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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.

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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.

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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,

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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

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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

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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

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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.

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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

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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

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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.

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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

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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

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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

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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

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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

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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.

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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.

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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.

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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).

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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

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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.

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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