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Banking Sector Performance in East Asian Countries:
The Effects of Competition, Diversification, and Ownership
Luc Laeven*
(The World Bank and CEPR)
Abstract: This paper takes stock of the bank restructuring process in five East Asian countries Hong Kong (China), Indonesia, the Republic of Korea, Malaysia, the Philippines, Singapore, and Thailand with a particular goal of assessing whether bank performance and stability has improved following the Asian financial crisis of 1997-98. We find that the banking systems in all East Asian countries look markedly different today than during the period before the crisis, both in terms of ownership and market structure. The ongoing process of consolidation of local banking markets and an increase in foreign ownership of banks have improved performance and stability. We conclude with several policy recommendations regarding foreign bank entry, bank consolidation, and bank governance going forward. * This paper was prepared as a background paper for East Asian Finance: the Road to Robust Markets published by the World Bank. The author would like to thank Stijn Claessens and Swati Ghosh for helpful comments and Ying Lin for excellent research assistance. This papers finding, interpretations, and conclusions are entirely those of the author and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent.
1
1. Introduction
In retrospect, we now know from the recent crisis experience in East Asia that banks
were taking excessive risks, largely unknown to small investors and depositors, although
bank performance varied markedly across banks depending on such factors as the quality
of management and the type of ownership (see Laeven (1999, 2002), among others).
Following the onset of the crisis in 1997-98, the banking systems of many countries in
the East Asia region, and especially the crisis-affected countries, have undergone major
restructuring efforts, often with major government involvement. Some banks in the
respective countries were taken over by the State, while others received government
support (Klingebiel, Kroszner, Laeven, and Van Oijen 2001). By now, many of these
nationalized banks have been sold to the private sector, mostly to domestic investors,
although foreign interest in local banks has also increased (both from outside the region
and from within the region, e.g. foreign equity investments by the Development Bank of
Singapore). As a result, the banking systems of most countries in the region look
markedly different today than before the crisis, both in terms of ownership and market
structure. This raises a number of policy-relevant questions: Should further consolidation
be encouraged? Should foreign banks be allowed to enter the market?
Commercial banks in many East Asian countries have traditionally been linked
through ownership to other financial institutions, such as merchant banks and finance
companies. In Korea, banks often owned merchant banks; in Thailand, banks often
owned finance companies; and in Malaysia, bank holding companies often include
commercial banking, investment banking, asset management, and insurance companies.
2
While the crisis has led some banks to focus on more traditional banking activities, other
banks have continued to expand the range of their activities, with increasing focus on
income from fee-based activities. This raises another important policy question: Is
diversification a better strategy than focusing on core activities?
In this paper, we address these questions by studying the performance and
stability of the banking systems in East Asia. We first assess whether performance and
stability have improved since the financial crisis of 1997-98, and then identify the
determinants of bank performance and stability today. Based on this analysis, we make
several predictions about the impact of the ongoing process of consolidation on the
performance and stability of these banking systems. We also derive some policy
recommendations regarding bank diversification, foreign bank entry, consolidation of
local banking markets, and bank governance more generally.
2. Methodology
Measures of bank performance can broadly be broken down in two categories: those
based on accounting information and those based on market information. Bongini et al.
(2002) show that accounting-based measures are lagging market-based measures, and
therefore a market-based approach would be the preferred choice.
As a market-based measure of bank-risk we will use the implicit deposit insurance
premium measure of risk developed in Laeven (2002b). This measure estimates the cost
of insuring all bank deposits in a particular banking system and can be interpreted, as
shown by Laeven (2002a), as a measure of bank risk. The riskier the banks in the system,
3
the costlier it will be to insure all bank deposits, and the higher the deposit insurance
premium. We will calculate this measure for the portfolio of all banks in the system to
allow for risk diversification, which can significantly reduce to cost of deposit insurance
(Laeven 2000b). The disadvantage of this method is that it can only be applied to listed
banks.
Most banks in the East Asia region are not listed (although many of the largest
banks are) and we will therefore focus on accounting-based measures of bank
performance. As accounting-based measures of bank performance we will use the ratio of
operating income to total assets. The banking literature has also developed methods to
calculate the X-efficiency of cost efficiency of banks using accounting-based
information. As argued by Laeven (1999), these methods heavily rely on reliable data on
nonperforming loans and measures of bank risk more generally. For most countries in
East Asia, such data is not available, and we therefore resort to simple financial ratios as
accounting-based measures of bank performance.
The basic model will look as follows:
ijtjtjtitijttijijt regulationmarketownershipBtimebankcountryePerformanc +++++++=
where Performance is a measure of bank measure; Country, Bank, and Time capture
country, bank, and time-specific effects, respectively; B is a vector of bank-specific
variables, such as liquidity ratios, capital adequacy ratios, and other CAMEL-type
indicators; Ownership is a bank-specific measure of bank ownership, such as type of
controlling owner; Market is a country-specific measure of market contestability, such as
market concentration or market share; Regulation is a country-specific measure that
4
includes bank regulatory and supervisory variables; and i denotes bank i, j denotes
country j, and t denotes year t.
As measure of bank performance, we use the ratio of operating income to total
assets. This measure has been widely used in the literature (together with pre-tax return
on total assets) as a measure of bank profitability. Note that operating income is gross
income before operating costs (including personnel expenses) and before taxes. It
includes net interest income and income from fees, commissions, and trading income.
Under the assumption that banks are profit maximizers, higher profits denote better bank
performance. Also, to the extent that high bank profits reflect greater stability of the
banking system, thus reducing the likelihood of costly bank runs and bank defaults, they
may improve a societys welfare. However, higher bank profits do not necessarily
enhance a societys welfare. If banks earn superprofits by extracting excessive rents from
consumers, then high profits may be an indication that the banking system is not
competitive and that consumer welfare is negatively affected. While it is generally
accepted that banks should have a positive franchise value to enhance financial sector
stability, very high profits are generally taken as a sign of lack of competition. In our
empirical analysis, we focus on within-country (rather than cross-country) variation in
bank profitability. This allows us to keep country effects, such as the competitiveness of
the banking system, and to analyze what drives differential performance of banks in a
given country.
Because the performance of banks may differ depending on the diversity of
activities they engage in, we construct an activity-adjusted performance measure based
on the work by Laeven and Levine (2005). Theory provides conflicting predictions about
5
the impact of greater diversity of activities on the performance of financial
intermediaries. As suggested by the work of Diamond (1991), Rajan (1992), Saunders
and Walter (1994), and Stein (2002), banks acquire information about clients during the
process of making loans that may facilitate the efficient provision of other financial
services, including the underwriting of securities. Similarly, securities and insurance
underwriting, brokerage and mutual fund services, and other activities may produce
information that improves loan making. Thus, banks that engage in a variety of activities
may enjoy economies of scope that boost performance. Alternatively, diversification of
activities within a single financial conglomerate may intensify agency problems between
corporate insiders and small shareholders with adverse implications on bank performance
(Jensen, 1986; Jensen and Meckling, 1986). Laeven and Levine (2005) find that, on
average, diversity of activities by banks destroys value and reduces bank performance.
We use the method developed by Laeven and Levine (2005) to control for the
possibility that the performance of different financial activities is inherently different. For
example, if securities underwriting is more income than loan making, then a bank that
does both may have higher operating income than a bank that only makes loans. We
abstract from these activity-effects on bank performance to identify the independent
impact of diversity by compare the operating income of diversified banks to the estimates
of operating income these banks would have if they were decomposed into a bank
specialized in loan-making activities and a bank specialized in non-lending activities.
Due to data constraints, we differentiate banks by (i) interest income versus non-
interest income and by (ii) loans versus other earning assets. Thus, we do not distinguish
among securities underwriting, brokerage services, and insurance underwriting. We
6
simply differentiate banks by lending versus non-lending activities. First, we construct
asset-based and income-based measures of the extent to which banks engage in loan
making activities or fee generating activities. One can think of specialized commercial
banks as converting deposits into loans, and one can think of specialized investment
banks as underwriting securities but not making loans.
Second, we construct asset-based and income-based measures of diversity. That
is, we measure the degree to which banks specialize in lending or non-lending services,
or whether they perform a diversity of activities. Lower values of these diversity indexes
imply more specialization, while higher values signify that the bank engages in a mixture
of lending and non-lending activities. Clearly there is a link between these diversity
measures and the measures of the degree to which banks engage in loan making or non-
loan making activities. If a bank only makes loans, it will be classified as having zero
diversity. The two measures, however, also capture different traits. The diversity indexes
measure diversity per se, while the activity measures gauge where each bank falls along
the spectrum from a pure lending bank to a pure fee-generating bank.
To measure where along the spectrum each bank falls from pure commercial
banking to specialized investment banking, we first construct an asset-based measure that
equals loans relative to total earning assets. Total-earning assets include loans, securities,
and investments. Very high values signal that the bank specializes in loan making, like
the specialized commercial banks mentioned above. Very low values of these ratios
signal that the bank is not specialized in loan making and indicates the financial
institution specializes in non-loan making activities.
7
The second measure of where each bank falls along the continuum from pure
lending to pure fee/trading-based activities is an income-based indicator that equals the
ratio of net interest income-to-total operating income. Total operating income includes
net interest income, net fee income, net trading income, and net commission income. In
terms of assessing where along the spectrum each bank falls, a specialized loan-making
bank will have a larger ratio of net interest income-to-total operating income, while a
specialized investment bank is expected to have a larger share of other operating income.
The asset-based measure suffers from fewer measurement problems than the
income-based measure, but we include both for robustness. In particular, since loans may
yield fee income, the income-based measure may overestimate the degree to which some
lending institutions engage in non-lending activities. Also, we would prefer to use gross
rather than net income to measure bank activities, but as noted above, we simply do not
have gross income for many banks.
Next, we construct two measures that focus on diversity per se. Asset diversity is
a measure of diversification across different types of assets and is calculated as
( )assetsearningTotal
assetsearningOtherloansNet 1 , where Other earning assets include securities
and investments. Total earning assets is the sum of Net loans and Other earning assets,
and |.| denotes the absolute value indicator. Asset diversity takes values between 0 and 1
and is increasing in the degree of diversification.
Income diversity is a measure of diversification across different sources of
income and is calculated as ( )
incomeoperatingTotal
incomeoperatingOtherincomeerestintNet 1 . Net
interest income is interest income minus interest expense and Other operating income
8
includes net fee income, net commission income, and net trading income. Income
diversity takes values between 0 and 1 and is increasing in the degree of diversification.
Since different banking activities may generate different income streams, it is
important to control for the degree to which banks engage in different activities when
comparing their performance. For example, if investment banking generates generally
more income than commercial banking, one needs to control for the extent to which the
bank is engaged in either activity in order to isolate the relationship between performance
and diversity per se. Thus, we compute an excess performance measure following a
modified version of the chop-shop approach introduced by LeBaron and Speidell
(1987) and Lang and Stulz (1994) and adopted and applied to banks by Laeven and
Levine (2005). The idea is to compare the operating income of each bank with the
operating income that would exist if the bank were chopped into separate financial
shops (pure-activity banks) that each specializes in a financial activity (e.g., lending or
fee/income generation).
Activity-adjusted j is our estimate of the ratio of operating income to total assets
that would prevail if bank j were divided into activity-specific financial institutions that
each generates income according to the s associated with each of those activity-specific
activities. At a general level, consider bank j that engages in n activities. Let ji equal the
share of the ith activity in the total activity of bank j, so that 11
==
n
i
ji . Let i equal the
ratio of operating income to total assets of financial institutions that specialize in activity
i (pure-activity ). Then,
=
=n
i
i
jijadjustedActivity1
9
More specifically, we primarily consider two banking activities: lending
operations versus non-lending operations, including trading, investments, and advisory
services. From an asset perspective, we focus on the distinction between investments in
loans and investments in securities or other companies. From an income perspective, we
focus on the distinction between interest income (mainly from loans) and non-interest
income, including fees, commissions, and trading income. For simplicity, we refer in
what follows to the first activity as commercial banking and to the second as
investment banking. Thus, 1 is the operating income of an activity-specific bank
focused on commercial banking, while 2 is the operating income of an activity-specific
bank focused on investment banking. With two activities, the definition of activity-
adjusted for bank j simplifies to the following:
))1(()( 211
1
2
2
1
1 jjjjjadjustedActivity +=+= (1)
In what follows, we compute two activity-adjusted measures. That is, we calculate
activity-adjusted based on both the asset and income measures of the share of bank
activity. Thus, 1j equals either the ratio of net interest income to total operating income
or the ratio of net loans to earnings assets for bank j.
Excess value equals the difference between a banks actual and the activity-
adjusted , so that the excess value for bank j is
))1(()( 211
1
2
2
1
1 jjjjj qvalueExcess +=+= (2)
Again, we compute two measures of excess value, one based on weights determined by
the asset composition of the bank and the other determined by the income composition of
the bank.
10
To measure activity-adjusted s and compute excess value, we construct 1 and
2 from banks that specialize in one activity. We follow the literature in defining what
constitutes specialization. For asset-based measures, banks where 90% of the assets are
associated with one activity are classified as specialized. In this case, 1 is the average
of banks with a ratio of net loans to earnings assets of more than 0.9. Similarly, for
income-based measures, specialized banks receive 90% of their income from one
activity, so that 1 equals the average of banks with a ratio of net interest income to
total operating income of more than 0.9. These pure-activity s are calculated by
averaging across banks from the different East Asian countries in our sample. Most
countries do not have a sufficiently large number of pure-activity banks to estimate pure-
activity s at the country-level. In the regression analyses below, we use country fixed
effects and year dummy variables to control for differences in across countries and
years.
In constructing activity-adjusted s and excess values, we need to compute j1
and j2, which are the shares of pure commercial banking and investment banking in
bank js activities. The weights are based on the relative importance of interest income to
total operating income in the case of the income diversity measure. In case of the asset
diversity measure, the weights are based on the relative importance of loans to total
earning assets.
In our empirical work we will also control for the ownership structure of the
banks. As shown by Caprio et al. (2004), among others, the type of ownership and the
cash flow rights of ultimate controlling shareholders are key determinants of bank
performance and valuation. We use hand-collected data on the type of the ultimate owner
11
of the bank. We do not have detailed enough information about the ownership structures
of all the banks in our sample to calculate the cash flow rights. This would require a
detailed study of the often times complex ownership structures of banks in East Asia (so-
called pyramidal structures). We consider a bank to be controlled by a shareholder, if
the controlling shareholder owns more than 50% of the control rights of the bank. We
consider four categories of ultimate ownership: state, foreign state, private domestic, and
foreign. We aggregate the stakes of all shareholders by each of these four categories and
determine ultimate ownership by attaching the ownership category to the group of
shareholders with the largest ownership stake.
Since many of the variables under consideration are bound to be endogenous (for
example, performance and ownership are expected to be endogenous), efficient
estimation of the above relationships will depend on the use of time series data. We will
thus construct a dataset that varies over time. This will not only help us to deal with
potential endogeneity issues but will also enable us to analyze whether effects have
changed over time, for example, whether the effect of foreign bank entry on local bank
performance has changed over time.
We also develop and estimate different types of bank competition measures. Here
we rely on the Panzar-Rosse (1982, 1987) approach developed in Claessens and Laeven
(2004, 2005), as well as on more traditional measures of bank concentration, such as the
3-bank concentration ratio and the market shares of individual banks. For most of these
measures, it is important to have data on a sufficiently large number of banks in the
respective countries, and therefore we are only able to implement this approach by
12
pooling country-level data over several years. As a consequence, we can only estimate
changes in competition over time across all countries in the region, not for individual
countries.
The Panzar and Rosse H statistics are 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 banks input prices. The H statistic is interpreted as follows. H
13
the sum of the coefficients on three main explanatory variables: interest expenses to total
funding, personnel expense to total assets, and other operating and administrative
expense to total assets.
3. Data
We collect financial data and ownership data on banks from Bankscope, a commercial
data provider of data on over 10,000 publicly listed and private banks around the world.
Most of the data come from audited financial statements. We also have data on the type
of specialization of the bank (i.e., whether the bank is a commercial bank, an investment
bank, a savings bank, a bank holding company, a development bank, etc.). Although non-
bank financial institutions are important players in the financial systems of some of the
East Asian countries (for example, the finance companies in Thailand and the merchant
banks in Korea), we focus on commercial banks. To enhance comparability of banks in
our sample, we limit the sample to banks identified by Bankscope as commercial banks,
savings banks, and bank holding companies with major commercial banking operations.
We collect data for the period 1994-2004 (when available) for 7 East Asian
countries: Hong Kong (China), Indonesia, the Republic of Korea, Malaysia, the
Philippines, Singapore and Thailand. We have data for about 2,157 bank-year
observations, although not all variables are available for all banks in all years. The
coverage of banks is particularly problematic during the early years of our sample period,
because Bankscope does not always keep information for banks that have failed during
the sample period. This produces a survivorship bias in the results. We also miss data on
14
many banks for the year 2004 because many banks have not yet reported their financial
statements for the year 2004 to Bankscope. This explains why the number of banks in our
sample drops from 213 in 2003 to only 122 in 2004.
Table 1 presents a breakdown of the banks in our sample by ownership category.
We distinguish between four different ultimate ownership categories: state, foreign state,
private, and foreign. Foreign state banks are banks that are owned by a foreign state. An
example is the Development bank of Singapore, which is majority owned by the
government of Singapore, and has operations in other East Asian countries. Private
denotes domestic banks that are majority-owned by domestic citizens. This includes
family owned banks as well as banks that are widely held by a large number of private
shareholders. Foreign banks are banks that are owned by foreign shareholders (excluding
foreign states). The latter group often includes subsidiaries of multinational banks but
also includes the Hong Kong and Shanghai Banking Corporation, the largest bank in
Hong Kong and one of the largest banks in the world, with stock market listings in
several countries and a large shareholder base around the world.
The table shows that the majority of banks in the East Asian countries are
privately-owned. However, the importance of family ownership and ownership by other
private parties has dwindled from almost 80 percent in 1994 to 36 percent in 2000, only
recovering somewhat to about 47 percent by the year 2004. State-ownership one the other
hand has increased over the same period, with the state controlling about 20 percent of
the banks in 1994 to about 30 percent in 2004. However, the foreign ownership category
has recorded the largest increase over this period. While foreigners owned a mere 1.5
percent of banking assets in East Asia in 1994, this number has increased to about 23
15
percent by the year 2004. The most important reason for these shifts in ownership
structure is the East Asian financial crisis and the governments response to the crisis.
Many family-owned and other privately-owned banks failed during the crisis, and unless
these banks were of systemic importance or had strong links to the political elite, they
were unlikely not to be bailed out. This explains the sharp drop in the number of private
banks post-1997. However, a significant share of these failed private banks did get bailed
out by the government, resulting in a temporary increase in state banks, reflected in the
significant increase in state banks during the years 1997-2000 from 21% to 37%. While
some of these banks are still in state hands, others have been successfully privatized to
the public, often to foreigners, explaining to a large extent the increasing importance of
foreign ownership.
Panel B of Table 1 reports the ownership breakdown by country. Although the
patterns of changes in ownership are broadly consistent across countries, there are some
differences. State ownership, for example, plays much less of an important role in Korea
and the Philippines than in the other countries. While ownership of banks by the state in
Korea increased after the 1997 financial crisis to about 21% in 2001, it decreased to only
7% by the year 2004. State-ownership in the Philippines stood at a level of about 18% by
year-end 2004. In Indonesia, Singapore, and Thailand, on the other hand, the state still
owns more than 50% of the banks (As measured in terms of total assets). The importance
of foreign shareholders of local banks also varies significantly across countries. While
foreigners are important shareholders of banks in Hong Kong and to a lesser extent in
Indonesia and Malaysia, they do not play an important role in the other East Asian
countries.
16
Next we look at the market structure of the East Asian banking systems. Panel A
of Table 2 reports for each country the average size of banks, the 3-bank concentration
ratio, and the average market share (all in terms of either total assets or total deposits).
Panel B reports values of the same variables for the year 2004. We find that Korean
banks are much larger on average than their counterparts elsewhere in the region, both in
terms of total assets and in terms of deposits, while banks in Indonesia and the
Philippines are much smaller. The typical bank in Korea has about US$ 30 billion worth
of total assets, while the average bank in Indonesia or the Philippines has about US$ 1.6
billion in total assets. These differences remain large and significant when we control for
differences in economic development using per capita GDP (not shown in the Table).
We also notice stark differences in the market concentration across the East Asian
banking systems with the banking systems of Singapore and Hong Kong being the most
concentrated and the banking systems of Korea and Malaysia the least concentrated,
although the average bank concentration in the region does not differ significantly from
the world average. The 3-bank concentration ratio (in terms of total deposits and for the
period 2004) varies from a low of 0.44 in Malaysia to a high of 0.82 in Hong Kong, and
the regional average of 0.56 is very similar to the average world-average of about 0.55
(as reported in Claessens and Laeven 2005). Bank concentration is often used as a
measure of bank competition, although work by Claessens and Laeven (2004) suggests
that concentration ratios are not highly correlated to measures of market contestability
and therefore capture other aspects beyond competition. Nevertheless, with this caveat in
mind, the figures suggest that competitive pressure may be low in some of the banking
markets of East Asia because of high concentration of bank assets and deposits.
17
Next we look at differences in bank performance. Panel C of Table 2 reports the
country averages of a commonly used measure of bank performance, the ratio of total
operating income to total assets. Operating income includes net interest income and
income from fees, commissions and other services. This figure is before operating
expense (such as labor costs) and before taxes. We find that the average bank in all
countries is profitable. The average ratio of operating income to total assets is about 4.8
percent over the period 1994-2004. Banks in the Philippines and Indonesia are the most
profitable, with operating income to total asset ratios of about 6 percent.
In Table 3, we report a measure of regulatory restrictions on banking in the East
Asian countries compiled by the Heritage Foundation, as well as similar measures of
government intervention and regulation in other aspects of the economy. Two things are
striking. First, banking is somewhat more regulated than other aspects of the economy
(such as trade and monetary policy) in all countries except Hong Kong and the
Philippines. Second, regulatory restrictions on banking have not changed much in any of
the East Asian countries over the period 1005-2004, despite the financial crisis. This
suggest that despite government intervention in the banking systems of most countries
following the crisis, this was not perceived by outside observers such as the Heritage
Foundation to negatively affect the freedom of banking in any of these markets. We
would have preferred to use more detailed data on specific bank regulatory variables (for
example on entry and capital regulation) from the World Bank Database on Bank
Regulation and Supervision and Barth et al. (2001) but because such data is only
available for two years in our sample we prefer to use the more aggregate measure of
banking freedom of the Heritage Foundation instead.
18
4. Empirical Results
We first assess the risk embedded in East Asian the banking systems. To this end, we
apply the option pricing methodology developed by Ronn and Verma (1986) and adapted
by Laeven (2002) to the sample of listed banks in each of the East Asian countries. We
assume that all bank debt is insured and that there is no regulatory forbearance. In
practice, regulatory forbearance can be substantial, especially around times of systemic
distress, which is also when the value of the put option of deposit insurance is highest. As
a result, we are underestimating the implicit cost of deposit insurance. It is also important
to note that most of the East Asian countries explicitly insure deposits; in three cases
coverage is even unlimited due to a government blanket guarantee on deposits installed
shortly after the onset of the 1997 financial crisis. Blanket guarantees on deposits were
enacted in Thailand in 1997 and in Indonesia and Malaysia in 1998. The Philippines was
the first country to region to adopt explicit deposit insurance, following the example of
the United States. The Philippines has had explicit deposit insurance since 1963 with
annual premium of 0.2% on deposits. Coverage limit on deposits in the Philippines has
been 100,000 Pesos since 1992. The Republic of Korea has enacted explicit deposit
insurance in 1996 with a coverage limit on deposits of 20 million Won which increased
to unlimited coverage in 1997 at the time of the crisis and was subsequentially reduced to
a coverage limit of 50 million Won (about 42000 US dollars) in 2003 with annual
premiums of 0.05%. Hong Kong and Singapore have no explicit deposit insurance
(Demirguc-Kunt et al. 2005).
19
Our deposit insurance measure estimates the implicit cost of insuring the deposits
in a particular banking system. Laeven (2002) shows that we can interpret higher implicit
deposit insurance premiums as a measure of banking system risk. By comparing the
implicit cost estimates with the actual premiums charged for deposit insurance, we can
also infer whether deposit insurance is underpriced. In countries that have not adopted
explicit deposit insurance, the estimates give an indication of how much it would cost to
insure all deposits in the system if deposit insurance were made explicit. We estimate the
implicit cost for the banking system as a whole, thereby allowing for diversification
potential by aggregating risks of banks that are not perfectly correlated. As Laeven
(2003) shows, the diversification potential can be substantial, particularly in large
banking systems with a diverse set of banks, thereby significantly reducing the cost of
deposit insurance.
Table 4 presents our estimates of deposit insurance for the year 1998 and for the
period 1996-2004. In all countries, the peak in the implicit cost of deposit insurance was
reached in 1998, which not surprisingly corresponds with the height of East Asian
financial crisis, except Indonesia where the peak of implicit premiums was reached in
1999 (not shown). In Thailand, the implicit annual deposit insurance premium on
deposits would be 0.63% of deposits, which is substantial for a bank with a typical
interest rate margin of 2-4%. Again, we note that these estimates are likely to
substantially underestimate the actual cost of deposit insurance because we have not
allowed for regulatory forbearance.
While the implicit cost of deposit insurance reached high levels around the time
of the crisis, the cost of insurance is much lower when calculated over the entire period
20
1996-2004. The implicit annual premiums on deposits vary from a low of 0.01% in Hong
Kong and Singapore (two countries that do not have explicit deposit insurance) to a high
of 0.84% in Indonesia. For the Republic of Korea we estimate an implicit cost of deposit
insurance amounting to an annual premium of 0.05% per annum, with is identical to the
actual premiums that banks are being charged today. For the Philippines, our implicit
premium estimates are substantially lower than those actually charged (but again, we
should keep in mind that we are potentially underestimating the cost of deposit
insurance).
Overall, the deposit insurance estimates summarized in Table 4 suggest that while
systemic risk increased dramatically in almost all East Asian banking systems during the
period 1997-98, that with the exception of Indonesia, systemic risk today is quite low.
Next, we measure the level of competition in each of the banking systems in our sample
and investigate whether the financial crisis has affected the level of competition. Table 5
presents estimates of the H-statistics developed in Claessens and Laeven (2004) for our
sample of banks. We report both the point estimate of the H-statistic and the standard
deviation of the H-statistic. We also report a test of perfect competition (i.e., the H-
statistic is equal to one) and monopoly (i.e., the H-statistic equals zero). We do not have
enough observations for each country to compute the market competition measure
developed in Claessens and Laeven (2004) for each country and year.
Panel A of Table 5 presents the estimates of the H-statistic when we estimate the
model described in section 2 and in more detail in Claessens and Laeveen (2004) for each
year using pooled OLS across all countries and include country fixed-effects.
21
On average, we find that the banking systems in our sample are not perfectly
competitive but rather display oligopolistic competition. Interestingly, the banking
systems were more competitive on average in 1994, prior to the financial crisis, than
today. The H-statistic in 1994 was about 0.83 on average, much higher than in 2004 when
the H-statistic averaged only 0.69. We also find that banking systems were least
competitive during the height of the financial crisis in 1998, when the H-statistic
averaged only 0.55. Since the crisis in 1998, competition has increased but has yet to
reach pre-crisis levels.
Next, we study cross-country variation in the level of bank competition. Panel B
reports estimates of H-statistics by country based on estimating the model for each
country using pooled OLS across years and including year fixed-effects. In panel C, we
estimate the model for each country using polled OLS across years and include fixed
bank effects as well as fixed year effects. We find that the Korean banking system is most
competitive, with an H-statistic of about 0.95-0.97 and not significantly different from
one, closely followed by Singapore and Hong Kong. The banking systems of Indonesia,
the Philippines and especially Thailand are the least competitive. Malaysias banking
system is somewhere in the middle in terms of competition.
Taking the estimates of the implicit deposit insurance measure of risk and the H-
statistic measure of competition together, the results suggest that the banking systems of
Hong Kong and Singapore are both stable and competitive, while the banking system of
Indonesia still embeds a lot of risk and is not very competitive.
In the remainder of this section of the paper, we analyze the relationship between
bank performance, diversity of bank activities, bank ownership, and regulations. In Table
22
6, we report OLS regressions with as dependent variable either the simple ratio of
operating income to total assets or the activity-adjusted ratio of operating income to total
assets. The difference between the two indicators of bank performance is explained in
section 2 of this paper and described in more detail in Laeven and Levine (2005).
The first four columns in Table 6 present results where the dependent variable is
the simple ratio of operating income to total assets. We include both the diversity
measure (Income diversity or Asset diversity) and an activity measure (Net interest
income to total operating income or Loans to total earning assets). We include the
activity measure to control for the mixture of activities conducted by each bank and to
therefore identify the relationship between valuation and diversity per se. We estimates
the regressions either for the year 2004 (columns (1) and (3)) or for the period 2000-2004
(columns (2) and (4)).
As in Laeven and Levine (2005), we find a diversification discount: the
coefficient on the income diversity and asset diversity variables enters negatively and
significantly (columns (1) to (4) in Table 6). Operating income of banks that engage in
multiple activities is much lower than if those banks were broken-up into financial
intermediaries that specialize in the individual activities. The results are consistent with
the view that diversification intensifies agency problems in financial conglomerates with
adverse implications on performance and these costs to diversification outweigh any
benefits accruing from economies of scope. Nevertheless, because we do not directly
measure agency problems, we cannot unequivocally conclude that intensified agency
problems in financial conglomerates drive the results. We can more confidently argue
that economies of scope are not sufficiently large to produce a diversification premium.
23
Next, we investigate the robustness of the diversification discount in banks to
controlling for bank-level and country-level characteristics. As dependent variable, we
use excess performance as described in section 2 of the paper. We only present regression
results that focus on income diversity (columns (5) to (8) of Table 6) but find similar
results when using asset diversity instead. We control for a number of bank-level traits
and also use country fixed-effects. The regressions are estimated for the year 2004.
When we control for numerous bank-level traits in Table 6, we continue to find a
negative, significant relationship between measures of the diversity of bank activities and
the performance of the bank. First, size is often thought to affect performance through
economies of scale. We therefore control for the logarithm of total assets (column (6)).
Furthermore, we also include the logarithm of total operating income as an alternative
measure of bank size (column (7)). Total operating income may better capture the
importance of a banks off-balance sheet items. While the logarithm of total operating
income enters the valuation regressions positively and significantly, we continue to find
that diversity is associated with lower valuation.
Second, competition in the product market may influence the governance of
banks, so that omitting information on the structure of the banking industry may lead to
inappropriate inferences regarding the relationship between performance and diversity.
Toward this end, we include each banks market share of deposits as an indicator of the
degree of competition facing the bank. Banks with a large market share may exert
market power and enjoy correspondingly higher performance. We find no evidence of
this.
24
Third, we include the ratio of total deposits to total liabilities
(Deposits/Liabilities). To the extent that a higher Deposits/Liabilities ratio implies that
the bank has access to low cost, subsidized funding (deposits generally being an
inexpensive source of funding and deposits generally enjoying government subsidized
insurance), then a higher Deposits/Liabilities ratio might signal higher valuations.
Fourth, we control for the book value capitalization of the bank (Equity/Assets).
A well-capitalized bank may have fewer incentives to engage in excessive risk-taking. If
this were the case, we would expect a positive correlation between the ratio of book value
of equity to total assets (Equity/Assets) and our excess performance measure. We find
that this is the case. Equity/Assets enters with a positive and statistically significant
coefficient.
Fifth, we control for past performance by including the lag of the growth in total
operating income. Past performance is commonly used as a proxy for growth
opportunities. We indeed find a strong relationship between current and past
performance. When including these variables, however, income and asset diversity still
enter negatively and significantly: There is still a significant diversification discount.
In Table 7 we control for bank ownership. In columns (1) to (4) we estimate the
performance regression for the subset of banks that belong to one of the following
ownership categories: state, foreign state, private domestic, and foreign. We find a
diversification discount for all four groups of banks. The diversification discount is
somewhat larger for domestic banks than for foreign banks. We also find that differences
in equity capitalization explain more of the variation in bank performance for state banks
and foreign banks than for private domestic banks.
25
In columns (5) to (7), we control for the share of private domestic ownership and
the share of foreign ownership (both measured in terms of total assets). The default
category is state banks. We find that private domestic banks perform slightly better than
state-owned banks in East Asia, although the difference is not statistically significant.
Foreign-owned banks on the other perform much better and the difference is statistically
significant. We find roughly a one-to-one correspondence between increases in foreign
ownership and increases in bank performance. So, if foreign ownership were to increase
by 10%, then the performance ratio would also increase by about 10%. This is a large
effect compared to the average ratio of operating income to total assets in the sample of
about 4%.
In Table 8 we also include country-level measures of banking sector regulations
other government regulations, and a measure of market concentration. All of the country-
level controls vary over time but in the reported regression we only use data for the year
2004. We find similar results when estimating the regressions over the period 2000-2004.
Specifically, in the first three columns we include a measure of government intervention
in banking (including regulatory restrictions on banks and state ownership of banks) from
the Heritage Foundation. We find that banking systems with less government
interventions (and that are less heavily regulated) perform better. The effect is
statistically significant. In columns (4) to (6), we also include other dimensions of the
economic freedom index computed by the Heritage Foundation, including a measure of
the fiscal burden of government (Fiscal policy), a measure of the effectiveness and
independence of monetary policy (Monetary policy), a measure of wage development and
price inflation (Price control), and a measure of the protection of property rights
26
(Property rights). Like the banking policy variable, all of these indexes are constructed
such that higher values denote more economic freedom.
When we control for other dimensions of economic freedom, banking sector
policy does no longer enter significantly. We find that fiscal and monetary policies are
the most highly correlated with bank performance.
In columns (7) to (9), we also include the 3-bank concentration ratio (measured in
terms of deposits) to control for the market structure of the banking system. We find that
banks in more concentrated banking systems generate more income, possibly because
they can extract more rents. Of the other country-level traits considered, fiscal policy has
the largest effect on bank performance, followed by monetary policy, banking policy, and
price controls. Property rights do not appear correlated with bank performance once we
control for these other country characteristics. Of course, these results should be
interpreted with caution because some of the country level characteristics are highly
correlated.
5. Conclusions
We study the effect of ownership, diversity of activities, and government policy on the
performance of banks in East Asia. We find that foreign banks perform significantly
better than domestic banks. Nevertheless, banking systems have been slow to open up to
foreigners. The results in this paper suggest that foreign ownership should be encouraged
and call for a revision of current policy adopted by countries on this topic.
We also find that some of the banking systems the Indonesian banking system
in particular are not very competitive and that competition is generally still at lower
27
levels than prior to the financial crisis in 1997-98. While our calculations suggest that
competition has improved somewhat since the crisis in most countries, much remains to
be done in this area. The entry of foreign banks may be one way to put competitive
pressure on local banks.
Further consolidation of local banks does not seem warranted. Bank concentration
ratios are already at par with the world average and banks in the more concentrated
markets seem to generate excessive rents. This suggests that existing banks should grow
by improving the quality of their services rather than through further consolidation.
Finally, we find that improvements in the area of fiscal and monetary policy are
equally important and needed to enhance banking sector stability and performance.
28
References:
Barth, James, Gerard Caprio, and Ross Levine (2001). Bank Regulation and
Supervision: A New Database, Policy Research Working Paper, World Bank. Bongini, Paola, Luc Laeven, and Giovanni Majnoni (2002). How Good is the Market at
Assessing Bank Fragility? A Horse Race Between Different Indicators, Journal of Banking and Finance 26(5), 1011-1028.
Calomiris, Charles, Daniela Klingebiel and Luc Laeven (2005), Financial Crisis Policies
and Resolution Mechanisms: A Taxonomy from Cross-Country Experience, in: Patrick Honohan and Luc Laeven (eds.), Systemic Financial Distress: Containment and Resolution, Cambridge: Cambridge University Press.
Caprio, Gerard, Luc Laeven, and Ross Levine (2004). Governance and Bank
Valuation, Policy Research Working Paper No. 3202, World Bank. Claessens, Stijn, Daniela Klingebiel and Luc Laeven (2005), Crisis Resolution, Policies,
and Institutions: Empirical Evidence, in: Patrick Honohan and Luc Laeven (eds.), Systemic Financial Distress: Containment and Resolution, Cambridge: Cambridge University Press.
Claessens, Stijn and Luc Laeven (2004). What Drives Bank Competition? Some
International Evidence, Journal of Money, Credit, and Banking 36(3), 563-583. Claessens, Stijn and Luc Laeven (2005). Financial Sector Competition, Financial
Dependence, and Growth, Journal of the European Economic Association 3(1), 179-207.
Demirg-Kunt, Asli, Baybars Karacaovali, and Luc Laeven, (2005). Deposit Insurance
around the World: A Comprehensive Database, Policy Research Working Paper 3628, Washington, DC: World Bank.
Demirg-Kunt, Asli, Luc Laeven, and Ross Levine (2004), Regulations, Market
Structure, Institutions, and the Cost of Financial Intermediation, Journal of Money, Credit, and Banking 36(3), 593-622.
Jensen, Michael C., 1986. Agency costs of free cash flow, corporate finance, and
takeovers. American Economic Review 76, 323-329. Jensen, Michael C. and William H. Meckling, 1976. Theory of the firm: Managerial
behavior, agency costs, and ownership structure. Journal of Financial Economics 3, 305-360.
29
Klingebiel, Daniela, Randall Kroszner, Luc Laeven, and Pieter van Oijen (2001). Stock Market Responses to Bank Restructuring Policies during the East Asian Crisis, Policy Research Working Paper No. 2571, World Bank.
Laeven, Luc (1999). Risk and Efficiency in East Asian Banks, Policy Research
Working Paper No. 2255, World Bank. Laeven, Luc (2002a). Bank Risk and Deposit Insurance, World Bank Economic Review
16(1), 109-137. Laeven, Luc (2002b). Pricing of Deposit Insurance, Policy Research Working Paper
No. 2871, World Bank. Laeven, Luc (2002). Financial Constraints on Investments and Credit Policy in Korea,
Journal of Asian Economics 13(2), 251-269. Laeven, Luc and Ross Levine (2005). Is There a Diversification Discount in Financial
Conglomerates? Journal of Financial Economics, forthcoming.
30
Table 1. Ownership of Banks
This table presents ownership data of the banks in our sample. Panel A presents the ownership of banks by category by year across the five East Asian countries.
Panel B presents the ownership of banks by category for select years by country. For each category, we report the percentage of total banking system
assets held
by banks of this ownership type. Between brackets we report the number of banks in each ownership category. The sample of banks includes all commercial
banks, savings banks, and bank holding companies in Bankscope. The sample of countries includes Hong Kong, Indonesia, Republic of Korea, M
alaysia,
Singapore, and Thailand.
Panel A: Bank ownership by year
Year
State
Foreign state
Private domestic
Foreign
Total number of banks
1994
19.20
(11)
0.25
(3)
79.01
(70)
1.52
(14)
98
1995
15.73
(14)
2.28
(4)
79.13
(86)
2.89
(18)
122
1996
18.35
(24)
2.26
(6)
73.53
(105)
5.82
(40)
175
1997
21.19
(40)
2.66
(9)
52.10
(153)
24.06
(57)
259
1998
24.90
(41)
2.78
(9)
47.34
(131)
24.76
(64)
245
1999
33.39
(46)
2.64
(13)
40.23
(118)
23.80
(66)
243
2000
36.91
(47)
2.47
(10)
36.44
(107)
24.08
(65)
229
2001
35.56
(47)
2.23
(10)
41.12
(103)
21.22
(60)
220
2002
33.61
(46)
1.93
(10)
42.65
(112)
21.70
(63)
231
2003
29.80
(43)
1.14
(7)
45.70
(102)
23.67
(61)
213
2004
29.04
(29)
0.88
(4)
47.23
(61)
22.88
(28)
122
31
Panel B: Bank ownership by country
Country
Year
State
Foreign state
Private domestic
Foreign
Total number of banks
Hong Kong
1996
7.35
29.79
18.33
44.68
19
2000
34.45
6.23
9.68
49.65
52
2004
31.02
2.88
10.51
55.60
33
Indonesia
1996
61.67
0.85
32.18
5.23
67
2000
82.25
0.34
13.03
4.47
59
2004
60.40
0.00
12.28
27.43
22
Korea
1996
0.00
0.00
100.00
0.00
20
2000
20.89
0.00
69.85
9.15
17
2004
7.20
0.00
83.53
8.99
13
Malaysia
1996
33.53
0.00
55.66
10.81
33
2000
44.78
0.00
38.52
16.81
37
2004
49.78
0.00
35.11
15.20
18
Philippines
1996
0.00
1.82
93.30
4.51
17
2000
16.45
0.31
81.65
1.46
28
2004
17.85
0.40
81.87
0.00
17
Singapore
1996
26.46
0.00
70.83
2.74
14
2000
46.90
0.00
46.12
6.82
21
2004
58.13
0.00
41.87
0.05
7
Thailand
1996
43.00
0.00
57.00
0.00
5
2000
53.33
1.43
40.00
5.30
15
2004
50.91
0.00
43.52
5.38
12
32
Table 2. Bank Size, M
arket Structure, and Operating Income
This table presents summary statistics of bank size, m
arket structure and operating income variables for the banks in our sample. Data are from Bankscope. Panel
A reports country-averages for the period 1994-2004 of total assets, total deposits, the 3-bank concentration ratio (in terms of assets and deposits), the average
market share (in terms of assets and deposits), and the number of banks included in the sample. Panel B reports averages of the same variables as in panel A for
the year 2004. Panel C reports the country-average and country-m
edian of the ratio of operating income to total assets for the period 1994-2004 and the country-
average of the ratio of operating income to total assets for the year 2004 by country.
Panel A: Structure variables, averages over the period 1994-2004
Country
Average assets
(US$bn)
Average deposits
(US$bn)
3-concentration
ratio (assets)
3-concentration
ratio (deposits)
Average market
share (assets)
Average market
share (deposits)
Number of
observations
Hong Kong
12.7
10.4
0.650
0.684
0.027
0.028
394
Indonesia
1.6
1.1
0.518
0.526
0.019
0.019
588
Korea, Rep. of
29.3
20.5
0.428
0.441
0.056
0.056
198
Malaysia
4.8
3.6
0.430
0.433
0.030
0.030
364
Philippines
1.6
1.1
0.519
0.528
0.037
0.038
290
Singapore
13.6
11.3
0.690
0.689
0.064
0.067
164
Thailand
9.5
8.2
0.630
0.641
0.089
0.089
123
Total
8.2
6.3
0.542
0.553
0.036
0.036
2121
Panel B: Structure variables, averages for the period 2004
Country
Average assets
(US$bn)
Average deposits
(US$bn)
3-concentration
ratio (assets)
3-concentration
ratio (deposits)
Average market
share (assets)
Average market
share (deposits)
Number of
observations
Hong Kong
24.9
19.3
0.634
0.639
0.030
0.030
33
Indonesia
4.6
3.6
0.568
0.576
0.045
0.045
22
Korea, Rep. of
78.3
52.7
0.443
0.446
0.077
0.077
13
Malaysia
12.5
9.2
0.448
0.443
0.056
0.056
18
Philippines
3.0
2.1
0.465
0.478
0.059
0.059
17
Singapore
47.5
40.7
0.829
0.824
0.143
0.167
6
Thailand
13.7
11.8
0.525
0.534
0.083
0.083
12
Total
22.2
16.4
0.557
0.562
0.057
0.058
121
33
Panel C: Operating income, summary statistics for the period 1994-2004 and the year 2004
Country
Operating income/Total assets,
average (1994-2004)
Operating income/Total assets,
median (1994-2004)
Number of
observations
Operating income/Total assets,
average (2004)
Number of
observations
Hong Kong
0.042
0.032
335
0.037
28
Indonesia
0.061
0.051
510
0.061
21
Korea, Rep. of
0.040
0.035
188
0.033
13
Malaysia
0.040
0.036
321
0.034
18
Philippines
0.062
0.057
283
0.067
17
Singapore
0.031
0.025
133
0.019
6
Thailand
0.032
0.030
103
0.043
12
Total
0.048
0.040
1873
0.044
115
34
Table 3. Measures of Institutions and Regulations
This table presents scores of the Freedom indexes of the Heritage Foundation for the countries in our sample. We present both the composite index of economic
freedom as well as the sub-components of this index. The index gives a score of 1 to 5, with higher scores denoting m
ore freedom. We have reversed the original
score. Ftotal is the composite index of economic freedom; Trade is an index of trade openness; Fiscal is an index of fiscal burden of government; Govint is an
index of government intervention in the economy; Monpol is an index of effectiveness and independence of monetary policy; forinv is an index of restrictions on
capital flows and foreign investm
ent; banking is an index of regulations and competition in banking and finance; Prices is an index of wage development and
prices; Property is an index of the protection of property rights; Regulation is an index of government regulation of the economy; and Inform
al is an index of
inform
al m
arket activity. Panel A shows the score for each index for the year 2004, by country. Panel B shows the average score of the banking freedom sub-
index for the period 1995-2004, by country.
Panel A: Index of Economic Freedom for the year 2004.
Country
ftotal
trade
fiscal
govint
monpol
forinv
banking
prices
property
regulation
inform
al
Hong Kong
4.7
5.0
4.1
4.0
5.0
5.0
5.0
4.0
5.0
5.0
4.5
Indonesia
2.2
3.0
1.9
2.0
3.0
2.0
2.0
3.0
2.0
2.0
1.5
Korea, Rep. of
3.3
2.0
2.6
3.5
4.0
4.0
3.0
4.0
4.0
3.0
3.0
Malaysia
2.8
3.0
2.4
2.0
5.0
2.0
2.0
3.0
3.0
3.0
3.0
Philippines
3.0
4.0
2.5
4.0
4.0
3.0
3.0
3.0
2.0
2.0
2.0
Singapore
4.4
5.0
3.4
2.5
5.0
5.0
4.0
4.0
5.0
5.0
5.0
Thailand
3.1
2.0
2.4
3.5
5.0
3.0
3.0
4.0
3.0
3.0
2.5
Total
3.2
3.5
2.7
2.9
4.2
3.2
3.0
3.4
3.2
3.1
2.8
Panel B: Index of Banking Freedom averaged over the period 1995-2004
Year
Hong Kong
Indonesia
Korea
Malaysia
Philippines
Singapore
Thailand
1995
4.0
3.0
4.0
3.0
3.0
4.0
3.0
1996
5.0
3.0
4.0
3.0
3.0
4.0
3.0
1997
5.0
3.0
4.0
3.0
3.0
4.0
3.0
1998
5.0
3.0
4.0
3.0
3.0
4.0
3.0
1999
5.0
2.0
3.0
3.0
3.0
4.0
3.0
2000
5.0
2.0
3.0
3.0
3.0
4.0
3.0
2001
5.0
2.0
3.0
2.0
3.0
4.0
3.0
2002
5.0
2.0
3.0
2.0
3.0
4.0
3.0
2003
5.0
2.0
3.0
2.0
3.0
4.0
3.0
2004
5.0
2.0
3.0
2.0
3.0
4.0
3.0
35
Table 4. Estimates of Cost of Deposit Guarantees
This table presents estim
ates of the fair value of deposit insurance based on the Ronn and Verma (1986) model of deposit insurance with zero regulatory
forbearance. We calculate the im
plicit premium for the country portfolio of listed banks, assuming that all bank debt is insured. We report the fair deposit
insurance premium (in %
of deposits) for the year 1998 and the average for the period 1996-2004.
Year
Hong Kong
Indonesia
Korea
Malaysia
Philippines
Singapore
Thailand
1998
0.04
0.44
0.24
0.56
0.12
0.09
0.63
Average 1996-2004
0.01
0.84
0.05
0.06
0.02
0.01
0.09
36
Table 5. Measures of Competition of the Banking System
This table presents estim
ates of the H-statistics developed in Claessens and Laeven (2004). W
e report the point estimate and the standard deviation (between
brackets) of the H-statistic. We also report a test of perfect competition (H-statistics equals one) and m
onopoly (H-statistic equals zero). In panel A, we estimate
the model for each year using pooled OLS across all countries and include country fixed-effects. In panel B, we estimate the model for each country using pooled
OLS across years and include year fixed-effects. In panel C, we estimate the model for each country using pooled OLS across years and include fixed bank
effects as well as fixed year effects. Underlying data are from Bankscope.
Panel A: Pooled OLS with country effects
Country
H-statistic
(st. dev.)
H0: H=1
(perfect competition)
H0: H=0
(monopoly)
Number of
observations
1994
0.83
Rejected
Rejected
71
(0.08)
1995
0.83
Rejected
Rejected
87
(0.04)
1996
0.69
Rejected
Rejected
129
(0.07)
1997
0.67
Rejected
Rejected
207
(0.04)
1998
0.55
Rejected
Rejected
165
(0.06)
1999
0.61
Rejected
Rejected
198
(0.07)
2000
0.64
Rejected
Rejected
192
(0.06)
2001
0.64
Rejected
Rejected
179
(0.07)
2002
0.64
Rejected
Rejected
195
(0.08)
2003
0.62
Rejected
Rejected
183
(0.07)
2004
0.69
Rejected
Rejected
110
(0.08)
37
Panel B: Pooled OLS with year effects
Country
H-statistic
(st. dev.)
H0: H=1
(perfect competition)
H0: H=0
(monopoly)
Number of
observations
Hong Kong
0.80
Rejected
Rejected
310
(0.05)
Indonesia
0.62
Rejected
Rejected
502
(0.03)
Korea, Rep. of
0.97
Not rejected
Rejected
97
(0.07)
Malaysia
0.80
Rejected
Rejected
315
(0.05)
Philippines
0.51
Rejected
Rejected
284
(0.05)
Singapore
0.71
Rejected
Rejected
96
(0.06)
Thailand
0.35
Rejected
Rejected
112
(0.13)
Panel C: Fixed bank effects with year effects
Country
H-statistic
(st. dev.)
H0: H=1
(perfect competition)
H0: H=0
(monopoly)
Number of
observations
Hong Kong
0.94
Not rejected
Rejected
310
(0.06)
Indonesia
0.71
Rejected
Rejected
502
(0.04)
Korea, Rep. of
0.95
Not rejected
Rejected
97
(0.07)
Malaysia
0.87
Rejected
Rejected
315
(0.03)
Philippines
0.79
Rejected
Rejected
284
(0.06)
Singapore
0.93
Not rejected
Rejected
96
(0.06)
Thailand
0.21
Rejected
Rejected
112
(0.11)
38
Table 6. Bank Performance, Diversity, Size, and M
arket Structure
This table reports OLS regressions. The dependent variable in columns (1) to (4) is the ratio of operating income to total assets and the dependent variable in
columns (5) to (8) is the activity-adjusted ratio of operating income to total assets. The regressions in columns (2) and (4) are estimated for the period 2000-2004;
all other regressions are estimated for the year 2004. All regressions include country fixed-effects. The regressions in columns (2) and (4) also use year fixed-
effects. W
e report W
hite (1981) heteroskedasticity-consistent standard errors in parentheses. Standard errors are clustered at the bank-level. * significant at 10%;
** significant at 5%; *** significant at 1%.
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Operating income/Total assets
Activity-adjusted Operating income/Total assets
Interest income/Total operating income
-0.064***
-0.096**
(0.021)
(0.037)
Income diversity
-0.039***
-0.057**
-0.047***
-0.035***
-0.036***
-0.035***
(0.012)
(0.023)
(0.006)
(0.005)
(0.006)
(0.006)
Net loans/Total earning assets
-0.004
0.012
(0.010)
(0.014)
Asset diversity
-0.014**
-0.024**
(0.007)
(0.010)
Log(Total assets)
-0.002*
(0.001)
Log(Total operating income)
0.002**
(0.001)
Market share deposits
-0.003
(0.014)
Deposits/Liabilities
0.012
0.014*
0.013
(0.008)
(0.008)
(0.008)
Equity/A
ssets
0.078***
0.102***
0.091***
(0.026)
(0.026)
(0.023)
Lag of growth in total operating income
0.014***
0.012***
0.013***
(0.003)
(0.003)
(0.003)
Observations
1873
898
1873
898
1669
1398
1398
1398
R-squared
0.19
0.17
0.13
0.08
0.30
0.45
0.45
0.44
39
Table 7. Activity-Adjusted Bank Performance and Bank Ownership
This table reports OLS regressions with as dependent variable the activity-adjusted ratio of operating income to total assets. In column (1), we estimate the
regression for the subset of state-owned banks. In column (2), we estimate the regression for the subset of foreign state-owned banks. In column (3), we estimate
the regression for the subset of privately-owned domestic banks. In column (4), we estimate the regression for the subset of privately-owned foreign banks.
Regressions are estimated for the year 2004. All regressions include country fixed-effects. We report W
hite (1981) heteroskedasticity-consistent standard errors
in parentheses. Standard errors are clustered at the bank-level. * significant at 10%; ** significant at 5%; *** significant at 1%.
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Income diversity
-0.045***
-0.047***
-0.041***
-0.026***
-0.037***
-0.039***
-0.038***
(0.011)
(0.008)
(0.009)
(0.009)
(0.006)
(0.006)
(0.006)
Log(Total assets)
0.002
0.003
-0.003*
-0.001
-0.002
(0.002)
(0.003)
(0.002)
(0.002)
(0.001)
Log(Total operating income)
0.003***
(0.001)
Market share deposits
0.008
(0.014)
Deposits/Liabilities
-0.007
-0.009
0.013
0.022
0.012
0.014*
0.013
(0.015)
(0.016)
(0.011)
(0.014)
(0.008)
(0.008)
(0.008)
Equity/A
ssets
0.122***
0.092
0.048*
0.110**
0.076***
0.098***
0.086***
(0.037)
(0.053)
(0.026)
(0.043)
(0.025)
(0.024)
(0.022)
Lag of growth in total operating income
0.011***
0.017***
0.004
0.030***
0.014***
0.012***
0.014***
(0.004)
(0.005)
(0.004)
(0.007)
(0.003)
(0.002)
(0.003)
Private domestic ownership
0.002
0.005*
0.003
(0.003)
(0.003)
(0.003)
Foreign ownership
0.007*
0.011***
0.009***
(0.004)
(0.004)
(0.003)
Observations
233
61
751
353
1398
1398
1398
R-squared
0.48
0.85
0.49
0.50
0.45
0.46
0.45
40
Table 8. Activity-Adjusted Bank Performance and Regulations
This table reports OLS regressions with as dependent variable the activity-adjusted ratio of operating income to total assets. In columns (1) to (6), we control for
regulations using individual subindexes of the Economic freedom index of the Heritage Foundation. We reversed the original indexes of economic freedom so
that they are increasing in quality. In columns (7) to (9), we also control for the 3-bank concentration ratio in terms of bank deposits. Regressions are estimated
for the year 2004. We report W
hite (1981) heteroskedasticity-consistent standard errors in parentheses. Standard errors are clustered at the bank-level. *
significant at 10%; ** significant at 5%; *** significant at 1%.
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Income diversity
-0.037***
-0.039***
-0.038***
-0.037***
-0.039***
-0.038***
-0.037***
-0.040***
-0.038***
(0.006)
(0.006)
(0.006)
(0.006)
(0.006)
(0.006)
(0.005)
(0.006)
(0.006)
Log(Total assets)
-0.002
-0.001
-0.001
(0.001)
(0.001)
(0.001)
Log(Total operating income)
0.003***
0.003***
0.003***
(0.001)
(0.001)
(0.001)
Market share deposits
0.011
0.011
0.004
(0.014)
(0.014)
(0.014)
Deposits/Liabilities
0.014
0.016*
0.014*
0.014
0.016*
0.015*
0.013
0.015*
0.014
(0.008)
(0.009)
(0.008)
(0.009)
(0.009)
(0.009)
(0.009)
(0.009)
(0.009)
Equity/Assets
0.076***
0.098***
0.087***
0.076***
0.098***
0.086***
0.077***
0.098***
0.085***
(0.025)
(0.024)
(0.022)
(0.025)
(0.024)
(0.023)
(0.025)
(0.024)
(0.022)
Lag of growth in total operating income
0.014***
0.012***
0.014***
0.013***
0.011***
0.013***
0.013***
0.011***
0.012***
(0.003)
(0.002)
(0.003)
(0.003)
(0.002)
(0.003)
(0.003)
(0.002)
(0.003)
Private domestic ownership
0.002
0.005*
0.003
0.002
0.005*
0.003
0.002
0.005*
0.003
(0.003)
(0.003)
(0.003)
(0.003)
(0.003)
(0.003)
(0.003)
(0.003)
(0.003)
Foreign ownership
0.007*
0.011***
0.009***
0.007*
0.011***
0.009***
0.007*
0.011***
0.008**
(0.004)
(0.004)
(0.003)
(0.004)
(0.004)
(0.003)
(0.004)
(0.004)
(0.003)
Banking policy
0.008**
0.008**
0.008**
0.003
0.003
0.004
0.006*
0.006*
0.006*
(0.003)
(0.003)
(0.003)
(0.003)
(0.003)
(0.003)
(0.003)
(0.003)
(0.003)
Fiscal policy
0.017***
0.019***
0.018***
0.015***
0.017***
0.016***
(0.004)
(0.004)
(0.004)
(0.004)
(0.004)
(0.004)
Monetary policy
0.003**
0.002*
0.003**
0.005***
0.004***
0.005***
(0.001)
(0.001)
(0.001)
(0.001)
(0.001)
(0.001)
Price control
0.005*
0.004
0.004*
0.005*
0.004
0.005*
(0.003)
(0.003)
(0.003)
(0.003)
(0.003)
(0.003)
Property rights
0.005
0.007*
0.006
0.003
0.004
0.004
(0.004)
(0.004)
(0.004)
(0.004)
(0.004)
(0.004)
3-concentration ratio (deposits)
0.045***
0.048***
0.046***
(0.014)
(0.013)
(0.014)
Observations
1398
1398
1398
1398
1398
1398
1398
1398
1398
R-squared
0.46
0.46
0.45
0.47
0.47
0.46
0.47
0.48
0.47
41