Analysis of Cost and Profit

8/8/2019 Analysis of Cost and Profit

1/22Electronic copy available at: http://ssrn.com/abstract=1692974

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Banks in China: An Analysis of Cost and Profit Efficiencies

By

Hong-Jen LinAssistant Professor of FinanceBrooklyn College, the City University of New York218 Whitehead HallBrooklyn, NY 11210

E-mail Address: [email protected] number: 718-463-1918


2/22Electronic copy available at: http://ssrn.com/abstract=1692974

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Abstract

This paper explores the cost and profit efficiencies of banks in

China from 1996 to 2005 and their relation to the economic growth.

We adopt the one-step stochastic frontier approach to estimate

the cost and profit frontiers and efficiencies. The profit

efficiency of banks has been improved after year 2000 while the

cost efficiency of them decayed during the same time period.

Nevertheless, according to our empirical results, the Big Fours,

private banks and banks with high the deposits-to-assets ratio

tend to have low volatility of the cost efficiency. The high

deposits-to-assets ratio also helps reduce the volatility of the

profit efficiency. Consequently, according to the empirical

results, the privatization of the Big Fours is the key to reduce

the volatility of cost efficiency in the banking operations in

China and high deposits-to-asset ratio contributes to both cost

and profit efficiencies.

JEL Classification: G21


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1. Introduction

China, one of the fastest growing economies all over the

world, is featured with its fragile banking systems. According

to He (2001) and Li and Ma (2004), the problems of the Chinese

banking system include its huge amount of Non-Performing Loans

(NPLs), weak capital base, and low profitability. In addition,

its banking businesses are underdeveloped and its client groupsare highly biased. In other words, banks in China focus on the

deposit-and-loan business for the related enterprises only,

particularly for the state-owned companies. These problems may

impair Chinas economy and even cause debacle of its economic

system in the near future. Therefore, the banking system in China

is the Achilles Heels of the economy (see Barth, Koepp, and

Zhou, 2004)

This problem deteriorates after China has decided to join

the WTO that requires China to open its financial markets to

foreign institutions (Li and Ma, 2004).The deregulation triggered

by its participation to the WTO have attracted more foreign

capitals flowing into Chinas banking system and more joint-

venture financial institutions have been established (Berger,

Hasan, and Zhou, 2005). 1 Will banks in China lose ground to

foreign competitors after 2006 when the promised openness of the

financial markets is realized?

In order to answer this question, we have to investigate

the performance of banks in China and find out the factors of the

performance of banks. In addition, a study in the time-varying

pattern of performance of banks helps predict future performance

of banks. In this study, we adopt the stochastic frontier

approach by considering factors of shift-in-variance and shift-

1.


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in-mean of cost and profit inefficiencies to depict the dynamics

of banks in China. First, the factors of performance of banks are

explored. Second, the fluctuations of bank performance aredepicted. Thus, we are able to address the problems of banks in

China via numerical methods and probably avoid these problems in

the future.

Most studies on Chinese banking are simply descriptive

(e.g., He, 2001; and Barth, Koepp, and Zhou, 2004 among others).

Not many of them evaluate performance of banks via numerical

approach and/or empirical research. Moreover, prior literature in

the field of cost or profit efficiency is dedicated to a largecross-section of banks within a nation. It ignores Chinas banks

in their samples under study due to the difficulty of data

collection. The exceptions are Chen, Skully, and Brown (2005) Lin

and Lin (2006) and Berger, Hasan, and Zhou (2005).

Among these studies, Chen, Skully, and Brown (2005) adopt

DEA approach while the other two use the stochastic frontier

approaches. Berger, Hasan, and Zhou (2005) have analyzed the

Chinese banking sector from 1994 to 2001. They find that foreign

ownership is positively and significantly related to profit

efficiency of banks while government ownership mitigates the

profit efficiency at the bank level. This research differs itself

from the prior studies in several aspects. First, Chen, Skully,

and Brown (2005) estimate technical, allocative, and cost

efficiencies while Berger, Hasan, and Zhou (2005) investigate

profit efficiency only. Neither of them combines production side

and profit side in one article and this study does incorporate

both of them. Second, the time periods under study of Lin and Lin

(2006), Chen, Skully, and Brown (2005), and Berger, Hasan, and

Zhou (2005) are before year 2000. No current updated information

is included. Third, this study considers the new approach of Wang

and Schmidt (2002)s stochastic frontier. The exogenous factors

and heteroscedasticity in the inefficiency are taken care of by

the models.


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The objective of this paper is threefold, first, to asses

the performance of banks in China under different categories:

sizes, ownership, and other firm-specific features, and second,to analyze the efficiency levels of banks before and after China

joined the WTO. Third, this study demonstrates that empirical

evidence that the new approach is superior to previous studies.

By doing so, we can observe an increase in the cost and profit

efficiencies of banks in China and further discuss how cost and

profit efficiencies affect economic growth of a country.

The remainder of the article is structured as follows.

Section 2 describes the methodologies of estimation under study.Section 3 introduces the data sources and summarizes the

statistics of variables. Section 4 estimates and analyzes the

empirical results of the cost and profit efficiencies. Finally,

Section 5 concludes.

2. Methodologies and Model Specifications

We adopt Wang and Schmidt (2002)s models to estimate the

cost and profit efficiencies of twelve banks in China from 1996

to 2005. The one-step estimation is superior to the two-step

estimation of Berger, Hasan, and Zhou (2005) in terms of the

statistical unbiasedness. This model allows the inefficiency term

to follow the shift-in-mean and shift-in-variance processes where

the exogenous variables explain the means or the volatility of

the cost or profit inefficiency.

Equation (1) describes the cost frontier of the banks under

study by considering three different distributions of cost

inefficiencies. Equation (1) is

( ) it it it uv f TC ++= ;p,y ititln (1)

where it TC ln is the natural logarithm of the ratio of the total

cost to the assets; ity is the output vector;2 itp is the input-

price vector. means the parameters to be estimated in the cost

frontier f-function. it v and it u are both random variables: it v is the

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normally distributed random error and it u is the cost inefficiency

term. The subscript it stands for the ith bank for the time

period t.

There are three variations of equation (1): equation (1.1)

represents the cost frontier with the truncated-normal

inefficiency, equation (1.2) with exponentially distributed

inefficiency and equation (1.3) with Gamma distributed cost

inefficiency.

The other specific parameters of equations (1.1) to (1.3) are

listed as follows. In equation (1.1), represents the truncated

point of the truncated normal distribution; = vu / ; and

= 22 vu + . In equation (1.2), is the parameter of the

exponential inefficiency; and v is the standard deviation of the

normal error. In equation (1.3), and are the parameters of

the Gamma-distributed inefficiency; and v is the standard

deviation of the normal error.

Equation (2) depicts the profit frontier. For other

equations, we try to use similar notations of variables as we did

in equation (1) if the natures of the variables are the same.

equation (2) is stated as follows.

( ) it it it uvgP += ;p,y ititln (2)

Again, ity is the output vector; itp is the input-price vector; and

represents the parameters to be estimated in the profit

frontier. The g-function means the optimal profit frontier. it v is

the normally distributed random error and it u is the cost

inefficiency term. it Pln is the natural logarithm of the net

income to the assets where it P is equal to the ratio of the net

income plus the maximum net loss in the sample divided by the

amount of the assets of a bank. 3

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Equation (2.1) considers the profit frontier with

truncated-normal inefficiency; equation (2.2) bears the

exponential profit inefficiency; and equation (2.3) assumes thatthe profit inefficiency is Gamma-distributed. The notations of

equations (2.1) to (2.3) are similar to those in equations (1.1),

(1.2) and (1.3). Here, three different distributions are assigned

to test if the results of the estimators of cost and profit

efficiencies are sensitive to the distribution of the

inefficiency term. If all of them render the same outcome, the

empirical results would be robust and concrete.

Here, we follow the intermediary approach to specify themodel. Thus, the total cost in equation (1) includes operating

expenses and interest expenses. In the itp vector, 1 p stands for

the natural logarithm of the interest rate and 2 p is the natural

logarithm of the wage level of banks. In the ity vector,

1 y =ln(loans/assets) and 2 y =ln(investments/assets), where loans

include all different types of loans and investments incorporate

both long-term and short-term investments of banks.According to Wang and Schmidt (2002), we extend the

original cost frontier and profit frontier to equations (3) and

(4), which consider shift-in-variance in the cost and profit

inefficiencies, respectively. Equation (3) includes two parts,

which are

( ) it it it uv f TC ++= ;p,y ititln and )exp(2 itz '=uit (3)where is the vector of coefficients and z is the exogenous

factors of the variance. The profit frontier is denoted in thesimilar way in equation (4)

( ) it it it uvgP += ;p,y ititln and )exp(2 itz'=uit (4).By doing so, we are order to depict the shift in variance of cost

and profit inefficiencies.

Equations (5) and (6) consider the shift-in-mean

inefficiencies for cost and profit frontiers, respectively. They


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are denoted as follows. The cost frontier (i.e. equation (5))

is

( ) it it it uv f TC ++= ;p,y ititln and it it wu += itz ' (5),

where )|,'~| 2uit it z N u , so it w is the residual and is the vectorof coefficients and the profit frontier.

( ) it it it uvgP += ;p,y ititln and it it wu += itz ' (6).

Similarly, ( )|,'~| 2uit it z N u in equation (6). Please note that all it u in equations (1) to (6) are positive inefficiency terms.

The z vector considers factors such as YR, TDD, BIG and

PRIVATE. YR is the time-period proxy where in year 1996 YR is

equal to 1, in year 1997, YR=2, etc. TDD is the ratio of the

total deposits to the total assets. BIG is the Big Fours proxy

when the bank is one of the Big Fours, BIG=1; if not, BIG=0. The

PRIVATE proxy denotes the ownership of the banks when the bank is

private owned, PRIVATE=1; else, PRIVATE=0.

Furthermore, both the g-function and f-function take the

translog functional form incorporating two input prices 1 p and 2 p ,

and two outputs 1 y and 2 y . Thus, the f-function is denoted as:

( ) 22921821227216215241322110 y y y y p p p p y y p p f ++++++++++=;p,y itit 2213121221111110 p y p y p y p y ++++ .

And the specification of g-function is the same, though the

estimation of parameters and coefficients differs

3. Data Sources and Descriptive Statistics

This study is based on the data of 15 banks in China. Table

1 lists the names, time periods, size proxy (i.e., part of Big

Fours or not), and the ownership of these banks. We collect the

income statements and balance sheets from the annual reports of

banks from websites for each individual bank. The state owned

banks include Citic Bank, Agricultural Bank of China, China

Everbright Bank, Bank of China, Communication Bank, Industrial

Bank, and Construction Bank of China. Among them, Agricultural

Bank of China, Bank of China, Industrial Bank and Construction


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Bank of China are called Big Fours. It is worth noting that

China Everbright Bank is the first state owned bank allows its

minor proportion of shares held by foreign institutions. The so-called private owned banks may not be 100% owned by the public.

Usually the Chinese government holds some proportion of shares.

Insert Table 1 Here

Table 2 summarizes the descriptive statistics of variables

in the sample under study. We also demonstrate the statistics of

two subsamples: Big Fours and Other Banks for the purpose of

comparison. The means and standard deviations are measured in

thousand Reminbi (Chinese currency) per annum.

It is worth noting that the total costs, net income, assets,

deposits, loans, and investments for Big Fours exceed these items

for the other banks. Moreover, these variables of Big Fours are

more volatile than their counterparts of the other banks in terms

of standard deviation. Particularly, the average net income of

the Big Fours is comparably more volatile than that of the other

banks. In other words, the big banks in China are not as stableas the other smaller banks in the net income. 4

Insert Table 2

4. Empirical Estimation and Analysis

In this Section, we report the empirical results of the

original and new stochastic cost and profit frontiers of

equations (1) to (6). Table 3 states the estimation of the

original stochastic frontier, that is, equations (1) and (2) and

Table 4 demonstrates the outcome of new stochastic frontier

models, equations (3) to (6).

Table 3 shows cost frontiers (equations (1.1) to (1.3)) and

profit frontiers (equations (2.1) to (2.3)). Among these results,

equations (1.1) and (2.1) assumes truncated normal distributed

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normal inefficiencies; equations (1.2) and (2.2) have exponential

distributed inefficiencies; and equations (1.3) and (2.3)

incorporate Gamma distributed inefficiency terms.

In equation (1.1), only 2 p and 22 p p are significant at the

5% level and 22 p y are significant (and negative) at the 10% level,

while other coefficients and parameters stay insignificant. In

equation (1.2), only the parameters and v are significant at

the 1% level while all coefficients in the cost frontier are not

significant. Equation (1.3) demonstrates a different pattern

where most coefficients and parameters are statistically

significant at the 1% level except for v . In the cost frontiers

equations (1.1) to (1.3), two input prices 1 p and 2 p negatively

contribute to the total cost and 1 y and 2 y positively affect the

dependent variable.

In the profit frontiers (i.e., equations (2.1), (2.2), and

(2.3)), 2 y 2 y is the only positively significant coefficient

across three equations. andv

are two significant parameters

across equations (2.2) and (2.3). Generally speaking, the

translog cost frontier captures the cost behavior better than the

translog profit function does the change in the net income.

Insert Table 3

In Table 4, equations (3) and (4) are the results of cost

and profit frontiers considering shift-in-variance of the

inefficiency terms, respectively; and equations (5) and (6) showsthe results of cost and profit frontiers considering shift-in-

mean of the inefficiency terms. All parameters in explaining

shift-in-variance of u are all significant at least at the 10%

level.

In equation (3), 2 p and 2 p 2 p are significant at the 10%

level, as we observed in equation (1), and the parameters in

explaining the shift-in-variance u are all significant at least


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and Brown (2005). 5 When big banks own the competitive strength in

the scale economy and network economy that enables big banks to

market and serve customers better. Even though Big fours sufferdeeply from a huge amount of NPLs, they may still outperform

other smaller banks (i.e., mainly mid-sized banks as we lack of

the data of several small banks) on the profit side. Our finding

is significantly different from that of Berger, Hasan, and Zhou

(2005). What cause these differences in empirical work? First, we

are using translog functional form of profit frontier while they

do not include the input prices and outputs in the equation.

Second, they have not updated the data to year 2005. And third,Berger, Hasan, and Zhou (2005) do not consider advanced model

considering heteroscedasticity and shift in mean of

inefficiencies suggested by Wang and Schmidt (2002).

Insert Table 4

Table 5 highlights the means of the cost and profit

efficiencies before and after year 2000. We find that the cost

efficiencies after 2000 are unanimously below those before 2000.In equation (1.1), the cost efficiency drops from 0.7772 to

0.7447, in equation (1.2), from 0.8047 to 0.7847 and in equation

(1.3), from 0.7760 to 0.6873. The decreases are obvious. The

evidence of profit efficiencies shows different patterns. In

equation (2.1), profit efficiency improves from 0.6524 to 0.6542;

in equation (2.2), from 0.6526 to 0.6541. But in equation (2.3),

it falls from 0.5329 to 0.5326. Among three equations, two

improves and one declines. We also find that the difference inprofit efficiencies before and after 2000 are not as large as

those in cost efficiencies. In short, the declining trend in cost

efficiencies is obvious and the improvement in profit

efficiencies is not as clear as the decline in the cost

efficiency. Therefore, according to the empirical results, we

have to watch over the operation of banks tightly in terms of

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both efficiencies since no clear trend of improvement is found in

terms of these two efficiencies. In short, the drop of cost

efficiencies is obvious and the pattern of increase in profitefficiencies is unclear. The banking market may not respond

positively to the news of participation of the WTO of China.

Insert Table 5

We have also measured the cost and profit functions and

efficiencies for two sub-samples of the state-owned and private-

owned banks. The results of the sub-samples do not change the

above conclusions and thus are eliminated to save spaces. A copy

of the results is available upon request.

6. Conclusions

This study estimates and analyzes the cost and profit

efficiencies of 15 commercial banks in China from 1996 to 2005.

We adopt Wang and Schmidt (2002)s stochastic frontier models

that incorporate shift-in-mean and shift-in-variance of cost and

profit inefficiencies.

The empirical results reveal that Big Four banks in Chinamay not operate less efficiently than other smaller banks.

Instead, their cost efficiencies are less volatile than those of

the other banks, and thus are more stable. The private ownership

also improve the stability of the cost efficiency of banks. That

is, the cost efficiency of private banks is more stable than that

of the other banks. Furthermore, the cost efficiency before 2000

is obviously above that after 2000, and the improvement of profit

efficiency before and after 2000 is not so significant inmagnitude. Also, the time-varying patterns of cost and profit

efficiencies are not increasing. It implies that the danger of

banking crisis in China still exists, which coincides with the

opinions of practitioners (He, 2001 and Barth, Koepp, and Zhou,

2004)

The major insight into the empirical results is that the

privatization of the Big Fours may be the solution to the future


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financial crises. This privatization also enforces banks in China

to follow the international accounting standard and the

classification of loans. Thus, these Chinese banks can bemonitored and regulated by the Bank of International Settlement

or other international organizations as well as the public

investors. Our hope is that, by doing so, banks in China will be

come transparent and efficient on both cost and profit sides.

Nevertheless, our efforts here are limited by availability

of the data. Since the financial statements come from the annual

reports of each bank from the websites, the formats of the

financial statements are not unified. Furthermore, our analysiscannot differentiate the different sources of capital as some

funding may come from the subsidy of the government. And the

definitions of subcategories may differ slightly. In addition, we

failed to collect the full financial statements of some banks so

we are forced to give up these observations in the study. In the

near future, as the openness of the Chinese banks advents, the

quality and availability of the data will be largely enhanced and

improved. Then we are able to investigate Chinese banks better in

depth and breadth.

Finally, this research paves the way to future study in

banking in several aspects. First, the new methodology will help

us discover how cost and profit efficiencies are improved over

time due to the superiority of the new estimation. Second, new

factors such as the new Basel Accord ans/or the information

technology investments (IT) in the banking industry will

contribute to the Chinese banking system. Third, the foreign

capital and private equities will flow into Chinese financial

system to improve the weak capital base and then change the

landscape of its banking industry. Therefore, it is worth keeping

track on the most updated change and/or evolution in the banks

and economy in China.


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Footnotes1 For instance, on June 17, 2005, the Bank of America Corp., one

of the largest retail banks in the U.S. announced that it willinfuse $3 billion to the China Construction Bank, one of the Big

Fours in Chinese banking sector, and acquire 9 percent of its

shares.

2 We take the comparative measure of the dependent variables and

outputs to alleviate the problem of heteroscedasticity in the

random error it v , which is caused by different sizes of banks.

3 Because the variables in the logarithm must be positive, we

have to artificially add one number over the amount of net income

(or loss) to make all of them are greater than zero to take the

logarithm.

4 International comparison of financial performance between

Chinese and foreign banks is complicated by the fact that Chinas

accounting standard and loan classifications differ from theinternational standard. Therefore, broader categories of loans

and investments are used in this study. See Li and Ma (2004).

5 Chen, Skully, and Brown (2005) find the big and small banks

operate more efficiently than the mid-sized banks from the cost

and production perspectives and here our inference from equation

(6) is on profit side.


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Table 1 Names of Banks Under Study

Bank Time Periods Big Four Ownership

Min Sheng Bank 1996-2004 Private

Citic Bank 1998-2003 State Owned

Agriculture Bank of China 1996-2003 Big Four State Owned

ShenZhen Development Bank 2000-2004 Private

Xiamen Bank 2003-2004

Partially

Private

Bank of China 1996-2004 Big Four State Owned

Merchant Bank 2001-2004 Private

Shanghai Pudong Development

Bank 1997, 1999-2004 Private

Communication Bank

1996-1999, 2002-

2004 State Owned

HuaXia Bank 2000, 2002-2004 Private

Fujian Industrial Bank 1998-2000

Partially

Private

Industrial Bank 1998-2004 Big Four State Owned

China Everbright Bank 1999 State Owned

Bank of Shanghai 2004 Private

Construction Bank of China 1999-2004 Big Four State Owned


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Table 2 Summary of Statistics

Net Income Total Costs Assets Deposits Loans Investments r wage inflation EGmean 3,527,346 44,475,184 1,321,793,510 1,027,375,818 712,834,590 216,320,909 3.13 8706 0.72 8.61std 6,910,588 46,548,237 1,494,071,349 1,238,845,727 924,758,530 329,620,621 2.00 3045 2.09 1.02

Net Income Total Costs Assets Deposits Loans Investmentsmean 7,187,300 97,603,233 3,056,587,100 2,393,218,433 1,681,855,023 505,739,183 - - - -std 10,283,131 25,713,571 1,001,702,742 1,013,769,312 866,041,022 392,597,662 - - - -

Net Income Total Costs Assets Deposits Loans Investmentsmean 1,331,374 12,598,355 280,917,356 207,870,249 131,422,329 42,669,945 - - - -std 1,135,953 18,357,885 238,721,980 196,655,263 122,580,701 43,531,545 - - - -

BIG FOURS

OTHERS

ALL BANKS

Note: Net income, total costs, assets, deposits, loans are in

1,000 Chinese Renminbi, and wage is in Chinese Reminbi. r,

inflation, and EG (economic growth calculated by GDP) are in %.

Std stands for the standard deviation of variables.


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Table 3 The Empirical Results of the Cost and Profit Frontier s

Cost Frontiers

Profit Frontiers Equation (1.1)

Equation (1.2)

Equation (1.3) Equation (2.1)

Equation (2.2)

Equation (2.3)

Panel 1: Primary Variables in the Cost or Profit Function

Variable Coefficient t-value Coefficient t -value Coefficient t-value Coefficient t-value Coefficient t-value Coefficient t-value Constant

127.6 823***

2.42

104.1299

1.56 183. 4097***

71.95 - 39.8472 -0.40 -47.2434

-0.47

-44.6529

- 0.42

1 p

-1.9209

-0.79

- 2.1695 -0.72 - 2.2265 ***

- 40.02

0.3241

0.06 0.3054

0.06

0.3637

0.07

2 p -26.84 03** -2.36

-21.7484 -1.55 -38.2870 ***

- 71.79

14.2350 0.69 15.8183

0.77

15.3244

0.70

1 y

5. 4905

0.86

8.0802 0.83 6.1557 ***

100.29 18.4321 1.13 18.5560

1.13

18.8008

1.09

2 y

5.9157

1.59

4.1064 0.73 8.6505 ***

71.68

- 0.2874

-0.02 -0.6015

-0.05

-0.5085

- 0.04

1 p 2 p

0.2247

0.90

0.2471 0.81 0.3645 ***

66.37

- 0.1044

-0.20 -0.1041

-0.20

-0.1085

- 0.20

2 p 2 p

1.3645** 2.20

1.0892 1.46 1.9471 ***

69.27

- 0.8411

-0.79 -0.9259

-0.86

-0.9002

- 0.79

1 p 1 p

0.0468

0.24

0.1110 0.54 - 0.2952 ***

- 49.44

0.4081

0.95 0.4211

0.99

0.4138

0.93

1 y 2 y

0.0525

0.16

0.1055 0.27 - 0.7691 ***

- 41.15

- 0.1585

-0.15 -0.1501

-0.14

-0.1128

- 0.10

1 y 1 y

0.0592

0.20

0.0247 0.07 0.8733 ***

48.15

0.0254

0.02 0.0189

0.02

-0.0209

- 0.02

2 y 2 y

0.0627

0.66

0.0454 0.53 0.2088 ***

32.85

0.5332**

2.18 0.5282**

2.17

0.527 9**

1.97

1 y 1 p

0.0593

0.38

0.0135 0.06 - 0.1126 ***

- 61.12

0.0362

0.06 0.0318

0.05

0.0191

0.03

1 y 2 p

-0.5229

-0.76

- 0.8032 -0.76 - 0.6003 ***

- 93.63

- 1.9963

-1.15 -2.0097

-1.14

-2.0371

- 1.10

2 y 1 p 0.0163

0.11

0.0658 0.29 0.2636 ***

74.01

0.0778

0.13 0.0812

0.14

0.0938

0.15

2 y

2 p

-0.6349* -1.69

- 0.4417 -0.76 - 0.9359 *** - 84.81 0.3892 0.29 0.4216 0.32 0.4136 0.29 Panel 2: Var i ables in the Cost or Profit Inefficiency

146.2759

0.02

--

-- --

--

- 191.2611 -0.01 -- --

--

--

55.4506

0.04

--

-- --

--

18.5748

0.02 -- --

--

--

7.3150 0.04

--

-- --

--

9.6186 0.02 -- --

--

--

-- --

2.9215***

8.48 0.6247 **

2.39

-- --

2.084 2***

3.38

2.7697**

1.97

v

-- --

0.1354***

3.57 0.0000 0.00

-- --

0.516 3***

5.95

0.4941***

3.36

-- --

--

-- 0.3058 ***

4.50

- - --

-- --

1.9195

0.84

Note: *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. , , and are the parameters of truncated normallydistributed ineff iciency u. and v are the parameters of exponentially distributed and Gamma - distributed inefficiency u , and is thededicated parameter of the Gamma distribut ionwhere

= v u / ; and = 22 vu + . u is the standard deviation of the truncated normal

inefficiency u,

and v is the standard deviation of the normal error

v .


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Table 4 Cost and Profit Frontiers Considering Shift in Mean andHeteroscedasticity in u

Equation (3) Equation (4) Equation (5) Equation (6)Variable Coefficient t-value Coefficient t-value Variable Coefficient t-value Coefficient t-value

Panel 1: Primary Variables in the Cost or Profit Function Panel 1: Primary Variables in the Cost or Profit Function

Constant 109.1982 1.77 26.7272 0.10 Constant 196.7589 5.54 -54.1818 -0.43

1 p -1.6065 -0.88 -2.1568 -0.11 1 p 1.8029 1.08 5.3582 1.102 p -23.2587* -1.75 0.3402 0.01 2 p -42.7337*** -5.58 16.6193 0.601 y 6.3919 0.71 18.7260 0.58 1 y 1.5638 0.26 21.2329 0.96

2 y 2.4097 0.54 2.5183 0.14 2 y 5.2101* 1.88 0.0618 0.01

1 p 2 p 0.2034 1.12 0.2054 0.10 1 p 2 p -0.1362 -0.80 -0.7412 -1.432 p 2 p 1.1964* 1.68 -0.1269 -0.04 2 p 2 p 2.2786*** 5.48 -0.9362 -0.60

1 p 1 p 0.0108 0.06 0.2978 0.44 1 p 1 p -0.3184** -2.41 0.3243 0.72

1 y 2 y 0.0703 0.18 -0.0811 -0.06 1 y 2 y -0.2412 -0.78 0.6932 0.52

1 y 1 y 0.0652 0.17 -0.0508 -0.04 1 y 1 y 0.3182 1.08 -0.6300 -0.48

2 y

2 y

0.0493 0.64 0.5218 1.50 2 y

2 y

0.0958 1.24 0.1941 0.78

1 y 1 p -0.0492 -0.18 -0.0012 0.00 1 y 1 p -0.0211 -0.13 0.3536 0.921 y 2 p -0.5852 -0.60 -2.0382 -0.58 1 y 2 p -0.0928 -0.15 -2.3100 -0.94

2 y 1 p 0.0855 0.44 0.1328 0.16 2 y 1 p 0.0502 0.41 -0.2094 -0.622 y 2 p -0.2515 -0.52 0.0578 0.03 2 y 2 p -0.5566* -1.87 0.2296 0.25

Panel 2: Mean of the Inefficiency u Panel 2: Variables explaining the mean of u

Constant -4.1240 -5.55 -0.9786 -4.39 YR -0.5297 -1.31 0.4007 0.13Panel 3: Variables explaining the heteroscedasticity

of u TDD 6.7012 0.72 -1.8537 -0.13

YR 0.4228*** 3.25 -0.4005 -0.96 BIG 17.2855 0.34 -1.0361* -1.70TDD -4.0034*** -3.20 -4.7460 -0 .47 Panel 3: Variance ParametersBIG -2.8759*** -2.30 4.1110 0.64 8.9885 1.42 69.9113 0.13PRIVATE -0.9868* -1.73 4.2832 0.66 1.1116 1.48 1.0568*** 5.23

Note: *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. , and are the

parameters of truncated normally distributed inefficiency u. =vu

/ ; and = 22vu

+ .u

is

the standard deviation of the truncated normal inefficiency u, and v is the standard deviationof the normal error v . YR: the year proxy=1, when the data are collected from statements in 1996;

TDD=the deposits to assets ratio; BIG=1 when the bank is one of the big fours.


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Table 5 Cost and Profit Efficiencies Before and After Year 2000

Cost Efficiencies Profit Efficiencieseq1.1 eq1.2 eq1.3 eq 2.1 eq 2.2 eq 2.3

Before 2000 0.7772 0.8047 0.7760 0.6524 0.6526 0.5329

After 2000 0.7447 0.7847 0.6873 0.6542 0.6541 0.5326


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References

Barth, J. R., Koepp, R., and Zhou, Z. 2004, Banking Reform in

China: Catalyzing the Nations Financial Future, SSRN WorkingPaper.

Berger, A. N., Hasan, I. and Zhou, M. 2005, Ownership, Financial

Liberalization, and Efficiency of Chinese Banks, Working Paper to

be presented in the 2005 FMA Annual Meeting, Chicago.

Chen, X., Skully, M. and Brown, K. 2005, Banking Efficiency in

China: Application of DEA pre- and post- Deregulation Eras, 1993-2000, China Economic Review 16 (3): 229-245.

He, L.-P. 2001, Facing the WTO Accession: Problems and Challenges

in Chinas Banking Industry, China Macroeconomic Information

Network, July 24, 2001.

Li, K.-W. and Ma, J. 2004 The Economy Intricacies of Banking

Reform in China the Chinese Economy, 37 (4): pp 50-77.

Lin, W. T., and Lin, H.-J. 2006, The Cost and Profit Efficiencies

and Scope Economies of Commercial Banks with Evidence from Taiwan

and China, Working Paper to be presented in the American

Association for Chinese Study Annual Conference, in Riverside, CA,

Oct. 21, 2006.

Lin, W. T., and Lin, H.-J and Chen, Y.H. 2006, Technological

Changes and Time-Varying Patterns of Cost Efficiencies in

Commercial Banks: An International Comparison, Working Paper.

Tadesse, S. 2005, Banking Fragility and Disclosure: International

Evidence, SSRN Working Paper.


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Wang, H.-J. and Schmidt, P. 2002 One-Step and Two-Step Estimation

of the Effects of Exogenous Variables on Technical Efficiency

Levels, Journal of Productivity Analysis, 18: 129-44

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