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Financial Liberalization And Bank Efficiency In China Finance Essay

Introduction

Financial liberalizations have been implemented in developing countries for several decades, bringing substantial changes for banking sectors against excessive government restrictions. Consequently, the sector has acquired more autonomous rights, less state-controlled, broad access to foreign banks and private institutions, and restructured national banking systems as well (Fry, 2005). As a result, numbers and types of financial institutions are both increased and banking sectors are more competitive. As Williams and Nguyen (2005) state, banks should be more efficient under competitive environments, reducing costs and increasing revenue, hence, coordinating limited resources effectively.

Both the economic growths in China and India are tremendous over the last decade and have been taking more important roles in globalization of trade. To boost internal liberty and external competitiveness in banking sectors and to provide stable capital resources for the overall economic constructions, the financial liberalizations have been implementing for several decades. In china, the promulgating of “Reform and Open up” policy in the late 1970s symbolizes the beginning of economic reform. The reform then speeded up in the early 1990s. In the meantime, Indian carried out their comprehensive economy reforms as well. As the two countries successively entered WTO, reforms measures are put an emphasis on generating a competitive banking system as the entry of foreign banks and more liberalized financial environment.

A great deal of empirical focuses has been put on exploring the effects of economic policies conducted on the financial market as well as the practical results (Jayaratne and Strahan, 1996, Wurgler, 2000, Beck et al., 2000, Levine, et al., 2000). Summarily, there are mainly two purposes. One is to seek a better way to establish a more sustainable and healthier financial system, which could possibly lead to an economic growth. The other is to protect the development of financial market as expected by every level of society. As studies reveal that, economic growth in developing countries is highly positively linked to more efficient financial market (King and Levine, 1993; Beck et al., 2005; Jappelli et al., 2005). There are also findings suggest that the growing up of financial infrastructure requires banks to have stability to support the development, such loans, credit scores, collateral (Berger and Udell, 2006).

In recent years, parts of researches are conducted to consider the effect of financial liberalization on bank efficiency in emerging economies. These studies examine the relationship by comparing bank efficiency before and after the initial financial reforms. The results are mixed. Some indicate significant improvements in banking efficiency have been achieved when comparing with the pre-reform period. While others show the performance of banks does not become better. Some studies have extended the research method with a two-step procedure. In the first step, bank efficiency scores are measured and then, by employing a censored regression, a set of explanatory variables are regressed, such as a dummy variable representing the reform and a number of bank-specific variables as well (Hao et al., 2001; Lski and Hassan, 2003; Ataullah et al., 2004).

As a follow up study, this paper conducts a comparative study by estimating commercial banking industry after the financial reforms in China and India. While most previous studies present the effect of financial liberalization as a whole by using all the variables relevant, this paper employs two components of the financial reforms, the improvement of competition level and the appearance of foreign banks, to estimate the effect on bank efficiency.

With this purpose, the analysis is divided into two procedures. First, utilizing the approach of Bhattacharyya et al., (1997) and Ataullah and Le (2006), Data envelopment analysis (DEA) technique is employed with a common “grand frontier” to make a comparative analysis for the two countries. The data covers 2003 to 2008, which is a crucial post-reform periods for both of the countries. The efficiency scores are measured by two-stage input-output models, a combination of a loan-based model and an income-based model. Finally, ordinary least squares are applied to examine the correlation between the DEA and two factors of financial reform elements as mentioned above. Furthermore, a set of internal bank-specific characteristics are included in the regression, bank size, operating expenses, return on assets (ROA)

The results suggest

Main empirical focus is put on the influences of minority foreign ownership and competition level in China and India. The results manifest that

To address these issues, the rest of the paper are structured with parts: section 2 displays main procedure and characteristic of the financial reforms in China and India, especially putting an emphasis of the entrance of foreign ownership. Section 3 reviews empirical researches on the relationship between financial liberalization, bank efficiency and performance in emerging economies, particularly China and India. Section 4 describes the methodology and data. Section 5 presents the empirical results, and Section 6 demonstrates the empirical findings. Section 7 concludes.

Literature review

In recent years, considerable exhaustive surveys of literatures examining banks efficiency and performance exist. Meanwhile, topics relevant are fairly broad and embedded. While some studies focus on determinants of efficiency such as size, capitalization, profitability, loans to assets (Casu and Girardone, 2004; Ariff and Can, 2008; Fethi and Pasiouras, 2009), abundant literatures pay attention to the relationship between bank efficiency and macro-economy under different economic and political systems throughout the world. The fields of macro-economy involve stock returns (Beccalli et al. 2006; Kirkwood and Nahm, 2006), bank ownership (Isik and Hassan, 2003a; Sathye, 2003), mergers and acquisitions (Wheelock and Wilson 2003; Hahn 2007a) and regulation liberalization (Ataullah and Le 2004; Chen et al., 2005) as well. Berger and Humphrey (1997) conducted an excellent review to consider the relationship between financial deregulation and the efficiency and productivity of bank operating. However, it’s still necessary to penetrate into the studies referring to emerging economies.

2.1 Financial liberalization, bank efficiency, and productivity in emerging economies

Since early 1980s, financial liberalizations have been implementing in many developing countries. It’s fairly obvious that studies about its impact on efficiency and productivity became more concerned since late 1990s. A certain number of studies make hypotheses that regulatory reforms have a positive relationship with bank efficiency and productivity. It is postulated that deregulation encourages more competitive and flexible environments, under which banks pay more attention to the operations.

However, globally, empirical evidences are mixed. In Turkey, Yildirim (2002) applied non-parametric DEA to measure technical efficiency of banks from 1988-1999 and the result shows that the efficiency after deregulation is not sustainable. In contrast, Isik and Hassan (2003b) concurred that a positive relationship exists between liberalization and banks’ X-efficiency, examining DEA of Turkish banks between 1980 and 1990. Gilbert and Wilson (1998) discover that as to comply with privatization and regulation reform in early 1990s, Korean banks essentially modified the mix of inputs and outputs, meanwhile, made a combination with advanced technology, and hence, effectively promoted productivity of banks. In contrast, Hao et al. (2001) confuted the findings of Gilbert and Wilson (1998) with a time invariant data, which from 1985-1995. By using a parametric Stochastic Frontier Approach (SFA), the researchers found that the financial reforms in Korea made little or no contribution to Korean’s bank X-efficiency. In addition to this, Hao et al. (2001) pointed out that the positive relationship may exist intertemporally and could also just be in a short-term. Moreover, the authors illustrate relationships between ownership and bank efficiency. The study indicates a positive relationship between bank efficiency and the numbers of foreign banks and it reveals a negative one when related to the amount of state-owned banks.

Ataullah and Le (2004) point out that financial liberalization is beneficial for foreign banks to conquer the barrier of being “foreignness” and promote the efficient use of resources. With the increscent amount of foreign banks in banking system of emerging market, it is believed that superior practices of management and technology are brought to banks in developing countries, which affirmatively enhance efficiency and productivity (Clasessens et al., 2001). In accord with the observation, Isik and Hassan (2003a) demonstrate that foreign banks in Turkish have better efficiency in bank operating than private domestic banks. Later on, Isik (2008) estimates TFP growth and the results support the previous findings. Similarly, Leightner and Lovell (1998) find that state-owned banks were less productive than foreign banks in Thailand.

2.2 Financial liberalization and bank efficiency in China

Studies exploring financial reform and Chinese bank efficiency in China are relatively limited and the results are mixed and contradictory. However, bank ownership is a common topic in most literatures investigating Chinese banks.

Chen et al. (2005) examine the effect of reforms initiated in 1995 on bank efficiency. 43 banks’ panel data are used to examine the efficiencies of cost, technology and allocation, covering the period 1993-2000. It is revealed that Big Four banks, which is state-owned and smaller joint-equity banks are both more efficient than medium-sized banks in cost. Meanwhile, with the reform, deregulation made a positive effect on bank efficiency.

On the contrary, it is continuously proved by a certain amount of studies with different approaches that state-owned banks are less efficient than smaller-sized banks in China. Applying an input distance function which covering a longer time period, 1993 to 2002, Kumbhakar and Wang (2005) demonstrate that relatively small scale banks are more efficient than Big Four banks. The study also indicates that deregulation doesn’t significantly promote bank efficiency. Consistent with the findings of Kumbhakar and Wang, Fu and Hefferman (2006) explore the cost X-efficiency in china from 1985-2002, by using a stochastic frontier approach. As the research shows, state-owned banks are less efficient than joint-stock banks. However, the first period of bank reforms, cost efficiency is higher.

Besides, Berger et al. (2009) estimate the change of ownership’s structure of Chinese banks and its effect on bank efficiency in China between 1994 and 2003. The study enlarges the concept of profit efficiency by embracing revenues and loan performance, instead of costs only. Moreover, as an emerging market, the effect of the entrance of foreign ownership on Chinese banking system is also checked. The results show that large state-owned banks are the least efficient of all the types of ownerships while foreign banks come out on top. Furthermore, a small number of foreign ownership prominently promotes the enhancement of bank efficiency as a whole.

There are two apparent characteristics in the studies about financial reform and bank efficiency and productivity. First, the results are considerably mixed. It is not necessarily that financial liberalization and deregulation give rise to the enhancement of bank efficiency and productivity. To a great extent, the results of reforms depend more on industry conditions than deregulation, fluctuating in different countries and also diverse reform levels (Fu and Heffernan, 2007). Secondly, there is only a small quantity of cross-country studies. It’s regrettable that until now there are no cross-country about Chinese bank efficiency and financial reforms. Lots of valuable information could be supplied by cross country studies, such as bank competitiveness in different countries, which is fairly important in the financial world with globalization (Berger and Humphrey, 1997).

2.3 Financial liberalization and bank efficiency in India

Although literatures about bank efficiency in India are productive, topics considering the relationship between financial reform and the change of bank efficiency are limited. While a little amount of the researches explore the relevance, the studies are presented either in pre-liberalization period, such as Bhattacharyya et al. (1997) or in post-liberalization period, like Sathye (2003) and Tabak and Tecles (2010). There are three studies examines the impact of financial reform. Interestingly, the results are mixed.

By using a translog cost function, Kumbhakar and Sarkar (2003) examine TFP growth of 50 banks in India from 1985-1996. 27 of the banks are private domestic banks and 23 are public sector banks, while no foreign banks are included. The evidence show there are little evidence show the reform facilitates bank productivity, particularly in state-owned banks.

By employing DEA, Atalullah et al. (2004) conduct a comparative analysis of India and Pakistan to examine the relationship between financial liberalization and bank efficiency. The study shows after the implement of reform, bank efficiency in both countries increased. Hence after, Atalullah and Le (2006) examine the effect of economic reform on bank efficiency. The reform comes down to three aspects, fiscal reforms, financial reforms and private investment linearization. The research conducted with a two-stage model. On the first stage, use DEA to analysis the relationship, and on the second stage, censored regression is used as the OLS and the GMM to examine the DEA scores. It is revealed that financial liberalization (economic reforms) positively promotes the increasing of bank efficiency.

Banking system and financial liberalization in China and India

China and India are fairly similar in economically and physically. Both have experienced tremendous growth rates over the last two decades. Since 1980, GDP of China and India grow at annual rates approach to 10% and 6%, respectively. Even after the Asian financial crisis in 2007 and global recession since 2007, China and India still keep a strong momentum of with positive growth while in most of western countries, negative GDP rates and sluggish markets. Some studies indicate that the growth capacity are strong and will be definitely continued in the two countries.

It is generally accepted that financial intermediaries effectively influence the growth of economy by coordinating limited financial resources in the economic market (Levine, 1997). Reflectingly, Fry (1995) demonstrates that as a matter of fact, it is commercial banks that play a vital role in all of the financial intermediations other than financial institutions and markets, which undertake an inessential mission in developing countries. The importance of commercial banks reveals that it’s extremely crucial for authorities of developing countries to ameliorate the economical environment which promote the bank efficiency and in return, enlarge the volume of intermediation and strength service and product quality (Ataullah and Le, 2006).

The history of banking system in China and India contribute to understanding of the general pattern of financial reforms in the two emerging economies.

3.1 China

In China, the financial reform and the change of banking system can mainly be divided into three parts.

3.1.1 1979-1992

Before 1978, China implemented socialist economic and financial system. People’s Bank of China (PBOC), the central bank, takes the role of issuing currency and the Economic Plan in each state. Since 1978, aiming to promote economic efficiency and optimize resource allocation an economic reform, China bring an economic reform into force and banking system. Naturally, the banking system was the focus of the significant, however, gradual reforms. To support the PBOC and the Big Four, in 1978, several large state-owned commercial banks were established. In 1985, the Big Four state-owned banks [1] , the Bank of China (BOC), Agricultural Bank of China (ABC), China Construction Bank (CCB) and Industrial and Commercial Bank of China (ICBC). Between 1985 and 1992, the Big Four were authorized to compete in all sectors and small and medium sized commercial banks were allowed to provide deposits and loans services to households and corporations. Thus, the first stage ended in 1992.

3.1.2 1992- WTO Entry in December 2001

The second stage started in 1993 until present. It is announced in the document “Decision on Financial System Reform” (Almanac of China’s Financial and Banking, 1994). One object of reform was to establish a competitive environment for commercial banks, in which state-owned coexist with other financial institutions. Hence, large quantities of reform measures were put into effect.

In 1990s, most of the loans from banks were allocated to state-owned enterprises (SOEs), which led to large of assets exacerbated. On one hand, SOEs were basically had no intend to repay. On the other hand, there is no specific deposit insurance and it’s pervasive that Chinese government stepped up to help the banks with financial problems, writing-off their bank loans or paying off the outstanding debts, so as to avoid the bank failure. In order to mitigate the deterioration of assets and improve the quality, in 1994, three policy banks were set up to take over the national projects from state-owned banks. In 1998, 270 billion RMB (US $32.6 billion) government bonds for 30 years were released to raising capital for the Big Four.

In 1995, two reforms with legal restrictions were carried out. PBOC was approved by “The 1995 Central Bank Law of China” as have the role of central bank and the power of local government to allocate the credit were weakened in substance. “The 1995 Commercial Bank Law of China” determined the nature of state-owned banks as commercial banks and pointed out the operate direction is commercial business. It shows a preference to market principles rather than policy oriented.

Before 1994, only in Shenzhen, a Special Economic Zones, could foreign banks open several branches and the restrictions of aspect of area. After that, foreign banks were permitted to operate in 23 cities and the scope of business extends continuously. At the end of 1999, total assets reached to 272 billion RMB (US $32,844 million).

While concern of bank taxes, as Xu and Zhang report (1995), there is no explicit uniform tax laws, corporations made a contract with government about the tax. The reform started in 1994 and in 1997, a 33% tax rate was settled down for all commercial banks.

3,1.3 After WTO entry

A series of new reforms measures were established after the entrance of WTO in December 2001 and some original regulations were revised to adapt to the requirement of WTO. In line with the agenda, interest rate should be more liberate, tax rates should be fairer to all the players, loosen the restriction to M&A and ownership control and more freedom of Chinese banking industry scope, both in operation and geography.

The Big Four are realized partial privatization by accepting a portion of foreign ownership. Bank of America and Singapore investment firm owns 9% and 5.1% shares of CCB. Royal bank of Scotland and Temasek both enjoy 10% stakes separately of BOC in 2006. For ICBC, three foreign banks reached a deal to share 10% stakes in total with $3.78 billion. Moreover, as one of the shareholder, Goldman Sachs offers employee training, assists risk control and direct in internal controls and corporation supervision.

Furthermore, to improve the efficiency of banking operating, Chinese banks are encouraged to have its stock listed so as to be monitored externally. Since 2005, Big Four banks have realized listed one after another, inside or outside mainland China. Notably, even the shares are issued in Hong Kong or other areas outside the mainland China; it is not subject to the foreign ownership with limitation of 25%.

3.2 India

In India, the main development process of banking industry can be divided in two periods, one is from 1950s to 1990, and the other period is post-crisis period, after the financial crisis from 1991-1992 in India, a magnitude of reforms have been done which produce the modern banking system of India today.

3.2.1 1950s-1990

Before 1950s, there was considerable limited government control in Indian financial system. Nevertheless, restrictions by the Reserve Bank of India, the central bank, were exerted gradually, yet, severely in the 1960s. The main implement was the control of interest rate. After noticing the inequitable distribution of bank credit, the government tightened its control over the credit allocation process. The statutory liquidity ratio was raised from 25% in 1966 to 38% in 1989.The cash reserve rate increased considerably from 3% to 15% during the same period. These high liquidity and reserve requirements enabled the Bank to purchase government securities at low cost. The extent of directed credit programs has also increased significantly since the nationalization of the 14 largest private banks in 1969. A number of priority lending rates were set at levels well below those that would prevail in the free market. This process culminated in the late 1980s when directed lending was more than 40% of the total.

3.2.2 After 1991-1992 financial crisis

The major phase of financial liberalization was undertaken in 1991 as part of the broader economic reform in response to the balance-of-payments crisis of 1990–91.The objective was to provide a greater role for markets in price determination and resource allocation. Consequently, interest rates were gradually liberalized, and the reserve and liquidity ratios were reduced significantly. However, despite this liberalization, the Indian financial system has continued to operate within the context of repressionist policies through the provision of subsidized credit to certain priority sectors. Liberalization of the directed credit programs is only limited to deregulation of priority lending rates, whilst significant controls on the volume of directed lending remain in place. Furthermore, the Bank has tightened supervision and regulation in recent years to ensure that these priority sector requirements are met.

Research Methodology

4.1 Methodology statement

A series of methods have been employed in banking performance assessment (Bauer et al., 1998), which can be mainly divided into two categories. One category is parametric approach, which is on basis of econometric techniques. The method is usually involved with estimation of a presupposed stochastic production, cost or profit function and the derivation of the measured scores by confirming whether it’s from residuals or dummy variables (Bauer et al., 1998). Stochastic frontier analysis is a popular application of this technique. In this approach, each decision-making unit (DMU) corresponds to a single optimized function.

While in another category of methods, on the contrary, performance measure of DMU is optimized. The non-parametric approach does not demand a specific function for the frontier. It is a piecewise linear program, in which an actual function envelops all the data in the sample (Thanassoulis, 2001). With such a function, efficiency scores can be measured by estimating the distance between the observations and the ‘envelope’ and the possibility of misspecification in the process of setting production function could be ruled out (Bauer et al., 1998). Data Envelopment Analysis (DEA) is representative for the method.

Both of the techniques hold strength and weakness. The parametric approaches allow the existence of noise when measuring efficiency scores; nevertheless, a specific functional form or abnormal efficiency distribution, such as exponential, gamma and half-normal distribution should be applied (Isik and Hassan, 2003b). While referring to the non-parametric approaches, though no pacific function or distribution of efficiency is required, two shortcomings are obvious. It is assumed the data measured without error and statistical noise. In addition, the technique has a large sensitivity to outliers (Berger and Mester, 1997; Yildirim, 2002). While numerous studied are conducted with the measurement of bank efficiency with different frontiers, it still cannot be conclude that which approach is exactly the best-practice frontier.

In this paper, a two-step procedure is employed to evaluate the effect of several elements of financial liberalization on bank efficiency. In the first step, the efficiency scores of commercial banks in China and India are measured by the Data Envelopment Analysis (DEA), covering 2002-2008. In the second step, the scores are regressed with a variety of variables in internal and external of bank sectors using OLS estimations to obtain a more veracious result.

4.1.1 Step 1— efficiency estimation by DEA

The DEA is applied to measure the efficiency of commercial banks in India and China during 2002-2008. DEA can be carried out by supposing constant returns to scale (CRS) or alternatively, variable returns to scale (VRS). Charnes et al. (1978) put forward a DEA model to estimate the overall technical efficiency (OTE) of banks, assumed CRS. However, it is debated that CRS is only applicable when the optimal scale is offered to all firms. As Coelli et al. (2005) propose, in real markets, a series of adverse factors obstruct a firm’s operation at optimal scale, such as imperfect competition, financial restriction and government regulations, etc., among others. Therefore, Banker et al. (1984) post variable returns to scale (VRS). In imperfect market, using CRS will lead to measurement error of technical efficiency (TE), in which scale efficiency (SE) contains. However, by using VRS, efficiency scores can be divided into two components, pure technical efficiency (PTE) and scale efficiency (SE). Hence, TE wouldn’t be affected by SE. SE is the difference between VRS TE and CRS TE, so SE can be regarded as a “residuals” (Coelli, 1997).

By using DEA, the best-practice production frontier of a sample of firms is structured through a set of correspondent input-output data with piecewise linear combination. This linear combination envelops all the firms with correspondent input-output data in the sample (Thanassoulis, 2001).

Following Bhattacharyya et al., (1997) and Ataullah and Le (2006), a single “grand frontier” is constructed to envelop the pooled input-output data of all the banks throughout the years in the India and China, covered in the study, that is, from 2002-2008. A best-practice benchmark is provided by this grand frontier. The efficiency of each bank in each year is measured against this benchmark. It is proposed that if the financial liberalizations have improved the bank efficiency, the efficiency scores will have a trend of increase. That is, the most efficient observations are of recent vintage.

Two advantages appear when using grand frontier approach. The key benefit is that trends of bank efficiency can be revealed with aggregated data which would not be available if the data is measured annually due to the benchmark are capable of changing in every year. The other advantage of applying the grand frontier instead of annual frontiers is that the former approach allows “an increase in degrees of freedom” which is extraordinary important for the efficiency measures using the DEA.

There are two main reasons for the employment of DEA in this paper. First, some existing studies exploring bank performance and efficiency have already use parametric techniques the measurement the situation of India and China. Hence, it’s worthy of exploring the effect of financial liberalization on the bank performance in China and India by DEA calculation so as to estimate whether the results support the previous studies. Second, Bhattacharyya et al. (1997) indicate that input/output prices may possibly be distorted attribute to the regulations and market imperfection in developing countries, especially excessive reform regulations on banking industry in the past decades. Therefore, employing parametric approaches may result in increased complexity of the cost and/or profit function measurement.

It is also feasible to acquire the efficiency trends by constructing a Malmquist (1953) productivity index, an approach which is generally used to reveal the tendency of change for bank productivity. Though the grand frontier approach is relatively less used, the single benchmark it provides contributes to a clearer trend throughout time. In consideration of the environment of reforms in China and India which features interim and transformable in a long term, the grand frontier approach is a better choice.

To measure the efficiency of banks, let the input data for commercial banks in the two countries be represented by

Where f = 1, 2, 3, … , F indexes banks, t = 1, 2, 3, … ,T indexes time periods, and, j = 1, …, J indexes inputs that banks in the two countries employ.

Let the output data be represented by

…, …,)

Where k = 1, 2, 3,…, K indexes outputs that banks in the two countries produce.

Then, the pooled production possibility set R for all the banks included in the years covered for the two countries can be represented as

R = { ( ,):

k= 1, 2, 3, … , K

j = 1, 2, 3, … , J

f= 1, 2, 3, … , F; t = 1, 2, 3, …, T

=1} (1)

Where the are intensity variables enable the production of convex combinations of observed ( , ). R represents the production technology, showing variable returns to scale and disposability of inputs and outputs.

To obtain the efficiency score of banks, it is presumed that banks pursue output maximization, given the inputs at their disposals. An output-oriented efficiency of each bank f in year t, E ( ,), is measured as the reciprocal of the solution to the DEA problem:

Max = [ ]-1 (2)

Subject to

, k = 1, 2, 3, …, K,

, j = 1, 2, 3, …, J,

0, f = 1, 2, 3, …, F, t = 1, 2, 3, …, T

= 1

The efficiency scores could be acquired once with each bank, countries and year. should be scaled up in sequence for bank with data ( , ) by the optimal value of . By so, the convex production frontier can be reached. Because of 1, it can get that 0 1. For example, 0.9 efficiency score of a bank indicates that the bank only produced 90% of the output should be produce, if the best practice in the industry was taken as the standard.

4.1.2 Step 2— OSL estimation by regression analysis

While the bank efficiency of the post-reforms period in China and India are measured, in the second step, factors crucial in the financial liberalization in both countries are examined so as to explore the effected factors to the variances of the scores.

The following model is used to generate a regression function:

= f ( ) (3)

Where stands for the technical efficiency of f-th banks in t-th time period with output orientation. represents the set of bank-specific variables with P factors. is the set of macroeconomic variables in financial liberalizations with Q elements. As mentioned above, the value of ranges from 0 to 1, so that Hao et al., (2001) demonstrate that it’s possible to use the logistical function for model (3) as:

= (4)

Where and are vectors of corresponding parameters. are individual variables of bank effects. are error term for white noise. Rely on the states above, the model (4) can be adapted as follows:

ln = = (5)

Where the term = ln is called the “log-odds” and could substitute the output-oriented technical efficiency of banks in China and India over 2002-2008.

Choice of variables

4.2.1 Variables in step 1

Dual role are possessed by banks. On one hand, as financial institutions, banks mobilize savings and provide services for transactions and document processing. Economic factors as production, land, labor and capital are employed in the progress of transaction. On the other hand, banks are financial intermediations, serving as both borrowers and lenders. They take the role of middlemen, financing firms for investment needs and individuals for consumption needs by channeling funds. This goes to an obvious problem. These services have direct or indirect correlation with financial assets and liabilities possessed by banks. However, it’s ambiguous to distinguish whether the services to customers are inputs to the production of assets or outputs. Situations exist that fees paid by customers sometimes don’t cover the cost of bank service, indicating double features of fees, without consistent methods to separate them or reasonable input price can be offered.

Based on the ambiguous internal item connection, Bergendahl (1998) argues that: “there have been almost as many assumptions of inputs and outputs as there have been applications of DEA”, Berger and Humphrey (1997) classify two main approaches to elaborate different emphasis on inputs and outputs. “Production approach” is based on the assumption that banks provide loans and deposits account services, identifying labour and capital as inputs and number and type of transactions or documents processed as outputs. “Intermediation approach” emphasizes the role of financial intermediation of banks, who are both savers and investors.

However, in the first step, intermediation approach is applied when choosing variables for two reasons. First, the role as financial intermediary continuously becomes more important position in banking industry globally. However, with production approach, the role is weakened and it excludes interest expense which holds the largest proportion of total costs in the production process. Second, as Berger and Humphrey (1997) state, the production approach are more suitable for the evaluation of bank branches efficiencies and the intermediation approach could be better to estimating financial institutions as a whole. The comparison of bank efficiency in India and China in this paper is financial intermediation orientation and place emphasis on the tendency analysis.

This paper indentifies the production process of financial institutions of a bank as follows: in line with Leightner and Lovell (1998) and Ataullah et al. (2004) as well as the extended study of Ataullah and Le (2006), two different, albeit relevant models are applied to specify the input and output of banks in China and India Model A is a loan-based model, postulating that banks generate interest and operating expenses to produce earning assets, among which, loan and advances. Model B is an income-based model, postulating that banks incur interest and operating expenses to obtain interest and non-interest income. Hence, the output variables for Model A are loans and advances while in Model B, the outputs are interest income and operating income. Interest expenses and operating expense are two input variables for both of the Models.

Several other studies applied investment in Model A as earning asset. However, it is not included in this paper for two reasons. First, investment is not a dependent item in the balance sheet. It blended into a series of other assets items, which are difficult to obtain accurate numbers. Second, the investment fields of banks are diverse in India and China, which could possible cause residuals. According to the above reasons, loans and advances are suitable items for earning assets as input variables.

4.2.2 Variables in step 2

In the second step, the purpose is to examine the impacts of financial liberalization on the bank efficiency in China and India, mainly focusing on competition and the presence of foreign banks. in model (3) represents external environmental factors, including two factors: the Herfindahl-Hirschman index of banks (HE), on the basis of total assets of banks, standing for the competitive level of banking industry; the other factor is the share of foreign ownership banks in total assets (FOA) representing the presence of foreign banks.

in model (3) stands for the relevant internal bank-specific factors. Quantities of literature results show that there is a strong positive relationship between efficiency of banks and internal factors in developed countries, so it is necessary to estimate the effect of internal factors (Casu and Molyneux, 2003; Hauner, 2004). Three factors are included: bank size, representing by logarithm of total assets (TA); operating expenses, as divided by total income (OE/TI) and return on assets (ROA).

Furthermore, the efficiency score of previous year (EFF1)

4.4 Data

The sample in this study consists of 56 commercial banks; 33 in China and 23 in India. The bank financial statement data were sourced mainly from the electronic databases, Bankscope and IBCA data. For some specific years, data for Indian commercial banks are from the website of the Reserve Bank of India, while in the case of China, huge amounts of data are collect from some official sources, such as annual Issues of China Statistical Yearbook and annual issues of Almanac of China’s Finance and Banking. Macroeconomic data were obtained primary from the IMF International Financial Statistics and the Asian Development Bank.

The test period covers 1993 to 2003, so as to study to effect of 1995 financial reforms held in both China and India. In 1995, India entered WTO while China issued two major legislative reforms. The beginning year is 1993 in the study, thus, encompassing the periods before 1995 financial reforms so as to compare the results from later years.

V. Empirical results

VII. Conclusions

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