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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH ELSEVIER

European Journal of Operational Research 98 (1997) 332-345

The impact of liberalization on the productive efficiency of Indian commercial banks A r u n a v a B h a t t a c h a r y y a a, *, C . A . K . L o v e l l

b,l, P a n k a j S a h a y c,2

a Marketing Sciences, Consumer Markets Division, AT&T, Somerset, NJ 08873, USA b Department o f Economics, University of Georgia, Athens, GA 30602, USA c ChristensenAssociates, 4610 UniversityAvenue, Madison, W153705-2164, USA

Abstract We examine the productive efficiency of 70 Indian commercial banks during the early stages (1986-1991) of the ongoing period of liberalization. We use data envelopment analysis to calculate radial technical efficiency scores. We then use stochastic frontier analysis to attribute variation in the calculated efficiency scores to three sources: a temporal component, an ownership component, and a random noise component. We find publicly-owned Indian banks to have been the most efficient, followed by foreign-owned banks and privately-owned Indian banks. We also f'md a temporal improvement in the performance of foreign-owned banks, virtually no trend in the performance of privately-owned Indian banks, and a temporal decline in the performance of publicly-owned Indian banks. We attempt to explain these patterns in terms of the government's evolving regulatory policies. © 1997 Elsevier Science B.V. Keywords: Banking; Data envelopment analysis; Efficiency measurement

1. Introduction The objective of this study is to measure, and to explain measured variation in, the performance of Indian commercial banks during the initial stages of the recent liberalization period. While many similar studies have evaluated the performance of banking sectors in the US and other advanced countries, very few studies have evaluated the performance of banking sectors in developing countries. Although Tyagarajan (1975), Rangarajan and Mampilly (1972), and Subrahmanyam (1993) have examined various issues relating to the performance of Indian banking,

" Corresponding author. Email: [email protected]. I Email: [email protected]. Email: pankaj@ lrea.com.

none of these studies have examined the efficiency of bank service provision. Indian banking is particularly interesting because of the diversity of bank ownership form, and the relationship between bank ownership and regulatory burden. Indian banks can be classified into three ownership groups: publicly-owned, privately-owned, and foreign-owned. Although the three groups of banks operate in the same markets, each group faces a different set of regulations, and these regulations have evolved through time. In light of this uneven and changing regulatory environment, we expect to find performance variation, both across groups of banks and through time. We seek to quantify, and also to explain, this anticipated performance variation. The first step in the analysis is the measurement

0377-2217/97/$17.00 © 1997 Elsevier Science B.V. All rights reserved. PII S 0 3 7 7 - 2 2 1 7 ( 9 6 ) 0 0 3 5 1 - 7

A. Bhattacharyya et al./ European Journal of Operational Research 98 (1997) 332-345

of bank performance. We associate performance with technical efficiency, the ability to transform multiple resources into multiple financial services, and we calculate technical efficiencies using data envelopment analysis (DEA). The second step in the analysis is the explanation of the variation in calculated efficiencies, in terms of temporal and ownership characteristics. Most similar studies which use DEA in the first step have relied on Tobit or other limited dependent variable regression models to explain variation in calculated efficiencies in the second step. However it is likely that the included explanatory variables fail to explain the entire variation in the calculated efficiencies, and the unexplained variation mixes with the regression residuals, adversely affecting statistical inference. We adopt a different approach, based on a stochastic frontier regression model, which allows us to decompose variation in calculated efficiencies into a systematic component and a random component. The systematic component contains temporal and ownership sources of efficiency variation. The random component contains an unexplained component and a separate white noise component. The paper is organized as follows. A brief review of the recent state of the Indian banking sector is provided in Section 2. In Section 3 the DEA model used to calculate efficiencies is developed, and the stochastic frontier regression model used to explain calculated efficiencies is described. The data are discussed in Section 4. The data consist of observations on resources consumed, financial services provided, and other exogenous characteristics, of an unbalanced panel of about 70 commercial banks over the period 1986-1991. Calculated efficiencies are summarized and explained in Section 5. We find calculated efficiencies to have varied substantially, both across ownership groups and through time. Section 6 concludes.

2. An overview of the Indian commercial banking sector Prior to 1970 the public sector contained only one bank, which was nationalized in 1951. Subsequently most of Indian banking was brought into the public sector by the nationalization of 14 major banks in

333

1970, and by the nationalization of six additional banks in 1980. The main goals of the nationalization program were: (i) to break the monopsony control of the large business houses over the country's banks; (ii) to spread banking services into the previously neglected suburban and rural areas; (iii) to mobilize deposits and direct funds toward investments in the public sector and loans to the priority sector (agriculture, small scale enterprises, and the export sector); and (iv) to make credit planning a part of the national economic plan. Details are available in Bhattacharyya (1993), Ghosh (1993), and Rangarajan (1993). Prior to nationalization, banking services were largely confined to metropolitan areas, and a major consequence of nationalization was the spread of services to suburban and rural areas. Thus at the end of 1964 only 10% of commercial banks were located in rural areas. This proportion increased to 45% in 1994. As a result, the number of bank branches increased from 8262 in 1969 to around 60000 in 1991, and the population served per branch declined from 65000 in 1969 to around 12000 in 1991 (Subrahmanyam, 1993). Although public sector banks played the major role in this branch network expansion process, private banks were also directed to expand into suburban and rural areas. With the nationalization of 14 large banks in 1970, public sector banks accounted for 85% of total deposits of the Indian banking system. The corresponding shares of the private banks and foreign banks were 6% and 9%, respectively. Similarly, with nationalization, the share of public sector banks amounted to 96% of the balances held by the banking system, and 88% of the paid-up capital and reserves of all banks. To comply with the stated objectives of bank nationalization, advances to the priority sector activities increased rapidly. The share of priority sector advances in the total credit of commercial banks increased from 14% in 1969 to 30% in 1980, and to 39% in 1985 (Thakur, 1990). In addition, Indian banks are required to lend 40% of their deposits to the priority sector. Foreign banks are required to lend only 12% of their deposits to the priority sector. However in recent years the share of priority sector advances in total credit has exhibited a downward trend for all three groups of banks. Thus for public sector banks the share of priority sector lending in

334

A. Bhattacharyya et a l . / European Journal o f Operational Research 98 (1997) 332-345

total lending has declined from 45% in 1986 to 38% in 1994. During the same period this proportion has declined from 51% to 28% for private sector banks, and from 11% to 8% for foreign-owned banks. All banks are currently required to invest 37.5% of their deposits in public sector securities. Directed investmerits have also exhibited a downward trend; Indian banks are now required to hold 30% of their net liabilities in government securities, down from 38.5% a few years ago (Economist, 1992). Beginning in 1985, the Reserve Bank of India (RBI), the central bank which controls most aspects of banking in the country, began relaxing its restrictions on the banks in response to pressure from international lending agencies, and also in response to changes taking place in international financial markets. In addition, the degree of protection afforded the banking sector has been gradually reduced in an effort to make the domestic banking sector internationally competitive. A system of flexible exchange rates on current account has been adopted. To strengthen the financial sector as a whole, and to encourage foreign investment, the Indian insurance sector and the stock market authorities have standardized procedures to register foreign institutional investors and foreign brokers. Lending rates have been freed, and deposit rates are subject only to a ceiling rate. A floor rate on advances has been prescribed, but banks are given freedom to charge higher rates according to their perceived risk on commercial loans. Banks now have increased freedom to determine their investment and credit policies. In addition, a merger between two public sector banks, New Bank of India and Punjab National Bank, has recently been approved. Finally, the Committee on the Financial System, appointed by the government of India in 1991, identified directed investment and credit programs as the two main sources of declining efficiency, productivity and profitability among commercial banks, and particularly among nationalized banks. Consequently subsequent liberalization policies have emphasized expansion of banking services, particularly in the private sector, and have relaxed the regulations and cut the red tape which hindered the banking sector, particularly its foreign component. As a consequence, there has been a steady increase in the number of foreign banks operating in metropolitan

centers, and these foreign banks have become leading players in several areas of business and have begun to set standards in the sector. A recent feature of the liberalization program is a consideration of privatization. It seems likely that industrial and business houses soon will be allowed to enter the banking sector. The private sector banks have been assured that they will be allowed to expand without fearing nationalization, and they are now allowed to raise up to 20% of their equity capital from foreign investors. To enable the public sector banks raise equity from capital markets, the Banking Companies Act is being amended. In addition, policy goals of the RBI have shifted toward the pursuit of profitability and capital adequacy. Following the Basle Committee norms, the RBI has stipulated that Indian banks, both public and private, must attain an 8% capital adequacy level. As of April 1992, banks satisfying the capital adequacy norm and prudential accounting standards have been allowed to open new branches without prior RBI approval. These recent policy changes indicate a desire to make Indian banking more competitive, and to level the playing field for the three ownership groups. However for institutional reasons, liberalization is evolving slowly, and banks, especially privately-owned banks and foreign banks, are proceeding cautiously in reacting to this changing environment. This is a good time to take stock of the trends in the performance of the three groups of bank.

3. Methodology Recent developments in the measurement of efficiency have followed two distinct trends. One is a nonparametric, nonstochastic approach developed by Chames et al. (1978), known as data envelopment analysis (DEA). The other is a parametric, stochastic approach developed by Aigner et al. (1977) and Meeusen and van den Broeck (1977), known as stochastic frontier analysis (SFA). In this study we combine the two approaches in a two-step procedure, using DEA in the first step to calculate technical efficiencies, and using SFA in the second step to explain variation in calculated efficiencies. DEA is eminently suitable for examining the performance of

A. Bhattacharyya et al. / European Journal of Operational Research 98 (1997) 332-345

Indian banks, due to the institutional framework in which they operate. Banks provide several financial services, which greatly complicates the application of SFA to quantity data to measure technical efficiency. In addition, regulations and other market imperfections distort prices, which would complicate the application of SFA to price and quantity data to measure cost, revenue or profit efficiency. However SFA does offer an attractive way of analyzing the variation in the technical efficiencies calculated using DEA.

where m = 1. . . . . M indexes outputs. Then the pooled production possibilities set can be expressed as

T={(ym,x,): F

Ym < E

x f ' ) > O,

where f = 1. . . . . F indexes banks, t = 1. . . . . T indexes time periods, and n = 1. . . . . N indexes inputs. Let the output data be represented by yft=(y{t

. . . . , y .ft. . . . .

y£') > 0,

~_,A:'y~',

F

x.~ E

m=l

..... M ,

T

E A/txf',

n = l . . . . . N,

f=lt=l

Aft>o,

X/i f ( X{' . . . . , x ft ......

T

f=l t=l

3.1. Using DEA to construct a 'grand frontier' In the first step we use DEA to construct a single 'grand frontier' which envelops the pooled inputoutput data of all banks in all years. This grand frontier provides a benchmark against which to calculate the efficiency of each bank in each year. If liberalization has caused performance to improve (decline) during the period, most of the efficient and near-efficient observations will be of recent (older) vintage. This information on trends in performance would not be available if we were to use DEA to calculate annual frontiers, since the benchmarks would likely change from year to year. Two additional benefits of the use of a grand frontier are an increase in degrees of freedom, and an increase in the variation in calculated efficiencies to be explained in the second step. It would also be possible to calculate trends in efficiencies by using DEA to construct a Malmquist (1953) productivity index, an approach that has been employed frequently to study bank productivity change. The approach we adopt has been much less frequently employed, but it offers the advantage of providing a single benchmark against which to evaluate performance and its change through time. In light of the evolving regulatory environment in which Indian commercial banks have operated, we prefer our 'grand frontier' approach. Let the input data be represented by

335

F

f=l ..... F;t=l

. . . . . T,

T

E E A f t = 1}, f--l t--I

(1)

where the A :t are intensity variables allowing the creation of convex combinations of observed (xy,, yyt). The production technology represented by T displays variable returns to scale and strong disposability of inputs and outputs. For the purpose of evaluation we assume that banks seek to maximize their service provision, given the resources at their disposal. A service-oriented measure of the efficiency of bank f in year t, E(x/t,yyt), is calculated as the reciprocal of the solution to the DEA envelopment problem Max

0= [E(x/',y/t)]-.

(2)

subject to F

T

Oyj'< Y'. ~,;~:'y~',

m=l

.....

M,

f=l t=l F

T

/t Y'. ~Af'x~'o, F

f = l . . . . . F,

t = l . . . . . T,

T

E EX:'=l. f=lt~l

This problem is solved once for each bank in each year. The optimal value of 0 is the factor by which yft must be scaled up in order for a bank with data (x/t, y/t) to reach the grand frontier. Since 0 > 1, 0 < E ( x ft, yfO < 1. Since the scaling is radial, any slacks remaining in the solution to the DEA envelopment problem are not incorporated into the effi-

336


ciency measures. Thus the Debreu (1951)-Farrell (1957) efficiency measures which we calculate may overstate the inclusive Koopmans (1951) definition of efficiency. We address the empirical significance of slacks in Section 5.

ciencies is captured by O ( z f ' ; / 3 ) , and the random part is captured by exp( - ~.ft). The SFA regression model is specified as a loglinear function of time-specific and bank-specific indicators, and Eq. (3) becomes

3.2. The second step regression analysis: rationale and method

T J In ~ft ~_. /30 "~- E / 3 t z o f t -~ E f l j z f t -~ 8ft, t=2 j=l

After calculating efficiencies, it is natural to seek to explain their variation. Earlier studies have sought to explain variation in calculated efficiencies by means of a second step regression, in which the calculated efficiencies are regressed on a set of exogenous variables using OLS or, because the efficiencies are censored variables, tobit methods. However a potentially serious shortcoming of this approach is that a part of the variation in the calculated efficiencies can remain unaccounted for, ending up mixing with the white noise error term and contaminating the estimated regression coefficients. Here we adopt a different approach, in which the unexplained part of efficiency variation is separated from the white noise error term. We specify the second step explanatory regression as an SFA model, rather than as an OLS or tobit model. In an SFA regression model the error term contains two components, a normally distributed white noise component, and a one-sided component, which in this case captures that part of efficiency variation which is not associated with the explanatory variables included in the model. This specification allows us to decompose variation in calculated efficiencies into systematic and random parts. The systematic part captures the effect of the exogenous variables on calculated efficiency variation, and the random part is captured by the one-sided error component. The entire variation in calculated efficiencies is thereby assigned to systematic and random sources. The second step SFA regression model is specified as ~St = O ( z f , ; / 3 ) exp( o s ' - z/I),

(3)

where ~/t is the vector of effficiencies calculated in the first step using DEA, Z ft is a vector of exogenous variables, /3 is a vector of parameters to be estimated, Vft is a symmetric white noise error component, and r f t > 0 is a random error component. The systematic part of variation in calculated effi-

(4)

where e ft = v f t - r f', the TD ft are time dummies, and the Z f t are bank-specific variables. The time dummies show how bank performance evolves through time relative to performance in 1986. Following Baltagi and Griffin (1988), we construct an index of efficiency change as

A(t)=flt-flt_ ,, t = 2 . . . . . T. This evolution can be attributed to any number of sources, but in the present context we interpret it as reflecting the impact of changes in the regulatory environment. The functioning and structure of a commercial bank depends to a large extent on its ownership form. This suggests a reformulation of Eq. (4) as T

In F_ft= /30 -1- E /3tTDft t--2 J K

+ E E/3:WDf'Z:' + : , j=lk=l

(5)

where the WD/t are ownership dummies corresponding to the three ownership forms. The ownership dummies are interacted with the exogenous variables so as to allow the exogenous variables to effect the three ownership forms in different ways. This specification allows us to estimate three sets of/3j parameters, one for each ownership group. The exogenous variables Z f t are described in Section 4. The specification in Eq. (5) is the standard SFA regression model, recast in a different role. Estimation is by maximum likelihood, which requires distributional assumptions on the two error components. We assume that 'r f' ~ N+ (0, o'~.2), vft~N(O,~r.2),

and that r :~ and v/t are distributed independently of each other and of TD/t, Z It,and W D :t.The log-likelihood function for the model is f - - A - In o - -

o'vo" / '


where tr 2 = o.2 + %2, @(.) is the standard normal cumulative distribution function, and A is a constant which can be ignored. Following Jondrow et al. (1982), we obtain estimates of ~.:t from the conditional mean of z It given the estimated residuals e/t. These are given by

-

4. The Indian commercial bank data

]

0"0"r =

-

cr~+ trv

337

(7) where 4,(.) is the standard normal density function. The estimated value of 7 ft provides an estimate of the random efficiency variation effect.

The Indian Banks' Association (IBA) provides a rich source of data on the operations of all commercial banks. Its Financial Analysis of Banks contains balance sheet and income data, as well as information on the number and type of branches, the number and type of staffs and fortnightly averages of several other key variables. These data are statutorily audited, and they are public information. The IBA already uses these data to calculate various financial performance ratios; see various issues of Financial Analysis of Banks (IBA, 1986-1992). We have gathered requisite data for 70 commercial banks over the period 1986-1991. However data are not available for all banks for all six years. Sixty-eight banks are observed in the year 1986.

Table 1 Summary statistics for Indian Banks: 1986-91

Services: Advances Mean SD Deposits Mean SD Investments Mean SD Resources: Interest expense Mean SD Operating expense Mean SD No. ofbanks: Public Priva~ Foreign Toml

1986

1987

1988

1989

1990

1991

64410 146432

67220 146107

77929 169068

86396 184725

89 181 198067

84 125 185906

122 445 258 688

128 400 266910

141680 299032

157086 328070

155 968 327047

157313 342715

42753 101 838

47432 107117

52397 117 148

57663 126067

59545 126435

63401 142517

9 007 20 329

9 852 22 780

13960 32468

13 410 31 338

14434 33346

15 353 37349

4 350 9 872

4 528 10 438

6236 14829

5783 12951

5790 13851

5758 13551

28 21 19 68

28 20 19 67

28 20 19 67

28 23 20 71

28 23 21 72

28 23 23 74

338

A. Bhattacharyya et aL / European Journal of Operational Research 98 (1997) 332-345

Data on 67 banks are obtained for the years 1987 and 1988. For the last three years, 1989-1991, data on 71, 72, and 74 banks are available. So the data set is an unbalanced panel, which we pool into a single sample having 419 observations. These data are summarized in Table 1, by year and by ownership group. The data for 1986 and 1987 cover the 12 months from January 1 through December 31. The data for 1988 cover the 15 months from January 1, 1988 through March 31, 1989 due to a change in the fiscal year; these data have been divided by 1.25 to convert them to a 12 month basis. The data for 1989, 1990, and 1991 cover the 12 month period from April 1 through March 31. In modeling commercial bank operations it is crucial to understand the objectives of the banking system, because such an understanding guides the selection of variables to be used in the analysis of bank performance. In India commercial banks are treated as intermediaries having the objective of mobilizing funds for directed lending and investment. This objective is consistent with the 'value added' approach to bank operations suggested, for example, by Berger and Humphrey (1992). In this approach interest expense and operating expense are incurred in the process of providing revenue-generating services, and all variables are measured in value terms. The alternative 'production' approach to bank performance analysis treats banks as using capital, labor, and other non-financial inputs to provide physical quantities of advances and deposits. Berger et al. (1987) provide a detailed discussion of the alternative approaches, and Coates (1990) provides a comprehensive discussion of the objectives of the Indian banking system, for which the production approach would be inappropriate. In the first step DEA efficiency measurement model, we include advances, investments, and deposits as outputs. Since nationalization, one of the major objectives of banking service in India is deposit mobilization, and all major banks have vigorously pursued this goal. As a result, the ratio of bank deposits to national income was 34.5% in 1991-1992 (RBI, 1991-1992). Banks spend a substantial share of their operating cost raising deposits. If we use Anand's (1993) estimates of transaction costs on 1991 data, approximately 25% of operating costs of Indian banks are expended on deposit mobilization.

To provide these three services, banks incur financial and operating costs. In this study these costs are captured by two types of expense, interest expense and operating expense. Descriptive statistics for all five variables are provided in Table 1; all variables are measured in hundred thousand 1980 rupees. Both service provision and resource use grew fairly steadily during the period of our study. The enormous size dispersion among banks also shows up, with standard deviations of the five variables generally being more than double their means. We now consider the exogenous variables used in the second step SFA explanatory regression. After nationalization, the government and the RBI emphasized expansion of the banking sector into the suburban and rural areas. This influenced both the number and the location of new branches, although the impact was greater on publicly-owned banks than on privately-owned banks. The freedom to open metropolitan branches was typically linked to the opening of suburban and rural branches. Foreignowned banks were treated still another way. They were not required to expand into suburban and rural areas, but they did face tight restrictions on the number of metropolitan branches they could open. It is of interest to examine how this controlled branch expansion policy influenced the performance of the three types of bank. Four branch-related variables included to capture these effects are: zr = Number of branches zs = Number of branches zu = Number of branches zm Number of branches =

in in in in

rural areas. suburban areas. urban areas. metropolitan areas.

Government regulations also compelled banks, again to varying degrees, to lend to the priority sectors. It is likely that a part of calculated efficiency variation can be attributed to this lending policy. In addition, in deference to Basle committee norms, the riskiness of bank portfolios has become an important issue. Increasingly, the decision-making freedom of banks has been linked to their capital adequacy; banks with satisfactory capital adequacy ratios are now allowed to open branches without prior RBI approval, and to raise equity from capital markets. It is therefore of interest to determine the impact of asset quality, as measured by capital adequacy, on bank performance. These two considerations lead us


to add two additional explanatory variables to the SFA regression model, namely: Zp zc

= The ratio of priority sector lending to total advances. = The capital adequacy ratio.

Since banks are obliged, to varying degrees, to invest in and lend to priority sectors, it would be of interest to examine whether such a directed lending policy has influenced the efficiency of the sample banks. The capital adequacy ratio is defined as the ratio of total capital to risk-weighted assets. This ratio indicates the coverage of banks' assets by owners' funds. Eight percent is regarded as the minimum standard by banks operating internationally. Most Indian banks fall short of this international standard. Their capital adequacy ratio was below 8% in all years, but showed improvement in the last year. As banks are required to adhere to these international norms, availability of capital is likely to restrict their growth into risky assets.

5. Empirical findings 5.1. DEA efficiency results

DEA was applied to 419 bank/year observations to construct a grand frontier, and to measure the

339

service-oriented radial efficiency of each observation. Results are summarized in Table 2. Our measure of technical efficiency does not incorporate non-radial slacks, and thus overstates the overall efficiency of the banks. The overstatement is not serious, however. The average ratios of non-radial slacks to target values of advances, deposits, and investments are 0.73%, 2.25%, and 4.00%, respectively. The average ratios for interest expense and operating expense are 3.82% and 5.03%, respectively. Each of these average ratios is dwarfed by the radial inefficiencies, although each average conceals a few large non-radial slacks for individual banks. We begin by considering variation in performance across the three ownership forms. Publicly-owned banks achieved the highest average efficiency, and the smallest average variation in efficiency. Foreign-owned and privately-owned banks achieved substantially lower average efficiencies. In the initial years, foreign-owned banks achieved the lowest average efficiency, but in the last two years their performance improved considerably. They also exhibited by far the largest variation in performance, perhaps reflecting differences in managerial philosophy and greater adaptability of banks from different foreign countries. The temporal performance pattern is also of interest. Overall average performance improved marginally after 1987. Public sector banks showed a significant decline in average efficiency,

Table 2 Average radial efficiency scores by ownership form: 1986-91 Bank group

Year 1986

1987

1988

1989

1990

1991

Average

82.51 12.34

79.25 13.19

79.96 14.48

79.19 15.21

80.76 13.80

80.44 14.94

80.35 14.01

89.70 6.97

88.78 6.72

89.79 7.04

88.21 9.55

85.77 9.99

82.14 10.49

87.40 8.90

77.33 9.24

76.58 9.43

74.85 9.00

74.46 10.19

75.11 8.47

77.06 11.67

75.88 9.63

77.65 16.22

68.01 13.94

70.88 18.52

72.06 20.02

80.26 19.76

81.73 21.26

75.37 18.92

All banks Mean SD

Publicly-owned Mean SD

Privately-owned Mean SD

Foreign-owned Mean SD

340


private Indian banks showed almost no change, and foreign-owned banks showed a remarkable increase in efficiency during the sample period. It is noteworthy that foreign-owned banks exhibited below-average performance through 1990, and improved dramatically to above-average performance in the last year of the sample period, when they were nearly as efficient as the public sector banks. A second feature of the DEA results concerns their variability. Domestically-owned banks exhibit less variability in performance than do foreign-owned banks. This finding is not surprising, and presumably reflects a greater familiarity with the regulatory system. The greater variability in the efficiency of foreign banks is also due to their dependence on less stable wholesale or corporate resources, interbank market borrowings, and refinance of assets. Domestic banks, on the other hand, have a more extensive branch network, assuring a more stable retail banking business. Overall performance variability tends to increase through time. This pattern holds clearly for publicly-owned and foreign-owned banks, and somewhat less clearly for private Indian banks. Liberalization may be leading to improved overall performance, but it is also creating winners and losers. We also conducted an analysis of returns to scale among the Indian commercial banks. We characterize returns to scale with the sign of the intercept of the supporting hyperplane in the DEA multiplier problem dual to (2). The intercept of the supporting hyperplane is not unique for frontier banks, however, since they are located at the intersection of two or more supporting hyperplanes. In general, banks exhibit increasing, constant or decreasing returns to scale at their optimal radial projection according as the maximum intercept of their supporting hyperplane is negative, the minimum and maximum intercepts bound zero, or the minimum intercept is positive, respectively; see Banker and Thrall (1992). Our analysis shows that most banks displayed decreasing returns to scale. Indeed every Indian bank in every year in the sample period operated in the decreasing returns to scale region of production technology. The persistence of diseconomies of scale could possibly result from the RBI's branching policy. Indian banks are required to open branches under the branching policy, but are not allowed to close unprofitable branches. This policy prevents optimizing resources

across the branch network because banks have neither control over the location of branches nor the ability to close loss-making branches. In sharp contrast, foreign banks exhibited increasing and constant as well as decreasing returns to scale. Foreign banks tend to have smaller branch networks, since they have not yet fully expanded their business and have not been forced by regulators to expand branch networks beyond their optimal size. Returns to scale characteristics of the frontier banks are summarized in Table 3. Of the 43 frontier banks, 33 displayed decreasing returns to scale (DRS). Only foreign-owned frontier banks showed any tendency toward increasing (IRS) or constant returns to scale (CRS). A final feature of the DEA results concerns the identity of the banks on the grand frontier. A total of 43 bank/year observations, approximately 10% of the sample, are rated as being radially efficient. Of these 43 best-practice observations, 29 come from the final three years of the sample period. Since performance variability is greatest during the final three years, this finding reinforces the 'winners and losers' conclusion reached above. Of the 21 bestpractice banks in the last two years, 13 are foreignowned, whereas prior to 1990 only eight of 22 best-practice banks were foreign-owned. This reinforces the conclusion reached above concerning the improved performance of the foreign-owned banks in recent years. In Table 4 the best-practice banks are listed by ownership type and by year. It is interesting to note that only two of 28 public sector banks are found to be efficient in the final year of the sample period. However six of 23 foreign-owned banks are found to be efficient during the same year. This clearly indicates that with the liberalization of the banking system in India, foreign-owned banks are not just playing an active role in Indian financial markets, they are beginning to set performance standards. Table 3 Returns to scale of frontierbanks, by ownershipform Ownership IRS CRS DRS Total Public Indian 0 0 18 18 Private Indian 0 0 4 4 Foreign 7 3 11 21 Total 7 3 33 43


5.2. Explaining calculated efficiency patterns using SFA

341

models are 322.74 and 224.61, respectively. Maximum likelihood parameter estimates of the unrestricted model appear in Table 5.

DEA has revealed variation in bank performance, both across ownership groups and through time. In this subsection we seek an explanation for these efficiency patterns. For the entire set of 419 bank/year observations we used SFA to regress calculated radial efficiencies against 22 variables (five time dummies and 3 × 6 - 1 = 17 interactions between the three ownership forms and the six environmental variables). (One interaction term is missing because foreign-owned banks do not operate in rural areas.) A likelihood dominance criterion model selection test due to Pollak and Wales (1991) was performed to determine whether the SFA regression model (5) dominates suitably restricted versions. At the 5% level of significance we were unable to reject the unrestricted model. The values of the log-likelihood function for the unrestricted and restricted

5.2.1. The temporal effect Relative to the base year 1986, and controlling for ownership form and the environmental variables, performance declined significantly between 1988 and 1991. As a consequence, over the entire period the Baltagi-Griffln performance index declined at an average annual rate of 2.23%. This declining trend presumably reflects a cautious adjustment of banks to rapid policy changes in a relatively unstable political environment. The large decline in efficiency observed in 1991 may be due to the share market scandal which broke out late that year. Several banks were alleged to have diverted public funds to the stock market, and this prompted the government and the RBI to take a more cautious approach toward deregulation.

Table 4 Frontier banks by ownership form and by year Banks

Ownership

Years 1986

Allahahad Bank Bank of America Banque Nationale de Paris State Bank of Patiala Sonali Bank Canara Bank Oman International Bank State Bank of India Jammu and Kashmir Bank Punjab National Bank Bank of Bahrain and Kuwait Central Bank of India United Bank of India British Bank of Middle East Citibank N.A. Indian Bank C~dit Lyonnais Bank of Nova Scotia Bank of Tokyo

Public Foreign Foreign Public Foreign Public Foreign Public Private Public Foreign Public Public Foreign Foreign Public Foreign Foreign Foreign

Soci~t~ G~n~rale Sanwa Bank

Foreign Foreign

1987

1988

1989

X

X

X

X X

X X X

1990

1991 X

X X X X X

X X

X X

X X

X X

X X X X

X X X

X

X X

X X X

X

X X X

342


To examine performance trends for each ownership form, we estimated the model (4) for each group of banks. We found very different temporal effects for the three ownership forms. Over the sample period foreign-owned banks experienced a 6.77% average efficiency increase, privately-owned banks experienced a negligible 0.07% average efficiency increase, and publicly-owned banks experienced a 2.69% average efficiency decline. This suggests that foreign-owned banks have adapted best to their relatively new environment, and that publicly-owned banks have adapted poorly to an environment which they know relatively well. Since environmental variables are controlled for in the analysis, the most likely explanation is that foreign-owned banks have prospered at the expense of publicly-owned banks in an increasingly competitive environment. With the liberalization of the banking sector and the resulting increase in the number of foreign-owned banks, the market share of the foreign-owned banks is increasing. Several foreign-owned banks compete directly with publicly- and privately-owned banks in various spheres of banking services; a case in point is the mobilization of savings of Non-Resident Indians (NRIs). By and large the foreign banks depend on deposits from NRIs. Due to their better customer service and their access to a well-developed international banking network, these banks have outperformed the Indian banks in obtaining business from

the NRIs. In particular, during the last two sample years falling interest rates in overseas markets have made savings and investments under various NRI schemes very attractive (Dobby, 1993). The poor performance of the privately-owned Indian banks could be due to the fact that these banks are still sceptical of the assurances of the central political authority; and particularly in the face of a continuing unstable political situation they are responding to the liberalization process very cautiously. 5.2.2. The ownership f o r m effects For the publicly-owned banks, none of the four branching variables is statistically significant; geographic restrictions on branch network expansion have had no perceptible impact on the performance of publicly-owned banks, For private sector banks, while the nonmetropolitan branches have had a significantly positive impact, branching in the metropolitan areas has had a significantly negative effect on performance. Branching into rural, suburban, and urban areas has enabled the private sector banks to provide much-needed financial services in these areas, and this has led to efficiency gains as well. This suggests a specialization of private sector banks in small towns and rural areas. For foreignowned banks, branch expansion into suburban and metropolitan areas also appears to have enhanced performance, although since there exists only one

Table 5 Maximum likelihood parameter estimates a Parameter

Parameter

/30 /387 flss

/389 /39o /391

3.9886

(0.0156)

/3rp

0.0234 - 0.0694 -- 0.0320 - 0.0502 -- 0.1100

(0.0146) (0.0227) (0.0190) (0.0283) (0.0284)

/3P /3P /3cP /3pP

0.162 0.13E 0.48E -0.71E - 0.4346 - 0.1489

/37

0.3919 (0.0294) - 0 . 1 9 2 - 2 (0.442- 2) 0.0122 (0.13E- 2) - 1.9761 (0.0803) - 1.9340 (0.1394)

/3rs /3ss /3s /3m s /3cs

0.47E - 0.83E 0.342 -0.90E - 0.6176

4 (0.47E - 4) 4 (0.11E - 3) 3 (0.31E - 3) 4 (0.44E - 3) (0.5224)

/3s

0.5890

(0.0371)

a Asymptotic standard errors are in parentheses.

0.2113 0.1715

2 (0.27E - 3) 2 (0.3TE - 3) 2 (0.76E - 3) 2 (0.22E - 2) (0.4568) (0.0755)

(0.0181) (0.0622)


suburban foreign-owned bank branch (of Standard Chartered), the estimated dummy coefficient applies to that bank only, and is not a reliable indicator of the impact of suburban branching of foreign-owned banks in general. These banks have been able to exploit their metropolitan office networks to expand efficiently, without having to expand into high-risk rural and suburban areas. The priority sector lending requirement has a statistically significant negative impact on the performance of foreign-owned and privately-owned Indian banks, but a statistically significant positive impact on the performance of publicly-owned banks. The first two results are plausible, but the third is difficult to explain. The public sector banks lend the highest proportion to the priority sector, and as a result could be receiving compensating benefits that are not captured in our model; alternatively, as Anand (1993) suggests, priority sector lending is as profitable as other public sector loans. It is also possible that despite the accounting norms requiring bad loans to be written off; priority sector loans and advances are gross figures. As a result, even if loans are of poor quality, they increase efficiency as we measure it. The capital adequacy variable has a statistically

343

insignificant impact on the performance of public sector banks, but for foreign-owned banks and private Indian banks it has a statistically significant adverse effect on performance. This suggests that the conservative behavior of the latter two groups accounts for their lower efficiency scores, implying a risk-return tradeoff in the industry. Banks with low-risk portfolios, as measured by a higher capital adequacy ratio, have been less efficient, probably because they have preferred safer and lower-earning portfolios over riskier but higher-earning portfolios. Capital adequacy norms, however, did not constrain the performance of public sector banks. This is because public sector managers have less control over the quality of their asset portfolios and the equity and reserve levels of their banks.

5.2.3. The random efficiency effect The magnitude of the random unexplained component of the calculated efficiency variation is summarized in Table 6. Using the maximum likelihood parameter estimates obtained from (5), that part of the efficiency measure that the systematic part of the model fails to capture is calculated using (7). The estimated conditional means of r given ~, therefore,

Table 6 Average residual efficiency effects by ownership form and by year Bank group

Year 1986

1987

1988

1989

1990

1991

Average

6.07 3.51 8.05

6.29 4.00 7.82

5.57 2.83 8.07

5.75 2.55 7.95

5.54 3.01 8.12

5.03 2.10 8.35

5.70 2.10 8.35

6.71 5.72 8.05

6.41 5.35 7.62

5.62 4.64 6.60

6.05 4.72 7.95

5.98 4.70 7.53

5.78 4.35 7.93

6.09 4.35 8.05

6.01 4.37 7.30

6.41 4.58 7.82

5.88 4.28 7.76

6.18 4.25 7.67

5.88 4.40 7.88

5.41 4.19 8.35

5.95 4.20 8.35

5.20 3.51 7.71

5.99 4.00 7.57

5.17 2.83 8.07

4.83 2.54 7.94

4.60 3.01 8.12

3.74 2.10 7.81

4.87 2.10 8.12

All banks Mean Min. Max.

Publicly-owned Mean Min. Max.

Privately-owned Mean Min. Max.

Foreign-owned Mean Min. Max.

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A. Bhattacharyya et al. / European Journal o f Operational Research 98 (1997) 332-345

measure the random component of the estimated DEA scores that cannot be explained by the included bank-specific variables. In this sense this component may also be called a residual efficiency effect. Our estimates show that on average, across all three ownership forms and throughout the sample period, 5.7% of calculated efficiency variation remains unexplained by the temporal and ownership form interaction effects. Not much deviation from this overall mean is observed for individual ownership forms or for individual years. However, for the foreign-owned banks the mean random efficiency effect is found to be smallest for each sample year and declining steadily over time. Banks with the largest and smallest random efficiency effects are also foreign-owned. The relatively small estimated random efficiency effects suggest that the SFA regression model (5) is well specified, and provides an adequate characterization of the determinants of calculated efficiency variation among Indian commercial banks during the sample period.

the least efficient, in utilizing the resources at their disposal to deliver financial services to their customers. However our most striking finding is the rise of the foreign-owned banks and the decline of the public sector banks. Foreign banks were the least efficient at the beginning of the sample period, but by the end of the period they were nearly as efficient as the publicly-owned banks, which exhibited a temporal decline in performance. The rise of the foreign-owned banks occurred despite the fact that their performance was hindered, not only by the existing regulations constraining their operations, but also to a significant degree by capital adequacy requirements and relatively modest priority sector lending requirements. Their rise appears to have occurred in part as a result of their ability to efficiently extend their small branch networks into metropolitan areas, while not having to extend further into rural areas, and in part as a result of a significantly positive temporal effect, which we have interpreted as an efficient adaptation to an increasingly competitive environment.

6. Summary and conclusions Acknowledgements The entire Indian economy is currently passing through a period of rapid economic liberalization. The banking sector of the economy, which since 1969 has grown up under protection and government regulatory control, has recently been moving gradually toward a more open and less regulated market system. In this study we have measured and endeavored to explain the performance of Indian commercial banks during the early phase of the government's liberalization program. To accomplish this task we have used DEA to calculate the efficiency of service provision for individual banks of three different ownership forms over a period of six years, and we have used SFA to attribute variation in calculated efficiencies to a set of temporal and government regulatory policy variables. We believe this to be the first such combination of DEA and SFA, and we believe that this study provides a methodological basis for future research into the performance of the banking sector in India and other developing economies. We have found the publicly-owned banks to have been the most efficient, and privately-owned banks

The authors are grateful to co-editor W.W. Cooper and two perceptive referees for their helpful comments on previous drafts of this paper, and to Bandi Prasad of the Indian Banks' Association for his help in collecting the data.

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