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Tan, Aaron Yong and Floros, Christos
Bank profitability and inflation: the case of China
Tan, Aaron Yong and Floros, Christos (2012) Bank profitability and inflation: the case of China.
Journal of Economic Studies, 39 (6). pp. 675-696. ISSN 0144-3585
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Bank profitability and inflation: the case of China
and Christos Floros
Economic and Finance subject group, Business School, University of
Portsmouth, PO1, 3DE, U.K.
This study examines the determinants of bank profitability in China over the period
2003-2009. The determinants are divided into three groups: bank-specific, industry-
specific and macroeconomic variables. The two-step General Method of Moments
(GMM) system estimator is used. The results show that there is a positive
relationship between bank profitability, cost efficiency, banking sector development,
stock market development and inflation. We report that low profitability can be
explained by higher volume of non-traditional activity and higher taxation. Moreover,
we confirm that there is a competitive environment in Chinese banking industry.
Furthermore, we propose policy actions that should be taken to improve bank
profitability in China.
The banking sector in China plays an important role in the development of financial
system and the economy as a whole. At the end of year 2008, the total deposits of
the whole banking industry account for more than 20% of GDP, higher than the 2006
and 2007 figures (17.5% and 16.8%, respectively). Further, the problem of
undercapitalization and a huge amount of non-performing loans demand prompt
solution. The profitability of the banking sector in China is still below international
standards (Garcia-Herrero et al., 2009). Understanding the factors influencing the
profitability of banking sector is helpful to solve these problems and is essential for
bank managers, government and shareholders.
A comprehensive banking sector reform with the aim of transforming banks into
market functioning and profitability institutions was started by the Chinese
government in 1997. The four state-owned commercial banks (SOCBs)
as the lending arms of the state owned enterprises (SOEs) are the focus of the
reform. There are mainly two ways in terms of restructuring, one is capital injection,
and the other one is to carve out the non-performing loans (NPLs).
This article seeks to examine the factors influencing the profitability of the Chinese
banking sector over the period 2003-2009. This period is the final round of reform
which focuses on banking modernization and partial privatization. The government
and banking regulatory authority allow foreign share purchases of any domestic bank,
and the banks are encouraged to be listed on Chinese stock exchanges in order to
improve their management, all of which are supposed to have a positive effect on
bank profitability. Although there have been several studies investigating the
profitability in developed countries, empirical works on factors affecting the
profitability of banks in developing countries, such as China, are relative scarce. This
is the first study which investigates three different groups of determinants affecting
Chinese banking profitability, namely the bank-specific, industry specific and
macroeconomic variables. The first group of determinants of profitability involves
bank size, credit risk, liquidity, taxation, capitalization, cost efficiency, non-traditional
activity and labour productivity. The second group of determinants describes
industry-structure factors that affect bank profitability which are concentration ratio,
banking sector development and stock market development. The third group relates
profitability to the macroeconomic environment within which the banking system
operates; in this context, we include inflation among the explanatory variables.
In this study, we include most comprehensive variables in analyzing the profitability
in the Chinese banking industry. Some of the variables are very important in the
development of banks and the policy making by the government. One of the
variables is labour productivity, which reflects the recruitment and management skills
The four state-owned commercial banks are Industrial and Commercial Bank of China(ICBC), China
Construction Bank(CCB), Agricultural Bank of China(ABC) and Bank of China(BOC), Bank of Communication is
classified as the new state-owned banks, so the total number of state-owned banks in China is 5.
of banks that is very important aspect of banking reform in China. The other variable,
which is called non-traditional activity, is an indicator of the development of banking
sector; we consider it to test whether the banking industry has been transferred from
traditional deposit-loan services to non-traditional activities oriented through several
grounds of reforms. These variables are not considered by most of the studies in the
context of Chinese banking industry. Furthermore, inflation is very important in the
country’s economy in the way that it exacerbates the so called friction in credit
market which is more severe in developing countries such as China. The financial
intermediaries ration credit leads to lower investment. The present and future
productivity may suffer, implying a low economic activity (Boyd and Champs 2006).
Nevertheless, inflation has important effects on banks under different aspects. First,
the bank lending is influenced by inflation. According to Boyd and Champ (2006),
some economist find that countries with higher inflation normally have small banking
and equity market, the amount of loan made by banks decreases through ration
credit especially to private sector. Second, the profitability is also affected by inflation.
Boyd and Champ (2006) find that there is a negative relationship between inflation
and bank profitability under the condition that banks may not be immediately aware
that inflation has stepped up. This paper examines these hypotheses using recent
data from China, i.e. banking and inflation data, to test the effect of inflation on bank
Our empirical results show that the profitability of Chinese banking sector is
explained by a lower volume of non-traditional activity, lower taxation, well-
developed banking sector, stock market and higher inflation. We also find that
profitability seems to persist to a moderate extent, which implies that departures from
a perfectly competitive market structure in China banking industry may be not that
The paper is divided into six sections. Section 2 reviews the existing literature on the
determinants of bank profitability. Section 3 outlines the empirical methodology.
Section 4 describes the Chinese banking market and data used. Section 5 presents
the main results and section 6 summarizes and concludes.
2. Literature review
There is a large amount of literature that examines the role of different factors in
determining the EU bank performance (Molyneux and Thornton, 1992; Staikouras
and Wood, 2003; Goddard et al., 2004). The determinants of European bank
profitability are firstly evaluated by Molyneux and Thornton (1992) for the period
1986-1989. The results show that liquidity is negatively related to bank profitability. In
addition, Staikouras and Wood (2003) examine the determinants of banks
profitability in the EU for the period 1994-1998. Using OLS and fixed effects models,
the empirical findings show that the profitability of European banks may be
influenced by factors related to changes in the external macroeconomic environment.
The performance of European banks across six countries is investigated by Goddard
et al. (2004). They find a relatively weak relationship between size and profitability.
The significant and positive relationship between off-balance business and
profitability is shown only for the UK.
There is a large number of studies on profitability of US banks (Smirlock, 1985;
Rhoades, 1985; Berger, 1995a; Goddard et al., 2001). Firstly, Rhoades (1985) uses
data from 1969-1978, and reports that there is a positive relationship between risk
and bank profitability in the US. Smirlock (1985) examines the profitability of US
banks during the period 1973-1978; the empirical findings suggest that size is
negatively related to bank profitability. Berger (1995a) uses data from 1980s, and
reports that profitability is positively related to market power and x-efficiency. The
profitability of US banks is also investigated by Goddard et al. (2001). Using data for
the period 1989-1996, the empirical results show that scale economies and
productive efficiency are positively related to profitability, while bank size has
negative impact on the profitability of the US banking industry. Further, the
determinants of foreign banks profitability based in Australia are considered by
Williams (2003) for the period of 1989-1993. He finds that GDP growth of a foreign
bank’s home country and non-interest income are positively and significantly related
to bank profitability.
Moreover, the profitability of bank-specific, industry-specific and macroeconomic
determinants of South Eastern European credit institutions is examined by
Athanasoglou et al. (2006). The empirical study shows that bank size, credit risk, and
capitalization have significant impacts on profitability, while the concentration is
positively related to bank profitability. In terms of macroeconomic variables, the
results are mixed among different countries.
Fewer studies have looked at the bank performance in emerging countries. The
performance of domestic and foreign banks in Thailand during the period of 1995-
2000 is investigated by Chantapong (2005). He finds that the profitability of foreign
banks is higher than domestic banks.
Guru et al. (2002) examine bank profitability for Malaysia during 1986-1995. The
results show that efficient expense management is one of the most significant factors
in determining the bank profitability. In terms of the macroeconomic variables,
inflation is found to have a positive relationship with bank profitability while the
negative relationship is obtained between interest rate and bank profitability.
The impact of bank characteristics, financial structure and macroeconomic
conditions on Tunisian banks’ profitability is examined by Ben Naceur and Goaied
(2008) for the period 1980 to 2000. The results suggest that the capitalization and
overhead expenses are positively related to profitability, while bank size exhibits the
negative effect. There is a positive relationship between stock market development
and bank profitability while no effect is found in terms of macroeconomic conditions.
The studies investigating the profitability of Chinese banking sector are relatively
scarce. The performance of the big four
, joint-stock and city commercial banks in
China is compared by Shih et al. (2007) using principle components analysis. The
results indicate that the joint-stock commercial banks perform better than state-
owned and city commercial banks. They argue that there is no relationship between
bank size and performance. Further, Fadzlan and Khazanah (2009) examine the
determinants of profitability of four state-owned and twelve joint-stock commercial
banks during the period of 2000-2007. The empirical findings suggest that size,
credit risk and capitalization are positively related to profitability, while liquidity,
overhead cost and network embeddedness have negative effects. The results also
show that there is a positive impact of economic growth and inflation on bank
Garcia-Herrero et al. (2009) explain the low profitability of Chinese banks for the
period 1997-2004. The results suggest that capitalization, share of deposits and x-
efficiency are positively related to bank profitability, while there is a negative effect of
concentration on bank profitability. Furthermore, the empirical findings indicate that
state-owned commercial banks are the main drag of bank profitability in China
whereas joint-stock commercial banks tend to be more profitable.
Heffernan and Fu (2008) use economic value added and net interest margin to
examine the determinants of performance for four different types of banks (state-
owned, joint-stock, city commercial and rural commercial banks). The empirical
findings suggest that bank listing and efficiency exert significant and positive
influence on bank performance. Real GDP growth rate and unemployment are found
to be significantly related to bank profitability. There are no effects of bank size and
off-balance-sheet activities on bank profitability. Finally, rural commercial banks
outperform the state-owned, joint-stock and city commercial banks.
3. Market and Data Description
3.1 Review of Chinese Banking industry
Until 1978, Chinese financial system followed the mono-bank model and was
operated based on socialist principles. The People’s Bank of China (PBOC) played
the dual role as central and commercial bank. A two tiered banking system,
consisting of the PBOC and State-Owned banks, was established during the first
stage of financial reform over the period 1979-1992. PBOC was free to serve as
central bank. In order to create a comprehensive environment and enhance
supervision in the banking sector, the Chinese Banking Regulatory Commission
(CBRC) and various ownerships of banks were established during the second stage
of reform from 1993 to present.
Big four include the following banks: ICBC, ABC, BOC and CCB.
Established by the state council in 2003, the CBRC is the primary government
agency and point of control for the commercial banks. The CBRC is responsible for
the supervision of the commercial banking operations, but also formulate rules and
regulations, authorize the establishment, changes, termination and scope business
of the banking institutions and conduct an onsite examination and offsite surveillance
of their operations. The objective is to protect the interest of depositors and maintain
market confidence through prudential and effective supervision.
The Chinese banking sector comprises five state-owned commercial banks
joint-stock commercial banks
(JSCBs), a big number of city commercial banks
(CCBs), policy lending banks, credit cooperative and foreign banks. The state-owned
commercial banks are assigned sector policy objectives, previously in the hand of
the PBOC under the mono-bank system. However, with the creation of the policy
lending banks in 1994, their responsibilities have been restricted to commercial
lending purposes. Further, the stockholders of JSCBs are made up of a diversified
group which includes local government as well as private and state-owned
enterprises. On the other hand, CCBs are local joint-stock commercial banks
established by local government, enterprises and residents. The establishment of the
Shenzhen city cooperative bank in July 1995 can be taken as the starting point when
China’s city commercial banking network begins its rapid, though arduous,
development on the Chinese financial platform. Unlike their JSCB counterparts, the
CCBs are not allowed to operate at the national or regional level, which is their major
competitive disadvantage. Therefore, due to their lack of scale, the CCBs have to
rely heavily on traditional lending activities with interest income consists of
approximately 95% of CCBs’ total revenue. In addition, the CCBs’ competitive
advantage stems from its strong relationship with local business fraternities and retail
customers. By the end of 2007, there are 124 city banks in China. Their assets
totalled RMB 3340 billion, possessing a market share of 6% among all depository
banking institutions (Rowe et al., 2009).
3.2. Data Description
Our banking data is composed of annual figures from 101 Chinese banks over the
period 2003-2009. The banks used in this study are five state-owned commercial
banks, twelve joint stock commercial banks and eighty four city commercial banks.
Furthermore, sixteen of them have already been listed on the stock exchanges in
China, hence the profitability of these banks is highly important for the shareholders.
Since not all banks have available information for all years, we opt for an unbalanced
panel not to lose degrees of freedom (i.e. the number of time series responses for
each unit is different; hence, the panel is unbalanced). In total, our sample contains
These are: Bank of China (BOC), Industrial and Commercial Bank of China (ICBC), Agricultural Bank of China
(ABC), China Construction Bank (CCB), and Bank of Communication.
These are: China Minsheng Banking Corporation, China Citic Bank, Shanghai Pudong Development Bank,
China Merchant Bank, Gungdong Development Bank, Hua Xia Bank, ShenZhen Development Bank,
Evergrowing Bank, Industrial Bank, China Everbright Bank, China Zheshang Bank and China Bohai Bank.
. The bank specific information is mainly obtained from Bankscope
database maintained by Fitch/IBCA/Bureau Van Dijk, which is considered as the
most comprehensive database for research in banking. The industry specific and
macroeconomic variables are retrieved from the website of China banking regulatory
commission and the World Bank database. The list of the variables used to proxy
profitability (including the notation), its determinants and descriptive statistics are
presented in the following table (Table 1). A summary of the expected effects of the
determinants, in accordance with the theory and previous literature, are also
included. More information about these effects is given in the next Section.
Table 2 shows summary statistics of the variables used in the present study. We find
that ROA is lower than NIM. There is a small difference in terms of bank size, cost
efficiency and liquidity comparing with other bank-specific variables (as seen from
the Min and Max values). The maximum amount of non-traditional business engaged
by the banks achieved is found to be 128.42, while the minimum amount is of -34.22.
The differences between the Min and Max values of banking sector development
and concentration are smaller than stock market development and inflation, which
suggests that the banking variables (of banking sector) are more stable than stock
market and macroeconomics in China.
<<Table 1 - about here>>
<<Table 2 - about here>>
Furthermore, Figure1 shows the inflation rate in China over 2003-2009. In 2003, the
inflation rate is 1.16%, the lowest point over the above period, while it achieves the
highest point in 2008, i.e. 5.86%. Notice that this is the highest inflation rate since
1997 due to the severe winter storm happened that year.
<< Figure 1 – about here >>
When estimating bank profitability, either measured by the ROA or NIM, we face a
number of challenges. First, it is endogeneity: more profitable banks may be able to
increase their equity more easily by retaining profits. The relaxation of the perfect
capital markets assumption allows an increase in capital to raise expected earnings.
Another important problem is unobserved heterogeneity across banks, which may be
very large in the Chinese case given differences in corporate governance. Finally,
the profitability could be very persistent for Chinese banks because of political
We tackle these three problems together by moving beyond the methodology used in
previous studies on bank profitability. Most previous studies use fixed or random
Similar study has been conducted by Shen and Lu (2008) who use 49 bank-level observations to
investigate the effect of different ownership structures on the profitability and risk of bank in China.
. In this paper, we employ the General Method of Moments (GMM), which
firstly used by Arellano and Bond (1991)
GMM is widely used in the investigation of
determinants of bank profitability. For instance, Athanasoglou et al. (2005) apply
GMM to a panel of Greek banks; Liu and Wilson (2009) and Dietrich and
Wanzanried (2010) also use a GMM approach for the Japanese and Switzerland
banking industries, respectively. This methodology accounts for endogeneity. The
GMM estimator uses all available lagged values of the dependent variable plus
lagged values of the exogenous regressors as instruments which could potentially
suffer from endogeneity. In our case, the variables treated as endogenous are the
dependent variables and capitalization. The GMM estimator also controls for
unobserved heterogeneity and for the persistence of the dependent variable. Overall,
this method yields consistent estimations of the parameters.
4.1 Performance measures (ROA and NIM)
Previous literature has used several measures of profitability, such as the ROA and
NIM (as reported before). ROA is widely used to compare the efficiency and
operational performance of banks as it looks at the returns generated from the
assets financed by the bank. For this reason, we choose ROA as one of our optional
dependent variables. Using ROA as dependent variable, we also provide
convenience in comparing our results to other findings reported in the literature.
Figure 2a shows the profitability of SOCBs ,JSCBs and CCBs over the examined
period. In general, the profitability of SOCBs and CCBs is higher than JSCBs, while
the profitability of SOCBs is higher than CCBs for the period 2003-2005 and 2007.
<< Figure 2a - about here>>
Another measure of profitability is the return on equity (ROE). ROE reflects the
capability of a bank in utilizing its equity to generate profits. Though not used widely
as ROA, it is also a standard indicator to compare financial performance among
different banks in developed countries.
Further, the NIM variable is used, which is focused on the profit earned on lending,
investing and funding activities. Figure 2b shows that: (i) the lowest and highest
profitability is obtained by CCBs in 2003 and 2008, and (ii) the profitability of CCBS
is higher than SOCBS in 2005-2006 and 2009. The profitability of JSCBs is the
lowest among these three groups of banks.
In this study, ROA and NIM are used as the performance measures, following a
recent study by Fadzlan and Khazanah (2009). ROE is not considered in this study
due to the fact that ROA and NIM are better representatives of bank profitability in
China (see Fadzlan and Khazanah, 2009).
Fixed or random effects are used by Maudos and Fernandez de Guerara (2004) and Claeys and Vennet (2005),
while Generalized Least Square and Weighted Least Square are employed by Angbazo(1997) and Demirguc-
Kunt and Huizinga(1999).
<<Figure 2b - about here>>
4.2 Bank-specific variables
The bank-specific variables included in our empirical analysis are LNTA (log of total
assets), PL (loan loss provisions/total loans), LA (loans/assets), TOPBT
(tax/operating profit before tax), ETA (shareholder’s equity/total assets), OETA
(overhead expenses/total assets), NIITA (non-interest income/total assets), and
TRNE (total revenue/number of employees).
Capitalization (ETA) has been demonstrated to be an important factor in explaining
the performance of financial institutions. Its impact on bank profitability is ambiguous.
A lower capital ratio suggests a relatively risky position; one might expect a negative
coefficient on this variable. (Berger, 1995). However, there are five reasons to
believe that higher capitalization should foster the profitability. First, banks with
higher capital ratio engage in prudent lending. Second, banks with more capital
should be able to lower their funding cost (Molyneux, 1993) because large share of
capital is an important signal of creditworthiness. Third, a well capitalized bank
needs to borrow less in order to support a given level of assets. This can be
important in emerging countries when the ability to borrow is more subject to stops.
Fourth, capital can be considered a cushion to raise the share of risky assets, such
as loans. When market conditions allow a bank to make additional loans with a
beneficial return, this should imply higher profitability. Finally, an increase in capital
may raise expected earnings by reducing the expected cost of financial distress
including bankruptcy (Berger, 1995).
Bank size (LNTA) is generally used to capture potential economies or diseconomies
of scale in the banking sector. This variable controls for cost differences, product and
risk diversification. There is no consensus on the direction of influence. On the one
hand, a bank of large size should reduce cost because of economies of scale and
scope (see Akhavein, Berger and Humphrey, 1997; Bourke, 1989; Molyneux and
Thornton, 1992; Bikker and Hu, 2002; Goddard, Molyneux and Wilson, 2004). In fact,
more diversification opportunities should allow to maintain (or even increase) returns
while lowering risk. On the other hand, large size can also imply that the bank is
harder to manage or it could be the consequence of a bank’s aggressive growth
strategy. Eichengreen and Gibson (2001) suggest that the effect of bank size on its
profitability may be positive up to a certain limit. Beyond this point, the impact of its
size could be negative due to bureaucratic and other factors. Hence, the size-
profitability relationship may be expected to be non-linear.
Furthermore, the literature argues that reduced expenses (OETA) improve the
efficiency, and hence, raise the profitability of a financial institution, implying a
negative relationship between the operating expenses ratio and profitability (Bourke,
1989; Jiang et al., 2003). However, Molyneux and Thornton (1992) find that the
expense variable affects European banking profitability positively. They argue that
high profits earned by firms in a regulated industry may be appropriate in the form of
higher salary and wage expenditures. Their findings support the efficiency wage
theory, which states that the productivity of employees increases with the wage rate.
This positive relationship between profitability and expense is also observed in
Tunisian case study (Naceur, 2003) and Malaysian study (Guru et al., 2002). The
proponents argue that these banks are able to pass their overheads to depositors
and borrowers in terms of lower deposit rates and/or larger lending assets.
Changes in credit risk (PL) may reflect changes in the health of a bank’s portfolio
(Cooper, Jackson and Patterson, 2003), which may affect the performance of the
institution. Duca and McLaughlin (1990), among others, conclude that variations in
bank profitability are largely attributable to variations in credit risk. Since inverse
exposure to credit risk is normally associated with decrease firm profitability. This
triggers discussion concerning not the volume but the quality of loans made. In this
direction, Miller and Noulas (1997) suggest that the financial institutions being more
exposed to high risk loans increase the accumulation of unpaid loans and decrease
Banks are also subject to direct taxation (TOPBT) through corporate tax and other
taxes. Although the tax rate on corporate profit is not a choice for banks, yet, the
bank management should be able to allocate its portfolio to minimise its tax. Since
consumers face an inelastic demand for banking services, most banks are able to
pass the tax burden to the consumers. Such a positive relationship between the tax
variable and profitability is confirmed by Demirguc-Kunt and Huizinga (1999), Bashir
(2000) for banks in Middle East and Jiang et al. (2003) for banks in Hong Kong.
Liquidity (LA), arising from the possible inability of banks to accommodate decreases
in liabilities or to fund increases on the assets’ side of the balance sheet, is
considered an important determinant of bank profitability. A larger share of loans to
total asset should imply more interest revenue because of higher risk. Thus one
would expect a positive relationship between liquidity and profitability (Bourke, 1989).
Graham and Bordeleau(2010) argue that profitability is improved for banks that
holding some liquid assets, however, there is a point at which holding further liquid
assets diminishes a bank’s profitability.
Empirical evidence from Athanasoglou et al. (2005) for banks in Greece shows that
there is a positive and significant relationship between labour productivity (TRNE)
and bank profitability. This suggests that higher productivity growth generates
income that is partly channelled to bank profits. Banks target high levels of labour
productivity growth through various strategies that include keeping the labour force
steady, ensuing high quality of newly hired labour, reducing the total number of
employees, and increasing overall output via increasing investment in fixed assets
which incorporate new technology.
Another important determinant, which is supposed to influence the bank profitability,
is the non-interest income ratio (NIITA). When banks are more diversified, they can
generate more income resources, thereby reducing its dependency on interest
income which is easily affected by the adverse macroeconomic environment. The
result of Jiang et al. (2003) show that diversified banks in Hong Kong appear to be
more profitable. However, fee-income generating businesses actually exert a
negative impact on banks’ profitability (Gischer and Juttner, 2001; Demirguc and
Huizinga, 1999). They attribute such a finding to the fact that those fee-income
generating businesses, such as trades in currencies and derivatives, credit cards
provisions, are subject to more intense competition, especially on an international
basis than those traditional interest income activities.
4.3 Industry-specific variables
Studies by Smirlock (1985), Bourke (1989), and Staikouras and Wood (2003)
suggest that industry concentration has a positive impact on banking performance.
The more concentrated the industry is, the greater the monopolistic power of the
firms will be. This, in turn, improves profit margins of banks. However, there are also
some studies that report conflicting results. For example, Naceur (2003) reports a
negative coefficient between concentration and bank profitability in Tunisia. Also,
Karasulu (2001) finds that the increasing concentration does not necessarily
contribute to profitability of the banking sector in Korea.
Many studies in the banking literature investigate whether financial structure plays a
role in determining banking performance
. In general, a high bank asset-to GDP ratio
implies that financial development plays an important role in the economy. This
relative importance may reflect a higher demand for banking services, which in turn,
attracts more potential competitors to enter the market. When the market becomes
more competitive, banks need to adopt different strategies moves in order to sustain
Demirguc-Kunt and Huizinga (1999) present evidence that financial development
and structure variables are very important. Their results show that banks in countries
with more competitive banking sectors, where bank assets constitute a large portion
of GDP generally have smaller margins and less profitable. Also, they notice that
countries with underdeveloped financial system tend to be less efficient and adopt
less-than-competitive pricing behaviours. In fact, for these countries, greater financial
development can help to improve the efficiency of the banking sector.
Stock market becomes larger, more active and more efficient as countries become
richer. Hence, developing countries generally have less developed stock markets. A
substantial body of literature (e.g. King and Levine, 1993a; King and Levine, 1993b;
Demirgüç-Kunt and Maksimovic, 1998; Levine and Zervos, 1998; Rajan and
Zingales, 1998; Demirgüç-Kunt and Huizinga, 1999; and Demirhuc-Kunt and
See Hassan and Banshir (2003), Demirguc-Kunt and Huizinga (2000).
Huizinga, 2001) have shown that stock market development leads to higher growth
of the firm, industry and country level. Specifically, Demirguc-Kunt and Maksimovic
(1998) show that firms in countries with an active stock market grow faster than
predicted by individual form characteristics.
Empirical evidence from Demirguc-Kunt and Huizinga (1999) and Bashir (2000)
show that banks have greater profit opportunities in countries with well-developed
stock markets. They argue that the larger equity markets in these countries give the
banks operating therein greater opportunities to expand their profits. Stock market
development leading to increased profitability for banks indicates complementarities
between bank and stock market finance, growth and development. This is because
stock market development and resulting improved availability of equity finance to
firms reduce their risks of loan default, increase their borrowing capacities and allow
them to be better capitalized. Also as stock markets develop, improved information
availability on publicly traded firms makes it easier for banks to evaluate and monitor
credit risks associated with them, simply put developed stock markets generate more
information about firms that is also useful for banks. This tends to increase the
volume and decrease the risk of business for banks, making higher profit possible.
Alternatively, the legal and regulatory environment that makes stock market
development possible may also improve the functions of banks.
4.4 Macroeconomic variables
To measure the relationship between economic conditions and bank profitability, the
annual inflation rate is used. Inflation is an important determinant of banking
performance. In general, high inflation rates are associated with high loan interest
rates and high income. Perry (1992), however, asserts that the effect of inflation on
banking performance depends on whether inflation is anticipated or unanticipated. If
inflation is fully anticipated and interest rates are adjusted accordingly, a positive
impact on profitability will be exerted. Alternatively, unexpected raises in inflation
causes cash flow difficulties for borrowers which can lead to premature termination
of loan arrangements and precipitate loan losses. Indeed, if the banks are sluggish in
adjusting their interest rates, there is a possibility that banks cost may increase faster
than bank revenue. Hoggarth et al. (1998) also conclude that high and variable
inflation may cause difficulties in planning and negotiating loans.
The findings of the relationship between inflation and profitability are mixed.
Empirical studies of Guru et al. (2002) for Malaysia and Jiang et al. (2003) for Hong
Kong show that high inflation rates lead to higher bank profitability. The study of
Abreu and Mendes (2001) nevertheless report a negative coefficient of inflation for
European countries. In addition, Demirguc-Kunt and Huizinga (1999) notice that
banks in developing countries tend to be less profitable in inflationary environments
particularly when they have a high capital ratio. In these countries bank cost actually
increase faster than bank revenue. Besides the inflation, GDP growth is supposed to
considered, however, because there is a multicollneality problem, this variable is
excluded from this study. In this study, we only consider inflation as an important
macroeconomic variable of the Chinese economy. Shen and Lu (2008) use the GDP
as the key macroeconomic variable to explain the bank profitability in China.
However, this study uses inflation to: (i) examine the determinants of bank
profitability in China and (ii) compare the results from inflation with those from GDP.
4.5 Econometric specification
We present a model which is able to capture the effects of bank-specific,
industry-specific and macroeconomic variables on profitability in China. Bank profits
show a tendency to persist over time, reflecting impediments to market competition,
informational opacity and/ or sensitivity to regional/macroeconomic shocks to the
extent that these are serially correlated (Berger et al., 2000); therefore, we adopt the
model proposed by Athanasoglou et al. (2008) where its dynamic specification
includes lagged dependent variable among the regressors. Our GMM model is
based on a general model which has the following linear form:
∑ ∑ ∑
= = =
1 1 1
Where is the profitability of bank i at time t, which i=1,…..,N, t=1,…..,T, c is the
constant term. ’s are the explanatory variables and the disturbance term,
with the unobserved bank-specific effect and the idiosyncratic error. This is
a one-way component regression model, where ～IIN(0, ) and independent
of ～(0, ). The ’s are grouped into bank-specific , industry-specific
and macroeconomic variables .
Equation (1) augmented with lagged profitability has the form (Athanasoglou et al.
∑ ∑ ∑
= = =
1 1 1
Where is the one-period lagged profitability and the speed of adjustment
to equilibrium. A value of between 0 and 1 implies that profit persists, but will
eventually return to their normal level. A value close to 0 means that the
industry is fairly competitive (high speed of adjustment), while a value of close
to 1 implies less competitive structure (very low adjustment).
Endogeneity, unobserved heterogeneity and correlation between regressors and
lagged dependent variable make fixed or random effects not suitable for the
estimation. Arellano and Bond (1991) derive a consistent GMM estimation for this
model. It is a single left hand-side variable that is dynamic depending on its own
past realizations. The Arellano and Bond (1991) estimation uses all available
lagged values of the dependent variable and lagged values of the exogenous
regressors as instruments; it is called difference GMM. This method is criticized
by Arellano and Bover (1995) and Blundell and Bond (1998) who argue that the
GMM difference estimator is inefficient if the instruments are weak. Hence, they
develop a new method which is called GMM system estimator and includes
lagged levels as well as lagged differences. Roodman (2006) argues that GMM
difference and system estimation can solve the problems of endogeneity,
unobserved heterogeneity, autocorrelation and profit persistence. Bond (2002),
however, argues that the unit root property makes the difference GMM estimator
bias while the system GMM estimator yields a greater precision result. Hence, in
our paper, the two-step GMM estimator (Liu and Wilson, 2009) is used to conduct
the empirical analysis.
Table 3 provides information on the degree of correlation between the
explanatory variables used in the multivariate regression analysis. The matrix
shows that, in general, the correlation between the independent variables is not
strong suggesting that multicollinearity problems are not severe or nonexistent.
Kennedy (2008) points out that multicollinearity is a problem when the correlation
is about 0.8, which is not the case here.
<< Table 3 – about here>>
5. Empirical results
We investigate empirically the determinants of bank profitability using annual data for
101 Chinese banks over the period 2003-2009. The complementary measures of
bank profitability, ROA and NIM, are used (as discussed above).
One of the issues confronted is to examine whether individual effects are fixed or
random. As indicated by the Hausman test on model (2), the difference in
coefficients between fixed and random model is zero, providing evidence in favour of
a random effect model. However, the least squares estimator of random effect model
in the presence of a lagged dependent variable among the regressors is both biased
and inconsistent. As mentioned in the methodology section, the two-step system
GMM estimation is used in order to get robust results.
There are mainly two reasons to use ROA as one of the measurement of bank
profitability. First, it shows the profit earned per unit of assets and reflects the
management ability to utilise banks’ financial and real investment resources to
generate profit (see Hassan and Bashir, 2003). Furthermore, Rivard and Thomas
(1997) argue that bank profitability is best measured by ROA because it is not
distorted by higher equity multipliers.
Table 4 shows the results from the econometric models. Starting with ROA, a high
significant coefficient of lagged profitability variable confirms the dynamic character
of model specification. For example, δ takes a value of approximately 0.22, which
means that profits seem not to persist; it implies that departures from a perfectly
competitive market structure in the Chinese banking sector is small. In contrast,
Garcia-Herrero et al. (2009) find that the statistical evidence for profit persistence in
Chinese banking sector is stronger.
In terms of taxation, the variable is negatively related to the bank profitability of
Chinese bank, indicating a negative relationship between taxation and bank
profitability. The more taxes paid by the bank, the higher cost incurred by the bank,
thus decrease the profitability. The result is supported by Hameed and Bashir (2003)
for Islamic banks from Middle East.
The coefficient of credit risk entered the regression model with a negative sign and
statistically significant indicating a negative relationship between credit risk and bank
profitability. Fadzlan and Royfaized (2008) find the same result in terms of Philippine
banking industry. This result is also supported by Liu and Wilson (2009) for
Japanese banks. Millar and Noulas (1997) suggest as the exposure of the financial
institutions to high risk loan increases, the accumulation of unpaid loans would
increase and profitability would decrease. However, the result of positive relationship
is found in Chinese banking industry by Fadzlan and Khazanah (2009).
We find that cost efficiency is highly significant and positively related to ROA; this is
in line with Abreu and Mendes (2001) for banking industry in Portugal, Spain, France
and Germany. It is also a testimony that banks have the ability to pass the overhead
expenses on customers through increasing lending rate and decreasing deposit rate.
The negative and significant relationship between non-traditional activity and ROA
implies that financial institutions that derive a higher proportion of their income from
non-interest sources, such as fee-based services, tend to report a lower level of
profitability. The empirical findings are not in line with those reported by Canals
(1993); he suggests that revenues generated from new business units have
significantly contributed to improve bank performance. However, this result is in line
with Wu et al. (2007) for Chinese banks. One explanation is that the main motivation
for Chinese banks to develop non-traditional activities is to attract new customers
rather than boost the profit; as a result, the fee charged for the non-traditional
services is very low, in some cases; this leads to a decrease in profitability.
Concerning the impact of labour productivity, it is positively related to profitability of
Chinese banks, indicating a positive relationship between bank profitability and
labour productivity. This is in line with Athanasoglou et al. (2005) for Greek banks.
This result suggests that higher productivity growth generates income that is partly
channelled to bank profits. Banks target high levels of labour productivity growth
through various strategies that include keeping the labour force steady, ensuring
high quality of newly hired labour (reducing the total number of employees) and
increasing overall output via increasing investment in fixed assets which incorporate
Turning to the industry specific factors, the concentration is significant and the sign
of the coefficient is negative indicating that there is a negative relationship between
concentration and bank profitability. This is in line with Garcia-Herrero et al. (2009)
for the Chinese banking industry and Naceur (2003) for Tunisian banks.
report a positive and significant effect of banking sector development on bank
profitability in China.
Further, a large proportion of bank assets in GDP indicate that there is a high
demand of bank services. According to the circumstance of banking industry in
China, the establishment of a new bank involves a very complicated procedure, and
the requirement and decision made by the government to open a new bank is very
strict. This makes a potential competitor difficult to enter the market, because the
demand is increasing which makes the profitability of existing bank increase.
The sign of stock market development is positive and this variable is significant at
1% level indicating there is a positive relationship between stock market
development and bank profitability. This finding confirms the empirical results of Ben
Naceur (2003) for Tunisian banks who suggests that as stock market enlarge, more
information become available. This leads to an increase number of customers to
banks by making easier the process of identification and monitoring of borrowers.
Consequently, this will contribute to a higher profitability. The positive relationship
between stock market development and bank profitability shows that there are
complementaries between stock market and banking development in China (this is in
line with the theory).
Turing into the macroeconomic variable, inflation is found to be significantly and
positively related to bank profitability. This implies that during the period of our study
inflation is anticipated which gives banks the opportunity to adjust the interest rates
accordingly, resulting in revenues that increase faster than costs, with a positive
impact on profitability. This result is consistent with the findings by Pasiouras and
Kosmidou (2007) for EU as well as Fadzlan and Khazanah (2009) and Garcia-
Herrero et al. (2009) for Chinese banks.
In order to check the robustness of the result, the NIM is used as an alternative
dependent variable while the C3 ratio is used instead of C5 ratio. The C3 and C5
ratios are the proportion of the largest three or five banks in terms of total assets to
the assets of the whole banking industry.
In terms of the NIM, we can see that most of the results are similar to what we obtain
from ROA; However, we find that there is a negative and significant impact of bank
size on bank profitability in China. This result is not in line with Fadzlan and
Khazanah (2009). Herffernan and Fu (2008) find that there is insignificant
relationship between bank size and profitability. The negative effect of bank size on
profitability could be due to bureaucratic reasons when banks become extremely
large. This is also reported by Pasiouras and Kosmidou (2007), Ben Naceur and
Goaid (2008).Furthermore, credit risk is significantly and positively related to NIM.
This is in direct contrast with the structure-conduct-performance (SCP) hypothesis and the findings of
Demirguc-Kunt and Huizinga (1999) and Hassan and Bashir (2003).
This result is confirmed by Fadzlan and Khazanah (2009) for the Chinese banking
industry. Thirdly, liquidity is found to be significantly and positively related to NIM.
This is in line with Fadzlan and Khazanah (2009); therefore, a larger volume of loan
will generate higher interest revenue because of higher risk.
<<Table 4 - about here>>
6. Summary and conclusion
This paper examines the determinants of profitability of five SOCBs, twelve JSCBs
and eighty-four CCBs covering the period from 2003-2009. Bank-specific, industry-
specific variables and a macroeconomic variable (inflation) are considered. We use
unbalanced bank-level panel data with totally 197 observations. Bank profitability is
measured by two different variables, the ROA and NIM.
The empirical findings suggest that higher cost efficiency, lower volume of non-
traditional activity, higher banking sector and stock market development tend to
increase profitability of Chinese banks. There are mixed findings about the effect of
risk on Chinese banking profitability in terms of ROA and NIM; in particular, small
bank size seems to increase the NIM of Chinese banks, while the higher NIM can
also be explained by the higher liquidity of Chinese banks. Higher labour productivity
leads to higher ROA of Chinese banks. The positive relationship found between
inflation and profitability in Chinese banking sector reflects the fact that the inflation
in China can be fully anticipated and the interest rates are adjusted accordingly. This
further implies that revenues increased faster than costs. This result is in line with
Pasiouras and Kosmidou (2007) for the European banks, Fadzlan and Khazanah
(2009) and Garcia-Herrero et al. (2009) for Chinese banks.
In summary, cost efficiency, non-traditional activity, banking sector development,
stock market development and inflation are related to bank profitability in China, no
matter if ROA or NIM is used as dependent variable. However, credit risk is
negatively related to ROA, but positively related to NIM; liquidity and bank size are
significantly related to NIM but not ROA, and labour productivity has a positive effect
on ROA only.
The findings of the current study have considerable policy relevance. First, Chinese
banks should take emphasize on the improvement of labour management and
training skills, the purpose of which is to increase their productivity and boost the
profitability. Furthermore, the government should gradually continue to open the
banking and stock market, as the well development of the financial sector is helpful
in increasing banks’ profitability in China.
Due to the fact that the results reported here are in line with previous studies for
European banks (e.g. Pasiouras and Kosmidou, 2007), the current study can be
extended by testing the relationship between inflation and other macroeconomic
variables, such as GDP, with bank competition to see whether similar results can be
obtained using data from EU, US and China. The cost efficiency in this study is
proxied by the ratio of overhead expenses over total assets. Further research should
also consider other efficiency variables as well as the slack based model and
bootstraps techniques for testing and measuring efficiency of large and small Asian
banks. Finally, we should examine the profitability of Chinese banks using data from
Abreu, M., & Mendes, V (2001), “Commercial Bank Interest Margins and Profitability:
Evidence for Some EU Countries”, presented on the 50th International Atlantic
Economic Conference, 17 May-20 May, Thessaloniki, Greece, available at:
Akhavein, J., Berger, A. N., & Humphrey, D. B (1997), “The effects of megamergers
on efficiency and prices: Evidence from a bank profit function”, Review of Industrial
Organization, 12,(1), 95–139.
Angbazo (1997), “Commercial bank net interest margin, default risk, interest-rate risk
and off-balance sheet banking”, Journal of Banking and Finance, 21, 55-87
Arellano, M., & Bond, S.R (1991), “Some tests of specification for panel data: Monte
Carlo evidence and an application to employment equations”, Review of Economic
Studies, 58, 277–297
Arellano, M., & Bover, O (1995), “Another look at the instrumental-variable
estimationof error-components models”, Journal of Econometrics, 68, 29–52.
Athanasoglou, P.P., Brissimis, S.N., & Delis, M.D (2005). “Bank-Specific,
Industry-Specific and Macroeconomic Determinants of Bank Profitability”,
Working Paper(No. 25), Bank of Greece, Greece, June.
Athanasoglou. P.P., Delis, M.D., and Staikouras, C. K. (2006), ‘Determinants of bank
profitability in the South Eastern European region’,WP No 47, Bank of Greece.
Athanasoglou, P. P., Brissmis, S. N., and Delis, M. D. (2008), ‘Bank-specific,
Industry-specific and Macroeconomic determinants of bank profitability’, Journal of
International financial Markets, Institutions and Money, Vol, 18, No. 2, 121-136.
Bashir, A (2000), “Determinants of Profitability and Rates of Return Margins in
Islamic Banks: Some Evidence from the Middle East”, Paper presented at the ERF’s
Seventh Annual Conference, 26 October to 29 October, Amman, Jordan, Available
Ben Naceur, S. (2003), “The Determinants of the Tunisian Banking Industry
Profitability: Panel Evidence”, Paper presented at the Economic Research
Annual Conference, 16-18 December, Marrakesh-Morocco,
available at: http://www.mafhoum.com/press6/174E11.pdf .
Ben Naceur, S., and Goaied, M (2008), “The determinants of commercial bank
interest margin and profitability: Evidence from Tunisia”, Frontiers in Finance and
Economics, 5,(1), 106–130.
Berger, A. N (1995), “The relationship between capital and earnings in banking”,
Journal of Money, Credit and Banking, 27, 432–456.
Berger, A. N. (1995a), ‘The Profit-Structure Relationship in Banking: Test of Market
power and Efficient Structure Hypothesis’, Journal of Money, Credit and banking,
Vol.27, No.2, 404-431.
Berger, A. N., Bonime, S. D., Covitz, D. M., & Hancock, D (2000), “Why are bank
profits so persistent? The roles of product market competition, informational opacity,
and regional/macroeconomic shocks”, Journal of Banking and Finance, 24, 1203–
Bikker, J., & Hu, H (2002), “Cyclical patterns in profits, provisioning and lending of
banks and procyclicality of the new basel capital requirements”, BNL Quarterly
Review, 221, 143–175.
Blundell, R., and Bond, S. (1998), Initial conditions and moment restrictions in
dynamic panel data model. Journal of Econometrics, 87, 115-143.
Bond, S.(2002), “Dynamic panel data models: a guide to micro data methods and
practice”, Portuguese Economic Journal, Vol.1 No.2, 141-62.
Bourke, P (1989), “Concentration and other determinants of bank profitability in
Europe, North America and Australia”, Journal of Banking and Finance, 13, 65–79.
Boyd, J. H., & Champ, B (2006), Inflation, banking and economic growth, Federal
Reserve Bank of Cleveland.
Canals, J (1993), Competitive Strategies in European Banking, Oxford, Clarendon
Chantapong, S (2005), “Comparative study of domestic and foreign bank
performance in Thailand: The regression analysis”, Economic Change and
Restructuring, 38,(1), 63–83.
Claeys, S., and Vennet, R. V(2005), “Determinants of bank interest margin in Central
and Eastern Europe: a comparison with the west”, working paper, Ghent University,
Cooper, M., Jackson, W., & Patterson, G (2003), “Evidence of predictability in the
cross section of bank stock returns”. Journal of Banking and Finance, 27, 817–850.
Demirguc-Kunt, A. & Huizinga, H (1999), “Determinants of Commercial Bank
Interest Margins and Profitability: Some International Evidence,” World Bank
Economic Review, 13, 2, 379-408.
Demirgüç-Kunt, A., & Maksimovic, V (1998), “Law, Finance and Firm Growth”.
Journal of Finance, 53, (6), 2107-2137.
Demirguc-Kunt, A., and Huizinga, H (2000), “ Financial Structure and Bank
profitability”, working paper(2430), The World Bank, August 2000.
Demirgüç-Kunt, A., & Huizinga, H (2001), Financial Structure and Bank
Profitability”.In Financial Structure and Economic Growth: A Cross-Country
Comparison of Banks, Markets, and Development, Cambridge, MA: MIT Press.
Dietrich, A., and Wanzenried, G. (2010), ‘Determinants of bank profitability before
and during the crisis: evidence from Switzerland. Available at SSRN:
Duca, J., & McLaughlin, M (1990), “Developments affecting the profitability of
commercial banks”,Federal Reserve Bulletin, 477–499.
Eichengreen, B., & Gibson, H. D (2001), Greek banking at the dawn of the new
millennium. CEPR Discussion Paper 2791, London.
Fadzlan, S & Kahazanah, N. B (2009), “Determinants of Bank profitability in a
Developing Economy: Empirical evidence from the China Banking Sector”, Journal of
Asia-Pacific Business, 10,4, 201-307.
Fadzlan, S., & Royfaized, R. C (2008), “Determinants of Bank Profitability in a
Developing Economy: empirical evidence from Philippines”, Asian Academy of
management Journal of Accounting and Finance. 4, 2, 91-112.
Garcia-Herrero, A., Gavila, S., and Santabarbara, D (2009), “What explains the low
profitability of Chinese banks?”, Journal of Banking and Finance, 33, 2080-2092.
Gischer, H., and Jutter, J. D (2001), Profitability and Competition in Banking
Markets: An Aggregative Cross Country Approach.
Goddard, J. A., Molyneux, P. M., and Wilson, J.O.S. (2001), European banking:
efficiency, technology and growth, Chichester, Wiley.
Goddard, J., Molyneux, P., & Wilson, J (2004), “Dynamic of growth and profitability
in banking”, Journal of Money, Credit and Banking, 36,(6), 1069–1090.
Graham, C., and Bordelean, E (2010), “ The impact of liquidity on bank profitability”,
working paper(2010-38), Bank of Canada, Canada, December 2010.
Guru B., Staunton, J., & Balashanmugam, B (2002), “Determinants of Commercial
Bank Profitability in Malaysia,” Paper presented at the 12
Finance and Banking Conference, 16-17 December, Sydney, Australian.
Hadad, M. D., Hall, M. J. B., Kenjegalieva, K., Santoso, W., Satria, R., and Simper, R.
(2008). Banking efficiency and stock market performance: ananalysis of listed
Indonesian banks. Working paper(2008-07), University of Loughborough, UK, July.
Hall, M. J. B., Kenjegalieva, K., and Simper, R. (2010). Accounting for environmental
factors, bias and negative numbers in efficiency estimation: a bootstrapping
application to the HongKong banking sector, working paper(2010-03), University of
Loughborough, UK, March.
Hassan, M. K., & Bashir, A. H. M (2003), “Determinants of Islamic banking
profitability”,Paper presented at the 10th ERF Annual Conference, 16–18
December.Morocco, available at: www.erf.org.eg/CMS/getFile.php?id=636
Hameed, A., & Bashir, M (2003), “Determinants of profitability in Islamic banks: some
evidence from the Middle East”, Islamic economic studies, 11, 1, 31-57
Hauner, D (2005), “Explaining efficiency differences among large German and
Austrian banks”, Applied Economics, 37,(9), 969–980.
Heffernan, S., and Fu, M (2008), “The Determinants of Bank Performance in China”,
working paper series(WP-EMG-03-2008), Cass Business School, City University ,UK,
Available at SSRN: http://ssrn.com/abstract=1247713
Hoggarth, G., Milne , A., & Wood, G (1998), “Alternative Routes to Banking Stability:
A Comparison of UK and German Banking Systems,” Bank of England Bulletin, 55-
Liu, H., & Wilson, J. (2009), “The profitability of banks in Japan: The road to
recovery?”, Working paper series. Cass business school, City University, UK, 10
Jiang, G., Tang, N., Law, E., & Sze, A (2003). “Determinants of Bank Profitability in
Hong Kong,” Hong Kong Monetary Authority Research Memorandum, September.
Karasulu, M (2001), “The Profitability of the Banking Sector in Korea,” IMF Country
Kennedy, P. (2008). A guide to econometrics. Malden, MA: Blackwell Publishing.
Kosmidou, K., & Zopounidis, C (2008), “Measurement of bank performance in
Greece”, South Eastern Europe Journal of Economics, 6,(1), 79–95.
King, R.G., & Levine, R (1993a), “Finance and Growth, Schumperer might be Right”,
Quarterly Journal of Economics, 108, 717-738.
King, R.G., & Levine, R (1993b), “Finance, Entrepreneurship and Growth: Theory
and Evidence”, Journal of Monetary Economics, 35, 513-542.
Levine, R., & Zervos, S (1998), “Stock Markets, Banks, and Economic Growth”.
American Economic Review, 88, 537-558.
Maudos and Fernandez de Guevava (2004),”Factors explaining the evolution of the
interest margin in the banking sectors of the European Union”, Journal of Banking
and Finance, 28,9,2259-2281.
Miller, S. M., & Noulas, A (1997), “Portfolio mix and large bank profitability in the
USA”, Applied Economics, 29, 505–512.
Molyneux, P (1993), “Structure and performance in European Banking”. Working
paper, University of Wales Bangor.
Molyneux, P., & Thornton, J (1992), “Determinants of European bank profitability: A
note”, Journal of Banking and Finance, 16, 1173–1178.
Naceur, S. B (2003), “The Determinants of the Tunisian Banking Industry Profitability:
Panel Evidence”,Paper presented at the Economic Research Forum(ERF) 10
Annual Conference, 16-18 December, Marrakesh, Morocco, available
at: .http://www.mafhoum.com/press6/174E11.pdf (accessed 19
Pasiouras, F., & Kosmidou, K (2007), “Factors influencing the profitability of
domestic and foreign commercial banks in the European Union”,Research in
International Business and Finance, 21,(2), 222–237.
Perry, P (1992), “Do Banks Gain or Lose from Inflation?”, Journal of Retail Banking,
14, 2, 25-30.
Rajan, R., & Zingales, L (1998), “Financial Dependence and Growth” , American
Economic Review, 88, 559-586.
Rhoades, S. A. (1985). ‘Market share as a source of market power: Implications and
some evidence’, Journal of Economics and Business, Vol. 37, No. 4, 343-363.
Rivard, R. J., and Thomas, C. R (1997), “The effect of interstate banking on large
bank holding company profitability and risk”, Journal of Economics and Business, 49,
Roodman, D. (2006), "How to do xtabond2: an introduction to difference and system
GMM in Stata", Working Paper No.103, Center for Global Development.
Rowe, W., Shi, W., & Wang, C (2009), “Board governance and profitability of
Chinese banks”, Electronic copy available at: http://ssrn.com/abstract=1368962
Shen, C. H., & Lu, C.H. (2008). Is there a silver lining in the cloudy performance of
Chinese banks?-an empirical investigation into the determinants of profitability and
risk. Fuchs, E.J., and Braun, F.Emerging Topics in Banking and Finance Nova
Science Publishers. New York. pp.9-26.:.
Shih, V., Zhang, Q., & Liu, M (2007), “Comparing the performance of Chinese banks:
a principle component approach”,
China Economic Review, 18, 15–34
Smirlock, M (1985), “Evidence on the (non) relationship between concentration and
profitability in banking”, Journal of Money, Credit, and Banking, 17, 69–83.
Staikouras, C., & Wood, G (2003), “The determinants of bank profitability
in Europe”, Paper presented at the European Applied Business Research
Conference, 9 June-13 June, Venice, Italy.
Williams, B(2003), “Domestic and international determinants of bank profits: Foreign
banks in Australia”, Journal of Banking and Finance, 27,(6), 1185–1210.
Wu,H. L., Chen, C. H., & Shui, F. Y (2007), “The impact of financial development and
bank characteristics on the operational performance of commercial banks in the
Chinese transitional economy”, Journal of Economic Studies, 34,:5, 401-414.
Table 1 Variables considered in this study
variables notation measurement Expected
ROA Net income/total
NIM Net interest
Bank size LTA Log of total assets ? Bank-
Credit risk LLPTA Loan loss
liquidity LA Loans/assets ? Bank-
taxation TOPBT Tax/operating profit
capitalization ETA Shareholder’s
Total assets of
largest 3 or 5
banks/total assets of
the whole banking
BSD Bank assets/GDP - Industry-
SMD Market capitalization
inflation IR Annual inflation rate ? macro World bank
Notes: + means positive effect, - means negative effect, ? means no indication.
Table 2 Descriptive statistics of all variables
Name Mean Standard Min Max
ROA 0.007 0.006 -0.003 0.11
NIM 2.85 1.11 1.89 3.76
Bank size 4.67 0.95 0.71 7.07
Credit risk 0.009 0.007 -0.002 0.042
liquidity 53.39 9.35 17.97 83.25
taxation 0.41 0.37 -4.56 3.18
capitalization 5.1 2.97 -14 31
Cost efficiency 0.012 0.004 0.004 0.04
13.91 15.2 -34.22 128.42
0.008 0.004 3.50e-06 0.019
14.54 1.95 10.19 16.29
20.61 2.5 14.66 22.12
51.98 15.49 16.86 63
77 49.47 31.9 184.1
inflation 2.5 2.17 -0.77 5.86
Table 3 Cross correlation matrix
ROA NIM size risk liquid taxation Capital Cost Non-
Labour C(3) C(5) Banking
NIM 0.44 1
size -0.03 -0.3 1
risk -0.15 0.21 -
liquid -0.04 0.26 0.03 -0.06 1
taxation -0.15 -0.02 0.03 0.15 0.3 1
capital 0.07 0.2 -
-0.09 -0.18 1
cost 0.16 0.51 -
0.17 0.21 0.06 0.09 1
-0.04 -0.53 -
0.09 -0.41 -0.07 -0.03 -0.07 1
Labour 0.19 0.11 0.29 -0.07 0.14 -0.09 0.02 -0.19 -0.26 1
C(3) -0.003 0.07 -
0.15 -0.003 0.22 -0.12 -
0.03 -0.01 1
C(5) -0.08 0.002 -
0.15 0.04 0.24 -0.18 0.01 0.04 -0.07 0.98 1
-0.03 -0.08 0 -0.07 -0.03 0.03 0.11 -0.01 0.1 0.07 -0.18 -0.17 1
0.29 0.31 -
-0.05 0.1 0.03 0.14 -0.11 -0.08 0.1 0.24 0.06 -0.29 1
inflation 0.06 0.15 -
0.11 -0.1 0.04 -0.04 -0.02 -0.01 0.13 0.79 0.72 -0.21 0.35 1
Table 4 Empirical results (two-step system GMM estimation)
coefficient t-statistic coefficient t-statistic
Lag of dependent
0.22*** 4.45 0.25*** 5.5
LTA -0.0002 -1.56 -0.07** -2.35
LLPTA -0.08* -1.86 52.41*** 5.89
LA -0.00002 -1.21 0.013*** 2.89
TOPBT -0.005*** -4.72 -0.54*** -3.76
ETA -0.00004 -1.39 -0.014 -1.54
CE 0.42*** 6.24 117.93*** 7.14
-2.92 -0.028*** -8.86
LP 0.24*** 5.08 3.13 0.31
C(3) 0.002 0.17
C(5) -0.00009* -1.84
BSD 0.00002*** 3.97 0.009*** 5.93
SMD 0.00002*** 8.36 0.004*** 10.74
IR 0.0003*** 5.79 0.04*** 4.43
F test 1397.01*** 1234.98***
sargan test 87.37*** 228.84***
AR(1) test Z=-2.49 P=0.013 Z=-2.45 P=0.014
AR(2) test Z=-0.37 P=0.713 Z=-1.74 P=0.082
the Sargan test is the test for over-identifying restrictions in GMM dynamic model estimation.
Arellano-Bond test that average autocovariace in residuals of order 1 is 0. ( no autocorrelation)
Arellano-Bond test that average autocovariance in residuals of order 2 is 0. ( no autocorrelation)
***,**,* are significant at 1,5 and 10 percent significance levels, respectively
Figure1 Inflation rate in China (2003-2009)
Figure 2 Profit changes of Chinese commercial banks (2003-2009)