toPS 3//3
POLICY RESEARCH WORKING PAPER           3113
What Drives Bank Competition?
Some International Evidence
Stijn Claessens
Luc Laeven
The World Bank
Financial Sector Operations and Policy Department
August 2003



I POLICY RESEARCH WORKING PAPER 3113
Abstract
Using bank-level data, Claessens and Laeven apply the         systems with greater foreign bank entry and fewer entry
Panzar and Rosse (1987) methodology to estimate the           and activity restrictions to be more competitive. They
extent to which changes in input prices are reflected in      find no evidence that the competitiveness measure
revenues earned by specific banks in 50 countries'            negatively relates to banking system concentration. Their
banking systems. They then relate this competitiveness        findings confirm that contestability determines effective
measure to indicators of countries' banking system            competition, especially by allowing (foreign) bank entry
structures and regulatory regimes. The authors find           and reducing activity restrictions on banks.
This paper-a product of the Financial Sector Operations and Policy Department -is part of a larger effort in the
department to study competition in the financial sector. Copies of the paper are available free from the World Bank, 1818
H Street NW, Washington, DC 20433. Please contact Rose Vo, room MC9-624, telephone 202-473-3722, fax 202-522-
2031, email address hvol@worldbank.org. Policy Research Working Papers are also posted on the Web at http:H/
econ.worldbank.org. Luc Laeven may be contacted at llaeven@worldbank.org. August 2003. (33 pages)
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about
development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The
papers cany the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this
paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the
countries they represent.
Produced by the Research Support Team



What Drives Bank Competition?
Some International Evidence
Stijn Claessens and Luc Laeven*
* Claessens is Professor in International Finance Policy at the University of Amsterdam and a
Research Fellow at the CEPR. Laeven is an economist at the World Bank. We thank Allen
Berger, Nicola Cetorelli, Hans Degryse, Enrica Detragiache, Gaston Gelos, Joseph Haubrich,
Hannah Hempel, Patrick Honohan, Ross Levine, Sherrill Shaffer, Bernard Yeung, and
co1nference participants at the World Bank and at the Federal Reserve Bank of Cleveland for
helpful comments.






INTRODUCTION
Competition in the financial sector matters for a number of reasons. As in other industries,
the degree of competition in the financial sector can matter for the efficiency of the production of
financial services, the quality of financial products and the degree of innovation in the sector. A
reason specific to the financial sector is the link between competition and stability, long
recognized in theoretical and empirical research and most importantly in the actual conduct of
prudential policy towards banks (Vives 2001). It has also been shown, theoretically as well
empirically, that the degree of competition in the financial sector can matter for the access of
finns and households to financial services and external financing, in turn affecting overall
economic growth, although not all relationships are clear.
While some of these relationships between competition and banking system performnance and
stability have been analyzed in the theoretical literature, empirical research on the issue of
competition, particularly cross-country research, is still in an early stage. A hindrance for the
cross-country research used to be data problems, as little bank-level data were available outside
the main developed countries, but recently established databases are allowing for better empirical
work. Another hindrance on the interpretation of existing empirical work has been that it did not
always take into account a number of theoretical issues. The long-existing theory of industrial
organization has shown that the competitiveness of an industry cannot be measured by market
structure indicators alone, such as number of institutions, or Herfindahl and other concentration
indexes (Baumol, Panzar, and Willig 1982). The threat of entry can be a more important
determinant of the behavior of market participants (Besanko and Thakor 1992). Theory also
suggests that performance measures, such as the size of the banking margins or profitability, do
not necessarily indicate the competitiveness of a banking system. These measures are influenced
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by a number of factors, such as a country's macro-performance and stability, the form and
degree of taxation of financial intermediation, the quality of country's information and judicial
systems, and bank specific factors, such as scale of operations and risk preferences. As such,
these measures can be poor indicators of the degree of competition.
Rather, testing for the degree of effective competition requires a structural, contestability
approach, along the lines pursued in much of the industrial organization literature. As in other
sectors, the degree of competition in the banking system should be measured with respect to the
actual behavior of (marginal) bank conduct. And the actual behavior should be related not only
to banking market structure, but also to entry barriers, including on foreign ownership, and the
severity of activity restrictions, as those can limit the degree of intra-industry competition.
Furthermore, the degree of competition from other forms of financial intermediation (capital
markets, non-bank financial institutions, insurance companies) will play a role in determining
banking system competitiveness. To date, however, few cross-country tests have taken this
approach.
These considerations suggest some advantages of using a more structural approach to
assessing the degree of competition in the financial sector. While one cannot expect to address
all issues, a more formal test of the degree of competition will allow one to overcome some of
these concems. It will also allow a comparison of results to other approaches to measuring
competition, such as using concentration ratios, the number of banks in a market, or outcomes
such as banking margins. Structural competition tests have been applied to banking systems in a
number of individual countries, but not on a broad cross-country basis. The purpose of this
paper is to estimate and document a measure of competition for a large cross-section of countries
and to find some factors helping explain differences. We specifically seek to analyze the role of
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entry and activity regulations, and the role of foreign banks in affecting the competitive
conditions of banking systems. Since the role of non-bank financial institutions in affecting the
overall competition in the financial sector have received limited attention, we also study those.
Using bank-level data and applying an adapted version of the Panzar and Rosse (1987)
methodology, we estimate the degree of competition in 50 countries' banking systems. We then
relate our competitiveness measure to countries' structural and regulatory indicators. We find
that systems with greater foreign bank entry, and lack of entry and activity restrictions have a
higher competitiveness score. We find no evidence that banking system concentration
negatively relates to competitiveness. Our findings confirm that contestability helps to
determine effective competition, especially through allowing (foreign) bank entry and
eliminating activity restrictions.
The paper proceeds as follows. Section 1 gives a review of related literature, both on the
effects of competition in the financial sector as well as measuring competition in general and in
the financial sector specifically. Section 2 discusses the methodology used to test for the degree
of competition in the banking market of a particular country. Section 3 presents the data we use,
the selection criteria we used for the sample we end up using, and the competitiveness measures.
Section 4 relates the measure of competition to some structural and policy variables and presents
the main empirical results. The section also reports several robustness tests. Section 5
concludes.
1.    LITERATURE REVIEW
There are several, related strands of literature. We highlight some findings of the growing
literature on the definition and effects of competition in the financial sector, and then review the
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empirical literature that has investigated the relationships between structural and regulatory
factors and performance, access to financing and growth, all as they relate to the competitive
structure of the banking systems. Since these papers mostly not attempt to test a specific
structural model, we review briefly the general theory on measuring competition and then review
some of the empirical papers that have applied structural competition tests to the financial sector.
A.    General Effects of Competition in Banking
As a first-order effect, one would expect increased competition in the financial sector to lead
to lower costs and enhanced efficiency, even allowing for the fact that financial products are
heterogeneous. Recent research has highlighted, however, that the relationships between
competition and banking system performance, access to financing, stability and growth are more
complex (for a recent review of the theoretical literature on competition and banking, see Vives
2001). Market power in banking, for example, may up to a degree be beneficial for access to
financing (Petersen and Rajan 1995). The view that competition is unambiguously good in
banking is more naive than in other industries and vigorous rivalry may not be the first best for
financial sector performance. This literature has also shown that technological progress lowering
production or distribution costs for financial services providers necessarily neither leads to more
or better access to finance.
B.    General Empirical Studies on Banking System Performance and Structure
A number of papers have investigated the competitive condition in banking systems. In one
of the first papers, Berger and Hannan (1989) investigate the commonly observed relationship
between market concentration and profitability using data for US banks during the period 1983-
85. They try to separate the effects of non-competitive price behavior from those of greater
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efficiency of firms with larger market shares and find that non-competitive price behavior could
explain the relationship. Other studies have focused on the effects of consolidation in the
banking systems (for a review of some of the earlier studies on consolidation and its effect on
bank lending terms, see Gilbert 1984; for a review of more recent studies on the effects of
consolidation, including studies on the effects of consolidation on access to financing, see
Berger, Demsetz, and Strahan 1999). While many of these papers are not formal structure-
performance-conduct tests, their results have been interpreted as indicative of the degree of
competition and/or its causes and consequences in the financial sector (Berger 1995).
A number of recent papers have investigated the effects of regulations and specific structural
or other factors presumed to relate to the competitive environment on banking performance. In a
broad survey of rules governing banking systems, Barth, Caprio and Levine (2001) document for
107 countries various regulatory restrictions in place in 1999 on commercial banks, including
various entry and exit restrictions and practices. Using this data, Barth, Caprio and Levine (2003)
document, among others, that tighter entry requirements are negatively linked with bank
efficiency, leading to higher interest rate margins and overhead expenditures, while restricting
foreign bank participation tends to increase bank fragility. These results are consistent with the
view that tighter entry restrictions tend to limit competition and emphasize that it is not the actual
level of foreign presence or bank concentration, but the contestability of a market that determines
bank efficiency and stability.
In a cross-country study on banking structure, Claessens, DemirgUg9-Kunt and Huizinga
(2001) investigate the role of foreign banks and show that entry by foreign banks makes
domestic banking systems more efficient by reducing margins. Using bank level data for 77
countries, Demirgiiu-Kunt, Laeven, and Levine (2003) investigate the impact of bank
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concentration and regulations on bank efficiency. They find that bank concentration has a
negative and significant effect on the efficiency of the banking system except in rich countries
with well-developed financial systems and more economic freedoms. Furthermore, they find
bank-level based support that regulatory restrictions on entry of the new banks, particularly
concerning foreign banks, and implicit and explicit restrictions on bank activities, are associated
with lower levels of bank margins. Their measure of bank efficiency, net interest margin, is not
necessarily an indicator of the actual degree of competitive conduct in a market, but may reflect
other factors, such as market power and risk preferences. They mitigate this problem by
controlling for a number of differences across banks and countries such that they can interpret
higher net interest margins as reflecting operational inefficiency. Our paper adds to this literature
by using an indicator that directly measures the actual degree of competitive conduct.
C.    Competition Testing: Theory
Most papers reviewed so far did not test for the degree of competition in the banking system
using a specific structural model. The theory of contestable markets has drawn, however,
attention to the fact that there are several sets of conditions that can yield competitive outcomes,
with competitive outcome possible even in concentrated systems. On the other hand, collusive
actions can be sustained even in the presence of many firms.
The concept of contestability has spanned a large theoretical and empirical literature covering
many industries. Two types of empirical tests for competition have been applied to financial
sector (and other industries). The model of Bresnahan (1982) and Lau (1982), as expanded in
Bresnahan (1989), uses the condition of general market equilibrium. The basic idea is that
profit-maximizing firms in equilibrium will choose prices and quantities such that marginal costs
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equal their (perceived) marginal revenue, which coincides with the demand price under perfect
competition or with the industry's marginal revenue under perfect collusion. This model allows
for an easy to use test statistic and a direct relationship to a natural measure of excess capacity.
Specifically, a parameter can be estimated which provides a measure of the degree of imperfect
competition, varying between perfect competition or full market power. One empirical advantage
is that one only needs to use industry aggregate data to estimate this parameter, although using
firm-specific data is possible as well.
The alternative approach is Rosse and Panzar (1977), expanded by Panzar and Rosse (1982)
and Panzar and Rosse (1987). This methodology, abbreviated here to the PR model, uses firm
(or bank)-level data. It investigates the extent to which a change in factor input prices is
reflected in (equilibrium) revenues eamed by a specific bank. Under perfect competition, an
increase in input prices raises both marginal costs and total revenues by the same amount as the
rise in costs. Under monopoly, an increase in input prices will increase marginal costs, reduce
equilibrium output and consequently reduce total revenues. The PR model also provides a
measure ("H-statistic") between 0 and 1 of the degree of competitiveness of the industry, with
less than 0 being a collusive (joint monopoly) competition, less than 1 being monopolistic
competition and 1 being perfect competition. It can be shown, if the bank faces a demand with
constant elasticity and a Cobb-Douglas technology, that the magnitude of H can be interpreted as
an inverse measure of the degree of monopoly power, or alternatively, as we do, as a measure of
the degree of competition.
The advantage of the PR model is that it uses bank-level data and allows for bank-specific
differences in production function. It also allows one to study differences between types of
banks (e.g., larges versus small, foreign versus domestic). Its drawback is that it assumes that
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the banking industry is in long-run equilibrium, but a separate test exists whether this condition
is satisfied. ' As we have access to bank-level information and as we want to study differences
among banks, we choose for the PR model (the empirical specification we use is explained in
section two).
D.     Competition Testing: Empirical Results for Banking Systems
A number of papers have applied either the Bresnahan or the PR methodology to the issue of
competition in the financial sector, although mostly to the banking system specifically. Cetorelli
(1999) provides more detail on these formal tests and reviews results of previous studies of
empirical banking studies. One of the first Bresnahan test is Shaffer (1989). For a sample of US
banks, he finds results that strongly reject collusive conduct, but are consistent with perfect
competition. Using the same model, Shaffer (1993) finds that the Canadian banking system was
competitive over the period 1965-1989, although being relatively concentrated. Gruben and
McComb (2003) finds that the Mexican banking system before 1995 was super-competitive, that
is marginal prices were set below marginal costs. Shaffer (2001) uses the Bresnahan model for
15 countries in North America, Europe, and Asia during 1979-91. He finds significant market
power in five markets and excess capacity in one market. Estimates were consistent with either
contestability or Cournot type oligopoly in most of these countries, while five countries were
significant more competitive than Coumot behavior would imply.
Shaffer (1982) applied the PR model to a sample of New York banks using data for 1979 and
found monopolistic competition. Nathan and Neave (1989) studied Canadian banks using the PR
methodology and found results consistent with the results of Shaffer (1989) using the Bresnahan
In case of short-ni, but not long-run equilibrium, the parameter H represents a one-tail test in the sense that a
positive value rejects any form of imperfect competition, but a negative value is consistent with a variety of
possibilities, including short-run competition (Shaffer 1983).
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methodology, i.e., a rejection of monopoly power. Several papers have applied the PR
methodology to European banking systems.2 Generally, the papers reject both perfect collusion
as well as perfect competition and find mostly evidence of monopolistic competition (Bikker and
Haaf 2001 summarize the results of some ten studies). Some studies have applied the PR
methodology to some non-North American and non-European banking systems. For Japan,
Molyneux, Thornton and Lloyd-Williams (1996) find evidence of a monopoly situation in 1986-
1988. Tests on the competitiveness of banking systems for developing countries and transition
economies using these models are few to date (Gelos and Roldos 2002, for example, using the
PR-methodology report that banking markets of eight European and Latin American countries
have not become less competitive, although concentration has increased).
Some studies find differences between types of banks. For example, De Bandt and Davis
(2000) find monopoly behavior for small banks in France and Germany while they find
monopolistic competition for small banks in Italy and for the large banks in all three countries in
their sample. This suggests that in these countries small banks have more market power, maybe
as they cater more to local markets.
2.    METHODOLOGY
We use the Panzar and Rosse (1982, 1987) (henceforth PR) approach to assess the competitive
nature of banking markets around the world. The PR H statistics is calculated from reduced form
bank revenue equations and measures the sum of the elasticities of the total revenue of the banks
with respect to the bank's input prices. The PR H statistic is interpreted as follows. H<0 indicates
2These papers include, among others, Molyneux, Lloyd-Williams, and Thornton (1994), Bikker and Groeneveld
(2000), and De Bandt and Davis (2000).
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a monopoly; H=1 indicates perfect competition; and O<H<l indicates monopolistic competition.
Nathan and Neave (1989) point out that this interpretation assumes that the test is undertaken on
observations that are in long-run equilibrium. We therefore also test whether the observations are
in long-run equilibrium.
A. Competitive Environment Test
We estimate the following reduced form revenue equations on pooled samples for each
country:
In( Pi,) = a + 6,/ In( Wl i, ) +  I2 In( W2i, ) + /33 In( W3J, ) +
+ r1ln(Y,id ) + Y2 ln(Y2ji, ) + 3 In(Y3i,)+                   (1)
+ 3D + Ei,
where Pi, is the ratio of gross interest revenue to total assets (proxy for output price of
loans), Wi,j1 is the ratio of interest expenses to total deposits and money market funding (proxy
for input price of deposits), W2,i, is the ratio of personnel expense to total assets (proxy for input
price of labor),3 W3, is the ratio of other operating and administrative expense to total assets
(proxy for input price of equipment/fixed capital). The subscript i denotes bank i, and the
subscript t denotes year t. This model is similar to models used previously in the literature to
estimate H-statistics for banking industries.
3 Due to lack of data on total employees, we do not express the unit cost of labor in terms of total employees but in
terms of total assets.
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We include several control variables at the individual bank level. Specifically, Y, i, is the
ratio of equity to total assets, Y2,;, is the ratio of net loans to total assets, and Y3 is iS the logarithm
of total assets (to control for potential size effects). D is a vector of year dummies for the years
1995 through 2001 (we drop the year dummy for the year 1994). We take natural logarithms of
all variables. We estimate model (1) both using OLS with time dummies and GLS with fixed
bank-specific effects (in the latter case a = ai). The H-statistic equals f,i + f82 + ,3 . In what
follows we refer to Hi as the H-statistic estimated using OLS, and to H2 as the H-statistic based
estimated using GLS with fixed-bank effects.
B. Equilibrium Test
Since the PR-model is only valid if the market is in equilibrium, we also estimate the
following equation for each country:
ln(ROAi,) = a + ,, ln(WJ,) + P2 hn(W2JI) + A3 ln(W3JI) +
+ y, ln(YJ,) + Y2 ln(Y2i, ) + r3ln(Y3,i1) +                (2)
+ SD + -i
where ROA is the pre-tax return on assets (pre-tax profits to total assets). Because return on
assets can take on (small) negative values, we compute the dependent variable as
ROA'=ln(l +ROA) where ROA is the unadjusted return on assets. We define the equilibrium E-
statistic as f,i + ,/2 +,83 . We test whether E = 0, again using a F-test. If rejected, the market is
assumed not to be in equilibrium. The idea behind this test is that, in equilibrium, returns on
bank assets should not be related to input prices. This approach for testing whether the
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observations are in long-run equilibrium has previously been used in the literature (see, for
example, Shaffer 1982 and Molyneux et al. 1996).
C. Alternative Specification
For robustness, we estimate an alternative reduced revenue equation where we include the
ratio of total revenue to total assets as the dependent variable, where total revenue is calculated
as gross interest revenue plus other operating revenues, such as fee income and commission
income. The dependent variable now includes also non-interest revenues, which arguable makes
it a more comprehensive measure of the overall degree of competition in banking services. The
other, explanatory variables remain the same. Again, we estimate the model using OLS and GLS
with fixed-bank effects and refer to H3 as the H-statistic based on model estimated using OLS,
and to H4 as the H-statistic estimated using GLS with fixed-bank effects. We also check
whether the market is in equilibrium using the same test as for the gross interest revenue to total
assets ratio.
3.    DATA AND RESULTS FOR THE COMPETITIVENESS INDICATOR
A.    Data
We use bank-level data from BANKSCOPE, a database containing bank financial statements
used in a number of other cross-country studies. We have panel data for the years 1994-2001
and we include all banks: commercial banks, savings banks, cooperative banks, and bank holding
companies. We use data from consolidated accounts if available, and otherwise from
unconsolidated accounts (to avoid double-counting).
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We start with the complete sample of banks in BANKSCOPE, resulting in a total number of
bank-year observations of 54,038 (on average 6,755 banks per year). The sample we end up
using is smaller, however, as we apply some selection criteria. First, we apply a number of
outlier rules to the main variables corresponding to the 1" and 99gh percentiles of the distributions
of the respective variables. This also deletes banks for which data on one of the main variables is
not available, such as data on interest expense or personnel expense. We also delete countries
with less than 50 bank-year observations (we need a reasonable number of bank-year
observations for each country to estimate the H-statistics; we set the minimum number of
observations to 50). This reduced sample consists of 37,107 bank-year observations. We also
delete countries with data for less than 20 banks since we need at least 20 observations per
country to get reasonable accurate H estimates for each country.4 Furthermore, some countries in
BANKSCOPE do not have adequate coverage of banks and only include the very large banks in
the country. This reduces the sample by another 1,273 bank-year observations.
The final sample consists of 35,834 bank-year observations (4,479 banks on average per
year). It is an unbalanced panel with the largest number of 5,002 bank observations for the year
1999.5 The final sample we use consists of 50 countries. A description and definition of the
variables can be found in Table 1. In terms of number of banks, banks from France, Germany,
Italy, Switzerland and United States dominate the sample. In each of these countries we have
more than 1,000 bank-year observations (see also Table 2).6
4We therefore drop observations from the following countries: Bahrain, Bolivia, Cyprus, El Salvador, Ireland,
Israel, Jordan, Kazakhstan, Republic of Korea, Nepal, Saudi Arabia, Slovak Republic, Slovenia, Sweden, Tbailand,
United Arab Emirates, and Vietnam.
5 The distribution of the sample across years is as follows: 3,934 banks in 1994; 4,327 banks in 1995; 4,633 banks
in 1996; 4,731 banks in 1997; 4,852 banks in 1998; 5,002 banks in 1999; 4,741 banks in 2000; and 3,614 banks in
2001. The total number of bank-year observations is 35,834.
6We need to exclude a large number of Japanese banks, because BANKSCOPE does not have data on personnel
expense for most large Japanese banks. Our sample of Japanese banks is therefore much smaller than the actual
number of Japanese banks.
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B.     Competitive Environment Indicator
We estimate the H-statistics on the basis of the four models. The four estimates vary in terms
of estimation technique, respectively Pooled OLS with time dummies (HI and H3) vs. Fixed
effects with time dummies (H2 and H4), and in terms of dependent variable, Gross interest
revenue as dependent variable (Hi and H2) vs. Total revenues as dependent variable (H3 and
H4). We find that the four measures generally provide close estimates of the H-statistic for each
country. This suggests that the methods are relatively robust. Since each estimation method has
some specific advantages and disadvantages, we take the average of the four estimates as our
measure of the competitiveness of various banking markets. The results for the average of the
four H-statistics, the standard errors, and the number of banks and observations used are reported
in Table 2.7
The H-statistic varies generally between 0.60 to 0.80, suggesting that monopolistic
competition is the best description of the degree of competition. There does not appear to be any
strong pattem among type of countries, although it is interesting that some of the largest
countries (in terms of number of banks and general size of their economy) have relatively low
values for the H-statistics. As small banks may operate more in local markets that are less
competitive, studying all banks may lead to a distorted measure of the overall competitiveness of
a banking system, especially in countries with a large number of banks, such as the United
States. In our empirical work, we will therefore investigate whether our results change when we
7We conducted the equilibrium tests for all the markets and found that the banking systems of most countries are in
"equilibrium" (not reported). We nevertheless did conduct a robustness test by excluding those countries that fail to
meet the equilibrium test at the 5 percent significance level, but our main results were not affected when excluding
these countries.
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compute H-statistics using data on large banks rather than all banks for countries with many
banks.
4.    DETERMINANTS OF THE COMPETITIVENESS INDICATOR
A.    Cross-Country Regressions
We next identify factors that can explain the assessment of the competitiveness of the
banking system across countries. To do so, we regress the average H-statistic on a number of
country characteristics.8 The regression model is as follows:
Hi = a + /iB, + e,
where Hi is the average H-statistic for country i, based on individual bank data for the period
1994-2001, and B, is a vector of country characteristics. We run these cross-country regressions
for our regular sample of 50 countries that includes only countries with at least 50 bank-year
observations and at least 20 banks. As a robustness, we also run the cross-country regressions
using a smaller sample of 39 countries that includes only countries with at least 100 bank-year
observations and at least 20 banks.
As explanatory variables we use a number of variables also used in other cross-country
studies to explain banking system performance, stability, and competitiveness. The explanatory
data fall in four categories: market structure, contestability, inter-industry competition and
general level of development. Data on banking structure and contestability typically refer to the
8 We also performed all regression results using the four individual estimates for each estimation technique as
robustness test (not reported).
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situation as of end-1999, which is towards the end of our data period. As in other studies, we
rely on the relative stability of the regulation and supervision frameworks. Data on inter industry
competition and the country's general development refer to the beginning of the estimation
period.
For the structure of the banking system, we use, from the date base established by Barth,
Caprio, and Levine (2001), three variables: the 5-bank concentration ratio, a measure of banking
system concentration; the Logarithm of the number of banks per million inhabitants in a
particular country, as proxy for the density of banks; and the Foreign bank ownership variable,
that is, the share in assets/numbers of banks which are foreign-controlled.9 All of these
measures have been used by others when investigating the impact of banking structure on
performance, stability, and efficiency.
For the contestability of the respective markets, we use, again from the Barth, Caprio, and
Levine (2001) data base, the Activity restrictions variable, indicating the limits imposed on
commercial banks to engage in securities markets, insurance and real estate activities with higher
scores indicating more restrictions; and the Entry fit test variable, an indicator of the severity of
the entry regime with higher scores indicating less severe restrictions. The Activity variable
refers to the legal rules in place while the Entry fit variable refers to the actual practices of the
supervisory agencies in the country.'I
We use two indicators to describe the competition coming from inter industry. To
investigate the impact of the degree of competition banks face from capital markets, we use the
size of the country's stock market capitalization to GDP. As a proxy for competition from non-
9 We also used from the Barth, Caprio, and Levine (2001) data base the degree of state-owned banks, but did not
find this variable to have significant effects and therefore did not add it.
'° We also used from the Barth, Caprio, and Levine (2001) data base the degree of entry applications denied, but
again did not find this variable to have significant effects and therefore did not include it.
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bank financial institutions, we use data collected by Beck, Demirguc-Kunt and Levine (2000) on
the amount of annual life insurance premiums collected divided by GDP. These data on stock
markets and life insurance refer to the year-end 1994. We expect to find positive coefficients for
both indicators as the more developed other parts of the financial sector are, the more
competitive pressure there will be on the banking system.
We also control for the countries' general economic development, macro-economic stability
and institutional framework as these can be expected to affect banking system performance.
Others have, for example, found that banking system structure indicators have a less close
relationship with competitiveness indicators in more developed countries (DemirgU9-Kunt,
Laeven, and Levine 2003). As a proxy for the general level of development of the country, we
use the logarithm of per capita GDP in 1995. We also expect that it will be less likely that a
banking system will be more competitive when it is subject to high inflation as prices of financial
services, such as interest rates, will be less informative. As an indicator for macro-economic
stability, we use the inflation rate in 1995. Both per capita GDP and inflation come from the
World Development Indicators (WDI). Finally, we want to investigate the role of the quality of
the country's overall institutional framework, especially the degree of protection of property
rights, which has been found to be an important foundation for a well-functioning financial
system. For this, we use an index of property rights from the Economic Freedom Index, used by
many others, with lower score indicating better protection of property rights. The index is the
average for the period 1995-99 and is obtained from the Heritage Foundation.
Table 3 reports the matrix of correlations between and among the dependent and independent
variables. As a start, it is useful to note that many of the correlations are not statistically
significant; out of the 55 correlations 21 are significant at the 10% level. Most of these
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significant correlations concern relationships among the independent variables that have been
documented extensively. There are, for example, significant positive correlations between GDP
per capita and the property rights index and our measures of financial markets development
(stock market capitalization and insurance penetration). General development as measured by
GDP per capita and property rights are also positively related, while inflation is negatively
related to GDP per capita and financial market development variables.
More interesting are the relationships of the independent variables with the competitiveness
measure. We find a statistically positive relationship between the competitiveness indicator and
the banking system concentration variable, which suggests that more concentrated banking
systems are more competitive. The correlation between the competitiveness indicator and the
number of banks per population, in logs, is not statistically significant. In terms of contestability
indicators, the only significant correlation with the competitiveness indicator is for the entry fit
test, at the 7% level. The correlation with the foreign bank ownership is positive, but statistically
significant only at the 16% level. The inter industry competition measures are not significantly
correlated with the competitiveness indicator, nor are the GDP per capita, inflation and property
rights variables.
Table 4 reports the base regression results regarding the cross-country determinants of the
average H-statistic. The results are presented in columns, depending on the categories of
independent variables included. All regressions include the two macro-economic variables we
have, GDP per capita and iinflation, to control for differences in economic development. Besides
these two macro variables, the column Structure only includes the banking structure variables,
i.e., the bank concentration, density of banks and foreign ownership variables. The column
Contestability includes only the contestability indicators, i.e., restrictions on the activities of
18



banks in terms of providing other types of financial services and the entry fitness test variables.
The column Inter-Industry investigates the impact of competition from other financial services
industries by including variables on the size of capital markets and insurance industries. The
fourth column combines two sets of indicators, the combined effects of banking structure and
contestability. It excludes inter industry competition variables, as those are not statistically
significant on their own. Finally, the last column Institutions investigates in addition to the
banking structure and contestability variables, the role of property rights. In all regressions, we
adjust the standard errors of the regression model using the White method to control for any
remaining heteroskedasticity in the error terms.
We run our regressions using weighted least squares as the H-statistics are generated
variables from the first step regressions with standard errors and we therefore face
heteroskedasticity in the dependent variable. We use as weights the inverse of the estimated
variance of a particular country's generated H-statistic, thus giving more value to those H-
statistics that are estimated with less error. We also conducted the cross-country regressions
using simple least squares as well as for each of the four individual H-statistics and we found
very similar results (not reported).
We find little evidence that variables describing the banking system structure can help
explain its measured competitiveness, or at least in the way typically posed. We find that bank
concentration is not negatively correlated with the H-statistic, as may be expected, but rather we
find a positive and statistically significant relationship, that is, more concentrated banking
systems face a greater degree of competition. Similarly, the density of banks variable is not
significantly positively related to the competition indicator, and, although not statistically
significant, has even a negative sign, that is, the fewer banks relative to population, the more
19



competitive the system is. It suggests that the H-statistic and the bank concentration measure
are two variables that cover different concepts, that is, bank concentration may not be a good
summary statistic for bank competitive environment. " It confirms findings in the general
industrial organization literature that the degree of competition is not necessarily related to
market structure. In terms of the degree of foreign bank ownership, more foreign bank
ownership seems to improve the level of competition in the home market, suggesting that the
nature of ownership matters for competition.
Of the contestability variables we use, we find that cross-country variations in bank
competition can be explained by differences in a lack of activity restrictions, with fewer
restrictions enhancing competition. We find a similar effect for the severity of entry fitness tests,
with less severe fitness tests positively affecting banking system competition. This suggests that
more contestable systems face greater competition.'2 In terrns of inter industry competition, we
find no evidence that there is an impact of the development of the stock markets or insurance
industry on the competitiveness of the banking system. In terms of the general level of
development, we find that the GDP per capita and the inflation rate are never statistically
significant and the signs of the coefficients are not always the same. This suggest no general
patterns in the degree of competition across countries of different levels of development.
When we include both market structure and contestability variables to explain the variation
in the competition indicator (column All), we find that market concentration, foreign bank
ownership, and activity restrictions are statistically significant. The signs are the same, i.e., more
" This result is, however, importantly influenced by the United States and some other countries with a large number
of banks. For these countries we find a low H-statistic while the markets have a relatively low degree of banking
system concentration. Excluding four countries with large number of banks, United States, France, Germany, and
Italy, we find no statistically significant relationship between the 5-bank concentration ratio and our measure of
competitiveness (not reported).
12 Since the effects of the degree of permitted contestability may vary by market structure, we also checked for
interaction effects between our entry and activity restrictions variables and the banking structure variables. We did
not find, however, any consistent results for these interaction effects (not reported).
20



concentrated banking system face greater competition, more foreign bank presence helps
competition, and fewer activity restrictions is associated with greater competitiveness. Again,
the general level of development and inflation variables are not significant. When including the
property rights index (column Institutions), these results are maintained, while the property rights
index itself is not significant. We find similar results when we use measures of the quality of the
legal system (not reported). This suggests that the quality of the institutional framework does not
exercise an independent effect on competition. In summary, it appears that assuring a
contestable system is the most important to guarantee a competitive banking system.
B.    Robustness test
We conduct a robustness check to verify that our results are not affected by the sample of
countries we focus on in the regressions. Specifically, we run the regressions on a smaller
sample of 39 countries that includes countries with at least 100 bank-year observations and at
least 20 banks (i.e., at least five year-observations on average per bank). This rule leads us to
exclude the following countries: Bangladesh, Czech Republic, Greece, Honduras, Japan, Latvia,
Mexico, Panama, Paraguay, Turkey, and Ukraine. The regression results based on this smaller
set of countries are reported in Table 5, where we follow the same specifications as in Table 4.
In the first column, the Structure regression, we find that banking system concentration and
the foreign bank ownership variables remain statistically significant. The signs remain the same
as for the larger sample: more concentrated banking systems and greater foreign bank entry is
associated with more competitive banking systems. The number of banks is again not
statistically significant. In the Contestability regression, we find that activity restrictions and
entry fit test remain statistically significant explanatory factors of banking system competition,
21



i.e., less restricted and more open banking system are more competitive. As before, we do not
find any evidence from competition from other segments of the financial services industry,
capital markets or insurance, affecting banking system competitiveness (the Inter-industry
regression). When including both Structure and Contestability variables, the All regression, we
find that the foreign bank ownership and the activity restriction variables remain statistically
significant. The signs remain the same as for the other regressions: more foreign bank ownership
and fewer activity restrictions lead to more competition. The concentration and entry-fit test
variables do no longer enter significantly. Finally, in the last regression, Institutions, we find
that the effects of foreign bank ownership and activity restrictions on competition are preserved,
and that the property rights index is again not statistically significant. In terms of overall
development, the inflation and per capita income variables are again not statistically significant
for any of the regressions in this sample of countries.
As mentioned earlier, studying all banks may lead to a distorted measure of the overall
competitiveness of a banking system, because small banks may operate more in local markets
that are less competitive. We therefore also conduct a robustness test where we estimated our
competitiveness indicator using data on large banks only for those countries with many banks,
i.e., the United States, France, Germany, and Italy. We then re-run the cross-country regressions
and do not find any differences with the results of Tables 4 or 5 (not reported).
22



5.    CONCLUSIONS
Using a structural model, we estimate competitiveness indicators for a large cross-section of
countries. When we relate our competitiveness indicator to a number of country characteristics,
we find that greater foreign bank presence and fewer activity restrictions in the banking sector
can make for more competitive banking systems. We also find some evidence that entry
restrictions on commercial banks can reduce competition. This suggests that being open to new
entry is the most important competitive pressure. We find no evidence that banking system
concentration is negatively associated with competitiveness. At the opposite, we find some
evidence that more concentrated banking systems are more competitive. Similarly, we have
some, although never significant evidence that the competitiveness of banking systems relates
negatively to the number of banks in the country. We find that these results remain using several
robustness tests.
While our results confirm much of traditional industrial organization theory that
contestability rather than structure is the most important for competition, the fact that structure
matters so little, or even in opposite ways to expectations, might surprise many involved with
competition policy in the financial sector. Competition policy in the financial sector has
traditionally centered on balancing franchise value, important for prudential concerns and related
to the so-called special nature of banks, with allowing more competition forces with greater
entry. This tradeoff implied that the preferred solution often was thought to be a more
concentrated system with less entry, although that resulted in less competitiveness. But our
results suggest that the tradeoff need not be between a more concentrated system and a less
23



competitive system. Having a contestable system may be more important to assure
competitiveness than a system with low concentration.
Our results on the lack of importance of market structure suggest that competition policy in
the financial sector is more complicated than perhaps previously thought. This may in part be
because financial services industries have been undergoing rapid changes, triggered by
deregulation and technological advances. These changes have made the definition of a financial
market and any particular financial service more complex, and may have made market structure
indicators less valuable measures of the competitive nature of financial systems. Developing
proper competitiveness tests and methodologies will remain an important area of research and
policy focus.
24



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Table 1        Description and definition of variables
Variable name        Description
H-statistic          H-statistic is calculated as the average of the implied H-statistics from four different
structural models estimated for each country the years 1994-2001 based on the Panzar-
Rosse (1987) approach, i.e., HI through H4. HI is estimated using pooled OLS with time
dummies and with gross interest revenues as dependent variable in the reduced form
revenue equations. H2 is estimated using pooled GLS with bank-specific effects and time
dummies and with gross interest revenues as dependent variable in the reduced form
revenue equations. H3 is estimated using pooled OLS with time dummies and with total
revenues as dependent variable in the reduced form revenue equations. H4 is estimated
using pooled GLS with bank-specific effects and time dummies and with total revenues as
dependent variable in the reduced form revenue equations. Source: Authors' calculations
using bank-level data from BANKSCOPE.
Concentration        5-Bank concentration ratio of deposits. Source: Barth, Caprio and Levine (2001).
Number of banks to   The logarithm of the ratio of the number of banks in the country and the total population
population           of the country. Source: Barth, Caprio and Levine (2001) and World Bank Development
Indicators.
Foreign bank         A measure of the degree of foreign ownership of banks, measured as the fraction of the
ownership            banking system's assets that is in banks that are 50 percent of more foreign owned.
Source: Barth, Caprio and Levine (2001).
Activity restrictions  A measure of a bank's ability to engage in activities other than banking (including
securities, insurance, and real estate). A higher score indicates more restrictions on banks
to engage in such activities. Source: Barth, Caprio and Levine (2001).
Entry fit test       A measure of entry restrictions on banks. A higher score indicates fewer restrictions on
entry into banking. Source: Barth, Caprio and Levine (2001).
Market capitalization  Stock market capitalization to GDP. Source: Beck et al. (2000).
to GDP
Insurance penetration  A measure of the size of the life insurance market measured as the ratio of the volume of
life insurance premiums to GDP. Source: Beck et al. (2000).
Property rights      Index of property rights from the Economic Freedom Index. Average of the index for the
period 1995-99. A lower score indicates better protection of property rights. Source:
Heritage Foundation.
Per capita GDP       Logarithm of per capita GDP in 1995 values. Source: World Bank Development
Indicators.
Inflation            Average over the period 1995-99 of the annual change in the consumer price index.
Source: World Bank Development Indicators.
28



Table 2       H-statistics of banking systems around the world
Country             H-statistic     Standard error   Number of banks  Number of observations
Argentina                        0.73          (0.06)             105                    278
Australia                        0.80           (0.11)             26                     126
Austria                          0.66           (0.04)            160                    760
Bangladesh                       0.69          (0.13)              28                    132
Belgium                          0.73          (0.05)              76                    371
Brazil                           0.83          (0.06)              96                    248
Canada                           0.67          (0.07)              49                    224
Chile                            0.66          (0.07)              31                    148
Colombia                         0.66          (0.08)              39                    167
Costa Rica                      0.92           (0.05)              30                    111
Croatia                         0.56           (0.09)              45                    196
Czech Republic                  0.73           (0.14)              25                     90
Denmark                         0.50           (0.05)             100                    646
Dominican Republic              0.72           (0.09)              27                    121
Ecuador                         0.68           (0.09)              35                    106
France                          0.69           (0.02)             355                   1,926
Germany                         0.58           (0.02)            2,226                 13,015
Greece                          0.76           (0.07)              21                     95
Honduras                        0.81           (0.11)              21                     68
Hong Kong, China                0.70           (0.07)              44                    243
Hungary                         0.75           (0.07)              26                    112
India                           0.53            (0.04)             60                    399
Indonesia                       0.62           (0.06)              97                    353
Italy                           0.60           (0.03)             472                   2,508
Japan                           0.47           (0.17)              44                    100
Kenya                           0.58           (0.11)              34                    106
Latvia                          0.66           (0.14)              24                     85
Lebanon                         0.69           (0.05)              63                    371
Luxembourg                      0.82           (0.04)              76                    277
Malaysia                        0.68           (0.06)              41                    228
Mexico                          0.78            (0.10)             27                     58
Netherlands                     0.86            (0.06)             44                    227
Nigeria                         0.67           (0.06)              42                    186
Norway                          0.57            (0.08)             48                    259
Pakistan                        0.48            (0.13)             21                    148
Panama                          0.74            (0.09)             32                     88
Paraguay                        0.60           (0.22)              23                     92
Peru                            0.72            (0.07)             24                    132
Philippines                     0.66            (0.05)             45                    237
Poland                          0.77           (0.06)              40                    138
Portugal                        0.67           (0.06)              31                    213
Russian Federation              0.54            (0.07)            106                    232
South Africa                    0.85            (0.05)             45                    186
Spain                           0.53            (0.03)            157                    839
29



Country              H-statistic      Standard error   Number of banks  Numnber of observations
Switzerland                       0.67            (0.03)             227                    1,048
Turkey                            0.46            (0.21)              34                      69
Ukraine                           0.68            (0.15)              30                      71
United Kingdom                    0.74            (0.04)             106                     569
United States                     0.41            (0.01)           1,135                   7,261
Venezuela                         0.74            (0.07)              55                     171
NOTES: The table displays the estirated average H-statistic for each country in the sample calculated for the years
1994-2001 using the Panzar-Rosse (1987) approach. The H-statistics are based on a sample that includes
observations from countries with a total number of at least 50 bank-year observations and observations on at least 20
banks. Standard errors of the H-statistics are reported to the right of the H-statistics between brackets. Further details
on the comnputation of the H-statistic and the data sources can be found in Table 1.
30



Table 3        Correlation matrix of main variables
Number of    Foreign                         Market
banks to     bank      Activity  Entry fit capitalization Insurance  Property Per capita
H-statistic  Concentration  population  ownership restrictions  test  to GDP    penetration  rights   GDP
Concentration                       0.38
(0.02)
Number of banks to population       -0.01        -0.26
(0.94)       (0.12)
Foreign bank ownership              0.25         -0.08        0.23
(0.16)       (0.68)      (0.20)
Activity restrictions              -0.26         -0.10       -0.55      -0.05
(0.10)       (0.54)      (0.00)     (0.79)
Entry fit test                      0.29          0.31        0.14       0.02      -0.17
(0.07)       (0.05)      (0.37)     (0.90)      (0.30)
Market capitalization to GDP        0.20         -0.13        0.13       0.08      -0.13     -0.03
(0.20)       (0.46)      (0.45)     (0.67)      (0.45)   (0.87)
Insurance penetration               0.02          0.08        0.15      -0.31      -0.19     -0.03       0.42
(0.89)       (0.63)      (0.39)     (0.10)      (0.28)   (0.88)      (0.01)
Property rights                     0.11          0.13       -0.51      -0.19       0.35      0.11       -0.36      -0.45
(0.45)       (0.43)      (0.00)     (0.29)      (0.03)   (0.49)      (0.02)     (0.00)
Per capita GDP                      0.01         -0.07        0.69       0.16      -0.45      0.04       0.28        0.50    -0.82
(0.94)       (0.66)      (0.00)     (0.38)      (0.00)   (0.81)      (0.07)     (0.00)   (0.00)
Inflation                           0.04          0.25       -0.54       0.10       0.42      0.12      -0.26       -0.51     0.59     -0.66
(0.76)       (0.14)      (0.00)     (0.60)      (0.01)   (0.47)      (0.10)     (0.00)    (0.00)   (0.00)
NOTES: H-statistic is the average H-statistic calculated using the Panzar-Rosse (1987) approach for the years 1994-2001. p-values in brackets below correlation
coefficients. A description of each variable and the data sources can be found in Table 1.
31



Table 4        Cross-country determinants of H-statistics
Structure     Contestability  Inter-industry  All     Institutions
Concentration               0.315**                                  0.203*        0.185**
(0.133)                                  (0.100)       (0.087)
Number of banks to population  -0.005
(0.024)
Foreign bank ownership      0.003***                                 0.003***      0.003***
(0.001)                                  (0.000)      (0.000)
Activity restrictions                     -0.039***                  -0.026***     -0.022***
(0.006)                    (0.006)      (0.006)
Entry fit test                            0.031***                   0.002         -0.008
(0.011)                    (0.016)      (0.013)
Market capitalization to GDP                            0.025
(0.061)
Insurance penetration                                   -0.166
(1.998)
Property rights                                                                    0.069
(0.056)
Per capita GDP              -0.040        -0.032        -0.062       -0.020        0.010
(0.034)       (0.023)      (0.056)       (0.020)       (0.030)
Inflation                   -0.057        0.024         -0.045       0.015         0.006
(0.040)       (0.030)       (0.054)      (0.026)       (0.024)
Number of countries         31            39            39           30            30
R-squared                   0.58          0.66          0.10         0.79          0.81
NOTES: Dependent variable is the average H-statistic for a particular country calculated using the Panzar-Rosse
(1987) approach for the years 1994-2001. All regressions are estimated using weighted OLS with heteroskedasticity-
consistent standard errors. As weights, we use for each country observation the inverse of the variance of the
generated H-statistic. A constant was added, but is not reported. Robust standard errors in parentheses. * significant
at 10%; ** significant at 5%; *** significant at 1%. A description of each variable can be found in Table 1.
32



Table 5       Robustness: Sample of countries with at least 100 bank-year observations
Structure     Contestability  Inter-industry  All      Institutions
Concentration               0.297**                                   0.188         0.169
(0.139)                                   (0.116)       (0.105)
Number of banks to population  -0.006
(0.030)
Foreign bank ownership      0.004***                                  0.003***      0.003***
(0.001)                                   (0.000)       (0.000)
Activity restrictions                     -0.039***                   -0.026***     -0.022***
(0.006)                    (0.007)        (0.007)
Entry fit test                            0.030**                     0.001        -0.008
(0.011)                    (0.018)        (0.015)
Market capitalization to GDP                            0.026
(0.062)
Insurance penetration                                  -0.024
(2.037)
Property rights                                                                     0.068
(0.067)
Per capita GDP              -0.042        -0.027       -0.065         -0.015        0.011
(0.051)       (0.026)      (0.061)        (0.024)       (0.032)
Inflation                   -0.063        0.027         -0.051        0.020         0.006
(0.049)       (0.036)      (0.062)        (0.033)       (0.032)
Number of countries         23            30            34            22            22
R-squared                   0.58          0.66          0.10          0.80          0.82
NOTES: Dependent variable is the average H-statistic for a particular country calculated using the Panzar-Rosse
(1987) approach for the years 1994-2001. The regression results are based on a sample of countries that excludes
countries with an estimated H-statistic that is based on a sample of less than 100 bank-year observations. All
regressions are estimated using weighted OLS with heteroskedasticity-consistent standard errors. As weights, we use
for each country observation the inverse of the variance of the generated H-statistic. A constant was added, but is not
reported. Robust standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. A
description of each variable can be found in Table 1.
33






Policy Research Working Paper Series
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Implications for the Regional Trade                                       89750
Agenda in Eastern and Southern Africa
WPS3091 Labor Market Policies and        Pierre-Richard Ag6nor  July 2003          M. Gosiengfiao
Unemployment in Morocco:        Karim El Aynaoui                          33363
A Quantitative Analysis
WPS3092 The Integrated Macroeconomic Model Pierre-Richard Agenor  July 2003        M. Gosiengfiao
for Poverty Analysis: A Quantitative  Alejandro lzquierdo                 33363
Macroeconomic Framework for the  Hippolyte Fofack
Analysis of Poverty Reduction Strategies
WPS3093 Migration and Human Capital in   Norbert M. Fiess       July 2003          R. lzquierdo
Brazil during the 1990s         Dorte Verner                              84161
WPS3094 Oil, Agriculture, and the Public  Norbert M. Fiess      July 2003          R. Izquierdo
Sector: Linking Intersector Dynamics  Dorte Verner                        84161
in Ecuador
WPS3095 The Emerging Project Bond Market:  Mansoor Dailami      July 2003          S. Crow
Covenant Provisions and Credit  Robert Hauswald                           30763
Spreads
WPS3096 International Survey of Integrated  Jose de Luna Martinez  July 2003       S. Coca
Financial Sector Supervision    Thomas A. Rose                            37474
WPS3097 Measuring Up: New Directions for  Piet Buys              July 2003         Y. D'Souza
Environmental Programs at the   Susmita Dasgupta                          31449
World Bank                      Craig Meisner
Kiran Pandey
David Wheeler
Katharine Bolt
Kirk Hamilton
Limin Wang
WPS3098 Governance and Economic Growth   Mark Gradstein         July 2003          H. Sladovich
37698
WPS3099 Creating Partnerships for Capacity  F. Desmond McCarthy  July 2003         D. Philage
Building in Developing Countries:  William Bader                          36971
The Experience of the World Bank  Boris Pleskovic
WPS3100 Governance of Communicable       Monica Das Gupta       July 2003          H. Sladovich
Disease Control Services: A Case  Peyvand Khaleghian                      37698
Study and Lessons from India    Rakesh Sarwal



Policy Research Working Paper Series
Contact
Title                           Author                  Date             for paper
WPS3101 Portfolio Preferences of Foreign  Reena Aggarwal         July 2003         A. Yaptenco
Institutional Investors         Leora Klapper                             31823
Peter D. Wysocki
WPS3102 Investing in Infrastructure: What is  Marianne Fay       July 2003         M. Fay
Needed from 2000 to 2010?       Tito Yepes                                87200
WPS3103 Ownership Structure and Initial  Reena Aggarwal          July 2003         A. Yaptenco
Public Offerings                Leora Klapper                             31823
WPS3104 Does Strict Employment Protection  Jan Rutkowski         August 2003       J. Rutkowski
Discourage Job Creation? Evidence                                         84569
from Croatia
WPS3105 Further Evidence on the Link between Allen N. Berger     August 2003       A, Yaptenco
Finance and Growth: An International lftekhar Hasan                       31823
Analysis of Community Banking and  Leora F. Klapper
Economic Performance
WPS3106 Governance Matters IlIl: Governance  Daniel Kaufmann     August 2003       A. Bonfield
Indicators for 1996-2002        Aart Kraay                                31248
Massimo Mastruzzi
WPS3107 More Favorable and Differential  Bernard Hoekman         August 2003       P. Flewitt
Treatment of Developing Countries:  Constantine Michalopoulos             32724
Toward a New Approach in the    L. Alan Winters
World Trade Organization
WPS3108 Stabilization and Association Process Bartlomiej Kaminski  August 2003     F. Sy
in the Balkans: Integration Options  Manuel de la Rocha                   89750
and their Assessment
WPS3109 The Impact of China's WTO        Elena lanchovichina     August 2003       S. Lipscomb
Accession on East Asia          Terri Walmsley                            87266
WPS31 10 Governance of Public Pension Funds: David Hess          August 2003       P. Braxton
Lessons from Corporate Governance Gregorio Impavido                       32720
and International Evidence
WPS31 11 International Trade and Wage    Gunseli Berik           August 2003       M. Ponglumjeak
Discrimination: Evidence from   Yana van der Meulen Rodgers               31060
East Asia                       Joseph E. Zveglich, Jr.
WPS3112 On the Timing of Marriage, Cantle,  Johannes Hoogeveen   August 2003       P. Sader
and Weather Shocks in Rural     Bas van der Klaauw                        33902
Zimbabwe                        Gijsbert van Lomwel