measuring the level of competition in the loan

8/12/2019 Measuring the Level of Competition in the Loan

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B NK OF TH IL ND

DP/01/2010

(English Version)

DISCUSSION P PER

E-Mail Address: [email protected]

Measuring the Level of Competition in the Loan

Market of the Thai Banking Industry Using the

Boone Indicator

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Introduction

This paper attempts to assess the level of competition in the loan market of the Thai banking

industry in the past 15 years and comparing it to another banking industry, notably that of the

United States. The results can be compared on an aggregate level (in a sense of an average of all

years) and by year. In addition, the results can be extended to other countries and other types of

market (such as deposits).

Assessing the level of competition has important policy implications, as it is linked directly to

efficiency and, hence, social welfare. The results of such assessment can later be used to craft

necessary policies to increase competition in a particular industry. Measuring competition has

always been a challenge to many industrial organization economists. This is because a traditional

measurement such as the Herfindahl-Hirschmann Index (HHI) may present only a partial picture

of competition. Moreover, using the price cost margin alone tends to misrepresent the development

of competition over time for markets with high policy relevance where only a handful of firms exist

(i.e. in a highly concentrated market) as mentioned in Boone et al. (2007).

Usually, measuring the level of competition in the banking industry is made much more difficult

by many reasons. The data availability on costs and prices by product-type is limited.1 Moreover,

the consolidation may be encouraged in an environment where banks are over-supplied, as claimed

by many to have happened in the U.S. before the Riegle-Neal Interstate Banking and Branching

Efficiency Act of 1994 was passed, allowing banks to merge across states for the first time. In this

latter case, looking at the HHI alone will be misleading.2

A new approach in measuring competition was introduced by Boone (2000, 2004), Boone et al.

(2004) and applied to the banking industry for the first time by van Leuvensteijn et al. (2007),

who measured the level of competition in 6 European Union countries and 2 non-EU countries (the

U.S. and Japan). The Boone measurement approach has many advantages. First, it is capable

of providing a micro-level analysis; for example, by product type offered by banks or by types of

1For further details, please see van Leuvensteijn et al. (2007)2Claessens and Laeven (2004) found that bank concentration varies positively with the level of competition, contrary

to the conventional belief of their negative relationship.

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banks (commercial, savings or cooperative banks) or by year, while the Panzar-Rosse model offers

the analysis only at the aggregate level. Secondly, as mentioned in van Leuvensteijnet al. (2007),

the Boone model requires very little data, compared to the Bresnahan model.

However, it is worth noting that the Boone approach relies on two key assumptions which may

or may not be true in a particular banking industry.

1. Banks will pass on at least part of their efficiency gains to their clients

2. Banks offer the same product quality and design and possess the same attractiveness of inno-

vations

There are a few key differences between this study and of van Leuvensteijn et al. (2007). First,

the marginal cost equation is calculated a little differently. Second, the process of estimating the

translog cost function for the U.S. in this study also includes the states fixed effects, which is

crucial in taking out differences between states in terms of different banking legislations that may

have biased the estimation. Third, the periods used for the U.S. estimation is from 1994-2004,3 not

1999-2004 as previously executed, to include the deregulation effects in the U.S. banking industry

during the 1990s. Finally, the data source for the U.S. used in this paper is from the CALL Report

instead of the Bankscope database.

I find that, compared to the U.S., the Thai banking industry is quite a lot less competitivedur-

ing the years 1994-2004, as measured by the competitiveness in the loan market. In addition, the

estimation of the translog cost function (TCF) points to the fact that Thai banks bore higher costs

than U.S. banks and, consequently, were operating inefficiently.

The paper is structured as follows. Section 1 provides the theory, the mathematical derivation

and assumptions behind the regression in this study, notably the translog cost function, the marginal

costs, and the Boone estimation equation. Section 2 presents the details on the data used in this

study. The regressions, results and analysis will be in Section 4 and the final remarks conclude

the paper. The literature review on measuring competition is presented in van Leuvensteijnet al.

(2007) and therefore omitted here.

3The data on securities used in this study was not available prior to 1994.

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1 Theory, Mathematical Derivation and Assumptions

This section presents the theory, the detailed mathematical derivation and also the assumptions

used to develop the translog cost function, the marginal costs and the Boone estimation equation.

1.1 The Theory

Theoretically, the Boone model relies on the notion that more efficient firms (i.e. with lower marginal

costs) will possess higher market shares (and consequently profits) and this effect of efficiency on

market shares will be stronger in the environment where the competition is more intense. This is

because an increase in competition should lead to a reallocation of output from inefficient firms to

more efficient firms. Although Boone has developed quite a few theoretical models for different kinds

of markets (see Boone, 2000, 2001 and 2004, and Boone et al. 2004), van Leuvensteijn et al. (2007)

relied on the Boone et al. (2004) model for their application of the theory to the banking industry.

In the model, bank i will face the linear demand of product offered qi in the form

p(qi, qj=i) = a bqi dj=i

qj (1)

with a constant marginal cost mci. ais an intercept which can be interpreted as a price a customer

is willing to pay to any bank that offers the first unit of product q. b measures the sensitivity to

price of a customer with respect to the product offered by bank i while d is the price sensitivity of

a customer to the same product offered by rival banks qj=i. Note that d can also be interpreted as

the measurement of the degree of substitutes of this product between banks. Ifd= 0, then bank i

will be the only bank offered product qi and other banks products qj has no commonality to the

product qi. Therefore it cannot be a substitute to qi and plays no role in the demand of qi. As d

increases, the more substitutable qj is to qi.

Given the remarks above, two key assumptions are imposed: 1) a > mci which means that the

price of the first unit chargeable to the customer has to exceed the marginal cost (otherwise the profit

is negative from the beginning); and 2) 0 < d b, which means the products cannot be perfect

substitutes. Next, assume that banks have an entry cost (fixed cost) of, the objective function of

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banks is to maximize the profit = (pi mci)qi by choosing the right level ofqi from the profit

function.4 The first order condition for the Cournot-Nash equilibrium is

a 2bqi dj=i

qj mci= 0. (2)

Without loss of generality, let there be Nbanks in the market of product q. The general closed

form of the solution is

qi(mci) =

2 bd

1

a

2 bd

+ N 1

mci+

jmcj

(2b+d(N 1))

2 bd

1 , (3)

relating the output qi and marginal costs mci.

The idea of Boone et al. (2004) is that competition can be enhanced through two channels

through an increase in d (products become closer substitutes) and through a decrease in an entry

cost . The authors showed that competition indeed reallocated output (in monetary term) from

inefficient firms to more efficient firms. Using the profit function = (pi mci)qi and equation

(3), it can be shown that profit is a quadratic function of output,

i= b[q(mci)]2. (4)

Therefore, it can be inferred from the analysis above that a higher level of competition increases

relative profits of a firm relative to a less efficient firm. Finally, the shift of output toward the more

efficient firms means that, in a more competitive environment, the more efficient firms will also gain

a market share. Therefore, competition will also lead to more efficient firms obtaining more market

shares.

Applying the Booneet al. (2004) model to the banking industry, van Leuvensteijn et al. (2007)

therefore used the market share approach and estimated the Boone indicator according to the fol-

lowing equation (assuming there are Nbanks in the system),

ln Si= +ln mci+ui (5)

whereSiis defined as the market share of bank i for productq,Si = qiNj=1

qjandmciis the marginal

cost.4as the marginal cost is assumed constant so the total cost is just mci qi

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From equation (5), it is clear that the effect of the change in the marginal cost mcion the market

share Si can be measured by , which is called the Boone indicator. Using the analysis above,

should be such that


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banks) in year t, t= 1...T. 0 is an intercept while dhi and dt are the dummies for bank types and

years respectively.

The explanatory variablesxijt can be grouped into two categoriesoutput components and input

components. There arethreeoutput componentsloans, securities, and other services. Threeinput

prices include wage rates, funding rates and other expenses. The output components enter the

equation as an amountwhile the input prices enter the equation as a rateor have been normalized

to be a share of some sort.

Without loss of generality, I define j = 1,...,Kand k = 1,...,Kin the following way.

j = k = 1 for the wage rates (variable name wage)j = k = 2 for the funding rates (variable name fund)

j = k = 3 for the other expenses (variable name othexp)j = k = 4 for loans (variable name loans)j = k = 5 for securities (variable name sec)j = k = 6 for other services (variable name othserv).

Moreover, additional restrictions are imposed on the coefficients of the TCF

1+2+3 = 1 (7)

1,k+2,k+ 3,k = 0, k= 1, 2, 3 (8)

k,1+k,2+k,3 = 0, k= 4, 5, 6 (9)

j,k = k,j j, k = 1, ...,K. (10)

The first and second conditions indicate the assumption of the linear homogeneity in the input

prices, which means that three linear input price elasticities (i, i = 1, 2, 3) add up to 1, whereas

the squared and cross terms of all explanatory variables (i,j) add up to zero. The last condition in

equation (10) is the symmetry restriction such that j,k= k,j forj, k= 1, . . ,K.

To estimate the translog cost function more accurately, the ratio of total equity to total assets

and its squared term are added to control for differences in loan portfolio risk across banks (Berger

and Mester (1997)). The appendix provides the TCF equation used in the estimation of the translog

cost function in detail.

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Using the general definition of marginal costs, the marginal cost for loans (j=4) is defined by

mci4t = Ci4t

xi4t=

Ci4t

xi4tln Ci4tln xi4t

. (11)

whereCi4t is the total cost and xi4tis the amount of loan issued by bankiin yeartandj = 4 denotes

the product for loans, using the definition on page 7. The second equality comes from applying the

logarithmic rule ln Ci4tln xi4t

=1

Ci4tCi4t

1

xi4txi4t

.

We can calculate the term ln Ci4tln xi4t

by differentiating the TCF in equation (6) with respect to

ln xi4t for each type h of banks (and the superscript h is removed) and applying the symmetry

j,k= k,j . Therefore,

ln Ci4tln xi4t

= 4+ 244ln xi4t+k=4

4kln xi4t+j=4

j4ln xijt

= 4+ 244ln xi4t+ 2k=4

4kln xikt (12)

Hence, the marginal cost for loans (from the notation j = 4), of bank i at time t, mci4t in

equation (11) becomes

mci4t = Ci4t

xi4t=

Ci4t

xi4t

4+ 244ln loans+ 2

k=4

4kln xikt

. (13)

for k = 4. Using the notation defined on page 7 ofj and k , we have that

mci4t = coststotalloans

[4+ 244ln loans+ 214ln wage+ 224ln fund+ 243ln othexp

+ 245ln sec+ 246ln othserv]. (14)

Finally, it is worth mentioning that the marginal cost calculation in equation (14) is a bit differ-

ent from equation (8) in the paper by van Leuvensteijn et al. (2007).

2 The Data

This section discusses the data used in detail and provides also the summary statistics for the

variables used in this study by country and year.

This study concerns the comparison of the competitiveness in the banking industry between two

countriesthe United States and Thailand. It employed two data sourcesthe CALL Report data

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of all U.S. banks and the Bankscope data for Thai banks. The U.S. is chosen as a comparative

country for two reasons. First, the CALL Report provides the data that is very much detailed and

complete. Second, the U.S. banking industry has been claimed to be very efficient and competi-

tive among countries around the world. It also went through interesting policy transformations in

the 1990s, notably the Riegle-Neal Interstate Banking and Branching Efficiency Act of 1994 was

passed, allowing banks to merge across states for the first time, and the Gramm-Leach-Bliley Act

of 1999, permitting banks and financial institutions to offer non-bank products like investments and

insurance-related services. These policies have consequences that would change the competitive dy-

namic in the U.S. banking industry which may be worth investigating.

The time periods in consideration is from the years 1994-2004 both for the U.S. and for Thailand.

The Bankscope data for Thailand contains only the commercial bank data so the regression by bank

type is no longer applicable.6 For the U.S., there is a mix of commercial, savings and cooperative

banks. Table 1 presents the number of banks in each country in each year.

TABLE 1: NUMBER OF BANKS USED FOR THE U.S. AND THAILAND BY YEAR

United States Thailandyear commercial banks savings banks cooperative banks commercial banks1994 10,430 526 84 13

1995 9,919 511 83 131996 9,509 508 83 131997 9,126 484 81 131998 8,760 467 77 131999 8,562 467 75 132000 8,295 449 75 132001 8,062 439 74 132002 7,871 420 73 132003 7,753 412 72 132004 7,613 390 70 12

This table presents the number of banks in each country by year and by type. The classification ofU.S. bank types follow the entity code classification in the CALL Report.

The gradual decrease in the number of banks in the U.S. has been a trend since the passage of

the Riegle-Neal in 1994. While the number of banks may have decreased over the years, the number

of U.S. bank branches have been increasing, as the merged banks are converted into branches.7

6Since the government-owned banks and SFIs may be affected by the central fiscal policies, I only consider com-mercial banks in this study.

7For more information on U.S. branches, please see Spieker (2004).

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I use the same system as in van Leuvensteijn et al. (2007). That is, total assets, loans, deposits,

equity and other non-interest income should be positive; the deposits-to-assets ratio should be less

than 0.98; loans-to-assets ratio should be less than 1; other income-to-assets ratio should be be-

low 0.2; personnel expenses-to-assets and other expenses-to-assets ratio should be between 0.0005

(0.05%) and 0.05 (5%); and finally the equity-to-assets ratio should be between 0.01 and 0.5.

Table 2 presents the summary statistics of the variables used in the TCF estimation (equation

(6)) by bank type and in aggregate of type.

TABLE 2: MEAN VALUES OF VARIABLES USED IN THE TCF ESTIMATION REGRESSIONFOR YEARS USED (in %)

United States Thailand

variable commercial savings cooperative all banks commercialcosts/total assets 5.41 5.42 5.33 5.41 7.11loan market share 0.01 0.01 0.002 0.01 7.83loans/total assets 59.35 64.22 67.39 59.66 69.23securities/total assets 26.85 26.04 22.61 26.77 20.55other services/total assets 10.41 6.23 5.50 10.16 11.19other expenses/fixed assets 146.95 113.92 113.96 145.03 39.19personnel expenses/total assets 1.63 1.25 1.41 1.60 0.81interest expenses/total deposits 5.04 4.13 3.46 4.98 5.81total equity/total assets 10.26 10.85 10.32 10.29 7.80

This table presents the summary statistics for the variables used in the TCF regression, each expressed asa percentage or a share of factors used to normalize it.

Recall that the explanatory variables are grouped into two categories. The output group vari-

ables are loans, securities, and other services (proxied by non-interest income). The securities for

the U.S. are book-value (instead of market-value) while the securities for Thailand are proxied by

other earning assets. The input prices are calculated as follows. The wagevariable is the ratio of

the personnel expenses to total assets while the fundvariable is calculated using the ratio of interest

expenses to total deposits. Finally, the other expenses (variable othexp) input price is defined as

the ratio of other non-interest expenses to fixed assets, although for Thai banks other-operating

expenses are used instead of other non-interest expenses. The costs is just the sum of interest ex-

penses, personnel expenses and other expenses.

From Table 2, it is obvious that the average loan market share per bank in Thailand is much

higher than in the U.S. (7.83% vs. 0.01%). This implies that the loan market in Thailand is much

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more concentrated. Thai banks also posssess a larger share of loans to assets than U.S. banks (69%

vs. 60%). In aggregate, Thai banks bear higher total costs than U.S. banks (7.11% vs. 5.41 %) which

may have been driven by high interest expenses, since the ratio of personnel and other expenses are

lower than U.S. banks. The low ratio of equity to total assets conincide with the perception that

Thai banks rely more on deposits as a source of fund.

3 The Regressions, Results and Analysis

The regression equations, results and analysis are discussed in this section. There are two regressions

performed in this paper. First, the translog cost function (TCF) is estimated using the fixed effect

panel data regression. Then, the marginal costs for each bank and year are calculated. Second, the

Boone indicator is calculated using the Generalized Method of Moments (GMM) with the marginal

cost variable being instrumented by its own one-year lag. The analysis is embedded in each section

dedicated to each step of the calculation.

3.1 Estimating the TCF and Calculating the Marginal Costs

The first step is to estimate the TCF and then calculate the marginal cost for each bank in each

year to be used in the Boone indicators calculation later. In order to do so, the TCF function in

equation (6) is estimated using the fixed-effect panel data regression. For the U.S., in addition to

the time fixed-effects, I also include the state fixed-effects to control for differences across states,

which include different banking legislation. The translog cost function estimation results for each

country and bank type (for the U.S. only) are presented in the appendix.

Prior to carrying out the regression and in addition to careful measures already taken, I minimize

the outliers problems further by winsorizing the top and bottom 1% of the data. This is to ensure

that the results obtained are not driven by any particularly large values of the variables. Finally, a

few observations may have negative or zero values. Since the TCF regression involves transforming

the variables into the natural logarithmic form, if the values of the variables are zero or negative,

they may not be defined. Therefore, these few observations are replaced by 1 and so ln(1) = 0.

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After estimating the TCF, all the coefficients needed in order to calculate the marginal cost of

loansnamely 4, 44, 45, 46, 41, 42, 43can be obtained. Table 3 presents the estimated values

of these coefficients.

TABLE 3: ESTIMATED COEFFICIENTS FROM THE TCF TO BE USED IN THEMARGINAL COST CALCULATION

United States Thailandcoefficient commercial savings cooperative commercial4 0.288*** 0.009 0.709*** 0.407

(0.064) (0.185) (0.009) (0.293)44 0.073*** 0.072*** 0.068*** 0.094***

(0.003) (0.009) (0.001) (0.019)245 -0.090*** -0.100*** -0.141*** -0.161***

(0.004) (0.010) (0.001) (0.025)246 -0.051*** - 0.017 -0.011*** 0.0002

(0.004) (0.013) (0.001) (0.011)

241 0.034*** 0.001 -0.026*** 0.118*(0.006) (0.018) (0.001) (0.063)

242 -0.015** 0.019 0.031*** -0.064(0.006) (0.021) (0.001) (0.048)

43 -0.009 -0.010 -0.002 -0.029

observations 93,664 4,995 844 139This table presents the estimated coefficients from the TCF (equation (11)) esti-mation through the fixed-effect panel data regression. Robust standard errors (forthe U.S.) and standard errors (for Thailand) appear below coefficients in parenthe-ses. ***, ** and * indicate statistical significance at 1 percent, 5 percent and 10percent, respectively. The coefficient 43 is indirectly estimated by the relationship43 = 41 42 so no standard error is reported. For more details on the results ofthe TCF estimation, please see Tables A1-A2 in the appendix.

From the coefficients in Table 3, the marginal cost for bank i in year t can be calculated using

equation (14). In order to get a clear picture of the marginal cost of loans in the banking industry

for each country in a year, the marginal cost of bank i is weighted by the loan market share (in %)

of that bank first and then added up within a year and a country. The results are presented in first

two columns of Table 4 below. In addition, the average marginal cost (i.e. equally-weighted by the

market share) by type and year is also calculated and presented in the last three columns on Table

4 for the cross-type comparison within the U.S. For comparison purposes, the plot of the first two

columns of Table 4 is presented as Figure 1 and the last 3 columns as Figure 2 in the appendix.

The estimated marginal costs for the whole banking industry (the first two columns) reflect that,

on average, Thai banks had higher marginal costs than U.S. banks. Consequently, Thai banks were

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TABLE 4: WEIGHTED AND AVERAGE MARGINAL COSTS OF ALL BANKS ANDSEPARATED BY BANK TYPES IN EACH COUNTRY BY YEAR

Weighted by Market Share Equally-Weighted Marginal Cost*

United States Thailand United Statesyear all banks all banks commercial banks savings banks cooperative banks

1994 5.73 7.54 5.93 5.39 5.501995 6.24 9.22 6.36 6.07 5.981996 6.02 9.47 5.78 6.21 5.981997 5.83 8.33 7.06 6.02 5.881998 5.88 10.17 3.85 6.06 5.931999 5.48 7.03 5.95 5.82 5.572000 6.06 5.52 6.45 6.05 5.642001 5.32 5.50 7.41 5.93 5.512002 4.20 4.86 5.06 5.04 4.552003 3.70 4.07 4.51 4.42 3.912004 3.55 3.48 4.23 4.09 3.57*The average of the equally-weighted marginal costs is presented in the 102 unit for an easiercomparison with the market-share-weighted marginal costs.This table presents the market-share-weighted marginal costs for the whole banking industry withina given year and a country. Also, the unweighted equally-weighted marginal costs by bank type and

by year is also offered.

operating inefficiently compared to U.S. banks, although Thai banks were catching up fast in terms

of efficiency. In general, the marginal costs of banks in both countries had gradually decreased during

this decade. Moreover, note that the surge in the marginal costs of Thai banks happened in 1998

and, hence, was consistent with the Asian Crisis lag effect. This huge jump of marginal costs (from

8.33 to 10.17) might have been contributed largely by the high interest rate policy implemented

after the crisis, which could have led to a huge increase in interest expenses. A small increase in

the marginal costs for U.S. banks in 2000 is in line with a gradual increase of the fed fund rates,

which resulted in more than 1% increase in total compared to the rate at the beginning of 2000.

The interest expenses of U.S. banks would have increased with this rate.

When comparing against types of banks in the U.S., commercial banks bear highest costs in

general and cooperative banks bear lowest costs. The finding that commercial banks in general

have the highest marginal costs is consistent with the cross-country results of van Leuvensteijn et

al. (2007). Finally, the deviation of the marginal costs estimated from the van Leuvensteijn et al.

(2007) paper would have been contributed by differences in the marginal cost equation (equation

(14)), the data source and the methodology in estimating the TCF function.

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3.2 Estimating and Calculating the Boone Indicator

After obtaining the marginal cost by banki and by yeart for each country, then the Boone indicator

can be estimated using the following equation.

ln Sit= +

Tt=2

tdt+ln mcit+uit (15)

where Sit is the loan market share of bank i in year t. dt is the year t dummy variables and mcit is

the marginal cost of bank i in year t.

Estimating equation (15) gives us the Boone indicator for each country throughout the time

periods used in this study. The estimations are carried out using the Generalized Method of Moments

(GMM) using the lag marginal costs as an instrument for the marginal costs of the current period.

The Hansen J-test is performed to test for the overidentification of the instruments.8

The variance

estimations are performed using kernel-based heteroskedastic and autocorrelation consistent (HAC).

Standard error reported therefore reflects the correction of both arbitrary heteroskedasticity and

arbitrary autocorrelation. The bandwidth in the estimation is set at two periods and the Newey-

West kernel is employed.

3.2.1 The Degree of Competition in the Whole Banking Industry

Table 5 presents the estimated Boone indicator for the banking industry in each country. The esti-

mation is carried out using the pooled sample of all banks and all year to calculate the overall Boone

indicator for the banking industry during the years in consideration.

TABLE 5: ESTIMATED BOONE INDICATOR FOR EACH COUNTRY

country Boone indicator z-value F-test Hansen J-test no. of banks

Thailand -2.28*** -4.59 2.65 0.00 125United States -3.12*** -50.69 260.50 0.00 86,517

This table presents the estimated Boone indicator from equation (15). ***, ** and * indicatestatistical significance at 1 percent, 5 percent and 10 percent, respectively. The F-statisticsare presented to show that the estimated value is statistically different from zero. HansenJ-Test is 0.00 since the equation is exactly-identified.

From Table 5, the Boone indicators () of both countries show that the Thai banking industry is

8The joint null hypothesis is that the instrument is uncorrelated with the error term. The chi-square test is usedto test whether the number of degrees of freedom equal to the number of overidentification restrictions, which is thenull hypothesis.

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quite a bit less competitivecompared to the U.S. banking industry. This can be seen from|Thai|=

2.28 < |US| = 3.12, since the theory indicates that the effect of the change in the marginal costs

on the loan market shares will be stronger in a more competitive environment. Also, the estimated

Boone indicator for the U.S. in this study is slightly lower (in absolute value) than the estimation

of van Leuvensteijn et al. (2007). This may be because the time periods covered in this study

include the periods of the merger wave in the U.S. banking industry in the 1990s, which provided an

additional policy shock on the structure of the loan market other than being affected by the change

in the marginal costs alone.

3.2.2 The Trend of the Degree of Competition Across Years

In this section, the Boone indicator is re-estimated by year for each country. Due to the small

number of data points in each year for Thailand, the estimated annual Boone indicator may not be

statistically different from zero. The estimated annual Boone indicators are presented in Table 6.

Also, the plots of the both countries Boone indicator trend as shown in Table 6 are presented as

Figure 3 in the appendix.

TABLE 6: THE ESTIMATED ANNUAL BOONE INDICATOR BY COUNTRY

United States Thailandyear Boone indicator z-value Boone indicator z-value

1995 -3.22*** -25.11 -10.99 -1.331996 -3.10*** -22.69 0.87 0.271997 -3.19*** -23.17 3.01 0.311998 -3.04*** -21.21 -10.09*** -4.331999 -3.54*** -20.55 -8.44*** -2.662000 -3.65*** -19.47 -1.56* -1.942001 -2.75*** -16.79 -1.59* -1.862002 -3.03*** -18.71 -1.17 -1.152003 -3.02*** -21.49 -1.58** -2.142004 -2.85*** -21.16 -2.72*** -4.10

This table presents the estimated annual Boone indicator for each country. Bothcountrys equations are exactly identified so the Hansen J-test is 0.00 and hence

omitted from the presentation. ***, ** and * indicate statistical significance at 1percent, 5 percent and 10 percent, respectively. The F-stat for the U.S. is 150.16.The F-stat for Thailand is 5.43. The year 1994 is omitted because the lag value ofthe marginal cost is used as an instrument.

From Table 6, the results indicated that the U.S. banking industry has remained competitive

despite the merger wave during the 1990s. Therefore, this finding is consistent with Jayaratine and

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Strahan (1998) and DeYoung (1999), whose studies found that the deregulation in the 1990s led to

more efficiency and found no evidence of a reduction in competition after the policies were imple-

mented. The Boone indicator for the U.S. banking industry fluctuates between the -2.75 and -3.75

band. The level of competition had not been decreasing despite the reduction in the number of banks

and an increase of an average loan market share of a bank. This is another evidence that looking

at the market concentration alone may be misleading in determining the level of competition, as

claimed by Boone et al. (2004).

Although the estimation for Thai banks is highly volatile, which is to be expected for such a small

sample size, some implications can be drawn from it. First, the Asian crisis may have changed the

structure of the loan market and the expenses of banks during a few years following 1997. Therefore,

the high value of estimated Boone indicator may have reflected that the loan market share is much

more sensitive to an increase in marginal costs for a country recovering from a severe financial crisis.

A more stable trend of the Boone indicator happens after the year 2000. The Thai banking

industry had become more competitive compared to previous periods and the Boone indicator reg-

istered at -2.72 at the end of 2004. It seems like the market for loans in Thailand was on an upward

trend when it comes to being competitive.

Concluding Remark

Measuring competition has always been an interesting issue as important policy implications can

be drawn from it. Boone et al. (2004) developed a model to measure competition, relying on the

theory that more efficient firms (i.e. with lower marginal costs) will possess higher market shares

and this effect will be stronger in a more competitive environment. The study by van Leuvensteijn

et al. (2007) applied the Boone concept to the banking industry for the first time.

Applying the similar concept as van Leuvensteijn et al. (2007), I measure the level of competition

in the loan market of Thai and U.S. banks through estimating the Boone indicator. Using the data

from the CALL Report (for the U.S.) and Bankscope (for Thailand) during the years 1994-2004,

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I find that the Thai banking industry is quite a bit less competitive during the years 1994-2004

compared to the U.S., as measured by the competitiveness in the loan market. In addition, the

estimation of the translog cost function (TCF) points to the fact that Thai banks were operating

more inefficiently, as they bore higher marginal costs than U.S. banks.

The policy implications that can be drawn from the results in this paper may lie upon the

question of how to increase competition in the Thai banking industry. As Thai banks have been

improving more in terms of competition, as can be seen from an upward trend of the absolute value

of the Boone indicator, but more measures may be taken to accelerate the process of liberalizing the

banking industry.

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References

Berger, Allen N. and Loretta J. Mester. (1997) Inside the Black Box: What Explains Differencesin the Efficiencies of Financial Institutions? Journal of Banking and Finance. Vol. 21, pp. 895947.

Boone, Jan. (2000) Competition. CEPR Discussion Paper Series. No. 2636.

Boone, Jan. (2004) A New Way to Measure Competition CEPR Discussion Paper Series. No.4330.

Boone, Jan, Rachel Griffith, and Rupert Harrison. (2004) Measuring Competition. Paper pre-sented at the Encore Meeting 2004.

Boone, Jan, Jan C. van Ours, and Henry van der Wiel. (2007) How (not) to measure competition.Center for Economic Research Discussion Paper. Tilburg University, No. 2007-32.

Bresnahan, Timothy. (1982) The oligopoly solution concept is identified. Economics Letters. Vol.10, pp. 8792.

Claessens, Stijin and Luc Laeven. (2004) What drives bank competition? Some internationalevidence. Journal of Money, Credit, and Banking. Vol. 36, pp. 563583.

Jayaratne, Jith and Philip E. Strahan. (1998) Entry Restrictions, Industry Evolution and DynamicEfficiency: Evidence from Commercial Banking.Journal of Law and Economics. Vol. 41, No. 1,pp. 239-273.

Panzar, John C. and James N. Rosse. (1987) Testing for monopoly equilibrium. Journal ofIndustrial Economics. Vol. 35, pp. 443456.

Spieker, Ronald L. (2004) Bank Branch Growth Has Been SteadyWill It Continue? Future ofBanking Study. Federal Deposit Insurance Corporation. Draft FOB200408.1.

Van Leuvensteijn, Michiel, Jacob A. Bikker, Adrian A.R.J.M. van Rixtel, and Christoffer KokSrense. (2007) A New Approach to Measuring Competition in the Loan Markets of the EuroArea.European Central Bank Working Paper Series. No. 768, pp 1-37.

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Appendix

1. Calculating the Translog Cost Function and the Marginal Costs in

Detail

Given the translog cost function equation

ln Chit = 0+

H1h=1

hdhi +

T1t=1

tdt+

Hh=1

Kj=1

jhln xijtdhi

+Hh=1

Kj=1

Kk=1

jkhln xijtln xiktdhi +vit,

and the classification ofk and l as given

j = k = 1 for the wage rates (variable name wage)j = k = 2 for the funding rates (variable name fund)j = k = 3 for the other expenses (variable name othexp)j = k = 4 for loans (variable name loans)j = k = 5 for securities (variable name sec)j = k = 6 for other services (variable name othserv),

the TCF can be rewritten in the following form (for each bank i of type h in year t so all these

indices are omitted here),

ln Costs = 0+tdt+1ln wage+2ln fund+3ln othexp+4ln loans

+ 5ln sec+6ln othserv+11ln wage2 +12ln wage ln fund

+ 13ln wage ln othexp+14ln wage ln loans+15ln wage ln sec

+ 16ln wage ln othserv+21ln fund ln wage+22ln fund2

+ 23ln fund ln othexp+24ln fund ln loans+25ln fund ln sec

+ 26ln fund ln othserv+31ln othexp ln wage+32ln othexp ln fund

+ 33ln othexp2 +34ln othexp ln loans+35ln othexp ln sec

+ 36ln othexp ln othserv+41ln loans ln wage+42ln loans ln fund

+ 43ln loans ln othexp+44ln loans2 +45ln loans ln sec

+ 46ln loans ln othserv+51ln sec ln wage+52ln sec ln fund

+ 53ln sec ln othexp+54ln sec ln loans+55ln sec2

+ 56ln sec ln othserv+61ln othserv ln wage+62ln othserv ln fund

+ 63ln othserv ln othexp+64ln othserv ln loans+65ln othserv ln sec

+ 66ln othserv2

Using the conditions in equation (7), 3 = 1 1 2 and applying the symmetry between the

coefficients of the same cross variable j,k= k,j in equation (10),

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ln Costs = 0+tdt+ ln othexp+1[ln wage ln othexp] +2[ln fund ln othexp]

+ 4ln loans+5ln sec+6ln othserv+11ln wage2 + 212ln wage ln fund

+ 213ln wage ln othexp+ 214ln wage ln loans+ 215ln wage ln sec

+ 216ln wage ln othserv+22ln fund2 + 223ln fund ln othexp

+ 224ln fund ln loans+ 225ln fund ln sec+ 226ln fund ln othserv

+ 33ln othexp2 + 234ln othexp ln loans+ 235ln othexp ln sec

+ 236ln othexp ln othserv+44ln loans2 + 245ln loans ln sec

+ 246ln loans ln othserv+55ln sec2 + 256ln sec ln othserv

+ 66ln othserv2

(16)

From equation (8), we can expand it to be such that

1,1+2,1+3,1 = 0

1,2+2,2+3,2 = 0

1,3+2,3+3,3 = 0.

Applying the symmetry j,k= k,j again yields

1,1+1,2+1,3 = 0 1,1 1,2 = 1,3 (17)

1,2+2,2+2,3 = 0 1,2 2,2 = 2,3 (18)

1,3+2,3+3,3 = 0 1,3 2,3 = 1,1+ 21,2+2,2 = 3,3 (19)

where the last part of equation (19) comes from applying equations (17)-(18) again to 1,3 and2,3.

Expanding equation (9) gives us

4,1+4,2+4,3 = 0

5,1+5,2+5,3 = 0

6,1+6,2+6,3 = 0

7,1

+7,2

+7,3

= 0.Applying the symmetry j,k= k,j again yields

1,4+2,4+3,4 = 0 1,4 2,4 = 3,4 (20)

1,5+2,5+3,5 = 0 1,5 2,5 = 3,5 (21)

1,6+2,6+3,6 = 0 1,6 2,6 = 3,6 (22)

1,7+2,7+3,7 = 0 1,7 2,7 = 3,7 (23)

Using equations (17)-(23) in equation (16), we have


+ 4ln loans+5ln sec+6ln othserv+11ln wage2 + 212ln wage ln fund

+ 2[11 12] ln wage ln othexp+ 214ln wage ln loans

+ 215ln wage ln sec+ 216ln wage ln othserv+22ln fund2

+ 2[12 22] ln fund ln othexp+ 224ln fund ln loans

+ 225ln fund ln sec+ 226ln fund ln othserv+ [11+ 212+22] ln othexp2

+ 2[14 24] ln othexp ln loans+ 2[15 25] ln othexp ln sec

+ 2[16 26] ln othexp ln othserv+44ln loans2 + 245ln loans ln sec


+ 66ln othserv2

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Rearranging yields


+ 4ln loans+5ln sec+6ln othserv

+ 11[ln wage ln othexp]2 + 212[(ln wage ln othexp)(ln fund ln othexp)]

+ 22[ln fund ln othexp]2 + 214(ln loans)[ln wage ln othexp]

+ 215(ln sec)[ln wage ln othexp] + 216(ln othserv)[ln wage ln othexp]

+ 224(ln loans)[ln fund ln othexp] + 225(ln sec)[ln fund ln othexp]

+ 226(ln othserv)[ln fund ln othexp] +44ln loans2 + 245ln loans ln sec


+ 66ln othserv2 (24)

Equation (24) gives the final form of the regression of the TCF.

Next, recall that the marginal cost function for loans is defined by

mci4t = Ci4t

xi4t= Ci4t

xi4t ln Ci4t

ln xi4t.

Basically, in order to determine ln Ci4tln xi4t

= lnCostsln loans

, we should differentiate equation (24) with

respect to ln loans and so

ln Costs

ln loans = 4+ 244ln xi4t+

k=4

4kln xi4t+j=4

j4ln xijt

= 4+ 244ln xi4t+ 2k=4

4kln xikt

= 4+ 244ln loans+ 214ln wage+ 224ln fund+ 243ln othexp

+ 245ln sec+ 246ln othserv

Hence, the marginal cost for loans is then

mci4t = C

xi4t=

Ci4t

xi4t

4+ 244ln xi4t+ 2

k=4

4kln xikt

= Costs

loans [4+ 244ln loans+ 214ln wage+ 224ln fund+ 243ln othexp

+ 245ln sec+ 246ln othserv],

which is equation (14).

2. The Results of the TCF Estimation of the U.S. and Thailand

The table below reports the estimated coefficients and other important statistics from the TCF

estimation regression.

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TABLE A1: THE RESULTS OF THE TCF ESTIMATION FOR COMMERCIAL BANKS OFBOTH COUNTRIES

Dependent variable: ln(costs)-ln(othexp)

Commercial Banks

explanatory variables coefficients US estimation Thai estimationintercept 0 1.713*** 3.616*

(0.474) (2.153)ln loans(com) 4 0.289*** 0.407

(0.064) (0.293)ln sec(com) 5 0.246*** -0.408*

(0.042) (0.239)ln othserv(com) 6 0.159*** 0.078

(0.046) (0.087)ln eqtyratio(com) 7 -0.666*** 0.039

(0.069) (0.055)ln loans2(com) 44 0.073*** 0.094***

(0.003) (0.019)ln sec2(com) 55 0.043*** 0.093***

(0.002) (0.013)ln othserv2(com) 66 0.025*** 0.003

(0.002) (0.003)ln eqtyratio2(com) 77 0.134*** -0.053***

(0.014) (0.017)ln wageln othexp(com) 1 0.661*** 0.922**

(0.068) (0.379)ln fundln othexp(com) 2 0.453*** -0.824***

(0.062) (0.293)(ln wageln othexp)2(com) 11 0.109*** 0.104***

(0.003) (0.035)(ln fundln othexp)2(com) 22 0.120*** 0.076***

(0.003) (0.026)[(ln wageln othexp)(ln fundln othexp)](com) 212 -0.225*** -0.384***

(0.006) (0.058)(ln loans)(ln wageln othexp)(com) 214 0.034*** 0.118*

(0.006) (0.063)(ln loans)(ln fundln othexp)(com) 224 -0.015** -0.064

(0.006) (0.048)(ln sec)(ln wageln othexp)(com) 215 -0.025*** -0.176***

(0.004) (0.044)(ln sec)(ln fundln othexp)(com) 225 0.010*** 0.082**

(0.004) (0.033)(ln othserv)(ln wageln othexp)(com) 216 -0.020** -0.003

(0.004) (0.015)(ln othserv)(ln fundln othexp)(com) 226 0.004 0.011

(0.005) (0.015)[ln loansln sec](com) 245 -0.090*** -0.161***

(0.004) (0.025)[ln loansln othserv](com) 246 -0.051*** 0.0002

(0.004) (0.011)[ln secln othserv](com) 256 0.005 -0.007

(0.003) (0.008)

Adjusted R2 - 0.992 0.993Total Observations - 99,188 139

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TABLE A2: THE RESULTS OF THE TCF ESTIMATION FOR SAVINGS ANDCOOPERATIVE BANKS FOR THE U.S.

Dependent variable: ln(costs)-ln(othexp)U.S. Banks

explanatory variables co efficients S avings Banks C oop erative Banks

ln loans 4 0.009 0.709***(0.185) (0.009)

ln sec 5 0.323*** 0.411***(0.083) (0.004)

ln othserv 6 0.128 -0.058***(0.107) (0.007)

ln eqtyratio 7 0.059 0.402***(0.119) (0.004)

ln loans2 44 0.072*** 0.068***(0.008) (0.001)

ln sec2 55 0.051*** 0.074***(0.005) (0.001)

ln othserv2 66 0.007 0.0132***

(0.004) (0.000)ln eqtyratio2 77 -0.015 -0.100***(0.025) (0.001)

ln wageln othexp 1 0.823*** 0.637***(0.162) (0.017)

ln fundln othexp 2 0.100 0.435***(0.151) (0.018)

(ln wageln othexp)2 11 0.096*** 0.124***(0.009) (0.002)

(ln fundln othexp)2 22 0.072*** 0.116***(0.007) (0.003)

[(ln wageln othexp)(ln fundln othexp)] 212 -0.174*** -0.242***(0.016) (0.004)

(ln loans)(ln wageln othexp) 214 0.001 -0.026***

(0.017) (0.001)(ln loans)(ln fundln othexp) 224 0.019 0.031***(0.020) (0.001)

(ln sec)(ln wageln othexp) 215 -0.011 0.032***(0.011) (0.001)

(ln sec)(ln fundln othexp) 225 0.002 -0.031***(0.009) (0.001)

(ln othserv)(ln wageln othexp) 216 -0.001 -0.003***(0.012) (0.001)

(ln othserv)(ln fundln othexp) 226 -0.015 -0.010***(0.011) (0.001)

[ln loansln sec] 245 -0.101*** -0.141***(0.009) (0.001)

[ln loansln othserv] 246 -0.017 -0.010***

(0.013) (0.001)[ln secln othserv] 256 0.001 -0.006***(0.005) (0.000)

dummies for savings and cooperative h 1.173 -4.994***(1.222) (0.489)

The tables A1-A2 present the results of the fixed-effect panel data regression of the translog cost function, as shownin equation (24). Robust standard errors (for the U.S.) and standard errors (for Thailand) appear below coefficientsin parentheses. ***, ** and * indicate statistical significance at 1 percent, 5 percent and 10 percent, respectively.

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measuring the level of competition in the loan

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