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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literature is already in public domain alongside unpublished consultancy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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quantile estimation for efficiency analysis with an application to Federal
Reserve check processing operations,Journal of Econometrics, Volume 145,
Issues 1-2, July 2008, Pages 209-225
Wheelock, D.C., Wilson, P.W., 2001. New evidence on returns to scale and
product mix among US commercial banks. Journal of Monetary Economics
47, 115132.Wilson, P.W., 2007. FEAR: A software package for frontier efficiency analysis
with R. Socio-Economic Planning Sciences (in press).
21
http://www.sciencedirect.com/science/journal/03044076http://www.sciencedirect.com/science?_ob=PublicationURL&_tockey=%23TOC%235940%232008%23998549998%23696819%23FLA%23&_cdi=5940&_pubType=J&view=c&_auth=y&_acct=C000014338&_version=1&_urlVersion=0&_userid=208107&md5=4ce377e1f36971ffa851d6ccb9a12694http://www.sciencedirect.com/science?_ob=PublicationURL&_tockey=%23TOC%235940%232008%23998549998%23696819%23FLA%23&_cdi=5940&_pubType=J&view=c&_auth=y&_acct=C000014338&_version=1&_urlVersion=0&_userid=208107&md5=4ce377e1f36971ffa851d6ccb9a12694http://www.sciencedirect.com/science?_ob=PublicationURL&_tockey=%23TOC%235940%232008%23998549998%23696819%23FLA%23&_cdi=5940&_pubType=J&view=c&_auth=y&_acct=C000014338&_version=1&_urlVersion=0&_userid=208107&md5=4ce377e1f36971ffa851d6ccb9a12694http://www.sciencedirect.com/science?_ob=PublicationURL&_tockey=%23TOC%235940%232008%23998549998%23696819%23FLA%23&_cdi=5940&_pubType=J&view=c&_auth=y&_acct=C000014338&_version=1&_urlVersion=0&_userid=208107&md5=4ce377e1f36971ffa851d6ccb9a12694http://www.sciencedirect.com/science/journal/030440767/31/2019 Trend in the Robust Non-Parametric Efficiency Estimates of Indian and Pakistani Banking Industries
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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)
Indian Banking Pakistani Banking
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
Indian Banking Pakistani Banking
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
Indian Banking Pakistani Banking
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