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8/10/2019 AES Paper Final

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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 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


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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


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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


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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 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


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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


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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


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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


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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


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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


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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.


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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 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


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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, ; ,..., ; ,..., .


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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


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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


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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 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


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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 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


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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.


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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.


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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.


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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.


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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.


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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 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


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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 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)


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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.


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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.


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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.


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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


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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.


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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


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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


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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


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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


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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


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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


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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


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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


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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



Source: Computed


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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


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Technical Efficiency of Banks (model 2): Summary Statistics


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




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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


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Allocative Efficiency of Banks (model 2); Summary Statistics


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


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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


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Economic Efficiency of Banks (model 2): Summary Statistics


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


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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


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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


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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

aes paper final

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