bank pproductivity in china 1997-2007

Bank productivity in China 1997–2007:Measurement and convergence

Kent MATTHEWS a,?,Nina (Xu)ZHANG b

a Cardiff Business School,Cardiff University,United Kingdom b

Citigroup,China

a r t i c l e i n f o a

b s t r a

c t

Article history:

Received 1September 2009

Received in revised form 20January 2010Accepted 17June 2010This study examines the productivity growth of the nationwide banks of China and a sample of city commercial,banks for the ten years to https://www.wendangku.net/doc/4619099853.html,ing a bootstrap method for the Malmquist index,estimates of the total factor productivity growth are constructed.Five different models of inputs and outputs based on variants of the Intermediation and Production approaches and non-performing loans are treated as a bad output,are examined for the purpose of arriving at a robust measure.The productivity growth of the state-owned commercial banks (SOCBs)is compared with the joint-stock banks (JSCBs)and city commercial banks (CCBs).In general,average TFP growth has been neutral over the period for the SOCBs and JSCBs but positive for the CCBs in the second part of the period.Ef ?ciency gains (catch-up)were obtained through cost reduction and technical innovation was associated with greater diversi ?cation of revenue away from interest earnings.The opening up of the banking market has not led to a discernible improvement in bank productivity growth.

?2010Elsevier Inc.All rights reserved.

JEL Classi ?cations:D24G21

Keywords:

Bank ef ?ciency Productivity Malmquist Bootstrap China

1.Introduction

Banking sector reform in China has been a gradual and on-going process since 1978.A further stage of reform was announced in 1993with the objective of creating an ef ?cient commercial banking sector.Following the conditions of the WTO,the Chinese banking market has been open to foreign competition since the end of 2006.Chinese banks have been encouraged to allow foreign banks and investors to take minority shareholding positions.The listing of four of the big ?ve banks on the international exchange during 2006–2007is supposed to usher in,not only foreign capital but also foreign managerial expertise to improve bank management,performance and productivity.Given the acceptance of larger stakes by foreign banks in the smaller commercial banks (to a speci ?ed limit of 25%share);it is no surprise that Chinese bank productivity has become a popular topic of research in recent years.

There have been a number of studies of Chinese banking productivity that have been published in Chinese scholarly journals,but to date only a few studies are available to non-Chinese readers.1The gradualist reforms of the banking sector and the potential of foreign competition would be expected to improve ef ?ciency and productivity in the banking sector.Evidence of improved performance has begun to emerge.

This paper is an empirical exercise in measurement and convergence.Its principal aim is to measure the productivity of the commercial banks in China for the period 1997–2007.Three issues are addressed in this paper,namely measurement,modeling strategy,and convergence.First,the measurement of output (and input)of banks is not a simple matter.Numerous studies of bank productivity by Chinese scholars employ a bewildering mix of inputs and outputs.We therefore consider several alternative measures of output as a means of obtaining robust results.

China Economic Review 21(2010)617–628

?Corresponding author.

E-mail address:matthewsk@https://www.wendangku.net/doc/4619099853.html, (K.Matthews).1

A recent exception is a study using non-parametric methods by Matthews,Guo,and Zhang (2009)and parametric methods by Khumbhakar and Wang (2007).The ?rst non-Chinese paper using the non-parametric approach to estimating bank ef ?ciency is that of Chen,Skully,and Brown (2005)

.1043-951X/$–see front matter ?2010Elsevier Inc.All rights reserved.doi:

10.1016/j.chieco.2010.06.004

Contents lists available at ScienceDirect

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618K.Matthews,N.(X.)Zhang/China Economic Review21(2010)617–628

Second,we use the Malmquist index of total factor productivity(TFP)as a means of translating inputs and outputs into a measure of productivity growth.The Malmquist index has the advantage of being able to decompose productivity growth into technological change,which captures a shift in the production frontier from ef?ciency improvement,which captures the movement towards the frontier.One of the problems associated with this approach is that it is constructed within the framework of Data Envelope Analysis(DEA),which is a non-parametric linear-programming method that applies observed input and output data to create a‘best practice’frontier.The main drawback of the DEA approach is that it assumes the inputs and outputs are measured without error and therefore do not permit statistical evaluation.Accurate reporting of Chinese bank data that meets international norms is a very recent phenomenon.This paper provides an inferential capability to the point-estimates of productivity through the use of non-parametric bootstrapping methods.

Third,we use the concepts of conditional beta-convergence and sigma-convergence from the growth convergence literature (Barro&Sala-i-Martin,1991,1992)to examine the properties of convergence of TFP.This paper poses the following questions. What has been the total factor productivity(TFP)growth of Chinese banks over the period1998–2007?What have been the driving factors in TFP growth?Has there been a signi?cant improvement in TFP growth in the second half of the period consistent with an increase in the pace of reform prior to the opening up of the banking market according to the WTO treaty.Finally,is there evidence of the convergence of TFP to peer group clusters?

The contribution of this paper is?rst,to extend the analysis of Matthews et al.(2009)to obtain a more robust statement of bank productivity growth by expanding the data set to include city commercial banks;second,to model non-performing loans in a consistent manner as a separate but undesirable output;third,to extend the range of models considered;and to extend the data sample.By using the results of5models,the paper uses1570simulated bank-year observations of productivity growth.The results show that the productivity of state-owned banks was neutral over this period and that technical progress was offset by negative catch-up(lead banks widening the gap with laggard banks).Additionally,this paper identi?es the main drivers of bank TFP growth convergence and identi?es the benchmark banks in each bank category.

The paper is organized on the following lines.The next section provides a brief background to the Chinese banking system. Section3discusses the methodology and literature relating to the Malmquist method and the bootstrap technology used in estimating bank productivity.Section4presents the banking data.Section5discusses the results and Section6concludes.

2.Chinese banking

In2007,the Chinese banking system consisted of8877institutions,including3policy banks,5large state-owned commercial banks(SOCB),12joint-stock commercial banks(JSCB),124city commercial banks(CCB),29locally incorporated foreign bank subsidiaries and the rest made up of urban and rural credit cooperatives and other?nancial institutions.2 Like many economies that have undeveloped?nancial and capital markets,the banking sector in China plays a pivotal role in ?nancial intermediation.Table1below shows that the ratio of total bank assets to GDP has increased from125%,in1997,to213% in2007.The market is absolutely dominated by the?ve state-owned banks,although their share of the market has been decreasing steadily through competition from the other commercial banks(JSCB and CCB).

Net interest margins(NIM)and return on average assets(ROAA)of the SOCBs are respectable by western standards but are well below levels that would be consistent with economies in the same stage of development(as for example India where NIM would be in the region of3.5%).Part of the reason is that interest rates were heavily controlled during this period and the remaining reason is the large amount of non-performing loans on the books of the commercial banks.The non-performing loans (NPL)ratio of the SOCBs has been falling from around50%3in1997to around8%in2007.

With the encouragement of the regulatory authorities,Chinese banks have in recent years,had to restructure their balance sheet,develop modern risk management methods,improve capitalization,diversify earnings,reduce costs and improve corporate governance and disclosure.4

Up until1995,control of the banking system remained?rmly under the government and its agencies.5Under state control,the banks in China served the socialist plan of directing credits to speci?c projects dictated by political preference rather than commercial imperative.Foreign banks and?nancial institutions were increasingly allowed to take a stake in selected Chinese banks. While control of individual Chinese banks remain out of reach for the foreign institution,6the pressure to reform management, consolidate balance sheets,improve risk management and reduce unit costs has increased with greater foreign exposure.

The theory of market contestability(Baumol,1982)suggests that incumbent banks will restructure weak balance sheets, reduce costs,and improve ef?ciency in preparation for the threat of entry.In their annual report on foreign banks in China, Pricewaterhouse Coopers7refer to the China Bank Regulatory Commission report on the opening up of the banking sector.The CBRC divides the pace of reform and innovation into three stages;1980–1993,1993–2002and2003–2006.In the third stage,more

2CBRC Annual Report2007.

3Estimate based on1998values.The1998values were obtained by adding back the Asset Management Company operations in1999back to the reported ?gures.This is the basic assumption used by Rodman(2005).An overestimate is likely to be small as Huang(2002)suggests that the mid-2002of?cial NPL ratio at23%is underestimated by12%.Liu(2009)estimates the overall NPL ratio was40–50%in the late1990s.

4CBRC Annual Report2006https://www.wendangku.net/doc/4619099853.html,/english/home/jsp/index.jsp.

5According to La Porta,Lopez-de-Silanese,and Shleifer(2002),99%of the10largest commercial banks were owned and under the control of the government in1995.

6There is a cap of25%on total equity held by foreigners and a maximum of20%for any single investor,except in the case of joint-venture banks.

7Pricewaterhouse Coopers(2007).

of the domestic banking business was opened up to external competition.Foreign banks were allowed to expand RMB business from the four major cities of Shanghai,Shenzhen,Tianjin and Dalian which existed at the time of accession to the WTO,to the rest of the country.RMB business activity was extended from foreign enterprises and individuals to cover domestic ?rms and residents.Quantitative restrictions on foreign banks RMB liabilities were lifted and capital requirements were brought into equality with domestic banks.Various restrictions on branch development were removed and branches were particularly encouraged in the under-banked geographical regions outside the east coast.The upshot of these and a number of other reforms is that Chinese banks should exhibit less inef ?ciency,and strong productivity improvements in this period,with marked improvements in the latter years as competition with foreign banks intensify.3.Methodology and literature

Data Envelope Analysis (DEA)can be used to evaluate the ef ?ciency of a ?rm by comparing it with a ‘best practice ’or output ef ?cient ?rm.An output ef ?cient ?rm is one that cannot increase its output unless it also increases one or more of its input,whereas an output inef ?cient ?rm is one that can increase its output without increasing its inputs.An output ef ?cient ?rm would have a score of 100%as being located on the output ef ?cient frontier whereas an output inef ?cient ?rm would be inside the frontier and have a score of less than 100%.Similarly an input ef ?cient ?rm is one that cannot reduce its inputs without reducing its output whereas an input inef ?cient ?rm can.

One of the earliest studies of bank ef ?ciency available to the non-Chinese reader is that of Chen et al.(2005).Using the DEA approach they examine the cost ef ?ciency of banks in the period pre-and post 1995to 2000.Their results suggest that ?rstly,the larger state-owned banks and smaller banks are more cost ef ?cient than medium sized banks.Secondly,the decomposition of cost ef ?ciency into technical ef ?ciency and allocative ef ?ciency showed that allocative ef ?ciency was lower than technical ef ?ciency.Thirdly,while ef ?ciency improved until 1996,thereafter there was a gradual decline to 2000.

The major drawback of the DEA approach is that the ef ?ciency scores obtained from a particular sample are con ?ned to that particular sample and cannot be compared with another sample in a different time period.This limitation does not allow the measurement of productivity growth,which allows for improvement in ef ?ciency as well as technical progress.

The idea of comparing the input of a decision making unit over two periods of time (period 1and period 2)by which the input in period 1could be decreased holding the same level of output in period 2is the basis of the Malmquist index.8F?re,Grosskop,and Norris (1994)developed a Malmquist productivity measure using the DEA approach based on constant returns to scale.The Malmquist productivity index (M)enables productivity growth to be decomposed into changes in ef ?ciency (catch-up)and to changes in technology (innovation).9

An illustration using the one input one output case is shown in Fig.1below.

Points A and B represent observations in periods t and t +1respectively.The rays from the origin S t and S t +1represent frontiers of production for periods t and t +1respectively.Relative ef ?ciency is measure in one of two ways.The relative ef ?ciency of production of a ?rm at point A compared to the frontier S t is described by the distance function d t (y t ,x t )=0a/0b.But compared with the period t +1frontier S t +1,it is d t +1(y t ,x t )=0a/0c.The relative ef ?ciency of production of a ?rm at point B compared to the period t +1frontier S t +1is d t +1(y t +1,x t +1)=0d/https://www.wendangku.net/doc/4619099853.html,pared with the period t frontier S t ,the relative ef ?ciency is d t (y t +1,x t +1)=0d/0c.The Malmquist index (M )of the total factor productivity (TFP)change is the geometric mean of the two indices based on the technology for periods t +1and t respectively.In other words:

M =

d t +1y t +1;x t +1àád t +1y t ;x t eTd t y t +1;x t +1

àád t y t ;x t eT

!12

e1T

8

Grosskopf (2003)provides a brief history of the Malmquist productivity index and discusses the theoretical and empirical issues related to the index.For the

decomposition of Malmquist productivity index,see Lovell (2003).9

A further decomposition can be conducted by separating the change in ef ?ciency into the change in pure ef ?ciency ×change in scale ef ?ciency.The change in ef ?ciency is constructed under CRS while the change in pure ef ?ciency and scale ef ?ciency is constructed under VRS.

Table 1

The Chinese banking market.

Sources:IMF International Financial Statistics,Individual Bank Annual Accounts,China Regulatory Banking Corporation Annual Report,Almanac of China's Finance and Banking,Fitch-Bankscope data base,National Bureau of Statistics of China.Variable

1997

2000

2007

Total assets to GDP 125.6%

147.1%

213.4%

SOCB employment

1670.4thousand 1540.8thousand 1492.1thousand Market share SOCB %assets 88.0%71.4%53.2.0%NPL ratio SOCB 49.8%30.8%8.1%ROAA SOCB a 0.2%0.2%0.9%NIM SOCB a

2.4% 2.0% 2.7%Cost-income ratio SOCB a

52.7%

55.8%

42.8%

SOCBs are Agricultural Bank of China,Bank of China,China Construction Bank,Industrial and Commercial Bank of China and Bank of Communications.a

Weighted average by asset share.

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K.Matthews,N.(X.)Zhang /China Economic Review 21(2010)617–628

In their study of productivity growth in industrialised countries,F?re et al.(1994)decompose Eq.(1)for changes in ef ?ciency (catch-up)and changes in frontier technology (innovation).This can be seen by expressing Eq.(1)as:

M =

d t +1y t +1;x t +1àád t y t t d t y t +1;x t +1àád t +1y t +1t +1àád t y t ;x t eT

d t +1y t t "#12

e2T

or

M =E t +1T t +1

where M

the Malmquist productivity index

E t +1a change in relative ef ?ciency over the period t and t +1(catch-up)

T t +1

a measure of technical progress measured by shifts in the frontier from period t to t +1

When M N 1it means that there has been a positive total factor productivity change between period t and t +1.When M b 1it means that there has been a negative total factor productivity change.

The use of the Malmquist method of evaluating productivity performance of banks has been a growth area of academic enquiry.Berg,Finn,and Eilev (1992)examined Norwegian banks 1980–1989and found productivity regress prior to deregulation and strong productivity gains due to catch-up after deregulation.The Malmquist decomposition was used by Wheelock and Wilson (1999)to examine bank productivity in the USA for the period 1984–1993.They reported a general drop in average productivity caused by failure to catch-up with outward shifts of the production frontier.Alam (2001)found that the deregulation period resulted in a productivity surge in the ?rst half of the 1980s followed by a productivity regress in the second half for large US banks.These results were con ?rmed by Mukherjee,Ray,and Miller (2001)who also used panel estimation to explain productivity growth in terms of bank size,product-mix and capitalisation.

Other studies of bank productivity using the Malmquist method have been Drake (2001)for the UK,Grifell-Tatjéand Lovell (1997)for Spain,Canhoto and Dermine (2003)for Portugal,Noulas (1997)for Greece,Fukuyama (1995)for Japan,and Isik and Hassan (2003)for Turkey.A pan-European study was conducted by Casu,Girardone,and Molyneux (2004)who compared parametric with the Malmquist method.Their ?nding is that productivity growth in European banking has been largely brought about by technological change rather than ef ?ciency improvement.Outside Europe,Worthington (1999)found that Australian Credit Unions exhibited strong technological progress after deregulation and Neal (2004)found that productivity improvements were mostly shifts in the frontier with the majority of banks having negative catch-up over 1995–1999.

The productivity of Chinese banking has also been the subject of numerous studies by Chinese scholars.Chen (2002),Zhang and Wu (2005)and Tang and Wang (2006)use the Malmquist method to examine the productivity trend of Chinese banks over the 1994–1999,1999–2003and 1997–2003periods respectively.Their basic ?ndings were that the large state-owned banks exhibited lower average growth compared with the joint-stock banks.In general average productivity growth was dominated by catch-up rather than technical innovation but that there had been a marked improvement in TFP in the latter years.10In contrast Ni and Wan (2006)found strong productivity improvement led by technical improvement rather than catch-up.Sun and Fang (2007)pose the question,whether foreign banks have stimulated an improvement in Chinese bank productive ef ?ciency?They ?nd that average TFP growth improved during the period 2001–2004consistent with the hypothesis that the threat of entry has had signi ?cant ef ?ciency effects on incumbent banks.

10

See also Hou and Wang (2006)which uses a two-stage panel estimation to explain productivity but inappropriately uses operating expenses as an explanatory variable when it is also an input in the construction of the M

index.

Fig.1.

620K.Matthews,N.(X.)Zhang /China Economic Review 21(2010)617–628

An alternative to the Malmquist non-parametric approach widely applied to Chinese banks is the stochastic frontier approach taken by Khumbhakar and Wang (2007).They apply the distance function approach implicit in the trans-log production function to estimate technical ef ?ciency,returns to scale and total factor productivity.They cover the same sample period as Chen et al.(2005)but in contrast ?nd that the state-owned banks were less ef ?cient than the joint-stock banks.In common with Chen et al.they report a general decline in ef ?ciency.However the contrasting ?ndings of Khumbhakar and Wang (2007)to those of Chen et al.(2005)highlight the sensitivity of the results to the method of estimation.

The studies using DEA are limited by two important issues.First,the results are conditional on the inputs and outputs employed.There is no consensus as to the appropriate measures of inputs and outputs used in the construction of Chinese bank productivity.On the input side,operational expenses or labour (where available),?xed assets and sometimes deposits in varying combinations are used most frequently.However,on the output side,the studies can be grouped into three variants.Some studies use asset stocks (loans and other earning assets)whereas others use income ?ows (interest earnings,non-interest earnings,net income,pro ?ts).A third group take an eclectic approach mixing assets with liabilities (deposits and loans)and stocks with ?ows (loans and pro ?ts)and even others mixing (assets,liabilities and income ?ows).

Second,the lack of statistical inferential capability makes it dif ?cult to evaluate the sensitivity of the estimates obtained relative to sample variation.In other words,the deterministic estimates of the Malmquist index cannot assign con ?dence levels to the measures of growth.The estimates obtained in the above studies represent measures of performance relative to an estimate of the true but unobserved frontier.Since these estimates are based on ?nite samples,they will be subject to sampling variation of the frontier and subject to ?nite sample bias.The bootstrap reduces ?nite sample bias and reduces,or even eliminates ?nite sample errors in the rejection probability of statistical tests (see Horowitz,2001).

Simar and Wilson (1998,1999,2000)propose a smooth bootstrapping methodology to examine the sensitivity of the DEA scores and Malmquist indices to sampling variations with the aim of assigning con ?dence intervals.The application of bootstrapping methods to the Malmquist productivity index remains an ongoing area of research (L ?thgreen &Tambour,1999).Relatively few studies have applied bootstrapping methods to measuring banking productivity.Gilbert and Wilson (1998)calculate con ?dence intervals for estimates of productivity in Korean banks in 1980–1994and conclude that the period had experienced signi ?cant productivity growth against the null hypothesis of no change between periods.Tortosa-Ausina,Grifell-Taté,Armero,and Conesa (2008),apply bootstrapping to Spanish savings banks over 1992–1998and con ?rm the common ?nding that productivity growth is dominated by technological progress in the post deregulation period.Murillo-Melchor,Pastor,and Tortosa-Ausin (2005)conduct a European wide study of bank productivity over the period 1995–2001using bootstrap techniques.They con ?rm the basic ?nding of Casu et al.(2004)that productivity gains were driven by technological progress but ?nd signi ?cant differences in inter-country performance.11

4.Banking data

This study employs an unbalanced panel of annual data (1997–2007)for the 5state-owned or state-controlled commercial banks (SOCB),9joint-stock commercial banks (JSCB)and 47city commercial banks (CCB).The total sample consisted of 314bank-year observations.The main source of the data was Fitch/Bankscope,and individual annual reports of banks.

Two approaches are normally taken in determining what constitutes bank input and output.The intermediation approach developed by Sealey and Lindley (1977)recognises that the main function of the bank is to conduct ?nancial intermediation.Under the intermediation approach,bank assets measure outputs and liabilities measure inputs.In contrast,the production approach recognises that the bank provides intermediation services and payment services to depositors.In the production approach,physical entities such as labour and capital are inputs while deposits are a measure of output.12Goldschmidt (1981)argues that deposits are both inputs and outputs depending on its use in intermediation services or payment services and suggests a weighting mechanism similar to the divisia approach of Barnett,Offenbacher,and Spindt (1984).Such a separation would need information about the term maturity of deposits.This information is not easily available for banks in China and in any case up until very recently deposit interest rates were regulated and did not re ?ect market fundamentals.

A further issue is the problem of non-performing loans which have been treated as an undesirable output in a number of studies.Park and Weber (2006)consider loans less non-performing loans (NPLs)as well as deposits as a valid output of the bank in their study of bank productivity in Korea,where NPLs are viewed as an undesirable output.Stripping out non-performing loans from the stock of loans for each bank creates a new output variable which replaces the stock of total loans and following Scheel (2001)we treat the inverse of NPLS as a positive output.13

Another argument for adjusting loans for NPLs is to mitigate the effect of the large loan portfolios held by the SOCBs on the ef ?ciency calculation.The unadjusted loan portfolio would bias the ef ?ciency score upwards for the SOCBs which have the largest share of loans but also the highest proportion of NPLs.

Finally,a variant of the production approach is to recognise that the services provided to depositors and loan obligors are re ?ected in the net ?ows of income to the bank.So services to the consumers of banking products whether it is intermediation services or other ?nancial services,will be re ?ected in the net interest earnings to the bank and net non-interest earnings.

11Alam (2001)also uses bootstrap con ?dence intervals to provide an inferential capacity to the point-estimates of productivity of large US banks.

12Freixas and Rochet (1997)propose a third approach that recognises the speci ?c activities of banks such as risk management and information processing.13

See Thanassoulis,Portela,and Despi ?(2008)for a discussion.

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622K.Matthews,N.(X.)Zhang/China Economic Review21(2010)617–628

Table2

Model structure.

Model type Inputs Outputs

1Deposits(RDEP),overheads(ROHD),?xed assets(RFA)Loans(RLOAN),other earning assets(ROEA),RFEE(net fee income)

2Deposits(RDEP),overheads(ROHD),?xed assets(RFA)Loans less NPLs(RPLOAN),other earning assets(ROEA),RFEE(net fee income),

RNPLs as undesirable output

3Overheads(ROHD),?xed assets(RFA)Loans(RLOAN),other earning assets(ROEA),RFEE(net fee income),Deposits(RDEP) 4Overheads(ROHD),?xed assets(RFA)Loans less RNPLs(RPLOAN),other earning assets(ROEA),RFEE(net fee income),

RNPLs as undesirable output,Deposits(RDEP)

5Overheads(ROHD),?xed assets(RFA)Net interest earnings(RNIE),net fee income(RFEE)

Following Drake(2001)we adopt a hybrid between the intermediation and production approaches.We also recognise that deposits may be viewed as an output or as an input.We therefore consider?ve types of models,which can act as boundaries for the intermediation and production approaches including undesirable outputs.Model1takes the intermediation approach as applied to numerous studies in the extant literature.There are three inputs;bank deposits and borrowed funds,?xed assets and operational costs,and three outputs;total loans,other earning assets,and non-interest income.Although non-interest income remains undeveloped in China,it is selected to re?ect the growing contribution of this area to banks'total income.Model2 separates NPLs from loans and treats NPLs as undesirable output.Model3recognises that bank services to depositors are directly related to the volume of deposits and so deposits are treated as an output and Model4allows deposits as an output and treats NPLs as an undesirable output.Model5is a proxy for the production approach and has only?xed assets and overheads as inputs but has net interest income and non-interest income as outputs.Model5is the closest to the concept of the neo-classical production function which uses stocks of capital and labour to produce a?ow of output.In this study overheads act as a proxy of labour and the outputs are the revenues generated from balance sheet and off-balance sheet business,which also subsumes the lower gross interest income generated by NPLs.Table2summarises the input/output structure of each model.

As an indicator of scale and evolution of the variables over the period,Table3presents the summary statistics of the input and output data by bank group for1999as representative of the?rst half of the period and for2007as representative of the second half.Since we are examining the movements in productivity over a period of nine years,the nominal values of data were de?ated by the consumer price index.

The groups represent collectively the?ve state-owned or controlled banks(SOCB),the joint-stock commercial banks(JSCB), and the city commercial banks(CCB).

The table highlights the rapid growth in the average loan book over this period,particularly for the SOCBs and JSCBs.The table also shows the decline in the average level of NPLs for the SOCBs in the eight years between1999and2007.In part this represents the transfer of tranches of NPLs from the four largest SOCBs to the Asset Management Companies in1999–2000and in2003.It also shows that the average rate of decline of NPLs by the CCBs was relatively faster.The?gures for the CCBs are not strictly comparable between the two periods given the unbalanced nature of the sample.While the summary statistics for the SOCBs and JSCBs are comparable,the number of CCBs in the sample for1999was9whereas in2007it was41.14

5.Empirical results

Positive productivity growth is measured by an estimate greater than unity.Productivity regress is indicated by an estimate of less than unity.We conduct two exercises in the measurement of bank productivity.First we estimate the standard Malmquist measure based on the deterministic Data Envelope Analysis,however this will be a biased estimate.Second,a bootstrap estimate of the median of2000bootstrap simulations is examined.15

In both cases a constant returns to scale technology was assumed.16The bootstrap algorithm of Simar and Wilson(1999)uses the conical hull of the observed data to estimate the production set,which amounts to assuming CRS.However,the Malmquist index provides consistent estimates of the true value irrespective of the returns to scale assumption but may give inconsistent results regarding the sources of productivity in the decomposition.17

We use the bootstrap results of all5models to estimate conditional convergence but for purposes of presentation Table4 shows the sample mean of the weighted(by group asset share)average of TFP and decomposition for each of the?ve models

14Although the sample is an unbalanced panel for the whole period,the TFP calculation necessarily has a balanced panel for each year of calculation.The estimates were weighted by asset share to give an aggregated estimate as a means of minimising potential bias.

15We also conduct a third exercise where the estimate of productivity growth is not signi?cantly different from unity as given by the95%con?dence intervals of the bootstrap,the?gure is constrained to the null of unity.The aggregated results did not look too different from the unconstrained bootstrap results and are not reported.A detailed statement of the bootstrap algorithm can be found in Matthews and Zhang(2009).

16If the production technology is variable returns to scale(VRS),the Malmquist TFP index can be further decomposed into frontier shift,pure ef?ciency change, and scale ef?ciency.See also Ray and Desli(1997).

17In a previous study looking at the productivity growth of the national banks of China for a shorter time period Matthews et al.(2009)used the third test of Banker(1996)on selected years and found that the null of CRS could not be rejected.

discussed above using the two alternative estimates of the pure DEA estimate and the unconstrained median bootstrap value.18The ?nal three rows of the table show the average for the 5models and the standard deviation in parenthesis.

The TFP productivity growth is decomposed into technical progress and ef ?ciency gains (catch-up)for each of the models.A number of points can be made about the results of Table 4.First,the bootstrap results are quantitatively different from the DEA estimates,indicating signi ?cant bias in the raw DEA results and henceforth we focus our attention on these.19Second,looking at models 1,2and 4,the SOCBs appear to have had average TFP regress over this period and only moderate growth in the case of model 3,where deposits are considered as an output and NPLs an undesirable output.Third,it would appear that the main driver of TFP growth for the national banks has been technical progress de ?ned by the ‘best practice ’banks.In most cases the best practice (benchmark)banks have shifted the frontier outwards leaving the average banks behind and further to catch-up.However,the ?gures suggest that the main driver of TFP growth for the CCBs has been catch-up (models 2,3,and 4).Technical progress as the driver of TFP is stronger in the case when NPLs are treated as an undesirable output (models 2and 4).Fourth,the results suggest that the TFP growth of the CCBs and JSCBs was higher relative to SOCBs in the case of models 2and 4where NPLs

18The multiplicative property does not hold because of the time weighting used in construction of the weighted averages.

19

Out of 1570bank-year estimates of TFP obtained by the DEA method for the 5models,36%was biased at the 95%based on the bootstrap results.

Table 3

Output –input variables 1999and 2007(million RMB)per bank/year de ?ated by the consumer price index 1997=1.Sources:Fitch/Bankscope,Almanac of China's Finance and Banking (various)and author calculations from web sources.Variable Description Bank group Mean Standard deviation Minimum Maximum RLOAN

Real stock of loans

SOCB 142,078783,54429,0242,464,4552,505,421986,477979,8953,464,731JSCB 48,57725,18616,64380,603386,374141,514194,756590,849CCB

11,2399611442033,09418,27228,210553138,379ROEA

Real stock of other earning assets

SOCB 685,486370,853224,2891,210,6722,496,7021,146,125873,9454,025,218JSCB 42,18924,77015,15774,369313,253133,885116,718568,532CCB

11,87513,207202438,14418,17933,9651254

172,276RFEE

Real net fees and commissions

SOCB 166434960791019,834

10,308

640331,032JSCB 786715177169116484075811CCB

111012835640277RNPL

Real non-performing loans

SOCB 642,448411,00050,7051,090,038203,324300,51120,482

738,243JSCB 82329834031,37284963750413616,922CCB

1388880370279235974943947RDEP

Real deposits and other sources of funds

SOCB 2,063,1331,097,08031,8303,249,6984,655,5741,956,5321,709,7347,016,662JSCB 86,10544,38834,818140,688616,877265,686233,1581,094,492CCB

23,30823,520532869,57932,81556,8122682281,241RFA

Real ?xed assets

SOCB 44,93524,472485667,99562,26022,90729,06088,802JSCB 236095193037954574200618007620CCB

440237122778319470112516ROHD

Real overhead and other non-interest costs

SOCB 25,82212,960616438,03163,99927,51619,42084,296JSCB 1339677584261676493014389413,225CCB

39132591161013338459232278RNIE

Real net interest earnings

SOCB 40,19221,76984464,969150,72949,22588,490202,585JSCB 2214978957391321,11211,803866948,866CCB

85965929716151095

1420

47

6724

623

K.Matthews,N.(X.)Zhang /China Economic Review 21(2010)617–628

are treated as undesirable outputs but that the technical innovation was stronger in the SOCBs.The reason for this is possibly because the distribution of NPLs is concentrated in the state-owned banking sector but also that the best practice banks in this group have had strong success in reducing their NPL ratios thus reducing their bad output at a faster rate.Finally,model 5which has a stronger resonance with the neo-classical production function shows strong TFP growth in the SOCBs and CCBs with technical progress being the main driver.

Table 4also shows that taking the average of all ?ve models to obtain a robust measure,TFP growth by the SOCBs and the JSCBs has on average been zero but productivity growth of the CCBs has been 15%a year.However this verdict belies sharp differences in the drivers between the bank groups and it is not clear if the difference between the growth rates of the bank groups is statistically signi ?cant.In the case of the SOCBs,technical innovation has been equally offset by regress in ef ?ciency.This means that the best practice SOCBs have shifted the frontier and widened the gap between them and the remaining SOCBs.A similar but much more moderate picture emerges for the JSCBs.But with the CCBs,ef ?ciency gains dominate suggesting that emulating the best practice banks have contributed the most to productivity growth.

The boundary is made up of the benchmark or best practice banks.The banks that make up the benchmark and de ?ne the extent of technical innovation may change from year to year and by model.However,it is instructive to identify the benchmark banks within each bank group as the bank that has the most frequent display of technical innovation and with highest average growth due to technical innovation.Table 5below presents the benchmark banks for each bank group.

Increasing deregulation as suggested by the CBRC and the opening up of the Chinese banking market post 2006would suggest that the second half of the sample period examined should see a signi ?cant improvement in TFP growth.To test for this,the sample was split into two periods 1998–2002and 2003–2007.Table 6below shows the annual weighted average of TFP growth in both periods for all ?ve models and the overall average.

The table shows that the average TFP growth of the SOCBs ranged from 0.1%a year to 13.8%a year in the ?rst half of the period but was universally negative in the second half.Given that Table 4indicates the main driver for TFP growth was technical progress,this suggests that the benchmark banks had raced ahead leaving the other banks in the group with more ground to catch-up,leading to an average productivity regress.The results for the ?rst half of the period also con ?rm the standard ?nding that the JSCBs outperformed the SOCBs,particularly when NPLs are treated as an undesirable output.But contrary to the ?ndings of some Chinese scholars this performance is not sustained in the second half of the period.20The ?nal column which shows the average TFP for the three bank groups con ?rm that productivity performance was weaker in the second half of the period.21

A heuristic conclusion is that the TFP growth of the CCBs was stronger than both groups of the national banks con ?rming the ?ndings of Ferri (2009)that city commercial banks have increased their performance and are challenging the traditional banks.

20

Khumbhakar and Wang (2007)?nd that overall TFP growth for the national banks in China over the period 1993–2002was 4.5%annually with the SOCBs showing an annual growth of 0.7%a year and the JSCBs showing an average growth of 6.1%.These ?gures fall within the boundary of the estimates obtained in this paper.21

This is also con ?rmed by a non-parametric test (Mann –Whitney z test)for the differences in the measures of TFP growth between the two periods which showed no evidence (at the conventional 5%level of signi ?cance)that the high productivity growth of the ?rst half of the period was sustained in the run-up to the opening up of the banking market to foreign competition.

Table 4

Weighted annual average of productivity growth 1998–2007(weighted by asset share).Model

Group

DEA standard linear-programming estimates Bootstrap unconstrained estimates TFP

Tech Catch-up TFP Tech Catch-up 1

SOCB 1.005 1.0350.9740.997 1.0460.996JSCB 0.9800.9870.9610.9750.9940.970CCB 1.015 1.0100.994 1.038 1.019 1.0182

SOCB 0.997 1.0850.9230.992 1.1020.946JSCB 1.032 1.0480.973 1.052 1.0850.999CCB 1.027 1.029 1.003 1.294 1.087 1.3523

SOCB 1.009 1.0990.948 1.006 1.1130.949JSCB 0.9670.9830.9820.9520.974 1.009CCB 1.0210.993 1.018 1.0080.979 1.2164

SOCB 1.008 1.2130.9350.996 1.1330.935JSCB 0.999 1.0240.957 1.038 1.053 1.008CCB 1.033 1.011 1.008 1.340 1.048 1.3395

SOCB 1.053 1.1420.941 1.054 1.0950.936JSCB 1.022 1.0290.966 1.019 1.0150.977CCB 1.109 1.1460.956 1.085 1.2060.976Average of 5models.Standard deviation in parenthesis

SOCB 1.014 1.1150.944 1.009 1.0980.952(.022)(.067)(.029)(.119)(.052)(.149)JSCB 1.000 1.0140.968 1.007 1.0240.993(.023)(.068)(.029)(.119)(.053)(.149)CCB

1.041 1.0380.996 1.153 1.068 1.180(.033)

(.069)

(.030)

(.116)

(.065)

(.149)

624K.Matthews,N.(X.)Zhang /China Economic Review 21(2010)617–628

However this conclusion is not fully supported by the detailed analysis of conditional convergence in the econometric evidence below.

The growth convergence literature distinguishes between unconditional β-convergence and conditional β-convergence .The former relates to convergence to a common point or trajectory and the latter relates to different points or steady-states de ?ned by peer group characteristics.These peer group characteristics are identi ?ed by bank speci ?c components that might explain productivity performance.

Recently a number of studies have emerged examining the convergence of bank ef ?ciency and productivity.Weill (2009)tests for β-convergence and σ-convergence of cost ef ?ciency of banks across 10EU countries and Casu and Girardone (2008)do the same for 15EU countries.Single country studies have been conducted by Fung (2006)for bank holding companies in the US and Fung and Leung (2008)for Chinese banks.In what follows,we examine conditional β-convergence and σ-convergence .We conduct panel estimation with all 5models to estimate a meta-β-convergence equation.The purpose of estimating the meta-convergence function is to identify the factors common to all the ?ve models.Following Fung (2006)and Weill (2009)we specify the following dynamic function.

Δln T i ;j ;t =α+βln T i ;j ;t ?1+γZ i ;t ?1+εi ;j ;t

e3T

Where T represents total factor productivity,i is the bank,j is the model and t is time.Z is a vector representing bank speci ?c and categorical variables,εis a stochastic term and αand βare parameters to be estimated.A similar function is speci ?ed for technical progress (frontier shift)and ef ?ciency gain (catch-up).

Following Weill (2009),Eq.(3)is supplemented by a speci ?cation for meta-σ-convergence (T )as shown in Eq.(4).

Δln T i ;j ;k ;t ?ln àT j ;k =α+βln T i ;j ;k ;t ?1?ln àT j ;k

+u i ;j ;k ;t

e4T

In Eq.(4),k is the bank category (SOCB,JSCB,CCB)and a bar over TFP indicates the average ?gure at time t for all the models and for banks within each category (k )and model (j ).Sigma (σ)-convergence describes the speed at which the dispersion of productivity narrows over time.Sala-i-Martin (1996)shows that β-convergence is a necessary,but not suf ?cient condition for σ-convergence.

The bank speci ?c variables that we experimented with were SIZE measured by the log of assets,the cost-income ratio (COST),and a measure of revenue diversi ?cation given by the proportion of fee income in total revenue (FEE).22Categorical variables included a dummy variable to distinguish between the categories of bank (JCSB=1if joint-stock bank,zero otherwise and CCB =1if City Commercial Bank,zero otherwise)and a dummy variable to separate the sample between the earlier and latter periods of the sample (DUM=0for 1998–2002,DUM=1for 2003–2007).All bank speci ?c variables were lagged one period to account for potential endogeneity.

Table 7summarises the results for TFP,technical innovation and ef ?ciency gain,estimated jointly with Seemingly Unrelated Regression (SURE).23The dependant variable is the change in the log of TFP or the change in the log of its components.The ?rst point to note is the high speed of adjustment.Such a rapid speed of adjustment has also been con ?rmed in the ?ndings of Fung and Leung (2008).What it means is that mean reversion occurs almost instantaneously (within the year).The results also suggest that lower TFP growth is associated with banks that have a higher cost-income ratio,and higher TFP growth is associated with banks that have diversi ?ed their revenue sources by developing non-interest income.In contrast to Khumbhakar and Wang (2007)we ?nd that the larger banks are associated with higher TFP growth.

The convergence functions for technical innovation (frontier shifts de ?ned by best practice)and ef ?ciency (catch-up)also shows near instantaneous adjustment.The decomposition of the TFP identi ?es the factors that drive technical progress and catch-22

We also used the proportion foreign share holding as a possible indicator of internal pressure for TFP growth and the NPL ratio as an indicator of drag on TFP growth but these variables were not statistically signi ?cant.23

The additive property of natural logarithms is that the sum of the log of technical innovation and log of ef ?ciency gain is approximately the log of TFP growth.Variables that explain the convergence of the components of TFP growth must necessarily explain TFP growth,unless they are equally offsetting in the components.

Table 5

Best practice banks.Bank group Model 1

Model 2

Model 3Model 4Model 5

SOCB

Bank of China

Bank of China

Bank of China

Bank of China

Bank of Communications Bank of Communications ICBC

JSCB China Minsheng China Minsheng China Merchant China Merchant China Merchant China Merchant CCB

Xiamen Xiamen Xiamen

Xiamen

Shanghai

Ningbo

Ningbo

625

K.Matthews,N.(X.)Zhang /China Economic Review 21(2010)617–628

up.Technical innovation is positively associated with banks that have diversi ?ed its revenue sources by developing non-interest income business.Whereas ef ?ciency gains (catch-up)are associated with banks that have reduced their cost-income ratio.This means that the benchmark banks are those that have developed a wider spread of non-interest bank business and the catch-up has been obtained through stronger cost control.The positive coef ?cient on the log of SIZE(?1)on the technical innovation equation suggests that larger banks are the ones that de ?ne the benchmark.Surprisingly the CCB dummy showed that there were equal and offsetting movements in catch-up and technical innovation for the CCBs.However,the interaction between CCB and DUM suggests that the City Commercial banks did better on technical innovation in the second part of the sample period than the national banks and therefore experienced a signi ?cantly higher rate of TFP growth in the second part of the period,supporting the argument of Ferri (2009).

The dummy variable (DUM)that de ?nes the second half of the sample period has a negative effect on TFP growth which is caused by lower technical innovation.This con ?rms the ?nding of Table 6that in general TFP growth was weaker in the second part of the period than in the ?rst.However,the interaction between DUM and Y (?1)shows that the speed of convergence to the group mean was faster in the second part of the period,suggesting that while the overall level of productivity declined there was greater convergence to the clusters de ?ned by the different bank groups.

The results for sigma-convergence (shown below —p values in parenthesis)demonstrate that the dispersion of productivity growth has also narrowed in this period.The results also indicate that the latter part of the period saw a greater narrowing of the dispersion of TFP across all three types of banks.

Δln T i ;j ;k ;t ?ln àT j ;k

=?:071?:965:000eT???:000eT???

lnT i ;j ;k ;t ?1?ln àT j ;k ?:069DUM :011eT??

lnT i ;j ;k ;t ?1?ln àT j ;k

àR 2

=:8930;RMSE =:328

6.Conclusion

This paper has used the Malmquist decomposition to quantify the productivity growth of Chinese banks in the period 1998–2007.The advantage of using the Malmquist method is that it separates the diffusion of technology (ef ?ciency gains)from

Table 7

SURE regression,1998–2007,dependant variable ΔY ;p values in parenthesis.

Total factor productivity growth

Technical innovation Ef ?ciency gain Intercept ?.023?.2300.195***(.767)(?.300)(.000)Y (?1)?1.009***?1.043***?1.011***(.000)(.000)(.000)DUM*Y (?1)?.150***?.109***?.145***(.000)(.000)(.000)

DUM ?.064**?.063***(.015)(.004)

COST(?1)?.004***?.004***(.000)(.000)FEE(?1)0.800*** 1.919***?1.166***(.000)(.000)(.000)

Ln(SIZE(?1))0.014**.015**(.015)(.011)CCB ?DUM 0.045*0.075***(.087)

(.006)CCB ?.120***.095***(.000)(.000)R 2

.7116.5845

.6395

***Signi ?cant at the 1%,**signi ?cant at the 5%,and *signi ?cant at the 10%.

Table 6

Total factor productivity growth in sub-samples (weighted averages).Years Bank group Model 1Model 2Model 3Model 4Model 5Average 1998–2002

SOCB 1.014 1.001 1.0270.996 1.138 1.035JSCB 1.005 1.132 1.018 1.176 1.141 1.094CCB 1.047 1.4810.954 1.359 1.206 1.2092003–2007

SOCB 0.9790.9830.9840.9960.9980.988JSCB 0.9460.9730.8860.8990.9380.928CCB

1.030

1.107

1.063

1.321

1.007

1.106

626K.Matthews,N.(X.)Zhang /China Economic Review 21(2010)617–628

advances in technology (frontier shifts).The paper also applies bootstrapping techniques to evaluate signi ?cant changes in productivity,ef ?ciency gains and innovation.Five models were examined to provide a robust measure of bank productivity performance.

In general,average TFP growth has been neutral over the period for the SOCBs and JSCBs but positive for the CCBs in the second part of the period.However,the weighted average ?gures mask wide differences in individual performance.The benchmark banks that de ?ne the production frontier have generated sharp increases in technical innovation,leaving a wider gap between them and the other banks in their respective groups.

In general,ef ?ciency gains (catch-up)were obtained through cost reduction.Technical innovation is associated with greater diversi ?cation of revenue away from interest earnings.However,our assessment of the performance of CCBs must be interpreted with caution.The number of CCBs in the sample count for one-third of the actual number of CCBs in China.It is possible that the CCBs that report data publicly are the better ones and there is a sample selection bias in favour of the improving segment in the group.

Except for the CCBs,we ?nd no evidence that innovation and reform in the second part of the sample period,coinciding with the opening up of the Chinese banking market,resulted in an improvement in bank productivity.This may in part be due to the fact that foreign banks still only command a small share of the banking market in China.It is also possible that domestic competition is particularly strong between local banks with the better CCBs challenging the more established national banks.We ?nd strong convergence of productivity growth to the peer group de ?ned by bank speci ?c variables.We ?nd some evidence that CCBs have improved technical progress in the latter part of the period and increased TFP growth.

We interpret the overall results in the following way.Reform of the banking system and domestic competition has resulted in an increase in bank productivity growth over 1998–2002.However,the threat of competition appears not to have made any impact on bank productivity growth in the second half of the period.This could be due in part to the ?nding that most of the technical innovation occurred in the ?rst part of the period.In general there was productivity regress over the period 2003–2007but the speed of convergence of productivity to peer group bank categories increased suggesting that the threat of the opening up of the banking sector to foreign competition had the effect of improving ef ?ciency in Chinese banks through emulating best practice.

Acknowledgments

We are grateful,without implication,to the Editor and three anonymous referees.We acknowledge the support of the Hong Kong Institute of Monetary Research in providing the ?rst named author the research space to undertake this work in April 2009.References

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硬件类常用英语词汇

硬件类常用英语词汇 下面是小编整理的硬件类常用英语词汇，希望对大家有帮助。 计算机英语词汇大全 常见硬件篇 CPU：Central Processing Unit，中央处理单元，又叫中央处理器或微处理器，被喻为电脑的心脏。 LD：Laser Disk，镭射光盘，又称激光视盘。 CD：Compact Disc，压缩光盘，又称激光唱盘。 CD-ROM：Compact Disc-Read Only Memory，压缩光盘-只读记忆(存储)，又叫“只读光盘”。 VCD：Video Compact Disc，视频压缩光盘，即人们通常所说的“小影碟”。 RAM：Random Access Memory，随机存储器，即人们常说的“内存”。 ROM：Read-Only Memory，只读存储器。 Seagate：美国希捷硬盘生产商。Seagate英文意思为“通往海洋的门户”，常指通海的运河等。 Quantum：英文含意为“定量，总量”。著名硬盘商标，美国昆腾硬盘生产商(Quantum Corporation)。

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