Ben Me Lech
Transcript of Ben Me Lech
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Acquisition of Information in Loan Markets and Bank Market Power
- An Empirical Investigation
Karl-Hermann Fischer
Department of Finance
Johann Wolfgang Goethe University Frankfurt
e-mail:[email protected]
Do commercial banks invest less in information gathering activity when they compete more
aggressively with each other? Does intensifying competitive pressure in bank loan markets affect the
quality of informational ties that bind borrowers and lending banks? Using survey data from German
manufacturing firms, we are able to directly measure information flows from loan applicants to banks.
We find that firms located in more concentrated banking markets have to transfer more project-
specific information to their lending banks. Furthermore we find that banks that systematically acquire
more information about their loan customers are able to provide liquidity without inducing additionalcostly transfer of information. Third, we find credit to be more readily available in more concentrated
banking markets. This latter result confirms recent US findings. However, our analysis of banks’
information acquisition offers first empirical evidence in favour an alternative explanation of why
credit availability systematically varies with bank market structure.
Key Words: Relationship Lending, Bank Market Power, Information Acquisition
JEL Classification: G21, L13
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I would like to thank the staff of the Ifo-Institute for Economic Research, Munich, for their help and hospitality.
I am especially indebted to Claudia Plötscher for not only providing access to the data base used in this study but
also for extremely helpful comments and suggestions. I also thank Jean Dermine, Reint Gropp, Craig McKinlay,Steven Ongena, Ulrich Rendtel and Erik Theissen for helpful suggestions and encouragement. Seminar
participants at INSEAD and Goethe University as well as participants of the EFA 2000 meeting in London also
provided helpful comments. All errors and inaccuracies remain the sole responsibility of the author.
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Acquisition of Information in Loan Markets and Bank Market
Power - An Empirical Investigation
Abstract:
Do commercial banks invest less in information gathering activity when they compete more
aggressively with each other? Does intensifying competitive pressure in bank loan markets
affect the quality of informational ties that bind borrowers and lending banks? Using survey
data from German manufacturing firms, we are able to directly measure information flows
from loan applicants to banks. We find that firms located in more concentrated banking
markets have to transfer more project-specific information to their lending banks.
Furthermore we find that banks that systematically acquire more information about their loan
customers are able to provide liquidity without inducing additional costly transfer of
information. Third, we find credit to be more readily available in more concentrated banking
markets. This confirms recent US findings. However, our analysis of banks’ information
acquisition offers first empirical evidence in favour an alternative explanation of why credit
availability systematically varies with bank market structure.
JEL Classification: G21, L13
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Do commercial banks invest less in information gathering activity when they compete
more aggressively with each other? There is now considerable interest in whether intensifying
competitive pressure in bank loan markets will affect the quality of informational ties that
bind borrowers and lending banks1. The idea of the transmission of project-specific private
information from a borrowing firm to a lending bank goes back at least to Fama’s (1985)
conjecture that banks provide inside financing as opposed to what was subsequently called
arm’s-length financing provided by capital markets. This notion could be seen as a major
building block of the modern theory of the commercial bank 2
and empirical research has
provided numerous pieces of evidence in favour of it. Although this view is now widely
accepted in general, what determines the intensity of a bank’s information acquisition is still
poorly understood. We do not yet know much about whether some banks acquire more
information and systematically build stronger ties to their customers than others3. What
determines banks’ information gathering activity cross-sectionally, or through time?
Furthermore, are there other related functions that banks perform – for example that of
providing liquidity services to their loan customers – that are affected by banks’ information
acquisition activity?
In this paper we analyse whether local bank market structure – proxying for bank
market power – affects banks’ information acquisition activity. To the best of our knowledge
this is the first empirical study that directly addresses information transmission within bank-
customer relationships. Using survey data from small and medium-sized German
manufacturing firms, we are able to directly measure information flows from firms to banks
within a loan application situation. We find that local bank market concentration has a
positive and significant impact on banks’ information gathering activity. Similarly to
Petersen/Rajan (1995), we take standard measures of the concentration of a local market as an
approximation for bank market power.4
In a second step the data set allows us to consider
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what could be termed a natural experiment on the hypothesised market structure/information
relationship: For example: if it is true that banks in more concentrated markets systematically
acquire more information in the normal course of a business relationship, these banks should
be able to provide liquidity at short notice without inducing additional costly transfer of
information and vice versa. As a consequence of this, the aforementioned relationship
between market structure and information transmission should be reversed for a subsample of
firms that have experienced a liquidity shock and, as a consequence, demand short-term
liquidity. Our results indicate that in such a situation informational requirements are
significantly lower for firms located in more concentrated banking markets as well as for
firms that already have a long-term relationship with the bank they approach.
In the last step of our analysis we assess whether the informational intensity within
lending relationships addressed in the first part of the study has consequences in terms of
firms’ financing patterns. Here we use information on the degree of discounts for early
payment which are taken by each firm as an approximation of credit availability. This last
step is thus a reappraisal of Petersen/Rajan’s (1995) study, using German data. The results are
similar to the US findings although the first two steps of the study offer an explanation for a
relationship between credit availability and bank market power that differs from
Petersen/Rajan’s as it is more directly related to information acquisition activity. The paper
proceeds as follows: In section I we briefly review the relevant theoretical literature and
derive testable hypotheses. Section II outlines the design of the study and presents our results.
Section III sums up and concludes.
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I Bank Market Power and Information Acquisition in Loan Markets
There is now intense academic interest in the welfare implications of increasing
competitive pressure in bank loan markets. Standard economic thought predicts positive
welfare effects that arise primarily from lower loan rates and larger loan volume.5
In banking
markets, however, this might not be the whole story as several bank products are
characterised by asymmetry of information between banks and their customers as well as
implicit risk sharing arrangements within multi-period contractual relationships. This seems
to be especially important for bank loan products such as lines of credit, loan commitments or
longer-term bank loans negotiated in spot markets. It is now widely accepted that long-term
relationships between banks and loan customers observable in real-world loan markets
provide a framework within which the solution of information problems is more efficiently
accomplished. It is also widely believed that bank uniqueness rests mainly on a bank’s ability
to acquire and evaluate borrower-specific private information. However, exactly how the
transmission of information within lending relationships takes place has remained largely
unexplored. For example, is learning-by-lending all that is needed to provide banks with
superior information? A learning-by-lending technology is assumed in most of the theory of
relationship lending.6
This notion is also implicit in most empirical studies that try to measure
informational intensities by using the duration of the bank-borrower relationship as a proxy.7
In contrast to this strand of the literature, in what follows we assume that information does not
only accumulate over time but information acquisition is a costly activity and a choice
variable of the bank. Thus the information problem becomes endogenous in the bank’s
incentives to invest in borrower-specific information, which might – among other things – be
influenced by competitive pressure in bank loan markets.
This raises the question of whether taking account of these characteristic features of
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bank loans in models of bank competition leads to results that go beyond the standard
industrial economics result. The theoretical literature offers at least three explanations of why
bank market power might affect banks’ information gathering activity. We review them
briefly here.
Mayer (1988) claimed that competitive financial markets might be detrimental to
economic welfare in that they make long-term relationships between financiers and
entrepreneurs harder to sustain. This notion was formalised in Petersen/Rajan (1995) who also
provided empirical evidence indicating that small and medium-sized US firms based in more
concentrated banking markets (i) take early payment discounts more often, (ii) show a
stronger reliance on debt financing by financial institutions and (iii) pay lower loan rates
when young, and higher loan rates when old compared to similar firms based in more
competitive markets. In the theoretical part of their paper, Petersen/Rajan focus on a bank’s
ability to share in the future borrower project surplus whenever it exercises market power
over the borrowing firm.
In their theoretical two-period model the bank becomes fully informed after the first
period because of a simple and costless learning-by-lending technology. In what follows we
will argue that costly information acquisition provides another mechanism that makes bank
market power a meaningful determinant of credit availability, although we do not address the
pricing dimension in our empirical study. From our perspective, assuming that information
acquisition activity eats up part of a bank’s resources is crucial but does not seem to be
unrealistic. Casual empiricism tells us instead that commercial banks devote considerable
resources to acquiring and processing borrower specific information. Obviously loan
availability as well as loan pricing should be affected by the accuracy and timeliness of the
information a lending bank has about a borrower’s prospects and credit risk.
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One reason why increasing bank competition might be detrimental to a lender’s
incentives to undertake costly screening/monitoring is that once a lender has granted a loan to
a firm, other potential lenders might be able to observe this at low cost. Given that the bank
has borne a non-trivial cost in screening the applicant, competitor banks can offer better terms
of lending as they free ride on the first bank’s screening effort. Thus, as a consequence of free
riding behaviour, underprovision of screening prevails in equilibrium in competitive credit
markets.8
Whether information spillover, competitor banks’ free riding, and switching behaviour
on the part of borrowing firms are empirically important, and whether they are related to
market structure in banking markets, are questions that remain to be analysed. There are,
however, several pieces of evidence that would seem to point in this direction. Petersen/Rajan
(1995) find that firms located in the most competitive banking markets are solicited
significantly more often by competitor banks than firms in the least competitive markets. The
difference seems to be especially pronounced for older firms where their mere survival as
well as a sequence of credit granting decisions by banks in the past has already disclosed
signals of project quality to competitor banks. Evidence of another type comes from so-called
bank uniqueness studies: It is now widely believed that announcements of bank loan
agreements systematically lead to re-evaluations of the borrowers by capital market
participants.9
What is often overlooked in interpreting these findings is that measured capital
market reactions are evidence of information spillover effects that take place in financial
markets10
.
A second fundamental hypothesis about the relationship between bank market power
and bank monitoring effort takes entrepreneurial incentive problems more directly into
account. In monitoring an entrepreneur acting under limited liability who – for well known
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reasons – is willing to shift to inefficient investment projects, a bank normally cannot commit
to a certain level of monitoring. As banks’ monitoring effort serves to deter entrepreneurs
from choosing an inefficient project, whenever a bank exercises market power it is able to
extract part of the incremental surplus created by monitoring (through more efficient
investment decisions). Market power thus acts like an implicit equity stake and serves to
reduce the bank’s own moral hazard problem – that of underproviding costly monitoring
effort ex post. From this perspective a monopolist bank has first-best monitoring incentives as
it is able to appropriate the full project surplus. This idea is formalised in Caminal/Matutes
(2000). There is a trade-off to be taken into account in that increasing bank market power
leads to increasing bank monitoring and thus more efficient allocation of credit and higher
credit availability. On the other hand, bank market power increases loan rates and makes the
incentive problems more severe, forcing the bank to ration credit in order to deal with
borrower moral hazard. As a result of these countervailing forces, the effect of bank market
power on social welfare crucially depends on the severity of the informational asymmetry
between banks and borrowers. However, bank market power is expected to increase
monitoring incentives when information acquisition is costly and the bank cannot commit to a
certain level of monitoring.11
The literature considered so far predicts that there is a positive correlation between
bank market power and banks’ costly information acquisition activity. What makes our
empirical contribution interesting is that other papers contradict this widely held view in that
they predict increasing informational intensity when banking markets become more
competitive.
The paper by Boot/Thakor (2000) adds a new perspective by analysing a bank’s
choice between different informational intensities in the loan products they offer within a
model of imperfect bank competition. The model is basically one of technology choice within
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a framework of product differentiation. Boot/Thakor then show that increasing interbank
competition could make information-intensive lending practices more attractive and for
certain levels of bank competitiveness even dominate pure transactional lending over the full
range of borrower types. This result rests mainly on the idea that, given increasing
competition from other banks, relationship lending allows heterogeneous banks to partly
insulate themselves against this pressure by offering a more differentiated product.12
After discussing the theoretical work that forms the basis for our empirical study,
several remarks are in order here: Firstly, our study is interested in bank behaviour given bank
market power – of which we believe information acquisition is a central aspect. Thus, this
paper regards bank market power as being exogenous. The question of how the qualitative
aspects of bank-borrower relationships that we address here feed back into banking market
structures obviously goes beyond the scope of our study.13
We will come back to this point in
section 3.7 below.
Secondly, the equilibrium bank behaviour in which this paper is interested should not
be confused with more explicit forms of bank-borrower relationships. It seems important to
make this point clear: The influence that bank market power might have on banks’
information acquisition does not preclude the possibility that some firms might voluntarily
offer banks a type of information monopoly precisely in order to overcome perceived credit
availability problems.14
Given our theoretical discussion above, one external determinant of
these problems (i.e. one that lies outside a firm’s own characteristics) could be bank market
power or a lack thereof.15
Thus, whenever firms can feasibly commit to a long-term
relationship with a bank to overcome credit availability problems caused by bank market
power, our tests would be biased against finding any effect of bank market power on
information gathering, whatever the direction of that relationship might be.16
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Given the theoretical literature surveyed above, another point deserves to be
mentioned: In Boot/Thakor (2000) a distinction is made between competition from banks and
competition from the capital markets, e.g. from investment banks acting as underwriters in the
corporate bond or commercial paper markets. For the design of our study we can safely say
that, for the sample of small and medium-sized German firms in our analysis, capital market
sources of finance during the sample period were hardly a viable alternative to bank loans. In
Germany, for example, only large firms had access to the commercial paper market or the
market for longer-term bonds and the German market for corporate bonds was thin by any
measure. Descriptive statistics from our sample firms confirm this prior as more than 85% of
all firms that wanted to raise debt financing turned to banks to apply for a loan17
. As the
empirical study relies on the cross-sectional variability of local bank market structures in
Germany it is primarily aimed at measuring the effects of interbank competition on banks’
incentives to invest in relationship-specific information and thus to form strong ties to their
borrowers.
II The empirical study
A Design of the study
Using survey data from small and medium-sized manufacturing firms, this study is in
a unique position to directly measure information flows from firms to banks within a loan
application situation. The study derives two measures to capture the amount and structure of
the information transmitted. We then endeavour to explain this flow of information within a
regression framework and control for firm characteristics, loan variables and standard
relationship variables. We are primarily interested in the relationship between local bank
market structure on the one hand and the information flow on the other. The German banking
market seems to be an excellent terrain for investigating the relationship between information
acquisition and bank market structure. Firstly, the German financial system is often referred to
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as a classical example of a so called bank-based system, where universal banks play a
dominant role in nearly every segment of the financial market and have built strong ties to the
corporate sector. Secondly, the German banking system, like other continental European
banking markets, was often said to be characterised by collusive behaviour and regulatory
capture, but is now expected to become more competitive as the deregulation and integration
of European financial markets progresses.18
Thirdly, the German banking system is regionally
segmented into many small local markets and thus offers a wide variety of local market
structures which seem to be the relevant market at least for the medium and small sized
segment of the corporate loan market. This latter point is widely recognised as being
applicable to the US banking market, as indicated by the use of local market structure
variables in empirical banking studies.19
It is however neglected in most empirical studies of
European banking markets.
The first step of our analysis, as described above, is to seek to ascertain the
determinants of banks’ information acquisition within the normal course of a business
relationship. As a second step, the data set allows us to consider what could be termed a
natural experiment on the hypothesised market structure/information relationship. For
example, if it is true that banks in more concentrated markets systematically acquire more
information in the normal course of a business relationship, these banks should be able to
provide liquidity at short notice without inducing additional costly transfer of information and
vice versa. As a consequence, the aforementioned correlation between market structure and
information transmission should be reversed in sign for a subsample of firms that have
experienced a liquidity shock and as a consequence demand short-term liquidity. This effect
might be especially important because information transmission from a borrowing firm to a
lending bank within a distress situation might be seriously prone to cheating behaviour on the
part of the firm. Information collected in the past might thus prove to be an extremely helpful
and reliable input into the bank’s decision making when confronted with a borrowing firm’s
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short-term liquidity needs. Whenever a borrowing firm demands liquidity at short notice, the
bank needs to distinguish between at least two likely explanations for the firm’s current
financial position. On the one hand, the firm might have experienced a drain of liquidity that
has relatively little effect on its prospects and thus its ability to make interest and principal
payments in the future. On the other hand, the liquidity shortage that the firm is currently
experiencing might simply be an indication that the firm is low quality and doomed to fail
anyway. The bank’s position here is that of being exposed to the risk of throwing good money
after bad . Obviously a bank that has acquired more borrower-specific information in the past
should be better able to see through the veil of the firm’s current financial position and assess
its prospects more accurately. As a consequence, less additional information is needed in a
situation where information transmission is especially costly and extremely prone to cheating
behaviour on the part of the firm. Furthermore the timeliness of a bank’s decision might be
crucial in such a scenario and the aspect of the costs to be borne by the firm should not be
neglected, especially if it is a small one and managerial capabilities are a scarce resource.
Note here that the design of the empirical study critically hinges on the distinction
between, on the one hand, information transmission during normal lending business and on
the other, informational requirements that allow banks to provide liquidity services to their
loan customers under exceptional circumstances in which qualitative aspects of the bank-
borrower relationship that have been built up in the past come to bear. Note further that recent
assessments of the functions that commercial banks perform emphasise their funding of
opaque, complex positions based on acquiring borrower-specific information on the one hand
and the provision of liquidity services to their customer on the other.20
Obviously these two
functions are deeply interrelated in that it is the information acquired that make banks a
unique low-cost source of liquidity.
In the third step of our empirical analysis we assess whether the informational
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intensity within lending relationships addressed in the first part of the analysis has
consequences in terms of firms’ financing patterns. We therefore use information on the
degree to which firms take advantage of early payment discounts as an approximation of the
availability of credit. This last step is a reappraisal of Petersen/Rajan’s (1995) study, using
German data, insofar as we too measure the impact of bank market structure as a determinant
of credit availability. The results that we obtain in this third step of our analysis are similar to
the US findings, but the first two steps of the study offer an explanation for a relationship
between credit availability and bank market power that directly focuses on banks’ information
acqusition.
B The data set
The data set used in this study is from the IfO Institute for Economic Research in
Munich, a leading economic research institution in Germany. In addition to its regular
Investment Survey, IfO conducted a Survey on Corporate Finance in 1997. A questionnaire
was sent to 4,833 manufacturing and construction firms spread all over Germany. As the aim
of the questionnaire was to assess the circumstances and motives of firms’ external financing
decisions, the first part of the survey dealt with equity financing whereas the second part
asked for the firm’s last attempt to raise debt financing. Note that the sample was limited to
firms in the manufacturing or construction sectors only, and is thus quite homogenous. We are
primarily interested in those firms that approached a bank to apply for a bank loan.
Among the 1,531 units that remained after elimination of all firms with incomplete data, we
identified 403 that applied for a bank loan in 1996.
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C. Information flow variables
Among other things, firms were asked for a list of information items that they had to
submit to the lending bank. Several items – among them cash flow projections, short term
financial statements, feasibility studies or long-term strategies – were offered as a pre-
specified category. Firms could also indicate other items, if any additional information was
transmitted. The firms’ answers to this question serve as a basis for our measure of
information flow to the bank. Our first variable, called INFOCOUNT, is designed simply to
count the information items reported. The assumption here is that a bank’s information
acquisition is a latent unobservable variable and mapped onto an ordinal scale represented by
INFOCOUNT. Obviously this measure is not without its problems. For example, the actual
informational content of the items considered here might overlap.
Our second measure therefore tries to capture the qualitative aspects as well as the cost
aspects that form the basis of the theoretical underpinnings of the empirical study. Here we
draw a distinction between what we refer to as “hard” information and “soft” information
items. Roughly speaking, hard information is defined as information that comes in numbers,
that could be processed automatically, benchmarked against industry averages or comparable
firms. Furthermore this type of information could generally be fed into analytical models, like
discriminant analysis, that are widely used by German banks. In contrast, soft information
could in general not even be reported in standardised fashion as it requires a firm’s managers
to report on their products, customers, investment projects and strategies.
We believe that this distinction is directly related to the cost aspects that drive the
theoretical models discussed above, in which information acquisition was modelled as a sunk
cost investment in bank-customer relationships. The assumption here is that soft information
is more costly to evaluate. We therefore define a second alternative information flow variable
that – as an ordered category – takes the value of 1 when only hard information items are
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transmitted and the value of 2 whenever, in addition, soft information items are transmitted.
Interestingly, in only 17% of all cases does a bank acquire only soft information, without
acquiring hard items at the same time. This observation might be an indication that the
ordering we have in mind is indeed real. For those observations where only soft information
items are transmitted one could assign INFOSTRUCTURE a value of either 1 or of 2. The
results reported below were achieved when we assigned a value of 1, but using the alternative
method does not change any of our results. We take as a base group those firms which
transmitted balance sheet information only. This is a statutory requirement for all loans larger
than DM 100,000 and is also stipulated in nearly every bank’s internal credit standards,
irrespective of loan volume, so that it does not provide us with any incremental information.
For this base group the variable INFOSTRUCTURE takes a value of 0. We believe that
INFOSTRUCTURE enables us to capture qualitative differences in rating styles among banks
as well as the cost implications of those styles, which are of theoretical interest.
D. Descriptive statistics
In table 1 we present descriptive statistics for three subsamples that were differentiated
according to the type of loan the firm applied for. 212 firms applied for a medium- to long-
term bank loan, 58 firms renegotiated their lines of credit and 133 firms applied for a loan
under a public sector loan programme, the most prominent of which is the ERP programme, a
successor to the former Marshall Aid Fund. It is important to note here that a line of credit in
Germany takes the form of a transaction account with an overdraft facility. A line of credit
thus is a classical bank product that provvides a loan customer with liquidity services21
.
[table 1 around here]
Firstly it shows that firms that renegotiated their lines of credit are in a poorer
financial position than firms in the other two groups. Differences in terms of early payment
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discounts taken are statistically significant for that group as compared to the other groups.22
The same holds true for the degree to which firms use their lines of credit on an annual
average basis. Furthermore the sales growth figures point to the causes of these differences in
liquidity position. Firms that renegotiated their lines of credit in 1996 experienced negative
sales growth from 1995 to 1996 on average. This stands in sharp contrast to both other groups
but is not statistically significant (p-value: 0.1518). If one looks at the sales growth figures
lagged one year, no such significant difference can be found. The same time pattern can be
observed if one looks at the percentage of firms that have a profit-to-sales ratio below 3%.
The percentage of firms with low profit-to-sales ratios is significantly higher in 1996 (p-value:
0.0153) among those that renegotiated their lines of credit although this difference does not
show up in the 1995 data. The evidence presented in table 1, together with our general notion
and priors about firms’ motives to renegotiate their lines of credit, leads us to conclude that
firms in that group have on average experienced a liquidity shortage and turn to their banks in
order to demand liquidity at short notice23
. We have already drawn attention to the importance
of this assumption for the design of our study (see above). Banks are assumed to perform a
distinct function in these cases as they provide liquidity services to their loan customers.
Another important result emerges from table 1. In our regression analysis below we
disregard those firms that applied for a bank loan provided under a public sector loan
programme (results for that group are displayed in the fourth column of table 1). In these
public sector programmes, banks merely pass on loans originated by a state-owned bank such
as KfW,24
a federally owned bank that originates loans under the so called ERP programme.25
In some of these cases the bank approached by the borrower does not even bear the full credit
risk inherent in the loan. Furthermore, and even more importantly for our purposes here, one
has to take into account that the informational requirements of loan applications in such a
programme are determined outside the bank-borrower relationship, e.g. in the loan
programme’s guidelines.26
As a consequence of these two fundamental weaknesses, we
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exclude these 133 firms from our sample. Table 1 shows that in terms of information items
transmitted the subsample is significantly different from the other two groups.
Wilcoxon/Mann-Whitney tests for two subgroups strongly reject the null when firms in this
group are compared to all other firms (p-value: 0.0019). Significant differences are also
displayed in terms of mean values for INFOCOUNT and INFOSTRUCTURE in table 1. It is
thus obvious that information flows within that subsample are quite different, confirming our
priors that other mechanisms are at work here.
Our next step is to assess whether firms located in more concentrated banking markets
are different from firms in highly competitive bank market areas. As bank market
concentration serves as a key variable in our study, this is an extremely important question to
ask. Table 2 therefore provides descriptive statistics and allows us to analyse whether marked
differences exist between firms from the more concentrated markets and their counterparts
located in more competitive markets. In addition to a sample split by market concentration we
found it important to look for differences between firms located in eastern and western
Germany. Besides likely differences in age, size, industry and financial position this
distinction is also recommended for our purposes, because banking markets in eastern
Germany are significantly more concentrated than local banking markets in western Germany.
Note that to obtain the results displayed in table 2, all observations with public sector loans
had already been eliminated; they will not be considered in what follows.
The general structural difference in terms of market concentration is mirrored in our
sample (the mean value of the Herfindahl is 0.169 for western Germany and 0.279 for eastern
Germany). Thus in order not to bias the interpretation of the descriptive statistics in table 2 we
decided to split the sample into eastern and western German subsamples.27
In most cases,
there are statistically significant differences between the eastern and western German
subsamples in terms of the firms’ characteristics given in table 2 (except for equity ratios).
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Within these subsamples, however, the differences between firms located in high
concentration banking markets and those in low concentration banking markets are never
statistically significant at conventional levels. Nevertheless an interesting result applies to
those variables that are often interpreted as indicators of firms facing liquidity constraints.
Nearly twice as many firms in low concentration markets reported that they “never” or
“infrequently” take early payment discounts offered by their suppliers. Remember that
Petersen/Rajan (1994, 1995) used early payment discounts taken by their sample firms as an
indicator of credit availability. Given our small sample sizes, the difference based on high and
low concentration subsamples turns out not to be significant at conventional levels for
western German firms (p-value 0.124) and a similar conclusion can be drawn for eastern
German firms. With respect to early payment discounts it is important to note that our sample
is composed entirely of firms from the manufacturing and construction sector and all sample
firms are regularly offered early payment discounts by their suppliers.28
Moreover, the use of credit lines follows a similar pattern to the use of early payment
discounts: Here 28.9% of the firms located in the more competitive markets subsample
reported that on average over the year lines of credit are drawn by more than 75%, whereas
only 19.5% reported comparable use of their lines of credit in the more concentrated markets
(p-value 0.1488). For both variables, the figures for eastern Germany show the same pattern
(fewer firms are liquidity-constrained in the high concentration subsample), although again
the differences are not statistically significant at conventional levels. We will, however, return
to these differences in the third step of our empirical study, where we do not restrict the
analysis to those firms that applied for bank loans in 1996 but use the full sample of firms
within a multivariate framework. For the moment we are interested in the determinants of
bank information acquisition activity, which we are attempting to measure by INFOCOUNT
and INFOSTRUCTURE. As indicated by the last two rows of table 2, there seem to be
marked differences between eastern and western Germany in terms of information acquisition
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by banks. Again, differences based on market structure split within these subsamples seem to
be of no statistical significance although they are always higher in the high concentration
subsamples.
[table 2 around here]
Like Petersen/Rajan (1995) we also checked for the industry composition of our
sample. This might be important because industry is a good proxy for business risk and
tangibility of assets, aspects that are likely to influence a bank’s perception of credit risk.
Figures 1 and 2 show the western German and eastern German subsamples according to the
firms’ industry classification. Here again we do not find marked differences between firms in
low concentration and firms in high concentration markets.
[figures 1 and 2 around here]
E. Regression results
We now study the determinants of the flow of information within a regression
framework. Table 3 displays ordered probit maximum likelihood estimates of the vector of
coefficients for a parsimonious specification for INFOCOUNT and INFOSTRUCTURE as
dependent variables. These specifications serve as our baseline model, and we report an all
extensions and robustness checks below.
A comparison of the results in column I and II of table 3 shows that they are almost
identical for INFOCOUNT and INFOSTRUCTURE as dependent variables. More precisely,
size – as measured by sales – seems to be a major determinant of banks’ information
acquisition activity. Estimated coefficients are negative and highly significant in all
regressions. From a theoretical perspective this is not very surprising, as size is often
interpreted as a proxy for informational problems in that smaller firms face more serious
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information problems when contracting with their financiers. As banks are seen as specialists
in bridging that informational gap by costly information gathering activity, one should not be
too surprised to see banks producing more information when granting loans to small firms.
Note, however, that we controlled for loan size by introducing three dummy variables. If the
loan size variables actually measured firm size effects, sales should not show up as being
significant. Accounting for loan size seems to be important, as indicated by the results of
Likelihood Ratio tests, but the relevance of size carries over to those specifications where loan
size is not controlled for. On the other hand, controlling for loan size in our baseline
regressions is motivated by the fact that most banks’ internal credit standards require more
information acquisition with larger loan volumes.29
As these internal credit standards are rigid
over time and not adjusted on a customer by customer basis, it seems appropriate to control
for this effect.
A firm’s financial position seems to impact bank information gathering activity in the
sense that firms that are in a poor financial position have to disclose more information to their
potential lenders. The coefficient for the dummy variable indicating whether the firm’s equity
ratio is below a 10% threshold is positive and significant in all regressions. The same holds
true for a dummy variable indicating whether the firm uses more than 50% of its line of credit
on an annual average basis. As an alternative to the latter we also used a dummy variable that
was assigned a value of 1 whenever the firm never or rarely took advantage of early payment
discounts offered by its suppliers and zero otherwise. This variable yielded qualitatively
identical results. In the brief literature survey above it was already mentioned that in theory as
well as in empirical studies of bank-borrower relationships the notion of learning-by-lending
plays a prominent role in the modelling of bank information acquisition. The duration of the
bank-borrower relationship is therefore often used as a metric for informational intensity in
relationship lending. Taking account of the duration of the relationship therefore seems to be
extremely important. Specifications reported in table 3 used a dummy variable indicating
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whether the firm had a relationship with the bank for more than 10 years. Estimated
coefficients have the expected sign but are not statistically significant. As the questionnaire
allowed us to partition the sample into 5 groups according to the duration measure30
we also
used groups of dummy variables in alternative specifications. Joint tests could reject the null
in these cases and, perhaps even more importantly, the signs were always as expected. Overall
we come to the conclusion that the information that a lending bank naturally accumulates over
time has an impact on information acquisition activity. However this paper argues that the
duration of bank-borrower relationships is not a sufficient metric to capture banks’ incentives
to gather borrower-specific information. The important question raised in this paper is
whether bank market structure – proxying for bank market power – is a determinant of banks’
information acquisition activity. We therefore included a measure of local bank market
concentration in our regression equations. As market share information for bank loans and
deposits is generally not available in Germany, we constructed concentration measures based
on branching information. Denote by MSi,j bank i’s market share in local market j. We
approximated MSi,j by the number of branches that bank i operates in j divided by the total
number of bank branches operated in market j. A local market on the other hand is identical to
a specific administrative regional unit. From this market share data we constructed a
Herfindahl index by squaring individual banks’ market shares and totalling them up. Several
alternative concentration measures were also computed and used in the regressions (see 3.6
below). Table 3 shows that local bank market concentration has a statistically significant
impact on information transmission activity.31
Results with respect to bank market structure
are extremely robust and statistically significant at conventional levels. They lead us to the
central empirical finding of this paper, that of a positive market concentration/information
acquisition relationship in bank loan markets.
To further investigate this relationship, we conduct something like a natural
experiment . The point here is to explore whether having collected more information in the
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past enables banks to provide their customers with liquidity at short notice. It is here that
those 58 firms that re-negotiated their lines of credit come into play.32
We therefore re-
estimated the specifications of table 3, simply adding those 58 firms and controlling for
specific effects of that group with respect to INFOCOUNT and INFOSTRUCTURE. The
results are displayed in table 4. To be more precise, we added a dummy variable RLC
indicating whether a firm renegotiated its line of credit. We also added two interaction terms
RLC*Herfindahl and RLC*duration>10. For both terms we expected a negative sign for
reasons set forth above. We introduced RLC*duration>10 into the specification as a check of
plausibility because the natural accumulation of borrower-specific information that occurs
over time might be relevant in situations where borrowers require liquidity, although it
seemed to have no statistically significant impact in normal times. When we interact
Herfindahl with RLC we interpret this as our natural experiment of our first hypothesis of
more information acquisition in more concentrated banking markets; when we interact RLC
with duration>10 we try to provide another check of plausibility as banks that have a longer
relationship with their loan customer should be able to provide liquidity at lower cost. Again
Column I of table 4 displays results obtained for INFOCOUNT as a dependent variable and
column II shows results for INFOSTRUCTURE.
Firstly, all results obtained in our first regressions (table 3) appear also to be valid for
the extended sample. The estimated coefficients for Herfindahl still appear to be positive and
highly significant. Coefficients for both interaction terms have a negative sign and are
themselves highly significant. For INFOCOUNT a Wald-Test cannot reject the null that the
overall effect of market concentration on information acquisition is zero for the RLC group,
although this combined effect has a negative sign. For INFOSTRUCTURE however the null
hypothesis is rejected at conventional levels in all specifications that we estimated. The
coefficient for the RLC dummy is negative, which must be interpreted as follows: Given the
model’s predictions about the effect of equity ratio and liquidity status on the information
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variable, renegotiations of credit lines are less information-intensive transactions than
applications for long and medium term loans.
F. Robustness/Extensions
Tables 4 and 5 present estimation results for parsimonious specifications. Therefore
considering extensions and checking the robustness of our results seems to be a natural and
important exercise. We performed these extensions for both our first-step regression
(excluding renegotiated lines of credit and interaction terms) as well as for our second-step
regressions (including renegotiated lines of credit and interaction terms). Here we briefly
report on the results obtained:
Eastern German localisation
Given the descriptive statistics in table 2 it seems natural to control for firms headquartered in
the eastern part of Germany as information transmission seemed to be more intensive there.
We did so by introducing a dummy variable into our regression equations. In not a single case
could we reject the null of the estimated coefficient being equal to zero. Estimated
coefficients were highly insignificant so that appropriately accounting for a firm’s financial
position seemed to leave no room for a separate eastern Germany effect. Furthermore, none of
the qualitative results reported in tables 4 and 5 were affected.
Industry classification
We controlled for industry by using a five-group industry classification. We also used more
differentiated industry classifications but that had no effect on the results reported. However,
controlling for industry seemed to be important, as indicated by Likelihood Ratio tests.
Local credit risk
Local banking markets might differ with respect to measures of aggregate credit risk that
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could not be accounted for by industry classification or the firm’s financial position. One
could think here of a purely local risk measure that is able to capture systematic risk factors
that are relevant only locally. Consider, for example, the possibility that a default by a large
firm might spill over to local suppliers, which might be affected as unsecured creditors of that
firm. We follow Berlin/Mester (1999) in using the local market’s rate of unemployment and
alternatively its one-year lag as a proxy for local credit risk. In no case did the estimated
coefficients show up to be statistically significant nor did any of our results change after
considering local unemployment.
Regional classification
Surprisingly, whether local markets were classified as rural, suburban or urban has no effect
on our measures of market concentration. Mean and median values for Herfindahl or CR3 are
nearly identical if one groups markets according to a regional classification. Nevertheless, we
controlled for regional effects by introducing dummy variables for rural and suburban areas
(taking urban areas as our base group). We find information acquisition to be more intense in
rural areas; estimated coefficients are marginally significant in most specifications. More
limited diversification possibilities in more rural areas could be one explanation for this result.
From this perspective, information acquisition and portfolio diversification might appear to be
alternative mechanisms to control portfolio credit risk. Note here that local banking markets
are dominated by banks that do business only in the specific region where they are
headquartered.33
Because this aspect raises more serious problems for our study we will refer
to it in section 3.7 below.
Firm age, limited liability, industrial group
We also controlled for firm age (using firm age in years as well as the logarithm of firm age in
years) and whether the firm is a corporation acting under limited liability. For the age variable
we censored the observations by restricting the variable to a maximum of 30 years. This was
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done in order not to bias our results because of some outliers in the sample. Furthermore we
introduced a dummy variable indicating whether the firm belongs to an industrial group. The
liability and group variables might proxy for important aspects of bank’s perceived credit risk.
None of these firm characteristics entered our regressions significantly. Even more important,
considering these variables in our specifications did not alter any of the results reported so far.
Stated purpose
For obvious reasons, it seems to be extremely important not only to control for the firm’s
current financial position, industry and size but also for the incremental investment to be
financed by the loan in question. We have good information on the stated purpose of the
financing. This leads us to introduce a vector of dummy variables indicating whether the loan
will be used for, say, R&D activity, acquisitions, replacement investment, and the like.
Interestingly, results of Likelihood Ratio tests indicated that controlling for purpose is
important in those specifications that use INFOCOUNT as a dependent variable but not for
those using INFOSTRUCTURE. Consistent with our priors with INFOCOUNT as a
dependent variable we find significant effects for acquisitions, growth investments and
rationalisation investments. However none of the results reported in tables 4 and 5 changed by
taking account of stated purposes.
Collateralisation
We have information on whether the bank required collateral to be posted for the loan in
question. Controlling for collateralisation requirements as a binary variable or the type of
collateral to be posted does not have any significant effect on our results. However, our
method of controlling for collateral is incomplete here as we do not have information on the
total degree of collateralisation for all loans taken by the firm, which seems to be much more
important in our case.
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Number of bank relationships
Given the overall perspective of the analysis it is quite interesting to control for the number of
bank relationships a firm has. On the one hand this is often used a proxy for the intensity of
the bank-borrower relationship – the more exclusive the relationship, the more intense the
relationship is expected to be – on the other hand it could also proxy for inside competition as
opposed to outside competition measured by market concentration. Although the number of
bank relationships never turns out to be statistically significant in our regression, a dummy
variable indicating whether the firm is a one-bank firm (the firm has only one bank
relationship) is robustly negative and highly significant. One should be cautious when
interpreting this finding, because only a few firms (around 5% of all observations) have only
one bank relationship. However a likely interpretation, reminiscent of the “lazy banks” notion
in Manove et al. (1999), is that inside competition might promote information acquisition
whereas outside bank market competition does the opposite.
Alternative measures of bank concentration
As alternatives to the Herfindahl we also considered CR3, the number of banks active in the
market, the ratio of the total number of branches to geographic market size (measured in
square kilometres), and the midpoint between a theoretical upper bound and lower bound for
the Herfindahl given the number of banks in the market.34
The qualitative results were
unaffected by these variations, although significance levels varied slightly but results were
always significant at conventional levels.
G. Caveats: selectivity, endogenous market structure and diversification effects
Several important points deserve mention here. The first has to do with our empirical
methodology: One might argue that results obtained for the chosen group of firms are
seriously biased by an obvious selection problem, namely that the flow of information from
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firms to banks, which forms the dependent variable in our regression, is only observed for
those firms that applied for a loan in the first place. We will elaborate on this problem shortly.
As the kind of analysis we have in mind simply asks for the probability that the dependent
variable falls into some category of interest, conditional on a vector of covariates, we can only
learn about that probability under the additional condition that a certain event – a loan
application – has taken place.35
Taking that statement seriously means that we could learn
nothing about the probability of interest for those firms that did not apply unless we strongly
assume that the selection that characterises our sample is purely exogenous. As a further
consequence, we could learn nothing about the probability of interest for the whole sample of
firms – unconditional on the event having taken place or not. There are standard methods at
hand that try to account for selectivity by posing strong assumptions about the joint
distribution of the error terms of a selection equation to be estimated and the equation of
interest. We will not report the results obtained with these methods here, although there is a
strong indication that our main qualitative results are indeed unaffected by taking account of a
selection process shaping our data. In particular, we strongly believe that loan application
situations are special in that an intensive exchange of information takes place. By this we
mean that even if one restricted one’s interpretation of our results solely to the group of firms
that actually apply for a loan, one could still learn something about the determinants of banks’
information acquisition activity.
In interpreting the results two other problems arise which we would like to discuss
briefly. Firstly, as already mentioned above, information acquisition might be seen as an
alternative to portfolio diversification as a means of controlling portfolio credit risk. The
information acquisition/market structure relationship that we measure might thus be shaped
by reverse causality in that markets with lower diversification opportunities show higher
concentration. It is important to note here that an overwhelming majority of German banks are
only doing business in a limited regional market, often identical to the market areas that we
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use in this study to identify local banking markets. This is not only true for all public sector
savings banks but also for mutual banks and a considerable number of private bankers and so-
called regional banks. We tried to control for portfolio effects by inserting a proxy measure
for diversification in our regression equation, very much like a concentration index. This
measure was based on sectoral shares of value added in that specific region. However only
rough sectoral information was available (agriculture, services, manufacturing, trade and
transport). In all our regressions this measure turned out to be highly insignificant. As an
alternative we constructed a probably more sensible measure of the diversification potential
within local banking market. This measure is based on the number of employees in a specific
industry in that particular local banking market. Using a classification scheme with eighteen
industries we measured the concentration of employment across industries which might be
proportional to loan demand across industries. Again this measure did not enter our
regressions in a significant way.
A second interpretation of our results that suggests reverse causality is that
information acquisition and building of strong informational ties with one’s customers is a
means to prevent entry. In a recent contribution, Dell’Ariccia et al. (1999) argue that
informational advantages of incumbent banks might be a deterrent to entry into banking
markets. One could think of a scenario where these informational advantages of incumbents
are not exogenous but endogenously determined to form barriers to entry into local markets.
The low rate of direct penetration of banking markets in Europe – even after implementation
of the single market programme and the common currency – is often interpreted as evidence
of informational entry barriers. The design of our study, however, is incapable of uncovering
this type of relationship.
H. Credit availability and early payment discounts
In the last step of our empirical study, we ask whether firms located in more concentrated
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banking markets are less liquidity-constrained for the reasons discussed above. If in more
concentrated markets banks know systematically more about their clients, there is reason to
believe that they could provide liquidity more easily because incentive problems are better
controlled for. Here we follow Petersen/Rajan (1995) in using information on the share of
early payment discounts taken by the firm as an indicator of credit availability. Within the
questionnaire, firms were asked how often they paid early, thereby taking advantage of
discounts offered by their suppliers. The questionnaire offered 4 categories
(never/rarely/frequently/always) which we take as an ordered category.36
Table 5 shows the
estimation results. Our primary interest here again is in the effect that local banking market
concentration has on the frequency of using early payment discounts offered by suppliers. The
coefficient for the Herfindahl is positive and highly significant in all regressions. Given that
firms’ early payment behaviour is a good indicator of credit availability, these results indicate
that credit is more readily available in more concentrated banking markets. This result is in
line with Petersen and Rajan’s (1995) findings for the local US banking markets. However the
results of the first two steps of our analysis offer a somewhat more focused explanation for
this observation: Petersen and Rajan’s argument points to intertemporal patterns in loan
contracting as a determinant of credit availability. This paper points to another mechanism
that might be important in that it emphasises systematic differences in banks’ accumulation of
borrower-specific information.
Generally, market structure variables seem to be of great importance as shown also by
estimated coefficients for the eastern German dummy, the rate of unemployment and at least
one of the regional dummies. Again the Herfindahl is of primary interest in these regressions.
All other coefficients have the expected sign. Coefficients for firm profitability and equity
ratio as well as sales (again as a size proxy) seem to be significant determinants of credit
availability. We also estimated the specification in table 5 for the western German and eastern
German subsample separately and obtained qualitatively identical results. In both regressions
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coefficients for the Herfindahl were positive and significant. The overall conclusion from this
last step of the analysis could be summarized as follows: Given that the usage of early
payment discounts by firms is a useful measure of credit availability, credit seems to be more
readily available in more concentrated banking markets. This result is in line with
Petersen/Rajan’s findings for small US firms.37
Unfortunately this study is not able to
distinguish between their explanation for the market power/credit availability correlation and
the one suggested by differences in information acquisition activity.
[table 5 around here]
III. Concluding remarks
In this paper we have argued that information gathering by banks forms the basis for
most of the theory of bank uniqueness. If, however, information acquisition is costly, the
competitive structure of the banking industry might be an important determinant of the
informational intensity of bank-borrower relationships. More recent theoretical assessments of
bank screening/monitoring in loan markets take this into account but offer conflicting
hypotheses about the nature of the impact of bank market structure on information gathering.
Our paper is, to the best of our knowledge, the first attempt to study the market
power/information relationship empirically. We are able to measure information flows from
loan applicants to banks within a loan application situation for a sample of small and medium-
sized German firms. In our study we then try to explain these information flows within a
regression framework. As could be expected, riskier firms and firms that are in a poor
financial position have to disclose more information to their potential lenders when applying
for a loan.
Local banking market structure as approximated by standard measures of market
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concentration seems to have a considerable influence on banks’ information gathering even if
one controls for loan characteristics as well as for standard relationship variables and market
variables different from concentration. We then ask whether there are situations where cross-
sectional differences in the equilibrium amount of information accumulated in the past might
be of importance in the sense that the transmission of incremental borrower-specific
information might be a substitute for less information acquired in the past. We find banks’
provision of liquidity to their loan customers at short notice to be such a situation. Expressed
the other way around: if a bank has systematically acquired more information in the past, less
additional information is needed to provide liquidity to a borrower that has experienced a
shortage of liquidity. Given the importance that banks’ liquidity provision to borrowing firms
has attracted in recent theories of bank uniqueness, we find this to be a very important aspect
of bank behaviour.
Finally we ask whether, due to superior information acquisition by their lenders, credit
is more readily available to firms in more concentrated markets. Using the share of early
payment discounts taken by the firm as a proxy for credit availability, we find a significantly
positive correlation between bank market concentration and credit availability. This last step
is a reappraisal of Petersen/Rajan’s (1995) study and confirms their findings for US firms.
The first part of our paper, however, offers a somewhat more specific explanation of why this
relationship seems to hold.
To the best of our knowledge, this is the first empirical analysis of a market
power/information acquisition relationship in bank loan markets and a lot more has to be done
in this area to fully understand the implications of recent shifts in the competitive structure of
financial markets. Our results indicate, however, that increasing competitive pressure within
banking markets has a negative impact on banks’ information acquisition and as a
consequence might hamper their function of providing liquidity to borrowers who have
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experienced a liquidity shock. Taking account of these interrelationships between acquisition
of private information on the one hand and the ability to provide liquidity at short notice on
the other might prove to be extremely important in assessing the welfare implications of
financial intermediary market power.
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Appendix
table 1
firms that applied for
medium- to long-termbank loan
firms that
renegotiated theirlines of credit
firms that applied for
a loan within a publicsector programme
N=212 N=58 N=133
percentage of firmswith equity ratio <10%
28.8% 39.7% c) 25.6%
percentage of firmsthat “never” or
“infrequently” takeearly payment
discounts offered bysuppliers
19.3% 32.8% b) 21.8%
percentage of firmsthat use more than50% of their lines ofcredit on average
57.5% 72.4% b) 54.1%
percentage of firmswith profit to salesratio <3% in 1996
52.8% 70.7% b) 54.8%
percentage of firmswith profit to salesratio <3% in 1995
50,7% 48,3% 53,8%
mean value of salesin 1996 in DM ’000.
[median]66,758[19.800]
87,149[14.500]
46,221[17.00]
sales growth from1995 to 1996 in % 1.1% -2.0% 5.9%
sales growth from1994 to 1995 in %
6.8% 4.6% 7.3%
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table 1 continued
firms that applied for
medium- to long-termbank loan
firms that
renegotiated theirlines of credit
firms that applied for
a loan within a publicsector programme
N=212 N=58 N=133
meanINFOCOUNT[median]
2.033[2]
2.068[2]
2.52 a) [2] a)
meanINFOSTRUCTURE
[median]
1.38[2]
1.36[1]
1.59 a) [2] a)
a)indicates differences compared to the complementary group that are statistically significant at the 1% level
b)indicates differences compared to the complementary group that are statistically significant at the 5% level
c) indicates differences compared to the complementary group that are statistically significant at the 10% level
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Table 2
Western German subsample Eastern German subsample
lowconcentration
subsampleN=90
highconcentration
subsampleN=87
lowconcentration
subsampleN=45
highconcentration
subsampleN=48
mean value Herfindahl 0.114 0.226 0.194 0.358
percentage of firms with
equity ratio ≤ 10%
28.8% 29.8% 33.3% 35.4%
percentage of firms that“never” or “infrequently”
take early paymentdiscounts offered by
suppliers
15.5% 8.0% 46.6% 37.5%
percentage of firms thatuse their lines of credit
more than 75% onaverage
28.9% 19.5% 51.1% 39.6%
mean number of bankrelationships [median]
4.5[4]
5.2[4]
3.5[3]
3.3[3]
percentage of firms thathave had relationship
with the bankapproached for loanapplication for more
than 10 years
86.6% 83.9% 13.3% 12.5%
mean value of sales for1996 in DM ’000
95,635 93,487 28,243 24,913
mean valueINFOCOUNT
[median]
1.71[1]
1.84[2]
2.42[2]
2.66[2.5]
mean valueINFOSTRUCTURE
[median]
1.02[1]
1.08[1]
1.47[2]
1.52[2]
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Figure 1 - Western German subsample by industry classification
0,00%
5,00%
10,00%
15,00%
20,00%
25,00%
30,00%
35,00%
primary
products
capital goods consumer
goods
food etc. construction
low concentration
high concentration
Figure 2 - Eastern German subsample by industry classification
0,00%
5,00%
10,00%
15,00%
20,00%
25,00%
30,00%
35,00%
40,00%
primary
products
capital goods consumer
goods
food etc. constrcution
low concentration
high concentration
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Table 3
Column
dependend variable
(I)
INFOCOUNT
(II)
INFOSTRUCTUREconstant
1.197***(0.501)
1.509**(0.598)
Sales -0,024***(0.006)
-0.023***(0.005)
equity ratio<10%0.496***(0.172)
0.603***(0.225)
use of line of credit on annual average>50%0.485***(0.172)
0.428**(0.185)
duration of relationship with bank>10 years-0.202(0.205)
-0.292(0.240)
Herfindahl
0.400**
(0.198)
0.477**
(0.223)
east German location0.192
(0.240)0.022
(0.278)
m 1 -
-
m 2 1.055*** (0.125)
1.256** (0.134)*
m 3 1.856*** (0.147)
-
m 4 2.427*** (0.161)
-
m 5 3.023*** (0.190)
-
N 212 212 log likelihood -323.351 -179.580 pseudo R 2 0.099 0.135 (Ordered Probit Maximum Likelihood estimates; standard errors in paratheses; *, **, and *** indicate significance at the 10%-, 5%-, and. 1%-level respectively; model includes a constant and therefore sets m 1=0; estimation considers four industry dummies (p-value Likelihood Ratio Test <0.05 for both regressions) and three dummies for the size of the loan (p-value Likelihood Ratio Test <0.01 in both regressions; Herfindahl ist der logarithm of the Herfindahl-Indexof the local banking market in which the firm is headquartered).
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Table 4
column
dependend variable
(I)
INFOCOUNT
(II)
INFOSTRUCTURE
constant1.303***(0.462)
1.655**(0.532)
sales 0.008***(0.002)
0.008**(0.003)
equity ratio<10%0.374**(0.153)
0.499**(0.189)
use of line of credit on annual average>50%0.408***(0.151)
0.3951**(0.161)
duration of relationship with the bank>10 years-0.232(0.199)
-0,332(0.223)
Herfindahl 0.420**(0.191)
0.513**(0.212)
line of credit-0.938(0.584)
-1.596**(0.689)
Herfindahl*line of credit (interaction)-0.779**(0.384)
-1.177***(0.413)
duration of relationship>10 years*line of credit(interaction)
-0647*(0.392)
-0.823*(0.440)
east german location0.208
(0.222)0.034
(0.246)
m 1 - -
m 2 1.073*** (0.112)
1.307*** (0.123)
m 3 1.809*** (0.131)
-
m 4 2.377*** (0.144)
-
m 5 2.949*** (0.167)
-
N 270 270 log likelihood -421.608 -233.072 pseudo R 2 0.075 0.116
p-value Wald Test 0.31 0.08 (Ordered Probit Maximum Likelihood estimates; standard errors in paratheses; *, **, and *** indicate significance at the 10%-, 5%-, and. 1%-level respectively; model includes a constant and therefore sets m 1=0; estimation considers four industry dummies (p-value Likelihood Ratio Test >0.1 for both regressions) and three dummies for the size of the loan (p-value Likelihood Ratio Test >0.1 in both regressions; Herfindahl ist der logarithm of the Herfindahl-Indexof the local banking market in which the firm is headquartered).
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1 See Danthine et al. (1999) for example.2
See also Boot (2000) who emphasises banks‘ investment in customer-specific information in his definition of
relationship banking.3 Nor do we know much about the likely differences of bank behaviour in different financial systems. Are bank-
borrower relationships in Germany or France qualitatively the same as in the United States?4 Fischer (2004) shows that the bank rate markups (for deposit as well as loan products) as well as bank rate
rigidity is systematically related to market structure variables used in this study. See also Fischer/Pfeil (2004).5
See Pagano (1993) for an assessment. Recently however it has been recognised that market power affects bank
behaviour by creating a franchise value that could be lost should the bank default. Creating franchise value
through restricting entry to banking markets might thus be an important means of preventing banks from taking
excessive risks.6 See Sharpe (1990) or Rajan (1992), for example.7 See Petersen/Rajan (1994) or Berger/Udell (1995), for example.8
See Anand/Galetovic (2000) or Cetorelli (1997) for models based on precisely this type of free rider problem in
financial intermediation. Furthermore Cetorelli/Gambera (2000) motivate their empirical study in pointing to
free-rider problems in financial intermediation.9
Following James (1987), much cross-sectional evidence has been added. See Lummer/McConnel (1989) and
Billet/Flannery/Garfinkel (1995) for example.10 This information spillover might have a strong impact on the competitive position of those banks that invested
in information production in the first place.11 A similar conclusion is derived by Manove et al. (2001).12 However, in this model banks first have to invest, at an earlier stage of the game, in their ability to create
incremental finance-related project surplus (sector specialization); therefore a caveat applies here. Although
relationship loans become more attractive when banking markets become more competitive, a bank’s incentivesto invest in sector specialization decrease with increasing banking market competitiveness. For empirical studies,
however, it is not easy to disentangle these two effects.13 See the recent paper by dell’Ariccia/Friedman/Marquez (1999) for a perspective on endogenous market
structure in banking based on incumbents’ informational advantages.14
In Germany the famous “house bank” relationship might serve as an example of such a more explicit type of
bank-customer relationship.
15 In a closely related paper Elsas (2004, forthcoming) finds that house bank relationships are more likely to beobserved in less concentrated banking markets.16 In terms of design, our study is thus quite similar to Petersen/Rajan (1995).17 Only loans within an industrial group are of any further importance, making around 8% of all oberservations
where firms wanted to raise debt.18 See Vives (1991), for example and Hackethal (2004) and Fischer/Pfeil (2004) for in-depth assessments of theGerman banking system.19
See among others Berger/Hannan (1989), Hannan (1991), Neumark/Sharpe (1991), Petersen/Rajan (1995).20 See for example Rajan (1998).21 Firms that renegotiate their lines of credit thus actually renegotiate the upper bound of the overdraft facility.22
With respect to most of the variables note that in order to improve the response rate, the survey offered pre-specified categories, so that firm managers only had to indicate the category into which their firm falls.23 Furthermore, when the questionnaire asked for the purpose of the financing a majority of these 58 firms
simply reported that they „were in need of liquidity“.24 KfW stands for Kreditanstalt f ür Wiederaufbau.25 See Edwards/Fischer (1994) for a further description of public loan programmes and specialised public sector
banks in Germany.26 For example, KfW issues a loan application form that regularly includes a detailed statistical questionnaire.
The loan applicant submits the application form together with the statistical questionnaire to its bank, which
resubmits it to KfW. KfW normally issues credit to the bank, rather than directly to the applicant. The bank then
passes the loan on to the applicant. Note that this implies information transmission outside the bank-borrower
relationship as predetermined by KfW’s guidelines.27
Obviously, this comes at a cost insofar as it makes our sample sizes very small.28 The design of the questionnaire allowed us to check this directly.29 This might be closely related to the observation that applications for larger loan are decided on higher levels
within a bank’s hierarchy than applications for smaller loans.30 These were: no relationship prior to the loan application / less than 2 years / 2 to 5 years / 6 to 10 years / morethan 10 years.31
As we report below, we also used alternative measures of market concentration and found a positive impact of concentration in every single specification. Results with respect to bank market structure – no matter what
specific metric is used – are extremely robust and always statistically significant at conventional levels.32
Again at this point it is extremely important to note that a line of credit in Germany takes the form of a
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transaction account with an overdraft facility and that for practical purposes it does not have a contractuallyagreed upon maturity.33 This aspect of German banking is often overlooked.34
The lower bound is 1/N, where N is the number of banks in the market; for computing the upper bound we
assumed a dominant firm and N-1 fringe firms with 1% market shares respectively.35
The author wants to thank Claudia Plötscher for making him aware of problems caused by sample selection.See Manski (1995) for an excellent exposition of selectivity problems.36 We do not intend to provide a broader discussion on the appropriateness of DISCOUNT as an indicator of
credit availability here. However one should take the possibility into account that trade credit of this type
becomes cheaper the longer the firm waits to pay the supplier. This is not controlled for in our analysis but might
be seen as a major drawback of that indicator of credit availability.37 Complementary evidence is provided by Zarutskie (2003).