Trend in the Robust Non-Parametric Efficiency Estimates of Indian and Pakistani Banking Industries

7/31/2019 Trend in the Robust Non-Parametric Efficiency Estimates of Indian and Pakistani Banking Industries

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Trend in the Robust Non-Parametric Technical

Efficiency Estimates of Indian and Pakistani

Banking Industries

Yaseen Ghulam* and Shabbar Jaffry

*Corresponding Author

University of Portsmouth, Portsmouth Business School, Department of Economics,

Richmond Building, Portland Street, PO1 3DE, UK

Email: [email protected]

Abstract

This study evaluates the effect of regulatory reforms on the technical

efficiency of Indian and Pakistani banking industries. We use newly

developed unconditional, hyperbolic quantile estimator of Wheelock and

Wilson (Wheelock D. C., and Wilson P. W., 2008, Non-parametric,

unconditional quantile estimation for efficiency analysis with an

application to Federal Reserve check processing operations, Journal of

Econometrics, 209-225). Contrary to general perception we conclude

that technical efficiency of Indian banking had worsen in post reform

period, while opposite can be said for Pakistani banking industry. Our

results are somewhat consistent and robust to the choice of inputs and

outputs. We conclude that improvement in the technical efficiency of

Pakistani banking in post reform period is as result of more competitive

banking industry and broadening of ownership rather than just banking

reforms.

JEL classification

C14, G21, L32

Keywords

Efficiency; Productivity; Indian banking; Pakistani banking; reforms;

technical efficiency; banking industry

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1. Introduction

Empirical research on the measurement of efficiency and productivity of a

firm is well established and ever expanding and is increasingly getting

very popular within the government, policy makers, less technical

management gurus and others. The outcome is being used to reward

best performing units and managers. A large and expanding body of

literature is already in public domain alongside unpublished consultancy

reports that seek to estimate the rate a firm is able to translate a given

quantity of inputs into quantity of outputs compared to peer firms in the

same industry during a chosen time period. Despite of the fact that the

measurement of productivity and efficiency had become a standard

practice with huge methodological development in the last few years

debate on the appropriate method of efficiency is still not concluded.

Two methods of efficiency measurement are popular vis--vis

regression based stochastic frontier analysis (SFA) and mathematical

linear programming led data envelopment analysis (DEA). DEA is a part

of a family of non-parametric estimator while SFA belongs to parametric

family with well-established statistical inference ability. Despite its

statistical soundness SFA estimator is less straightforward when

encountered with multiple outputs alongside assuming priori functionalform (translog being the most flexible and popular functional form

among applied efficiency researchers). But flexibility of functional form

brings costs, which plague the derived results. We had seen a lot of

recent developments in the literature in examining the properties of

DEA estimator led by Simar and Wilson (1998, 1999a, 1999b, 2000a,

2000b, 2001a, 2001b), Daraio and Simar (2005), Daouia and Simar

(2007) and Wheelock and Wilson (2008). A family of non-parametric

estimator is in use by applied researcher i.e. DEA ,Free Disposal Hull

(FDH), order-m, and conditional and unconditional quantile hyperbolic

estimator. This study evaluates the effect of deregulation on Indian and

Pakistani banking industry by a family of non-parametric estimators

alongside the limitation of each estimator. Ours is the first study using

newly developed robust non-parametric estimator to estimate technical

efficiency of two emerging developing markets banking industries. The

results derived from the study are more robust to the choice of

input/output orientation and input/out selection

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The structure of the paper is as: next section presents overview of Indian

and Pakistani banking industries as well as a discussion of major policy

reforms in early and late 1990s. Section 2 provides summary of

empirical literature on two countries banking efficiencies analysis.

Conceptual framework and estimation techniques are discussed in

section 3. The last two sections are dedicated for our estimation results

and discussion and conclusions.

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2. Developments in the Indian and Pakistani Banking Industries

Last two decades had seen a dramatic shift in the way both countries

banking industries operate in term of operational decisions such as

interest rate setting, credit allocation or strategic decisions such as

branch expansion, mergers and acquisition and risk management

practices etc. The changes in regulatory practices had resulted in a

significant change in the ownership from public to private sector

through complete or partial privatisation and in some cases by stock

offering in both countries. This has resulted in the rationalisation of

branches and headcount and market driven interest rates on deposits

and loans. Banks are moving from historical focussed industrial sector

to consumer and home finance lending.

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Traditionally Indian banking industry had operated through a mix of public,

private and foreign ownerships. Despite of the fact that private

ownership was allowed in Indian banking industry, public sector banks

dominated the market share for the last so many decades. In post

reforms period, the dominance of public sector banks had declined

significantly but nonetheless still hold a larger share compare to both

foreign and domestic private owned banks. Pakistani banking industry

on the other hand operated with just two ownerships since

nationalisation of banks in 1970s. With domestic private ownership not

allowed, banking industry was dominated by public sector banks

(holding 95% of the market share) alongside with a number of foreign

owned banks with smaller market share and unable to exert any

influence in the direction of how the banks operated. Foreign banks

concentrated on customers in posh urban localities with perceived

better customer service compare to public sector banks with outdated

practices. However starting from 1990, series of regulatory reforms

were introduced to change the face of traditional banking industry.

Domestic private ownership of banks was allowed coupled with selling

of most of big public sector banks to private investors. Contrary to

Indian government who adopted less aggressive attitude in term oftransforming the ownership structure from public to private, Pakistani

government had been more proactive in selling the bank ownership.

New face banking industries are mix of three ownerships, but private

sector banks lead the way in case of Pakistani banking but for Indian

banking industry the role of public sector still dominant though with less

power compare to pre-reform period (see Table 1)1. This shift in

ownership alongside other regulatory reforms was introduced to

encourage competition which will lead to greater efficiency in the use of

bank resources and credit allocations.

1 For detail of regulatory reforms and importance of banking industry for both Indiaand Pakistan see Jaffry et al (2009).

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This study seeks to evaluate the effect of regulatory reforms on the Indian

and Pakistani banking industries operation efficiency during the early

and late 1990s. In the pursuit of getting more reliable estimates of

efficiency scores the study uses more robust non-parametric estimator.

Evaluation of performance for these two banking industries has become

a topic of great interest after a series of reforms were introduced in

both countries at the same time.

3.1 Effect of Regulatory Reforms on Banking-Review of Literature

Studies in regard to effect of regulatory reforms on the efficiency of Indian

and Pakistani banking had been forthcoming in recent years. Some

studies such as Bhaumik and Dimova used simple ratios of profitability

and noted a catching up phenomenon by initially less profitable banks in

post reforms period in particular, after 1999. Bhattacharyya et al (1997)

used DEA and concluded efficiency declined during their sample period

and contrary to general perceptions public sector banks were more

efficient compare to both privately and foreign owned banks. However,

this study suffers from curse of dimensionality and strict convexity

assumption of the envelope and conclusions drawn from this study may

be less reliable to conclude. Studies carried out by Saha and Ravisankar(2000) and Mukherjee (2002) concluded the almost same but also

suffers DEA related problems. Studies by Sathye (2003) and Das and

Ghosh (2004) during 1992-95 and 1996-99 supported the above

conclusion but did not address small sample, convexity and input/output

dimension issue. Shammugam and Das (2004) Sansarma (2006) while

estimating efficiency and productivity of Indian banks by using

parametric SFA estimator concluded that efficiency/productivity did

improved in post reforms period and public sector banks outperformed

private and foreign owned banks. However, priori functional form and

other econometric and theoretical assumptions render the conclusions

subject to debate.

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Some studies carried out to analyse the effect of regulatory reforms on

efficiency/productivity of Pakistani banks are also either inconclusive or

can be criticised in term of choice of inputs/outputs or estimator to

evaluate the efficiency and productivity. Among those studies carried

out for Pakistani banking is Pitti and Hardy (2005) who by using

parametric estimator for cost and profit efficiency concluded that banks

had become profit efficient and dispersions in efficiency score increased

immediately after first wave of reforms and most of the efficiency

improvement however was contributed by domestic privately owned

banks. Second wave of reforms though contributed a decline in profit

efficiency. These conclusions were largely supported by Iimi (2004) for

the sample period 1998-2001 in estimating the cost efficiency of

Pakistani banking with parametric estimator. DEA based studies of

Ataullah et al (2004), Hovercroft and Ataullah (2006) and Jaffry et al

(2007) all suggested improvement in efficiency/productivity in post

reforms period. However, in all of these above-mentioned studies, no

serious effort was made to correct the estimates for the issues

highlighted in the following section which costs serious doubts about the

conclusions drawn from these studies.

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Our study aims to address these issues and use a newly established robust

non-parametric hyperbolic -quantile estimator to assess the effect of

regulatory reforms on the efficiency of public, private and foreign owned

banks of India and Pakistan.

4. Methodology

Standard production possibility set consistent with micro economic theory

can be represented as:

(4.1)

Where vector of inputs are presented as and output vector

and as a upper boundary of is representation of production

frontier. Standard practice is to estimate distance from an arbitrary

point to (which is boundary of production possibility

curve) along a particular path. Input/output distance function of

Shephard (1970) is defined as:

(4.2)

(4.3)

Input distance function measures the distance from to in a direction

orthogonal to output vector while output distance function orthogonal to

input vector x. Under constant return to scale (CRS), output distance

function id reciprocal of input distance function. However, variable

return to scale implies significantly different results with the choice of

orientation (input or output) particularly with respect to the size of the

operation of a firm. Fre et. al. (1985) measured efficiency along a

hyperbolic path from a point to and represented as:

(4.4)

The above unknown true distance function of a production set are

estimated from a set of realized input/output

combination of a sample firm. Traditionally is replaced with an

estimator of the production set to obtain an estimator of input/output

oriented distance function estimates. Deprins et al (1984) proposed a

free disposal hull (FDH) of the observations as:

(4.5)

Assuming variable return to scale (VRS), DEA estimator is obtained byreplacing with convex hull of by:

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(4.6)

A lot of progress has been made so far to develop asymptotic properties of

DEA and FDH estimator. However, both estimators suffer from following

problems, which make the estimates derived from these estimators less

reliable and statistically meaningless.

DEA and FDH estimator convergence is strictly based on the few condition

being met such as )1(2

++

qp

n

for DEA and )(1

qp

n

+

for FDH estimator where n is

number of decision making units (DMU)

2

and p is number of inputs andq is number of outputs. Hence in a case where banks is producing 5

outputs using 3 inputs one would need many more observations to get

the convergence of DEA and FDH estimator which in our Pakistani and

Indian population of banks is not sufficient in pre and post deregulation

period. Further similar to typical pattern of high hetrogeniety in the size

of the banks, possibility an extreme observation in the sample is the

real possibility. Hence results derived from estimator such, as DEA and

FDH are likely to be biased upward or upward. Further, a very popular

parametric estimator is based on the idea of estimating a composite

error response function with error term based on the idea of Aigner et al

(1977) and Meeusen and Vanden Broeck (1997). Theoretical research

has however, proved that in case of extreme hetrogeniety, in the

sample translog functional form can lead to misspecification of model

and produces unreliable efficiency estimates (example of such studies

highlighting this issue include Cooper and Mclaren (1996), Banks et al

(1997), Wheelock and Wilson (2001) and Wilson and Carey (2004).

Wheelock and Wilson (2008) noted that extension of translog functional

form also does not guarantee robust estimates.

2 Number of banks in our study.

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Under non-parametric approach, production set is estimated by different

methods such as Free Disposal Hull (FDH) or convex hull of the FDH,

which is also called DEA. These estimators does not require any priori

functional form but eventually does not allow increasing return to scale

at different scales of operation. Recently a new estimator based on the

idea of partial frontier rather than the full envelope has been developed

such as order-m and order- (for details of these estimators

see Cazal et al (2002) for order-m estimator and Daouia (2003),

Aragan et eal (2005) and Daouia and Simar (2007) for conditional

order- and unconditional hyperbolic order- quantile where inputs

and outputs are adjusted simultaneously (hyperbolic) thus avoiding the

priori assumption of input/output orientations. In the following sectionwe provide the summary of derivation order- quantile estimator.

Quantile Estimator for Technical Efficiency Estimates

As per production possibility set in 4.1, we can define statistical model with

the assumption that i) production set is compact and free disposal ii)

sample observations are realisation of identically

independently distributed (iid) random variables with probability density

function with support vector . Any point can be said to be

on the frontier of if for any and that point can be

iii) it is assumed that at the frontier, the density is strictly positive

and sequentially lipschits continuous.

Now if we assume as the kth element of y, k=1,.,q and let

denote the vector y with the kth element

deleted. Now let assume is kth element of y for each k=1,., 1 definefunction as:

(4.7)

where production set can be defined by the function .

the density function above implies a probability function

(4.8)

the above function provides the probability of drawing an observation from

that weakly dominate DMU operating at

Now hyperbolic distance function can be written as:

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(4.9)

-quantile distance function can be defined as:

(4.10)

the hyperbolic -quantile is defined as:

(4.11)

For estimation of and corresponding and its empirical analogue is

defined as:

(4.12)

with as an indicator function. Now estimator of is obtained by

replacing with to achieve

(4.13)

and now by computation of becomes univariate issue and an exact

solution can be achieved to get the estimator. A non-parametric

estimator of the hyperbolic -quantile distance function is

given by:

(4.14)where integer part of denotes by , strictly positive integers and

represented by and jth largest element of set s, which is

), is .

An estimator of distance to the full frontier, is obtained by setting =1

and treating resulting estimator as . An alternative method is

that given a point , one can find initial values that would

bracket the solution so that and

and then solve for using bisection

method. Wheelock and Wilson (2008) developed an algorithm to

estimate using the bisection method to estimate -quantile

hyperbolic . We use hquan routine by Wilson (2006) FEAR

library to get our -quantile frontier estimates.

5.Data

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Our population of commercial banks covers 19 years of data (1985-2003) on

inputs and outputs, encompassing a significant part of the pre and post

reform period. The complete data set consists of unbalanced population

of 72 Indian (of which 1986: 27 public, 21 private and 16 foreign; 2003:

27 public, 18 private and 17 foreign) and 41 Pakistani (of which 1986: 5

public, 0 private and 14 foreign; 2003: 3 public, 18 private and 10

foreign) banks covering the period 1985-2003. The chosen period

covers both pre and post deregulation period and atleast 3 economic

cycles (1985-90,1991-99 and 2000-2003). For the purpose of this study

we treat loans (consumer, industrial and others), investment

(government and private), time deposits, saving deposits, demand

deposits and branches as outputs and inputs include number of

employees, value of building and equipment measured by fixed assets

and capital and reserves. The choice of inputs and outputs is somewhat

consistent to Jaffry et al (2009). In subsequent analysis we altered the

choice of inputs and outputs in our sensitivity analysis exercise. All the

nominal monetary values were converted to real numbers by deflating

by CPI alongside deleting few observations which were deemed extreme

values compare to all other years figures for a particular banks and

share of those deleted observation was 0.5% of the full population ofbanks. We use BANKSCOPE and other secondary data sources to

compile of our data for the analysis.

6.Results

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First we use and present estimates of technical efficiency derived through

simple traditional DEA and FDH estimators (Table 2). We estimated

efficiency scores for each year of our sample for each bank using that

year production frontier. We present both input and output oriented

estimates in our subsequent discussion. For Indian banking DEA

estimator show roughly 6% technical inefficiency irrespective of

input/output orientation with inefficiency going down on the average

from 8% to 5% in post reform period. While for Pakistan technical

inefficiency estimates based on input orientation are 9% on the average

during the sample period. Based on output orientation, we have

different conclusion. On the average inefficiency level had gone up from

5% to 11-12% in post reform period in input orientation, while output

orientation indicating an increase in efficiency in post reform period. our

FDH estimator tells different story i.e. where inefficiency estimates

appears to be almost zero with no change in post reform period for both

India and Pakistan.

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Small sample size and slow convergence rate may have rendered DEA and

FDH estimates doubtful. In our next step, to get meaningful estimates

with root-n convergence rate without imposing convexity assumption

like DEA we used -quantile estimator and reached on different results

for both Indian and Pakistani banking industries. Table 3 & 4 show -

quantile estimates for 2003. Table 3 presents estimates for Indian

banking where banks had been sorted as per their efficiency level (for

= 0.90). Estimates show a significant variation in efficiency levels with

most efficient bank using only 5.7% of the input amount and producing

roughly 17% more output than a bank (a hypothetical) located on =

0.90 quantile frontier along a hyperbolic path from the first bank. Least

efficient bank on the contrary used 65% of the input and produced 1.5

times of a bank on -quantile frontier. Not surprisingly, all the estimates

are less than 1 which indicates the fact that a very high percentage of

banks are located on FDH frontier. We also observe the fact that the

choice of does not change the ranking of banks significantly (with

efficiency level increasing as the value of increases). Foreign banks

appear to be more efficient compared to public and domestic privates

sector banks. On the average public sector banks seems to be least

efficient banks.

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Similar to India, Pakistani banks also show considerable variation in

efficiency levels with most efficient bank consuming 3% of the input and

producing 30 times more output than a hypothetical bank on =0.9

quantile frontier along a hyperbolic path. The least efficient bank used

80% of the inputs and produced 1.5 times more output than a bank

located -quantile frontier. Contrary to Indian banking, public sector

banks appear to be not highly inefficient compare to their counterpart in

private and foreign ownership (these results should be interpreted with

caution because the fact that by 2003 we had only 3 banks remaining in

public ownership with two banks offering specialised products to

treasury and women entrepreneurs). Variation in inefficiency however,

is more widespread compare to Indian banks. Table 5 shows lower andupper level bootstrap confidence level estimates and difference

between lower and upper level estimates appear to be statistically

significant.

Table 6 presents the results from our robust -quantile estimator. Indian

banking industry appears to be not responding to regulatory reforms

(efficiency marginally declined in post reform period as compare to pre-

deregulation period). On the contrary, Pakistani banks appears to be

responding favourable to regulatory changes where efficiency increased

around 7% in post reform period. When -quantile results are compared

with other estimator such as order-m in input and output orientation, our

conclusions does not change.

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We carried out sensitivity analysis by changing the selection of inputs and

outputs. In our model1 we dropped capital and reserves as inputs and

kept loans and investment as outputs. In model2, similar to model1 we

dropped capital and reserves and replaced it with number of branches

while loans and investments treated as outputs. In model3 we dropped

fixed assets and capital and reserves from the list of inputs and

replaced them with three deposits (fixed, saving and demand deposits)

alongside branches and employees, while loans and investment treated

as outputs. In all our permutation (Table 7), broad conclusions remains

same except for Indian banking, efficiency improved as per model2

contrary to base model (irrespective of orientation). For Pakistan,

irrespective of choice of inputs and outputs technical efficiency had

declined in post reform period (in particular after second generation of

reforms).

In subsequent analysis Table 8, we used -quantile hyperbolic estimates to

see the effect of regulatory reforms on the efficiency of banks classified

by three types of ownerships. For India, foreign and private sector banks

appear to be more efficient compared to publicly owned banks in both

pre and post reform period. However, public sector banks appear to betrying catching the private sector banks in post reform period. For

Pakistan however, for all three types of ownerships, banks improved

their efficiency in post reforms period. Further, public sector banks

appear to be more efficient compare to both foreign and domestic

private banks.

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We conclude that Pakistani banking industry had experienced a some

improvement in technical efficiency in post reforms period across all the

ownerships. However, we are unable to say the same for Indian banking

industry. Similar to some other studies, public sector Indian banks were

less efficient compared to domestic private and foreign owned banks.

Public sector banks though showed some improvement in efficiency

after second generation of reforms. We also noticed a greater level of

hetrogeniety in the efficiency levels across three ownerships and it

remained even after post reforms years for both Indian and Pakistani

banking industries. We conclude that introduction of domestic private

banks and full-hearted aggressive reforms had promoted arms length

banking which in term improved the resource use.

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with R. Socio-Economic Planning Sciences (in press).

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Table 1 Banking Structures of Indian and Pakistani BankingIndustries

Structure of banking systemPakistan (values in billion Pakistani rupees and shares in %)

19902003

banks value share banks valueshare

Assets

Private 17 33 7 32 133959-Domestic - - - 171122 47-Foreign 17 33 7 15277 12Public 7 465 93 5980 41

Total 24 499 100 372380 100

Source: Jaffry et al (2008)India (values in billion Indian rupees and shares in %)

1990 2003banks value share banks value share

Assets

Private 46 259 9 70 2500 15-Domestic 23 107 4 30 1200 7-Foreign 23 152 5 40 1300

8Public 28 2569 91 27 14665 85

Total 74 2828 100 97 17165 100Source: Jaffry et al (2008)

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Table 2: Indian and Pakistani Banking:Traditional Non-Parametric Technical Efficiency Estimates

DEAi DEAo FDHi FDHo DEAi DEAo FDHi FDHo

Indian Banking Pakistani Banking

1985 1.064 1.056 1.000 1.000 1.062 0.941 1.000 1.000

1986 1.085 1.075 1.000 1.000 1.068 0.941 1.000 1.000

1987 1.108 1.103 1.000 1.000 1.027 0.974 1.000 1.000

1988 1.073 1.065 1.000 1.000 1.027 0.973 1.000 1.000

1989 1.088 1.079 1.000 1.000 1.036 0.933 1.000 1.000

1990 1.090 1.086 1.000 1.000 1.052 0.964 1.000 1.000

1991 1.062 1.060 1.000 1.000 1.063 0.949 1.000 1.000

1992 1.082 1.080 1.001 1.000 1.103 0.929 1.000 1.000

1993 1.061 1.056 1.004 1.002 1.097 0.905 1.000 1.000

1994 1.054 1.049 1.003 1.001 1.098 0.912 1.000 1.000

1995 1.038 1.043 1.001 1.000 1.132 0.889 1.000 1.000

1996 1.044 1.044 1.000 1.000 1.123 0.906 1.000 1.000

1997 1.041 1.040 1.001 1.000 1.109 0.900 1.000 1.000

1998 1.036 1.036 1.000 1.000 1.115 0.902 1.000 1.0001999 1.047 1.048 1.000 1.000 1.100 0.905 1.000 1.000

2000 1.071 1.073 1.002 1.000 1.125 0.885 1.000 1.000

2001 1.052 1.059 1.002 1.000 1.115 0.896 1.004 0.994

2002 1.038 1.040 1.000 1.000 1.124 0.888 1.000 1.000

2003 1.055 1.055 1.000 1.000 1.156 0.864 1.000 1.000

1985-91 1.081 1.075 1.000 1.000 1.048 0.954 1.000 1.000

1992-03 1.052 1.052 1.001 1.000 1.117 0.898 1.000 0.999

1992-97 1.053 1.052 1.002 1.001 1.110 0.907 1.000 1.000

1998-

03 1.050 1.052 1.001 1.000 1.123 0.890 1.001 0.9991985-

03 1.063 1.060 1.001 1.000 1.091 0.919 1.000 1.000

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Table 3: Indian Banking: Robust Non-Parametric TechnicalEfficiency Estimates 2003

Bank Ownership a=0.85 a=0.90 a=0.95

Mashreq Bank foreign 0.022 0.057 0.118State Bank of India public 0.068 0.100 0.135Abu.dhabi CommercialBank foreign 0.078 0.103 0.156

Bank of America foreign 0.093 0.108 0.126Oman International Bank foreign 0.067 0.111 0.219Bank of Bahrain and Kuwait foreign 0.086 0.131 0.226Bank of Nova Scotia foreign 0.117 0.151 0.171Nainital Bank private 0.095 0.162 0.201Bank of Tokyo Mitsubishi foreign 0.127 0.162 0.312Credit Lyonnais foreign 0.091 0.169 0.250Citibank foreign 0.167 0.210 0.232Societe Generale foreign 0.099 0.217 0.500Ratnakar Bank Ltd private 0.141 0.240 0.314BNP Paribas foreign 0.202 0.248 0.318ABN Amro Bank foreign 0.199 0.253 0.293Deutsche Bank foreign 0.189 0.271 0.322

HSBC India foreign 0.271 0.292 0.442Sangli Bank Ltd private 0.295 0.330 0.470Punjab National Bank public 0.288 0.341 0.590Catholic Syrian Bank Ltd private 0.300 0.341 0.418Lakshmi Vilas Bank Ltd private 0.318 0.351 0.520Bharat Overseas Bank Ltd private 0.291 0.354 0.529Standard Chartered Bank foreign 0.341 0.379 0.416Punjab Sind Bank public 0.330 0.382 0.480Lord Krishna Bank Ltd private 0.258 0.383 0.757United Western Bank Ltd. private 0.331 0.410 0.474City Union Bank Ltd. private 0.270 0.419 0.641South Indian Bank Ltd private 0.388 0.419 0.556Bank of Rajasthan Ltd private 0.327 0.424 0.489

Canara Bank public 0.345 0.426 0.675State Bank of Saurashtra public 0.372 0.445 0.756Bank of India public 0.392 0.450 0.724Dhanalakshmi Bank Ltd private 0.277 0.451 0.491State Bank of Indore public 0.370 0.453 0.669American Express Bank foreign 0.408 0.457 0.512Federal Bank Ltd private 0.422 0.459 0.555Central Bank of India public 0.420 0.462 0.530

Tamilnad Mercantile Bank Ltd private 0.372 0.473 0.576State Bank of Mysore public 0.409 0.475 0.681ING Vysya Bank Ltd private 0.461 0.492 0.565Karnataka Bank Ltd private 0.440 0.500 0.582Karur Vysya Bank Ltd private 0.402 0.507 0.609UCO Bank public 0.456 0.509 0.589Syndicate Bank public 0.458 0.511 0.650Bank of Baroda public 0.394 0.513 0.676Indian Overseas Bank public 0.506 0.525 0.680State Bank of Bikaner and

Jaipur public 0.519 0.536 0.602State Bank of Travancore public 0.497 0.542 0.624Allahabad Bank public 0.458 0.542 0.721State Bank of Hyderabad public 0.467 0.550 0.619

Jammu and Kashmir Bank Ltd private 0.543 0.555 0.607Oriental Bank of CommerceLtd. public 0.459 0.561 0.596Union Bank of India public 0.498 0.570 0.685Bank of Maharashtra public 0.517 0.578 0.711

United Bank of India public 0.538 0.592 0.732Corporation Bank Ltd. public 0.570 0.610 0.675

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Dena Bank public 0.497 0.622 0.714Andhra Bank public 0.599 0.623 0.701State Bank of Patiala public 0.573 0.633 0.689Vijaya Bank public 0.551 0.650 0.784

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Table 4: Pakistani Banking: Robust Non-Parametric Technical EfficiencyEstimates 2003

Bank Ownership a= 0.85 a= 0.90 a= 0.95Rupali Bank Foreign 0.034 0.034 0.051Allied Bank of Pakistan Ltd. Private 0.126 0.140 0.207Habib Bank AG Zurich Private 0.148 0.194 0.306

Bank of Tokyo Foreign 0.107 0.200 0.278Bank of Punjab Private 0.186 0.211 0.269First Women Bank Ltd. Public 0.148 0.257 0.277Deutsche Bank A.G. Foreign 0.259 0.270 0.484Hong Kong & ShanghaiBank Foreign 0.240 0.276 0.671National Bank of Pakistan Public 0.165 0.286 0.491Habib Bank Ltd. Public 0.172 0.299 0.657Bank of Khyber Private 0.290 0.300 0.379Bank Alhabib Private 0.263 0.324 0.415Muslim Commercial BankLtd. Private 0.316 0.338 0.580Askari Commercial Bank Private 0.312 0.338 0.684Bank Alfalah Private 0.324 0.351 0.505PICIC Commercial Bank Private 0.296 0.354 0.442Albaraka Islamic Inv Bank Foreign 0.243 0.355 0.500Bank Indosuez Foreign 0.186 0.357 0.404Standard Chartered Bank Foreign 0.348 0.379 0.509Metropolitan Bank Ltd. Private 0.333 0.392 0.561Citibank N.A. Foreign 0.372 0.395 0.677American Express BankLtd. Foreign 0.332 0.411 0.521Algemene Bank Nederland Foreign 0.390 0.419 0.445United Bank Private 0.303 0.420 0.527Union Bank Ltd. Private 0.472 0.477 0.652KASB Private 0.401 0.550 0.891Prime Commercial BankLtd. Private 0.553 0.558 0.689Soneri Bank Private 0.620 0.626 0.825

Bolan Bank Private 0.572 0.670 0.804Saudi Pak Private 0.579 0.697 0.719Faysal Bank Private 0.578 0.804 0.958

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Table 5: Hyperbolic Quantile Efficiency and Bootstrap CI Estimates(2003)


Bank Type lo hi Bank Type lo hi

Mashreq Bank foreign 0.057 0.055 0.058 Rupali Bank Foreign0.03

4 0.034 0.034

State Bank of India public 0.100 0.095 0.104 Allied Bank of Pakistan Private0.14

0 0.140 0.140

Abu.dhabi Commercial Bank foreign 0.103 0.097 0.108 Habib Bank AG Zurich Private0.19

4 0.194 0.194

Bank of America foreign 0.108 0.102 0.114 Bank of Tokyo Foreign0.20

0 0.200 0.200

Oman International Bank foreign 0.111 0.105 0.117Bank of Punjab Private0.21

1 0.211 0.211

Bank of Bahrain and Kuwait foreign 0.131 0.123 0.141 First Women Bank Ltd. Public0.25

7 0.257 0.257

Bank of Nova Scotia foreign 0.151 0.139 0.163Deutsche Bank A.G. Foreign0.27

0 0.270 0.270

Nainital Bank private 0.162 0.148 0.176 Hong Kong & Shanghai Foreign0.27

6 0.276 0.276

Bank of Tokyo Mitsubishi foreign 0.162 0.149 0.175National Bank ofPakistan Public

0.286 0.286 0.286

Credit Lyonnais foreign 0.169 0.154 0.183 Habib Bank Ltd. Public0.29

9 0.299 0.299

Citibank foreign 0.210 0.187 0.233 Bank of Khyber Private0.30

0 0.300 0.300

Societe Generale foreign 0.217 0.193 0.242 Bank Alhabib Private0.32

4 0.324 0.324

Ratnakar Bank Ltd private 0.240 0.211 0.270Muslim CommercialBank Private

0.338 0.338 0.338

BNP Paribas foreign 0.248 0.217 0.279 Askari Commercial Bank Private0.33

8 0.338 0.338

ABN Amro Bank foreign 0.253 0.220 0.286 Bank Alfalah Private0.35

1 0.351 0.351

Deutsche Bank foreign 0.271 0.233 0.308 PICIC Commercial Bank Private0.35

4 0.354 0.354

HSBC India foreign 0.292 0.248 0.336

Albaraka Islamic Inv

Bank Foreign

0.35

5 0.355 0.355

Sangli Bank Ltd private 0.330 0.274 0.387 Bank Indosuez Foreign0.35

7 0.357 0.357

Punjab National Bank public 0.341 0.283 0.405Standard CharteredBank Foreign

0.379 0.379 0.379

Catholic Syrian Bank Ltd private 0.341 0.282 0.405 Metropolitan Bank Ltd. Private0.39

2 0.392 0.392

Lakshmi Vilas Bank Ltd private 0.351 0.286 0.415 Citibank N.A. Foreign0.39

5 0.395 0.395

Bharat Overseas Bank Ltd private 0.354 0.292 0.420 American Express Bank Foreign0.41

1 0.411 0.411

Standard Chartered Bank foreign 0.379 0.304 0.455Algemene BankNederland Foreign

0.419 0.419 0.419

Punjab Sind Bank public 0.382 0.310 0.454 United Bank Private0.42

0 0.420 0.420

Lord Krishna Bank Ltd private 0.383 0.310 0.461 Union Bank Ltd. Private0.47

7 0.477 0.477

United Western Bank Ltd. private 0.410 0.325 0.495 KASB Private0.55

0 0.550 0.550

City Union Bank Ltd. private 0.419 0.334 0.501 Prime Commercial Bank Private0.55

8 0.558 0.558

South Indian Bank Ltd private 0.419 0.326 0.509 Soneri Bank Private0.62

6 0.626 0.626

Bank of Rajasthan Ltd private 0.424 0.332 0.512 Bolan Bank Private0.67

0 0.670 0.670

Canara Bank public 0.426 0.332 0.516 Saudi Pak Private0.69

7 0.697 0.697

State Bank of Saurashtra public 0.445 0.345 0.545 Faysal Bank Private0.80

4 0.804 0.804Bank of India public 0.450 0.345 0.554Dhanalakshmi Bank Ltd private 0.451 0.351 0.558State Bank of Indore public 0.453 0.348 0.558American Express Bank foreign 0.457 0.349 0.563

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Federal Bank Ltd private 0.459 0.353 0.567Central Bank of India public 0.462 0.351 0.568

Tamilnad Mercantile BankLtd private 0.473 0.361 0.597State Bank of Mysore public 0.475 0.358 0.589ING Vysya Bank Ltd private 0.492 0.368 0.617Karnataka Bank Ltd private 0.500 0.371 0.633

Karur Vysya Bank Ltd private 0.507 0.375 0.635UCO Bank public 0.509 0.387 0.648Syndicate Bank public 0.511 0.386 0.644Bank of Baroda public 0.513 0.383 0.643Indian Overseas Bank public 0.525 0.389 0.661State Bank of Bikaner &

Jaipur public 0.536 0.398 0.682State Bank of Travancore public 0.542 0.386 0.688Allahabad Bank public 0.542 0.395 0.696State Bank of Hyderabad public 0.550 0.394 0.702

Jammu and Kashmir BankLtd private 0.555 0.399 0.714Oriental Bank of CommerceLtd. public 0.561 0.400 0.722Union Bank of India public 0.570 0.403 0.736

Bank of Maharashtra public 0.578 0.401 0.750United Bank of India public 0.592 0.419 0.773Corporation Bank Ltd. public 0.610 0.427 0.811Dena Bank public 0.622 0.422 0.818Andhra Bank public 0.623 0.426 0.821State Bank of Patiala public 0.633 0.421 0.840Vijaya Bank public 0.650 0.434 0.863Indian Bank public 0.742 0.462 1.032

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Table 6: Indian and Pakistani Banking: Robust Non-ParametricTechnical

Efficiency Estimatesorderm5i

orderm5o

cquan

cquani cquano

orderm5i

orderm5o cquan cquani

cquano


1985 0.444 0.2810.32

9 0.310 0.520 0.610 0.169 0.465 0.715 0.620

1986 0.388 0.1790.27

9 0.258 0.474 0.634 0.154 0.498 0.697 0.572

1987 0.399 0.1680.28

2 0.269 0.442 0.620 0.146 0.395 0.656 0.590

1988 0.398 0.1720.29

3 0.272 0.426 0.599 0.161 0.362 0.606 0.528

1989 0.394 0.1780.30

7 0.290 0.412 0.610 0.183 0.396 0.629 0.542

1990 0.390 0.1820.30

4 0.269 0.425 0.637 0.214 0.351 0.658 0.532

1991 0.377 0.215

0.30

4 0.262 0.440 0.583 0.196 0.418 0.612 0.405

1992 0.394 0.1940.31

7 0.270 0.436 0.575 0.212 0.422 0.586 0.410

1993 0.400 0.2280.34

8 0.292 0.436 0.594 0.218 0.370 0.642 0.397

1994 0.387 0.2050.33

5 0.287 0.450 0.580 0.210 0.382 0.678 0.383

1995 0.377 0.2150.33

2 0.270 0.447 0.595 0.246 0.361 0.663 0.425

1996 0.389 0.2240.33

4 0.291 0.464 0.582 0.241 0.350 0.544 0.442

1997 0.412 0.2350.35

9 0.331 0.457 0.563 0.237 0.347 0.593 0.451

1998 0.411 0.219

0.38

9 0.338 0.445 0.581 0.233 0.320 0.695 0.395

1999 0.418 0.2070.38

6 0.356 0.437 0.577 0.196 0.328 0.693 0.381

2000 0.423 0.2010.36

4 0.346 0.441 0.588 0.181 0.323 0.722 0.377

2001 0.445 0.2230.36

0 0.369 0.468 0.569 0.158 0.337 0.644 0.411

2002 0.440 0.2230.35

5 0.372 0.484 0.545 0.128 0.312 0.528 0.408

2003 0.445 0.2210.35

5 0.377 0.470 0.563 0.150 0.333 0.563 0.439

1985-91 0.399 0.196

0.300 0.276 0.449 0.614 0.175 0.412 0.653 0.541

1992-03 0.412 0.216

0.353 0.325 0.453 0.576 0.201 0.349 0.629 0.410

1992-97 0.393 0.217

0.338 0.290 0.448 0.581 0.227 0.372 0.618 0.418

1998-03 0.430 0.216

0.368 0.360 0.457 0.570 0.174 0.325 0.641 0.402

1985-03 0.407 0.209

0.333 0.307 0.451 0.590 0.191 0.372 0.638 0.458

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Table 7: Indian and Pakistani Banking: RobustNon-Parametric Technical Efficiency Estimates

model1

model2

model3

model1

model2 model3


1985 0.453 0.580 0.347 0.688 0.806 0.544

1986 0.471 0.578 0.301 0.728 0.807 0.557

1987 0.482 0.565 0.292 0.566 0.693 0.390

1988 0.418 0.577 0.309 0.529 0.724 0.437

1989 0.442 0.608 0.331 0.607 0.716 0.464

1990 0.418 0.644 0.306 0.517 0.641 0.432

1991 0.443 0.631 0.316 0.550 0.694 0.417

1992 0.435 0.608 0.316 0.604 0.737 0.478

1993 0.505 0.629 0.345 0.578 0.661 0.420

1994 0.505 0.631 0.342 0.592 0.696 0.416

1995 0.493 0.645 0.345 0.580 0.701 0.396

1996 0.480 0.627 0.350 0.608 0.755 0.468

1997 0.492 0.606 0.353 0.585 0.696 0.4011998 0.529 0.613 0.391 0.547 0.652 0.375

1999 0.522 0.610 0.386 0.543 0.647 0.393

2000 0.484 0.599 0.349 0.540 0.637 0.359

2001 0.480 0.575 0.332 0.513 0.634 0.307

2002 0.472 0.545 0.320 0.478 0.609 0.310

2003 0.474 0.509 0.331 0.471 0.546 0.3141985-

91 0.447 0.598 0.315 0.598 0.726 0.4631992-

03 0.489 0.600 0.347 0.553 0.664 0.3861992-

97 0.485 0.624 0.342 0.591 0.708 0.430

1998-03 0.493 0.575 0.352 0.515 0.621 0.343

1985-03 0.474 0.599 0.335 0.570 0.687 0.415

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Table 8: Indian & Pakistani Banking: Robust Non-Parametric TechnicalEfficiency Estimates

India PakistanPublic Private Foreign Public Private Foreign

1985-91 0.479 0.254 0.193 0.375 NA 0.4301992-03 0.457 0.363 0.238 0.307 0.440 0.337

1992-97 0.436 0.326 0.259 0.307 0.473 0.3731998-03 0.479 0.400 0.217 0.307 0.407 0.3001985-03 0.465 0.323 0.222 0.332 0.440 0.371

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Fig 1: Indian Banking Efficiency Estimates by Ownership

Fig 2: Pakistani Banking Efficiency Estimates by Ownership

0.100

0.150

0.200

0.250

0.300

0.350

0.400

0.450

0.500

0.550

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

Efficiency

Public Private Foreign

0.050

0.100

0.150

0.200

0.250

0.300

0.350

0.400

0.450

0.500

0.550

0.600

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

Efficiency

Public Private Foreign

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Trend in the Robust Non-Parametric Efficiency Estimates of Indian and Pakistani Banking Industries

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