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Oligopoly and Conduct in Banking - An Empirical Analysis -∗

Karl-Hermann Fischera and Hannah Sabine Hempellb

Preliminary Version: January 10, 2005 - Please do not quote!

Abstract

This paper analyzes competitive conduct in banking. Examination of the German case turns out to be particularly promising. Germany’s so called three-pillar system, characterized by a unique group structure and significant state involvement, has been heavily attacked recently and is now under close scrutiny. We apply techniques from New Empirical Industrial Organization (NEIO) to an extensive micro data set covering almost all German banks for the time period from 1993 to 2001. Based on an empirical cost approach we estimate Lerner indices taking account of bank individual effects. From our analysis three main results emerge. Firstly, we find competitive pressures increased (market power declined) during our sample period - very much in line with conventional wisdom. However, market power currently exercised by German banks is moderate but still significant. Secondly, banks’ pricing policies are largely consistent with regional classifications based on our estimated Lerner indices, supporting the validity of our estimates of bank market power. Finally, we find cross regional differences in average Lerner indices to be attributable to a significant extent to regional structural and economic factors. Finally, we find cross regional differences in average Lerner indices to be attributable to a significant extent to local bank concentration, however, local demand side factors seem to be of even more economic significance.

Key Words: Oligopoly, Market Power, Banking

JEL Classification: L13, G21, D43

∗ The views expressed in this paper represent the authors' personal opinion and do not necessarily reflect the views of the Deutsche Bundesbank. a Finance Department, JW Goethe University Frankfurt, Germany, e-mail: kafische@wiwi.uni-frankfurt.de; b Corresponding author; Research Centre, Deutsche Bundesbank, Wilhelm-Epstein-Strasse 14, D-60431 Frankfurt/M., Germany,e-mail: Hannahs.Hempell@bundesbank.de.

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1. Introduction There has been much talk but few empirical evidence on the structure and profitability of

Continental European banking and German banking in particular. This paper tries to fill part

of the gap by providing a detailed empirical analysis of competitive conduct in German

banking. Our motivation is twofold: Firstly, any study of competitive conditions in industries

considered to be of vital importance for the functioning and growth of the overall economy

should be interesting in its own right. Based on recent cross country research, there is now

widespread agreement among economists that financial markets and banking structures in

particular do indeed matter. Secondly, examination of the German case turns out to be

particularly promising, given that Germany’s so called three-pillar structure, characterized by

a rather rigid group structure and significant state involvement, has been heavily attacked

recently and is now under close scrutiny, both academic and political.

In this paper we apply techniques from New Empirical Industrial Organization (NEIO)

pioneered by Bresnahan and others to an extensive micro data set covering almost all German

banks for the time period from 1993 to 2001. However our strategy to econometrically

identify the Lerner Index described in section 3 is different from most other studies as we

build our analysis on an empirical cost approach. While our results with respect to the level of

market power exercised by German banks are somewhat sensitive to the estimation technique

employed, several conclusions emerge. Exercise of market power by German banks has

declined over our sample period very much in line with conventional wisdom. However,

when our regressions account for bank individual effects in terms of total cost and pricing

behavior, the level of market power exercised by German banks is moderate but still

significant (see section five). To put these results in perspective, it is worth noting, however,

that in banking, unlike other industries, systemic stability is a major concern given that risk-

shifting moral hazard might have a serious role to play. Moderate departures from marginal

cost pricing might, therefore, well be welfare enhancing.1 To check the plausibility of our

estimates of Lerner indices, in section six we examine regional pricing policies of banks and

find them to be largely consistent with classifications based on our estimates.

When we ask for the sources of market power exercised by sample banks, the specific

structure of German banking comes into play. Compared to other European countries, the

German banking market is still heavily fragmented in that many small regional institutions

serve only small regional markets. This is despite a pronounced consolidation process in

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terms of the sheer numbers of institutions merged.2 The specific governance structures that

prevail in public and co-operative banks in Germany, however, have retarded signigicant

industry consolidation in terms of volume and national market shares; moreover, the principle

of market demarcation is still applied especially in the deposit and loan business.

Accordingly, in section seven we investigate whether regional economic and structural factors

have explanatory power for average cross regional differences in competitive conduct. Lerner

indices seem to be attributable to a significant extent to regional structural and economic

factors. While local bank concentration does have an impact, demand side factors measured

at the local level seem to have even more explanatory power.

2. Motivation Based on recent cross-country empirical research, among economists it is now commonplace

to assume that financial markets do indeed matter for economic growth. From this

perspective, it is nothing more than a derivative to conclude that banks and the structure of

banking markets also have an important role to play, simply because in most countries the

banking system is a dominant part of the financial system. In Germany, for instance,

Demirgüc-Kunt/Levine (2002) report that total bank assets make 121 % of GDP, a rather

large figure in international comparison.

In empirical work, therefore, the institutional framework of banking markets has been studied

to assess its likely impact on the stability of the financial system, the financing of firms and

overall economic growth in a cross-section of countries.3 Cross-country differences in bank

regulation, the legal framework for enforcing creditor rights, market structure and competition

and government ownership of banks are suspect of being relevant determinants of banking

market performance. For our study of the German case two aspects seem to be especially

relevant: We strive to measure competitive conduct in a market, where government

involvement is substantial. While on the one hand, we have to take this structural aspect into

account in the design of our study, on the other hand our results also provide a contribution to

the current political and academic debate.

1 Furthermore, bank products are especially information sensitive and banks’ incentives to acquire information need to be sustained. 2 During our sample period from 1993 to 2001 the number of banks declined from 3,880 to 2,700. 3 See Demirgüc-Kunt et al. (2003) and La Porta et al. (2002) for instance.

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In the arena, La Porta et al (2002) found that government ownership of banks correlates

negatively with subsequent financial development and subsequent rates of growth in GDP.4

Obviously, these results are important from the German perspective, because in Germany

state involvement in banking is substantial. La Porta et al (2002) measure the share of

government ownership in German banks’ assets as 51.90 % in 1970 and a still remarkable

36.36 % in 1990.5

We will skip a detailed description of the German banking market here for the sake of brevity

and refer instead to Bundesbank (2001, 2002), Hackethal (2004) and Fischer/Pfeil (2004) for

further discussions. However, what remains to be taken account of is that in the German

financial market the group of state-owned banks, comprising local savings banks owned by

municipalities as well as Landbanks owned by the states, is a dominant player in all areas of

domestic finance. Together with the group of co-operative banks and private banks, they

make up what is called the three-pillar structure of German banking. Discussions about the

likely benefits of privatizing state-owned banks in Germany are going on for years now. The

discussion has been revived recently and the German system has come under close scrutiny,

both domestic and international (see, for instance, IMF (2003)). However, for any such

assessment, several basic questions need to be answered: One of those is about the overall

competitiveness of German banking and the contribution of each of the three pillars to

competitive conduct. Unfortunately, as of yet not much empirical evidence on this issue is

available. A thorough assessment of competitive conduct in German banking, however, seems

to be a necessary prerequisite for any plans to restructure German banking. This study,

therefore, does not only supplement cross country empirical work with competition and

government ownership in banking, but the results we derive seem also be important for the

current political debate on the German banking system.

3. Methodology and Empirical Implementation Structural indicators of banking markets, measures of market share distribution and

concentration, still are an indispensable part of any thorough industrial economics analysis.

However, assessments based on descriptives alone turn out to be seriously limited. Empirical

studies based on structural econometric models of oligopoly behavior instead allow for a more

4 Supporting evidence comes from Sapienza’s (2004) study of Italian government owned banks. 5 As noted by Fischer/Pfeil (2004), although based on only observing the largest banks in the country, these figures capture the share of government ownership in Germany fairly well.

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profound analytical assessment of the extent of competition in the Banking industry. Methods

of the New Empirical Industrial Organization proposed by Bresnahan (1989) and others offer

a consistent framework for the empirical analysis of the exercise of market power of firms,

groups of firms or industries as a whole.

There have been earlier applications to the banking industry using aggregate time series data

for national banking markets.6 Closer examination of these previous studies reveals that they

often encounter serious problems with respect to data and methodology. Our approach,

heaviliy influenced and stimulated by Angelini/Cetorelli’s 2003 contribution, tries to

circumvent at least part of these problems as we do not rely on industry time series data.

Furthermore, we rely on a broad measure of banking outputs to cope with the multi-product

nature of universal banking.

Like several other studies of bank market power, we take a generalization of a quantity setting

oligopolist’s first-order condition as our starting point.

(1) iiii Qpqcp θ

∂∂

−′=

Here ip is the price for oligopolist i, ic′ is his marginal cost, iq his output quantity, while Q

denotes total industry output. By varying θ pricing rules derived from (1) cover most of static

oligopoly theory.7 In a conjectural variations equilibrium, θ has the interpretation of iqQ ∂∂ / ,

an oligopolist’s conjectures about rivals’ responses. Under the “as-if interpretation” advocated

by Bresnahan (1989) an average of θ over all firms in an industry still provides a useful

measure of the “collusiveness of conduct” even if firms conduct is not of the conjectural

variations type. Furthermore, aggregating over industry participants and rearranging (1) yields

θ=∂∂′−Qp

pQ

pcp

i

ii

Thus θ provides an elasticity adjusted measure of how much price diverges from marginal

cost known as the Lerner index; a widely accepted measure of the exercise of market power.

6 See, for example, Shaffer (1989, 1993), Suominen (1994), Ribon/Yosha (1999), Uchida/Tsutsui (2004). Adams et al. (2002) account for separate conduct on the loan and deposit side. 7 See Genesove/Mullin (1989).

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In fitting this structural model to industry data, econometric identification of θ becomes the

major issue. For instance, employing time series data, with non-constant marginal cost

econometric identification of θ requires Qp ∂∂ / to vary over time. Most studies in banking

follow Bresnahan’s (1982) proposal here and estimate a demand function from industry time

series data allowing for shifts in demand elasticity. These rotations of the demand curve then

serve to identify θ in the above supply relation. Both equations could in principle be estimated

consistently using instrumental variables techniques.

While other papers in banking have followed this path, our approach in this paper is similar

to Angelini/Cetorelli (2003) and differs for the following reasons: As pointed out by Corts

(1999) application of Bresnahan’s model only yields estimates of the marginal response of

price to variations in demand. Estimation of the Lerner index, the divergence of price from

marginal cost, is only achieved by mapping the marginal effect onto an equilibrium concept

based on conjectural variation. For reasons discussed below, even if the conjectural variations

assumption provided a useful approximation, data problems and specific industry practice

alone might seriously hamper this second step of the analysis. Our approach, therefore, is to

directly measure the divergence of price from marginal cost. It is build upon a thorough

empirical analysis of banks’ total cost using flexible functional forms from which an estimate

of marginal cost is derived. Accounting for unobservable individual effects in total cost

derived marginal cost is then mapped into oligopolists’ first-order conditions.

Unlike other studies, we are not restricted to aggregate industry data. Instead, we employ an

extensive micro dataset covering almost all German banks over the 1993 to 2001 period. The

cross-sectional dimension of our data allows for much more detailed examination of marginal

cost and pricing suggesting identification of the Lerner index on a period by period basis.

Within our framework from the above generalized first-order condition the following supply

relation could be derived.

( )i

ij ijii qQ

QpqtwqCp∂∂

∂∂

−′= ∑ ,,

which simplifies to ( ) ( ) i

i

i CCp λεθ

+′=−′= .~. . Setting Qq

qQ i

i

i

∂∂

≡θ and Qp

Q 1~∂∂

=ε ,

the Lerner index then calculates as

(2) ( )i

i

i

ii

i

ii ppp

CpL λεθ

εθ

=⋅

==′−

= ~. .

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As the dependent variable p in our supply relation, we do not use actual transaction prices but

an indirect measure of price calculated as the ratio of income to a corresponding measure of

earning assets.

We simultaneously estimate the above supply relation and a translog cost function as

developed by Christensen et al. (1973). This functional form is the workhorse and still marks

the state-of-the-art in empirical cost analysis.8 As a flexible functional form, no restrictions on

partial elasticities of substitution are imposed. In the translog cost function total cost are

specified as a function of a measure of output q and factor prices wi . As factor prices we

consider prices of staff (w1), of refinancing (w2), and of other administration (w3). Because we

consider interest paid on deposits in the cost function, the divergence of price from marginal

cost provides a compound measure market power exercised in loan and deposit markets.

Applying Shepherd’s lemma (1971) differentiating the cost function with respect to input

prices yields factor share equations as a system of cost minimizing conditional factor demand

functions. In principle these factor share equations could as well be estimated simultaneously

improving the efficiency of our estimates.9

(3) ( ) ∑ ∑= =

+++++=3

1

3

11

2100 lnlnlnln

2lnln

jij

jjiijiii wcqwqcqcbC

iiii wwbwwb 315214 lnlnlnln ++ ( ) iijj

jii wbwwb υ+++ ∑=

+2

3

16326 lnlnln

From the translog functional form marginal cost is easily derived and plugged into the supply

relation via coefficient restrictions.

(4) iij

ijjii

ii uwcqcc

qC

p ++⎟⎟⎠

⎞⎜⎜⎝

⎛++= ∑

=+ λ

3

1110 lnln

We estimate the system of equations (3) and (4) simultaneously using instrumental variable

techniques. In an extended estimation approach, we additionally account for unobservable

individual effects by employing fixed effects estimation of our system.

As already mentioned, identification of the conduct parameter could alternatively be achieved

by modelling variations in demand elasticity through time. Following this approach, however,

requires much more detailed information with respect to price and quantity than is usually

available in banking. For instance, one would optimally use observed prices for sharply

defined banking products for which corresponding industry output should as well be directly

8 See Kelly/Ying (2003) for a recent application to the US cable television industry.

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observable. While this seems to be less of a difficulty in many other industries, in banking it

poses a serious problem. Prices for many bank products typically comprise interest charges as

well as fees. This is true for many of banks’ loan and deposit products. If accessible at all, in

most databases only the interest component of price is available to researchers making banks’

pricing schedules hard to compare cross-sectionally and through time. Furthermore, recorded

interest rates and loan and deposit quantities often do not perfectly correspond. So in

Neven/Röller (1999), for example, these authors use total loans to the corporate sector as a

measure of industry output but their German sub-sample only includes interest rates on very

short term lines of credit as a price proxy.10 Unfortunately, short term loans in Germany only

make a relatively small fraction on overall corporate loan volume. As is well known from the

studies of a credit channel of monetary policy transmission, aggregates of short term loans

and of longer term loans often do react quite differently to changes in the stance of monetary

policy (e.g. changes in short term interest rates).

Even more fundamental, measurement of new loans originated in a specific sub-period is far

from trivial with total loans outstanding - as available from official monetary and banking

statistics and often used in empirical work - being only a crude and inadequate approximation.

Repayments of principal, prolongations of existing lines of credit, restructurings,

renegotiations and commitments in most cases are not adequately accounted for. Because

maturity structures and repayment schedules of such total loan aggregates are not constant

through time, approximations of period by period loan demand are seriously misleading.

Finally cross-subsidization between different products offered to one customer might have

been a widespread practice in universal banking systems in the past. Cross subsidization,

therefore, not only takes place across product globally but even across products within a

single customer relationship. Finally, even with good price and quantity data for specific bank

products at hand, a comparably serious difficulty comes with assigning marginal cost

schedules to such separated products and services. Given these potential difficulties and the

restrictions set by the data at hand, we believe that directly measuring the divergence of prices

from marginal costs yields more valid estimates of market power exorcized.

4. Data The micro data used in the following estimations stem from the bank balance sheet statistics

(BISTA) and the bank profit and loss accounts (PaL) all of which are compiled at the

9 See also Kelly/Ying (2003).

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Deutsche Bundesbank. They basically comprise yearly data from all credit institutions

reporting to the Deutsche Bundesbank from 1993 to 2001.11 Altogether the (unbalanced)

sample used is made up of 24,238 observations starting with 3,510 in 199412 and declining

somewhat substantially to just 2,327 in 2001 as a reflection of the aforementioned significant

consolidation (see table 1 in the appendix for a summary on the data). The vast majority of

observations in the sample pertain to banks from the cooperative sector (17,780) while around

twenty percent (4,738) stem from the savings banks sector and only seven percent (1,720)

from private commercial banks. Just over ninety percent of the observations originate from

banks located with their main office in the western part of Germany.

The data set is not adjusted for bank mergers, i.e. two merging banks are treated as separated

banks up to the year they are merged from where on only the ‘take over’ bank is accounted

for. Thereby, it is implicitly assumed that the behavior of that bank does not change due to the

merger with respect to its business mix.13 However, the impact of mergers on changes in local

market structure is accounted for in section 5. Angelini/Cetorelli (2003), by contrast,

explicitly analyze the long-run impact of consolidation in Italian banking; they find no

evidence that banks involved in M&A gained market power. For Germany, the effects of

mergers for Lerner indices remains to be analyzed in an updated version of this paper.

The main variables mentioned before calculated from these data are the approximated multi-

output price variable (p), overall costs (C), interest-bearing assets (q), approximated input

prices of staff (w1)14, of refinancing (w2), and of other administration (w3). Here p is the ratio

between the sum of interest income from loans and money market transactions as well as

commission income on the one side and the sum of loans to customers and banks on the other.

10 Bikker’s (2003) study also uses total loans in combination with interest rates on short term lines of credit. 11 From our sample, we excluded mortgage banks and special purpose banks as well as “non-typical” banks, i.e. institutes with no or hardly any deposit/loan business with non-bank customers etc.; together, we excluded 4.4% of all observations available. 12 Since lagged values are used as instruments, here we only comment on data starting in 1994. 13 Greater deviations in the results for an adjusted data set are not expected since the majority of the mergers took place among small cooperative banks which supposedly operate under similar competitive conditions and with a similar business mix. However, Fischer (2000) found significant differences in post merger pricing for some banks in Germany depending on their mergers’ influence on regional market structures. The results of other authors for merger adjusted and unadjusted data sets (e.g. Kishan, Opiela (2000), p. 127) support our practice, yet their analysis referred to different banking markets (US). Nevertheless, adjustments such as treating the merging banks as only one bank throughout the entire sample period (e.g. Peek, Rosengren (1995), p. 631, Kishan, Opiela (2000), p. 127), also make implicit assumptions about the banks’ behavior. 14 In the absence of data on the individual bank’s number of staff we approximate this input price by dividing staff expenses by the potentially more staff intensive balance sheet positions, i.e. loans to non-banks, deposits from non-banks as well as debt securities and stocks.

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q reflecting the output of a bank covers all interest bearing assets (for a detailed description of

the variables see the appendix on p.22).

The mean price p ranges from close to 9 percent in 1994 to somewhat more than 7 percent in

2001 mirroring the overall interest rate decline during that period (see table 2 in the appendix

for some more detailed descriptive statistics). Average costs C increased substantially from

around 40 mill Euro per bank in 1994 to close to 100 mill Euro in 2001 which is mainly

caused by a significant increase in average bank size reflected here in the increase of mean q

from around 530 mill Euro in 1994 to 1.4 bill Euro in 2001. While there has been limited

variation over time in the approximated input prices for staff and other administration (w1, w3)

- not withstanding substantial cross-sectional variations -, prices for refinancing (w2) partially

again mirrored the decline in market interest rates.

For the extensions on determinants of banks’ regional market power in section seven, we

additionally draw on data from the Bundesbank’s bank branch statistics15 and regional

statistical data on the district level collected by the Federal Statistical Office covering all 436

districts (Kreise and municipalities) in Germany. As market structure variables, we use the

number of banks active in a local market16, the local concentration ratio of the largest three

banks (CR3), and the local Herfindahl index (HHI). Both concentration measures are

calculated on the basis of branches per bank in the respective district. 17 Among the further

data employed reflecting local market characteristics are the number of new housing licenses

per capita, the disposable income per capita, and the investments per manufacturing firm

located within a district.18

15 Here our original data are restricted to the coverage of the years 1996, 1998, and 2000. As the variation over time is rather limited, for the structural indicators developed from these statistics we use observations for 1996 as structural proxies for the years before 1996, observations from 1998 for 1997 and 1999, and observations from 2000 as proxies for 2001. 16 Defined as the number of banks operating at least one branch in a local market. 17 On a local level, market share information for bank loans and deposits is generally not available in Germany. This is primarily due to supra-regional banks not tied by market demarcation. These institutions do not report on how their business is split on a local level (Kreise). Therefore, we constructed concentration measures based on branching information. Denote by MSij bank i’s market share in a local market j delineated by an administrative district (Kreis or municipality). We approximated MSij by the number of branches that bank i operates in market j divided by the total number of bank branches operated in market j. From this market share data we constructed a Herfindahl index (HHI) by summing up the squared individual banks’ market shares. The CR3 is accordingly calculated as the sum of the largest three market shares in market j. 18 Averaged over the available time period from 1995 to 2001.

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4. Empirical Results In order to estimate the prevailing degree of competition and its development over time, we

use the estimation equations (4’ and 5) – a price relation and a translog cost equation - derived

from the structural econometric model in section 2. We apply three types of estimations to

this equation system: three stage least squares (3SLS) cross sectional estimations on a yearly

basis, a 3SLS pooled estimation for the whole sample, and 3SLS with fixed effects; for the

latter two we include time dummies to take account of changes over time.

(4’) tit

ttj

tijjtiti

titi udumdconswcqcc

qC

p ,

8

1

3

1,1,10

,

,, lnln +++⎟⎟

⎠

⎞⎜⎜⎝

⎛++= ∑∑

==+

λt is then either estimated by only the constant (cons) in the cross sectional case or the

constant plus the coefficient (dt) of the respective time dummy (dumt) per year for the pooled

estimations and the fixed effects estimations. The estimated Lerner indices (Lt) are then

calculated as t

tt p

Lλ

= , where tp is the average price over all banks included in the

respective estimation in year t.

The estimation results differ somewhat dependent on the type of estimation method used (see

tables 3 and 4 in the appendix); however, for all three types we find a decline in the estimated

Lerner indices over time (see figures 1 to 3 in the appendix). The actual degree of market

power reflected in the Lerner indices for cross sectional estimations and pooled estimations

ranges from around 0.1 in 1994 to zero (or little below)19 in 2001. For fixed effects

estimations, the estimated degree of market power is somewhat higher ranging from around

0.19 in 1994 to 0.12 in 2001 (see figure 4 in the appendix).

The differences in the estimated levels is mainly due to the different ability to take account of

bank specificities in the cross sectional or pooled estimations on the one side and the fixed

effects estimation on the other. In the former, we were only able to take account of rather

simple and general “bank specific” features such as the affiliation with different banking

groups (savings banks, cooperative banks, and private commercial banks) by introducing

dummies for the respective characteristics when estimating the banks’ cost functions. In the

19 Negative values for Lerner indices do not seem plausible at first sight, however, as these are ex post-Lerner indices unexpected business developments may well push Lerner indices temporarily below zero. Here, for 2001 such effects of unexpectedly weak developments with high provisioning and fainting credit extensions seem to be reasonable explanations.

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latter, individual effects are explicitly accounted for in the estimations, which allows us not to

force these artificially in the estimation of the overall cost function and, thereby, in the

estimation of marginal costs relevant for the estimated Lerner index.20

When comparing estimates of the overall sample with a sample restricted to banks having

their main office registered in the western part of Germany, we find somewhat higher values

for West Germany, especially when looking at the results from the fixed effects estimations.

This may partially be explainable by the difficult general economic situation in the eastern

part of Germany21 which leads to a more challenging business situation for banks located in

these regions.22 Local market structures by contrast, tend to be more concentrated in the

eastern parts of Germany. This seems to be highly consistent with economic models of market

entry in banking and reinforces the notion that there are common factors affecting both

profitability and market structure.

Interpreting our results at this stage, they indicate a decrease in market power during the

sample period regardless of the estimation method applied. Furthermore, the actual level of

average market power has been moderate according to our estimations. If - for illustrative

purposes - interpreted under the “as if”-interpretation (as proposed by Bresnahan (1989))

within a (hypothetic) symmetric Cournot oligopoly, it ranges roughly from a six firm local

oligopoly in 1994 to an eight firm local oligopoly in 2001 or from a twelve firm local

oligopoly in 1994 to a perfectly competitive market in 2001 for fixed effects results and

pooled results, respectively.

6. Estimates of market power and bank pricing patterns Is there any way to check the plausibility of our estimates of Lerner indices? Banks’ ability to

exercise market power vis-a-vis their customers should ultimately show up in their pricing

policies. So for instance, a reasonable check would be to ask the following question: Are loan

rates higher and deposit rates lower for banks whose estimated Lerner indices are higher on

average and vice versa. Unfortunately to conduct such a preliminary test we only had interest

20 See the high positive coefficient in the cost function in tab. 4 reflecting the average individual effect. 21 The economic situation in east Germany is mainly characterized by low economic growth, high unemployment, higher rates of delinquent loans, and lower per capita income. 22 The majority of which is limited to these regions in their business due to demarcations rules applying to savings banks as well as cooperative banks.

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rate data for the 1992 to 1995 period available.23 The data are from the former Bundesbank’s

monthly bank interest rate statistics and are based on a survey conducted regularly among a

sample of German banks. While most observations are bank specific, larger banks active in

more than one regional market could provide multiple observations per month. The

appropriate unit of observation for these institutions is the bank branch located in some

regional market. While trimmed averages of reported interest rates are published in the

Bundesbank’s monthly reports, we had access to bank individual time series of reported

interest rates for several types of deposit accounts and loan products. Given the above

mentioned structure, the data are especially useful for examining cross regional differences in

pricing behavior.24 Furthermore, the data at hand were anonymized with respect to the

reporting banks25 but could be assigned to the region where the reporting bank/branch is

located. Tables 5 and 6 provide a short description of the data with respect to the number of

observations, number of reporting institutions/branches and number of regional markets

considered.

As our estimates of Lerner indices are bank specific we only use those of German savings

banks and co-operative banks. The so called regional principle, that is the principle of market

demarcation assures that these institutions do only business with respect to loans und deposits

within the region where they are headquartered. We aggregate individual Lerner indices to

obtain unweighted average regional Lerner indices (see section seven, pp.14, for a more

detailed description). We then group available time series of bank loan and deposit products

according to whether they were reported from banks/branches located in high, medium or low

Lerner regions. How to exactly distinguish high, medium and low Lerner areas is somewhat

arbitrary, but the results are quite robust to the choice of regional classification. For the results

reported in the remainder, we defined low Lerner regions as those with average Lerner indices

below 0.15. High Lerner regions were those with average Lerner indices above 0.2 with

medium regions lying in between. Table 7 displays some descriptive statistics on market

structures according to this proposed grouping. As can be seen from table 7, averages of

standard structural measures like the regional market Herfindahl based on branching data is

consistent with our estimated Lerner indices. However, reported figures for maximum and

minimum Herfindahl indicate that within our groups there is considerable overlap with

23 For a follow-up version of this paper, we will update the interest rate data on which this part of our analysis is based. 24 The full sample of interest rate observations is extensively analysed in Fischer (2000). 25 This was done for confidentiality reasons.

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respect to these structural measures of market concentration. The main findings of our little

exercise are displayed in figures 5 through 7. These are simple plots of time series averages of

interest rates reported by sample banks for loan and deposit products. Aggregation across

individual time series was conducted according to the grouping of regional markets as

discussed above. The pattern of cross regional pricing according to our classification is quite

clear. Rates charged on corporate loans are higher and rates paid on customers’ deposit

accounts are lower in markets characterized by higher average estimates of Lerner indices. As

is obvious from figures 5 to 7 the extend to which estimated Lerner indices provide an

explanation for variations in bank pricing differs across products. Overall, pricing patterns

observable in Germany’s regional banking markets largely confirm our estimates of Lerner

indices and an according classification of markets as being more or less competitive

respectively. We, therefore, conclude that our structural econometric model of oligopoly

conduct yields reasonable estimates of the ordering of the price cost margin across banks and

markets.

7. Determinants of Banks' Regional Market Power In a further step we first try to identify the relevance of the structure – i.e. concentration – of

local markets for the degree of market power executed by banks in their respective markets.

To obtain further information on the relevance of additional local market characteristics, we

additionally test for the significance of such characteristics in explaining the average

estimated degree of market power of banks within a local market delineated by an

administrative district (Kreis or municipality).26 To take account of the endogeneity of market

structure variables when explaining market power, we complement our analysis with

instrumented variable two stage least squares regressions.

As measures for the degree of market power executed by the individual banks, we calculate

the bank specific average Lerner indices using the estimated coefficients c from the fixed

effects estimations described in section 3 (see table 4 in the appendix) as follows:

i

T

tti

i T

LL

i

∑== 1

,

with ti

jtijjti

ti

titi

ti p

wcqccqC

pL

,

3

1,1,10

,

,,

,

lnˆlnˆˆ⎥⎥⎦

⎤

⎢⎢⎣

⎡⎟⎟⎠

⎞⎜⎜⎝

⎛++−

=∑=

+

26 Here we restrain the analysis to cooperative banks and savings banks due to their more clearly limited business areas.

15

For the calculation of district specific average Lerner indices, we restrict ourselves to savings

banks and cooperative banks as only for them we can sensibly suppose that they are

predominantly active in limited local markets as they have agreements on regional

demarcations in their statutes. The district specific Lerner indices are calculated as the

arithmetic mean of the bank specific Lerner indices ( iL ) of the banks predominantly active in

the respective district27:

Figures 8 and 9 in the appendix show the distribution of these Lerner indices and table 8 lists

some further descriptive details..

As market structure or concentration variables, we use the local HHI, the local CR3, and the

number of banks active in a local market (for some descriptive details see section 3, p. 10, and

the tables 9 in the appendix). While the candidates for additional right hand side variables are

numerous, we focus on the structural interpretation of our cross-sectional regressions. It is

worth noting that, for instance, in an asymmetric Cournot equilibrium the market share

weighted average of individual Lerner indices equals the HHI divided by the elasticity of

demand. Although in our regressions we only use unweighted averages of our estimated

Lerner indices of a subset of active oligopolists in a given market (savings banks and

cooperative banks), we nevertheless view our specifications as a valid approximation to this

structural relationship.

In selecting further regressors, we, therefore, focus on variables likely to reflect the regional

elasticity of demand for banking services. All our right hand side variables are measured at a

local level. We use the ratio of the average number of new housing per capita

(housinglicdens) serving as indicator for the demand for housing loans, average disposable

income per capita, and average investment per manufacturing firm.28 These variables not only

reflect potential shifts in demand for bank services but are also likely to have an impact on

cross regional variations in demand elasticity. Obviously, disposable income per capita might

be a more relevant indicator for liability side bank products like deposits or investment

services. Additionally, the average ratio of negatively qualified enterprises to the number of

27 To avoid distortions in our indices by banks only existing in the beginning of the sample period, we only include banks which have still been active in 1999. However, our results are not affected by this selection. 28 If not indicated otherwise, all additional regional variables stem from the Federal Statistical Office’s regional statistics averaged over the years from 1995 to 2001.

j

N

iji

j N

LL

j

∑== 1

,

16

all enterprises registered within a district (crefo risk indicator)29 is included. This variable

deserves special mention as it not only reflects the risk attached to commercial borrowers and

thus accounts for the likely effects of loan losses on our estimates of market power. In

addition, it might serve as an aggregate measure of borrower opaqueness which we expect to

correlate with bank dependency in external financing.30

In cross sectional OLS regressions (see table 10 in the appendix), we find the local HHI, the

local CR3, and the number of banks all to be highly significant for average district Lerner

indices with expected signs (positive for the two measures of concentration and negative for

the number of banks). Adding the local market characteristics described above increases the

explanatory power of the regressions further; here again, all variables are highly significant.

Important for regressions using the whole sample (including 104 districts from the eastern

part of Germany) is to include a dummy variable for these districts. As mentioned before, the

economic situation as well as the market structures still differ significantly for the two parts

which here is reflected by the highly significant negative coefficient of the dummy variable.

To take account of likely endogeneity of our market structure variables, we additionally

estimate these regressions with instrumental variable two stage least squares (see tab. 11 in

the appendix). As an instrument for the market structure variables, we use the number of

banks active within a district as of 1985 (1991 for the eastern part).31 The instrument chosen

correlates well with the market structure variables but can be expected to exert no influence

on equilibrium pricing behavior for our sample period. Because of the large number of

mergers among small banks that have taken place in the meantime correlation between our

instrument and the endogenous variable is strong but far from perfect. Again the market

structure variables prove to be highly significant. However, in the instrumented regressions,

the coefficients for the concentration variables are substantially higher than in the OLS

estimates described before. The variables reflecting economic market characteristics yield

similar results to the OLS estimates.

29 Negatively qualified enterprises are those that show signs of loan delinquencies or outright default. Data provided by Creditreform for the years 1996 to 1999 by postal codes; they were converted into Kreis level data weighting postal code areas by population figures of the year 2000. 30 See Diamond (1991). 31 Approximated by the sum of the number of banks per district in 1995 and the cumulated number of mergers between banks registered within the district since 1985 up to 1995. 31 For the eastern parts of Germany, however, our recursive calculations end in 1991as a natural consequence of German unification. Minor inaccuracies stem from our inability to properly account for market exits by branch closings.

17

With respect to the signs of the estimated coefficients, we would expect positive coefficients

in every single case (except for the number of banks as an inverse proxy for bank

concentration). For the measures of bank concentration a positive impact on Lerner indices

can easily be derived from most of oligopoly theory.32 Higher values of our demand side

variables imply an outward shift in the demand function for bank services. In addition, as

discussed above, they have the potential to lead to a decrease in demand elasticity. Lower

demand elasticities in turn increase banks’ ability to price above marginal cost. These

expectations regarding the signs of coefficients are met by our regression results (see tab. 11

in the appendix).

To assess the economic significance of our regressors we performed comparative statics

exercises based on our regression coefficients. From the results, we arrive at the preliminary

conclusion that each demand side regressor has a similar impact on the Lerner index as the

respective structural variable employed (the predicted effect of a two standard deviation move

in each variable is roughly two percent in terms of the Lerner index – only manufacturing

investments yield somewhat lower figures). We, therefore, conclude from the above analysis

of determinants of banks’ regional market power that local market concentration as well as

other local economic characteristics primarily related to the demand for bank products prove

to be highly significant for the degree of banks’ market power executed at a local level. Our

findings, however, further indicate that demand related factors seem to have an even higher

economic significance for differences in banks’ regional market power.

8. Conclusions In the paper we analyzed competitive conduct in banking, its variation across local markets,

and determinants of market power executed on a local level. We apply techniques from New

Empirical Industrial Organization (NEIO) to the case of Germany, a banking system

characterized by a strong consolidation process among small banks, a rather rigid group

structure (a so-called three-pillar system), and significant state involvement. For our analyses

we used an extensive micro data set covering almost all German banks for the time period

from 1993 to 2001. Based on an empirical cost approach we estimated Lerner indices taking

account of bank individual effects. Our results indicate a decrease in market power during the

sample period regardless of the estimation method applied. However, when accounting for

32 However, Angelini, Cetorelli (2003) find a negative impact of market concentration on estimated regional Lerner indices in Italy.

18

bank individual effects in terms of total cost and pricing behavior, the level of market power

exercised by German banks is moderate but still significant. Pricing patterns observable in

Germany’s regional banking markets are largely consistent with classifications based on our

estimates and confirm the plausibility of our estimated Lerner indices. We therefore, conclude

that our structural econometric model of oligopoly conduct yields reasonable estimates of the

ordering of the price cost margin across banks and local markets.

Despite a pronounced consolidation process, the German banking market is still heavily

fragmented and for the majority of small banks (cooperative banks and savings banks) the

principle of market demarcation of their respective groups of affiliation still applies.

Accordingly, we investigated whether regional structural and economic factors have

explanatory power for average cross regional differences in competitive conduct. We found

both local market concentration as well as other local economic characteristics primarily

related to the demand for bank products to be highly significant for the degree of banks’

market power executed at the local level. Our findings, however, further indicate that demand

related factors seem to have an even higher economic significance for differences in banks’

regional market power than local market concentration. Our analyses confirm the importance

of regional market characteristics and local market concentration for the performance of local

banking markets despite rapidly proceeding international financial market integration.

With these results, we tried to fill part of the gap of empirical evidence on the structure and

profitability of Continental European banking and German banking in particular, a necessary

prerequisite for any discussion about the future of Germany’s three-pillar system. For

Germany, more specifically, these findings might support a more empirically based line of

reasoning in the discussions on plans to restructure German banking from the regulatory and

political side.

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22

Appendix

Definitions of main variables in detail: p = Price = (interest income from loans and money market transactions + commission income)/(loans and advances to banks and customers)

C = cost = interest expense + commission expense + other operating expenses + staff expenses + depreciation, write-down on intangible and tangible assets + depreciation and write-down on receivables and certain securities and additions to provisions for the loan business

q = interest bearing assets = loans and advances to banks and customers + debt securities and other fixed-interest securities

w1 = staff “price”= staff expenses /(loans and advances+ debt securities and other fixed-interest securities + shares + customer accounts)

w2 = refinancing “price” = interest expenses / customer and bank accounts, certified liabilities, subordinated liabilities, profit participation rights

w3 = other operating “price” = other operating expenses / total assets

Table 1: Sample descriptives – number of banks / observations

overall cooperative

sector savings banks

sector

private commercial

banks West1) 1994 3,510 2,615 646 249 3,177 1995 3,435 2,573 619 243 3,135 1996 3,330 2,495 607 228 3,040 1997 3,240 2,415 598 227 2,959 1998 3,055 2,246 594 215 2,793 1999 2,801 2,030 578 193 2,559 2000 2,540 1,789 561 190 2,318 2001 2,327 1,617 535 175 2,118

∑observations 24,238 17,780 4,738 1,720 22,099

1) Location of main office in western part of Germany.

Table 2: Descriptives for main variables (C and q in thousand Euro)

means P C q w1 w2 w3 1994 0.0894 40,679 531,285 0.011 0.044 0.0079 1995 0.0837 43,161 596,647 0.011 0.042 0.0080 1996 0.0796 45,208 679,319 0.011 0.038 0.0079 1997 0.0769 49,829 772,637 0.010 0.037 0.0080 1998 0.0746 56,809 883,804 0.010 0.036 0.0080 1999 0.0725 64,863 996,404 0.010 0.033 0.0080 2000 0.0752 86,939 1,220,045 0.011 0.035 0.0085 2001 0.0733 99,399 1,439,232 0.010 0.035 0.0082

mean 0.0787 58,189 850,678 0.010 0.038 0.0080

sd 0.0160 643,847 9,414,807 0.005 0.008 0.0038

Source: BISTA and PaL statistics of Deutsche Bundesbank

23

Table 3: Regression results from pooled 3SLS estimation

Table 4: Regression results from fixed effects 3SLS estimation

*** Indicating significance at the 1percent level.

Price equation (p) Cost equation (lnC)

coefficient z-value coefficient z-value Cq 0.8309*** (65.09) lnq 0.8309*** (65.09)

lnqCq -0.0008*** (-7.63) lnq2 -0.0017*** (-7.63) lnw1Cq -0.0211*** (-9.17) lnw1 0.4733*** (12.35) lnw2Cq -0.0310*** (-9.96) lnw2 0.1465** (3.77) lnw3Cq -0.0039* (-1.78) lnw3 0.2837*** (7.02) dum94 0.0071*** (22.59) lnqlnw1 -0.0211*** (-9.17) dum95 0.0057*** (18.25) lnqlnw2 -0.0310*** (-9.96) dum96 0.0060*** (19.50) lnqlnw3 -0.0039* (-1.78) dum97 0.0048*** (15.34) lnw1lnw2 0.0000 (-0.01) dum98 0.0034*** (10.77) lnw1lnw3 0.0118* (2.37) dum99 0.0037*** (11.43) lnw2lnw3 -0.1705*** (-27.96) dum00 0.0019*** (5.78) lnw12 0.0003 (0.10) dum01 (dropped) lnw22 0.0177*** (9.19) _cons -0.0002*** (-0.64) lnw32 0.0510*** (15.98)

spk 0.0306*** (16.49) priv -0.0693*** (-23.92) east 0.0949*** (40.94) dum* (all significant) _cons (dropped) obs parameters "R2" chi2

Price equation 24229 12 0.5164 3500000 Cost equation 24229 25 0.9958 269000000

Price equation (p) Cost equation (lnC)

coefficient z-value coefficient z-value dCq 0.7069*** (33.12) dlnq 0.7069*** (33.12)

dlnqCq 0.0015*** (4.07) dlnq2 0.0029*** (4.07) dlnw1Cq -0.0338*** (-7.44) dlnw1 -0.0599 (-1.14) dlnw2Cq 0.0252*** (5.55) dlnw2 -0.0080 (-0.16) dlnw3Cq -0.0235*** (-5.47) dlnw3 0.3660*** (7.13) ddum94 0.0079*** (34.83) dlnqlnw1 -0.0338*** (-7.44) ddum95 0.0061*** (29.12) dlnqlnw2 0.0252*** (5.55) ddum96 0.0060*** (30.28) dlnqlnw3 -0.0235*** (-5.47) ddum97 0.0048*** (24.27) lnw1lnw2 -0.0467*** (-7.81) ddum98 0.0033*** (16.45) lnw1lnw3 -0.0172*** (-2.68) ddum99 0.0035*** (16.86) lnw2lnw3 -0.1018*** (-14.84) ddum00 0.0022*** (10.47) dlnw12 -0.0418*** (-11.14) ddum01 (dropped) dlnw22 0.0669*** (29.43)

_cons 0.0088*** (9.17) dlnw32 0.0338*** (7.75) ddum* (all significant) _cons 1.7821*** (6.93) obs parameters "R2" chi2

Price equation 24087 12 0.4932 68016.62 Cost equation 24087 21 0.8799 133813.55

24

Figure 1: Lerner indices calculated from cross sectional 3SLS estimations*

Figure 2: Lerner indices calculated from pooled 3SLS estimations* Figure 3: Lerner indices calculated from fixed effects 3SLS estimations* * “Lerner indices overall” covering estimated Lerner indices for the whole sample, “Lerner indices west” only for banks situated with their main office in the western part of Germany. For estimation details on pooled 3SLS estimations and fixed effects 3SLS estimations, see tables 3 and 4.

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

1994 1995 1996 1997 1998 1999 2000 2001

Lerner indicesoverallLerner indiceswest

-0.02

0

0.02

0.04

0.06

0.08

0.1

1994 1995 1996 1997 1998 1999 2000 2001

Lerner indicesoverallLerner indices

0.1

0.12

0.14

0.16

0.18

0.2

0.22

1994 1995 1996 1997 1998 1999 2000 2001

Lerner indicesoverallLerner indiceswest

25

Figure 4: Summary of Lerner indices calculated from various types of 3SLS

estimations*

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

1994 1995 1996 1997 1998 1999 2000 2001

pooled pooled (west)cross-sectional cross-sectional (west)fixed effects fixed effects (west)

* Summary of figures 1 to 3.

26

Tab. 5: Interest rates on deposit accounts (1992-1995)*

Product mean standard deviation

# of observation

# of reporting banks/branches

# of regional markets considered

Time Deposits (<50.000 €) 5.10 % 1.67 % 16.761 365 114

Time Deposits (50.000 € < and < 500.000 €) 5.61 % 1.72 % 16.490 360 114

Savings Deposits (maturity 3 Month) 2.38 % 0.52 % 16.539 361 114

Tab. 6: Interest rates on loan products (1992-1995)*

Product mean standard deviation

# of observation

# of reporting banks/branches

# of regional markets considered

Lines of Credit (<500.000 €) 12.21 % 1.68 % 16.184 354 114

Lines of Credit (>500.00 € and < 2,500.000 €) 10.18 % 1.73 % 7.693 242 92

Discount Loans (< 50.000 €) 8.16 % 2.18 % 15.645 349 113

* own calculations based on monthly anonymized individual data from the Bundesbank’s bank interest rate statistic.

Tab. 7: Descriptive statistics for regional markets grouped according to proposed classification scheme of low, medium and high Lerner indices

Group

mean average

estimated Lerner1

mean Herfindahl

maximum Herfindahl

minimum Herfindahl

# of banks/branches

in group

# markets in group

Regional markets with low average estimated Lerner

indices

0.125 0.194 0.388 0.098 178 42

Regional markets with medium

average estimated Lerner indices

0.172 0.217 0.384 0.073 163 55

Regional markets with high average estimated Lerner

indices

0.214 0.242 0.384 0.137 40 17

1 See table 4 in the appendix and section 7, pp. 14, for a closer description of the calculation method.

27

Lines of Credit (<500.000 €)

10.00

11.00

12.00

13.00

14.00

15.00

Month (1992/01 to 1995/12)

Ave

rage

Inte

rest

Rate

(%)

low Lerner medium Lerner high Lerner

Figure 5: Averaged bank interest rates for lines of credit (<500.000 Euro) in

regional markets grouped by level of estimated Lerner indices Figure 6: Averaged bank interest rates for lines of credit (>500.000 Euro and

< 2.5 mill Euro) in regional markets grouped by level of estimated Lerner indices

Lines of Credit (>500.000 € and < 2,500.000 €)

7.50

8.50

9.50

10.50

11.50

12.50

Month (1992/01 to 1995/12)

Ave

rage

Inte

rest

Rate

(%)

low Lerner medium Lerner high Lerner

28

Figure 7: Averaged bank interest rates for savings deposits (maturity of up to

3 months) in regional markets grouped by level of estimated Lerner indices

Savings Deposits (maturity 3 month)

1.80

2.00

2.20

2.40

2.60

2.80

3.00

Month (1992/01 to 1995/12)

Ave

rage

Inte

rest

Rate

(%)

low Lerner medium Lerner high Lerner

29

Table 8: Average Lerner indices based on fixed effects estimations*

by bank by district mean 16.08 % 16.04 % median 16.95 % 16.30 % sd 8.78 % 4.12 %

West Germany only: mean 17.92 % 18.23 % median 18.74 % 18.30 % sd 8.69 % 3.31 %

* See table 4 in the appendix and section 7, pp. 14, for a closer description of the calculation method.

Table 9: Descriptives of market structure variables on the district level*

no_banks cr3_kreis1 hhi_kreis1 mean 16.8 65.7 .2276 min 4 27.25 .0486 max 88 96.2 .5791

* Source: bank branch statistics of the Deutsche Bundesbank; own calculations. 1 Calculation based on the number of bank branches per local market.

30

Figure 8: Average bank specific Lerner indices1

Figure 9: Average county specific Lerner indices2 (calculated from cooperative banks and savings banks only)

1 Based on 3SLS fixed effects estimations as reported in tab. 4, for details on the calculation see section 7, pp. 14.

2 Based average bank specific Lerner indices as displayed in fig. X above, for details on the calculation see section 7, pp. 14

0.1

.2.3

krei

s_le

rner

0 10 20 30 40 50Frequency

-1-.5

0.5

1le

rner

0 500 1000Frequency

31

Table 10: Regression results for average district Lerner indices (calculated from cooperative banks and savings banks only; cross-sectional estimations)* hhi_kreis 0.0513 0.0506 0.046 0.050

(2.12)** (2.13)** (1.95)* (2.09)** housinglicdens 5.7049 5.8001 6.09

(4.38)*** (4.49)*** (4.68)*** creforisk 0.0155 0.0173

(3.08)*** (3.38)*** disposable income 0.000002

(1.69)*

east -0.0292 -0.0319 -0.05 -0.0465 (-5.86)*** (-6.50)*** (-6.57)*** (-5.91)***

cons 0.1563 0.1408 0.1142 0.07446 (29.11)*** (22.21)*** (10.71)*** (2.88)***

R2 0.07 0.108 0.125 0.129

cr3_kreis 0.0347 0.0307 0.0268 0.0311

(2.07)** (1.86)* (1.64) (1.88)* housinglicdens 5.5817 5.6933 5.9798

(4.27)*** (4.39)*** (4.59)*** creforisk 0.0155 0.0174

(3.08)*** (3.39)*** disposable income 0.000002

(1.76)*

east -0.0286 -0.0308 -0.0488 -0.0453 (-5.87)*** (-6.42)*** (-6.48)*** (-5.81)***

cons 0.145 0.1322 0.107 0.0634 (13,56)*** (12.13)*** (7.90)*** (2.25)**

R2 0.07 0.106 0.123 0.127

no_banks_kreis -0.0008 -0.0006 -0.0006 -0.0007 (-4.04)*** (-3.18)*** (-2.85)*** (-3.32)***

housinglicdens 4.77 4.958 5.1885 (3.59)*** (3.76)*** (3.94)***

creforisk 0.0145 0.0167 (2.88)*** (3.28)***

disposable income 0.000003 (2.27)**

east -0.03 -0.0312 -0.048 -0.0431 (-6.60)*** (-6.94)*** (-6.53)*** (-5.66)***

cons 0.1818 0.1656 0.1384 0.088 -0.0008 -0.0006 -0.0006 -0.0007

R2 0.095 0.119 0.134 0.142 * dependent variable: average estimated Lerner indices (for details on calculation see section 7, pp 14, 432 observation (Kreise) covered.

32

Tab. 11: Results for average Kreis-Lerner indices (instrumented regressions)

0.137*** .081*** -.0006 *** market structure

variable hhi_kreis (2.75) cr3_kreis (2.73) no_banks_kreis (- 2.74)

6.623*** 6.335*** 5.799*** housing licenses per capita (3.69) (3.51) (3.24)

.015** .016** .0158** Crefo risk indicator (2.52) (2.57) (2.58)

.000003** .000003*** .000003*** Disposable income per capita (2.43) (2.60) (2.66)

.0000014*** .0000013** .0000013*** investments per manufacturer (2.60) (2.35) (2.58)

- .042*** - .039*** - .033*** east (-4.50) (-4.31) (-3.98)

.046 .018 .082*** constant (1.58) (0.54) (3.15)

F-Statistic (6, 425) / R2 7.33*** / 8.02 7.21*** / 8.56 7.66*** / 11.84

* Dependent variable: average Kreis Lerner indices calculated from estimated individual Lerner indices (fixed effects estimation) from cooperative banks and savings banks only; instrumental variable regressions (2SLS) with robust standard errors; 432 observations (Kreise); instruments for market structure variables: approximated number of banks in the market as of 1985, and regressors listed above; robust standard errors; t-values in parentheses.