0.048*** -0.036***

(0.005) (0.009) (0.012)Ln(Disposable income) 0.011*** 0.012*** 0.001

(0.001) (0.002) (0.003)Other banks 0.007*** 0.015*** 0.003

(0.002) (0.003) (0.005)Previous arrears -0.037**

(0.019)

Fixed capital loan 0.016*** 0.008*** 0.009***(0.001) (0.002) (0.003)

Annuity loan -0.000 -0.002 -0.011(0.006) (0.008) (0.009)



41

Add-on loan 0.009**(0.004)

Observations 93,494 60,544 40,189Method OLS Panel FE Panel FER² (adjusted) 0.069 0.185 0.186Industry-fixed effects yes no noFirm-fixed effects no yes yesBranch-fixed effects yes no noTime-fixed effects yes yes yes



42

Table 6. Granted and requested loan amounts over bank relationships

This table reports results from fixed effects regressions for the subsample of Repeat clients (loans disbursed tofirms that take out more than one loan from the Bank during the observation period). Standard errors arereported in parentheses and account for clustering at the firm level. The dependent variables are Ln( Granted amount) which is the natural logarithm of the granted loan amount in EUR in columns (1) and (2) andLn( Requested amount) which is the natural logarithm of the requested loan amount in EUR in columns (3) and(4). All explanatory variables are defined in Table 1. ***, **, * denote significance at the 0.01-, 0.05- and 0.1-level.

(1) (2) (3) (4)Dependent variable Ln(Granted amount) Ln(Requested amount)Loan number_2 0.230*** 0.136***

(0.009) (0.010)Loan number_3 0.406*** 0.204*** 0.290*** 0.230***

(0.016) (0.016) (0.017) (0.016)Loan number_4 0.551*** 0.362*** 0.412*** 0.364***

(0.023) (0.025) (0.024) (0.026)Loan number_5 0.651*** 0.484*** 0.484*** 0.457***(0.031) (0.036) (0.032) (0.037)

Previously rationed -0.005 -0.023(0.018) (0.019)

Loan number_3*Previously rationed -0.001 -0.199***(0.023) (0.024)



Loan officer change -0.187*** -0.171***

(0.012) (0.012)Leverage -0.627*** -0.646*** -0.537*** -0.570***

(0.027) (0.042) (0.028) (0.043)Ln(Assets) 0.139*** 0.099*** 0.123*** 0.083***

(0.009) (0.017) (0.010) (0.018)Asset growth 0.007* 0.008*

(0.004) (0.004)Ln(Disposable income) 0.125*** 0.114*** 0.108*** 0.110***

(0.007) (0.011) (0.007) (0.012)Other banks 0.179*** 0.214*** 0.155*** 0.205***

(0.008) (0.016) (0.009) (0.017)Previous arrears -0.315*** -0.245***

(0.057) (0.056)Fixed capital loan 0.363*** 0.378*** 0.350*** 0.363***

(0.008) (0.012) (0.008) (0.012)Annuity loan 0.574*** 0.489*** 0.560*** 0.488***

(0.026) (0.032) (0.027) (0.033)Add-on loan -0.020 -0.034**

(0.016) (0.016)Observations 60,544 40,189 60,544 40,189Method Panel FE Panel FE Panel FE Panel FER² (within) 0.307 0.252 0.249 0.237Industry fixed effects no no no no

Firm-fixed effects yes yes yes yesBranch fixed effects no no no noTime-fixed effects yes yes yes yes



Table 7. Ex-post loan performance

This table reports marginal effects from Probit regressions for the full sample and the subsample of Repaid loans (loans that were repaid before the end of the observation period). Standard errors are reported inparentheses and account for clustering at the firm level. The dependent variable is Arrears which is a dummyvariable indicating whether interest or principal payments are past-due more than 30 days at the maturity date of the loan or at the end of the observation period. All explanatory variables are defined in Table 1. ***, **, *denote significance at the 0.01-, 0.05- and 0.1-level.

(1) (2) (3) (4)All loans Repaid loans

Dependent variable ArrearsShare granted < 1 0.017*** 0.018*** 0.018*** 0.019***

(0.001) (0.001) (0.002) (0.002)Share granted > 1 0.006** 0.004

(0.003) (0.005)Loan number_2 0.001 0.001 0.001 0.001

(0.001) (0.001) (0.002) (0.002)Loan number_3 0.003 0.003 0.000 0.000(0.002) (0.002) (0.003) (0.003)

Loan number_4 0.001 0.001 -0.003 -0.003(0.002) (0.002) (0.004) (0.004)

Loan number_5 0.005* 0.005* -0.000 -0.000(0.003) (0.003) (0.005) (0.005)

Loan officer change 0.019*** 0.019*** 0.036*** 0.036***(0.001) (0.001) (0.003) (0.003)

Sole proprietorship 0.010*** 0.010*** 0.005 0.005(0.002) (0.002) (0.003) (0.003)

Ln(Age) -0.011*** -0.011*** -0.013*** -0.013***(0.001) (0.001) (0.001) (0.001)

Leverage 0.031*** 0.031*** 0.029*** 0.029***(0.003) (0.003) (0.005) (0.005)

Ln(Assets) -0.002*** -0.002*** -0.003*** -0.003***(0.001) (0.001) (0.001) (0.001)

Ln(Disposable income) 0.003*** 0.003*** 0.001 0.001(0.001) (0.001) (0.001) (0.001)

Other banks -0.001 -0.001 0.001 0.001(0.001) (0.001) (0.002) (0.002)

Fixed capital loan -0.000 -0.001 0.003* 0.003*(0.001) (0.001) (0.002) (0.002)

Annuity loan 0.024*** 0.024*** 0.016*** 0.016***(0.003) (0.003) (0.003) (0.003)

Observations 92,755 92,755 38,831 38,831R² (pseudo) 0.097 0.098 0.094 0.094Industry fixed effects yes yes yes yesFirm-fixed effects no no no noBranch fixed effects yes yes yes yesTime-fixed effects yes yes yes yes

Documents

Credit Rationing in Small Business Bank Relationships