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Efficiency Analysis of Conventional and Islamic Banks in Indonesia using Data Envelopment Analysis1
Ascarya, Diana Yumanita, and Guruh S. Rokhimah Center for Central Banking Education and Studies, Bank Indonesia
Jl. M.H. Thamrin 2, Sjafruddin Prawiranegara Tower, 20th fl., Jakarta 10110, Indonesia Email: [email protected]; [email protected]; [email protected]
ABSTRACT This study will measure and compare the efficiency of Conventional and Islamic banks in Indonesia using nonparametric approach data envelopment analysis (DEA). These measurements will provide comprehensive and robust results of efficiency of individual bank compare to its peer group in every aspect considered.
The results show that Islamic banks are slightly more efficient than conventional banks. However, they are improving and converging to a high level of efficiency. Moreover, income has become the most efficient, while labor is always inefficient in both banks. Deposit is improving in conventional banks, while it is worsening in Islamic banks. Financing has been a problem in conventional banks, while it is always high in Islamic banks. Therefore, Islamic banks should redirect their marketing and communication strategies to focus more on targeting floating customers, while the shortage in human resource should be given serious attention with short term and long term strategies.
JEL Classification: C14, G21, G28 Keywords: Islamic Banking, Performance, Efficiency, Data Envelopment
Analysis
1 Paper submitted to Airlangga University International Seminar and Symposium “On Implementations of Islamic Economics to Positive Economics in the World as Alternative of Conventional Economics System: Toward Development in the New Era of the Holistic Economics,” Airlangga University, Surabaya, Indonesia, August 1-3, 2008.
1. Introduction
1.1 Background
Islamic banks have been in existence since early 1960s. The first Islamic bank
established in 1963 as a pilot project in the form of rural savings bank in a small
town of Egypt, Mit Ghamr. After that, Islamic banking movement came back to
life in mid 1970s. The establishment of Islamic Development Bank in 1975
triggered the development of Islamic banks in many countries, such as Dubai
Islamic Bank in Dubai (1975), Faisal Islamic Bank in Egypt and Sudan (1977),
and Kuwait Finance House in Kuwait (1977).
Joharris (2007) predicted that there are over 276 Islamic financial institutions (IFI)
in the world, spread over 70 countries - sprawling from London, New York and
Zurich to the Middle East, Africa and Asia with capitalization in excess of US$13
billion. These include banks, mutual funds, mortgage companies and takaful
providers. The pool of money held by Muslim is predicted more than US$3.0
trillion. At present, there is an estimated US$1 trillion Islamic fund in the market.
Moreover, global Islamic capital market is growing at 15% - 20% per annum,
including deposits in Islamic banks which are estimated to be over US$560
billion. A large part of the banking and Takaful concentration is in Bahrain,
Malaysia, and Sudan. A significant part of mutual funds concentrate in the Saudi
Arabian and Malaysian markets in addition to the more advanced international
capital markets.
In Indonesia, Islamic financial institutions started to emerge in early 1980s with
the establishment of Baitut Tamwil-Salman in Bandung dan Koperasi Ridho Gusti
in Jakarta. The first Islamic Bank in Indonesia, Bank Muamalat Indonesia,
established in 1992. The development of Islamic bank has been accelerated since
Bank Indonesia (the central bank of Indonesia) allowed conventional banks to
open Islamic branch. This Islamic branch can offer Islamic banking products and
services separated from its conventional parent with its own infrastructure,
including staff and branches.
By April 2008, the Islamic banking system in Indonesia is represented by 3
Islamic banks, 28 Islamic branches, and 118 Islamic Rural Banks, with 730
offices and more than 1250 office channeling spread throughout the country. They
offer comprehensive and wide range of Islamic financial products and services
and cater 1.97% of the banking market share. It is expected that the Islamic
banking industry in Indonesia would reached 5% of the banking market share in
2011.
Despite these impressive achievements, Islamic banking in Indonesia has
experiencing a slower growth in the past two years. There are many factors that
could be attributed to this slower growth. One of these factors is the
competitiveness of Islamic Banks within the banking system, since, under dual
banking system, they have to compete head to head with conventional banks.
One important aspect of competitiveness is efficiency. Inefficiency would become
a great disadvantage to face a fierce competition in the banking industry. To win
the competition, Islamic banks should know the strengths and weaknesses of
themselves as well as of their competitor. Know yourself and know your
competitor is a halfway to success. Therefore, analysis of the efficiency of Islamic
banks in comparison with conventional banks is very important to provide a big
picture of the strengths and weaknesses of Islamic banks and their competitors.
Despite of the importance, there are very limited studies comparing the efficiency
of Islamic and conventional banks within a country, especially in Indonesia.
Therefore, there should be a study that measure efficiency of Islamic and
conventional banks in Indonesia to provide comparison and to improve the
robustness of previous measurements. These measures could also be used as a
guide for Islamic banks to improve their weaknesses to be able to compete head to
head with conventional banks and to achieve the intended goals to improve the
market share. Moreover, the goal to strengthen Islamic banking structure could be
achieved.
1.2 Objective
The objective of this study is to measure and compare the efficiency of
conventional and Islamic banks in Indonesia using nonparametric approach data
envelopment analysis (DEA). These measurements will provide comprehensive
and robust results of efficiency of individual bank compare to its peer group in
every aspect considered.
1.3 Scope of Study
Islamic banks included in this study are all full fledged Islamic banks and
business unit Islamic banks in Indonesia, while conventional banks included in
this study, to be comparable, are those with asset less one million US$ in real
term. The measurement will compare the efficiency of conventional and Islamic
banks in Indonesia using nonparametric approach (DEA).
1.4 Data and Methodology
The time frame of this study is 2002 – 2006. The data used in this study are the
data of published annual financial statements (balance sheets and income
statements) of conventional and Islamic banks in Indonesia, with total asset less
than one million US$ in real term.
This study will apply Data Envelopment Analysis (DEA). DEA is a non-
parametric and non-deterministic method to measure relative efficiency of
production frontier, based on empirical data of multiple inputs and multiple
outputs of decision making units. The non parametric nature of DEA makes it
does not need assumption of the production function. DEA will generate
production function based on data observed. Therefore, misspecification can be
minimized. DEA can be applied to analyze different kind of inputs and outputs
without initially assigning weight. Moreover, the efficiency produced is a relative
efficiency based on observed data. Preference of decision maker can also be
accommodate in the model.
1.5 Benefit of the Study
The results of this study will be very useful for many stakeholders of conventional
and Islamic banks in Indonesia, especially the regulator (Bank Indonesia), to
formulate appropriate policy recommendations to improve the synergy between
conventional and Islamic banks in facilitating intermediation to the real sector.
Conventional and Islamic banks in Indonesia will also benefit from this study to
see where they are in the competitiveness of the banking system. They will also be
able to determine the potential improvements of weak aspects
2. Literature Review
Banking efficiency has been a very important issue in a transition economy. All
countries in transition have been encounter at least with one banking crisis, and
many with more than one crisis (Jemrić and Vujčić, 2002). Banking efficiency is
also an important issue in a developing open economy, since most of them have
also been faced a banking crisis in the past. Malaysia and Indonesia are no
exception. There are many studies about banking efficiency using parametric and
non-parametric methods. Moreover, those studies are applied to conventional as
well as Islamic banks. Some of the studies applying non-parametric approach
DEA will be discussed.
Five of those studies that measure efficiency of Islamic banks using DEA
application are conducted by Yudistira (2003), Ascarya and Yumanita (2006,
2007a, and 2007b), and Sufian (2006). Yudistira measured the efficiency of 18
Islamic banks from various countries during 1997 – 2000 using intermediation
approach, since intermediation is a fundamental principle of Islamic banking.
Ascarya and Yumanita (2006) measured the efficiency of Islamic banks in
Indonesia during 2002 – 2004 using intermediation and production approaches,
since Islamic banking not only can be viewed as intermediary institution, but can
also be viewed as a production entity. Their studies in 2007 compared the
efficiency between Islamic bank in Malaysia and Indonesia, as well as between
conventional and Islamic banks in Indonesia during 2002-2005 using
intermediation approach. Meanwhile, Sufian measured the efficiency of Islamic
window banks in Malaysia during 2001 – 2004 using intermediation approach
with the same reason as that of Yudistira.
Other studies of banking efficiency using DEA are done by Jemrić and Vujčić
(2002) and Hadad et al. (2003). Jemrić and Vujčić measured efficiency of banks
in Croatia during 1995 – 2000 using intermediation and production approach,
since banking is not just functioned as intermediary, but also as a producer of
loans and investments. Meanwhile, Hadad et al. measured efficiency of banks in
Indonesia during 1995 – 2003 using asset approach to see the impact of merger
and acquisition.
The efficiency measurement, parametric or non-parametric, of financial institution
like banks can be approached from their activities. There are three main
approaches to explain the relationship between input and output of banks. Two
approaches, namely, production (or operational) approach and intermediation
approach, apply the classical microeconomic theory of the firm, while one
approach, namely modern (or assets) approach applies modified classical theory
of the firm by incorporating some specificities of banks’ activities, namely risk
management and information processing, as well as some form of agency
problems, which are crucial for explaining the role of financial intermediaries
(Freixas and Rochet, 1998). The production approach describes banking activities
as the production of services to depositors and borrowers using all available
factors of production, such as labor and physical capital. The intermediation
approach describes banking activities as intermediary in charge of transforming
the money borrowed from depositors (surplus spending units) into the money lent
to borrowers (deficit spending units). Meanwhile, the asset approach or the
modern approach tries to improve the first two approaches by incorporating risk
management, information processing, and agency problems into the classical
theory of the firm. The summary of approaches applied by previous authors can
be read in table 2.1.
Table 2.1 Summary of Non-parametric Approach Applied
Author Input Output
Intermediation Approach
Ascarya &
Yumanita’07b
Labor Costs; Fixed Assets; Total Deposits Total Loans; Income
Mochtar et.al‘07 Labor Costs; Total deposits, other
operating/overhead expenses
Total earning assets (financing/loans;
dealing securities; investment securities;
placements with other banks)
Zamil &
Rahman‘07
Staff cost; capital (net book value of
premises and fixed asset); Total deposits &
loanable funds
Loans and advances; Income (total
interest income, non-interest income and
income form IBS)
Ascarya &
Yumanita’07a
Labor Costs; Fixed Assets; Total Deposits Total Loans; Income
Sufian’06 Labor Costs2; Fixed Assets; Total Deposits Total Loans; Income
Ascarya &
Yumanita’06
Staff Costs; Fixed Assets; Total Deposits Total Loans; Other Income; Liquid Assets
Yudhistira’03 Staff Costs; Fixed Assets; Total Deposits Total Loans; Other Income; Liquid Assets
Jemrić &
Vujčić’02
No. of Employees; Fixed Assets & Software;
Total Deposits
Total Loans; Short-term Securities
Production Approach
Ascarya &
Yumanita’06
Interest Costs; Staff Costs; Operational
Costs
Interest Income; Other Operational
Income
Jemrić &
Vujčić’02
Interest & Related Costs; Commissions for
Services & Related Costs; Labor Related
Adm. Costs; Capital Related Adm. Costs
Interest & Related Revenues; Non-interest
Revenues
Asset Approach
Hadad et.al’03. Staff Costs to Total Assets; Interests Costs
to Total Assets; Other Costs to Total Assets
Financing to Connected Party; Financing
to Other Party; Financial Papers
2 As data on the number of employees are not readily made available, this study uses personnel expenses as a proxy measure.
From those studies it can be concluded that asset approach is an advanced
approach that views bank not only has a classical function of intermediary, but
also has other various new functions. Therefore, asset approach is not suitable to
be applied to Islamic banking which focuses on extending financing to the real
sector. Production approach can be applied for Islamic banking, since this
approach views Islamic bank as a general business unit. However, it becomes too
general, so that the very essence of Islamic banking is not represented.
Meanwhile, intermediation approach can be applied for Islamic banking since this
approach views Islamic banking as an intermediary institution. However, the input
and output variables should be selected carefully to really reflect the true essence
of Islamic banking. Input and output variables selected by Sufian (2006) are the
closest to the characteristics of Islamic banking. Some refined modifications
might be needed to make it more representative.
3. Methodology
Three well known approaches that widely applied to measure efficiency are
parametric Stochastic Frontier Analysis (SFA) and Distribution Free Analysis
(DFA), as well as nonparametric Data Envelopment Analysis (DEA). SFA, DFA
and DEA applications are derived from the theory of efficiency. Therefore, this
chapter will first discuss the theory of efficiency, the measurement of efficiency,
the connection of SFA, DFA and DEA to efficiency theory, and then discuss DEA
in details. Moreover, bank’s efficiency can be measured from its functions. Three
approaches to measure the efficiency of bank’s functions are intermediation
approach, production approach, and modern or asset approach. The theory of
efficiency in general, its relation to SFA, DFA and DEA, and the measurement of
bank’s efficiency can be described in figure 3.1.
Figure 3.1 Theory of Efficiency
3.1 The Theory of Efficiency
The concept of efficiency rooted from the microeconomic concept, namely,
consumer theory and producer theory. Consumer theory tries to maximize utility
or satisfaction from individual point of views, while producer theory tries to
maximize profit or minimize costs from producer point of views.
In the producer theory, there is a production frontier line that describes the
relationship between inputs and outputs of production process. This production
frontier line represents the maximum output from the use of each input. It also
represents the technology used by a business unit or industry. A business unit that
operates on the production frontiers is technically efficient. Figure 3.2 shows the
production frontier line.
Efficiency
Consumer Theory
Producer Theory
(Production
Technical Efficiency
Allocative
Constant Return to Scale
V i bl R t t
Theory Measurement l
BANK EFFICIENCY
Intermediation Approach
Production Approach
Modern Approach
Input – Output Concept
Econ
omic
Ef
ficie
ncy
Minimum Input Maximum
Para
met
ric
SFA
, TFA
, DFA
Non
para
met
ric
Figure 3.2 Production Frontier Line
Considered from economic theory, there are two different types of efficiency,
namely technical efficiency and economic efficiency. Economic efficiency has
macro economic point of view, while technical efficiency has micro economic
point of view. The measurement of technical efficiency limited to technical and
operational relationship in a conversion process of input to output. Whereas, in
economic efficiency price can not be considered as given, since price can be
influenced by macro policy (Sarjana, 1999). According to Farell (1957),
efficiency comprises of two components, namely:
a. Technical efficiency describes the ability of a business unit to maximize
output given certain amount of input.
b. Allocative efficiency describes the ability of a business unit to utilize inputs
in optimal proportion based on their price.
When the two types of efficiency combined, it will produce economic efficiency.
A company is considered to be economically efficient if it can minimize the
production costs to produce certain output within common technology level and
market price level.
Kumbhaker and Lovell (2000) argue that technical efficiency is only one of many
components economic efficiency as a whole. Nevertheless, in order to achieve
economic efficiency a company should produce maximum output with certain
amount of input (technical efficiency) and produce output with the right
combination within certain price level (allocative efficiency).
3.2 The Measurement of Efficiency
In the past few years, performance measurement of financial institution has
increasingly focused on frontier efficiency or X-efficiency (rather than scale
efficiency), which measures deviation in performance of a financial institution
from the best practices or costs-efficient frontier that depicts the lowest production
costs for a given level of output. X-efficiency stems from technical efficiency,
which gauges the degree of friction and waste in the production processes, and
allocative efficiency, which measures the levels of various inputs.
Frontier efficiency is superior for most regulatory and other purposes to the
standard financial ratios from accounting statements, such as, return on asset
(ROA) or cost/revenue ratio, that are commonly employed by regulators,
managers of financial institutions, or industrial consultants to assess financial
performance. This is because frontier efficiency measures use programming or
statistical techniques that removes the effects of differences in input prices and
other exogenous market factors affecting the standard performance ratios in order
to obtain better estimates of the underlying performance of the managers (Bauer,
et al., 1998).
Frontier efficiency has been used extensively in regulatory analysis to measure the
effects of merger and acquisition, capital regulations, deregulation of deposit
rates, removal of geographic restrictions on branching and holding company
acquisitions, etc., on financial institution performance. Furthermore, Bauer et al.
(1998) argue that the main advantage of frontier efficiency over other indicators
of performance is that it is an objectively determined quantitative measure that
removes the effects of market prices and other exogenous factors that influence
observed performance.
Tools to measure efficiency could be parametric and non-parametric. Parametric
approach to measuring efficiency uses stochastic econometric and tries to
eliminate the impact of disturbance to inefficiency. There are three parametric
econometric approaches, namely:
1. Stochastic frontier approach (SFA);
2. Thick frontier approach (TFA); and
3. Distribution-free approach (DFA).
These approaches differ in the assumptions they make regarding the shape of the
efficient frontier, the treatment of random error, and the distributions assumed for
inefficiencies and random error. The parametric methods have disadvantages
relative to the non-parametric methods of having to impose more structure on the
shape of the frontier by specifying a functional form for it. However, an advantage
of the parametric methods is that they allow for random error, so these methods
are less likely to misidentify measurement error, transitory differences in cost, or
specification error for inefficiency (Bauer, et al., 1998).
Meanwhile, non-parametric linear programming approach to measuring efficiency
uses non-stochastic approach and tends to combine disturbance into inefficiency.
This is built based on discovery and observation from the population and
evaluates efficiency relative to other units observed. One of the non-parametric
approaches, known as data envelopment analysis (DEA), is a mathematical
programming technique that measures the efficiency of a decision making unit
(DMU) relative to other similar DMUs with the simple restrictions that all DMUs
lie on or below the efficiency frontier (Seiford and Thrall, 1990). The
performance of a DMU is very relative to other DMUs, especially those that cause
inefficiency. This approach can also determine how a DMU can improve its
performance to become efficient.
DEA was first introduced by Charnes, Cooper, and Rhodes in 1978. Since then its
utilization and development have grown rapidly including many banking-related
applications. The main advantage of DEA is that, unlike regression analysis, it
does not require an a priori assumption about the analytical form of the production
function so imposes very little structure on the shape of the efficient frontier.
Instead, it constructs the best practice production function solely on the basis of
observed data, and therefore the possibility of misspecification of the production
technology is zero. On the other hand, the main disadvantage of DEA is that the
frontier is sensitive to extreme observations and measurement error (the basic
assumption is that random errors do not exist and that all deviations from the
frontier indicate inefficiency). Moreover, there exists a potential problem of “self
identifier” and “near-self-identifier”.
3.3 Non-parametric Approach DEA
Data envelopment analysis or DEA is a methodology for analyzing the relative
efficiency and managerial performance of productive or decision making units
(DMUs), having the same multiple inputs and multiple outputs. DEA allows us to
compare the relative efficiency of (Islamic or conventional) banks by determining
the efficient banks as benchmarks and by measuring the inefficiencies in input
combinations (slack variables) in other banks relative to the benchmark (Jemrić
and Vujčić, 2002). DEA provides an alternative approach to regression analysis.
While regression analysis relies on central tendencies, DEA is based on extremal
observations. While the regression approach assumes that a single estimated
regression equation applies to each observation vector, DEA analysis each vector
(DMU) separately, producing individual efficiency measures relative to the entire
set under evaluation (Jemrić and Vujčić, 2002).
DEA is a non-parametric, deterministic methodology for determining the relative
efficient production frontier, based on the empirical data on chosen inputs and
outputs of a number of DMUs. From the set of available data, DEA identifies
reference points (relatively efficient DMUs) that define the efficient frontier (as
the best practice production technology) and evaluate the inefficiencies of other,
interior points (relatively inefficient DMUs) that are below the efficient frontier
(Jemrić and Vujčić, 2002). Besides producing efficiency value for each DMU,
DEA also determines DMUs that are used as reference for other inefficient
DMUs.
∑
∑
=
==⋅⋅ m
iii
p
kkk
xv
yDMUofEfficiency
10
10
0
μ
DMU = decision making unit n : number of DMU evaluated
m : different inputs xij : number of input i consumed by DMUj
p : different outputs ykj : number of output k produced by DMUj
There are two DEA models that are most frequently used, namely, the CCR model
(Charnes, Cooper, and Rhodes, 1978) and the BCC model (Banker, Charnes, and
Cooper, 1984). The main difference between these two models is the treatment of
return to scale. The CCR assumes that each DMU operates with constant return to
scale, while the BCC assumes that each DMU can operate with variable return to
scale.
CCR model assumes that the ratio of additional input and output is equal (constant
return to scale). It means that an additional input of x times will produce
additional output of x times. Another assumption is that every DMU operates on
an optimal scale. Therefore the efficiency of DMU can be measured as a
maximum of a ratio weighted outputs to weighted inputs. Meanwhile, BCC model
assumes that every DMU has not (or not yet) operated on optimal scale. This
model assumes that the ratio of additional input and output is not equal (variable
return to scale). It means that an additional input of x times will not produce
additional output of exactly x times, but it can be less or greater than x times.
Generally, the efficiency score of CCR model for each DMU will not exceed the
efficiency score of BCC model. This is because BCC model analysis each DMU
“locally” (i.e. compared to the subset of DMUs that operate in the same region of
return to scale) rather than “globally (Jemrić and Vujčić, 2002). Furthermore, a
business or DMU, like bank, has similar characteristics one to another. However,
each bank usually varies in size and production level. This indicates that size
matters in relative efficiency measurement. CCR model represents (the
multiplication of) pure technical and scale efficiencies, while BCC model
represents technical efficiency only. Therefore, the relative scale efficiency is a
ratio of CCR model and BCC model.
BCCkCCRkk qqS ,, /=
If the value of S = 1 means that the DMU operates in the best relative scale
efficiency, or in optimal size. If the value of S is less than 1 means that there still
exists scale inefficiency of the DMU. Therefore, the value of (1-S) represents the
level of inefficiency of the DMU. Consequently, when a DMU is efficient under
BCC model, but inefficient under CCR model, this means that the DMU has scale
inefficiency. This is because the DMU is technically efficient, so that the
inefficiency that exists comes from the scale.
SETEOE ×= --> TEOESE /=
OE: overall efficiency of CCR Model; TE: technical efficiency of BCC Model.
3.4 Measuring the Activity of Banks
The efficiency measurement, parametric or non-parametric, of financial institution
like banks can be approached from their activities. There are three main
approaches to explain the relationship between input and output of banks. Two
approaches, namely, production (or operational) approach and intermediation
approach, apply the classical microeconomic theory of the firm, while one
approach, namely modern (or assets) approach applies modified classical theory
of the firm by incorporating some specificities of banks’ activities, namely risk
management and information processing, as well as some form of agency
problems, which are crucial for explaining the role of financial intermediaries
(Freixas and Rochet, 1998).
3.4.1 Production Approach
The production approach describes banking activities as the production of services
to depositors and borrowers using all available factors of production, such as labor
and physical capital. This approach, initiated by Benston (1965) and Bell and
Murphy (1968), considers banks as producer of deposit accounts to depositors and
loans to borrowers. Therefore, this approach defines input as number of
workforce, capital expenses on fixed assets and other materials, and defines output
as the sum of all deposit accounts or other related transactions.
According to Freixas and Rochet, (1998), the production approach suits well the
case of a local branch that is “financially transparent” in the sense that the money
collected from depositors is fully transferred to some main branch. Similarly, all
the money lent to borrowers is made available by the same main branch. The only
outputs of the local branch are its services to depositors and borrowers, and its
only inputs are labor and physical capital.
Parametric measurement of production approach has some difficulties. First,
disaggregation of costs prevents the study of scale and scope economies. Second,
production approach suffers from a basic problem on what the relevant measure of
output volumes is. Third, Cobb-Douglas specification for monotonicity of average
cost prevents the existence of an efficient size.
The first difficulty has been addressed by Baumol, Panzar, and Willig (1982) and
the existence of Functional Cost Analysis (FCA) program that allowed separate
cost functions to be estimated for all product lines. Disaggregated cost data for
five categories of banking activities identified are demand deposits, term and
savings deposits, real estate loans, consumer loans, and business loans. Cost
functions of the Cobb-Douglas type (one per activity i) are as follows:
constrawaQC iiiiiii +−++= log)1(logloglog ε
i = 1, …, 5, Ci (total cost), Qi (volume of output), wi (wage rate), ri (interest)
The second difficulty is to choose output volume among the number of accounts,
the number of operations on these accounts, or the dollar amounts. Among these
three output volumes, the dollar amounts are more readily available. To correct
possible biases, heterogeneity factors for homogenizing the data (size, activity,
and composition of accounts) are introduced.
The third difficulty, the monotonicity of average cost (increasing if εi > 1,
decreasing if εi < 1, and constant if εi = 1), has been addressed by Benston,
Hanweck, and Humprey (1982) by applying a more convenient specification of
translog cost function, in which the logarithm of the cost is quadratic with respect
to the logarithms of output and input prices. They find that a U-shaped average
cost function with an efficient size between 10 and 25 million dollars of deposits,
which is surprisingly small (Freixas and Rochet, 1998).
Moreover, Gilligan and Smirlock (1984), Gilligan, Smirlock, and Marshall
(1984), Berger, Hanweck, and Humprey (1987), and Kolari and Zardhooki (1987)
use a multiproduct cost function, which allows the discussion of scope economies
and cost complementarities. But, the results are not conclusive (Freixas and
Rochet, 1998).
3.4.2 Intermediation Approach
The intermediation approach describes banking activities as intermediary in
charge of transforming the money borrowed from depositors (surplus spending
units) into the money lent to borrowers (deficit spending units). In other words,
deposits that are typically divisible, liquid, short-term, and risk less are
transformed into loans that are typically indivisible, illiquid, long-term, and risky.
Therefore, this approach defines input as financial capital (the deposits collected
and the funds borrowed), and defines output as the volume of loans and
investment outstanding.
According to Freixas and Rochet, (1998), the intermediation approach is
complimentary to the production approach and is more appropriate to the case of a
main branch, which is not directly in contact with customers. In this case, the total
volume of loans granted by the local branches is in general different from the total
volume of deposit collected. Therefore, the main branch may have to borrow (or
invest) on financial markets.
Results of parametric measurement of the intermediation approach do not differ
substantially from those of the production approach. But, this approach also has
some difficulties. First, there is problematic behavior in determining deposits as
output or input. There is not enough supporting argument in selecting the
variables and their positions. Second, there is problematic behavior of the multi-
product translog cost function when some of the outputs tend toward zero (the
logarithms become infinite).
On the first problem, one interesting findings are given by Hancock (1991) who
runs a linear regression of bank’s profit on the real balances of the items in bank’s
balance sheet without presuming a priori which correspond to outputs and which
to inputs. When these coefficients are positive they correspond to outputs
(intuitively, the bank’s profit increases when they increase), and when they are
negative they are correspond to inputs. She found that loans and demand deposits
are outputs; whereas labor, physical capital, materials, and cash are inputs.
However, Hughes and Mester (1993) found that deposits are inputs.
On the second problem, several contributions have tried to correct it. For example,
Hunter, Timme, and Yang (1990) use another specification (Minflex-Laurent) of
the cost function, and McAllister and McManus (1992) adopt nonparametric
approach.
3.4.3 Modern Approach
The modern approach tries to improve the first two approaches by incorporating
risk management, information processing, and agency problems into the classical
theory of the firm. This approach introduces a possible discrepancy between
bank’s manager and owner in profit maximization behavior. If bank’s managers
are not risk neutral, they will typically chose a level of financial capital that is
different from the cost minimizing one.
Parametric measurement of the modern approach done by Hughes and Mester
(1994) find that, for larger banks, an increase in size (holding default risk and
asset quality constant) significantly lowers the price of uninsured funds (too big to
fail). Moreover, Berger and De Young (1997) find support for the “bad luck
hypothesis” (problem loans cause banks to increase spending on monitoring).
Also, “decreases in bank capital ratios generally precede increases in non-
performing loans…evidence that thinly capitalized banks may respond to moral
hazard incentives by taking increased portfolio risks” (Freixas and Rochet, 1998).
4. Data Analysis
4.1 Data Description
The data needed for this empirical analysis comes from financial statements of
conventional and Islamic banks in Indonesia in the period of 2002 – 2006. There
are six types of conventional banks, namely public bank listed on capital market,
conventional domestic foreign exchange bank, conventional domestic bank,
conventional regional bank, conventional mixed bank owned by domestic and
foreign investors, and conventional foreign bank owned by foreigner. While
Islamic banks in Indonesia are of three types, namely, full-fledged Islamic bank,
conventional bank that have separate Islamic branch or Islamic business unit, and
Islamic Regional Development Branches. The data of type and number of banks
included in this study can be read in table 4.1.
[Insert Table 4.1]
This study will adopt a modification of intermediation approach to better reflect
Islamic bank activities, as also adopted by Sufian (2006), Ascarya and Yumanita
(2007a and 2007b). Accordingly, we assume that conventional and Islamic banks
produce Total Loan/financing (y1) and Income (y2) by employing Total Deposit
(x1), Labor (x2), and Fixed Asset (x3). Liquid assets are not included in this study
as output variable, since banks are naturally not in the business of financial
instruments in the financial markets, but in the business of providing financing to
the real sector. As data on the number of employees are not readily made
available, we use personnel expenses as a proxy measure. The aggregate series of
inputs and outputs of conventional and Islamic banks included in this study can be
read in table 4.2.
[Insert Table 4.2]
4.2 Results and Analysis
The efficiency of conventional and Islamic banks in Indonesia is measured in
several ways by applying non-parametric DEA method. To make a comparable
measurement, conventional and Islamic banks are pooled together annually to
form a common frontier. All banks for each year (2002-2006) are pooled to
measure efficiency.
Summary of DEA results can be read in table 4.3 in the appendix and figure 4.1
below. The results suggest that overall efficiencies of conventional bank have
exhibited continuous improvement, except in 2004, and have reached the highest
mean of 0.85 in 2006. The decomposition of overall efficiency into its technical
and scale efficiency components suggest that technical efficiency has been
improving significantly from 0.67 in 2004 to 0.88 in 2006, while scale efficiency
has always been high and stable to reach 0.96 in 2006. These imply that during
the period of study, conventional banks in Indonesia have been operating at
efficient scale; while technically have been operating at higher and higher
efficiency.
[Insert Table 4.3]
Meanwhile, the overall efficiencies of Islamic bank have also been improving,
except in 2004, and have reached the highest mean of 0.88 in 2006. The efficiency
decomposition suggests that scale efficiency has been improving significantly in
the last two years from 0.85 in 2004 to 0.99 in 2006, while technical efficiency,
after 3 years trend of improvement, has been stagnant since 2005 at its highest
level of 0.89. These show that during the period of study, Islamic banks in
Indonesia have been operating at high and improving level of efficiency, except
between 2003-2004 where Islamic banks were experiencing aggressive expansion
(read figure 4.1, right).
Figure 4.1 Efficiency of Conventional and Islamic Banks in Indonesia using
DEA
Overall, it can be concluded that during the period of observation from 2002 to
2006 conventional and Indonesian Islamic banks in Indonesia have showed
continues improvement, except in 2004, and have converged to a comparable high
level of efficiency. Conventional banks mostly improved in technical efficiency,
while Islamic banks improved in scale and technical efficiencies. In sum, overall
efficiency of Islamic bank is only slightly better than conventional bank.
Note: Public: conventional public bank listed on capital market; Domestic fx: conventional domestic foreign exchange
bank; Domestic: conventional domestic bank; Regional C: conventional regional bank; Mixed: conventional bank
owned by domestic and foreign investors; Foreign: conventional foreign owned bank; Islamic: average Islamic bank;
Full Fledged: Islamic full fledged bank; Full Branch: Islamic full branch bank; Regional I: Regional Islamic bank.
Figure 4.2 Group Efficiency of Conventional and Islamic Banks in Indonesia
using DEA
Figure 4.2 and table 4.4 in the appendix show bank efficiency of each group using
DEA method. During 2002-2006, regional conventional bank has become the
most improved conventional bank, while regional Islamic bank has become the
most improved Islamic bank. Foreign conventional bank has become the most
efficient in 2006 (1.00). Moreover, Islamic full fledged bank has almost always
become the most efficient Islamic bank.
[Insert Table 4.4]
Moreover, the scale efficiency of Islamic banks can also be viewed from the trend
of the return to scale (RTS)3 measured by DEA. Scale efficient banks exhibit
constant return to scale (CRS). Banks experiencing economies of scale exhibit
increasing return to scale (IRS), which means that the bank operates at a wrong
scale of operation. Banks experiencing diseconomies of scale exhibit decreasing
return to scale (DRS). Figure 4.3 and table 4.5 show the results of return to scale.
[Insert Table 4.5]
The number of conventional banks operating at efficient scale (CRS) has been
increasing since 2004 from 8% to 36%, after a decrease during 2003-2004.
Conventional banks experiencing economies of scale (IRS) have been increasing
during the period of study, except in 2004. Moreover, conventional banks
experiencing diseconomies of scale (DRS) have been decreasing sharply during
the period of study, except in 2004 (read figure 4.7, left).
3 RTS are the increase in output that results from increasing all inputs. There are three possible cases. (1) Constant Returns to Scale or CRS (RTS=0), which arise when percentage change in outputs = percentage change in inputs; (2) Decreasing Returns to Scale or DRS (RTS=-1), which occur when percentage change in outputs < percentage change in inputs; (3) Increasing Returns to Scale or IRS (RTS=1), which occurs when percentage change in outputs > percentage change in inputs.
Figure 4.3 Return to Scale of Conventional and Islamic Banks in Indonesia
Meanwhile, Islamic banks operating at efficient scale (CRS) have been decreasing
in 2002-2004 from 43% to 8%, and have been sharply increasing in 2004-2006
from 8% to 63%. Islamic banks experiencing diseconomies of scale (DRS) have
been increasing in 2002-2004 from 43% to 77%, and have been sharply
decreasing in 2004-2006 from 77% to 26%. Moreover, Islamic banks
experiencing economies of scale (IRS) have been slightly decreasing during the
period of study from 14% to 11% (read figure 4.3, right). Aggressive expansion of
Islamic banking industry in Indonesia during 2003-2004 has been reflected in the
increase of DRS and the decrease of CRS Islamic banks.
Overall, it can be concluded that conventional and Islamic banks in Indonesia
have been experiencing improvement in scale efficiency, especially Islamic banks.
At the end of 2006, there are more Islamic banks operating at scale efficient
(CRS), while there are more conventional banks operating at economies of scale
(IRS).
Other than generating efficient frontier, one salient feature of DEA is that it can
generate set of references for inefficient DMUs (banks) to benchmark to. Table
4.6 shows conventional and Islamic banks that are referenced by other inefficient
banks in 2002-2006. In the first three years of observation, there are always more
conventional banks on efficient frontiers that set as benchmarks for other
inefficient banks to make improvements. Conversely, in the last two years of
study, there are always more Islamic banks benchmarked by other inefficient
banks.
[Insert Table 4.6]
Another useful feature of DEA is that it can identify the source of inefficiency for
each DMUs or Islamic banks. The results in table 4.7 and figure 4.8 are generated
by running each year data of each country, separately.
[Insert Table 4.7]
In 2002-2006, conventional banks (read figure 4.4, left) have always been
efficient in generating income, since they have been able to provide sophisticated
diverse banking services that are demanded by general customers, such as e-
banking, internet-banking, phone-banking, sms-banking, and so on. Conversely,
labor has always been inefficient part of conventional banks. This could be
attributed to the nature of service industry where the most important capital is
skilled and experienced human capital. Moreover, deposit has been improving,
while loan extension or financing has been worsening. These can be attributed to
high policy rate (the rate of Bank Indonesia Certificate or SBI) which is higher
than deposit rate that makes conventional banks race to accumulate deposit and
put the excess liquidity into SBI which serves like investment instrument with
higher rate than deposit rate. No wonder that LDR has always been low and SBI
outstanding has always been increasing. Conventional banks do not have to
extend loan to make easy profit.
Figure 4.4 Potential Improvements for Conventional and Islamic Banks in
Indonesia
Meanwhile, financing has always been the most efficient elements in Indonesian
Islamic banks, except in 2004 and 2006 (read figure 4.4, right), as also showed in
the figure of FDR that have always been high around 100%. In early 2004, there
was a temporary shift of deposits from conventional banks to Islamic banks due to
the release of fatwa of the haram-ness of interest by DSN-MUI (National Shariah
Advisory Council of Indonesia) in late 2003. Income has been improving
considerably from the most inefficient element in 2002 to the most efficient
element in 2006, due to the improvements of Islamic banks in providing financial
services comparable to those provided by conventional banks. Deposits have been
worsening, and have become the most inefficient element in 2006. The problem of
deposits in Indonesian Islamic banking intensified due to its high growth, but
limited customer base. Islamic banks have been targeting faithful customers which
counted for only 1-10 percent, while they have not been focusing on floating
customers which counted for 80 percent. Islamic banks should shift their
marketing and communication strategies to target more on floating customers.
Moreover, the problem of deposit is also due to the high interest rate mentioned
before, so that floating customers opt to put their money into conventional bank.
Meanwhile, labor has always been inefficient part of Islamic bank. This is the case
in Indonesia, and most countries adopting dual banking system, where the supply
of human resource is always lagging behind the demand of this still fast growing
industry. Even though there are more and more universities and higher
educational institutions offering Islamic Economic and Finance, the number of
graduates are still could not catch up with the demand. The consequence of this is
either the wage goes up or/and the human resource quality goes down. Therefore,
Indonesian Islamic banks should give more attention to human resource to
improve their efficiency. Moreover, other elements of input can also be improved
further in less priority than human resource.
Note: Domestic fx: conventional domestic foreign exchange bank; Domestic: conventional domestic bank; Regional C:
conventional regional bank; Mixed: conventional bank owned by domestic and foreign investors; Foreign: conventional
foreign owned bank; Islamic: average Islamic bank; Full Fledged: Islamic full fledged bank; Full Branch: Islamic full
branch bank; Regional I: Regional Islamic bank.
Figure 4.5 Efficiency of Conventional and Islamic Banks in Indonesia using
DEA vs. OCOI Ratio
Figure 4.5 and table 4.8 show DEA results compare to their respective traditional
OCOI (operating costs divided by operating expenses) and ROA (return on
assets). It can be shown that efficient conventional banks, like regional and
foreign banks, always have lower OCOI ratios, while efficient Islamic banks do
not always have lower OCOI ratios. Moreover, conventional banks that have
better (lower) OCOI usually have better profitability (ROA), while Islamic banks
that have better OCOI do not always more profitable (better ROA).
[Insert Table 4.8]
5. Conclusions and Recommendations
5.1 Conclusions
Overall, conventional banks exhibit improving and convergence measure of
high efficiency to those of Islamic banks. However, Islamic banks are
relatively slightly more efficient than conventional banks in all three
measures (technical, scale, and overall efficiencies) during the period of
study. This can be attributed, among others, to efficient financing activities.
Financing to deposit ratios has always been high around 100 percent,
reflecting high contribution of Indonesian Islamic banking to the real sector.
Conventional and Islamic banks show a convergence in the characteristics
of inputs and outputs, where income has become the most efficient element,
while labor or human resource should be given top priority for
improvements. Income from banking services come from sophisticated
diverse banking services provided by conventional and Islamic banks, such
as e-banking, internet-banking, phone-banking, sms-banking, and so on.
Conversely, labor has always been inefficient part of conventional and
Islamic banks. This could be attributed to the nature of service industry
where the most important capital is skilled and experienced human capital.
Moreover in Islamic banking, the supply of human resource is always
lagging behind the demand of this still fast growing industry.
The differences between conventional banks and Islamic banks are shown
in deposit and financing. Deposit is improving in conventional bank, while
it is worsening in Islamic bank, due to high interest rate and competition.
High and increasing SBI rate which is higher than deposit rate makes
deposits flow to conventional banks. Meanwhile, conventional banks
always have low loan to deposit ratio (LDR), while Islamic banks always
have high financing to deposit ratio (FDR). Moreover, conventional banks
can put their excess liquidity into SBI and do not have to extend loan to
make profit, while Islamic banks have to extend financing to make profit.
5.2 Recommendations
Islamic banks in Indonesia are still young and small, so that socialization
and expansion should be the number one priority to make Islamic bank
familiar to public and to reach economies of scale and critical mass in the
shortest time possible. Other than organic expansion that naturally slow, to
accelerate expansion Islamic banks in Indonesia (i.e. the government)
should also have the political will, commitment, and courage to expand
inorganically by converting one state owned conventional bank into Islamic
bank, preferably the one that have large networks.
Funding (deposits) has become a new problem in Indonesian Islamic
banking. Head to head competition with conventional banks should force
Islamic banks to redirect their marketing and communication strategies to
focus more on floating customers.
Human resource has always been a problem in Islamic banks in Indonesia,
specifically due to high demand and short supply. The improvement of the
human resources could be done with two strategies, namely, short term and
long term. In the short term, education and training should be conducted for
every level of management. In the long term, special fields of study in
Islamic economic and finance should be opened in graduate and
undergraduate levels, as well as inserting Islamic economic and finance
curriculum in high school.
The improvement of the human resources from the regulator side could be
done by requiring banks to spend minimum budget for human resources
development. Moreover, the government or regulator could give incentives
by financing participation in human resources development. The regulator
could also provide free training for Islamic bank officers.
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Appendix
Table 4.1 Data of Conventional and Islamic Banks
DEA 2002 2003 2004 2005 2006
Conventional 72 101 97 63 44
Public 1 1 1 -
Domestic FX 15 22 22 14 9
Domestic 24 38 32 14 27
Regional 22 20 23 20 3
Mixed 8 16 14 9 4
Foreign 2 5 5 5 1
Islamic 7 8 13 19 19
Domestic Full Fledged 2 2 3 3 3
Domestic Full Branch 4 5 7 9 9
Regional Full Branch 1 1 3 7 7
Table 4.2 DEA Inputs and Outputs Data (Real US$.000)
2002 2003 2004 2005 2006
Conventional Financing 15,697 8,330,315 8,950,342 6,086,346 9,829,870
Income 241,318 1,949,255 1,782,812 982,839 2,097,861
Deposit 12,813 7,174,381 7,419,603 4,831,932 10,941,882
Labor 22,534 253,859 262,873 129,032 386,983
Asset 9,481 19,945,804 20,392,106 14,204,088 17,972,278
Islamic Financing 347,468 580,334 1,041,176 1,093,134 1,485,325
Income 51,847 83,175 140,256 141,101 255,105
Deposit 110,371 541,520 940,023 885,359 1,284,758
Labor 8,580 12,427 19,084 20,173 32,909
Asset 433,713 830,556 1,400,265 1,395,608 2,024,293
Conventional : Islamic Financing 0.05 14.35 8.60 5.57 6.62
Income 4.65 23.44 12.71 6.97 8.22
Deposit 0.12 13.25 7.89 5.46 8.52
Labor 2.63 20.43 13.77 6.40 11.76
Asset 0.02 24.02 14.56 10.18 8.88
Table 4.3 Summary of Non-parametric DEA Efficiency
Efficiency Measures 2002 2003 2004 2005 2006
Conventional Overall Efficiency 0.45 0.72 0.62 0.69 0.85
Technical Efficiency 0.58 0.76 0.67 0.73 0.88
Scale Efficiency 0.82 0.95 0.94 0.95 0.96
Islamic Overall Efficiency 0.70 0.79 0.69 0.81 0.88
Technical Efficiency 0.74 0.86 0.80 0.89 0.89
Scale Efficiency 0.94 0.92 0.85 0.91 0.99
Table 4.4 Summary of Non-parametric DEA Efficiency by Group
BANK 2002 2003 2004 2005 2006
Conventional 0.45 0.72 0.62 0.69 0.85 Public 0.23 1.00 1.00
Domestic fx 0.44 0.70 0.53 0.62 0.80
Domestic 0.46 0.81 0.64 0.71 0.85
Regional C 0.32 0.51 0.56 0.64 0.97
Mixed 0.79 0.80 0.77 0.82 0.80
Foreign 0.75 0.75 0.71 0.72 1.00
Islamic 0.70 0.79 0.69 0.81 0.88 Full Fledged 0.76 0.85 0.76 0.96 0.97
Full Branch 0.73 0.86 0.68 0.87 0.88
Regional I 0.45 0.36 0.65 0.67 0.83
Table 4.5 Summary of DEA Return to Scale
2002 2003 2004 2005 2006 Bank Share Bank Share Bank Share Bank Share Bank Share
Conventional CRS 14 19% 21 21% 8 8% 8 13% 16 36%
IRS 10 14% 41 40% 26 27% 24 38% 25 57%
DRS 49 67% 40 39% 64 65% 31 49% 3 7%
TOTAL 73 102 98 63 44
Islamic CRS 3 43% 3 38% 1 8% 9 47% 12 63%
IRS 1 14% 1 13% 2 15% 1 5% 2 11%
DRS 3 43% 4 50% 10 77% 9 47% 5 26%
TOTAL 7 8 13 19 19
Table 4.6 Summary of DEA Reference Set
2002 2003 2004 2005 2006 Bank Freq Bank Freq Bank Freq Bank Freq Bank Freq
Conv MIXED 66 Conv MIXED 64 Conv MIXED 101 Conv MIXED 57 Conv DOM 28
Conv DOM 49 Conv FOREIGN 56 Conv FOREIGN 95 Islamic REG 23 Islamic FF 21
Conv DOM fx 39 Conv DOM 46 Conv DOM fx 26 Islamic FB 21 Islamic FB 18
Conv DOM fx 39 Conv DOM+ 45 Conv MIXED 24 Islamic FB 21 Islamic REG 13
Conv MIXED 24 Conv DOM 21 Conv PUBLIC 22 Islamic FF 19 Islamic REG 12
Conv DOM 17 Conv DOM 19 Islamic FF 16 Islamic REG 12
Conv MIXED 10 UFJ 16 Conv DOM 15 Conv DOM fx 12
Islamic FF 9 Islamic FB 14 Conv MIXED 10 Conv DOM 10
Islamic FB 5 Conv DOM 12 Islamic FB 7 Conv DOM 7
Conv MIXED 1 Conv MIXED 3 Conv FOREIGN 5 Conv REG 7
Conv FOREIGN 2 Conv MIXED 1 Islamic FB 6
Islamic FB 5
Conv DOM 5
Conv REG 2
Conv MIXED 1
Conv FOREIGN 0
Table 4.7 Summary of DEA Potential Improvements (in %)
2002 2003 2004 2005 2006
Conventional Financing 15% 13% 1% 17% 15%
Income 0% 1% 2% 1% 3%
Deposit 29% 34% 37% 26% 23%
Labor 28% 27% 35% 37% 33%
Asset 29% 25% 26% 20% 26%
Islamic Financing 4% 0% 25% 0% 14%
Income 38% 9% 21% 20% 0%
Deposit 13% 28% 21% 26% 35%
Labor 30% 35% 17% 30% 25%
Asset 16% 28% 17% 25% 25%
Table 4.8 Summary of Non-parametric DEA vs. OCOI and ROA Ratios
BANK OE TE SE OCOI ROA
Conventional Public
Domestic fx 0.80 0.86 0.93 1.05 0.20
Domestic 0.85 0.88 0.97 0.99 1.01
Regional C 0.97 1.00 0.97 0.64 4.52
Mixed 0.80 0.87 0.92 0.79 2.87
Foreign 1.00 1.00 1.00 0.64 2.50
Islamic Full Fledged 0.97 1.00 0.97 0.86 2.25
Full Branch 0.88 0.89 0.99 0.76 2.36
Regional I 0.83 0.85 0.98 0.95 2.95
