Download - AES Paper Final
-
8/10/2019 AES Paper Final
1/54
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 andintroduction 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
-
8/10/2019 AES Paper Final
2/54
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
-
8/10/2019 AES Paper Final
3/54
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
-
8/10/2019 AES Paper Final
4/54
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 ofreserve 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
-
8/10/2019 AES Paper Final
5/54
of bank branches expanded significantly to reach 117 in1990 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
-
8/10/2019 AES Paper Final
6/54
the banks 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
-
8/10/2019 AES Paper Final
7/54
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
-
8/10/2019 AES Paper Final
8/54
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
-
8/10/2019 AES Paper Final
9/54
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
-
8/10/2019 AES Paper Final
10/54
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.
-
8/10/2019 AES Paper Final
11/54
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 branchesof 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
-
8/10/2019 AES Paper Final
12/54
Max e
u y
v x
j j
j
J
i i
i
I
0
0 0
1
0 0
1
(1)
Subject to
u y
v xn N
j j
n
j
J
i i
n
i
I
0
1
0
1
1 1
; ,..., ;
v u i I j J i j0 0 0 1 1, ; ,..., ; ,..., .
-
8/10/2019 AES Paper Final
13/54
transformed the above non-linear programming problem into a
linear one as follows:
Max h u yj jj
J0 0 0
1
(2)
Subject to
v x u y v xi ii
I
j j
n
j
J
i i
n
i
I0 0
1
0
1
0
1
1 0
, ;
n N v u i I j J i j 1 1 10 0,..., ; ; ; ,..., ; ,... .
The variables defined in problem (2) are the same as those defined
-
8/10/2019 AES Paper Final
14/54
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
-
8/10/2019 AES Paper Final
15/54
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 bankingactivities 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
-
8/10/2019 AES Paper Final
16/54
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 methodscomplementarily 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
-
8/10/2019 AES Paper Final
17/54
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 thanstaff 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.
-
8/10/2019 AES Paper Final
18/54
18
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.
-
8/10/2019 AES Paper Final
19/54
19
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.
-
8/10/2019 AES Paper Final
20/54
20
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.
-
8/10/2019 AES Paper Final
21/54
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.
-
8/10/2019 AES Paper Final
22/54
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 to0.754 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
-
8/10/2019 AES Paper Final
23/54
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 butlower 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)
-
8/10/2019 AES Paper Final
24/54
24
TABLE 6. Technical Efficiency of Banks (Central Banks 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.
-
8/10/2019 AES Paper Final
25/54
25
TABLE 7. Allocative Efficiency of Banks (Central Banks 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.
-
8/10/2019 AES Paper Final
26/54
26
TABLE 8. Overall Economic Efficiency of Banks (Central Banks 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.
-
8/10/2019 AES Paper Final
27/54
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. BNPIs 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 optimumlevel 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 banks AE was at its optimum level throughout all
the years. In the case of Barclays pursuing the central bank's
-
8/10/2019 AES Paper Final
28/54
TABLE 9. Summary of Means (Central Banks Objectives)
TechnicalEfficiency
AllocativeEfficiency
Overall EconomicEfficiency
MCB
Barclays
HSBC
Baroda
Habib
BNPI
SBM
IOIB
SEAB
Delphis
0.979
0.912
0.989
0.995
1.000
0.922
1.000
1.000
0.956
0.965
0.983
0.968
0.887
0.759
1.000
0.857
0.927
0.916
0.937
0.897
0.962
0.881
0.876
0.754
1.000
0.786
0.927
0.916
0.895
0.866
Mean 0. 972 0. 913 0. 886
Source: Computed.
-
8/10/2019 AES Paper Final
29/54
-
8/10/2019 AES Paper Final
30/54
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
-
8/10/2019 AES Paper Final
31/54
Avkiran, N.K. 1999a. An Application Reference for Data
Envelopment Analysis in Branch Banking: Helping the Novice
Researcher.International Journal of Bank Marketing17: 206-220.
Avkiran, N.K. 1999b. The Evidence of Efficiency Gains: The Role
of Mergers and the Benefits to the Public. Journal of Banking and
Finance23: 991-1013.
Athanassopoulos, A.D. and Giokas, D. 2000. The Use of Data
Envelopment Analysis in Banking Institutions: Evidence from the
Commercial Bank of Greece.Interfaces30: 81.
Barr, R., Seiford, L. and Siems, T. 1994. Forecasting Bank Failure:
-
8/10/2019 AES Paper Final
32/54
Productive Efficiency: Techniques and Applications (H. O. Fried, C.
A. K. Lovell, and S. S. Schmidt, eds.), New York: Oxford University
Press.
Bhattacharyya, A., Bhattacharyya, A. and Kumbhakar, S.C. 1997.
Changes in Economic Regime and Productivity Growth: A Study
of Indian Public Sector Banks Source. Journal of Comparative
Economics25:196-201.
Berg, S. A., Forsund, F. R and Jansen, E. S. 1992. Malmquist
Indices of Productivity Growth during the Deregulation of
Norwegian Banking. Scandinavian Journal of Economics 94:
S211-S228.
-
8/10/2019 AES Paper Final
33/54
Berger, A. N., Hunter, W. C., and Timme, S. C. April. 1992. The
Efficiency of Financial Institutions: A Review of Research, Past,
Present and Future.Journal of Banking andFinance17(2/3): 221-
249.
Bundoo, S.K. and Dabee, B. 1999. Gradual Liberalisation of Key
Markets: The Road to Sustainable Growth in Mauritius. Journal of
International Development11: 437-464.
Charnes, A., Cooper, W.W. and Rhodes, E. 1996. Measuring the
Efficiency of Decision Making Units. European Journal of
Economic Research 2: 429-44.
-
8/10/2019 AES Paper Final
34/54
Evanoff, D.D. and Israilevich, P.R. 1991. Productive Efficiency in
Banking.Economic Perspectives15:11-32.
Fanelli, Jose M., and Rohinton Medhora. 1998.Financial Reform in
Developing Countries, (London, MacMillan Press).
Fre, R., Grosskopt, S. Lindgren, B., and Roos, P. 1994.
Productivity Developments in Swedish Hospitals: A Malmquist
Output Index Approach. In Data Envelopment Analysis: Theory,
Methodology and Applications (A. Charnes, W. W. Cooper. A. Y.
Lewin, and L. M. Seiford, eds.). Boston: Kluwer Academic
Publishers, 253-272
-
8/10/2019 AES Paper Final
35/54
Fried, H. O. and Lovell, C.A.K. 1994. Enhancing the Performance
of Credit Unions; The Evolutions of a Methodology. Recherches
Economiques de Louvain, 60: 431-47.
Fry, M. J. 1995. Money Interest, and Banking in Economics
Development,(Baltimore: Johns Hopkins University Press).
Gilbert, R. 1984. Banking Market Structure and Competition: A
Survey."Journal of Money, Credit and Banking, (November).
Goldfield, S.M. and Sichel, D.E. 1987. Money Demand: The
Effects of Inflation and Alternative Adjustment Mechanisms.
Review of Economics and Statistics,69: 511-5.
-
8/10/2019 AES Paper Final
36/54
Jankee, K. 1999. Financial Liberalisation and Monetary ControlReform in Mauritius. Research Journal 12 University of
Mauritius.
Jagtiani, J. and Khanthavit, A. 1996. Scale and Scope Economies at
Large Banks: Including Off-Balance Sheet Products and Regulatory
Effects, 1984-1991. Journal of Banking and Finance 20: 1271-
1287.
Leightner, Jonathen E., and C. A. Knox Lovell. 1998. The Impact
of Financial Liberalization on the Performance of Thai Banks.
Journal of Economics and Business 50:115-31.
-
8/10/2019 AES Paper Final
37/54
-
8/10/2019 AES Paper Final
38/54
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
-
8/10/2019 AES Paper Final
39/54
banks with the two largest banks allocating about 75 per cent of totalloans. 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
-
8/10/2019 AES Paper Final
40/54
Assets Deposits Profitbefore
Tax
InterestIncome Non-interest
Income
Loans Provisionfor Loan
LossesForeign 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 1.22Barclays 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 1.731994-20042 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 0.32
2004 1.9 2.1 1.0 2.1 1.1 0.8 7.49
1994-20042 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 27
-
8/10/2019 AES Paper Final
41/54
Summary Statistics on Banks
Rs millions 2001 2002 2003 2004
Charge for bad and doubtful
debts
407 685 936 814
Total advances of banks 71507 74715 85839 89037
Interest income 10572 10572 12154 1325Interest 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) SummaryStatistics
Year mean minimum gap (%)
1994 0.967 0.943 3.3
-
8/10/2019 AES Paper Final
42/54
42
Chart 1
0
0.2
0.4
0.6
0.8
1
1.2
1992 1994 1996 1998 2000 2002 2004 2006
Efficiencyscores
Years
Mean and minimun efficiency scores of technical efficiency
mean
min
-
8/10/2019 AES Paper Final
43/54
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 28.11998 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
-
8/10/2019 AES Paper Final
44/54
44
Chart 2
0
0.2
0.4
0.6
0.8
1
1.2
1992 1994 1996 1998 2000 2002 2004 2006
Efficiencyscores
Years
Mean and minimum allocative efficiency scores
mean
min
-
8/10/2019 AES Paper Final
45/54
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 31.71998 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
-
8/10/2019 AES Paper Final
46/54
46
Chart 3
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1992 1994 1996 1998 2000 2002 2004 2006
Efficiencyscores
Years
Mean and Minimum economic efficiency scores
min
mea
n
-
8/10/2019 AES Paper Final
47/54
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 1.31998 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
-
8/10/2019 AES Paper Final
48/54
48
Chart 4
0
0.2
0.4
0.6
0.8
1
1.2
1992 1994 1996 1998 2000 2002 2004 2006
Efficiencyscores
Years
Mean and minimum efficiency scores
min
mean
-
8/10/2019 AES Paper Final
49/54
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 6.42003 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
-
8/10/2019 AES Paper Final
50/54
50
Chart 5
0
0.2
0.4
0.6
0.8
1
1.2
1992 1994 1996 1998 2000 2002 2004 2006
efficiencyscores
Years
Mean and Minimum efficiency scores
min
mea
n
-
8/10/2019 AES Paper Final
51/54
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 5.21998 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
-
8/10/2019 AES Paper Final
52/54
52
Chart 6
0
0.2
0.4
0.6
0.8
1
1.2
1992 1994 1996 1998 2000 2002 2004 2006
Efficiencyscores
Years
Mean and minimun efficiency of banks
min
mea
n
-
8/10/2019 AES Paper Final
53/54
53
Chart 7
0
5
10
15
20
25
30
35
40
45
1992 1994 1996 1998 2000 2002 2004 2006
Efficiencyscores
Years
Gaps of efficiency measures
te1
ae
1
oe
1
-
8/10/2019 AES Paper Final
54/54
54
Chart 8
0
2
4
6
8
10
12
14
16
18
1992 1994 1996 1998 2000 2002 2004 2006
Efficiencymeasures
Years
Efficiency gaps in model 2
te2
ae
2
oe
2