Efficiency of commercial banks after financial...

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

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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

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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.

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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

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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

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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.

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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

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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.

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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

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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

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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,

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Max (1)

Subject to

where are positive known outputs and inputs of the nth

DMU and are the variable weights to be determined by

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 given by the problem (1) is the DEA

efficiency score assigned to . Since every DMU can be

, this optimisation problem is well defined for every DMU.

If the efficiency score , satisfies the necessary

condition to be DEA efficient; otherwise it is DEA inefficient.

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It is difficult to solve problem (1) as stated, because the objective

function is non-linear and fractional. Charnes et al., however,

transformed the above non-linear programming problem into a

linear one as follows:

Max (2)

Subject to

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 ensures that

the base 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 weight sets. In each program, the

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constraints are held constant while the ratio to be maximised is

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

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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

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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

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of premises and fixed assets, net of depreciation. Loanable funds

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.

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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

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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.

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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.

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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.

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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

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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 Efficiency

Allocative Efficiency

Overall Economic Efficiency

MCBBarclaysHSBCBarodaHabibBNPISBMIOIBSEABDelphis

0.8890.9691.0000.8621.0000.9730.9920.9840.9540.985

0.7670.5510.8700.8680.3730.6420.9170.8870.5330.975

0.6770.5320.8700.7490.3730.6250.9090.8720.5080.961

Mean 0.961 0.738 0.709

Source: Computed.

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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.

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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.

25

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.

27

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.

28

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.

30

TABLE 9. Summary of Means (Central Bank’s Objectives)

TechnicalEfficiency

AllocativeEfficiency

Overall EconomicEfficiency

MCBBarclaysHSBCBarodaHabib BNPISBMIOIBSEABDelphis

0.9790.9120.9890.9951.0000.9221.0001.0000.9560.965

0.9830.9680.8870.7591.0000.8570.9270.9160.9370.897

0.9620.8810.8760.7541.0000.7860.9270.9160.8950.866

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

31

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

32

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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40

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

41

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 before Tax¹

Interest Income¹

Non-interest Income¹

Loans¹ Provision for Loan 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 0.821994-20042 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 0.762004 26.0 25.6 39.9 26.8 35.7 29.0 0.451994-20042 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 1.16200 2.5 2.7 1.5 2.9 1.0 2.1 1.071994-20042 2.5 2.5 2.3 3.0 1.5 2.4 1.02

42

Assets¹ Deposits¹ Profit before Tax¹

Interest Income¹

Non-interest Income¹

Loans¹ Provision for Loan 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 0.631994-20042 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 2.222000 6.1 6.6 4.5 6.0 7.0 6.2 1.561994-20002 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 0.012004 2.9 3.1 1.3 2.7 2.6 1.9 1.731994-20042 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 0.322004 1.9 2.1 1.0 2.1 1.1 0.8 7.491994-20042 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 9.272004 0.7 0.7 1.4 0.9 0.5 0.3 13.571994-20042 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 0.652004 5.3 5.0 3.1 5.4 3.1 3.9 0.851994-20042 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 5.942004 1.0 1.1 0.1 1.3 0.6 0.9 0.751994-20042 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 1994-2004³ share out of total loans.

Source: Computed from Banks’ Balance Sheets.

43

Summary Statistics on Banks

Rs millions 2001 2002 2003 2004Charge for bad and doubtful debts

407 685 936 814

Total advances of banks 71507 74715 85839 89037Interest income 10572 10572 12154 1325Interest expense 6857 6371 7232 7584Non-interest income 4521 4201 4922 5845Staff and operating expenses 2954 2941 3653 3954Operating profits 3594 3353 4275 4521Capital 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 3.31995 0.97 0.752 31996 0.981 0.877 1.91997 0.981 0.812 1.91998 0.958 0.761 4.21999 0.961 0.857 3.92000 0.94 0.814 62001 0.96 0.825 42002 0.95 0.82 52003 0.94 0.825 62004 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.

44

Chart 1

1992 1994 1996 1998 2000 2002 2004 2006

0

0.2

0.4

0.6

0.8

1

1.2

Mean and minimun efficiency scores of technical efficiency

mean

min

Years

Effic

ienc

y sc

ores

45

Allocative Efficiency of Banks Summary Statistics

Year mean min gap (%)1994 0.799 0.536 20.11995 0.797 0.583 20.31996 0.622 0.418 37.81997 0.719 0.418 28.11998 0.623 0.336 37.71999 0.805 0.495 19.52000 0.804 0.441 19.62001 0.81 0.81 192002 0.82 0.54 182003 0.75 0.55 252004 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.

46

Chart 2

1992 1994 1996 1998 2000 2002 2004 2006

0

0.2

0.4

0.6

0.8

1

1.2

Mean and minimum allocative efficiency scores

mean

min

Years

Effi

cien

cy s

core

s

47

Economic Efficiency of Banks (model 1): Summary Statistics

Year min mean gap (%)1994 0.536 0.773 22.71995 0.332 0.772 22.81996 0.29 0.61 391997 0.418 0.683 31.71998 0.388 0.597 40.31999 0.506 0.773 22.72000 0.518 0.754 24.62001 0.625 0.885 11.52002 0.68 0.885 11.52003 0.52 0.885 11.52004 0.82 0.825 17.5


Source: Computed.

48

Chart 3

1992 1994 1996 1998 2000 2002 2004 2006

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Mean and Minimum economic efficiency scores

minmean

Years

Effic

ienc

y sc

ores

49

Technical Efficiency of Banks (model 2): Summary Statistics

Year min mean gap (%)1994 0.918 0.987 1.31995 0.833 0.98 21996 0.727 0.94 61997 0.825 0.987 1.31998 0.948 0.992 0.81999 0.837 0.977 2.32000 0.727 0.94 62001 0.925 0.952 4.82002 0.725 0.954 4.62003 0.79 0.856 14.42004 0.93 0.985 1.5


Source: Computed

50

Chart 4

1992 1994 1996 1998 2000 2002 2004 2006

0

0.2

0.4

0.6

0.8

1

1.2

Mean and minimum efficiency scores

minmean

Years

Effic

ienc

y sc

ores

51

Allocative Efficiency of Banks (model 2); Summary Statistics

Year min mean gap (%)1994 0.841 0.908 9.21995 0.866 0.938 6.21996 0.622 0.9 101997 0.971 0.96 41998 0.739 0.887 11.31999 0.692 0.899 10.12000 0.791 0.9 102001 0.812 0.925 7.52002 0.941 0.936 6.42003 0.826 0.852 14.82004 0.741 0.842 15.8


Source: Computed.

52

Chart 5

1992 1994 1996 1998 2000 2002 2004 2006

0

0.2

0.4

0.6

0.8

1

1.2

Mean and Minimum efficiency scores

minmean

Years

effic

ienc

y sc

ores

53

Economic Efficiency of Banks (model 2): Summary Statistics

Year min mean gap (%)1994 0.851 0.896 10.41995 0.833 0.919 8.11996 0.622 0.845 15.51997 0.859 0.948 5.21998 0.778 0.88 121999 0.692 0.878 12.22000 0.662 0.845 15.52001 0.817 0.9385 6.152002 0.881 0.945 5.52003 0.854 0.854 14.62004 0.822 0.913 8.65


Source: Computed.

54

Chart 6

1992 1994 1996 1998 2000 2002 2004 2006

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Mean and minimun efficiency of banks

minmean

Years

Effic

ienc

y sc

ores

55

Chart 7

1992 1994 1996 1998 2000 2002 2004 2006

0

5

10

15

20

25

30

35

40

45

Gaps of efficiency measures

te1ae1oe1

Years

Effic

ienc

y sc

ores

56

Chart 8

1992 1994 1996 1998 2000 2002 2004 2006

0

2

4

6

8

10

12

14

16

18

Efficiency gaps in model 2

te2ae2oe2

Years

Effic

ienc

y m

easu

res

57

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