economic reforms, efﬁciency and productivityeba-zuiz/2008/kumbhakar_wang_2007.pdfabstract this...

J Regul Econ (2007) 32:105–129DOI 10.1007/s11149-007-9028-x

ORIGINAL ARTICLE

Economic reforms, efficiency and productivityin Chinese banking

Subal C. Kumbhakar · Dan Wang

Published online: 10 April 2007© Springer Science+Business Media, LLC 2007

Abstract This paper analyzes the impact of banking reforms on efficiency and totalfactor productivity (TFP) change in Chinese banking industry. Using an input distancefunction, we find that joint-equity banks are more efficient than wholly state-ownedbanks (WSOBs). Furthermore, both WSOBs and joint-equity banks are found to beoperating slightly below their optimal size, suggesting potential advantages in expan-sion of their businesses. Overall, TFP growth was 4.4% per annum for the sampleperiod 1993–2002. Joint-equity banks experienced much higher growth in TFP (5.5%per annum) compared to the WSOBs (1.4% per annum).

Keywords Deregulation · Efficiency · Productivity · Technical change ·Stochastic frontier · Input distance function

JEL Classifications C23 · G21 · G28

1 Introduction

In the past two decades, China has a unique experience of going through the transitionfrom a command economy to a market economy. In spite of its great achievementin economic development, its largely dysfunctional banking system has crippled pro-gress in many respects. In the past, credit culture in the banking system was neithercompetitive nor market based. Loans were often made without questioning borrow-ers’ credibility and projects’ profitability while most borrowers (almost exclusively

S. C. Kumbhakar (B) · D. WangDepartment of Economics, State University of New York, Binghamton, NY 13902, USAe-mail: [email protected]

D. Wange-mail: [email protected]

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106 S.C. Kumbhakar, D. Wang

state-owned enterprises) defaulted on their loans. As a result, a significant proportionof the loans turned into non-performing loans, which severely prevented banks fromextending credits to projects that had the potential for higher rates of return. Since theearly 1980s the Chinese government has gradually implemented a series of policiesaiming at upgrading its banking system by restructuring and deregulating. After twodecades of effort, the banking industry has undergone some institutional and structuralchanges. The question that we address here is whether these institutional changes havereally enhanced the performance of the banks as expected by the Chinese governmentand the central bank.

Effects of deregulation on efficiency and performance of banking sector in indus-trialized economies has been widely studied (Humphrey and Pulley 1997; Wheelockand Wilson 1999; Berger and Mester 2003; Kumbhakar et al. 2001, among others). Inrecent years, interest has been focused on transitional economies where markets andinstitutional structures are different from those of developed countries. Since publicsector banks are dominant in the transitional economies, the ownership-performancenexus has particularly been one of major research interests. However, the findingsfrom empirical studies on the impact of privatization/diversification of ownershiphave been mixed. Kraft and Tirtiroglu (1998) found increased efficiency of newlyprivatized banks after the deregulatory period 1990 in Croatia. Bhattacharyya et al.(1997) showed that Indian state-owned banks were more efficient than private domes-tic banks. Kumbhakar and Sarkar (2003) found no improvement in TFP growth forpublic banks in India after deregulation but observed that private banks had benefitedfrom deregulation. Bonin et al. (2004) compared the cost and profit efficiency of 225banks from 11 former Soviet republics and found that government-owned banks werenot necessarily less efficient than domestic private banks while foreign-owned bankswere generally more cost efficient than other banks. Fries and Taci (2005) conducteda similar study on 15 east European countries and concluded that the majority of for-eign-owned private banks performed most efficiently and the domestic-owned privatebanks were relatively less efficient, while state-owned banks were the least efficient.

Bank size is another important issue in the efficiency literature. Most studies indi-cate that large asset size tends to promote higher efficiency. Higher productive andprofit efficiency were associated with large and medium-sized banks in the Thai bank-ing industry (Leightner and Lovell 1998; Laeven 1999). Hasan and Marton (2003)found that large Hungarian banks were more cost and profit efficient. Similar resultsare also found in the studies on banking systems in developed countries (e.g., Hugheset al. 1999; Hunter and Timme 1986).

Studies on the Chinese banking sector are far less and comprehensive, primarily dueto difficulties in obtaining data since public accounting requirement for most banks inChina was absent. A number of researchers (e.g., Bonin and Huang 2001; Bhattasali2002; DaCosta and Foo 2002; Gordon 2003; Lardy 1999; Mo 1999) have focused oncase studies, mainly describing the history and problems of reforms in China’s bankingsector. Only a few studies (Park and Sehrt 2001; Leung et al. 2003a,b; Li et al. 2001;Shirai 2002) have provided empirical assessments on the effects of reform measureson bank performance. Although a variety of methods and perspectives of reformswere discussed in these studies, two major findings stand out. First, WSOBs tend toperform worse in terms of profitability and cost efficiency (measured as accounting

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Economic reforms, efficiency and productivity in Chinese banking 107

ratios) compared to their peers (other types of banks). Second, an inverse relationshipis found between size and performance of domestic banks.

Overall, the above studies have either compared the accounting ratios of expensesor income or the interest margin across banks, or have used reduced form of regres-sion to find the coefficients of the potential correlates to these financial accountingratios. However, the recent banking literature (e.g., DeYoung 1997) suggests thatregression-based approaches (e.g., the stochastic frontier approach), which control forthe different characteristics and environmental conditions associated with individualbanks give more accurate estimates of efficiency than the simple descriptive statisticslike accounting ratios. In the present analysis we assess the effectiveness of reformmeasures, by focusing on the change in efficiency and productivity, in an environmentwhere transformation has been gradual.

In this paper we examine technical efficiency and total factor productivity changesof 14 national banks in China over the period 1993–2002. During this time, bankingsector reforms were mostly implemented. Our methodology utilizes the input-dis-tance function approach, which has been adopted in some recent banking studies(e.g., Marsh et al. 2003; Cuesta et al. 2002). Distance functions have the advantageof accommodating multiple inputs and multiple outputs, which is quite common inthe banking industry. In addition, the use of the distance function does not requireexplicit behavioral assumptions like cost minimization and profit maximization. Con-sequently, no price information is needed for estimation. This is particularly importantwhen assumptions about perfectly competitive markets are unlikely to be met or whenprice information is not available or accurate.1 Though no explicit behavioral assump-tions are necessary, under some conditions the primal input distance function is dualto the cost function, which enables us to give the input distance function results a costfunction interpretation without estimating the cost function per se. The other featureof our methodology is the one-step estimation procedure that incorporates explanatoryfactors into the efficiency analysis while estimating the input distance function. Thisone-step estimation is superior to two-step estimation procedure2 that is commonlyseen in the literature (e.g., Nikiel and Opiela 2002; Berger and Mester 1997; Clarkand Siems 2002).

The objective of this paper is twofold. First, it investigates the effects of possibleforces including ownership type, capital adequacy ratio, bank size and environmentalfactors on technical efficiency in order to find channels for improvement for policypurposes. Second, we examine the dynamic pattern of total factor productivity changeby decomposing it into scale effects, technical change, technical efficiency change andchange induced by bank characteristics and environmental forces.

The remainder of the paper is organized as follows. Section 2 describes the insti-tutional and regulatory changes in China’s banking sector. Section 3 presents thetheoretical framework and the empirical model. Section 4 discusses data sources and

1 Inaccurate prices are even worse than no prices since it may explain less of the variance of cost or profitand thereby leading to more error in the estimation of inefficiency (Berger and Humphrey 1997).2 Wang and Schmidt (2002) present extensive Monte Carlo results to show that the two-step estimationmethod suffers from serious biases. They suggested using the single-step approach.

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definition of the variables used in the study. Section 5 discusses the empirical results,and Sect. 6 concludes the paper.

2 Institutional and regulatory changes in the Chinese banking industry

During 1979–1993, the Chinese government took the first round of financial reforminitiatives as a part of its overall economic reform. This financial reform was aimedat institutional reconstruction and entailed the creation of a central bank (People’sBank of China) and the establishment of four state-owned banks. The four whollystate-owned specialized banks3(WSOBs), usually called the Big Four, were initiallyfounded4 as fiscal budget distributors to the state-owned enterprises (SOEs) in specificsectors of the economy. Before 1986, the banks were under the direct control of theMinistry of Finance, and were not granted decision-making powers. In other words, thelending decisions of these banks were not based on debtors’ credibility and projects’profitability, but were based on government investment plans in the form of a creditplan set by the central bank. The credit plan was carried out by the local governments,often colluding with the central bank’s plan. The four WSOBs were responsible forsupplying both working capital loans and fixed-asset loans to SOEs. In China, theSOEs historically were not profit-driven, but played a social role in the economy bytaking responsibilities of maintaining urban employment and providing social insur-ance and welfare, pensions, medical care, housing and education. The performance ofthe SOEs worsened since the beginning of industrial reform in 1984 and the number ofloss-making SOEs increased dramatically, especially after the 1990s. Consequently,the WSOBs accumulated an enormous amount of non-performing loans (NPLs)5 anddead debts from lending to the SOEs. To mitigate the rapid growth in non-performingloans, the Chinese government and central bank launched a second wave of financialreforms in 1994. During this second wave, the Chinese government also focused oncommercializing the WSOBs, promoting competitiveness by easing the entry of bothdomestic new banks and foreign banks, alleviating the problems of non-performingloans and relaxing reserve requirements. This second stage of the liberalization processculminated in 1998.

Three major measures were taken to facilitate the transition of the WSOBs frompractitioners of the central government investment plan to real commercial banks.First, three policy banks6 were established in 1994 and it was envisaged that thesebanks would assume the policy-lending activities of the specialized WSOBs. Also,the WSOBs were legally allowed to make their lending decisions independently. Sec-ond, a sum of 1.4 trillion RMB7 non-performing loans of the WSOBs (roughly 20% of

3 These are Bank of China (BOC), Industrial and Commercial Bank of China (ICBC), China ConstructionBank (CCB) and Agriculture Bank of China (ABC).4 BOC, CCB and ABC were founded in 1979, and ICBC was established in 1984.5 About 80% of NPLs of Chinese state-banks were from the bankrupt SOEs (Hu 2000).6 These are Export-Import Bank of China, China Development Bank, and Agricultural Development Bankof China.7 RMB is the Chinese currency, a.k.a. RenMinBi.

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their total loans outstanding) were bought at their face values by four assets manage-ment companies (AMCs) founded in 1998. In exchange, RMB 550 billion of centralbank borrowings was reduced from the WSOBs’ liabilities, while the rest appeared ontheir asset sides as investments on bonds issued by AMCs. Third, in 1998, the creditplan for both working capital loans and fixed investment loans was removed. In lieuof this credit plan, an indicative non-binding target was set by the central bank asan indirect monetary policy but this target served only as a reference for commercialbanks to plan their business. However, these efforts cannot be expected to solve theWSOBs’ problem of policy lending and non-performing loans fundamentally, sincethe WSOBs did not really gained their independence in their lending decisions fromthe local governments. The WSOBs are all national banks with a branch and subordi-nate branch in each province, district, and county. Under the current political system,the senior managers are usually directed by the Communist party committee of thecity in which the bank is located, and the local governments always exert influenceover the appointment and dismissal of bank managers. In order to maintain a goodrelationship with the local government officials, the bank managers usually meet therequirement of local governments to solve financial problems of local enterprises.Thus, the branches of the four WSOBs were known as “the second budget” of thelocal government. Even after the Commercial Banks law came into force in 1995, thissituation didn’t improve much.

While the commercialization process of the WSOBs was in progress, competitionwas introduced by relaxing the barriers to entry for new banks. As a result, in thelate 1980s new commercial banks began to emerge and many city and rural creditcooperatives were transformed to city banks. With respect to the newly establisheddomestic commercial banks, China Mingshen Bank is the only real private bank, whilethe other nine are jointly owned by state and private companies and are still subjectto ownership restructuring towards privatization. For simplicity, we refer to these 10newly established banks as joint-equity banks. These newly established banks andtransformed city banks are expected to form a private banking sector in the future. Inaddition, foreign banks began to acquire licenses to open representative offices andbranches. Benchmarked by the Commercial Bank Law enacted in 1995, a legal com-mercial banking system was established, comprising of 104 commercial banks, and alarge number of city and rural credit cooperatives, as well as 190 foreign banks withbranches and representative offices.

After the East Asian financial crisis, the Chinese central bank recognized the impor-tance of risk management in the banking sector and adopted a new risk managementsystem of five-tier classifications of loans in 1998. According to the guidelines of thenew risk management system, loans are categorized into five groups: standard, atten-tion, substandard, doubtful, and non-performing, based on their risks contents ratherthan payment status.8 In addition, the commercial banks in China were allowed to set

8 The loan classification system before 1998 was based on payment status, which means a loan was clas-sified as NPL only when the repayment of principal was not fulfilled. Since many loans were bullet loansso that no repayment of principal was required until the end of loan term, some de facto NPLs were stilllabeled as healthy loans even when the credit risk of the loans was signaled by the failure of repayment ofinterests. Also, in the case of multiple loans extended to a single borrower, individual loans were classifiedas NPLs only when the contractual terms of each loan were violated. See Lardy (1999) for more details.

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aside a special provision for the loss of NPLs. This provision is exempt from taxes,but the amount of the provision must be negotiated with the Ministry of Finance everyyear. Although the write-offs of bad loans may reduce the banks’ profit in the shortterm, these are beneficial to the health of the banks in the long run.

In 1998 the reserve requirement for commercial banks was reduced from 13% to8% and the requirement of 7% for excess reserves was removed. This gave banks morefreedom to allocate their funds. The WSOBs lowered their reserves in line with thedrop in the requirement and a sum of RMB 270 billion was invested in governmentbonds especially issued by Ministry of Finance for recapitalization.9

In the late-1980s some flexibility in the lending rate was introduced so that bankswere allowed to adjust their lending rates within a certain margin of the administratedrate set by the central bank. Such flexibility was not available to interest rates ondeposits. Moreover, inter-bank market interest rate ceilings were lifted in 1996.

Besides the major reforms described above, other measures were simultaneouslyundertaken. New accounting principles, which are consistent with the basic ideas ofthe International Accounting Standards, were adopted in July 1993; nationwide moneymarket and bond markets were established, and regional inter-bank markets were uni-fied through a computer network system in 1996 to become national in order to helpfree flow funds upon demand.

For a better insight of the effect of reform measures on the performance of China’sbanking sector, we go beyond the simple descriptive statistics and use a stochasticinput distance function, which allows us to draw inference on efficiency, productivitygrowth and technical change, while controlling for variations in quantities of inputs andoutputs and also in bank characteristics and environmental factors. The next sectionbriefly describes the input distance function model.

3 Methodology

In the case of multiple inputs and multiple outputs, the typical parametric productionfunction approach is incapable of describing the production technology structure. Dis-tance functions introduced by Shephard (1953) are commonly adopted in the literatureto characterize this structure of multiple outputs produced by multiple inputs. An inputdistance function measures the maximum amount by which the input usage can beradially reduced but still remain feasible to produce a given vector of outputs. It isformally defined as DI (y, x, t) = max{λ : x/λ ∈ L(y)} where x and y are the inputand output vectors, and t represents time trend that is introduced to capture technicalchange. The input isoquant, expressed as Isoq L(y) = {x : DI (y, x, t) = 1}, shownin Fig. 1 corresponds to the sets of input vectors that have an input distance functionvalue of unity, which means any further radially contracted sets of input vectors areincapable of producing the given output vector y.

9 The recapitalization was indeed implemented by two swap transactions: the reserves were swapped forbond investment on the assets side and borrowings from central bank were swapped for equity on theliability side (Mo 1999).

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Fig. 1 An input distancefunction with two inputs. Note:X A is the actual usage ofcombination of input X1 and X2to produce output y; L(y)represents the input isoquant forproducing output y; λ is theinput distance function while θ

is the technical efficiency

X2

L(y)

xA

xA/ λ =θ xA

X1

In the context of the input distance function, input-oriented technical efficiencymeasures how close the actual usage of the input vector is to the minimum (optimal)usage of inputs on the input isoquant. A more compact definition of input-orientedtechnical efficiency is given by TE(y, x, t) = min{θ : DI (y, xθ, t) ≥ 1}. When theinput vector is on the input isoquant (Isoq L(y)), there is no technical inefficiencyand TE(y, x, t) = 1. If the input vector falls beyond the input isoquant, there existstechnical inefficiency and TE(y, x, t) <1.

It is clear from above that the input distance function and technical efficiencyare closely related. Their relationship can be explicitly expressed as TE(y, x, t) =[DI ((y, x, t))]−1. Figure 1 gives a visual demonstration of this for the case of twoinputs. In Fig. 1, output (y) can be produced by input vector (xA) but it can alsobe produced with less input xA/λ or θxA. Thus, DI (y, x, t) = λ >1 and 0 <

TE(y, x, t) = θ < 1. We can rewrite the relationship TE(y, x, t) = [DI (y, x, t)]−1 asTE(y, x, t) ∗ [DI (y, x, t)] = 1. If we replace technical efficiency (TE) with exp(−u)

where non-negative values of u represents the input-oriented technical inefficiency, weget DI (y, x, t) exp(−u) = 1. After rearrangement, the above equation can be writtenas ln DI (y, x, t) = u. The inefficiency term u can depend on a vector of covariates(Z). This will be discussed later.

Input distance functions are non-decreasing, concave, and linearly homogeneousin x and non-increasing and convex in y (Färe and Primont 1995). Färe and Primont(1995) also showed how to derive returns to scale (RTS) using input distance function,which will be discussed in the derivation of the TFP change formula. In addition,Atkinson and Cornwell (1998) showed that under certain conditions, the dual rela-tionship between distance function and cost function allows technical change (costdiminution) to be expressed as −Ct (y, w, t) = − ∂ ln DI (y,x,t)

∂t . This relationship gives

− ∂ ln DI (y,x,t)∂t a cost saving interpretation of technical change without any price infor-

mation.For estimation purposes, we assume a following flexible functional form (the tran-

slog) for the input distance function with M outputs (y), J inputs (x) and time t , t =1, 2, …, T, viz.

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ln D1 = a0 +J∑

j=1

a j ln(x j ) +M∑

m=1

bm ln(ym) + 1

2

J∑

j=1

J∑

k=1

a jk ln(x j ) ln(xk)

+1

2

M∑

m=1

M∑

l=1

bml ln ym ln yl +J∑

j=1

M∑

m=1

g jm ln x j ln ym + j0t

+1

2j00t2 +

J∑

j=1

γ j t t ln(x j ) +M∑

m=1

θmt t ln(ym) (1)

Subscripts i and t represent the individual bank and time period, respectively. Fornotational ease, we omit the subscripts i and t .

Since our sample data covers a relatively long time period (1993–2002), we allowfor changes in both the technology and efficiency over time. Thus, the time trend var-iable is included in both the frontier function and efficiency so that we can examineseparately the temporal patterns of technical change and technical efficiency change.The time-invariant technical change hypothesis can be tested by imposing the follow-ing restriction on the parameters, viz., j0 = j00 = γ j t = θmt = 0.

As mentioned earlier, input distance functions must satisfy certain theoretical prop-erties, such as homogeneity of degree one in inputs x and symmetry of the cross effects.These conditions impose some constraints on the parameters of the input distancefunction in (1), viz.,

J∑

j

a j = 1,

J∑

j

a jk =J∑

j

g jm =J∑

j

γ j t = 0, blm = bml and a jk = akj .

Imposition of the homogeneity restrictions is equivalent to rewriting the functionby normalizing all the inputs by one input (Karagiannis et al. 2004), viz.,

ln(DI /x1) = ln DI − ln x1 = a0 +J∑

j=2

a j ln(x j/x1) +M∑

m

bm ln(ym)

+1

2

J∑

j=2

J∑

k=2

a jk ln(x j/x1) ln(xk/x1) + 1

2

M∑

m=1

M∑

l=1

bml ln ym ln yl

+J∑

j=2

M∑

m=1

g jm ln(x j/x1) ln ym + j0t + 1

2j00t2 +

J∑

j=2

γ j t t ln(x j/x1)

+M∑

m=1

θmt t ln(ym) (2)

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Since ln DI (y, x, t) = u, the above relation after appending a stochastic error term,v, can be written as

− ln x1 = α0 +J∑

j=2

a j ln(x j/x1) +M∑

m

bm ln(ym)

+1

2

J∑

j=2

J∑

k=2

a jk ln(x j/x1) ln(xk/x1) + 1

2

M∑

m=1

M∑

l=1

bml ln ym ln yl

+J∑

j=2

M∑

m=1

g jm ln(x j/x1) ln ym + j0t + 1

2j00t2 +

J∑

j=2

γ j t t ln(x j/x1)

+M∑

m=1

θmt t ln(ym) − u + v, (3)

where u is the one-sided error component that is normally distributed (truncated atzero from below); v is normally distributed (with mean zero and constant variance)error term that captures unmeasured factors and measurement errors; and u and v areassumed to be independently distributed.

Two questions are often raised in estimating distance functions: (i) possible end-ogeneity of the input and output variables that appear on the right-hand-side (rhs)of (3) and (ii) interpretation of the parameters of the distance function. Like a costfunction, the output variables are treated as exogenous in input distance functions.This is not objectionable since banks are providing services to its customers and theseservices are exogenously determined (not in the control of individual banks). By thesame argument outputs are treated as exogenous in the cost function approach. Theexogeneity of input ratios that appear on the rhs of (3) follows if we assume that allthe inputs are affected in the same way by exogenous shocks. This exogeneity assump-tion is standard in the literature and has been used in every input distance functionapplication (see Morrison-Paul et al. 2000; Karagiannis et al. 2004; and referencescited in there). It is shown in Färe and Primont (1995) that input distance functionis dual to the cost function. Estimation of cost functions require price informationwhich is either difficult to obtain or there may not be much variations, especially in acompetitive market. In view of this, it is ideal to estimate the input distance functionfrom which one can obtain cost implications. Furthermore, the estimated parametersof the input distance function can be used to obtain information about the underlyingtechnology such as measures of elasticity of substitution, returns to scale, and the like(see Morrison-Paul et al. 2000 for more on this).

Following Kumbhakar et al. (1991), Battese and Coelli (1995), the one-sided errorterm, u, is assumed to be normally distributed (truncated at zero from below) with itsmean dependent on some exogenous variables (z) including the time variable t . Thus,

uit ∼ N (µi t , σ2u ) with uit ≥ 0 and

µi t = ω0 + ω1t + ω2t2 + z′i tδ; or, uit = ω0 + ω1t + ω2t2 + z′

i tδ + εi t ; (4)

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where εi t ≥ −(ω0 + ω1t + ω2t2 + z′i tδ) is the unexplained component of technical

inefficiency. The z variables include bank characteristics as well as environmentalvariables that are possible sources of technical inefficiency. The inclusion of the t2

term in µi t allows for a flexible temporal pattern of technical efficiency change sincethe technical efficiency change does not necessarily have to be monotonic (as in theBattese and Coelli 1992 parameterization, viz., uit = ui exp(θ(t − T ))).

The input distance function specified in (3) and (4) can be estimated using a single-step maximum likelihood (ML) method. The log-likelihood function for a particularyear (t) is

ln Lt = constant − I

2ln(σ 2

v + σ 2u ) −

∑

i

ln

(µi t

σu

)

+∑

i

ln

(µ∗

i t

σ ∗

)− 1

2

∑

i

(eit + µi t )2

σ 2v + σ 2

u(5)

where µi t = ω0 + ω1t + ω2t2 + z′i tδ, µ∗

i t = σ 2v µi t −σ 2

u eit

σ 2v +σ 2

u, σ ∗2 = σ 2

v σ 2u

σ 2v +σ 2

u, and eit =

vi t − uit .The above log likelihood function (after summing it up over all the time periods) is

maximized to obtain ML estimates of all the parameters in the model (a, b, g, j, γ, θ,

ω, δ, σ 2v , σ 2

u ). These estimates can then be used to obtain bank specific estimates oftechnical inefficiency. Using Jondrow et al. (1982) decomposition, these estimates ofuit are

E(uit |eit ) = µ∗i t + σ ∗ φ(µ∗

i t/σ∗)

(µ∗i t/σ

∗)(6)

Once the input distance function specified in (3) and (4) is estimated, returns toscale, technical inefficiency, and total factor productivity change can be computedfrom the estimated parameters. Moreover, the TFP change can be further decomposedinto a scale component, a technical change component and a technical efficiencychange component, all of which can be calculated from the estimated coefficients ofthe model. Using the input distance function, the formulas for TFP change ( ˙TFP =∑

m Rm ym −∑j S j x j ) and its components with input-oriented non-neutral technical

inefficiency are10:˙TFP =

∑

m

Rm ym −∑

j

S j x j = (1 − RTS−1)∑

m

Rm ym

+∂ ln DI /∂t − ∂u/∂t +∑

k

Ik.

Zk (7)

where RTS−1 = −∑Mm=1

∂ ln DI (y,x,t)∂ ln ym

; Rm = pm ym/∑

m pm ym = ∂ ln DI /∂ ln ym∑m ∂ ln DI /∂ ln ym

;

S j = w j x j/∑

j w j x j = ∂ ln DI /∂ ln x j∑j ∂ ln DI /∂ ln x j

= ∂ ln DI /∂ ln x j ; Ik = ∂ ln e−u

∂ ln Zk=

10 The detailed derivation of this equation can be found in Karagiannis et al. (2004).

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− ∂u∂ Zk

Zk . Finally, p is the output price vector and a dot over a variable indicatesits rate of change.

In (7), the first term measures scale effect and RTS stands for returns to scale. Thepresence of economies of scale (excess capacity) is indicated by increasing RTS andthis is often used as a justification for bank mergers and acquisitions. This is because inthe presence of scale economies, cost savings can be obtained from expanding output(cost elasticity of output being less than unity). Alternatively, the scale size is efficientwhen RTS is unity. One can test the hypothesis of constant (unitary) returns to scaleby imposing the following restriction on the parameters in the input distance function,viz.,

M∑

m=1

bm = 1,

M∑

m=1

bml = 0∀l,J∑

j=1

g jm = 0∀m,

M∑

m=1

θmt = 0. (8)

It can be easily seen from (7) that as long as there is an increase in aggregate out-puts (

∑m Rm ym > 0) increasing returns to scale contributes positively to the total

factor productivity growth, and vice versa. If the technology exhibits constant returnsto scale, then the scale effect is zero. The second term in (7) is technical change.Technical efficiency change is decomposed into pure technical efficiency change (theneutral component) given by the third term, and the non-neutral component inducedby some exogenous forces (the z variables) given by the fourth term in (7). Thesevariables include bank characteristics and environmental factors. If we assume timeinvariant and neutral technical efficiency, then both the third and fourth terms are zero.In such a case, total factor productivity change is affected only by the scale component(either increasing or decreasing returns to scale), and technical change.

From TFP change we define the TFP index using 1993 as the base year, viz.,

TFPt+1 = (1 + �TFPt+1)TFPt , t = 1, 2, . . ., T and TFP1(1993) = 100. (9)

4 Data

In our study, sample data covers the most influential fourteen banks in China for thetime period 1993–2002. It is an unbalanced panel data with 132 observations. Thesefourteen banks consist of the four WSOBs and 10 joint-equity banks, which accountfor about 70% and 9%, respectively, of the market share of total assets. Furthermore,these banks control about 80% of the entire banking market in China at the end of year2000.11 Four WSOBs are solely owned by the state, and the 10 joint-equity banks areeither entirely private-owned (China Mingsheng Bank), or jointly owned by privateenterprises and local governments. City banks, which evolved from mergers of urbancredit cooperatives and the remaining urban and rural credit cooperatives, have histori-cally served the needs of local small businesses and have different business scopes andbusiness strategies than their peer domestic banks. Therefore, we exclude them from

11 Data source is the report of Chinese Banking Industry in 2000 from China Economic InformationNetwork.

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our analysis. The foreign banks are also not included in the analysis because many ofthe regulations have been primarily targeted to domestic banks. Besides, their busi-nesses were largely confined to foreign currency transactions and dealt with foreignenterprises only. This makes the comparison of foreign and domestic banks difficult.

Three main sources of data are used in this study. First, the financial data from1993 to 2000 are drawn from the BankScope database, which is published by Bureauvan Dyk. All financial data (excluding financial ratios) are converted in US dollars.Since BankScope does not provide information on employment and branches, thenumbers of employees and branches for each bank from 1993 to 2002 are obtainedfrom the second source, China Economic Information Network. In order to extend ouranalysis, we added two more years of data by going beyond the BankScope database.Supplemental financial data of 2001 and 2002 are taken from the Almanac of China’sFinance and Banking 2003.12

Table 1 reports summary statistics of some of the variables that are pertinent to ouranalysis. For comparison purposes, variables for the WSOBs and the joint-equity banksare reported separately. For brevity, only information for selected sample years andthe averages of sub-periods of 1993–1998 and 1999–2000 are reported. The period1993–1998 represents the deregulatory period when the primary reform initiativeswere undertaken, while the post-deregulation period refers to the period 1999–2002.

The joint-equity banks experienced rapid growth during the sample period. This isreflected in both expansion of branches and their scales of operation. Their averagetotal assets were increased almost by 200% from $9.96 M in the deregulatory periodto $26.49 M in the post-deregulatory period. In contrast, although the WSOBs are stilldominant in terms of their total assets and number of branches and employees, a declin-ing trend in the number of branches has been seen during the sample period. The equityratio for joint equity banks gradually dropped from 10% in 1993 to 3% in 2002, whilethose for WSOBs has been fairly stable at around 4–5%. Thus we see convergence ofcapital adequacy rates between the two groups. The lower ratio of fixed assets to totalassets in the joint-equity banks indicates that joint-equity banks are relatively moreefficient in allocating the funding sources than the WSOBs. This is because the fundsused on fixed assets can not be used on loans or other earning assets for earning purpose.

12 In order to construct consistent data series from two different data sources, several issues were carefullyhandled. Since the applied accounting standards differ across banks, the treatments of items in their financialstatements vary too. First, we looked into the items of balance sheets of 1997 and 1998 for each bank fromAlmanac of China’s Finance and Banking 1998, and aggregated the data to construct the variables that areused in our study. Then, we converted the figures of our aggregate data (which were in Chinese currency)in US dollars using exchange rate of the last of calendar date. Historical exchange rates were obtained fromthe website of Bank of China (http://www.bank-of-china.com/info/qpindex.shtml). Next, we compared ouraggregate figures in US dollars to the values of corresponding variables given in BankScope data set forthe same bank in the same year. If the results are perfectly matched, it indicates that the correspondingitems we chose from the balance sheets to construct our variables are consistent with those used in theBankScope data set. By taking the procedures described above, we were able to identify the formula usedin the BankScope for constructing their data set. Then using the same formulas we constructed our data setfor 2001 and 2002 from the source of Almanac of China’s Finance and Banking 2003. By doing so, oursupplemented data sets of 2001 and 2002 are consistent with the BankScope data sets of 1993–2000.

123


Tabl

e1

Des

crip

tive

stat

istic

s

Var

iabl

es19

9319

9920

0219

93–1

998

1999

–200

2@

lJo

int-

equi

tyW

SOB

sJo

int-

equi

tyW

SOB

sJo

int-

equi

tyW

SOB

sJo

int-

equi

tyW

SOB

sJo

int-

equi

tyW

SOB

sba

nks

bank

sba

nks

bank

sba

nks

Num

ber

ofem

ploy

ees

5293

3894

3395

7142

9457

1283

236

6962

7676

4128

5111

252

3944

12

Num

ber

ofbr

anch

esn/

a13

3382

4311

1029

1350

4898

951

n/a

1417

9247

2510

7997

Tota

lass

ets

(TA

)60

0625

9944

1739

032

1120

3707

641

3773

9959

2624

6326

488

3632

68

Equ

ityto

tota

lass

ets

(%)

10.3

93.

695.

085.

383.

094.

847.

853.

804.

225.

25

Fixe

das

sets

toTA

(%)

0.93

1.44

2.42

2.07

1.14

1.79

1.59

1.77

1.68

2.00

Loa

nto

TA(%

)52

.55

60.4

749

.18

60.8

256

.07

56.9

749

.92

58.0

651

.85

57.6

1

Oth

erea

rnin

gas

sets

toTA

(%)

37.5

115

.97

41.5

429

.09

38.6

236

.24

41.1

427

.95

40.9

234

.15

Tota

lear

ning

asse

tsto

TA(%

)90

.06

76.4

490

.71

89.9

194

.69

93.2

291

.06

86.0

192

.77

91.7

6

Lab

orpr

oduc

tivity

2597

369

1987

791

3741

1183

1973

601

2810

977.

13

Loa

nto

depo

sits

(%)

88.7

810

1.56

61.2

868

.01

69.2

662

.15

68.4

684

.46

64.6

763

.58

Not

es:T

otal

asse

tsan

dla

bor

prod

uctiv

ityar

em

easu

red

inth

ousa

ndU

Sdo

llars

123


The ratio of loans to total assets indicates that loans were the primary earning source(about 57% of total assets on average during the sample period). The WSOBs haveslightly higher concentration on loans (58% of total assets) compared to the joint-equity banks (52% of total assets). There was also a large increase in the share ofother earning assets to total assets in WSOBs (from 16% in 1993 to 36% in 2002).This change in the product mix reflects the structural change in the WSOBs, whichis indicative of the transformation of the WSOBs from specialized banks to commer-cial banks. The other notable feature of China’s banking industry is reflected by theratio of total earning assets to total assets which, for the WSOBs, increased from 76%in 1993 to 93% in 2002. This indicates a remarkable efficiency improvement in thefund utilization by the WSOBs. In contrast, the joint-equity banks had a stable fundutilization rate that stayed above 90% for every year.

Labor productivity is traditionally used to measure productivity of a firm. For thepresent application, it is calculated as total earning assets divided by full time equiv-alent employees. All banks experienced labor productivity growth during the sampleperiods. Joint-equity banks consistently outperform WSOBs with much higher laborproductivity during each year of the sample period. However, we observe a remark-able growth of labor productivity in WSOBs from $369 in 1993 to $1183 in 2002,increasing by more than three times.

For estimating input distance function, we need information on input and outputquantities. Banking industry is characterized by multiple inputs and outputs. There isno consensus in the literature on what banks produce. Some authors used the numberof transactions provided for account holders as outputs, including loan applications,checks, credit reports and the like. Sealey and Lindley (1977) criticized this measureof outputs in financial firms by stating that those authors failed to carefully analyzeboth the technical and economic aspects of production at the financial firms. Theydeveloped a theory of production and cost for financial firms. According to their the-ory, banks function as the intermediary between depositors and investors, and varioustypes of earning assets are treated as outputs while deposits along with capital andlabor are used as inputs in the production process. In our study deposits,13 borrowedfunds, fixed assets and labor are considered as inputs, while loans and other earn-ing assets are treated as two outputs. The outputs, inputs, bank characteristics andenvironmental variables are as follows:

Outputs: y1 is total loans net; y2 is other earning assets.Inputs: x1 is labor; x2 is total fixed assets; x3 is total deposits plus total borrowedfunds.Bank characteristics and environmental variables (the Z variables): z1 is ratio ofequity to total assets; z2 is ownership dummy; z3 is deregulation dummy; and z4includes four dummy variables for different bank sizes.

All outputs and inputs except labor are measured as of the end-of-year values in mil-lions of US dollars. Labor is measured as the total number of employees in thousandsat the end of year. Total net loans is the aggregate total loans minus allowance and

13 Hughes and Mester (1993) provided an empirical test of the role of deposits in bank production, and theresults indicated that deposits should be treated as inputs.

123


reserves (aggregate total loans being the sum of short term customer loans, short termfunding, viz., discounts of acceptances, banker’s drafts and bills of exchange and longterm customer loans). Other earning assets include balances due from central bank andother depository institutions, inter-bank funds sold, securities purchased under agree-ment of resale, investment in securities available to sell, and investment in securitiesheld-to-maturity net of risk reserve. Total fixed assets are the book value of total fixedassets and premises net of depreciation. Total deposits plus borrowed funds includeindividual deposits (demand deposits, time deposits, and saving deposits), depositsfrom commercial banks and other depository institutions, government deposits, shortterm borrowing from central bank, inter-bank funds purchased, securities sold underagreement of repurchase, and short term and long term bonds outstanding.

Capital adequacy (the ratio of equity to total assets) is an important aspect of bankcharacteristics and it is a proxy for the market-based risk. This ratio measures theextent to which the bank owners (shareholders) put their own money at risk relativeto the total assets. The ownership dummy variable takes a value of unity if the bankis wholly state-owned and a value of zero otherwise. A deregulation dummy vari-able is constructed to separate the pre- and post-deregulation periods. Although thereform measures were continuously carried out after 1998, major initiatives that hadshaped the new structure of China’s banking industry all took place during the period1993–1998. The last year (i.e., 1998) featured the transition from direct interventionof government on credit plan to market-based non-binding target, partial disposal ofnon-performing loans, the re-capitalization of banks, and the deregulation of interestrates on loans (to some limited extent). Therefore, we define the period 1993–1998 asthe pre-deregulatory period and 1999–2002 as the post-deregulatory period. Finally,bank size is considered to be a factor that may affect technical efficiency. Hunter andTimme (1986) argued that larger banks were better equipped to use new technologyand were also associated with extensive network of branches and offices coveringmore regions and serving more customers, both of which helped them to exploit theresulting cost savings and/or efficiency gains. If true, this would suggest that biggerbanks are more technically efficient. Instead of introducing total assets or log of totalassets as a continuous variable into the model, we create four dummy variables tosegment the population of banks into four groups by their quartiles. The advantageof doing so is to capture the effects of scale on efficiency while avoiding potentialmisspecification by imposing a linear relationship between bank size and efficiency.The first bank size dummy (for the first quartile) is dropped from the model (used asa reference group) to avoid exact multicollinearity problem.

5 Empirical results

Before we present the results from the translog model specified in (3) and (4), westart with a simple Cobb–Douglas (CD) function form. The CD model is a specialcase of the translog model and the likelihood ratio test rejects the CD specification atthe 1% level of significance. Because of this, we are not reporting the CD results indetails. In the CD specification returns to scale are identical for each individual bankand for every year. We find modest economies of scale (RTS = 1.08) from this model.

123


0.4

0.6

0.8

1

1.2

1.4

1.6

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Year

elacS

otnru te

RWSOBs Joint Equity

Fig. 2 Returns to scale of WSOBs and joint-equity banks

The joint-equity banks are found to be more efficient than the WSOBs. However, thetechnical efficiencies of joint-equity banks are found to be deteriorating over timewhile those of WSOBs were relatively stable.

We now return to the translog model and report results on returns to scale, technicalchange, technical efficiency change, TFP change, etc., in greater details.

5.1 Returns to scale (RTS)

The translog model allows returns to scale to vary for each bank with different volumesand mix of outputs. First, we test the null hypothesis of constant returns to scale.14

The likelihood ratio test rejects the parametric restrictions in (8) at the 1% level ofsignificance (the value of the test statistics is 177.36 and the degrees of freedom is 6).Average RTS for each year and each ownership category are reported in column fourof Table 2 and plotted in Fig. 2.

All, except for joint-equity banks in 1993, were operating under increasing RTS.Scale economies for the WSOBs are found to be consistently higher than those ofjoint equity banks. This result suggests that these Chinese banks were operating belowtheir efficient scale and they could increase their operations without increasing averagecosts. Furthermore, a comparison of temporal behavior of RTS between the WSOBsand the joint-equity banks shows (see Fig. 2) that the WSOBs experienced a slightdecreasing trend in RTS (from 1.38 to 1.30) while the joint-equity banks experienceda slight increase in RTS (from 1 in 1993 to 1.06 in 2002).

5.2 Technical efficiency

Inefficiency (u) in an input distance function shows the rate at which input use canbe reduced without reducing outputs. Alternatively, it is rate by which inputs are over

14 Since the parameter estimates of input distance function cannot be directly interpreted, we decided notto report them to conserve space. The estimated parameters are used to compute returns to scale, technicalefficiency, total factor productivity change and its components. These results are reported in Table 2 andare discussed below.

123


Tabl

e2

Effi

cien

cy,R

TS,

�T

FPan

dT

FPin

dex

byow

ners

hip

Yea

rO

wne

rshi

pT

ER

TS

Scal

eef

fect

TC

TE

CZ

effe

ct�

TFP

TFP

inde

x

Join

t-eq

uity

(10)

0.96

390.

9963

0.00

35−0

.003

910

0

1993

Who

llyst

ate-

owne

d(4

)0.

4584

1.38

09−0

.058

1−0

.003

910

0

Tota

l(14

)0.

8720

1.06

63−0

.007

7−0

.003

910

0

Join

t-eq

uity

(10)

0.96

201.

0161

0.00

100.

0045

−0.0

020

0.03

320.

0366

103.

66

1994

Who

llyst

ate-

owne

d(4

)0.

4890

1.36

12−0

.060

7−0

.043

7−0

.002

00.

0046

−0.1

018

89.8

2

Tota

l(14

)0.

8760

1.07

89−0

.010

2−0

.004

3−0

.002

00.

0280

0.01

1410

1.14

Join

t-eq

uity

(10)

0.94

931.

0384

0.00

080.

0049

−0.0

002

0.04

140.

0469

108.

52

1995

Who

llyst

ate-

owne

d(4

)0.

4739

1.38

370.

0695

−0.0

429

−0.0

002

0.00

610.

0361

93.0

6

Tota

l(14

)0.

8305

1.12

470.

0133

−0.0

070

−0.0

002

0.03

500.

0449

105.

69

Join

t-eq

uity

(10)

0.93

631.

0409

0.00

960.

0102

0.00

16−0

.006

40.

0138

110.

0219

96W

holly

stat

e-ow

ned

(4)

0.46

981.

3836

0.04

45−0

.038

20.

0016

0.00

400.

0123

94.2

0

Tota

l(14

)0.

8030

1.13

880.

0183

−0.0

036

0.00

16−0

.003

80.

0134

107.

11

Join

t-eq

uity

(10)

0.92

941.

0727

0.01

230.

0037

0.00

340.

0144

0.03

3711

3.74

1997

Who

llyst

ate-

owne

d(4

)0.

4827

1.36

020.

0194

−0.0

285

0.00

34−0

.003

1−0

.008

893

.37

Tota

l(14

)0.

8017

1.15

490.

0143

−0.0

055

0.00

340.

0094

0.02

1610

9.42

Join

t-eq

uity

(10)

0.91

561.

0702

0.00

950.

0124

0.00

520.

0118

0.03

8911

8.17

1998

Who

llyst

ate-

owne

d(4

)0.

4944

1.34

490.

0394

−0.0

181

0.00

52−0

.052

1−0

.025

590

.99

Tota

l(14

)0.

7952

1.14

870.

0180

0.00

370.

0052

−0.0

064

0.02

0511

1.67

Join

t-eq

uity

(10)

0.89

051.

0841

0.01

680.

0159

0.00

710.

0319

0.07

1612

6.63

1999

Who

llyst

ate-

owne

d(4

)0.

4777

1.34

210.

0219

−0.0

102

0.00

710.

0108

0.02

9693

.68

Tota

l(14

)0.

7726

1.15

780.

0183

0.00

840.

0071

0.02

590.

0596

118.

33

Join

t-eq

uity

(10)

0.88

151.

0829

0.02

480.

0267

0.00

89−0

.002

40.

0580

133.

9720

00W

holly

stat

e-ow

ned

(4)

0.46

601.

3483

0.02

26−0

.004

50.

0089

0.00

220.

0292

96.4

2

Tota

l(14

)0.

7628

1.15

870.

0242

0.01

780.

0089

−0.0

011

0.04

9812

4.21

Join

t-eq

uity

(10)

0.82

491.

0674

0.01

540.

0429

0.01

070.

0332

0.10

2314

7.67

123


Tabl

e2

cont

inue

d

Yea

rO

wne

rshi

pT

ER

TS

Scal

eef

fect

TC

TE

CZ

effe

ct�

TFP

TFP

inde

x

2001

Who

llyst

ate-

owne

d(4

)0.

4664

1.32

200.

0130

0.00

840.

0107

−0.0

047

0.02

7499

.06

Tota

l(14

)0.

7225

1.14

010.

0148

0.03

300.

0107

0.02

240.

0809

134.

26

Join

t-eq

uity

(10)

0.79

661.

0641

0.01

710.

0529

0.01

250.

0085

0.09

1116

1.12

2002

Who

llyst

ate-

owne

d(4

)0.

4568

1.30

350.

0305

0.02

010.

0125

0.01

310.

0763

106.

62

Tota

l(14

)0.

6995

1.13

250.

0209

0.04

350.

0125

0.00

980.

0868

145.

92

Join

t-eq

uity

0.90

331.

0545

0.01

220.

0182

0.00

430.

0183

0.05

55

All

year

Who

llyst

ate-

owne

d0.

4735

1.35

010.

0238

−0.0

176

0.00

43−0

.003

30.

0135

Tota

l0.

7894

1.13

280.

0152

0.00

870.

0043

0.01

260.

0445

Not

es:

Sinc

eth

esa

mpl

eda

taw

ere

reco

rded

atdi

scre

tein

terv

als

rath

erth

anco

ntin

uous

time,

the

chan

geof

outp

uts

and

the

chan

geof

bank

char

acte

rist

ics

and

envi

-ro

nmen

tal

vari

able

s(Z

vari

able

s)th

atar

eus

edin

the

form

ula

(7)

toca

lcul

ate

the

TFP

(tot

alfa

ctor

prod

uctiv

ity)

chan

gear

epr

oxie

dby

the

Torn

qvis

tap

prox

imat

ions

:

Rm

y m≈

�R

mlo

gY

m=

1 2(R

mt+

Rm

t−1)

log

(Y

mt

Ym

t−1

)an

dz s

=�

z s=

z st−

z st−

1,t

here

fore

the

scal

eco

mpo

nent

,the

effe

cts

indu

ced

byZ

vari

able

san

dov

eral

lTFP

chan

gear

eno

tava

ilabl

efo

rth

efir

stye

ar,1

993.

The

tota

lof

14ba

nks

isdi

vide

din

totw

ogr

oups

byow

ners

hip:

4jo

int-

equi

tyba

nks

and

10w

holly

stat

e-ow

ned

bank

s.T

Eis

tech

nica

leffi

cien

cy,i

ndic

atin

gth

epr

opor

tion

ofin

puts

that

are

used

effic

ient

lyby

aba

nk;R

TS

isre

turn

sto

scal

e,m

easu

ring

the

scal

eec

onom

ies

ofa

bank

.Sca

leef

fect

isth

eco

mpo

nent

ofT

FPch

ange

that

isre

late

dto

retu

rns

tosc

ale.

TC

isth

ete

chni

calc

hang

e,re

pres

entin

gth

esh

ifto

fpro

duct

ion

fron

tier.

Tech

nica

leffi

cien

cych

ange

desc

ribe

sho

wth

epr

opor

tion

ofin

puts

used

effic

ient

lych

ange

sov

ertim

e,ei

ther

clos

erto

oraw

ayfr

ompr

oduc

tion

fron

tier.

Itin

clud

estw

opa

rts

ofef

fect

s,pu

rete

chni

cale

ffici

ency

chan

ge(T

EC

)an

dno

n-ne

utra

leff

ects

(Zef

fect

s)th

atar

ein

duce

dby

bank

char

acte

rist

ics

and

envi

ronm

enta

lfac

tors

.Las

t,T

FPch

ange

isth

esu

mof

scal

eef

fect

,TC

,TE

Can

dZ

effe

cts.

TFP

inde

xis

calc

ulat

edfr

omfo

rmul

a(8

)to

depi

ctth

edy

nam

ics

ofT

FPov

ertim

e,re

lativ

eto

the

base

year

of19

93

123


0

0.2

0.4

0.6

0.8

1

1.2

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Year

ET

WSOBs Joint Equity

Fig. 3 Technical efficiency of WSOBs and joint-equity banks

used (cost is increased) to produce a given output vector. Thus, if u = 0.05, cost isincreased by 5% due to input over-use, therefore, cost efficiency is ((1−u)100 = 95%).The results in Table 2 (column 3) show that the joint-equity banks are consistentlymore efficient than the WSOBs during the sample period. For WSOBs, the averagetechnical efficiency for the entire sample period was 0.47 thereby meaning that, onaverage, these banks could have reduced their cost by 53% by eliminating technicalinefficiency. Similarly, for joint-equity banks average TE was 0.90, which indicatesa potential cost saving of 10%, on average. This large discrepancy of technical effi-ciency between these two types of bank is attributed to difference in their ownership.15

Figure 3 depicts the trends of technical efficiency for both WSOBs and joint-equitybanks during the period of 1993–2002. We observe that technical efficiency was quitestable for the WSOBs while it deteriorated slightly for the joint-equity banks. Fromthe figure, we also find that discrepancy in technical efficiency between WSOBs andjoint-equity banks has a tendency to converge over time.

Note that quality of service is not accounted for in estimating the model. Conse-quently, improvement in quality of outputs might show up as reduction in efficiency.For example, improvement in the quality of assets might be caused by partial dis-posal of non-performing loans from the portfolio of the bank assets. Disposal of non-performing loans will be reflected in the balance sheet as a reduction in the stock valueof loans. This will lead to a decrease in total net loans with no corresponding changein the input usage, and thus will be reflected in technical inefficiency because moreinputs are used without any increase in outputs. Therefore, the decline in efficiencymay not necessarily imply that banks are getting worse over time.

5.3 Effects of bank characteristics and environmental factors (zs) on technicalefficiency

We allowed technical efficiency to vary across banks and over time. It is affectedby bank characteristics that are unique to each bank and environmental factors that

15 We discuss this point later at length.

123


generally affect all banks in the same manner. The bank characteristics and environ-mental factors enter into technical inefficiency via the mean (µ) of technical ineffi-ciency in (5). The ratio of equity to total assets is negatively related to technicalinefficiency. It has a coefficient of −1.98, which suggests that increasing the ratio by1% will help reduce the technical inefficiency by 1.98%. This result is consistent withother banking studies (e.g., Hughes and Mester 1998). The logic behind the result isthat it meets the owners’ incentives to closely monitor the management of bankingbusiness so that the bank management makes prudent decisions, which help to controlrisks. On the other hand, equity also provides a cushion against insolvency. Thus,banks with higher levels of capital have a greater capacity to absorb the losses frombad loans.

The positive sign on the coefficient of ownership dummy (0.53) shows that WSOBswere operating less efficiently than the joint-equity banks.16 The result is consistentwith what property rights hypothesis and public choice theory suggest (e.g., Levy1987). The property rights theory states that private ownership is associated withmore efficient management and thus joint-equity firms with partly private ownershipshould outperform the wholly public firms in general (e.g., Alchian 1965; de Alessi1980). This result is quite intuitive in the sense that in a state owned bank there isless incentive for the managers to operate efficiently because they are not certainwhether the accumulated assets and profits belong to the state or the bank itself. Sincethe WSOBs are still indirectly controlled by local governments, the communist partymight affect the performance of the WSOBs and joint-equity banks. There are stilltoo many implicit “policy” loans being advanced by the WSOBs to incompetent stateowned enterprises, and government officials are inclined to pursue their own interestsor the interests of pressure groups. All these factors impede the WSOBs from pursuingtheir goal of offering their services at the lowest possible cost. In addition, multipleand frequently changing objectives of public banks arising from government’s pol-icy-oriented interests also exacerbate these problems because outcomes of managerialdecisions become more difficult to measure and monitor (Estrin and Perotin 1991).Our results show that compared to the joint-equity banks, the WSOBs are overstaffedand their branch structure is based not on economic criteria but on the administrativestructure of the government. This fact is reflected in Table 1 (larger average number ofemployees and branches, and hence lower labor productivity). Improving efficiencyby downsizing their staffs and branches is difficult for the WSOBs because of labormarket conditions and the social insurance system in China. Therefore, the indirectpressure (from the local government) of increasing employment might also lead to aless efficient operation of the WSOBs than their peers.

The bank size coefficients are found to be positive and increasing with bank size.This result indicates that larger banks tend to be less efficient. Furthermore, we see thatthe marginal effect of size on technical efficiency drops from 0.09 to 0.07 and then to

16 We examine the ownership issue further. Instead of one dummy variable differentiating the WSOBs andjoint-equity banks, we introduced two dummy variables: one for listed joint-equity banks, the other fornon-listed joint-equity banks, with WSOBs as the reference group. The results from model with two own-ership dummy variables indicate that the listed joint-equity banks rank highest in terms of efficiency scores,non-listed joint-equity banks rank lower than the listed ones, while WSOBs are in the bottom, suggestingthat the diversified ownership is associated with efficient performance.

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0.04 as the size of a bank increases, thereby indicating a diminishing negative effect oftotal assets on technical efficiency and thus cost savings. Alternatively, one can arguethat bureaucratic problems associated with large size does harm to the performanceof banks and it may damage the overall technical efficiency.

Finally, the coefficient of post-deregulation dummy is positive (0.0024) but notsignificant. This result shows that deregulation did not improve the performance ofbanks. This phenomenon, however, is not unique to the transition economies. Dereg-ulation might have mixed effects on the performance of banks. For example, the aidsfrom government to help reduce the non performing loans of the four WSOBs bytransferring bad loans to assets management companies increased WSOBs’ capitaland improve their risk resistance capacities. On the other hand, it might bring out themoral hazard problem. In the context of this study, moral hazard hypothesis states thatbanks are not prudent when making lending decisions since the government bailout isexpected when loans are in default. This might, in turn, make them more inefficient.

Mixed effects can also be attributed to competition that stemmed from the relax-ation of barriers to entry. Intuitively, the competitive pressure forced the banks tonarrow down their net interest margins as the central bank further relaxed the strictregulation on interest rates and allowed more flexible lending interest rates in 1998.As a result banks sought for other sources of earning in order to generate enoughrevenues to cover their operating costs. Consequently, banks became less prudent ontheir decision-making and were more likely to involve in risky activities.

5.4 Productivity growth

Different concepts of productivity have been applied in the banking literature, includ-ing cost productivity, profit productivity (for example, Berger and Mester 2003), andtotal factor productivity (Bauer et al. 1993). Here we examine TFP change and itscomponents.

Overall, TFP in the Chinese banking industry grew at the rate of 4.4% annually. TFPof the WSOBs and the joint-equity banks grew at the rate of 1.4% and 5.6%, respec-tively, per year. Figure 4 illustrates the trend of TFP changes for both the WSOBs andjoint-equity banks. We find that joint-equity banks outperform the WSOBs in eachyear with higher TFP changes. WSOBs experienced negative TFP growth in 1994,1997 and 1998, followed by a steady positive growth since 1999.

The components of TFP change are reported in Table 2 and Fig. 5, from which itcan be seen that TFP growth for the WSOBs were mostly coming from scale compo-nent while for the joint-equity banks it was a combination of scale effects, technicalchange and the Z effects. Negative technical changes for the WSOBs during 1993–2000 substantially lowered their TFP growth while consistent positive technical change(technical progress) for the joint-equity banks played an important role in their steadyTFP growth. Pure technical efficiency change (∂u/∂t) plays a very limited role inaffecting TFP changes for both the WSOBs and joint-equity banks. The effects of thez variables were not monotonic but varied from year to year as bank characteristicsand environmental conditions changed. This can be seen in Table 2, column 8.

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-0.15

-0.1

-0.05

0

0.05

0.1

0.15

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Year

gnahcP

FT

eWSOBs Joint Equity

Fig. 4 Dynamics of total factor productivity change

Technical Change

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Year

WSOBs Joint Equity

Scale Effect

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Year

WSOBs Joint Equity

TEC Dynamics

-0.006-0.004-0.002

00.0020.0040.0060.0080.01

0.0120.014

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Year

WSOBs Joint Equity

Z Effect

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Year

WSOBs Joint Equity

Fig. 5 Dynamics of components of TFP change

From Fig. 6 we observe diverging trends of the TFP index for the WSOBs and thejoint-equity banks. Relative to the base year (1993), the joint-equity banks experienceda steady growth and their TFP increased by 61% over the 10-year period from 1993to 2002. In contrast, after a sharp fall in 1994, TFP index of the WSOBs recoveredslowly and it did not regain their 1993 level until 2001. By the end of 2002, TFP indexof the WSOBs reached 107, showing a 7% increase in 10 years.

6 Summary and conclusions

In this paper we evaluated the performance of the Chinese banking industry duringits reform period, 1993–2002. Our focus was on the largest fourteen banks in China,including four wholly state owned banks (WSOBs) and 10 joint-equity banks withpartial or sole private ownership. First, we compared labor productivity (a simpledescriptive statistics) for both the WSOBs and joint-equity banks and found that laborproductivity has increased during the entire sample period for both bank types. We

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0

20

40

60

80

100

120

140

160

180

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Year

edniP

FT

x

WSOBs Joint Equity

Fig. 6 Dynamics of total factor productivity index

then estimated a stochastic input distance function to investigate technical efficiency,RTS and TFP change. Results showed the presence of increasing returns to scale,indicating that most banks were operating below their efficient scale. On average, theinputs were over-used (which increased cost) by almost 21%. The mean technical effi-ciency (TE) of the WSOBs and joint-equity banks were 47% and 90%, respectively.Thus the joint-equity banks were, on average, 43% more efficient.

We also examined the role of bank characteristics and environmental forces inexplaining technical efficiency. Private ownership in joint-equity banks was found toimprove technical efficiency. Similarly, a higher capital adequacy ratio was associ-ated with high efficiency: larger banks tend to be less efficient. In order to investigatewhether the banking reforms had a significant impact on the performance of thesebanks, we separated the sample period into two sub-periods, viz., the deregulatoryperiod (1993–1998 when the primary reform initiatives were undertaken) and thepost- deregulatory period (1999–2002). We found no evidence to support the viewthat deregulation improved efficiency significantly.

Our results showed moderate improvement in TFP growth (4.5% annually) in theChinese banking industry for the sample period of 1993–2002, compared to the rate ofreal GDP growth of between 7% and 8% during the same period. Since productivitychange is affected by factors other than efficiency, TFP change was further decom-posed into technical change, TE change, scale effects and non-neutral TE changesinduced by bank characteristics and environmental factors. We found that positiveTFP change for the joint-equity banks was contributed by scale economies, technicalchange and technical efficiency gain. In contrast, the scale effect was the main drivingforce behind TFP growth in the WSOBs. Overall, the joint-equity banks gained 61%in their TFP from 1993 to 2002, while the gain for the WSOBs was only 7%.

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