Applied Economics, 2004, 36, 1915–1924
Financial liberalization and bank efficiency: a comparative analysis of India and Pakistan A L I A T A U L L A H * , T O N Y C O C K E R I L L and HA N G L E y Durham Business School, University of Durham, Mill Hill Lane, Durham DH1 3LB, UK and yDepartment of Economics and Politics, Nottingham Trent University, Nottingham, NG1 4BU
This paper provides a comparative analysis of the evolution of the technical efficiency of commercial banks in India and Pakistan during 1988–1998, a period characterized by far-reaching changes in the banking industry brought about by financial liberalization. Data Envelopment Analysis is applied to two alternative input–output specifications to measure technical efficiency, and to decompose technical efficiency into its two components, pure technical efficiency and scale efficiency. The consistency of the estimated efficiency scores are checked by examining their relationship with three traditional non-frontier measures of bank performance. In addition, the relationship between bank size and technical efficiency is examined. It is found that the overall technical efficiency of the banking industry of both countries improved gradually over the years, especially after 1995. Unlike public sector banks in India, public sector banks in Pakistan witnessed improvement in scale efficiency only. It is also found that banks are relatively more efficient in generating earning assets than in generating income. This is attributed to the presence of high non-performing loans. In addition, it is found that the gap between the pure technical efficiency of different size groups has declined over the years.
I . I N T R O D U C T IO N After decades of excessive government regulations and restrictions, the implementation of financial liberalization has brought substantial changes in the banking sector of developing countries: The sector has become relatively less state-directed, more competitive, and open to foreign banks and non-bank financial institutions.1 While considerable research has gone into the ‘macroeconomic’ impacts of these changes, only a handful of studies have empirically examined the impact of financial liberalization on the efficiency of banks in developing countries. Until recently, the empirical studies on the efficiency of banks have
primarily concentrated on the banking industry of developed countries, especially of the USA (see Berger and Humphrey, 1997; Isik and Hassan, 2003). This paper contributes to the burgeoning literature on the efficiency of banks in developing countries by providing a comparative analysis of the evolution of the technical efficiency of commercial banks in two South Asian economies, namely India and Pakistan, before and after the implementation of financial liberalization in the early 1990s. Financial liberalization is an integral element of the ongoing Economic and Structural Reforms (ESRs) in India and Pakistan (see Ahluwalia, 1999; Zaidi, 1999). Financial liberalization includes, inter alia, a gradual deregulation
*Corresponding author. E-mail:
[email protected] 1 See Fry (1995) for changes in the financial sector in developing countries after the implementation of financial liberalization programmes. Applied Economics ISSN 0003–6846 print/ISSN 1466–4283 online # 2004 Taylor & Francis Ltd http://www.tandf.co.uk/journals DOI: 10.1080/000368404200068638
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1916 of interest rates and state-directed credit policies, reduction in banks’ reserve requirements, entry of non-bank financial institutions, reduced restrictions on entry and operations of private (domestic and foreign) banks, and privatization of public sector banks2 (see SBP, 2000; Arun and Turner, 2002). Prior to the financial liberalization, the governments of India and Pakistan followed a policy of ‘social control’ that emphasized controlling banks’ operations either through state directives or through nationalization. In India, the 14 largest commercial banks were nationalized in 1969, and six more banks in 1980. In Pakistan, all domestic private banks were nationalized and merged during the mid 1970s to form five large public sector banks. By the late 1980s, these nationalized banks controlled more than 90% of the total deposits and the earning assets of the banking industry (see SBP, 2000; Arun and Turner, 2002). Operations of private sector banks, especially foreign banks, were restricted to a few large cities only. In addition, the governments stipulated lending targets to priority sectors (e.g. agriculture), imposed low ceilings on interest rates on loans and deposits, directed public sector banks to open branches in rural and semi-urban areas, and made it mandatory for banks to hold government securities in their asset portfolios in order to finance growing fiscal deficits (see Sen and Vaidya, 1998; Zaidi, 1999). Under this policy of ‘social control’, the governments determined the direction and prices of financial services provided by the banking industry; banks themselves had little control over their inputs and outputs. This policy of ‘social control’, though augmented deposit mobilization and provision of loans to the priority sectors, resulted in a deterioration of banks’ profitability and capital base and in an unsustainable accumulation of non-performing loans in public sector banks’ asset portfolios. In both countries, on average the share of non-performing loans in total loans and advances in public sector banks was above 20% (see SBP, 2000; Arun and Turner, 2003). In this context, a key objective of the financial liberalization of the early 1990s was to revive the banking industry by reducing government regulations and restrictions, underpinning on-site and off-site bank supervision, strengthening the capital base of public sector banks, privatizing public sector banks, and elevating competition through entry of new foreign and domestic private banks. These measures are expected to enable and encourage banks to enhance their efficiency, i.e. their ability to transform inputs into outputs, which, in turn, is expected to enhance economic growth by increasing the volume of funds intermediated in the economy.
A. Ataullah et al. Although some studies have examined the performance of commercial banks in India, only a recent study by Kumbhakar and Sarkar (2003), by using econometric technique to measure the total factor productivity of domestic banks, has analysed a time period long enough to shed some light on the impact of financial liberalization. In the case of Pakistan, only one study has measured the efficiency of commercial banks by using a parametric Distribution Free Approach (DFA). These studies are extended by employing non-parametric Data Envelopment Analysis (DEA) to calculate the efficiency of commercial banks in India and Pakistan before and after the liberalization. Following Bauer et al. (1998), the consistency of the DEA-based efficiency scores are checked by examining their relationship with three traditional nonfrontier based performance indicators. In addition, the relationship between size and the pure technical efficiency of banks is examined.The comparative analysis of Indian and Pakistani banking industries suggests that a similar financial liberalization programme in two developing countries may lead to different outcomes in terms of its success in fostering the technical efficiency of banks operating in those countries. The rest of the paper is structured as follows. Section II briefly reviews some recent studies on financial liberalization and the efficiency and productivity of banks in developing countries. Section III provides an overview of the measurement of technical efficiency using DEA. Section IV presents empirical findings. Section V concludes.
II. FINANCIAL LIBERALIZATION AND THE EFFICIENCY OF BANKS IN DEVELOPING COUNTRIES Although many developing countries initiated financial liberalization in the early 1980s, only recently have a few studies examined its impact on the efficiency and productivity of banks operating in these countries. These studies postulate that financial liberalization enhances the efficiency and productivity of banks by creating a competitive and flexible environment in which banks have more control over their operations. For example, financial liberalization allows banks to set interest rates on their assets and liabilities that were previously determined by the government. The empirical evidence on the impact of financial liberalization on the efficiency of banks is mixed. Leightner and Lovell (1998) measure the total factor productivity growth of Thai banks during 1989–1994 to evaluate the financial liberalization of the late 1980s. Using two alternative input–output models, one based on commercial banks’
2 During the sample period used in this study, the legislative changes in India allowed public sector banks to tap the capital market to the extent of 49% of their total capital (see Bhide et al., 2002). In Pakistan in contrast, a major portion of two public sector banks, Muslim Commercial Bank and Allied Bank of Pakistan, was sold to private investors (see SBP, 2000).
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Financial liberalization and bank efficiency objective to generate revenue and the other based on central bank’s objective to intermediate funds, they construct a Malmquist total factor productivity index for Thai banks. Leightner and Lovell find that the productivity of banks improved after the liberalization. Using a similar approach, Gilbert and Wilson (1998) also find that financial liberalization in Korea had positive impacts on the productivity of the Korean banking industry during the early 1990s. In contrast, Hao et al. (2001) use a parametric Stochastic Frontier Approach (SFA) to measure the efficiency of Korean banks, and do not find any positive relationship between the measured efficiency and financial liberalization. Isik and Hassan (2003) employ DEA to construct a Malmquist total factor productivity index for Turkish banks during 1980–1990, and suggest that the performance of banks improved after the implementation of financial liberalization. In contrast, Yildirim (2002) analyses the technical efficiency of Turkish banks between 1988 and 1999 using non-parametric DEA, and finds that the Turkish banks did not achieve any sustained efficiency gains over the sample period. Although some recent studies have measured the efficiency of Indian banks, their analysis is restricted either to the pre-liberalization period (see Bhattacharyya et al., 1997) or to a single year in the post-liberalization period (see Sathye, 2003). Only a recent study by Kumbhakar and Sarkar (2003) investigates the impact of financial liberalization by calculating growth in the total factor productivity (TFP) of 23 public sector banks and 27 private domestic banks during 1985–1996 (their study excludes foreign banks). Kumbhakar and Sarkar (2003) measure TFP growth by estimating a translog cost function, and decompose TFP growth into a technological change, a scale, and a miscellaneous component. They find considerable over-employment of labour in Indian banks and find little evidence to suggest that the liberalization enhanced the productivity of banks, especially that of public sector banks. Kumbhakar and Sarkar suggest that public sector banks in India have become too dominant to feel the impact of changes in the economic environment brought about by financial liberalization. Hardy and de Patti (2001) examined the cost and revenue efficiency of 33 banks in Pakistan during 1981–1998 by utilizing DFA. They find that during the post-liberalization period, both costs and revenues of banks increased, and therefore conclude that the benefits of improvements in revenue efficiency were transferred to customers, e.g. borrowers and depositors. However, it is submitted here that during the post-liberalization period, the interest rate margin of the banking industry in Pakistan increased considerably (see SBP, 2000). That is, banks 3
charged higher interest rates on their loans, but did not transfer the higher rates to their depositors. Moreover, there has been constant criticism in the domestic media on the quality of services provided by Pakistani banks, especially by public sector banks. Therefore, it may be difficult to justify Hardy and de Patti’s conclusion that benefits of improvement in banks performance, if any, were transferred to customers.
I I I . M EA S U R E M E N T O F T E C H N I C A L EFFICIENCY USING DEA The technical efficiency of a firm refers to its success/ failure in transforming its inputs into outputs. It is a relative concept as its measurement requires a standard of performance against which the success/failure of the firm is assessed. Broadly speaking, the contemporary empirical studies employ parametric or non-parametric frontier techniques to measure the efficiency of firms relative to an estimated ‘best-practice’ frontier that represents the optimal utilization of resources (see Berger and Mester, 1997).3 The parametric approaches usually involve econometric estimation of a prespecified stochastic production, cost or profit function (see Bauer et al., 1998, pp. 93–96). In contrast, non-parametric DEA does not require the specification of a particular functional form for the frontier. Instead, the production frontier is constructed through a piecewise linear combination of the actual input–output correspondence set that envelops the input–output correspondence of all the firms in the sample (see Thanassoulis, 2001). Hence, efficiency measurement is not contaminated by a possible misspecification of the production function (see Bauer et al., 1998). The main weakness of the DEA is that measurement error and statistical noise are assumed to be non-existent (Berger and Mester, 1997; Yildirim, 2002). In this paper DEA is employed for two reasons. First, as discussed above, the existing studies have already employed parametric techniques to investigate the impact of financial liberalization on the performance of banks in India and Pakistan. Therefore, it is pertinent to examine whether the efficiency scores obtained through DEA calculation support the conclusions reached by the existing studies. Second, as Bhattacharyya et al. (1997, p. 335) point out, regulations and other market imperfections in developing countries (especially decades of excessive regulation in the banking industry) may distort input/output prices, and, therefore, may complicate the measurement cost and/or profit function using parametric approaches.
Parametric techniques are: Stochastic Frontier Approach, Distribution Free Approach, and Thick Frontier Approach. Non-parametric approaches are: Data Envelopment Analysis, and Free Disposal Hull (see Bauer et al., 1998).
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A. Ataullah et al.
A simple DEA model Consider N decision-making units (DMUs) (commercial banks in the present case) producing J outputs using I inputs (see Thanassoulis, 2001 for details). To measure the efficiency of a DMU, Charnes et al. (1978) proposed the use of the maximum of the ratio of weighted outputs to weighted inputs for that DMU, subject to the condition that similar ratios for all other DMUs in the sample be less than or equal to 1. Mathematically, PJ o o j¼1 wj yj max eo ¼ PI o o ð1Þ i¼1 vi xi subject to PJ o n j¼1 uj yj PI o n 1 i¼1 vi xi voi , uoj 0
n ¼ 1, . . . , N i ¼ 1, 2, . . . , I;
j ¼ 1, 2, . . . , J
where ynj and xni are positive known outputs and inputs, respectively, of the nth DMU, and voi , uoj are the variable weights to be determined by solving linear problem 1. The DMU being measured is indicated by the index ‘o’. The optimization is defined for every DMU in the sample. If the efficiency score eo ¼ 1, the DMUo is 100% efficient within the sample; otherwise it is DEA inefficient. Charnes et al. (1978) transformed the above into the following linear programming problem: XJ o o max ho ¼ uy ð2Þ j¼1 j j subject to XI
vo x o i¼1 i i
¼1
n ¼ 1, . . . , N i ¼ 1, 2, . . . , I
XJ j¼1
voi "
uoj ynj
XI
vo xn i¼1 i i
0
uoj "
j ¼ 1, 2, . . . , J
" is an arbitrary small positive number introduced in the above problem to ensure that all of the known inputs and outputs have positive weights. When h ¼ 1, DMU is DEA efficient; otherwise it is DEA inefficient with respect to other DMUs in the sample. The problem is solved N times to obtain an efficiency score for each DMU in the sample. The DEA is carried out by assuming either Constant Returns to Scale (CRS) or Variable Returns to Scale (VRS). The estimation with these two assumptions allows the overall technical efficiency (OTE) to be decomposed into two collectively exhaustive components: pure 4
technical efficiency (PTE) and scale efficiency (SE) (see Thanassoulis, 2001). PTE refers to managers’ capability to utilize firms’ given resources, while SE refers to exploiting scale economies by operating at a point where the production frontier exhibits constant returns to scale. Input–output specification and data source The first step in measuring efficiency using DEA is to specify the inputs and outputs of banks.4 Following Leightner and Lovell (1998) two different, albeit complementary, input–output models for banks in India and Pakistan are specified: Model A (loan-based model) postulates that banks incur operating and interest expenses to produce loans and advances, and investments; Model B (income-based model) postulates that banks incur operating and interest expenses to produce interest and noninterest income. The analysis covers the period from 1988 to 1998. The sample includes all the commercial banks in India and Pakistan for which data for at least three years are available. This will allow, to some extent, one to see whether the efficiency of a bank is due to the capability of managers or due to some random factors that cannot be controlled for in the DEA calculations. In the case of both the countries, the commercial banks included in the sample control over 95% of total assets, deposits, and loans of the commercial banking industry. Data for commercial banks in Pakistan are obtained from various issues of Banking Statistics of Pakistan published annually by the State Bank of Pakistan. In the case of India, data from 1990 to 1998 are obtained from the recently uploaded data set on the website of the Reserve Bank of India,5 and data from 1988 and 1989 are obtained from various issues of Financial Analysis of Banks published by the Indian Banks’ Association. Banks having zero recorded values for one or more outputs or inputs variables in any year are excluded from the sample for that year in recognition of the fact that the DEA is sensitive to outliers (see Yildirim, 2002, p. 2294).
I V . E M P IR I C A L F IN D I N G S Trends in the efficiency of commercial banks in India and Pakistan Tables 1 and 2 present the average annual efficiency scores of commercial banks in India and Pakistan, respectively, using output-oriented DEA calculated separately for
In the case of banks, there is no agreement on the inputs and outputs. This disagreement is due to dual nature of some of the services that banks provide. For example, bank deposits can be regarded as banks’ inputs as they are the main inputs for loan production. On the other hand, high value added deposits, like integrated saving and checking accounts, can be regarded as banks’ outputs. See, for example, Berger and Humphrey (1997) for various approaches to specify banks’ inputs and outputs, especially the ‘intermediation approach’ and the ‘production approach’. 5 Website: http://www.rbi.org.in/annualdata/index.html
1988
1989
Loan-based model (model A) OTE ABs 67.0 67.3 PSBs 73.3 75.1 DPBs 63.1 62.9 FBs 64.7 64.1 PTE ABs 85.0 84.8 PSBs 89.2 90.2 DPBs 81.3 83.6 FBs 84.6 80.6 SE ABs 78.8 79.3 PSBs 82.1 83.2 DPBs 77.6 75.2 FBs 76.5 79.5 Income-based model (model B) OTE ABs 58.0 58.2 PSBs 49.9 52.5 DPBs 59.3 57.3 FBs 64.9 64.7 PTE ABs 80.1 79.1 PSBs 81.0 81.5 DPBs 79.9 75.7 FBs 79.4 80.2 SE ABs 72.5 73.6 PSBs 61.6 64.4 DPBs 74.2 75.8 FBs 81.7 80.7
1990
1991
1992
1993
1994
1995
1996
1997
1998
1988–1991
1992–1994
1995–1998
70.2 75.5 67.9 67.2 87.2 93.0 85.2 83.5 80.4 81.2 79.6 80.5
67.0 75.5 61.7 63.7 84.8 90.6 80.9 82.7 78.8 83.3 76.2 77.0
68.7 78.3 67.2 60.6 88.3 92.1 89.8 83.0 77.6 85.0 74.8 73.0
71.0 76.9 61.9 74.2 88.3 91.9 85.0 87.9 80.3 83.7 72.8 84.4
70.5 73.7 68.1 69.7 83.1 90.3 76.6 82.5 85.0 81.6 88.9 84.5
79.1 79.5 75.8 82.1 88.6 92.5 87.3 85.9 89.4 85.9 86.8 95.6
80.4 80.4 74.7 86.1 88.8 92.8 82.5 91.1 90.6 86.6 90.6 94.5
78.0 82.7 76.1 75.1 88.4 93.5 82.7 89.0 88.3 88.5 92.0 84.4
82.1 83.2 79.5 83.4 91.4 94.8 87.2 92.2 89.8 87.8 91.2 90.5
67.9 74.8 63.8 64.9 85.5 90.8 82.8 82.8 79.3 82.5 77.1 78.4
(15.0) (7.2) (20.0 (7.7) (13.7) (8.5) (17.0) (11.2) (12.1) (7.7) (10.8) (8.1)
70.1 76.3 65.7 67.9 86.5 91.4 83.6 84.4 80.9 83.4 78.5 80.4
(13.5) (6.1) (17.8) (8.8) (12.6) (8.1) (15.8) (10.4) (10.9) (6.5) (9.2) (7.4)
79.9 81.4 76.5 81.6 89.3 93.4 84.9 89.5 89.5 87.2 90.1 91.1
(17.8) (6.7) (24.4) (13.9) (17.1) (6.7) (21.2) (14.0) (14.5) (4.9) (18.2) (9.1)
59.3 52.5 58.3 67.0 81.4 83.5 79.3 81.4 72.9 62.9 73.5 82.3
60.6 53.8 61.2 66.9 81.8 82.1 80.1 83.1 74.1 65.5 76.3 80.5
59.6 57.2 57.7 63.8 81.1 84.2 77.6 81.4 73.5 67.9 74.3 78.4
60.5 52.0 61.6 68.0 82.6 84.3 81.4 82.0 73.4 61.7 75.7 82.9
62.6 54.3 64.7 68.8 83.7 86.1 79.6 85.5 75.0 63.1 81.3 80.5
67.9 57.1 64.4 82.3 86.4 87.3 82.3 89.6 78.5 65.4 78.3 91.9
65.6 54.6 70.7 71.4 84.7 89.1 81.9 83.0 77.9 61.3 86.3 86.0
69.4 60.8 70.8 76.7 86.7 89.0 85.5 85.5 80.3 68.3 82.8 89.7
71.6 66.2 72.2 76.5 87.8 90.1 86.0 87.4 81.6 73.5 83.9 87.5
59.0 52.2 59.0 65.9 80.6 82.0 78.7 81.0 73.3 63.6 74.9 81.3
(14.7) (7.4) (17.3) (8.4) (10.4) (12.7) (7.8) (8.5) (9.3) (8.5) (13.9) (4.5)
60.9 54.5 61.3 66.8 82.5 84.9 79.5 82.9 74.0 64.2 77.0 80.6
(15.7) (6.1) (14.3) (6.4) (13.2) (9.7) (8.8) (12.6) (13.8) (5.1) (12.3) (8.7)
68.6 59.5 69.4 76.6 86.4 88.9 83.9 86.3 79.6 67.0 82.8 88.7
(18.4) (5.4) (23.0) (14.9) (15.5) (7.5) (18.9) (14.0) (18.2) (4.1) (18.6) (13.0)
Financial liberalization and bank efficiency
Table 1. Technical efficiency of commercial banks in India
Note: Abs ¼ All banks; PSBs ¼ public sector banks; DPBs ¼ domestic private banks. Foreign Banks: OTE ¼ overall technical efficiency; PTE ¼ pure technical efficiency; SE ¼ scale efficiency. Figures in parentheses are standard deviations.
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1920
Table 2. Technical efficiency of banks in Pakistan 1988
1989
Loan-based model (model A) OTE ABs 41.1 37.9 PSBs 38.5 33.5 DPBs n.a. n.a. FBs 43.7 42.3 PTE ABs 88.1 88.9 PSBs 92.6 93.2 DPBs n.a. n.a. FBs 83.7 84.6 SE ABs 46.9 43 PSBs 41.6 35.9 DPBs n.a. n.a. FBs 52.2 50 Income-based model (model B) OTE ABs 45.8 51.7 PSBs 33 37.5 DPBs n.a. n.a. FBs 58.5 66 PTE ABs 83.7 87.5 PSBs 85.6 89.3 DPBs n.a. n.a. FBs 81.9 85.7 SE ABs 55 59.5 PSBs 38.6 42 DPBs n.a. n.a. FBs 71.4 77
1990
1991
1992
1993
1994
1995
1996
1997
1998
1988–1991
1992–1994
1995–1998
35.6 34.4 n.a. 36.8 86.7 91.2 n.a. 82.1 41.2 37.7 n.a. 44.8
40.2 38.4 n.a. 42 86.9 89.5 n.a. 84.3 46.4 43 n.a. 49.8
44.1 32.5 48.2 51.7 76 84 65.3 78.7 59.4 38.6 73.8 65.7
35.9 28.4 36.7 42.5 68.1 78.5 51.5 74.3 54.9 36.1 71.3 57.2
37.6 25.5 42.5 44.9 74.8 86.1 56.6 81.6 53.2 29.6 75.1 55
39.9 33.5 46.4 39.8 71.3 75.4 58.5 79.9 57.8 44.4 79.3 49.8
42.4 41.7 45 40.4 73.3 74.1 65.5 80.2 58.4 56.2 68.7 50.4
53.9 45.7 55.8 60.3 77.9 76.7 70 87 69.6 59.6 79.7 69.3
57.4 48.6 59.7 63.9 80.1 78.1 72.6 89.6 71.9 62.2 82.3 71.2
38.6 (17.32) 36.1 (10.45) n.a. (n.a.) 41.1 (14.37) 87.6 (14.65) 91.6 (5.74) n.a. (n.a.) 83.7 (19.21) 44.3 (12.21) 39.4 (9.34) n.a. (n.a.) 49.1 (13.36)
39.0 28.6 42.2 46.2 72.9 82.8 57.5 78.1 55.8 34.6 73.4 59.1
(15.0) (9.0) (n.a.) (12.4) (12.7) (5.0) (n.a.) (16.6) (10.6) (8.1) (n.a.) (11.5)
47.8 42.0 51.4 49.9 75.6 76.1 66.4 84.1 64.1 55.2 77.3 59.3
(10.8) (6.5) (n.a.) (8.9) (9.1) (3.6) (n.a.) (11.9) (7.6) (5.8) (n.a.) (8.3)
46 31.6 n.a. 60.3 84.3 86.2 n.a. 82.4 54.9 36.6 n.a. 73.2
47.4 37.3 n.a. 57.5 84.5 88.5 n.a. 80.6 56.7 42.1 n.a. 71.4
52.6 37.4 61.8 58.8 78.5 76.7 80.1 78.7 66.8 48.7 77.1 74.7
52.8 37.7 55.2 65.4 82 79.4 80.3 86.2 64 47.5 68.7 75.9
48.4 37.2 45.4 62.6 76.8 79.1 69.1 82.1 63 47 65.7 76.3
56.9 37.3 60.3 73.1 79.3 80.5 72.2 85.1 71.9 46.3 83.5 85.9
63.8 34.5 80.9 75.9 84.9 75.8 88.9 89.9 73.6 45.5 91 84.4
64.4 35.3 82.4 75.5 86.4 81.2 86.4 91.5 73.8 43.4 95.4 82.5
65.6 40.9 79.3 76.6 85.2 79.6 87.5 88.4 76.2 51.4 90.6 86.6
47.7 (17.7) 34.7 (5.2) n.a. (n.a.) 60.5 (17.2) 85.0 (16.7) 87.4 (5.6) n.a. (n.a.) 82.6 (17.8) 56.5 (18.2) 39.8 (19.9) n.a. (n.a.) 73.2 (9.1)
51.2 37.4 53.7 62.2 79.0 78.4 76.3 82.3 64.6 47.7 70.3 75.6
(18.7) (5.5) (n.a.) (18.1) (17.6) (16.4) (n.a.) (18.9) (19.3) (21.0) (n.a.) (9.6)
62.6 36.9 75.1 75.2 83.9 79.3 83.5 88.7 73.9 46.6 90.0 84.8
(16.1) (4.8) (n.a.) (15.6) (15.2) (14.1) (n.a.) (16.2) (16.6) (18.1) (n.a.) (8.3)
Note: ABs ¼ All banks; PSBs ¼ public sector banks; DPBs ¼ domestic private banks. Foreign banks: OTE ¼ overall technical efficiency; PTE ¼ pure technical efficiency; SE ¼ scale efficiency. Figures in parentheses are standard deviations. n.a. refers to the time period when domestic private banks were not allowed to operate in Pakistan.
A. Ataullah et al.
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Financial liberalization and bank efficiency each country using annual frontiers.6 The banking industry has been divided into three groups according to ownership: public, foreign, and domestic private. The whole period (i.e. 1988–1998) is divided into three subperiods: 1988– 1991 refers to the pre-liberalization period, 1992–1994 is considered as the transition period, and 1995–1998 represents the post-liberalization period when the liberalization programme is expected to have some impact on the efficiency of banks. The banking industry in both the countries exhibits very low OTE, and witnessed little improvement until 1995. In the case of India, this is consistent with Kumbhakar and Sarkar’s (2003) findings. In both the countries, the major source of low OTE was low SE, which has not been examined by the previous empirical studies on Indian and Pakistan banking industry. The low level of SE could be attributed to governments’ restrictions on private banks to extend their operations, and governments’ direction to public sector banks to extend their branch network to rural and suburban areas. These policies hindered banks’ ability to exploit scale economies. The limited improvement in OTE until 1995, it is submitted, suggests that banks adapted slowly and cautiously to the changes brought about by the liberalization (see Bhattacharyya et al., 1997). The average OTE of the Indian banking industry improved from 67.9% (Model A) and 59.0% (Model B) in the pre-liberalization period to 79.9% (Model A) and 68.6% (Model B) in the post-liberalization period. In case of Pakistan, the OTE of the banking industry increased from 38.6% (Model A) and 47.7% (Model B) in the preliberalization period to 47.8% (Model A) and 62.6% (Model B) in the post-liberalization period. Unlike in India, where improvement in the OTE was due to improvement in both PTE and SE, the improvement in the OTE of the banking industry in Pakistan was due only to improvement in SE, especially after 1995–1996 when the government allowed public sector banks to reduce the number of employees and close unprofitable branches in rural areas. In Model A, the average PTE of the banking industry in Pakistan declined from 87.6% during the pre-liberalization period to 75.6% during the post-liberalization period, while in Model B PTE declined from 85.0% (pre-liberalization) to 83.9% (post-liberalization). This decline in PTE in the banking industry was due to a sharp decline in the PTE of public sector banks even when the PTE of foreign banks and private domestic banks improved during this period. The PTE of public sector banks declined from 91.6% (Model A) and 87.4% (Model B) in the preliberalization to 76.6% (Model A) and 79.3% (Model B) in the post-liberalization period. It could be argued that,
6
unlike in India, the financial liberalization process in Pakistan failed to encourage the managers of public sector banks to utilize their resources more efficiently. This could be due to the fact that although both the countries followed a similar financial liberalization programme, the economic environment in Pakistan was marred by high political instability during the 1990s. This high political instability could have undermined the Pakistani government’s commitment to the liberalization process, and, therefore, failed to encourage public sector banks to enhance their resource utilization. Like Kumbhakar and Sarkar (2003), it is found that, unlike private sector banks, public sector banks in both India and Pakistan were relatively slow in improving their efficiency over the years. Following Kumbhakar and Sarkar, it is suggested that this group has become too dominant (controlling more than 90% of the assets of the banking industry) to feel any need to quickly transform itself in the face of competition from smaller foreign and private domestic banks. Also, public sector banks’ huge non-performing loans and extensive branch-networks might have made them inflexible even if they wanted to adapt to the changing environment. However, after 1995–1996, public sector banks exhibit more improvement in their efficiency. This could be due to a slight intensification of competition as a result of adopting new financial technology (e.g. computerization of bank branches and Automated Teller Machines) and the introduction of new financial products (e.g. credit cards and car financing schemes) by private banks, especially foreign banks. Private sector banks, especially foreign banks, in both the countries witnessed improvement in both PTE and SE. Non-performing loans and the gap between the two input–output models As non-performing loans (NPLs) are a major problem for the banking industry in India and Pakistan, it is crucial to examine their impact on the evolution of technical efficiency. This could be achieved by using NPLs as another input, usually non-discretionary, that banks use (see Berger and Humphrey, 1997). However, as the bank-level data on NPLs are not available for India and Pakistan, an attempt is made to examine their impact by taking a closer look at the difference between the efficiency scores obtained from the two models, i.e. the loan-based model and the income-based model. Model A postulates that banks produce loans, advances and investments with given resources, while Model B postulates that banks produce income with given resources. Consequently, the outputs of Model B (income) depend primarily on the outputs of Model A
For DEA estimation, we use DEAP 2.1 is used, developed by Tim Coelli of the University of New England, Australia.
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A. Ataullah et al.
Table 3. Correlation between frontier and non-frontier based measures India
Pakistan
Model A PTE ROA TC/TA TC/TR
0.075* 0.166* 0.075**
Model B SE 0.035* 0.060** 0.055
PTE 0.304** 0.088* 0.321**
Model A SE 0.050* 0.046 0.095**
PTE
Model B SE
0.014* 0.019* 0.099**
0.002 0.032** 0.055*
PTE 0.015** 0.096 0.264**
SE 0.094* 0.032 0.072**
Note: PTE ¼ pure technical efficiency; SE ¼ scale efficiency; ROA ¼ return on assets; TC/TA ¼ total costs/total assets; TC/TR ¼ total costs/total revenue. *Spearman Rank Correlation is statistically significant at 5% level. **Spearman Rank Correlation is statistically significant at 1% level.
(loans, advances and investments).7 However, if banks are unable to enhance their income-based efficiency even when they are able to improve their loan-based efficiency, this could be due to the presence of high NPLs in their portfolios. In the case of Indian public sector banks, at the start of financial liberalization in 1991–1992, the NPLs as a percentage of total advances were around 24% (see Bhide et al., 2002). This percentage, however, gradually declined to 16% in 1997–1998. The gap between the efficiency scores obtained from two input–output models follows a similar trend: during the early years, public sector banks were much more efficient in generating loans, advances and investments than in generating income. During the postliberalization era, however, this gap gradually declined. In the case of the public sector banks in Pakistan, the level of NPLs increased after the implementation of the financial liberalization from around 18% of total advances to around 26% (see SBP, 2000). The gap in the efficiency scores of Pakistani public sector banks also increased over the years. A similar gap between the efficiency scores of private sector banks also exists in the two countries. However, as the level of NPLs of private banks is much lower than in the public sector banks, the gap between the efficiency scores obtained from the two models is also lower. This gap in the efficiency scores from the two models may reflect the impact of the presence of high NPLs. That is, over the years, the presence of NPLs impeded banks’ ability to generate income even when they were relatively more efficient in generating earning assets.8 It could be argued that if the liberalization programme fails to enhance the efficiency of banks to generate income from their resources, it could, in the medium- and long-run, impede
their ability to intermediate between savers and borrowers and to enhance the quality of their services, which, in turn, may negatively influence the process of economic growth.
Consistency of the DEA efficiency scores As suggested by Bauer et al. (1998), for the frontierbased efficiency scores to be useful, the estimated scores should be positively correlated with the traditional nonfrontier based measures of performance used by regulators, managers, and industry consultants: ‘Positive rank-order correlations with these measures would give assurance that the frontier measures are not simply artificial products of the assumptions made regarding the underlying optimisation concept’ (Bauer et al., 1998, p. 108). Table 3 presents the Spearman Rank correlations between the PTE and SE of the banking industry in India and Pakistan generated by DEA and three non-frontier based measures of bank performance, namely return on assets (ROA), total operating and interest cost per rupee of assets (TC/TA), and total cost per rupee of revenue (TC/TR). The first measure is expected to have a positive correlation with the frontierbased efficiency scores, while the latter two are expected to have a negative correlation. The results in Table 3 suggest that most of the DEA-based efficiency scores are consistent with the three non-frontier based performance measures. Only in case of Pakistan, ROA is not consistent with the loan-based PTE of banks. That is, there is an unexpected negative correlation between loan-based PTE and ROA of banks. This could be due to the increasing NPLs of public sector banks in Pakistan, which suggests that even when banks were becoming more efficient in generating
7 This is especially the case for the commercial banks in developing countries where, unlike in developed countries, fee income is very low for commercial banks, and banks rely on traditional loans and government securities for income. 8 Another possible explanation for this gap between the efficiency scores obtained from the two models could be that banks transferred the benefits of improvement in their efficiency to their customers because though banks produced more loans, advances and investments (i.e. intermediated more funds) with given inputs, they did not extract more income from this intermediation process. However, increasing interest margins in both the countries, coupled with constant criticism in the domestic media about the quality of customer services provided by banks, especially public sector banks, may cast some doubt on this interpretation.
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Financial liberalization and bank efficiency loans and advances, the profitability of banks (i.e. their ROA) was deteriorating.
Bank size and pure technical efficiency The evidence on the relationship between size and PTE of banks is mixed. For example, in the context of Singaporean banking sector, Leong and Dollery (2002) find that larger banks, due to complexity of their operations, exhibit higher inefficiencies. In contrast, Yildirim (2002) find a positive relationship between size and PTE of Turkish banks. This positive relationship is attributed to larger banks’ market power and their ability to diversify credit risk in an uncertain macroeconomic environment. Berger and Humphrey (1997) also find a positive relationship between size and efficiency for the US banking industry. To examine the relationship between size and PTE, the banking industry in India and Pakistan was divided into four quartiles according to their size, where the size of each bank is determined by the total assets of that bank as a percentage of the total assets of the whole commercial banking industry. Figure 1 presents the evolution of PTE of different size groups. The figure suggests that in both the countries, during the pre-liberalization period, the largest
banks outperformed the smaller ones. However, over the years, the gap between the largest group and other groups declined, and in case of Pakistan, the gap virtually disappeared. The catching-up of smaller banks could be due to their higher flexibility, which allowed them to adapt to changes in the banking industry brought about by the financial liberalization programme. In contrast, the declining efficiency of the largest group, which primarily constitute public sector banks, could be due to their complex and politically-determined bureaucratic organizational structure that impeded their ability to keep up with smaller private domestic and foreign banks, which were quicker to adopt new financial technology (e.g. Automated Teller Machines) and to introduce new financial products (e.g. car financing and credit cards) (see SBP, 2000; RBI, various issues).
V. CONCLUSION This paper provides a comparative analysis of the evolution of the technical efficiency of the banking industry in India and Pakistan before and after the implementation of the financial liberalization programme of the early 1990s. Using non-parametric DEA, it is found that the
India Model A
Model B
75
75
% Efficiency
100
% Efficiency
100
50 25 0 1988
1990
1992
1994
1996
50 25 0 1988
1998
1990
Year
1992
1994
1996
1998
1996
1998
Year
Pakistan Model A
Model B 100
75
% Efficiency
% Efficiency
100
50 25 0 1988
1990
1992
1994
1996
1998
75 50 25 0 1988
Year
Largest 3rd Quartile
1990
1992 Year
2nd Quartile Smallest
Fig. 1. PTE scores by size quartile for commercial banks
1994
1924 overall technical efficiency of the banking industry improved following the financial liberalization, especially after 1995–1996. In the case of India, efficiency increased due to improvement in both pure technical efficiency and scale efficiency. In Pakistan, however, the increase in overall technical efficiency was due primarily to an improvement in scale efficiency. The results suggest that the efficiency of commercial banks is much higher in Model A, which uses earning assets as outputs, than in Model B, which uses income as output. This gap in efficiency scores obtained from the two models could be due to the presence of high nonperforming loans in the asset portfolios of banks in the two countries. It is argued that even when banks are becoming more efficient in increasing the quantity of loans, advances and investments, this efficiency is not being translated into higher efficiency in generating income. The results also suggest that the implementation of the financial liberalization closed the efficiency gap between large and small banks. The results suggest that there is still room for improvement in the efficiency of banks in both the countries. A major problem, however, is the presence of high non-performing loans. It should be noted that in developing countries the non-performing loans accumulate not only due to the ineffectiveness of banks’ managers but also due to other factors, such as economic downturns, politicians, pressure on banks, managers to provide loans to clients who may not have economically viable projects, or the weakness of legal system to support the recovery of non-performing loans (see, e.g. Bhide et al., 2002 for the limited success of Debt Recovery Tribunals in India). A step forward for the liberalization programme, therefore, is not only to deregulate interest rates and enhance the level of competition but also to strengthen the institutional structure to support good practices in the banking industry. A C KN O W L E DG E ME N T S Helpful comments by Canan Yildirim and the participants of the 4th Annual International Economics and Finance Society Conference, London 2003, are gratefully acknowledged. The usual disclaimer applies.
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