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					                    I.     INTRODUCTION


The objective of this study is to measure and explain the measured
variation in the performance and productive efficiency of Mauritian
commercial banks in the post-financial liberalisation period. A wide
range of financial reforms have been instituted since the 1980s
which included measures such as liberalisation of interest rates,
removal of quantitative controls on credit, lifting of barriers on
competition, privatisation of public financial institutions and
introduction of market-based securities. The major aims of the
reforms have generally been to raise both the level of investment
and the efficiency of its allocation and in addition, to enhance the
provision of financial services to all sectors of the economy. These
reforms were geared towards the liberalisation of the financial
system in order to enhance efficiency in the mobilisation and
allocation of financial resources (Jankee 2001, World Bank 2003).


The main impetus for this study lies on the objectives of
policymakers to increase efficiency in the financial system and
develop Mauritius into a regional financial center. Given the
importance of the banking sector in the Mauritian economy, an
examination of efficiency in the banking sector will provide useful
guidance to policy makers towards understanding and assessing the
process of the banking sector reform. Such a study has been
motivated by a number of factors. First, in contrast to developed
countries, very limited evidence has been obtained on these issues



                                 1
particularly in the case of developing countries (see Humphrey,
1993, 1997 for an extensive survey). Moreover, a wide range of
methods has been developed, broadly classified under parametric
and non-parametric, to study bank efficiency and productivity
(Bauer et al., 1998; Avikaran, 1999).


Given that no such study has been undertaken for Mauritius, such a
work attempts to extend the literature in various ways. First, an
examination of the impact of financial deregulation on the efficiency
(allocative and technical) and productivity (TFP) of banks would
contribute to the literature on a small and deregulated open economy
in the African region. Moreover, we also incorporate different
objectives of the banking industry in our analysis of efficiency and
productivity. Following Leightner and Lovell (1998), two aspects
are examined. Firstly, we look at banks as profit maximisers and
secondly, as banks pursuing the objectives of the Central Bank in
terms of financial stability and economic performance. We apply the
non-parametric approach Data Envelopment Analysis (DEA) (see
Coelli, 1996), a widely applied technique to estimate efficiency
scores and productivity of banks for the period 1994-2004. These
estimates can be used to compare Mauritius with other countries in
terms of efficiency of the banking system.


The outline of this paper is as follows: in section 2, we give an
overview of the Mauritian banking sector. Section 3 briefly reviews
the literature on issues relating to bank efficiency, productivity and



                                  2
financial deregulation. Section 4 discusses the Data Envelopment
Analysis (DEA) and the methodology that is used to compute
efficiency and productivity of banks for the period 1994-2004. The
estimated efficiency scores are discussed in section 5. Section 6
concludes the paper and highlights the policy implications.


         II.    AN OVERVIEW OF THE MAURITIAN
                       BANKING SECTOR


Before discussing the efficiency issues, in this section I will discuss
the main features of the Mauritian banking sector (see Bundoo and
Dabee, 1998; Junglee 2001; Jankee 1999; Worldbank 2003).
Mauritius is a small island economy in the Indian Ocean and
inherited a bank-dominated financial system at the time of
independence in 1968. As compared to many developing countries
especially in the African region, the Mauritian financial system was
quite developed with 11 banks. The first set of control has been the
regulation of banks’ interest rates by monetary authorities
throughout the 1970s until late 1980s. Other repression policies
consisted of the imposition of cash ratio and liquid asset ratio that
were gradually tightened over the years as well as the exchange
control on current and capital transactions. In the mid 1970s, the
monetary authorities tightened their control over the financial
system in an attempt to regulate credit expansion and allocate it to
productive sectors.




                                  3
In the early 1980s, the control over the overall credit was modified
whereby sectors were categorised into priority and non-priority and
ceilings were imposed respectively on both types of sectors.
Furthermore, banks were individually subject to a certain quantum
of credit depending upon their extent of deposit mobilisation and
credit creation. The early 1980s were marked by the beginning of
the process of gradual liberalisation of the financial system. Controls
over interest rates were gradually lifted. Exchange control on current
transactions was no longer imposed as from mid 1980s. By late
1980s, interest rates were fully liberalised. However, quantitative
controls in the form of reserve requirements and credit ceilings
continued to be imposed. The 1990s were marked by the relaxation
of most of the remaining banking sector controls. Credit ceilings
were gradually abolished and the exchange control act was
suspended by mid 1990s. The cash ratio and liquid asset ratio were
gradually lowered and the liquid asset ratio was brought down to
zero in 1997.


The financial liberalisation programme was also accompanied by
other market-oriented reforms such as a free float exchange rate, the
auctioning of Treasury bills and the setting up of a secondary market
for   government    securities   amongst    others.   Most    recently,
transactions involving the repurchase of bank reserves and foreign
currency swaps have increased enormously. In addition, bank
branches expansion has also contributed largely to the institutional
development of the banking sector. From 32 in the 1970, the number



                                  4
of bank branches expanded significantly to reach 117 in 1990 and
163 in 2003.        Some other specific details on banking sector
developments are given in the appendix.


             III.     OVERVIEW OF LITERATURE


The banking sector has attracted considerable theoretical and
empirical research during the preceding decades. Studies have
involved a number of issues including the role of banks in financial
development, bank efficiency, pricing behaviour of banks and
banking regulation. Prior studies on bank efficiencies concentrated
on estimating cost functions and measuring economies of scale and
scope with the implicit assumption that banks being studied operate
efficiently (Gilbert, 1984). Many researchers who have claimed the
importance of investigating inefficiencies in the banking units have
questioned this assumption. Since then, this issue has led to
considerable research. However, one issue of recent interest has
been the effects of deregulation on the performance of banks (see
Berger, 1993, 1997 for a review). The literature distinguishes two
main types of bank efficiency. The first is operational efficiency as
introduced by Farell (1957) to measure efficiency and the second is
X-efficiency as introduced by Leibenstein (1966) to explain
differences in efficiency between banks. The concept of operational
efficiency is purely technical and can be defined as the product of
technical efficiency and allocative efficiency (see Coelli, 1996).
While technical efficiency tells us how far the bank output is from



                                  5
the bank’s isoquant, allocative efficiency captures inefficiencies due
to the fact that the bank has picked up a suboptimal input
combination given input prices.


A number of factors have motivated research on bank efficiency
(Berger et al., 1993, 1997; Hardy et al., 2001). First, there is the
mainstream economic thinking that improving the efficiency of
financial systems is better implemented through deregulatory
measures aiming at increasing bank competition on price, product,
services and territorial rivalry (Smith, 1997; Fry, 1995). However,
empirical evidence on the impact of financial deregulation on bank
efficiency has been mixed. Berger and Humphrey (1997) stated that
the consequences of deregulation might essentially depend on
industry conditions prior to the deregulation process as well as on
the type of deregulation measures implemented. The deregulation on
the asset side of the balance sheets that focused on the liberalisation
of the volume and the interest rates of bank lending resulted in the
improvement of both efficiency and productivity of Norwegian
banks (Berg et al., 1992). Turkish banks had a similarly experience
(Zaim, 1995). But the impact of liberalisation on Indian banks
resulted in varied productivity efficiency depending on the type of
ownership (Battarcharyay et al., 1997).


Berger and Humphrey (1997) undertook a comprehensive survey of
130 studies that apply the parametric and non-parametric frontier
efficiency analysis to financial institutions in 21 countries.



                                  6
A number of issues had been raised and tested relating to bank
efficiency and financial deregulation. These issues mainly included
the alternative methodologies used to estimate different types of
efficiencies, namely technical efficiency, allocative efficiency, scale
efficiency, pure technical efficiency, cost efficiency and change in
factor productivity (see Coelli, 1996). Moreover, researchers have
also tested empirically the extent to which factors such as market
share, total assets, credit risk, technology and scale of production,
bank branches, ownership and location, quality of bank services and
diversity of banking products, financial deregulation and managerial
objectives determine bank efficiencies.


Das (2002) examined the effects of financial deregulation on risk
and productivity change of public sector banks in India for the
period 1995-2001. They found evidence that capital; non-
performing loans and productivity were entwined, with each
reinforcing and to a certain degree complementing the other. They
also found that higher capital led to a rise in productivity whilst
higher loan growth reduced productivity. Moreover, increased
government ownership tended to increase productivity.


Leightner and Lovell (1998) using the best practice production
frontiers,   constructed   the   Malmquist    growth    indexes    and
productivity indexes for each Thai bank, for 1989-1994,
incorporating two different specifications of the services that the
bank provides, one derived from the objectives of the bank itself and



                                  7
the other derived from the objectives of the Bank of Thailand. They
found higher productivity growth of banks when the bank objective
of profit-maximisation was pursued as compared with the model
when the Bank of Thailand's objective was achieved.


Laeven (1999) used DEA to estimate the efficiencies of the
commercial banks in Indonesia, Korea, Malaysia, Philippines and
Thailand for the years 1992-1996. They also included risk when
analysing the performance of banks and found that foreign banks
took lower risk as compared with family-owned banks.


Battacharyay et al. (1997) examined productive efficiency of 70
Indian commercial banks during the early stages of the on-going
liberalisation process. They estimated the technical efficiency scores
using DEA and then used stochastic frontier analysis to attribute
variation in the calculated efficiency scores to three sources,
temporal, component, ownership component and random noise
component. They found public owned banks to be the most efficient
followed by foreign banks and privately owned banks. Hardy et al.
(2001) estimated the effects of banking reform on the profitability
and efficiency of the Pakistani banking system. They estimated the
profitability, cost and revenue frontiers to derive measures of
efficiency of the banking system relative to the best available
practice. They found that financial market reform has increased both
revenues and costs but did not increase overall profitability and led
to convergence in efficiency.



                                  8
Jagtiani and Khantavit (1996) examined the impact of risk-based
capital requirements on bank cost efficiencies in the US banking
industry. They found that the introduction of risk-based capital
requirements led to a significant structural change in the banking
industry both in terms of efficient size and optimal product mixes.
Their results implied that regulations encouraging large banks to
expand production and product mixes resulted in a less efficient
banking industry.


Sathye (2001) empirically investigated the X-efficiency, both
technical and allocative, in Australia. He used the non-parametric
method of DEA to estimate the efficiency scores. He found that
banks in the sample had low levels of efficiency as compared with
the banks in the European countries and in US. Efficiency in
Australia came mainly from the waste of inputs (technical
efficiency) rather than choosing the incorrect input combinations
(allocative efficiency). Moreover, domestic banks were found to be
more efficient than foreign owned banks.


                    IV.    METHODOLOGY


Overtime, a number of methods have been used to measure the
performance of banks. The use of financial ratios has been criticised
because of its reliance on benchmark ratios (see Yeh 1996). These
benchmarks could be arbitrary and misleading. Further, Sherman
and Gold (1985) noted that financial ratios do not capture the long



                                 9
term performance and aggregate many aspects of performance such
as operations, marketing and financing. In recent years, there has
been an increasing use of frontier analysis methods to measure bank
performance. In the frontier analysis methods, the institutions that
perform better relative to a particular standard are separated from
those that perform poorly. Applying a non-parametric or parametric
frontier analysis does such separation to firms within the financial
services industry.


The parametric approach includes stochastic frontier analysis, the
free disposal hull, thick frontier and the distribution free approaches,
while the non-parametric approach is the data envelopment analysis
(DEA) (see Molyneux et al., 1996). Furthermore after Charnes et al.
(1978) who coined the term DEA, “a large number of papers have
extended and applied the DEA methodology” ( Coelli, 1996). In the
present study, we employ the non-parametric method (DEA). This
approach has been used since a lot of “recent research has suggested
that the kind of mathematical programming procedure used by DEA
for efficient frontier estimation is comparatively robust (Seiford and
Thrall, 1990.)


IV.1 Data Envelopment Analysis (DEA)

The Data Envelopment Analysis is a linear programming technique
initially developed by Charnes et al. (1978) to evaluate the
efficiency of public sector non-profit organisations (see Molyneux et




                                  10
al., 1996). DEA involves the use of linear programming methods to
construct a non-parametric piecewise frontier over the data so as to
calculate efficiency relative to this frontier. Thus, DEA calculates
the relative efficiency scores of various decision making units
(DMU) in a particular sample. The DMUs can be banks or branches
of banks. The DEA measure compares each of the banks/branches in
that sample with the best practice in the sample. It tells the user
which of the DMUs in the sample are efficient and which are not.


The ability of the DEA to identify possible peers or role models as
well as simple efficiency scores gives it an edge over other methods.
Fried and lovell (1994) have given a list of questions that DEA can
help to answer. Details about the various frontier measurement
techniques are found in the works of Bauer et al. (1989), Bauer
(1990), and Leightner and Lovell (1998) etc. There are a number of
software options for running DEA. This study uses the Software
(DEAP) developed by Coelli (1996) to calculate the technical,
allocative and cost efficiency scores of banks. Methodologically, the
characteristics of DEA can be described through the original model
developed by Charnes, Cooper and Rhodes. Consider N units (each
is called a DMU) that convert I inputs into J outputs, where I can be
larger, equal or smaller than J. To measure efficiency of this
converting process for a DMU, Charles et al. propose the use of the
maximum of a ratio of weighted outputs to weighted inputs for that
unit, subject to the condition that the similar ratios for all other
DMUs be less than or equal to one. That is,



                                 11
                   J

                 u
                  j 1
                         0
                         j    y0
                               j

Max     e0        I
                                                                           (1)
                 v
                  i 1
                         i
                          0
                              x   0
                                  i




Subject to

  J

u
 j 1
        0
        j   yn
             j

  I
                  1; n  1,..., N ;
v
 i 1
        0 n
        i i x

vi0 , u 0  0; i  1,..., I ; j  1,..., J .
        j




where       y n , x n are positive known outputs and inputs of the nth
              j     j


DMU and           vi0 , u 0 are the variable weights to be determined by
                          j

solving problem (1). The DMU being measured is indicated by the
index 0, which is referred to as the base DMU. The maximum of the

objective function                    e 0 given by the problem (1) is the DEA
efficiency score assigned to                    DMU 0 . Since every DMU can be
DMU 0 , this optimisation problem is well defined for every DMU.
If the efficiency score e
                                          0
                                               1 , DMU 0 satisfies the necessary
condition to be DEA efficient; otherwise it is DEA inefficient.
It is difficult to solve problem (1) as stated, because the objective
function is non-linear and fractional. Charnes et al., however,


                                                  12
transformed the above non-linear programming problem into a
linear one as follows:

                J
Max    h0   u0 y 0
               j j                                                         (2)
               j 1



Subject to
 I                    J                  I

v
i 1
        x  1,
       0 0
       i i            u
                      j 1
                              0
                              j   y n   vi0 xin  0;
                                    j
                                        i 1



n  1,..., N ; vi0  ; u 0  ; i  1,..., I ; j  1,... J .
                          j



The variables defined in problem (2) are the same as those defined
in problem (1). An arbitrarily small positive number,                      is
introduced in problem (2) to ensure that all of the known inputs and
outputs have positive weight values and that the optimal objective
function of the dual problem to problem (2) is not affected by the
values assigned to the dual slack variables in computing the DEA

efficiency score for each DMU. The condition             h 0  1 ensures that
the base     DMU 0 is DEA efficient; otherwise it is DEA inefficient,
with respect to all other DMUs in the test. A complete DEA model
involves the solution of N such problems, each for a base DMU,

yielding N different         (vin , u n ) weight sets. In each program, the
                                      j

constraints are held constant while the ratio to be maximised is




                                             13
changed. Finally, these DEA problems are solved in the paper using
the computer software developed by Coelli (1996).


IV.2 Sources and Selection of Inputs and Outputs

The definition and measurement of banks' outputs have been a
matter of long standing debate among researchers. For defining
inputs and output, prior studies in banking literature have followed
three main approaches, namely the production approach, the
intermediation approach and the modern approach (user cost)
(Berger and Humphrey, 1992; Freixas and Rochet, 1997). The first
two approaches apply the traditional microeconomic theory of the
firm to banking and differ only in the specification of banking
activities. The third approach goes one step further and incorporates
some specific activities into the classical theory (Leightner and
Lovell, 1998). In the production approach, banking activities are
described as the production of services to depositors and borrowers.


Traditional production factors such as land, labour and capital are
used as inputs to produce desired outputs. A main problem with this
method is that of measurement of outputs. Researchers have used
the number of accounts and number of operations on these accounts
or the dollar amounts as outputs depending on the availability of
data. Sherman and Gold (1985), Ferrier and Lovell (1990), Fried et
al., (1993), Rosen and Paradi (1997), and Athannasopolous and




                                 14
Giokos (2000) followed this approach. The inputs include the
number of employees and physical capital.


Next, we have the intermediation approach, which is in fact
complementary to the production approach and describes banking
activities as transforming money borrowed from depositors into the
money lent to borrowers. The transformation activity originates
from the different characteristics of deposits and loans. Deposits are
typically divisible, liquid, and risk-free while on the other hand,
loans are indivisible and risky. In this approach inputs are financial
capital, deposits collected and funds borrowed from financial
markets and outputs are measured in terms of the volume of
outstanding loans and investments. This approach has been found to
be more relevant for financial institutions as it is inclusive of interest
expenses, which often account for one half or two thirds of total
costs (Berger and Humphrey, 1997). Barr et al., (1994),
Athannasopolous and Giokos (2000) and Sathye (2001) are some
recent studies using the intermediation approach.


The modern approach has the novelty of integrating risk
management and information processing into the classical theory of
the firm. One of the most innovative parts of this approach is the
introduction of the quality of banks' assets and the probabilities of
banks' failure in the estimation of costs. It can be argued that this
approach is embedded in the previous approaches (Freixas and
Rochet, 1997). The third approach perhaps can best be represented



                                   15
through the ratio-based CAMEL approach. In this approach, capital
adequacy, asset quality, management, earnings and liquidity are
derived from the financial tables of the bank and are used as
variables in the performance analysis (Mercan and Yolaan, 2000).
Some recent works have also tried to use all these methods
complementarily in the analysis of efficiency by incorporating
objectives of the bank as well as the central bank (see Leigthner and
Lovell, 1998; Das, 2002). In this paper, we use an intermediation
approach which considers banks as financial intermediaries and uses
volume of deposits, loans and other variables as inputs and outputs.


         V.    ANALYSES OF EFFICIENCY SCORES


Given the price information and considering the behavioural
objective of banks, we have used the cost minimisation DEA
program to measure technical efficiencies, allocative efficiencies
and overall economic (cost) efficiencies. We investigate bank
efficiencies with respect to banks maximising their revenues (model
1) and when banks are pursuing the Central Bank’s objective
financial soundness and economic performance (model 2).


Bank's Own Objective (model 1)

The inputs used are labour, capital and loanable funds whilst outputs
comprise interest income and non-interest income. Staff costs have
been used as a measure of labour. Capital represents the book value
of premises and fixed assets, net of depreciation. Loanable funds


                                 16
include time and savings deposits and other borrowed funds. The
price of labour has been derived by dividing total staff expenses by
the number of employees of respective banks. A proxy for the price
of capital is derived by dividing the book value of premises and
fixed assets, net of depreciation by operating expenses other than
staff expenses. The sample size consists of 10 banks and is
comparable to other works carried out using DEA. The sample size
exceeds the rule of thumb given by Soteriou and Zenios (1998) and
Dyson et al., (1998) which state that the sample should be larger
than the product of the number of inputs and outputs. According to
Evanoff and Israeillevich (1991), DEA can be used in small
samples. The study has been carried out over the period 1994 to
2004, which represents more or less the post-deregulation period.


The data on inputs and outputs of the 10 banks are not reported here
because of its magnitude. Such data were fed into the program
DEAP. However, table 5 in the appendix gives some summary
statistics of the banking industry over the years. The charge for
doubtful debts has increased from Rs 407 millions in 2002 to attain
Rs 817 millions in 2004. Total advances, interest and non-interest
income as well as the overall expenses of banks have increased over
the years under study.


Then we obtained the different efficiency scores for each bank.
Technical efficiency scores are presented in Table 2, Allocative
efficiency scores are given in Table 3. Moreover, the overall



                                 17
TABLE 2. Technical Efficiency of Banks (Banks’ Own Objective)

Years       1994    1995     1996    1997     1998      1999    2000    2001    2002    2003    2004
MCB        0.823    0.945   0.936    0.812    0.923     0.909   0.873   0.854   0.795   0.926   0.895
Barclays   1.000    1.000   1.000    0.886    1.000     1.000   0.900   0.856   0.954   1.000   1.000
HSBC       1.000    1.000   1.000    1.000    1.000     1.000   1.000   1.000   0.965   0.897   0.812
Baroda     0.953    0.752   0.877    0.804    0.761     0.888   1.000   0.925   0.859   0.965   0.854
Habib      1.000    1.000   1.000    1.000    1.000     1.000   1.000   1.000   1.000   0.825   0.925
BNPI       1.000    1.000   1.000    1.000    1.000     1.000   0.814   0.825   0.825   0.963   0.915
SBM        0.943    1.000   1.000    1.000    1.000     1.000   1.000   0.985   0.925   0.916   0.856
IOIB       1.000    1.000   1.000    1.000    1.000     0.954   0.935   0.950   0.950   0.960   0.890
SEAB       1.000    1.000   1.000    1.000    0.897     0.857   0.926   0.850   0.820   0.850   0.950
Delphis    0.955    1.000   1.000    1.000    1.000     1.000   0.938   0.940   0.950   0.950   0.960
Mean       0.967    0.970   0.981    0.981    0.958     0.961   0.940   0.960   0.950   0.940   0.930

Source: Computed.




                                                   18
TABLE 3. Allocative Efficiency of Banks (Banks’ Own Objective)

Years       1994     1995     1996    1997     1998     1999     2000    2001    2002    2003    2004
MCB        0.857    0.803    0.592    0.899   0.570     0.821    0.826   0.850   0.820   0.860   0.850
Barclays   0.536    0.583    0.418    0.639   0.373     0.702    0.608   0.950   0.720   0.750   0.820
HSBC       1.000    1.000    0.761    0.901   0.529     0.920    0.976   0.920   0.930   0.950   0.940
Baroda     0.838    0.997    0.770    0.742   0.845     0.881    1.000   1.000   0.850   0.820   0.930
Habib      0.401    0.332    0.290    0.298   0.356     0.495    0.441   0.250   0.440   0.460   0.520
BNPI       0.824    0.744    0.463    0.842   0.336     0.646    0.636   0.620   0.540   0.550   0.620
SBM        0.984    0.889    0.783    0.762   1.000     1.000    1.000   1.000   0.960   0.850   0.960
IOIB       1.000    0.998    0.737    0.688   0.816     0.990    0.983   0.950   0.960   0.925   0.930
SEAB       0.619    0.620    0.404    0.418   0.433     0.590    0.646   0.650   0.560   0.650   0.630
Delphis    0.935    1.000    1.000    1.000   0.968     1.000    0.920   0.960   0.940   0.930   0.920
Mean       0.799    0.797    0.622    0.719   0.623     0.805    0.804   0.810   0.820   0.750   0.960

Source: Computed.




                                                   19
TABLE 4. Overall Economic Efficiency of Banks (Banks’ Own Objective)

            1994     1995    1996    1997    1998     1999    2000     2001    2002    2003    2004
MCB         0.705    0.759   0.554   0.730   0.526    0.746   0.721    0.852   0.808   0.825   0.963
Barclays    0.536    0.583   0.418   0.566   0.373    0.702   0.547    0.903   0.837   0.840   0.962
HSBC        1.000    1.000   0.761   0.901   0.529    0.920   0.976    0.960   0.948   0.952   0.856
Baroda      0.799    0.750   0.675   0.597   0.643    0.782   1.000    0.963   0.855   0.865   0.896
Habib       0.401    0.332   0.290   0.298   0.356    0.495   0.441    0.625   0.720   0.725   0.856
BNPI        0.824    0.744   0.463   0.842   0.336    0.646   0.518    0.723   0.683   0.745   0.854
SBM         0.928    0.889   0.783   0.762   1.000    1.000   1.000    0.993   0.943   0.952   0.856
IOIB        1.000    0.998   0.737   0.688   0.816    0.944   0.919    0.950   0.955   0.925   0.963
SEAB        0.619    0.620   0.404   0.418   0.388    0.506   0.598    0.750   0.690   0.526   0.968
Delphis     0.893    1.000   1.000   1.000   0.968    1.000   0.863    0.950   0.945   0.745   0.935
Mean        0.773    0.772   0.610   0.683   0.597    0.773   0.754    0.885   0.885   0.885   0.825

Source: Computed.




                                                     20
economic efficiency score, which is a product of technical efficiency
and allocative efficiency, are given in Table 4. The summary of
means of these scores is presented in Table 5.

Table 2 presents the technical efficiency (TE) scores in the case
where banks followed their own objective. The TE of individual
banks ranged between 0.752 and 1 while the average TE of all banks
collectively has been quite high ranging between 0.939 and 0.981. A
striking point to note is that the TE of MCB has been consistently
below the optimal level, which is 1, despite it being the largest bank
in Mauritius. The allocative efficiency (AE) of banks ranged from
0.290 to 1 (Table 3). The Habib bank which is a small bank, showed
relatively lower AE, ranging between 0.290 and 0.495.


Similarly, SEAB also showed low levels of AE that ranged between
0.404 and 0.646. Surprisingly, Barclays which is the fourth largest
bank in the country also registered relatively low AE in the range on
0.373 and 0.702 while in a small bank like Baroda, AE ranged from
0.742 and 1. The average AE of banks collectively was in a lower
range of 0.622 and 0.805 as compared to technical efficiency.


The overall efficiency (OE) of banks ranged from 0.290 to 1. The
OE of MCB ranged from 0.526 to 0.759 while SBM's OE fluctuated
between 0.762 and 1. It is surprising that a bank as small as IOIB
registered high OE ranging between 0.688 and 1 while HSBC, the
third largest bank registered OE in the range of 0.761 and 1 (except




                                 21
for 0.529 in 1998). Barclays, which ranks just after the HSBC in
terms of size, showed relatively, lower OE fluctuating between
0.373 and 0.702. The overall economic efficiency of banks however
showed a declining trend in the first few years, falling from 0.773 in
1994 to 0.597 in 1998. It picked up to 0.773 in 1999 but fell again to
0.754 in 2000. The low levels of average overall efficiency are due
to lower allocative efficiency (that is the suboptimal input-output
mix given prices) rather than technical inefficiency. Table 5 reports
the summarised results across banks as well as over the period 1994
through 2004.

TABLE 5. Summary of Means (1994-2004) (Banks’ Own Objective)

                  Technical         Allocative         Overall
                  Efficiency        Efficiency        Economic
                                                      Efficiency
 MCB                0.889             0.767             0.677
 Barclays           0.969             0.551             0.532
 HSBC               1.000             0.870             0.870
 Baroda             0.862             0.868             0.749
 Habib              1.000             0.373             0.373
 BNPI               0.973             0.642             0.625
 SBM                0.992             0.917             0.909
 IOIB               0.984             0.887             0.872
 SEAB               0.954             0.533             0.508
 Delphis            0.985             0.975             0.961

 Mean               0.961             0.738              0.709

Source: Computed.




                                 22
According to Berger and Humphrey (1997), the world mean
efficiency value is 0.86 within the range of 0.55 (UK) - 0.95
(France). The overall efficiency score of all banks collectively in
Mauritius, over the period 1994 through 2004, is estimated at 0.71
which is within the range of the scores found in other countries but
lower than the world mean efficiency. A lower mean efficiency
score than the world mean would have important policy
implications. Firstly, there is a need for Mauritian banks to further
improve their efficiency so as to achieve the world best practice and
secondly, the government should help banks by creating an
appropriate policy environment that promotes efficiency.


Central Bank's Objective (model 2)

In order to incorporate the objective of economic growth and
financial soundness, we use a different set of outputs namely loans
and investments by the bank and one additional input which is the
provision for loans losses. These data are processed using DEAP to
generate efficiency scores to capture the impact of Central Bank’s
objectives. The different measures of efficiency are given in the
Table 6 – 9. One interesting result is that inefficiencies of banks are
lower when the Bank of Mauritius objectives of economic growth
and financial soundness are pursued as compared with the case
when banks are only maximising their own objective.




                                  23
TABLE 6. Technical Efficiency of Banks (Central Bank’s Objectives)
            1994     1995     1996    1997     1998        1999   2000    2001    2002    2003    2004
MCB         0.956    1.000    0.950   1.000   1.000    0.996      0.950   0.960   0.950   0.830   0.850
Barclays    0.918    0.833    0.873   0.948   1.000    0.936      0.873   0.850   0.852   0.790   0.850
HSBC        1.000    1.000    1.000   0.925   1.000    1.000      1.000   1.000   0.860   0.920   0.930
Baroda      1.000    0.962    1.000   1.000   1.000    1.000      1.000   1.000   0.982   0.796   0.825
Habib       1.000    1.000    1.000   1.000   1.000    1.000      1.000   0.988   0.859   0.916   0.936
BNPI        1.000    1.000    0.727   1.000   1.000    1.000      0.727   0.925   0.725   0.852   0.745
SBM         1.000    1.000    1.000   1.000   1.000    1.000      1.000   0.985   0.956   0.952   0.936
IOIB        1.000    1.000    1.000   1.000   1.000    1.000      1.000   1.000   0.952   0.963   0.985
SEAB        1.000    1.000    0.939   1.000   0.976    0.837      0.939   0.936   0.940   0.952   0.965
Delphis     0.998    1.000    0.906   1.000   0.948    1.000      0.906   0.925   0.936   0.895   0.954
Mean        0.987    0.980    0.940   0.987   0.992    0.977      0.940   0.952   0.954   0.856   0.985

Source: Computed.




                                                      24
TABLE 7. Allocative Efficiency of Banks (Central Bank’s Objectives)
            1994     1995     1996    1997    1998     1999    2000    2001    2002    2003    2004
MCB         0.908    1.000   1.000    0.971   1.000    1.000   1.000   1.000   0.925   0.936   0.944
Barclays    0.927    0.989   0.998    0.990   0.890    0.985   0.998   0.985   0.925   0.925   0.889
HSBC        0.964    0.907   0.791    0.999   1.000    0.756   0.791   0.812   0.852   0.936   0.942
Baroda      0.841    0.866   0.622    0.896   0.739    0.728   0.622   0.633   0.625   0.825   0.741
Habib       1.000    1.000   1.000    1.000   1.000    1.000   1.000   0.925   0.985   0.936   0.988
BNPI        0.875    0.842   0.910    0.993   0.778    0.692   0.910   0.895   0.941   0.952   0.899
SBM         0.800    0.972   1.000    0.859   0.855    1.000   1.000   0.658   0.988   0.758   0.985
IOIB        0.846    1.000   0.889    0.945   0.871    0.973   0.889   1.000   0.925   0.952   0.985
SEAB        1.000    0.962   0.891    0.947   0.874    0.993   0.891   0.825   0.936   0.952   0.940
Delphis     0.916    0.842   0.896    1.000   0.865    0.863   0.896   0.856   0.825   0.936   0.863
Mean        0.908    0.938   0.900    0.960   0.887    0.899   0.900   0.925   0.936   0.852   0.842

Source: Computed.




                                                      25
TABLE 8. Overall Economic Efficiency of Banks (Central Bank’s Objective)
          1994      1995    1996    1997    1998    1999     2000    2001    2002    2003    2004
MCB       0.868     1.000   0.950   0.971   1.000   0.996    0.950   0.980   0.938   0.883   0.897
Barclays 0.851      0.824   0.871   0.939   0.890   0.922    0.871   0.918   0.889   0.858   0.870
HSBC      0.964     0.907   0.791   0.924   1.000   0.756    0.791   0.906   0.856   0.928   0.936
Baroda    0.841     0.833   0.622   0.896   0.739   0.728    0.622   0.817   0.804   0.811   0.783
Habib     1.000     1.000   1.000   1.000   1.000   1.000    1.000   0.957   0.922   0.926   0.962
BNPI      0.875     0.842   0.662   0.993   0.778   0.692    0.662   0.910   0.833   0.902   0.822
SBM       0.800     0.972   1.000   0.859   0.855   1.000    1.000   0.822   0.972   0.855   0.961
IOIB      0.846     1.000   0.889   0.945   0.871   0.973    0.889   1.000   0.939   0.958   0.985
SEAB      1.000     0.962   0.837   0.947   0.853   0.831    0.837   0.881   0.938   0.952   0.952
Delphis   0.914     0.842   0.812   1.000   0.820   0.863    0.812   0.891   0.881   0.916   0.909
Mean      0.896     0.919   0.845   0.948   0.880   0.878    0.845   0.939   0.945   0.854   0.914

Source: Computed.




                                                    26
The technical efficiency (TE) of banks ranged from 0.727 to 1
(Table 6). Unlike banks pursuing their own objective, in this case
MCB registered higher TE ranging between 0.950 and 1. BNPI’s TE
was generally at its optimum level except in 1996 and 2000 when it
fell to 0.727. Surprisingly, Baroda's TE was also at its optimum
level in all periods except in 1995 while it showed lower TE when
pursuing its own objective.


The allocative efficiency (AE) of banks pursuing the central bank's
objective ranged from 0.622 to 1, which is higher than the range of
0.290 to 1, registered when banks pursued their own objective
(Table 7). Habib bank’s AE was at its optimum level throughout all
the years. In the case of Barclays pursuing the central bank's
objective, it registered much higher AE between 0.890 and 0.998.
MCB and SBM also showed much higher AE when pursuing the
central bank's objective.


The overall efficiency of banks when they incorporate the Central
Bank’s objectives fluctuated between 0.622 and 1 over the period of
study (Table 8). Except for BNPI, Baroda and HSBC, which
registered some low levels of overall efficiency in certain years, all
other banks showed overall efficiency higher than 0.80. The average
OE of all banks collectively had an erratic behavior in the first four
years, fluctuating between 0.845 and 0.948.




                                 27
TABLE 9. Summary of Means (Central Bank’s Objectives)
               Technical     Allocative   Overall Economic
               Efficiency    Efficiency      Efficiency
 MCB             0.979         0.983            0.962
 Barclays        0.912         0.968            0.881
 HSBC            0.989         0.887            0.876
 Baroda          0.995         0.759            0.754
 Habib           1.000         1.000            1.000
 BNPI            0.922         0.857            0.786
 SBM             1.000         0.927            0.927
 IOIB            1.000         0.916            0.916
 SEAB            0.956         0.937            0.895
 Delphis         0.965         0.897            0.866

 Mean            0. 972        0. 913           0. 886

Source: Computed.

It would be interesting to compare efficiencies of banks under these
two conditions, namely when banks are pursuing their own
objectives and banks are incorporating the objectives of the Central
bank. In both conditions, as indicated in Table 4 and Table 6, we
find that banks have been maintaining relatively high scores of
average technical efficiency from 1994 to 2004. But the difference
in efficiencies is more explained in terms of allocative efficiencies.
From Table 3 and Table 7, it is found that the mean allocative
efficiency scores of banks over the period were lower in the case
when banks are pursing their own objectives rather than the central
bank’s objectives. Thus, the overall economic efficiency of banks
turns out to be lower when the central bank’s objectives are taken
into account. The mean allocative efficiency score for the period



                                 28
under study is estimated at 0.709 when banks pursue their own
objectives as compared with a figure of 0.886 when central bank’s
objectives are incorporated.


Moreover, it should be noted that DEAP provides a relative
performance measure. It is a comparative analysis and we separate
those, which are good relative to those, which are bad, worse or
worst.   These are reported in the tables and diagrams in the
appendix.    Mean efficiency score means an average value of
efficiency over the years, minimum means the minimum efficiency
value that the bank has had over the years while gap (in percentage)
means how much less, the non best practice banks produce the best
practice banks on average. Tables 12 to 17 show these summary
statistics for TE. AE and OE for both models, respectively. The
mean and minimum efficiency behaviour of the same are illustrated
in charts 1 to 6 while the corresponding evolution of efficiency gaps
for both models are shown in charts 7 and 8.


We find a lot of fluctuations in the gaps over the period of study in
the case of technical efficiency, allocative efficiency and overall
economic efficiency in both models. In terms of the overall
economic efficiency, in the case when banks are maximising their
objectives, the gap has been reduced especially since 2001
indicating a general increase in efficiency of non-best practice
banks. However, in the case of model 2, there is no clear trend as
the gaps have been fluctuated but with relatively lower values as



                                 29
compared with model 1. This conforms to our earlier results that
efficiencies are lower when banks are pursuing the objectives of
financial soundness.


                       V.     CONCLUSION


To sum up, in this paper, we have computed technical, allocative
and economic indicators of banking performance in terms of
efficiency scores under two different situations. First, when banks
pursue their own objectives to maximize revenues and second, when
banks pursue the objectives of the central bank, namely financial
stability and economic performance. An analysis of the efficiency
scores confirms that in both situations technical efficiencies have
been relatively high. However, the differences in overall economic
efficiencies were due to lower allocative efficiencies. The mean
allocative efficiencies of banks were lower when banks pursued
their own objectives rather than the central bank’s objectives.


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                                   37
                             APPENDIX

The Mauritian banking sector can be categorised into three domestic
banks, five foreign banks and two foreign banks locally
incorporated. Two of the domestic banks, namely MCB and SBM,
hold about 70 per cent of the total banking assets and deposits,
dominating the loan banking landscape. Although MCB has
maintained its deposit share over the period 1994 and 2004, the
share of deposits of the SBM has declined from 29.8 per cent in
1994 to 24.6 percent in 2004. Among the foreign banks, HSBC has
the largest share of assets and deposits of about 9 per cent, followed
by Barclays with a share of about 6 per cent. Among the foreign
banks locally incorporated, the deposit share of Delphis increased
from about 2 per cent to 4.4 per cent in 1997 as it took over a bank
in liquidation. It gradually rose to 5.0 per cent in 2004. The ratio of
profit after tax out of the total is relatively high for the SBM as
compared to MCB although the deposit share of the SBM (28 per
cent on average) is much lower than that of the MCB with an
average deposit share of 43 per cent.


For most banks, non-interest income coming mainly from profit
derived   from foreign currency transactions and            fees and
commissions, is as important a source of banking income as interest
income. However, for HSBC and Barclays, non-interest income has
been the major source of income over the period 1994-2004. Total
loans represent about 60 per cent of bank's total assets. The share of
loans out of total loans is largely comparable to the deposit share of



                                  38
        banks with the two largest banks allocating about 75 per cent of total
        loans. The credit-deposit ratio, which describes the extent to which
        banks fund their loan activities out of deposits and which also
        measures the extent of risk has been on an upward trend over the
        period 1994-2004, rising from 67.3 per cent in 1994 to 79.2 per cent
        in 2004. Banks' provision for loan losses as a ratio of total loans
        averaged to 0.9 per cent over the period 1994-2004. Baroda and
        Habib made the highest provisioning while MCB, SBM and Delphis
        had relatively lower provisions for loan losses.


        The upshot of the above analysis is that the banking sector
        developments indicate symptoms of lopsided growth and banking
        sector concentration. Profitability, total assets and deposits of banks
        have expanded but varied across banks.

        Selected Banking Sector Indicators¹ (per cent)
                        Assets¹ Deposits¹   Profit   Interest Non-      Loans¹ Provision
                                            before   Income¹ interest          for Loan
                                            Tax¹              Income¹          Losses³
Local Banks:
MCB     1994             44.2      44.1      32.1      36.3     46.1     45.5     0.83
        2000             45.6      43.9      38.2      45.6     44.8     50.2     0.82
                    2
        1994-2004        44.5     41.28      40.85    45.23     44.4     49.5     0.82
SBM     1994             29.7      29.8      31.3      32.0     21.7     28.7     0.76
        2004             26.0      25.6      39.9      26.8     35.7     29.0     0.45
        1994-20042       29.7      29.1      35.4      30.8     31.8     27.0     0.58
IOIB    1994              2.2      2.2        4.1      3.1      1.7      2.1      1.16
        200               2.5      2.7        1.5      2.9      1.0      2.1      1.07
        1994-20042        2.5      2.5        2.3      3.0      1.5      2.4      1.02



                                            39
                            Assets¹ Deposits¹   Profit   Interest Non-      Loans¹ Provision
                                                before   Income¹ interest          for Loan
                                                Tax¹              Income¹          Losses³
   Foreign Banks:
   HSBC     1994              7.4      7.3       13.5      8.2      9.3      8.1     0.05
            2004             10.0      10.9      10.8      9.9      10.7     6.7     0.63
            1994-20042        8.8      9.1        9.6      8.7      10.4     8.3     1.22
   Barclays 1994              6.6      6.7        6.7      7.7      10.5     5.8     2.22
            2000              6.1      6.6        4.5      6.0      7.0      6.2     1.56
            1994-20002        6.1      6.3        5.3      6.2      8.4      5.5     1.89
   BNPI     1994              3.9      4.0        5.5      5.0      4.9      4.3     0.01
            2004              2.9      3.1        1.3      2.7      2.6      1.9     1.73
                        2
            1994-2004         3.3      3.4        3.6      3.6      3.9      3.1     0.85
   Baroda   1994              2.2      2.2        3.3      3.1      1.5      1.9     0.32
            2004              1.9      2.1        1.0      2.1      1.1      0.8     7.49
                        2
            1994-2004         2.0      2.1        1.7      2.2      1.2      1.3     3.06
   Habib    1994              0.8      0.9        1.4      1.2      0.9      0.6     9.27
            2004              0.7      0.7        1.4      0.9      0.5      0.3     13.57
                        2
            1994-2004         0.8      0.8        1.2      0.9      0.7      0.4     8.51
   Foreign Banks Locally Incorporated:
   Delphis 1994               1.6      1.5        1.2      1.7      1.4      1.5     0.65
            2004              5.3      5.0        3.1      5.4      3.1      3.9     0.85
            1994-20042        3.6      3.5        2.7      3.9      2.8      3.3     0.94
   SEAB     1994              1.3      1.2        1.0      1.7      1.8      1.6     5.94
            2004              1.0      1.1        0.1      1.3      0.6      0.9     0.75
                        2
            1994-2004         1.2      1.1        0.5      1.4      1.1      1.2     1.86
  ¹ share out of total.
  ² mean share out of total over the period 1994-2004
  ³ share out of total loans.
Source: Computed from Banks’ Balance Sheets.




                                                40
Summary Statistics on Banks

 Rs millions                     2001     2002    2003       2004
 Charge for bad and doubtful     407      685      936       814
 debts
 Total advances of banks        71507    74715    85839      89037
 Interest income                10572    10572    12154      1325
 Interest expense               6857     6371     7232       7584
 Non-interest income            4521     4201     4922       5845
 Staff and operating expenses   2954     2941     3653       3954
 Operating profits              3594     3353     4275       4521
 Capital                        8754     8598     8965       8457
Source: Commercial Banking Reports.

Technical Efficiency of Banks (model 1) Summary Statistics

Year       mean       minimum        gap (%)
1994       0.967       0.943           3.3
1995        0.97       0.752            3
1996       0.981       0.877           1.9
1997       0.981       0.812           1.9
1998       0.958       0.761           4.2
1999       0.961       0.857           3.9
2000        0.94       0.814            6
2001        0.96       0.825            4
2002        0.95        0.82            5
2003        0.94       0.825            6
2004        0.93       0.812            7

Gap: (1-mean)*100 indicates how much less, in percentage, the non-
best practice banks produce the best practice banks on average.
Source: Computed.




                                41
Chart 1

                                 Mean and minimun efficiency scores of technical efficiency


                      1.2




                       1
  Efficiency scores




                      0.8



                                                                                                       mean
                      0.6
                                                                                                       min



                      0.4




                      0.2




                       0
                        1992   1994       1996        1998           2000        2002    2004   2006


                                                             Years




                                                                            42
Allocative Efficiency of Banks Summary Statistics
Year          mean        min         gap (%)
1994          0.799      0.536          20.1
1995          0.797      0.583          20.3
1996          0.622      0.418          37.8
1997          0.719      0.418          28.1
1998          0.623      0.336          37.7
1999          0.805      0.495          19.5
2000          0.804      0.441          19.6
2001           0.81       0.81           19
2002           0.82       0.54           18
2003           0.75       0.55           25
2004           0.96       0.52            4

Gap: (1-mean)*100 indicates how much less, in percentage, the non-
best practice banks produce the best practice banks on average
Source: Computed.




                                 43
Chart 2

                                      Mean and minimum allocative efficiency scores


                      1.2




                       1




                      0.8
  Efficiency scores




                                                                                                    mean
                      0.6
                                                                                                    min




                      0.4




                      0.2




                       0
                        1992   1994   1996         1998                2000   2002    2004   2006

                                                          Years




                                                                  44
Economic Efficiency of Banks (model 1): Summary Statistics
Year           min         mean        gap (%)
1994          0.536        0.773         22.7
1995          0.332        0.772         22.8
1996           0.29         0.61          39
1997          0.418        0.683         31.7
1998          0.388        0.597         40.3
1999          0.506        0.773         22.7
2000          0.518        0.754         24.6
2001          0.625        0.885         11.5
2002           0.68        0.885         11.5
2003           0.52        0.885         11.5
2004           0.82        0.825         17.5
Gap: (1-mean)*100 indicates how much less, in percentage, the non-
best practice banks produce the best practice banks on average
Source: Computed.




                               45
Chart 3

                                      Mean and Minimum economic efficiency scores


                       1


                      0.9


                      0.8


                      0.7
  Efficiency scores




                      0.6
                                                                                                  min
                      0.5                                                                         mea
                                                                                                  n
                      0.4


                      0.3


                      0.2


                      0.1


                       0
                        1992   1994    1996        1998           2000     2002     2004   2006

                                                          Years




                                                                  46
Technical Efficiency of Banks (model 2): Summary Statistics
Year           min         mean        gap (%)
1994          0.918        0.987          1.3
1995          0.833         0.98           2
1996          0.727         0.94           6
1997          0.825        0.987          1.3
1998          0.948        0.992          0.8
1999          0.837        0.977          2.3
2000          0.727         0.94           6
2001          0.925        0.952          4.8
2002          0.725        0.954          4.6
2003           0.79        0.856         14.4
2004           0.93        0.985          1.5

Gap: (1-mean)*100 indicates how much less, in percentage, the non-
best practice banks produce the best practice banks on average

Source: Computed




                               47
Chart 4


                                             Mean and minimum efficiency scores


                      1.2




                       1




                      0.8
  Efficiency scores




                                                                                                  min
                      0.6                                                                         mea
                                                                                                  n



                      0.4




                      0.2




                       0
                        1992   1994   1996           1998           2000     2002   2004   2006

                                                            Years




                                                                    48
Allocative Efficiency of Banks (model 2); Summary Statistics

Year          min        mean         gap (%)
1994         0.841       0.908           9.2
1995         0.866       0.938           6.2
1996         0.622         0.9           10
1997         0.971        0.96            4
1998         0.739       0.887          11.3
1999         0.692       0.899          10.1
2000         0.791         0.9           10
2001         0.812       0.925           7.5
2002         0.941       0.936           6.4
2003         0.826       0.852          14.8
2004         0.741       0.842          15.8

Gap: (1-mean)*100 indicates how much less, in percentage, the
non-best practice banks produce the best practice banks on average

Source: Computed.




                                 49
Chart 5

                                             Mean and Minimum efficiency scores


                      1.2




                       1




                      0.8
  efficiency scores




                                                                                                  min
                      0.6                                                                         mea
                                                                                                  n



                      0.4




                      0.2




                       0
                        1992   1994   1996           1998           2000     2002   2004   2006

                                                            Years




                                                                    50
Economic Efficiency of Banks (model 2): Summary Statistics

Year           min         mean        gap (%)
1994          0.851        0.896         10.4
1995          0.833        0.919          8.1
1996          0.622        0.845         15.5
1997          0.859        0.948          5.2
1998          0.778         0.88          12
1999          0.692        0.878         12.2
2000          0.662        0.845         15.5
2001          0.817       0.9385         6.15
2002          0.881        0.945          5.5
2003          0.854        0.854         14.6
2004          0.822        0.913         8.65

Gap: (1-mean)*100 indicates how much less, in percentage, the
non-best practice banks produce the best practice banks on average

Source: Computed.




                               51
Chart 6

                                             Mean and minimun efficiency of banks


                      1.2




                       1




                      0.8
  Efficiency scores




                                                                                                   min
                      0.6                                                                          mea
                                                                                                   n



                      0.4




                      0.2




                       0
                        1992   1994   1996            1998           2000     2002   2004   2006

                                                             Years




                                                                     52
Chart 7

                                            Gaps of efficiency measures


                      45


                      40


                      35


                      30
  Efficiency scores




                                                                                               te1
                      25
                                                                                               ae
                                                                                               1
                      20                                                                       oe
                                                                                               1


                      15


                      10


                      5


                      0
                       1992   1994   1996       1998            2000      2002   2004   2006

                                                       Years




                                                               53
Chart 8

                                              Efficiency gaps in model 2


                        18


                        16


                        14
  Efficiency measures




                        12


                                                                                                te2
                        10
                                                                                                ae
                                                                                                2
                        8                                                                       oe
                                                                                                2


                        6


                        4


                        2


                        0
                         1992   1994   1996     1998            2000       2002   2004   2006

                                                       Years




                                                               54

				
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