IS THERE A GAP OF BANKING EFFICIENCY BETWEEN ACCESSION AND NON-
ACCESSION COUNTRIES IN CENTRAL AND EASTERN EUROPE
Except when reference is made to the work of others, the work described in this thesis is
my own or was done in collaboration with my advisory committee. This thesis does not
include proprietary or classified information.
Tong Wu
Certificate of Approval:
Daniel M. Gropper Steven B. Caudill, Chair
Professor Regions Bank Professor
Economics Economics
Valentina M. Hartarska Stephen L. McFarland
Assistant Professor Acting Dean
Agricultural Economics Graduate School
and Rural Sociology
IS THERE A GAP OF BANKING EFFICIENCY BETWEEN ACCESSION AND NON-
ACCESSION COUNTRIES IN CENTRAL AND EASTERN EUROPE
Tong Wu
A Thesis
Submitted to
the Graduate School of
Auburn University
in Partial Fulfilment of the
Requirement for the
Degree of
Master of Science
Auburn, AL
December 15, 2006
iii
IS THERE A GAP OF BANKING EFFICIENCY BETWEEN ACCESSION AND NON-
ACCESSION COUNTRIES IN CENTRAL AND EASTERN EUROPE
Tong Wu
Permission is granted to Auburn University to make copies of this thesis at its discretion,
upon request of individuals or institutions at their expense. The author reserves all
publication rights.
Signature of Author
Date of Graduation
iv
VITA
Tong Wu, son of xiufen Wang and Lianfeng Wu, Was born on May 24, 1983, in
Fushun, LiaoNing Province, China. He attended the Development Zone first high school
in Dalian and graduated in 2001. He attended the Renmin University of China in the
same year, and received his degree of Bachelor of Art in Economics in July 2005. He
entered graduate school, Auburn University in August 2005, where he held a graduate
teaching assistantship. He will marry to Yang Liu in January of 2007.
v
THESIS ABSTRACT
IS THERE A GAP OF BANKING EFFICIENCY BETWEEN ACCESSION AND NON-
ACCESSION COUNTRIES IN CENTRAL AND EASTERN EUROPE
Tong Wu
Master of Science, December 15, 2006
(Bachelor of Art, Renmin University of China, 2005)
71 Pages typed
Directed by Steven B. Caudill
This thesis divides the Central and Eastern European countries into two groups:
accession countries which have or will attend Europe Union and non-accession countries
which are not willing to attend Europe Union. The accession countries are supposed to
have motivation to improve their own cost structure and banking efficiency, which may
make them more efficient than non-accession countries. This thesis first utilizes the
Seemingly Unrelated Regression technique to obtain the Allen Partial Elasticity, and then
adopts Stochastic Frontier Approach to get X-efficiency. This thesis finds that accession
countries are more efficient than non-accession countries, and the structure of the banking
system has significant effect on efficiency.
vi
ACKNOWLEDGEMENTS
I would like to thank my parents, Xiufen Wang and Lianfeng Wu. Without their
financial support and emotional encourage, I would have not had the chance to come to
USA. I also want to thank Department of Economics in Auburn University for giving me
the opportunity to learn more about Economics. A special thanks will be given to my
committee, Dr. Caudill, Dr. Gropper and Dr. Valentina Hartarska for their guidance and
advices through the Graduate School career.
vii
Style Manual of journal used American Economic Review
Computer Software used Microsoft Word 2003 and Limdep 8.0
viii
TABLE OF CONTENTS
LIST OF TABLES IX
CHAPTER I: INTRODUCTION 1
CHAPTER II: LITERATURE REVIEW 4
CHAPTER III: THEORETICAL MODEL 11
CHAPTER IV: DATA AND METHODOLOGY 16
CHAPTER V: EMPIRICAL RESULT 37
CHAPTER VI: CONCLUSION 53
REFERENCES 56
APENDIX 59
ix
LIST OF TABLES
Table 4.1 Accession countries and non-accession countries in this thesis 24
Table 4.2 Data statistics of variables (2000)-accession countries 25
Table 4.3 Data statistics of variables (2000)-non-accession countries 25
Table 4.4 Data statistics of variables (2001)-accession countries 26
Table 4.5 Data statistics of variables (2001)-non-accession countries 26
Table 4.6 Data statistics of variables (2002)-accession countries 27
Table 4.7 Data statistics of variables (2002)-non-accession countries 27
Table 4.8 Data statistics of variables (2003)-accession countries 28
Table 4.9 Data statistics of variables (2003)-non-accession countries 28
Table 4.10 Data statistics of variables (2004)-accession countries 29
Table 4.11 Data statistics of variables (2004)-non-accession countries 29
Table 4.12 The statistics of banks in Armenia (Total Deposit) 30
Table 4.13 The statistics of banks in Belarus (Total Deposit) 30
Table 4.14 The statistics of banks in Georgia (Total Deposit) 31
Table 4.15 The statistics of banks in Croatia (Total Deposit) 31
Table 4.16 The statistics of banks in Kazakstan (Total Deposit) 32
Table 4.17 The statistics of banks in Poland (Total Deposit) 32
Table 4.18 The statistics of banks in Romania (Total Deposit) 33
Table 4.19 The statistics of banks in Russia (Total Deposit) 33
x
Table 4.20 The statistics of banks in Ukraine (Total Deposit) 34
Table 4.21 The statistics of banks in Czech (Total Deposit) 34
Table 5.1 Seemingly unrelated regression (SUR) result-2000 43
Table 5.2 Allen Partial Elasticity of Substitution in 2000(accession countries) 44
Table 5.3 Allen Partial Elasticity of Substitution in 2000(non-accession countries) 44
Table 5.4 Seemingly unrelated regression (SUR) result-2001 45
Table 5.5 Allen Partial Elasticity of Substitution in 2001(accession countries) 46
Table 5.6 Allen Partial Elasticity of Substitution in 2001(non-accession countries) 46
Table 5.7 Seemingly unrelated regression (SUR) result-2002 47
Table 5.8 Allen Partial Elasticity of Substitution in 2002(accession countries) 48
Table 5.9 Allen Partial Elasticity of Substitution in 2002(non-accession countries) 48
Table 5.10 Seemingly unrelated regression (SUR) result-2003 49
Table 5.11 Allen Partial Elasticity of Substitution in 2003(accession countries) 50
Table 5.12 Allen Partial Elasticity of Substitution in 2003(non-accession countries) 50
Table 5.13 Seemingly unrelated regression (SUR) result-2004 51
Table 5.14 Allen Partial Elasticity of Substitution in 2004(accession countries) 52
Table 5.15 Allen Partial Elasticity of Substitution in 2004(non-accession countries) 52
Table 6.1 Structure Test for Null Hypotheses that accession and the Non-accession
countries have the same cost structure 59
Table 6.2 X-efficiency for the accession countries and the non-accession countries 59
xi
Table 6.3 X-efficiency for individual countries 60
1
CHAPTER I
INTRODUCTION
In May, 1
st
, 2004, eight Central Eastern European countries entered into the EU
(European Union), two more countries will join the EU in 2007. In order to be the
member of EU, many Central and Eastern European countries need made a transition in
the banking system during the 1990s. The main goal of those countries, performing the
bank reforms, was to decrease the intervention of the government. At the same time, most
of the eastern and central European banks adopted the Germany General Bank system in
law frame, style of management and technique index, etc. there are more and more
commercial banks which has close relationship with small firms entering into the
financial market. Many global financial institutions and big banks in EU supply direct
guidance and support to eastern and central European countries including reforming
scheme, consulting group, and banking export. This process is thought to have positive
effect on the performance of eastern and central European countries? banking according
to Weill (2000)
There has been much research on comparing the bank efficiency between Eastern
and Central European countries and developed Western European countries. Scholtens
(2000), Riess et al. (2002) illustrated that compared with the developed Western
European countries, there is a backward trend in the Eastern and Central European
countries. There are two important points in the bank transition: solving bad loans and
2
privation. When more and more foreign banks enter into the financial market in Eastern
and Central European countries, the banks in those countries have a tendency of merging
to relieve the pressure brought by the big Western European banks. The financial
market in Eastern and Central European countries are undeveloped and sensitive to the
intense competition which is illustrated by Scholtens (2000). Under the pressure of the
foreign countries? bank, the banking structure in Eastern and Central European countries
have a tendency to become less efficient.
The purpose of this paper is to fill the gap of the literature to compare the bank
efficiency between the Eastern and Central European countries which have attended the
EU and the ones which do not enter EU. Compared with the developed countries in
Western Europe, the banks in Eastern and Central European banks certainly have a very
big range of efficiency, but this does not have an important policy implications. Actually,
it has more sense to compare the banking efficiency between the new EU member and the
countries which did not attend the EU such as Russia. These two groups of banks have
the similar basis, and after comparing the banking efficiency, it can get assist in
developing policy regards to the financial implications of joining the EU.
An effective method to research on the change of the banking efficiency is to
illustrate the cost structure?s variation. There is a lot of literature which discusses the cost
structure in US banking and can supply a good theory to study European banking. This
paper will adopt two forms of translog cost function: the traditional translog function
form and transcendental translog functional form. The data set for this paper is
2000-2004, because in 1990s the transition in these countries banks had begun, through
2004 they undertook improving bank efficiency. The author will utilize the translog
3
function form to calculate the Allen Partial Elasticity first which can reflect the change in
cost structure of banks. These Eastern and Central European banks are undeveloped, and
the change of the substitution elasticity should lead to an interesting interpertation.
The Allen Partial Elasticity can show the difference of cost structure between the
accession countries and non-accession countries, however, AES is not an effective
method to compare the banking efficiency for these two groups of countries. We can
know the cost structure is different for accession countries and non-accession countries,
but we can not judge which structure is better, and can not provide policy
recommendations. Therefore, this thesis calculates X-efficiency by utilizing Stochastic
Frontier Analysis (SFA). After obtain the X-efficiency, we can compare the banking
efficiency for these two groups of countries.
This thesis will proceed as follows: A literature review is illustrated in Chapter 2;
the theoretical model and regression method is provided in Chapter 3; definitions of the
variables and data analysis are described in Chapter 4; empirical regression results are
analyzed in Chapter 5, and the conclusion will be drawn in Chapter 6
4
CHAPTER II
LITERATURE REVIEW
There is not a lot of literature on the banking efficiency of Central and Eastern
European countries. Generally speaking, these countries are in various phases of
economic transition, and the literature is primarily focused on the effect of ownership
change on banking efficiency. This thesis will not investigate this topic in particular, but
this type of literature will provide some economic background and introduce important
policies that are vital to understanding the issues discussed in this paper. In this chapter,
the literatures on ownership change in Central and Eastern European banking will be
illustrated in order to provide the basic framework for the analysis done in this thesis.
In his paper, Drakos (2003) investigates the banking efficiency of eleven Central
and Eastern European countries during the 1993-1999 period (8 countries are included in
this thesis). He examines the interest margin, and how decrease of the interest margin can
express an improvement of banking efficiency. Drakos (2003) concludes that ownership
has significant effect on banking efficiency. In addition, he finds that the entry of foreign
big banks will increase competition, which can improve banking efficiency in these
transition countries. The effect of a decrease of the interest margin is illustrated by
empirical result, and Drakos (2003) suggests that transition countries should adopt more
effective policies to encourage entry of foreign banks into their respective markets.
Next, Fries and Taci (2002) utilize data for consumer loans in 16 transition
5
countries during the 1994-99 period. they use the real growth of bank loans as a standard
to judge the growth of banks. Their empirical results show that high interest rates and
inflation rates in the transition countries have a negative effect on the growth of banks.
They found that the pace of the is basically the same as the growth of GDP in the
transition countries. Their primary focus is not banking efficiency, but they also arrive at
the same conclusion as Drakos (2003). They conclude that the entry of foreign big banks
has a significant positive effect on the growth of banks in these transition countries. In
addition, they suggest that transition countries should continue to open their financial
market in order to encourage large foreign banks enter into their respective markets.
In a companion study Fries et al (2002) investigate the performance of banks in
16 transition countries during the 1994-99 period. They examine whether banks in these
countries are taking excessive risk to make profit. In this paper, they find that there are
some small private banks taking risk, but this is not true for the whole banking system.
Contrary to the two papers mentioned above, Fries, Neven, and Seabright (2002) do not
focus on the effect of foreign banks, but instead they illustrate that the reform process of
transition countries matters significantly. Second, they estimate a multi-product revenue
function and cost function. Finally, they focus on the deregulation and suggest relaxing
the regulation policy to increase competition in the financial market of these transition
countries.
Next, Hasan and Marton (2003) investigate the cost and profit inefficiency of
banks in Hungary during the 1993-98 period. They estimate a multiproduct translog
functional form using the stochastic frontier approach (SFA). The distinguishing feature
of this paper is that they utilize just one transition country to describe an age of transition
6
in its banking system. Hasan and Marton conclude that the entry of foreign banks has a
positive effect on improving the cost and profit inefficiency of the local banks.
On the contrary, Nikiel and Opiela (2002) use only one transition country, Poland,
to investigate this question; however, they find a different result from the previously
mentioned studies. The above studies all conclude that foreign banks are more cost and
profit efficient, and the entry of foreign big banks has a positive effect on the transition
countries? financial markets. Nikiel and Opiela first estimate the cost efficiency. They
conclude that the cost efficiency is not determined by the banks type (foreign bank or
domestic bank), but instead by the type of consumers. They find that if a bank focuses on
supplying service to foreign consumers, the bank is cost efficient. Next, Nikiel and
Opiela also examine the profit efficiency question, and find that banks which mainly
supply services to foreign customers generally have low profit. Finally, they conclude
that cost-efficiency is more important than profit-efficiency, because foreign banks may
have profit-inefficiency in the short-run, but they will maximize their profit in the long
run by being more cost efficient.
Similarly, there are two case studies for Croatia on similar topic. Kraft and
Tirtiroglu (1998) investigate X-inefficiency and scale-inefficiency for new and old banks
in Croatia by using the SFA method. The data used in this study is obtained from banks
in Croatia during the 1994-95 period. The empirical results show that new private banks
are more X-inefficient and more inefficient in scales compared with existing banks in
Croatia. Kraft and Tirtiroglu (1998) conclude that this result is caused by simply selling
banks to persons, instead of rebuilding the structure of the financial market. They suggest
the Croatia government formulate proper policies to make new banks more competitive,
7
which will improve Crotia?s financial market.
Jemric and Vujcic (2002) utilize a different method, Data Envelopment Analysis
(DEA), to estimate the banking efficiency in Croatia using a dataset during the
1995-2000 period. This dataset is slightly after the one adopted by Kraft and Tirtiroglu
(1998), and their conclusion is the opposite. Jemric and Vujcic (2002) first conclude that
foreign banks are the most efficient, and that new banks are more efficient than old ones.
This finding is totally opposite with what Kraft and Tirtiroglu (1998) found. This is
probably due to the different environment of these two periods. The political environment
in Croatia was not stable during 1994-1995, but became more stable after 1995. One
important contribution of Jemric and Vujcic (2002) is that they investigate the factors
which can determine the efficiency of Croatian banks. They find that a banks? size does
not necessarily determine its banking efficiency; however, number of labor, fixed assets
and number of bad loans are all significant factors for banking efficiency.
Next, Grigorian and Manole (2002) try to find some general conclusion for the
Central and Eastern European countries. The regression method adopted in this paper is
also DEA. They point out that a change of ownership will not improve banking efficiency,
but technology brought by advanced foreign banks will make local banks more efficient.
Lastly, Grigorian and Manole (2002) also illustrate several disadvantages of the DEA
method which may create some bias in the dataset.
Yildirim and Philippatos (2002) adopt SFA (Stochastic Frontier Approach) and
DFA (Distribution Free Approach), two methods to estimate banking inefficiency in 12
transition countries in Central and Eastern Europe during the 1993-2000 period. They
focus primarily on the degree of inefficiency, and conclude that foreign banks are cost
8
efficient, but profit inefficient.
The literature above relate to the economic background of banking efficiency in a
few transition countries in Central and Eastern Europe. In addition to these works, there
is some other technical literature which estimates the AES and X-efficiency for the
banking. This kind of literature supplies the technical background for this thesis, and is
crucial to understand the development of this thesis.
Humphrey (1981) is the first important literature for the Allen Partial Elasticity
(AES). The dataset utilized in this paper is for big US money center banks during
1970-1975 period. He utilizes a full information maximum likelihood estimation (MLE)
technique to estimate a translog profit function. Two elasticities are obtained in
Humphrey (1981): liability substitution elasticity and own elasticity of demand. This
paper was an excellent basis for other literature, which utilized the translog functional
form to examine the cost structure in the banking system.
The next important study examining AES is Murray and White (1983). They
utilize a dataset for credit unions in British Columbia during 1976-1977 period. The
functional form utilized by Murray and White (1983) is a multiproduct translog cost
function, which includes three outputs (Mortgage lending, other loans and investment)
and four inputs (labor, capital, demand deposits and time deposits). They examine the
cost structure by using Seemingly Unrelated Regression (SURE) to estimate a translog
function jointly with inputs share equations. In addition Murray and White (1983)
compare the translog functional form with other functional forms including CES and
Cobb-Douglas.
Similar to Murray and White (1983), Mester (1987) also utilize a translog cost
9
function to estimate the AES. She focuses on the cost structure of saving and loans. The
dataset consist of saving and loans in California in 1982. Mester (1987) also utilizes the
SURE technique to estimate the AES. After examining the scale, scope and the Allen
elasticity of substitution in saving and loans, she concludes that small saving and loans
does not have significant cost disadvantage compared with large saving and loans in the
dataset. Different from the two literatures above, Mester (1987) calculates the standard
error for the AES, which makes the regression results comparable to other papers.
Noulas et al (1990) investigate US Commercial Banking focusing on the effect of
deregulation. Noulas et al (1990) utilize the dataset of US large banks in 1986. They
notice that the banks are merging and the average scale of bank increase significantly
during this period. Noulas et al (1990) examine how the change of the banking
environment influences the cost structure of US commercial banks. This topic is also
important in Central and Eastern Europe. Also Noulas et al (1990) adopt a useful
classification method, which is also utilized in this thesis. They define a bank with more
than 1 billion deposits as a big bank, and a bank with less than 1 billion deposits as a
small bank. They also adopt a multiproduct translog cost function with four outputs, four
variable input and one fixed input.
Now, we will introduce some literature that focuses on banking efficiency. Hunter
and Timme (1993) examine the cost efficiency and AES for US banks by using the
Federal Reserve data from 1983-1990. The regression technique adopted in Hunter and
Timme (1993) is Full Information Maximum Likelihood (FIML). This paper also has a
distinct feature compared with the literature above. Hunter and Timme (1993) estimate
two cost functions including a total cost function and a variable cost function. They also
10
estimate the cost function jointly with share equations. There are six outputs in the total
cost function, and four outputs in the variable cost function. Lastly, there are five inputs
included in the cost model.
Next, Turati (2002) is an excellent study to discuss scale, scope and AES in
European banking. He focuses on the European banking system including France,
Germany, Italy, Spain and the UK from 1992-1999. There are three cost functions in
Turati (2002). They all include three inputs (Labor, Capital and Deposit) which are the
same as are used in this thesis, and two outputs (loans and other assets). His findings
show a relatively small difference between cost efficiency of these European countries.
Lastly, Williams and Gardener (2003) examine the issue of X-efficiency for
European banks by using Stochastic Frontier Analysis (SFA). They suppose that the
regional European banks are most efficient in local area because of the information
asymmetry. This is probably because the regional banks master some specific
information of the local consumers. The dataset is of large regional banks in Europe
during 1990-1998. This paper concludes that the regional banks are efficient, and that the
competitiveness of the banking environment has a positive effect on banking efficiency.
11
CHAPTER III
THEORETICAL MODEL
This chapter will illustrate the translog cost functional form, the formulas for the
Allen Partial Elasticity, the SURE technique, and the SFA technique used to calculate the
X-efficiency for different groups of banks in Central and Eastern Europe. The cost
function form utilized here is a multiproduct translog cost function.
The cost function of a bank is a very useful tool to analyze the structure of a
financial market. We can easily obtain an abundance of useful measures such as
Economies of Scale and the Elasticity of substitution from the cost function. The general
cost function for banks can be written as follows: C(P, Y)=MIN{PX: X?V(Y), P>0}
where P is the input price vector, Y is the output vector, and the V(Y) is the requirement
set.
The general cost function can be illustrated by microeconomic theory, and it is
very important to choose a proper functional form to be utilized in an empirical project.
The proper functional form must be flexible and have the fewest restrictions. The
functional form should be positive, not decreasing in the cost, homogenous and concave.
In addition it should have real values. The translog function form can satisfy all the
restrictions above. The functional form of the mutiproduct translog cost function utilized
in this thesis will be illustrated as follows:
LnTC=?
0
+?
1
lnY
1
+?
2
lnY
2
+?
3
lnY
3
+?
1
lnPK+?
2
lnPL+?
3
lnPD+1/2(?
11
lnY
1
lnY
1
+?
12
lnY
1
lnY
12
2
+?
13
lnY
1
lnY
3
+?
22
lnY
2
lnY
2
+?
23
lnY
2
lnY
3
+?
33
lnY
3
lnY
3
)+1/2(?
11
lnPKlnPK+?
12
lnPKlnPL+
?
13
lnPKlnPD+?
22
lnPLlnPL+?
23
lnPLlnPD+?
33
lnPDlnPD)+?
11
lnY
1
lnPK+?
12
lnY
1
lnPL+?
13
l
nY
1
lnPD+?
21
lnY
2
lnPK+?
22
lnY
2
lnPL+?
23
lnY
2
lnPD+?
31
lnY
3
lnPK+?
32
lnY
3
lnPL+?
33
lnY
3
ln
PD+?
In this equation, Y
i
expresses the ith output (i=1, 2, 3, 4); PK, PL and PD are the
input price for the capital, labor and deposit; ?, ?, ? and ? are parameters which are to be
estimated. The condition of positively linear homogeneity in input prices is illustrated as
follows:
??=1, ??=0, and ??=0
So far, we have discussed the proper cost functional form. Next we can simply
find the parameters using OLS for the cost function. However, the coefficients may not
be efficient. There is a better method to obtain the efficient estimators, SURE, which
consists an entire system of equations including the cost function jointly with share
equations. After utilizing Shephard?s lemma, we can obtain the cost share equations as
follows:
S
1
=?
1
+?
11
lnPK+?
12
lnPL+?
13
lnPD+?
11
lnY
1
+?
21
lnY
2
+?
31
lnY
3
+e
1
S
2
=?
2
+?
21
lnPK+?
22
lnPL+?
23
lnPD+?
12
lnY
1
+?
22
lnY
2
+?
32
lnY
3
+e
2
S
2
=?
3
+?
31
lnPK+?
32
lnPL+?
33
lnPD+?
13
lnY
1
+?
23
lnY
2
+?
33
lnY
3
+e
3
Next we will use the SURE method to estimate the parameters. There is often a
correlation among disturbance terms between the cost and cost share equations, and if the
correlation is ignored, the OLS will estimate the inefficient estimators. In this thesis, the
Seemingly Unrelated Regression (SURE) will be used to obtain efficient and unbiased
estimators. Because the sum of the three share equations equals to 1, the cost share
13
equations are not linearly independent, which yields a singularity problem. In order to
solve this problem, one of the share equations should be dropped, and the regression
result will be the same. Because dropping any of them gives us the desired result, it does
not matter if we drop input price of labor, capital or deposit.
After obtaining parameters for the translog cost function by using SURE, the
Allen Partial Elasticity (AES) can be calculated. AES can express the elasticity of an
input ratio with respect to another input ratio. It can illustrate the relationship between the
different inputs, and can supply the information for analyzing structure of the financial
markets. Because three inputs are included in this thesis, there are six useful AES. The
formulas for the AES are illustrated as follows;
?
11
= (?
11
+S
1
(S
1
-1))/S
1
?
12
= (?
12
+S
1
S
2
)/ (S
1
S
2
)
?
13
= (?
13
+S
1
S
3
)/ (S
1
S
3
)
?
22
= (?
22
+S
2
(S
2
-1))/S
2
?
23
= (?
23
+S
2
S
3
)/S
2
S
3
?
33
= (?
33
+S
3
(S
3
-1))/S
3
Where ?
11,
?
22
and ?
33
are own AES, and ?
12,
?
13
and ?
23
are cross AES. S
1
is the
cost share for Capital, S
2
is the cost share for Labor, and S
3
is the cost share for Total
Deposit. Therefore, the AES is calculated by using the parameters obtained in the SURE
estimation and the input cost shares. An important topic here is what can be used as the
variable of cost share. There are three values for cost share: actual value for the cost share,
mean value of the actual cost share, and the fitted cost share. It should be noted that, in
this thesis, the mean value of the actual cost share will be utilized.
14
The AES can be only utilized to obtain the information of the cost structure for
the whole financial market in the Central and Eastern European countries. The main
purpose of this thesis is to investigate the different cost structure and banking efficiency
between the countries which have or will attend the Europe Union (accession countries)
and the countries that have not and will not attend EU. Therefore a Chow test will be
utilized in this thesis to test this difference. Many foreign big banks enter into financial
markets in accession countries, and the accession countries probably adopt some new
advanced technology. Therefore, the cost structures in these two groups of countries may
be different. A Chow test is used to test the hypothesis that the regression coefficients are
different in these two groups of countries. However, this thesis will still utilize the SURE
method to estimate the parameters for these two groups of data.
We can know whether the two groups of countries have different cost structure by
using a Chow test. However we do not know which cost structure is better. Even if we
know the structures are different, we cannot supply valid policy suggestions. Therefore
we need to do a new regression to obtain further information.
To do this, this thesis will utilize the Stochastic Cost Approach (SFA) to estimate
the cost efficiency for each bank. It will divide the countries into two groups: the
accession countries which will attend EU, and the non-accession countries which will not
attend the EU. Next it will calculate the X-efficiency for every bank using SFA in the
same group, and then get the average X-efficiency for each group.
In SFA analysis, the error term for the cost function is not ?? (0, ?
2
), but it is a
composite expression: e=V+U. Because the cost function utilized in this thesis is translog
function, the formula is e=lnV-lnU, where V is the cost inefficiency. We can choose two
15
types of distribution for the cost inefficiency: Half Normal and Truncated Normal.
Stevenson (1980) shows that truncated normal distribution is more flexible than the Half
Normal distribution. Therefore, this thesis will adopt the Truncated Normal distribution
for lnV. On the contrary, lnU is random error and follows the symmetric normal
distribution. In order to get the X-efficiency, we have two steps: first we use SFA
technique to obtain the expected value of inefficiency conditional on the estimated value
of residual e: E (V/e); second, we can obtain the X-efficiency by comparing each bank
with the most efficient bank in that group. We can utilize e
(lnV)
to get the cost inefficiency
V, where the smallest V illustrates the most efficient bank. Then we can get the
X-efficiency for every bank by this formula: X
i
=V
best
/V
i
where X
i
is the X-efficiency for
the bank i, V
best
is the smallest value for the cost inefficiency in the group, which shows
the most efficient bank, and V
i
is the cost inefficiency for the bank i. lastly, X
i
is between
0 and 1, and the X
i
is bigger when a bank is more efficient.
16
CHAPTER IV
DATA AND METHODOLOGY
The primary use of this chapter is to describe the data used in this thesis, and also
describe how the variables are defined. All the data utilized in this thesis is from
bankscope. It contains a lot of useful information for both public and private banks. As a
global dataset, the bankscope covers 25000 banks around the world until August 2004.
This database contains detailed information on the consolidated or unconsolidated
balance sheet and income statement. The report also includes information in varying
degrees of standards which you can use to search and analyze banks across borders. The
database is huge, and contains 200 types of data and 36 kinds of ratios for every bank.
The bankscope database has three main advantages: first, the data provided in this
database is all given in standardize format, which can be easily utilized and compared;
second, it supplies the detailed enough information for researchers to investigate
individual banks; third, it supplies the locations of the banks which makes for easy
comparison between banks in different countries. The disadvantage of this dataset is that
the data is not a random sample. It is supplied on a volunteer basis by commercial banks,
and at the same time there is some missing data which makes having comparison study of
different years somewhat difficult.
The dataset in this thesis covers the 2000-2004 period. The data contains public as
well as commercial banks; however, commercial banks are the main part of the dataset.
17
The main purpose of this thesis is to illustrate the difference in banking efficiency in
diverse countries, and because of this, public and commercial banks do not need to be
differentiated. The translog cost function in this thesis includes four outputs and three
input price, which are independent variables. The dependent variable is total cost, and all
variables are all utilized in the translog form.
TOTAL COST is the dependent variable and is an important standard used to
estimate banking efficiency. The method utilized in this paper is the intermediation
method which uses the actual dollar value as the variable. The total cost is a sum of all
the input cost. In this paper, the total cost is calculated by adding up the cost in capital,
labor, and commission. By calculating the total cost, we can also get a very important
variable for this thesis, share of the input. The variable of share will be utilized to
construct the share equation which will be used to calculate AES. Every variable of share
illustrates an input share of the total cost. For example, capital?s share equals the total
payment to capital divided by total cost.
There are three variables of output, loans, investment, commission revenue, and
the detailed definitions of the outputs will be given as follows.
First, LOANS (Y1) is the net loans. The net loans are calculated by total loans
subtracting loan reserves. Total loans contain the sum of commercial loans, credit cards,
real estate loans, and installment loans. Loans reserve is the amount of reserve for
commercial banks to give to the central banks. The loans reserve cannot be utilized by the
banks, so it is most reasonable to utilize net loans as one of the output.
Second, IVESTMENT (Y2) is taken directly from the reports of bankscope.
Investment is an important source of output in banks, and it can express the ability of
18
banks to earn money. It contains Securities, Tax-Exempt Securities, stocks and bonds,
and other forms of investment.
Third, COMMISSION REVENUE (Y3) is the income from the fee which is
charged by banks for the service of facilitating transactions, such as buying or selling
securities or real estate. It is not a big part of the output, but this variable may have a
relationship with the number of employee, and so on, so this paper includes this as an
output for banks.
Next, there are three kinds of input, capital (X1), labor(X2) and deposit(X3).
There are three input price: price of capital (PK), price of labor (PL) and price of deposit
(PD).
PRICE OF CAPITAL (PK) has two parts. The first is the physical capital cost and
the second is the financial capital cost. The physical capital cost contains the occupancy
cost on the building and equipment, and the reserve for physical capital. The financial
capital cost includes dividends paid on stock, and interest payment on securities and so
on. The price of capital is calculated by totaling the payment for capital divided by the
amount of capital.
Next, PRICE OF LABOR (PL) is easy to understand. It is calculated by dividing
the sum of wages by the number of employees. The data for this variable is based on the
full time employee and is equal to the average annual wage for all employees.
Lastly, PRICE OF DEPOSIT (PD) is calculated by dividing the interest payment
for the total deposit by the total dollar value of deposit. The deposit includes all types of
accounts such as demand deposit, time deposit and other deposits.
These are the definitions of the input prices. It should be noted that the input
19
prices cannot be gotten directly from the report, but must be calculated, and the
calculation method will be illustrated next. There are two main types of expense for
banks. The first is interest expense and the second is the non-interest expense. It is easy to
understand that the interest expense is the payment for a deposit. Therefore, the interest
payment divided by the total deposit is equal to the price of deposit. The non-interest
expense can be divided into two parts: the expenses for the employees and the other
expenses. The expenses for the employees are calculated by dividing by the number of
employee and getting the price for labor. The non-interest payment excluding the
personal payment is the expense for the capital, and we can use this divided by the asset
expense to get the price for the capital.
General data analysis: the median and mean values for all the four output in
accession countries are bigger than the data in non-accession countries in the whole 5
years? dataset. However, the non-accession countries own the bigger maximum value.
This situation illustrates that in average the banking scale is larger in accession countries.
There are many small banks in non-accession countries, such as the bank with the total
deposit just 1 million dollars. And the minimum data for total deposit in accession
countries is 7 million. There is a very huge deviation between median and average value
in non-accession countries, because there is a very big bank in Russia,
SBERBANK-Savings Bank of the Russian Federation, which has more than ten billion
dollars in loans, and it increased the average value. The same situation happens in all the
three output.
After examining the dataset, we can find that this bank does not have high input
price. The price of capital and labor in SBERBANK-Savings Bank of the Russian
20
Federation are all lower than the average across countries. The price of deposit can
expressed as a weighted interest rate, and the SBERBANK-Savings Bank of the Russian
Federation has a high interest rate in comparison to the average. The biggest banks do not
have the highest input price, but have the very large total cost. It is interesting to examine
the cost efficiency of the Russian banks which contain most of big banks in the Central
and Eastern European banks.
Data analysis for cost share: in 2000, the average cost share of the capital, labor
and deposit for accession countries are 25%, 16% and 59%. This illustrates that the
largest part of the cost is the interest cost, and the cost of the capital is larger. In order to
enter Europe Union, accession countries in Central and Eastern Europe adopt more
advanced technique which makes the cost of capital a larger fraction of the overal costs.
Compared with accession countries, the non-accession countries do not have the
dominant cost share. The share for capital, labor and deposit is 37%, 25%, 38%. The
non-accession countries have similar share with these three inputs.
In 2001, the accession countries have nearly the same cost structure as 2000. The
average cost share is 23%, 17% and 60% for capital, labor and deposit. This illustrates
that the process of entry of foreign big banks continues and the accession still try to
improve the techniques for banking. The non-accession countries? cost structure also
stays the same, which is 33%, 27% and 40% for capital, labor and total deposit. In rest of
three years, the non-accession countries do not have any significant change in cost
structure, because they do not have any motivation to improve the banking efficiency.
The cost share for accession countries in 2002 is 25%, 23% and 52%. The share of capital
and labor becomes similar, which illustrates that the banking technique in accession
21
countries is more mature. In The 2003 and 2004, the cost structure for accession
countries stays the same. The accession countries owns nearly same cost share for
capital and labor, but have a higher share for deposit.
In the Data analysis for the individual country, the total deposit is the most
important resource for banks, and most of the cost and profit for banks is made by
utilizing deposits. The investment and loan is the primary method to use deposits, and the
expense of the interest is the biggest part of the cost for Central and Eastern European
banks and it has a very big relationship with total deposits. At the same time, the total
deposits, which is an important standard for bank?s scale, is often used to divide the
banks into different groups. Therefore, it follows that this thesis will utilize the data of the
total deposits in the different countries to analyze the structure difference in the Central
and Eastern European banks. There are several small countries which only have a few
banks in the dataset, and some years contain no data for these banks. For these small
countries, if they have all the data for all five years, they will have a statistical table; and
if they miss any year of data, they will be treated as random data, but are not given a table
to describe the structure of that country. The tables will include the number of banks,
minimum, maximum, median, mean, the number of big banks, and the size change. One
billion dollars in total deposits is the standard to judge the scale of the banks. It is a big
bank if its total deposit is over one billion. The size change variable means the difference
between the mean values of the deposit compared with the 2000 mean value. There are
nine Central and Eastern European countries on the statistical tables in this thesis. We can
divide the nine countries into four groups which each have different market structures.
The First group of countries includes Armenia, Belarus and Georgia. This group
22
of countries owns very few banks in the dataset. They all have less than six banks for
each year. They have nearly no big banks, except Belarus which has one big bank. The
bank scale is small among these three countries. In 2000, all the countries had total
deposit less than 15 million dollars, and Armenia only had 4656.45 thousand dollars.
They all have a big increasing trend during the dataset. In 2004, the mean scale of banks
in Armenia is 1289.93% compared with 2000. Belarus increased the most, in 2003, when
it increase more than 36 times its data in 2000; Georgia increased the least, with its
biggest scale being in 2003 which was 790.75% versus the data in 2000. however, this is
still a big increase compared with countries in other group.
The Second group is the accession countries. There are seven countries in this
dataset which have or will attend the Europe Union. They are: Bulgaria, Poland, Romania,
Czech, Hungary, Slovenia, and Slovakia. All the seven countries are included in the
tables, and they all have similar situations. They do not have a lot of banks, but all have
big banks which own more than one billion in total deposits. This is especially true for
Poland which does not have more than 5 banks in the dataset, but every year has 1 big
bank, and in 2004 all three banks in Poland are big banks. Romania and Czech has more
banks than Poland, and nearly always has one big bank. Poland has a big commercial
bank, Bank Pekao SA which is the biggest commercial bank in Poland. Romania and
Czech also has two big banks: Romanian Commercial Bank SA and Ceskoslovenska
Obchodni Banka. Compared with Romania, Poland?s banks expand more rapidly,
Increase an average of 703.77% during the five years. Romania and Czech increase
174.97% and 135.69% respectively compared with the data in 2000. Slovenia and
Slovakia are very similar. They all have less than 9 banks, but often have 3 large banks.
23
These two countries? banking scale did not increase significantly, until 2004 when they
arrive at the biggest scale.
The third group includes Kazakstan and Ukraine. They do not have big banks in
the 2000 and 2001, but have big banks in the rest of the 3 years. The increased speed of
scale is not big in this group: only 315.13% and 224.83% respectively. These two
countries do not have one dominant super big bank, but have 2 or three big banks which
have similar scale and can compete with each other. Kazakstan has 3 big banks: Bank
Turan Alem, Kazkommertsbank and OJSC Halyk Savings Bank of Kazakhstan; Ukraine
has 2 big public banks, and 1 bank named Privat bank. This group of countries is notable
because it has competition in the banking system.
The last group includes just one country, Russia. It is the biggest country in
Central and Eastern Europe and has a lot of banks. Russia owns the biggest bank scale in
this dataset. Every year it has mean total deposits of more than 1 billion dollars. At the
same time, it has mostly big banks. In particular, there is one super big bank in Russia,
SBERBANK which is the central bank of Russia. This bank is always the biggest bank in
the dataset, and it brings up the mean scale of Russian banks significantly. In contrast
with the other groups, Russia has a decreasing trend of the bank?s scale, which is quit odd
when compared to the significant, positive growth seen in the other groups.
24
Table 4.1 Accession countries and non-accession countries in this thesis
Accession countries
Non-accession countries
Bulgaria(07)(BG) Albania(AL)
Poland(PL) Armenia(AM)
Romania(07)(RO) Belarus(BY)
Czech(CS) Croatia(HR)
Hungary(HU) Georgia(GE)
Slovenia(SI) Kazakstan(KZ)
Slovakia(SK) Moldova(MD)
Russian(RU)
Ukraine(UA)
Kyrgyzstan(KG)
25
Table 4.2 Data statistics of variables (2000)-accession countries
standard Y1 Y2 Y3 PK PL PD
Minimum 6690.82 20700.00 111.07 0.10 7.37 0.01
Maximum 3959220.37 8667151.51 144561.51 218.91 46.50 3.42
Median 253284.94 207442.14 6654.57 0.72 18.03 0.07
Mean 416845.99 684845.38 18168.20 8.37 19.18 0.17
share 0.25 0.16 0.59
All values are in thousand dollars except the input price
Table 4.3 Data statistics of variables (2000)-non-accession countries
standard Y1 Y2 Y3 PK PL PD
Minimum 1822.78 1721.52 101.27 0.08 1.07 0.004
Maximum 10171388.53 10002429.19 431271.31 26.75 59.31 0.24
Median 50707.08 47349.68 3276.38 0.79 8.81 0.05
Mean 294577.49 361710.34 14938.24 1.74 11.02 0.06
share 0.37 0.25 0.38
All values are in thousand dollars except the input price
26
Table 4.4 Data statistics of variables (2001)-accession countries
standard Y1 Y2 Y3 PK PL PD
Minimum 5188.00 15334.12 300.00 0.12 3.63 0.01
Maximum 7875389.50 7390708.44 367453.54 637.53 51.54 3.18
Median 278427.93 337192.37 10597.69 0.63 16.48 0.05
Mean 634748.50 991195.38 31903.07 15.74 18.55 0.14
share 0.23 0.17 0.60
All values are in thousand dollars except the input price
Table 4.5 Data statistics of variables (2001)-non-accession countries
standard Y1 Y2 Y3 PK PL PD
Minimum 1321.13 742.42 56.62 0.04 1.44 0.01
Maximum 13155962.54 9676489.91 475096.22 13.89 46.13 0.85
Median 58969.20 44046.83 3456.39 0.74 7.54 0.05
Mean 338242.84 333706.77 14540.08 1.20 11.14 0.06
share 0.33 0.27 0.40
All values are in thousand dollars except the input price
27
Table 4.6 Data statistics of variables (2002)-accession countries
standard Y1 Y2 Y3 PK PL PD
Minimum 1817.00 2516.78 86.26 0.11 0.10 0.01
Maximum 6850137.69 11397266.18 362593.23 12.28 50.99 1.09
Median 247461.11 344704.77 9408.01 0.71 18.03 0.05
Mean 670501.38 1044194.42 30776.93 1.21 19.16 0.07
share 0.25 0.23 0.52
All values are in thousand dollars except the input price
Table 4.7 Data statistics of variables (2002)-non-accession countries
standard Y1 Y2 Y3 PK PL PD
Minimum 86.78 817.54 37.51 0.08 0.36 0.01
Maximum 14623598.05 12630328.27 532638.65 14.21 69.04 1.34
Median 69628.50 50967.43 3019.28 0.81 7.64 0.05
Mean 362614.84 337960.33 14604.97 1.49 12.22 0.07
share 0.33 0.27 0.40
All values are in thousand dollars except the input price
28
Table 4.8 Data statistics of variables (2003)-accession countries
standard Y1 Y2 Y3 PK PL PD
Minimum 4817.96 1894.42 136.43 0.14 6.62 0.01
Maximum 9226774.72 12554182.58 420845.81 12.02 686.13 2.25
Median 337158.53 220005.05 8244.33 0.75 18.81 0.04
Mean 957493.26 984659.78 42350.96 1.17 29.69 0.08
share 0.31 0.27 0.42
All values are in thousand dollars except the input price
Table 4.9 Data statistics of variables (2003)-non-accession countries
standard Y1 Y2 Y3 PK PL PD
Minimum 262.50 906.36 22.69 0.04 0.72 0.00
Maximum 24641519.12 16177372.08 627995.76 9.19 104.55 3.04
Median 125238.54 81907.69 4248.36 0.77 9.04 0.05
Mean 595369.08 437716.35 19632.65 1.28 14.79 0.08
share 0.32 0.27 0.41
All values are in thousand dollars except the input price
29
Table 4.10 Data statistics of variables (2004)-accession countries
standard Y1 Y2 Y3 PK PL PD
Minimum 20567.72 18733.90 278.57 0.08 7.03 0.01
Maximum 13464307.50 7205979.26 508209.00 4.45 67.74 0.24
Median 541996.78 297429.49 12756.65 0.62 18.65 0.04
Mean 1477176.29 1106965.90 53590.97 0.88 21.12 0.05
share 0.29 0.28 0.44
All values are in thousand dollars except the input price
Table 4.11 Data statistics of variables (2004)-non-accession countries
standard Y1 Y2 Y3 PK PL PD
Minimum 17400.00 10523.02 438.46 0.18 2.31 0.02
Maximum 5193298.42 4841685.47 155582.52 11.24 97.44 0.34
Median 380489.25 166166.34 10303.19 0.70 10.72 0.05
Mean 882590.56 545232.67 29685.85 1.11 15.60 0.06
share 0.27 0.26 0.46
All values are in thousand dollars except the input price
30
Table 4.12 The statistics of banks in Armenia (Total Deposit)
Variable 2000 2001 2002 2003 2004
NO. bank 1 1 6 6 2
Minimum 4656.45 4292.02 7087.32 9722.08 53707.19
Maximum 4656.45 4292.02 31564.23 38080.21 66422.90
Median 4656.45 4292.02 23016.04 23291.52 60065.04
Mean 4656.45 4292.02 20098.02 23513.34 60065.04
Big bank 0 0 0 0 0
Size change 100% 92.17% 431.62% 504.96% 1289.93%
All values are in thousand dollars except the input price
Table 4.13 The statistics of banks in Belarus (Total Deposit)
Variable 2000 2001 2002 2003 2004
NO. bank 1 3 4 4 1
Minimum 13595.32 8800.00 18900.00 34800.00 73629.14
Maximum 13595.32 1253086.23 1399600.00 1499769.48 73629.14
Median 13595.32 40716.09 155265.05 218529.55 73629.14
Mean 13595.32 434200.78 432257.53 492907.14 73629.14
Big bank 0 1 1 1 0
Size change 100% 3193.75% 3179.46% 3625.56% 541.58%
All values are in thousand dollars except the input price
31
Table 4.14 The statistics of banks in Georgia (Total Deposit)
Variable 2000 2001 2002 2003 2004
NO. bank 4 4 6 6 2
Minimum 1164.56 1699.03 3492.82 5638.55 55068.49
Maximum 30329.11 33980.58 61722.49 83759.04 177753.42
Median 13696.20 15533.98 21339.71 28626.51 116410.96
Mean 14721.52 16686.89 24864.43 34096.39 116410.96
Big bank 0 0 0 0 0
Size change 100% 113.35% 168.90% 231.61% 790.75%
All values are in thousand dollars except the input price
Table 4.15 The statistics of banks in Croatia (Total Deposit)
Variable 2000 2001 2002 2003 2004
NO. bank 18 22 23 23 7
Minimum 19668.19 18046.91 21565.30 27406.44 111834.92
Maximum 2275943.25 4536022.02 5804017.50 7278150.02 8308674.30
Median 131282.72 140713.26 131560.90 197973.53 1136195.91
Mean 326602.20 566485.16 706608.89 1037471.08 2923909.81
Big bank 1 3 4 6 4
Size change 100% 173.45% 216.35% 317.66% 895.25%
All values are in thousand dollars except the input price
32
Table 4.16 The statistics of banks in Kazakstan (Total Deposit)
Variable 2000 2001 2002 2003 2004
NO. bank 7 12 14 16 14
Minimum 18346.02 26195.07 82.15 76.97 55066.92
Maximum 566492.75 820193.08 1093304.70 1807148.80 2869292.31
Median 99833.91 67671.44 133975.74 253443.70 486845.76
Mean 145991.40 196810.98 262566.62 475961.64 904912.42
Big bank 0 0 2 3 3
Size change 100% 134.81% 179.85% 326.02% 619.84%
All values are in thousand dollars except the input price
Table 4.17 The statistics of banks in Poland (Total Deposit)
Variable 2000 2001 2002 2003 2004
NO. bank 5 4 5 5 3
Minimum 91687.84 375489.70 108172.95 734334.90 1479902.35
Maximum 908408.95 15225464.84 14071062.95 13779057.69 7348615.90
Median 623109.50 770303.87 787383.30 1041194.40 1538690.48
Mean 548786.45 4285390.57 3198388.25 4509153.12 3455736.24
Big bank 0 1 1 1 3
Size change 100% 780.88% 582.81% 821.66% 629.71%
All values are in thousand dollars except the input price
33
Table 4.18 The statistics of banks in Romania (Total Deposit)
Variable 2000 2001 2002 2003 2004
NO. bank 9 10 14 16 5
Minimum 26100.00 22400.00 13400.00 16500.00 95693.09
Maximum 3447553.85 3479466.50 4275516.81 4853132.66 7252571.09
Median 71143.87 89084.27 114170.85 169805.92 226594.76
Mean 472073.46 483244.01 515975.29 610799.77 1693866.37
Big bank 1 1 1 2 1
Size change 100% 102.37% 109.30% 129.39% 358.81%
All values are in thousand dollars except the input price
Table 4.19 The statistics of banks in Russia (Total Deposit)
Variable 2000 2001 2002 2003 2004
NO. bank 23 36 48 56 29
Minimum 22613.64 4861.06 13003.23 30979.99 36938.67
Maximum 22657024.61 24746476.87 28309905.96 41692491.81 6328900.00
Median 260553.98 137582.95 153441.63 251318.13 598590.20
Mean 1559572.34 1222797.86 1169213.22 1508776.92 1304015.78
Big bank 6 6 8 11 11
Size change 100% 78.41% 74.97% 96.74% 83.61%
All values are in thousand dollars except the input price
34
Table 4.20 The statistics of banks in Ukraine (Total Deposit)
Variable 2000 2001 2002 2003 2004
NO. bank 11 13 18 18 10
Minimum 17600.00 1113.52 4500.79 7052.42 49000.00
Maximum 557291.38 872000.00 1007276.27 1667600.00 1962800.00
Median 63961.73 112445.22 128549.34 188089.66 441500.00
Mean 185431.92 248387.19 265820.96 435776.59 717619.56
Big bank 0 0 1 3 3
Size change 100% 133.95% 143.35% 235.01% 387.00%
All values are in thousand dollars except the input price
Table 4.21 The statistics of banks in Czech (Total Deposit)
Variable 2000 2001 2002 2003 2004
NO. bank 10 11 16 21 1
Minimum
7917.912 9178.411 2202.181 4672.897 1712336.150
Maximum
10221405 9761328 15998673 19480861 1712336.150
Median
592248.7 1236703 936004.1 181445.4 1712336.150
Mean
1549347 1783356 2605543 2308043 1712336.150
Big bank 4 6 8 6 1
Size change 100%
115.10% 168.17% 148.97% 110.52%
All values are in thousand dollars except the input price
35
Table 4.22 The statistics of banks in Hungary (Total Deposit)
Variable 2000 2001 2002 2003 2004
NO. bank 3 3 2 4 2
Minimum
184572.4 175706.9 513168.4 656613.1 4275107
Maximum
6145861 6911314 558083.1 13544801 17506878
Median
337098.3 916306.5 535625.8 1783869 10890992
Mean
2222511 2667776 535625.8 4442288 10890992
Big bank 1 1 0 2 1
Size change 100%
120.03% 24.10% 199.88% 490.03%
All values are in thousand dollars except the input price
Table 4.23 The statistics of banks in Slovenia (Total Deposit)
Variable 2000 2001 2002 2003 2004
NO. bank 7 7 9 9 4
Minimum
90143.2 111952.2 162540.1 240675 336713
Maximum
1103164 1158912 1920139 2412685 8572743
Median
634901.5 694595.7 992005.3 1347036 2156909
Mean
541293.8 644392.6 924634.3 1252286 3305819
Big bank 1 2 4 6 3
Size change 100%
119.05% 170.82% 231.35% 610.73%
All values are in thousand dollars except the input price
36
Table 4.24 The statistics of banks in Slovakia (Total Deposit)
Variable 2000 2001 2002 2003 2004
NO. bank 5 7 9 9 3
Minimum
107930.1 112391.9 112266.5 87763.46 1016886.26
Maximum
3440765 3791425 4632756 4861683 6466701.291
Median
359080.3 665007.1 765253.7 704694.5 1523722.058
Mean
944027.8 1520910 1611901 1438707 3002436.536
Big bank 1 3 3 3 3
Size change 100%
161.11% 170.75% 152.40% 318.05%
All values are in thousand dollars except the input price
37
CHAPTER V
EMPIRICAL RESULT
This thesis adopts two regression techniques: SURE (Seemingly Unrelated
Regression) and SFA (Stochastic Frontier Analysis). SURE is utilized to estimate the
parameters which will be used to obtain the AES (Allen Partial Elasticity), and SURE is
also used to run the Chow test to test for a significant difference between the parameter
estimates of the two models. SFA is used to obtain the X-efficiency in order to examine
the efficiency difference between the two different groups of countries.
In order to utilize SURE, this thesis adopts a system of equations which includes
the cost function and the cost share equation. The dataset contains banks in Central and
Eastern European countries during 2000-2004 period. There are three outputs and three
input price in the cost function. The outputs are LOANS, INVESTMENT, and
COMMISSION REVENUE. The input prices are price of capital, price of labor and price
of total deposit. The real values for the parameters in the cost function and share
equations (?, ? and ?) are the same. This will increase the efficiency of the estimators,
because there are no new parameters that needed to be estimated other than the ones in
the cost functions. Since all variables are in the form of logarithms, every parameter can
be interpreted as the elasticity of the independent variable with respect to the change of
total cost. In theory, all the variables need be strictly positive and can be satisfied by the
log form. In addition, the total cost will increase when the output and the input price
38
increase, and this condition needs all the parameters estimated for output and input price
to be positive. Therefore, the seven parameters for the three outputs and three input price
(?
1
, ?2,
?
3
, ?4, ?1, ?2and?3) must be positive. In actuality the regression result shows that
all these seven parameters have the positive signs.
From the regression result table of SURE; we know that all the parameters for the
output and input price are positive for every year. This means that the cost function
satisfies the restriction in every year. All the weighted R
2
is above 0.95 which is a
common result for the SURE method. However, not all the parameters are significant in
all the year.
The input of labor and capital are strong substitutes for each other in the dataset
for accession countries, with the exception 2001. The relationship of substitution can be
illustrated by its positive sign. The highest AES for these two variables is 4.573 in 2004,
and lowest AES is 0.547 in 2001. In the whole dataset, the change of AES for labor and
capital is very significant. The relationship between these two variables is strong
complement in 2000, which is very strange. Combining the SURE result with AES, we
can find a big structural change during the 2000-2001. In 2000 and 2001, the t-value for
the parameters of price of labor are 5.768 and 1.042 respectively, which means that the
price of labor has a very significant effect on the total cost in 2000, but is not significant
in 2001. During this period, many foreign big banks entered into the financial market in
several accession countries, and this process brings more advanced banking techniques.
The new techniques and machines need banks to hire more employees and some exports,
which makes labor has a significant effect. During this period, both capital and labor
increase significantly, and these two variables are strong complements. After 2001, the
39
cost structure begins to be stable and the capital and labor becomes to be substitutes
again.
The relationship of labor and total deposit is substitutes for each other during
2000-2004. Total deposits is the basis for banks to produce INVESTMENT and LOANS.
And labor can express the ability for the Central and Eastern European banks to earn
money. Actually, the prospect of a bad loan is a big problem there, and with more
financial officers these banks may have a better chance to calculate the probability of bad
loans, and at the same time improve the efficiency to utilize the total deposit. They can
utilize less total deposit to obtain more loans; therefore the labor and deposit are
substitutes. Biggest AES is 8.786 in 2000, and the smallest AES is 1.192 in 2003.
The relationship of capital and deposit is also substitutes for each other in
2000-2003. This relationship is similar with the relationship between labor and deposits.
Capital includes the physical capital and financial capital. They all have a positive
influence on LOANS and INVESTMENT. Actually this variable can illustrate a technical
level which can improve the efficiency of banks, and its increase will decrease the
amount of input of total deposits.
The AES for the non-accession countries illustrates that all the inputs are
substitutes for each other. The AES structure does not have any big change during
2000-2004. All the AES are close to 1, and have only small difference during the dataset.
We can ascertain that the non-accession countries do not have incentive to improve the
cost structure of their banking systems, which may make them have low banking
efficiency.
Next, the countries in the dataset can be divided into two groups: the accession
40
countries (including the ones will attend EU in 2007) and the non-accession countries.
For the accession countries, there are more foreign big banks entering into their financial
market, which will bring advanced techniques. This process will improve the banking
efficiency and cost structures in these countries. At the same time, in order to attend EU,
the government in accession countries will adopt a series of policies which will change
the environment for banks. This process may make these two groups of countries have
different cost structures. Because of this, a Chow test is used to check the difference of
cost structure in these two groups.
After dividing the data into two groups, the observations for the accession
countries are not enough for 2004. Therefore, this paper only contains the chow test for
2000-2003. The result shows that in 2000, it accepts the hypothesis that there is no
difference of cost structure in these two groups. However, in 2001, 2002 and 2003, there
is a significant difference in the cost structures between the two groups of countries. The
F-value in these three years are all significant at the 5% level. Although there is a
difference, we cannot judge which group has better cost structure. In order to answer this
question, we need a more powerful technique to supply more information.
This paper divided the countries into the accession countries and non-accession
countries. The banks in the accession countries are more efficient than the non-accession
ones for every year. Because there is not enough data to attain large degree of freedom in
2004, the X-efficiency will be calculated in the combined data from the four years. In
2000, the average X-efficiency for accession countries is 93.01%. The most efficient
country is Czech, which owns the average X-efficiency as 99.86%. The most inefficient
country, which has X-efficiency of 86.20%, is Slovenia. In 2001, the average
41
X-efficiency is 92.01%. The most efficient country is still Czech, but the most inefficient
country changes into Poland. The X-efficiency for accession countries continues a trend
of decreasing. It falls 89.92% in 2002, and has a little increase in 2003. Actually, the most
efficient country in the whole dataset is always Czech, and different year has individual
inefficiency country, and in average, Slovenia is most inefficient with the number of
86.13%.
The X-efficiency for non-accession countries in 2001 is pretty low, which is only
70.65%. Russia has an X-efficiency of 76.20%, which is the lowest efficiency in this year.
Croatia has the largest X-efficiency in this year: 89.41%. The banking efficiency in
non-accession countries is higher in 2002, the X-efficiency was 74.16%. Croatia had
average banking efficiency (69.97% is still better than Russia (68.77%). The countries
which do not have many big banks have high banking efficiency. In this dataset in 2002,
Armenia is most efficient; whose X-efficiency is 80.58%. In 2003, the average
X-efficiency for non-accession countries is 75.82%. Banking system in Croatia and
Kazakstan are realitively efficient, and they obtain the X-efficiency: 79.25% and 82.30%
respectively. Similarly, Russia obtains the lowest efficiency 71.51%. From the
X-efficiency, we can know that the biggest country, Russia, always has the low efficiency.
The three countries with medium financial market, Croatia, Kazakstan and Ukraine have
higher efficiency, but still pretty low compared with the accession countries. These three
countries have similar structure with accession countries, but have low efficiency, and
this situation suggests these three countries to try to attend EU.
The average X-efficiency in this 4 years period for the accession countries is
90.34%, and the average X-efficiency in this 4 years period for the non-accession ones is
42
75.82%. Therefore, the Banks in the accession countries are more efficient than banks in
the non-accession countries. However, the efficiency of banks in accession countries is
lowered during this 4 year period.
43
Table 5.1 Seemingly unrelated regression (SUR) result-2000
Accession Non-accession Accession
Non-accession
Para-
meter
Esti-
mate
t-value Esti-
mate
t-value Para-
meter
Esti-
mate
t-value Esti-
mate
t-value
?
0
1.906 7.010 0.322 4.305 ?
12
-0.258 -3.197 0.039 0.892
?
1
0.220 0.903 0.024 0.209 ?
13
-0.039 -0.479 0.085 1.213
?
2
0.911 5.986 0.544 7.696 ?
22
0.154 0.980 0.002 0.070
?
3
0.516 2.235 0.427 3.473 ?
23
0.730 4.674 -0.108 -1.502
?
1
0.639 5.768 0.132 2.320 ?
33
-0.068 -2.570 0.387 4.835
?
2
0.136 0.667 0.041 0.428 ?
11
-0.213 -3.859 -0.141 -2.502
?
3
0.768 3.251 0.774 6.130 ?
12
-0.171 -1.089 -0.168 -1.952
?
11
-0.239 -3.372 0.136 3.292 ?
13
0.128 1.095 -0.335 -3.144
?
12
0.259 3.404 -0.195 -3.946 ?
21
0.096 2.251 0.032 0.905
?
13
0.087 1.153 -0.126 -2.043 ?
22
0.179 1.114 0.114 1.986
?
22
0.060 1.615 0.098 3.877 ?
23
0.140 2.957 0.374 4.375
?
23
-0.248 -3.692 -0.031 -0.618 ?
31
0.154 2.544 0.106 2.044
?
33
0.054 1.448 0.131 4.348 ?
32
-0.078 -0.928 0.025 0.551
44
Table 5.2 Allen Partial Elasticity of Substitution in 2000(accession countries)
Input capital Labor deposit
Capital
-0.349
-5.571
0.734
Labor
0.130
8.786
deposit
-0.520
Table 5.3 Allen Partial Elasticity of Substitution in 2000(non-accession countries)
Input capital Labor deposit
Capital
-0.559
1.453
1.605
Labor
-0.743
0.074
deposit
-0.337
45
Table 5.4 Seemingly unrelated regression (SUR) result-2001
Accession Non-accession
Accession Non-accession
Para-
meter
Esti-
mate
t-value Esti-
mate
t-value Para-
meter
Esti-
mate
t-value Esti-
mate
t-value
?
0
0.863
2.243 0.148 3.127
?
12
-0.018
-0.311
-0.031 -0.995
?
1
0.008 0.042 0.260 4.963 ?
13
-0.026 -0.398 0.043 1.065
?
2
0.559 2.080 0.512 9.568 ?
22
-0.108 -1.136 -0.039 -1.207
?
3
0.386 1.655 0.266 4.632 ?
23
0.478 3.952 0.058 0.935
?
1
0.143 1.042 0.035 0.963 ?
33
-0.381 -6.201 0.138 3.536
?
2
0.067 0.258 0.013 0.219 ?
11
-0.075 -1.524 -0.006 -0.164
?
3
0.194 0.570 0.993 11.489 ?
12
0.064 0.882 0.014 0.293
?
11
0.101 1.576 0.078 2.516 ?
13
0.060 0.576 -0.006 -0.109
?
12
-0.239 -3.179 -0.248 -5.348 ?
21
0.107 2.483 0.022 0.596
?
13
-0.045 -0.509 0.003 0.052 ?
22
0.153 1.916 0.140 4.251
?
22
0.069 2.033 0.098 5.696 ?
23
-0.248 -2.461 0.097 2.504
?
23
0.100 1.212 0.078 1.980 ?
31
-0.040 -1.057 -0.009 -0.302
?
33
-0.002 -0.055 -0.011 -0.339 ?
32
-0.208 -2.294 -0.135 -3.543
?
11
0.008 0.551 -0.023 -1.712 ?
33
0.210 1.739 -0.011 -0.227
46
Table 5.5 Allen Partial Elasticity of Substitution in 2001(accession countries)
Input capital Labor deposit
Capital
-0.737
0.547
0.808
Labor
-1.447
5.607
deposit
-1.044
Table 5.6 Allen Partial Elasticity of Substitution in 2001(non-accession countries)
Input capital Labor deposit
Capital
-0.737
0.649
1.323
Labor
-0.878
1.538
deposit
-0.255
47
Table 5.7 Seemingly unrelated regression (SUR) result-2002
Accession Non-accession Accession
Non-accession
Para-
meter
Esti-
mate
t-value Esti-
mate
t-value Para-
meter
Esti-
mate
t-value Esti-
mate
t-value
?
0
0.219
3.575 0.226 4.183
?
12
0.101
1.580
0.016 0.594
?
1
0.343 4.692 0.302 6.584 ?
13
-0.344 -8.925 -0.138 -3.359
?
2
0.179 3.819 0.396 7.858 ?
22
0.069 4.026 -0.004 -0.222
?
3
0.334 5.196 0.296 5.969 ?
23
0.105 1.383 -0.097 -2.793
?
1
0.096 1.506 0.005 0.126 ?
33
-0.468 -10.753 -0.271 -10.599
?
2
0.128 1.441 0.039 0.767 ?
11
-0.013 -0.253 0.027 1.266
?
3
0.030 0.422 0.393 5.585 ?
12
0.061 1.086 0.030 0.990
?
11
0.065 2.902 0.096 6.956 ?
13
0.370 6.594 0.101 2.811
?
12
-0.043 -1.614 -0.312 -10.552 ?
21
-0.083 -2.873 -0.017 -0.685
?
13
-0.110 -2.404 0.088 2.684 ?
22
0.177 3.133 0.007 0.189
?
22
0.007 0.461 0.119 7.269 ?
23
-0.205 -5.068 -0.023 -0.662
?
23
-0.024 -0.858 0.057 2.074 ?
31
0.065 1.762 0.031 1.085
?
33
0.089 3.446 -0.041 -1.967 ?
32
-0.289 -5.075 -0.044 -1.133
?
11
0.023 0.905 -0.019 -1.386 ?
33
-0.218 -4.647 -0.070 -1.772
48
Table 5.8 Allen Partial Elasticity of Substitution in 2002(accession countries)
Input capital Labor deposit
Capital
-0.654
2.853
1.552
Labor
-0.465
1.922
deposit
-1.350
Table 5.9 Allen Partial Elasticity of Substitution in 2002(non-accession countries)
Input capital Labor deposit
Capital
-0.734
1.179
0.051
Labor
-0.745
0.112
deposit
-1.269
49
Table 5.10 Seemingly unrelated regression (SUR) result-2003
Accession Non-accession
Accession Non-accession
Para-
meter
Esti-
mate
t-value Esti-
mate
t-value Para-
meter
Esti-
mate
t-value Esti-
mate
t-value
?
0
0.107
1.663 0.098 1.771
?
12
0.106
1.539
0.010 0.303
?
1
0.372 4.216 0.618 9.940 ?
13
-0.125 -1.687 -0.024 -0.627
?
2
0.472 6.766 0.185 3.408 ?
22
0.072 2.349 -0.040 -1.592
?
3
0.050 0.597 0.159 2.827 ?
23
0.212 1.738 -0.111 -2.678
?
1
0.133 1.682 0.022 0.454 ?
33
-0.245 -4.106 -0.102 -3.946
?
2
0.031 0.319 0.115 1.868 ?
11
0.008 0.151 0.100 2.415
?
3
0.575 4.876 0.415 6.136 ?
12
0.076 0.722 0.023 0.568
?
11
0.197 3.835 0.137 7.196 ?
13
0.172 1.925 0.047 1.374
?
12
-0.180 -3.009 -0.242 -6.282 ?
21
-0.009 -0.211 -0.106 -2.497
?
13
-0.157 -2.288 0.023 0.657 ?
22
0.232 3.078 0.042 0.911
?
22
0.058 1.665 0.080 3.260 ?
23
-0.085 -1.190 0.006 0.176
?
23
0.058 0.970 0.042 0.918 ?
31
-0.102 -2.768 -0.016 -0.446
?
33
0.017 0.430 -0.034 -1.675 ?
32
-0.271 -3.287 -0.072 -1.657
?
11
-0.083 -2.350 -0.003 -0.144 ?
33
-0.026 -0.424 -0.005 -0.184
50
Table 5.11 Allen Partial Elasticity of Substitution in 2003(accession countries)
Input Capital Labor deposit
Capital
-0.911
2.118
0.027
Labor
-0.462
2.966
deposit
-1.260
Table 5.12 Allen Partial Elasticity of Substitution in 2003(non-accession countries)
Input Capital Labor deposit
Capital
-0.688
1.111
0.815
Labor
-0.882
0.009
deposit
-0.835
51
Table 5.13 Seemingly unrelated regression (SUR) result-2004
Accession Non-accession
Accession Non-accession
Para-
meter
Esti-
mate
t-value Esti-
mate
t-value Para-
meter
Esti-
mate
t-value Esti-
mate
t-value
?
0
-0.415
-8.201 0.312 7.290
?
12
0.066
0.732
0.024 4.027
?
1
0.131 0.571 0.005 0.114 ?
13
-0.053 -0.815 -0.022 -2.147
?
2
0.253 0.907 0.051 1.234 ?
22
-0.582 -8.546 0.044 2.849
?
3
0.733 3.117 0.077 2.168 ?
23
-0.271 -3.908 -0.033 -3.721
?
1
0.282 1.982 0.106 3.633 ?
33
-0.304 -6.463 -0.033 -1.572
?
2
0.071 0.300 0.057 1.615 ?
11
0.041 0.656 0.098 5.146
?
3
0.190 1.066 0.851 17.042 ?
12
0.792 7.613 -0.106 -5.230
?
11
0.049 0.540 0.058 4.165 ?
13
0.614 9.804 -0.032 -1.173
?
12
-0.180 -1.340 0.102 3.562 ?
21
-0.481 -4.718 -0.036 -2.003
?
13
-0.008 -0.150 -0.075 -5.305 ?
22
0.644 3.819 0.004 0.201
?
22
-0.834 -9.883 -0.126 -2.677 ?
23
-0.550 -4.660 0.054 2.266
?
23
1.734 10.272 -0.014 -1.120 ?
31
0.488 6.894 -0.053 -2.114
?
33
-0.798 -11.335 -0.009 -0.464 ?
32
-1.263 -13.262 0.058 3.633
?
11
-0.058 -1.851 -0.066 -1.610 ?
33
0.266 4.444 -0.033 -1.975
52
Table 5.14 Allen Partial Elasticity of Substitution in 2004(accession countries)
Input Capital Labor deposit
Capital
-0.970
4.573
0.546
Labor
-2.758
1.192
Deposit
-1.519
Table 5.15 Allen Partial Elasticity of Substitution in 2004(non-accession countries)
Input capital Labor deposit
Capital
-0.571
1.079
1.352
Labor
-0.387
1.065
deposit
-0.980
53
CHAPTER VI
CONCLUSION
In 2004, eight Central and Eastern European transition countries entered into the
Europe Union, and Bulgaria and Romania will be admitted to the EU in 2007. These
countries are defined as accession countries in this thesis. In order to attend EU, the
accession countries have motivation to improve banking efficiency during 2000-2004
period which is the dataset utilized in this thesis. The governments in accession countries
have made a series of policies to encourage the entry of foreign big banks, which will
bring a lot of technology to the financial market in accession courtiers. The adoption of
the new technology is supposed to improve the cost efficiency and change the cost
structure of accession countries. This thesis examined the different cost structure between
these two groups of countries.
After analyzing the data, we can find the different banking structure in the
accession countries and non-accession countries. Accession countries have higher
average scale of banks, but non-accession countries have a lot of small banks. In this
thesis, the bank scale is measured by total deposit: the bank with more than 1 billion
dollars? total deposit is defined as big bank, and the bank with total deposit less than 1
billion dollars is called small bank. Many non-accession countries do not have any
large bank at all. Nearly every accession country has more than 1 large bank in each
year. Russia is a special country in the dataset, because it owns the most of the large
54
banks. This thesis will compare the cost structure and banking efficiency in these two
groups of countries.
In order to obtain the information for the cost structure, it is necessary to calculate
Allen Partial Elasticity (AES). This thesis adopts multiproduct translog cost function,
which is jointly estimated with cost share equations. The translog functional form can
satisfy all the restriction for the cost function. There are 4 outputs (loans, investment,
commission revenue and total deposit), and 3 input price (price of capital, price of labor
and price of total deposit) including in this cost model. All the values for the outputs are
real dollar values, and dataset utilized in this thesis is obtained from the bankscope for
2000-2004 period.
There are two main regression method utilized in this thesis: Seemingly Unrelated
Regression (SURE) and Stochastic Frontier Approach (SFA). SURE is adopted to
estimate the parameters, which is used to calculate the AES; SFA is utilized to obtain the
X-efficiency to judge the banking efficiency. Chow test is also estimate to examine
whether there is difference of cost structure in these two groups of countries.
All the parameters for the 3 outputs and 3 input price are positive, after running
SURE test. This satisfies the homogeneity and non decreasing cost condition. The
parameter for the price of deposit is always most significant in the non-accession
countries, which shows that the total cost is sensitive for the interest cost in these
countries. The weighted R
2
in this thesis is compatible with other studies.
The AES illustrates that all the inputs are substitute with exception for capital and
labor in the accession countries, which are complements in 2001. Capital is a very strong
substitute for labor in 2000, and this may be because there are many foreign big banks
55
entering into the accession countries at the same time. The non-accession countries have
all input as substitute and the AES stay stable around 1. The accession countries only
have strong substitution relation among all the three input in 2000, and after that period,
the cost structure also stay the same for the accession countries. In order to test whether
there is significant difference between these two groups, this thesis adopts Chow test.
The empirical result shows that except 2000, the cost structures of these two groups are
significantly different in 5% level.
Finally, this thesis adopts the X-efficiency to measure the banking efficiency for
the accession countries and non-accession countries. The accession countries always have
higher X-efficiency than non-accession countries, but both of these two groups have a
trend of decreasing in the banking efficiency. After examining the banking efficiency
for the individual country, we can find that the most efficient country often has a
medium-size financial market, such as Czech in accession countries. The most
inefficient country is often the country with the very huge financial market, which is
Russia with the lowest efficiency. For the different country in the non-accession group,
the author finds that Russia often has low efficiency, but the countries with middle
financial market such as Croatia, Ukraine and Kazakstan have higher efficiency. These
three countries have similar structure of financial market compared with accession
countries, but have pretty slow efficiency. This situation illustrates the process of
attending EU has a significant positive effect on the accession countries? financial market,
which suggests Croatia, Ukraine and Kazakstan also attend EU to improve their financial
market.
56
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59
APPENDIX
Table 6.1 Structure Test for Null Hypotheses that accession and the Non-accession
countries have the same cost structure
Year 2000 2001 2002 2003
F
Va lue
2.844
3.133
3.105
6.274
Table 6.2 X-efficiency for the accession countries and the non-accession countries
Year accession Non-accession
2000
2001
93.01%
92.01%
83.52%
70.65%
2002
89.92%
74.16%
2003 90.34%
75.82%
60
Table 6.3 X-efficiency for individual countries
Country 2000 2001 2002 2003 average
Czech 99.86% 97.44% 97.91% 94.18% 97.35%
Poland 87.40% 80.44% 96.61% 87.30% 87.94%
Romania 88.15% 93.58% 88.12% 87.66% 89.38%
Slovenia 86.20% 89.39% 82.50% 86.43% 86.13%
Slovakia 99.06% 91.05% 79.94% 91.09% 90.29%
Russia 76.20% 68.77% 71.83% 71.51% 72.08%
Ukraine 82.75% 74.06% 74.66% 79.08% 77.64%
Kazakstan 85.72% 69.16% 79.78% 82.30% 79.24%
Croatia 89.41% 69.97% 76.37% 79.25% 78.75%
Hungary 99.84% 88.02% 99.97% 95.94%
Georgia 88.88% 76.79% 66.12% 80.92%