Cost frontier efficiency and risk-return analysis in an emerging market

International Review of Financial Analysis 14 (2005) 283–303

Cost frontier efficiency and risk-return analysis

in an emerging market

Ananth Rao*

College of Business Administration, Dubai University College, PO Box 14143, Dubai, United Arab Emirates

Abstract

The paper investigated cost efficiencies and its relationship with risk-return behavior of banks in

United Arab Emirates (U.A.E.). The major findings are that there were 10–25% inefficiencies in

these banks under different cost specifications. On the risk-return front, lower liquidity and lower

capitalization risks coupled with higher ROE significantly improved the cost efficiencies of the

banks. Further, domestic banks were relatively cost efficient than foreign banks. These findings are

useful to emerging market participants in their investment decisions, as also the policymakers and

bank regulators to monitor inefficient banks in the context of revised Basel capital norms.

D 2004 Elsevier Inc. All rights reserved.

JEL classification: C21; C51; D57; F30; G21; M31; M41; N25; O16

Keywords: Emerging market; U.A.E. banks; Cost efficiency; Risks-returns; Liquidity and capitalization risk

1. Introduction

The United Arab Emirates (U.A.E.) is one of the fast emerging markets in the gulf region.

U.A.E. has long played an important role as a regional financial center for the Middle East

based on its location, the legacy of an open and liberal trade regime, and proactive stance of

the governments of the emirates to promote modern and technologically advanced

infrastructure. Its well-developed banking system is the second largest in the gulf

cooperation council (GCC) in terms of total assets. This is illustrated by the degree of

1057-5219/$ -

doi:10.1016/j.

* Tel.: +971

E-mail add

see front matter D 2004 Elsevier Inc. All rights reserved.

irfa.2004.10.006

4 2072630; fax: +971 4 2242151.

ress: [email protected].

A. Rao / International Review of Financial Analysis 14 (2005) 283–303284

banking presence in the U.A.E. economy where 25 foreign banks and 21 domestic banks

operated at the end of 2002, with total bank lending to private sector representing more than

50% of GDP.

There are signs, however, that this financial sector landscape could be poised for

change. This is driven in the near term, by the increased competition from smaller banks to

gain market share within the domestic market; in the medium term by further liberalization

of the financial services sector as part of WTO commitments and the development of

capital markets. While the top five national banks still represent about half of the banking

system’s total assets, along with deposits, loans, and advances, their dominance has seen

gradual erosion since the mid-1990s with smaller banks making inroads in all of these

categories. Further competition is expected, since the U.A.E. will in all likelihood open up

the banking sector to the GCC in the near-term. Alternative sources of financing, as the

capital markets develop further, is also expected to put competitive pressure on the

banking sector as corporations that have traditionally depended on bank borrowing

diversify their funding profile and strategies (IMF Country Report, 2003).

There has been a marked increase in activity in securities markets since the establishment

of the Dubai Financial Market (DFM) and the Abu Dhabi Securities Market (ADSM) in

March and November of 2000, respectively. Out of the publicly traded companies, the

banking sector has the largest contribution in terms of trading volume followed by insurance

sector and services sector in both the securities exchanges. While the formal exchanges have

seen a significant rise in shares traded, the trading is thin andmarket capitalization have been

significantly low compared to other emerging economies in the region. The activities in the

non-listed companies that continue to be traded in the OTC market have seen a substantial

decline during 1998–2001. The bond market is in its nascent stages.

In this background, the current research is motivated by a set of concerns such as: are the

banks in U.A.E. cost efficient?1 If so, what is the magnitude of these cost efficiencies in

relation to other economies? Are smaller banks more cost-efficient than larger banks? Are

foreign banks more cost-efficient than domestic banks? The second set of concerns are

related to relationship of cost efficiencies with risk-return behavior of banks such as: How

are cost efficiencies related to various banks’ risks inherent in an emerging economy such

as capital risk, default risk, and liquidity risk over time? These concerns are of current

interest as policymakers would be concerned about whether inefficient banking firms pose

additional risks to the banking system and its safety net. Lastly, the research addresses the

third set of concerns: how is return characteristics of the banks and investors related to the

banks’ cost efficiencies? Both the individual and the institutional Investors would be

interested to know the relationship between the firm-specific efficiencies and the value of

bank stocks.

In banking research, there is a large body of literature studying the efficiency of financial

institutions, with an increasing focus on X-efficiency.2 These studies strongly suggest that

X-efficiency in banking is large, typically accounting for 20% or more of costs, and

dominate scale and scope efficiencies (Berger & Humphrey, 1997). The amount of

attention that banking efficiency research has received is understandable. Their findings

2 X-efficiency refers to managerial quality at the banks.

1 In this paper, the term efficiency specifically refers to cost frontier efficiency.

A. Rao / International Review of Financial Analysis 14 (2005) 283–303 285

have obvious implications for banks’ management, who seek to improve operating

performance, and for policymakers, who are concerned about banking competition, bank

safety, and soundness. Research that shows a positive relation between finance and growth

(see, for example, Levine, 1999; Levine & Zervos, 1998; Beck, Levine, & Loayza, 2000)

prompts additional studies to focus more narrowly on the banking system (Demirguc-Kunt

& Levine, 1999; Levine, Loyayza, & Beck, 2000). A natural extension of this line of

inquiry is to investigate how the banks in the emerging U.A.E. market, if inefficient, can

remain economically viable and not be driven out of the banking system in the emerging

market scenario. This is more important for the policymakers and regulators where

inefficient banking firms pose additional risks to the banking system and its safety net in

the context of the recent Basel II norms. For the investors, it is more important to know the

relationship of risk-return with bank efficiencies.

Banking efficiency research has been conducted quite extensively for US and European

financial institutions. Further, there is no systematic research in developing economies that

has studied the relationship of risk-return measures with the cost frontier efficiency

behavior of banks. A major gap in the bank efficiency and risk-return literature is the scant

evidence on banks in U.A.E. in particular and the gulf region in general. This shortfall is

substantial due to the pivotal role of the U.A.E. being played in the gulf region by its

recent establishment of international financial centre and deregulation of financial sector

for promoting international capital flow to the region. The effective implementation of

these measures requires empirical evidence on the level of frontier efficiencies in the

U.A.E. financial institutions. Without research on these bank cost frontier efficiencies, no

inference about deregulation and related policy implications to banking industry structure

could be made. Further, investors are not aware of the risk-return relationship with bank

efficiencies to make their investment decisions in the emerging U.A.E. economy. The

current research is motivated by these factors and sheds light on the micro-production of

banking outputs and assist the investors as well as bank managements to suitably adjust

their investment, financing, and portfolio decisions. We apply Frontier analysis, which is a

sophisticated way to benchmark the relative performance of production firms (including

banking firms) by empirically estimating the banking industry’s cost frontier efficiencies3

and their risk-return relationship.

We hypothesize that there exists cost inefficiencies in U.A.E. banking system, smaller

banks aremore cost-efficient than larger banks, domestic banks are less cost-efficient than its

foreign counterparts; however, the cost efficiency has improved over time due to adoption of

cost-effective measures by the U.A.E. banks’ management. In stage 2 analysis, we estimate

the relationship of cost efficiencies derived in stage 1 analysis, with the risk-return measures.

We hypothesize that cost efficiencies are significantly related to various risks and return

characteristics of both the investors and the banks. Hypotheses that are more specific are

formulated and tested in the following pages. The rest of this paper is organized as follows.

Section 2 discusses the banking literature related to this study. Section 3 briefly discusses

the measurement of cost efficiency in banking. Section 4 describes the data and analyzes

the empirical findings. Section 5 summarizes and concludes the study.

3 A best-practice bank is one that has the lowest expected costs given the business conditions specified in its

cost function and reflects the best use of technology to respond to market forces and other business conditions.


2. Literature review

The efficiency of the financial services industry has long been a focus of banking

research. A large body of literature that spans a half-century exists on banking efficiency in

the U.S (e.g., see surveys in Berger & Humphrey, 1997; Berger & Strahan, 1998; Berger,

Demsetz, & Strahan, 1999). Likewise, a more recent but growing literature on European

banking efficiency is developing (e.g., see Altunbas, Gardner, Molynex, & Moore, 2001;

Molynex, Altunbas, & Gardener, 1997; Sheldon, 1999). Studies prior to the 1980s tended to

report U-shaped cost curves with economies of scale exhausted by $100–500 million for the

most part. Results for economies of scope (i.e., joint production of outputs) weremixed, with

most authors concluding that banks do not gain efficiencies from providing multiple

financial services to the public. Altering the path of efficiency research, Berger and

Humphrey (1991) showed that U.S. banks could improve their cost efficiency more by

reducing frontier inefficiencies than by reaching some optimal level of scale and scope

economies to minimize average costs. Subsequent research further investigated this issue by

using both parametric and non-parametric frontier estimation methods (e.g., see Lovell,

1993; Mitchell & Onvural, 1996). Moreover, recent research has expanded the analyses to

consider both cost and profit efficiency (e.g., Berger & Humphrey, 1997; Berger & Mester,

1997; and others), as well as risk variables (e.g., see Berger &DeYong, 1997; Berg, Fbrsund,& Jansen, 1992; McAllister & McManus, 1993; Mester, 1996; and others). In general,

studies have confirmed Berger and Humphrey’s result that cost and profit frontier

inefficiencies outweigh output inefficiencies associated with scale and scope economies

by a considerable margin.

Berger and Humphrey (1997) also survey over 100 studies that apply frontier efficiency

analysis to financial institutions in 21 countries. Berger et al. (1999) review the literature that

provides international comparison of banking efficiency. Altunbas, Liu, Molyneux, and Seth

(2000) study the efficiency and risk in Japanese banking. Okuda (2000) estimates the cost

function of Philippine domestic banks. Leightner (1999) uses linear programming techniques

to evaluate the performance of Thailand’s finance and securities’ companies over the 1990–

1995 periods. Huang, Fu, & Huang (1999) examine the efficiency of Taiwan’s farmers’ credit

union. In short, studies of the efficiency of U.A.E. financial institutions in particular and GCC

in general are practically non-existing, compared to research on U.S. and European banking.

3. Methodology

3.1. Bank efficiency

The general concept of efficiency refers to the difference between observed and optimal

values of inputs, outputs, and input/output mixes. Efforts to measure how efficiently a firm

produces outputs with its inputs have led to the development of a number of efficiency

concepts, including scale efficiency, scope efficiency, economic efficiency, and X-

efficiency. Economic efficiency builds on scale and scope efficiency by incorporating

prices and thereby allowing the firm to react to price changes and potentially gain market

power in input or output markets. The concept of X-efficiency or managerial efficiency goes


one-step further in the sense that it measures efficiency in implementing an existing production

plan with given prices and technologies. Berger, Hunter, and Timme (1993) have defined X-

efficiency as the economic efficiency of any single firmminus scale and scope efficiency effects,

thereby allowing for sub-optimal (beneath the frontier) operations. As stated earlier, we employ

stochastic frontier regression models developed by Aigner, Lovell, and Schmidt that allow us to

measure X-efficiency. According to Berger and Humphrey (1991) and Berger et al. (1993), the

significance of scale and scope inefficiencies (amounting to 5%) is less important in the banking

industry than X-inefficiencies in the range of 20–25%).4

Concerning the measurement of X-efficiency, Bauer, Berger, Ferrier, and Humphrey

(1997) imposed six consistency conditions and examined the extent to which SFA models,

thick frontier models (TFA), distribution free models (DFA), and data envelopment analysis

(DEA) meet these consistency conditions. They found that the choice between these

different models did not appear to significantly alter efficiency measures. However, SFA has

two important advantages: (1) allowance for measurement error, which is an important factor

since measuring bank production can be difficult due to non-availability of public data and

the choice of a set of inputs and outputs, and (2) generation of firm-specific efficiency

estimates, which are important for the bank managements to improve their operational

efficiency. Following analysis involves two stages. Stage 1 estimates cost efficiencies using

SFA. Stage 2 examines the relationship of impact of risk-return factors with cost-efficiency

estimates that are derived from stage 1 analysis.

3.2. Stage 1 analysis—cost-efficiency estimation

To measure the cost inefficiency (Ci) for the individual bank i=1. . .N, we use the

stochastic frontier methodology of Aigner, Lovell and Schmidt (1977). In this method, a

banking firm’s observed total cost is modeled to deviate from the cost-efficient frontier due

to random noise and possibly X-inefficiency as in Eq. (1):

Ln TCi ¼ f Ln Xið Þ þ �i ð1Þwhere TCi is the total cost for bank i; Xi=(Ln Wi, Ln Qi, Ln Zi) are the set of exogenous

bbusiness conditionsQ that affect costs, specifically variable input prices (Ln Wi), variable

output quantities (Ln Qi) expressed in natural logs, environmental variables (Ln Zi which

are zero–one dummy indicator variables indicating size and type of the bank and year

dummy for 1998–2001).5 Finally, ei is a two-component stochastic error term of the form

as in Eq. (2):

�i ¼ Ui þ Vi: ð2Þ

4 See also Berger and Humphrey (1997) and Molyneux et al. (1997).5 This equation is based on the premise that banks minimize their cost objective function under the restrictions

imposed by the transformation function: minimizeWVX subject to technology constraint T(X,Y,Z)=0, which is non-

linear. The corresponding Lagrangean function can be formulated as TC=WVX�kT(�) whereW is a vector of input

prices and T is the non-linear technology constraint. Taking the first derivative and solving yields the conditioned

factor demand equation, or the restricted input requirement set (please see Hughes & Mester, 1993)

Xi*=Xi*( Y,W,Z). Substituting these into the cost function gives the minimum cost level: TC=W’Xi*( Y,W,Z).


The first part Ui is a one-sided error component that captures the effects of inefficiency

relative to the stochastic cost frontier. This error term denotes an inefficiency factor that is

zero for best-practice banks and raises costs above the best-practice level for other banks

because of both technical inefficiency6 and allocative inefficiency.7 The second part Vi is a

symmetric error component that permits random variation of the frontier across firms, and

captures the effects of measurement error, other statistical dnoiseT, and random shocks

outside the bank’s control. The cost efficiency (Ci) of bank i can be expressed as the expected

value of Ui conditional on �i (Jondrow, Lovell, Materov, & Schmidt, 1982)8 in Eq. (3):

Ci ¼ E Uij�ið Þ ¼ rk= 1þ k2� ��

/ �ik=rð Þ=H �ik=rð Þ þ �ik=rð Þð Þ½ ð3Þwhere k is the ratio of ru/rv, r2=ru

2+rv2, H is the cumulative standard normal density

function (cdf) and / is the standard normal density function (pdf).

It is well known that either the cost or the production functions uniquely define the

bank technology, which one is to be estimated depends on one’s assumption and/or data.

The behavioral assumption underlying direct estimation of the bank cost function is that it

requires the data on input prices but not input quantities, and the cost frontier yields

information on the extra cost of technical and allocative inefficiency.9 To specify the SFA

cost function in Eq. (1), we employ three alternative functional forms10: translog, flexible

Fourier, and the fixed-effect frontier regression.11

3.2.1. Model 1: translog specification

We specify the translog model as in Eq. (4):

Ln TCit ¼ ao þ RidjZi þ RiaiLn Xi þ cLn W þ 0:5Riai Ln Xið Þ2

þ 0:5 Ln Wð Þ2 þ RiRjbj Ln Xið Þ Ln Xj

� �þ Rigi Ln Xið Þ Ln Wð Þ ð4Þ

where TCit is normalized total cost of the ith bank at year t (including interest costs)

expressed in natural logs normalized by price of input labor cost; Zi are set of environmental

variables represented through indicator variables such as type of banks whether domestic or

foreign banks, size of banks whether smaller or larger banks and year dummies for 1998–

2001; Xi are outputs expressed in natural logs; and W is deposit cost normalized by input

labor prices expressed in natural logs. Consistent with the intermediation approach used by

6 Refers to errors in minimum inputs relative to outputs or maximizing outputs relative to inputs.7 Refers to errors in responding to relative prices in choosing inputs or outputs.8 LIMDEP econometric software (Version 8) developed by William Greene (2002) provides the estimates of Ci

using maximum likelihood estimation for different error specification. We specify David, Fletcher, and Powell

(DFP) algorithm for iterations and convergence of maximum likelihood estimates.9 The earlier version of the current research considered estimates of ray scale, scope, and change in productivity

measures. Due to the lower number of observations with the resultant lower degrees of freedom, these estimates

were unstable. Hence, they were excluded from the purview of the discussion. We thank the unanimous referees

on this note. Future studies could examine these measures once more data becomes available.10 Earlier studies focused on either the translog or flexible Fourier forms for estimating cost inefficiencies. This

paper considers all the three forms to estimate cost efficiencies to determine the suitability of a particular form to

the U.A.E. banking industry. This also forms the research base for future investigations in the U.A.E. on cost-

efficiency studies of not only banks but also other firms.11 The current study is probably the first of its kind in the research literature to use SFA in fixed-effect regression

form.


most SFA studies, three measures of banking outputs are included: book value of

investments (X1), net loans (X2), and off-balance sheet commitments and contingencies (X3).

Investments include securities, equity investments, and all other investments reported on the

balance sheet. Loans aggregate commercial and industrial, real estate, consumer, and other

outstanding credit. Loans are net of provisions for bad and doubtful debts. Off-balance sheet

activities include loan commitments, letter of credits (both commercial and standby), futures

and forwards contracts, and notional value of outstanding interest rate swaps. Two input

prices are utilized: the unit price of labor (W1) and the unit price of customer deposits (W2).

The linear homogeneity restrictions are imposed by normalizing the total cost and input

deposit price by unit price of labor. The standard symmetry restrictions are applied in the

translog function. Factor share equations as per Shepherd’s Lemma are not imposed to avoid

undesirable assumption of perfect allocative efficiency (Berger & Mester, 1999).

3.2.2. Flexible Fourier specification

The approximating powers of the flexible Fourier form derive from the capacity of a

Fourier series to represent any function exactly. The flexible Fourier form represents a semi-

non-parametric approach to the problem of using the data to infer interrelationships among

the variables when the true functional form of the relationships is unknown (Mitchell &

Onvural (1996)).12 An exact representation of a function may require a Fourier series

having an infinite number of trigonometric terms, but the coefficient of these terms could

only be estimated with a data set having an infinite number of observations. Given a finite

number of observations, a researcher is forced to choose a subset of the trigonometric

terms with which to represent a cost function. Gallant (1981) asserts that a Fourier series

representation of an unknown function can achieve a given level of approximation error

with fewer trigonometric terms when it includes a second-order polynomial in the

explanatory variables. Despite its superior properties, the flexible Fourier form has sparsely

been used to study the efficiency of banks, McAllister and McManus (1993) and Mitchell

and Onvural (1996) being the exceptions. We specify the flexible Fourier as in Eq. (5):

Ln TCit ¼ ao þ RidjZi þ Riailn Xit þ cLn W þ 0:5Riai Ln Xitð Þ2 þ 0:5 Ln Wð Þ2

þ RiRjbj Ln Xitð Þ Ln Xjt

� �þRigi Ln Xitð Þ Ln Wð Þþ

X3

j¼1

djcosXj þ hjsinXj

� �

þX3

j¼1

X3

k¼j

djkcos Xj þ Xk

� �þ hjksin Xj þ Xk

� �� þ

X3

i¼1

X3

k¼j

X2

l¼k

½djklcos Xj þ Xk þ X1

� �þ hjklsin Xj þ Xk þ X1

� � þ � ð5Þ

12 The previously cited bank efficiency studies have shown that the translog cost functions are locally stable

in bank applications. Further, translog represents a second-order Taylor series approximation of an arbitrary

function at a point. Even in translog form, there are some criticisms as the estimates from such a cost equation

for all small and large banks are incompatible (McAllister & McManus, 1993). For this reason, flexible Fourier

functional form is specified as an alternative model, which can potentially approximate any function well over

the entire range of data (Gallant, 1981). The approximating power of the flexible Fourier form derives from the

capacity of a Fourier series to represent any function exactly. An additional advantage of this form is its

capacity to reveal bias resulting from use of the translog form, since the translog is nested within the flexible

Fourier, as seen in Eq. (5).


The definitions of set of variables TC, X, Z, andW are the same as in translog model. The

terms in the first two rows in Eq. (5) represent translog part of the flexible Fourier cost

function, while the terms in the last two rows represent the truncated Fourier series. In this

study due to limited sample size, we use the trigonometric terms through pre-testing, i.e.,

retaining significant trigonometric terms. The chosen vectors produce sine and cosine

terms having pairs of outputs, and pairs of outputs coupled with input prices as arguments.

3.2.3. Fixed-effects regression13

In this specification, the annual balance sheet and income statements are used to construct

the variables and SFA is specified through standard linear regression (OLS) format. To

examine whether production costs vary systematically across the banks and over time, the

following frontier fixed-effect OLS model is estimated as in Eq. (6):

Ln TCit ¼ a þ bLn Xit þ cDi þ dTt þ � ð6Þwhere TCit is the observed cost for the ith bank expressed in natural logs at year t, Xit are the

vector of control variables expressed in natural logs,Di is the vector of bank-specific dummy

variable (size and type), and T the vector of time-specific dummy variables. a, b, c, and d are

the vectors of regression coefficients; e is the error term as defined in Eq. (2). Four control

variables, viz. levels of Investments, net loans, off-balance sheet commitments, and

deposit input prices normalized by labor costs, describe the environment of the bank

similar to first specification but with no translog terms.

3.3. Stage 2 analysis: relationship of risk-return measures with cost efficiencies

In this stage, the cost efficiencies estimated in the afore-stated three models are

examined with regard to specific risk variables.14 The model is specified as on OLS in

non-log form as in Eq. (7):

Cit ¼ a þ bXit þ cDi þ dTt þ � ð7Þ

where Cit are the cost efficiencies15 of the ith bank in period t obtained in stage 1 analysis.

Xit are the set of risk variables, viz. the ratio of loan loss provision to total loans (denoting

default risk), the ratio of cash and due from banks to total assets (denoting liquidity at the

bank), the ratio of equity to total assets (denoting capitalization risk), the ratio of retail

deposits to total deposits, and the ratio of net loans to total earning assets (denoting banks’

output mix in the asset portfolio). Di is size and type indicator variables as discussed in Eq.

(1) and Tt are the year dummies.

The ratio of loan loss provisions16 to total loans is used to proxy default risk or loan

quality. We hypothesize that the loan quality is endogenous in the quality of manage-

13 We thank the unanimous referees for suggesting to include this as an alternative specification to obtain

adequate degrees of freedom.

15 Higher cost efficiencies or lower cost inefficiencies are complementary measures to each other.16 The banks did not report problem loans in their financial statements. Therefore, we use loan loss provision

rather than problem loans to proxy for loan quality.

14 These are accounting ratios derived from the 1998–2001 balance sheet and accounting data of the banks.


ment17; an inefficient bank with high costs would have more problem loans, so that loan

loss provisioning would be positively related to higher operating costs. This results in

lower cost-efficient operations; thus, a negative sign is expected on this coefficient with

cost-efficiency measure. Similar explanation is in Berger and DeYoung (1997).

The cash ratio, i.e., ratio of cash and dues from banks to total assets, controls the

liquidity risk of the bank. We hypothesize that, while higher liquid assets as a share of total

assets reduce the bank’s liquidity risk, they may be less costly to handle as these assets

may involve less interest costs,18 lower transaction costs, and storage and protection costs.

Thus, the cash (liquidity) ratio is expected to have a negative sign on the coefficient with

cost-efficiency measure.

The ratio of equity capital to total assets captures the capital risk and risk

preference of banks’ management. We hypothesize that, to the extent that well-

capitalized banks reflect high quality management, these banks are likely to be more

cost efficient in producing banking outputs by their cautious risk taking behavior.

Thus, the equity ratio is expected to have a positive sign on coefficient with the cost-

efficiency estimates.

The ratio of retail deposits to total deposits and the ratio of net loans to total earning

assets are included to control output mix in the asset portfolio of banks. We hypothesize

that retail deposits are less costly to service than wholesale deposits. The lower this ratio, it

indicates that banks borrow more from non-retail sources implying that banks are more

efficient in their funding decision to stay competitive in the business with the resultant

reduction in cost inefficiencies. Thus, the coefficient on this ratio is expected to be positive

with the cost-efficiency measures.

Similarly, we hypothesize that higher net loans in the total earning assets are more

costly and riskier than investment securities leading to more non-systematic risk at the

banks. This requires more efficient management of risky portfolio by the banks through

cost-efficient operations in their asset portfolio. Thus, the ratio of net loans to earning

assets (loan ratio) is expected to have a positive sign on the coefficient with the cost-

efficiency measures.

While these set of variables address the risk aspects of the bank, it would be interesting

to note how the investors’ earning measures such as EPS and ROE influence the cost-

efficiency measures at the banks. One could argue that the efficiency measure influences

EPS and ROE positively, with the contention that higher efficiency improves the

profitability of the bank and, in turn, increases return to investors. However, from the

behavioral finance perspective, we would like to take a different stand analogous to

sustainable growth mode.19 In this behavioral approach, we hypothesize that higher EPS

and ROE reflect better management behavior, which is reflected through higher efficiency

at the banks. Coupled with this approach and market inefficiency in U.A.E. financial

market, we argue that higher EPS and ROE imply greater confidence of the investors in

18 During the study period, there was a gradual decline in the interest rate in the U.A.E. economy.19 ( g=b*ROE) where firm’s growth rate g is a positive function of retention rate (b) and ROE. Higher retention

rate and higher ROE leads to a sustainable higher growth rate of equity in the long-term.

17 Loan loss provisioning is still at the discretion of the management in U.A.E. banks.


the banks evidenced by higher efficiency of banks’ management. Thus, a positive sign is

expected on these coefficients with cost-efficiency measures.20

With regard to indicator variables, because of their larger resources and volume of

business, larger banks21 seem to be more cost-efficient relative to smaller banks. Hence, a

negative sign is expected on the bank size dummy. Similarly, by virtue of their vast

international experience under various business conditions, foreign banks are expected to

be more cost-efficient relative to domestic banks. Thus, the coefficient on this bank type

dummy is expected to be negative. With regard to time trend, we hypothesize that banks

have improved their cost efficiencies during 1998–2001 through restructuring their human

resources, employing modern technology, prudent funding and investment practices, and

appropriate risk control. Hence, positive sign is expected on this coefficient. The set of

hypotheses in the paper is summarized below:

1. Bank size Smaller banks are less cost-efficient relative to larger banks. Hence, negative sign is

expected on the bank size indicator variable.

2. Type of bank Domestic banks are less cost-efficient relative to foreign banks. Hence, negative sign is

expected on the bank type indicator variable.

3. Time trend effect Cost efficiencies of U.A.E. banks in general increase over time. Hence, positive sign is

expected on year indicator variable.

4. Default risk Cost efficiencies are negatively related to loan quality.

5. Liquidity risk Cost efficiencies are negatively related to banks’ liquidity.

6. Capitalization Cost efficiencies are positively related to banks’ capitalization.

7. Return measures Cost efficiencies are positively related to ROE and EPS.

8. Deposit portfolio Cost efficiencies are positively related to the ratio of retail deposits to total deposits.

9. Loan portfolio Cost efficiencies are positively related to the ratio of net loans to earning assets.

4. Data and analysis of results

Aggregate data on bank outputs and inputs as specified under translog specification

were pooled from 37 commercial banks’ financial statements22 published during 1998–

2001.23 The summary statistics of the data are stated in Table 1. We specify three

stochastic frontier models (SFA) for estimating cost X-efficiency (Ci) for each bank over

1998–2001 period: (a) a translog cost function model as in Eq. (4), (b) a flexible Fourier

model as in Eq. (5), and (c) a fixed-effects frontier regression specification as in Eq. (6).

Total costs and the numerator used to construct the input prices are flow variables that

20 While one may ridicule this hypothesis, we have evidence that substantial market inefficiencies exist in the

economy (please see Rao, 2000). U.A.E. financial market is fast emerging where the two official bourses started

functioning one in Dubai and the other in Abu-Dhabi in 1999. Trading is very thin and it takes some more time

for the financial market to absorb these market inefficiencies. CAPM is currently inappropriate to the U.A.E.

financial market for valuing bank stocks.

22 These banks represented almost 81% of total banking industry’s assets in U.A.E. Two Islamic banks were

excluded, as their operations are different from those of commercial banks.23 This period was chosen since consistent set of data for all banks were available only from 1998 onwards.

21 Banks with median total assets b2133 million AED are categorized as smaller banks (indicator variable=1)

and those N2133 million AED are categorized as larger banks (indicator variable=0).

Table 1

Summary statistics of business environmental variables of U.A.E. banks

Year 1998 Year 2001

Mean S.D.# Skew Mean S.D.# Skew

Panel A: all U.A.E. banks (N=37)

Total assetsa 5434 8142 2.2 6725 9284 1.8

Net profita 102 146 1.8 23 181 1.7

Total costsa 313 445 2.1 362 458 1.6

Outputs ( Yj) ( j=1–3)

Investmentsa (X1) 350 1061 4.5 697 1909 3.8

Net loansa (X2) 2841 4332 2.2 3323 4943 2.2

Off-balance sheeta,b (X3) 3845 5315 2.4 5247 9718 3.9

Inputs (Wm) (m=1–2)

Cost of depositsc (w2) 0.12 0.04 0.8 0.14 0.05 0.8

Cost of labord (w1) 0.06 0.10 5.8 0.05 0.09 5.3

Accounting ratios

Loan loss provisions/gross loans (%) 13.9 15.9 2.3 14.8 15.0 2.9

Cash and CE to total assets (%) 0.08 0.01 2.4 1.2 2.4 5.5

Equity to total assets (%) 15.2 8.6 2.0 14.7 8.1 1.8

Retail deposits to total deposits (%) 90.1 10.5 (3.1) 83.8 11.9 (1.4)

Net loans to total earning assets (%) 92.6 11.1 (2.5) 81.6 16.6 (1.6)

Earning per share (EPS) AED 7.85 13.5 2.4 7.96 16.3 2.5

Return on assetse (%) 1.9 1.2 0.01 1.2 3.1 (4.9)

Return on equityf (%) 3.6 3.7 2.3 3.1 4.5 (1.2)

Panel B: U.A.E. domestic banks (N=16)

Total assetsa 9234 10836 1.2 11205 11986 0.9

Net profita 175 178 0.9 227 217 0.9

Total costa 518 590 1.1 554 574 0.9


Investmentsa (X1) 795 1525 2.9 1582 2697 2.4

Net loansa (X2) 5109 5674 1.1 5914 6312 1.2




Cost of labord (w1) 0.042 0.01 0.4 0.036 0.01 0.1

Accounting ratios


Cash and CE to total assets (%) 0.9 0.9 2.5 1.0 0.7 1.6


Retail deposits to total deposits (%) 91.9 4.4 0.4 86.1 8.2 (0.2)



Return on assetse (%) 2.5 1.1 0.9 2.5 0.7 0.5

Return on equityf (%) 33.3 19.7 0.8 35.2 16.2 0.6

Panel C: U.A.E. foreign banks (N=21)

Total assetsa 2523 3269 2.1 3312 4371 2.0

Net profita 46 83 3.3 44 94 2.6

Total costa 156 188 2.2 216 279 2.0


(continued on next page)


Year 1998 Year 2001

Mean S.D.# Skew Mean S.D.# Skew

Panel C: U.A.E. foreign banks (N=21)

Investmentsa (X1) 11 25 2.2 24 39 1.9

Net loansa (X2) 1113 1540 2.8 1350 2154 3.2




Cost of labord (w1) 0.079 0.13 4.4 0.069 0.12 4.0

Accounting ratios


Cash and CE to total assets (%) 0.7 0.4 (9.0) 1.3 3.1 4.4


Retail deposits to total deposits (%) 88.8 13.4 (2.5) 82.0 13.9 (1.3)



Return on assetse (%) 1.4 1.1 (0.8) 0.2 3.9 (4.1)

Return on equityf (%) 38.2 46.8 2.0 27.2 57.8 0.6

a In million AED (US$1=3.67 AED).b Includes off-balance sheet items such as: loan commitments, LCs, futures and forward contracts, and notional

value of outstanding swaps.c In AED for 100 AED of customer deposits.d In million AED per full-time equivalent employee.e (Net profit/Average total assets)*100.f (Net profit/Average total equity)*100.# S.D. represents standard deviation—measure of dispersion.

Table 1 (continued)


reflect accumulated activity over 1998 and 2001, while the output variables are averages of

beginning-of-year and end-of-year values. Because natural log of zero is undefined, a

small positive amount (1, which represents 1000 Arab Emirate Dirham (AED—the official

currency)) of output is added to each of the elements of outputs for all banks. In all the

three model specifications for identification purpose, the indicator variables for foreign

banks and larger banks are excluded so that the estimated coefficients measure the cost

efficiency of the domestic banks relative to the foreign banks. The bank-specific indicator

variable tests whether there are systematic differences in costs across smaller banks

relative to larger banks as also across domestic banks relative to foreign banks.24 Again,

for identification purpose, the year dummy for 1998 is excluded so that the time dummies

measure the time effect on cost efficiencies relative to 1998.

4.1. Stage 1 analysis: general business environment of U.A.E. banks during 1998 and

2001

Panel A in Table 1 reports the summary statistics of business environment variables, viz.

banking outputs, input prices, total assets, and total costs of 37 commercial banks in U.A.E.

24 The researcher sincerely thanks the unanimous referees for advising to include these aspects in the analysis to

differentiate it from other researches.


The average volume of business environment variables, viz. investments, loans, and off-

balance sheet items as well as input costs, viz. cost of deposits and labor were higher with

higher variability in 2001 as compared to 1998. Although net profit of all banks increased in

2001 relative to 1998, ROA and ROE declined in 2001 and were more variable relative to

1998. The banks’ cost of deposits increased from 0.12 AED per 100 AED in 1998 to 0.14

AED per 100 AED in 2001 as the banks increased their borrowing from non-retail deposits

sources to fund their operations. On the other hand, average per-employee cost marginally

decreased from 0.06 million AED in 1998 to 0.05 million AED in 2001 implying

reallocation of human resources of banks thus partly reducing their total operating costs.

Banks’ loan quality (indicated by ratio of loan loss provisions to gross loans) increased

from a level of 13.9% in 1998 to 14.8% in 2001. Liquidity of banks (as measured by ratio of

cash and cash equivalents to total assets) improved from 0.8% in 1998 to 1.2% in 2001. Banks

were marginally less capitalized at 14.7% in 2001 compared to 15.2% in 1998. The share of

retail deposits to total deposits decreased from 90.1% in 1998 to 83.8% in 2001 implying

scarcity of cheaper source of funds for banks in 2001. Similarly, ratio of net loans to total

earning assets decreased from 92.6% in 1998 to 81.6% in 2001 implying that banks reduced

their exposure to risky loans in 2001 by improving loan quality through more provisioning for

bad debts and reducing loans in their asset portfolio. These management practices resulted in

marginally higher EPS to investors, which increased from an average of AED 7.85 in 1998 to

AED 7.96 in 2001. ROA and ROEmarginally declined implying declining profit margins and

decreased asset utilization reflecting intense competitive business conditions at the banks in

2001 than in 1998.

Panel B reports the summary statistics of 16 domestic commercial banks. Panel C reports

the summary statistics for 21 foreign commercial banks. The business environment of these

two types of banks was more are less similar to that of overall banks as discussed above except

that; level of deposit-cost and per-employee cost were higher for foreign banks in both the

periods compared to domestic banks. This implies relatively higher operating costs at the

foreign banks due to lower average volume of business compared to domestic banks. Further,

domestic banks were better capitalized than foreign banks25 for both types of banks in 2001

compared to 1998. In summary, the business environment of banks during 1998–2001 was

mixed, i.e., decline in capitalization, an improvement in loan quality through increased

provisioning for bad debt, decrease in net loans, decreased per employee cost, increase in

deposit costs, and decreased availability of retail deposits. These banks appear to be under

more stress in 2001 than in 1998 both in terms of funding and investment operations.

Cost-efficiency estimates from the three specifications are reported in Table 2. Results

indicate that the estimated models fit the data reasonably well with adjusted R2 ranging from

0.62 to 0.70 supporting our model design. X-efficiency estimates averaged 0.796 in translog

specification, 0.747 in flexible Fourier specification, and 0.896 in fixed-effects frontier

specification.26 This implies that U.A.E. banks experienced operating inefficiencies of

25 They were in general above the 10% norms prescribed by the U.A.E. Central Bank implying higher degree of

safety as per Basel II norms.26 An additional SFA fixed-effects model was specified without natural log transformation as suggested by an

unanimous referee. However, the coefficients were insignificant, log-likelihood estimate was not encouraging,

and efficiency estimates were unstable and not reliable relative to the three models under discussion. Hence, the

model results are excluded from the discussion.

Table 2

Cost function model estimation results

Translog:

model 1

t-ratios Flexible Fourier:

model 2

t-ratios Fixed-effects:

model 3

t-ratios

Dependent variable Ln(NTC) Ln(NTC) Ln(NTC)

Mean cost efficiency 0.796*** (10.34) 0.747*** (10.67) 0.896*** (11.49)

ru2 0.0017 0.0995 0.1431

rv2 0.0074 0.0766 0.1269

Adjusted R2 0.65 0.7 0.62

N 148 148 148

Degrees of freedom 126 119 136

Log-likelihood �136.98 �130.66 �159.29

Intercept 15.9993*** (4.83) 11.9287 (2.53) 4.8028 (9.23)

Ln[net loans (NL)] �2.5678* (�1.88) �2.5544 (�1.29) 0.6427*** (6.31)

Ln(investments (INV)) 1.0355*** (2.50) 0.6581 (1.18) 0.0963*** (2.86)

Ln[off-balance

sheet items (OBS))

0.1983 (0.43) 0.308 (0.48) �0.1143* (�1.99)

Ln(normalized

input (NW))

�1.3506 (�1.51) �1.6966 (�1.36) 0.4431*** (4.30)

0.5Ln(NL)2 0.454 (1.47) 0.2665 (0.63)

0.5Ln(INV)2 0.0742** (2.03) 0.0536 (1.21)

0.5Ln(OBS)2 �0.1588 (�1.35) �0.2264** (�1.92)

0.5Ln(NW)2 0.1879 (0.83) �0.0057 (�0.02)

Ln(NL)*Ln(INV) �0.2376** (�2.17) �0.1506 (�1.21)

Ln(NL)*Ln(OBS) 0.1247 (0.92) 0.2213 (1.33)

Ln(NL)*Ln(NW) 0.4666*** (2.67) 0.2409 (0.80)

Ln(INV)*Ln(OBS) 0.0463 (0.94) 0.0421 (0.56)

Ln(INV)*Ln(NW) �0.2062*** (�2.68) �0.0503 (�0.38)

Ln(OBS)*Ln(NW) �0.0982 (�1.10) 0.0759 (0.61)

Cos[(Ln(INV)+Ln(OBS)] �0.0808 (�0.53)

Sin[(Ln(NL)+Ln(OBS)] �0.1254 (�1.03)

Cos[(Ln(NW)+Ln(NL)] 0.1198 (0.25)

Cos[(Ln(NW)�Ln(INV)] 0.2399 (1.39)

Cos[(Ln(NW)�Ln(OBS)] 0.1522 (1.26)

Cos[(Ln(NW)+Ln(OBS)] 0.2784 (1.42)

Sin[(Ln(NW)+Ln(INV)] 0.1347 (0.92)

Size (small banks=1,

large banks=0)

�0.1268 (�0.83) �0.1367 (�0.68) 0.5076*** (�3.37)

Local (domestic banks=1,

foreign banks=0)

�0.2721* (�1.82) �0.1928 (�0.80) 0.4594*** (�2.86)

1999 �0.0999 (�0.72) �0.1168 (�0.85) �0.0521 (�0.37)

2000 �0.0335 (�0.22) �0.0403 (�0.27) �0.1285 (�0.85)

2001 0.9889*** (4.73) 0.8479*** (4.34) 1.043*** (6.1)

E 1.2556*** (9.33) 1.3019*** (8.56) 1.1163*** (9.36)

r 0.1383*** (2.29) 0.1255** (2.28) 0.1921*** (3.11)



20.4% under SFA translog cost functional form, 25.3% under SFA flexible Fourier cost

functional form, and 10.4% under SFA fixed-effect frontier cost functional form. This also

implies that the fixed-effects frontier and translog specification underestimated cost

inefficiencies compared to flexible Fourier form and this finding is consistent with the

study by Berger and Humphrey (1997). As in other SFA studies, the distribution of cost-

efficiency scores is highly skewed (e.g., the range in all the three models was between a

minimum of 0.0368–0.0629 to a maximum of 4.662–5.2134). These results are generally

consistent with previous European studies, which reported bank cost X-efficiency

estimates of 0.8–0.936 with skewed distribution (e.g., see Ruthenberg & Elias, 1996;

Dietsch, Ferrier, & Weill, 1998; Vander Vennet, 1999). Further, coefficients of k and r are

quite significant indicating that significant inefficiencies existed in U.A.E. banks during

1998–2001.

Coefficient values of k and r are used to computeUi and Vi components of the error term

as in Eq. (2). rui denotes both technical and allocative inefficiency, which is controllable

by the banks, while rvi captures the effects of measurement error, which are uncontrollable

by the banks and the analysts. Results in Table 2 indicate that the technical and allocative

inefficiencies in U.A.E. banks ranged from 0.1% in translog from to 14.31% in fixed-

effects frontier form. On the other hand, uncontrollable inefficiencies ranged from 0.7% in

translog form to 12.7% in fixed-effects frontier form. This indicates that controllable

inefficiencies outweighed the uncontrollable, random, and measurement inefficiencies at

the bank. This signifies the fact that the U.A.E. banks could reduce their operating

inefficiencies by further improving the allocation of their resources (labor, capital, and

other resources).

A closer look at the likelihood estimates and significance of coefficients in all the three

specifications indicate that there was not much gain from specification of cost function in

translog SFA form (model 1) and flexible Fourier SFA form (model 2) compared to fixed-

effects SFA form (model 3). This is consistent with the earlier findings of Bauer et al. (1997)

that the choice between different models did not appear to significantly alter efficiency

measures. Further, model 1 and model 2 results with respect to individual variables are

difficult to interpret due to second order and interaction variable effects trigonometric

variables. For example, the negative and significant coefficient for net loans (NL or X1) in

model 1 would appear to imply lower total costs as loans are increased, all else the same.

However, this interpretation does not take into account the nonlinear cost implications of

loans captured in squared net loans (NL2) and multiple interactions of net loans with other

Notes to Table 2:

Figures in parenthesis are t-statistics.

Model 1: translog model: dependent variable: natural log transformation of total cost normalized by cost of labor

input with exponential error term specification.

Model 2: full flexible Fourier model: dependent variable: natural log transformation of total cost normalized by

cost of labor input with exponential error term specification.

Model 3: simple stochastic frontier model with no translog and flexible Fourier forms: dependent variable:

natural log transformation of total cost normalized by cost of labor input with exponential error term specification.

* VSignificant at 7–9% level (two-tailed t-test) that estimates are different from 0.

** Significant at V2–5% level (two-tailed t-test) that estimates are different from 0.

*** Significant at V1% level (two-tailed t-test) that estimates are different from 0.


variables in the model. For these reasons, it is not possible to accurately interpret the impact

of the environmental variables through individual estimated coefficients in model 1 and

model 2. These model specifications are no doubt important for computing scale and scope

coefficients and changes in productivity measures, which are beyond the scope of this paper.

While we will use the cost-efficiency estimates obtained from these three models for stage 2

risk-return analysis, the results in fixed-effects SFA model (model 3 results in Table 2) are

used to interpret the coefficients of the bank environmental variables on the efficiency.

The net loans and investments variables are significantly positively related to total cost

implying that the higher the bank scale of operations in these two outputs mix, the higher is

the total cost to the bank, which is plausible. However, off-balance sheet variable has a

significantly negative sign implying that banks could reduce its total costs by engaging in off-

balance sheet activities besides loans and investments functions. This reinforces the fact that

banks could benefit by expanding their operation through diversifying their portfolio to both

balance sheet and off-balance sheet activities so that there is cost sharing across these

functions. In the light of competitive pricing structure of loans and deposits services, the

banks operate on thin net interest margins and increase non-interest earnings. Hence, off-

balance sheet activities are very important for banks to generate non-interest revenue.

The deposit cost input variable normalized by labor input cost has a significantly

positive sign implying that if the price of deposit costs relative to labor costs decrease, the

relative total costs would decrease too. This motivates banks to borrow at low cost funds

and increase its asset portfolio through loans and investments. One way the banks could do

this is to increase their share of retail deposits to non-retail deposits by attracting new retail

deposit customers and retaining existing customers through effective customer relationship

management (CRM).

The size indicator variable has a significantly negative sign implying that the smaller

banks have lower total costs and managed their operational costs effectively. Conversely,

the larger banks in the region have relatively higher total costs. The type indicator variable

has a significantly negative sign implying that the domestic banks have lower total costs

relative to foreign banks. This is true because majority of the businesses in the U.A.E. are

handled by domestic banks through a wider network of around 260 branches compared to

foreign banks, which have around 101 branches. Panels B and C in Table 1 reflect this

business environment of both the domestic and the foreign banks.

With regard to time indicator variable, only year 2001 is significantly positive relative

to 1998 while the decline in total costs was insignificant in 1999 and 2000 relative to 1998,

the decline was insignificant. On the other hand, the total costs significantly increased in

year 2001 relative to 1998. This is plausible since the business environment of banks in

2001 was mixed, i.e., decline in capitalization, an improvement in loan quality through

increased provisioning for bad debt, decrease in net loans resulting in lower earnings,

decreased per-employee cost, increase in deposit costs, and decreased availability of retail

deposits. All these factors pushed up the total costs at the banks in 2001 relative to 1998.

4.2. Stage 2 analysis: risk-return relationship with cost efficiencies

Here, we analyze and test the set of hypotheses formulated earlier in the paper about the

relation between risk-return behaviors of banks with the cost-efficiencies derived in the stage 1


analysis. As stated earlier, there is awider gap in this area in the banking research literature. The

current study hopefully fills a part of this gap to provide assistance to the bank managements

and the investors to suitably adjust their investment, financing, and portfolio decisions.

Table 3 reports the results of OLS estimation specified as in Eq. (7). The dependent

variable Cit denotes the cost-efficiency estimates obtained from the three SFA models

specified in stage 1 analysis.27 As can be seen from the regression diagnostics, viz. the

adjusted R2, log-likelihood function estimates, F-values, chi-square values, DW statistics

and significance of coefficients, the model 3 outperformed the other two models. Hence,

we shall use model 3 results below in our discussion of relationship of risk-return variables

with cost-efficiency measures.

In model 3, the coefficient on the ratio of cash and cash equivalent to total asset (X2) is

significantly negative confirming the hypothesis that higher liquidity (lower liquidity risk) at

the banks tend to increase the banks’ cost efficiencies during the study period. Interestingly,

the default risk variable (X1) and the two variables controlling the bank portfolio mix, viz.

ratio of retail deposits to total deposits (X4) and net loans to earnings assets (X5), are

insignificant. The results suggest that banks’ cost efficiencies are neither related to loan

quality nor portfolio mix (expressed as ratios of retail deposits to total deposits, and net loans

to total earning assets), all of which capture managerial quality at the banks.

The ratio of equity to total assets variable measures capitalization risk (X4). This variable

is significantly positive supporting the hypothesis that well-capitalized banks reflect higher

quality management and such bank managements are cautious in their risk taking behavior.

This finding lends credence to the fact that individual and institutional investors look at well-

capitalized banks in U.A.E. to be not only less risky but also more efficient for making their

investment decision. This finding is of greater importance to all the parties in the context of

the fast emerging U.A.E.’s economy and the revised Basel recommendations, which require

banks to capitalize adequately to cover overall risk including operational risk.

On the return side of the analysis, the EPS as an earning measure was negative and

insignificant. This is plausible since financial markets in U.A.E. are just developing,

fundamental equity valuation is still in its infancy, and EPS has lesser importance to

investors as an investment criterion. However, the alternative accounting measure of

earnings, i.e., ROE is significantly positive. This finding supports our hypothesis that higher

ROE of banks positively influences the cost-efficiency measure at the banks. The

implication is that, in U.A.E. financial market, investors look at ROE as the criterion for

their investment decision, while bank managements look at it as criterion for measuring

managerial performance. Although ROE is an accounting measure, it is quite powerful in

terms of Dupont analysis. It comprises of profit margin (profitability measure), assets

utilization (efficiency measure), and leverage (equity multiplier) components. Higher ROE

implies higher score on each of these components, which in turn results in increased

efficiency of operations. Thus, ROE captures fundamentals of banks in place of traditional

EPS in emerging markets and signals the investors that banks with higher ROE are more

cost-efficient and less risky.

27 The dependent variable in model 1 is the cost-efficiency estimates derived from stage 1 of translog

specification; in model 2, it is the cost efficiencies derived from flexible Fourier specification and, in model 3, it is

the cost efficiencies derived from the fixed effects frontier regression.

Table 3

OLS estimates of cost efficiencies with risk-return variables of U.A.E. banks

Variables Model 1 Model 2 Model 3

Dependent variables Ci SFA translog Ci SFA flexible Fourier Ci SFA fixed effects

Adjusted R2 0.45 0.44 0.46

Log-likelihood �149.4 �137.7 �150.5

F-value 11.14*** 10.46*** 11.49***

Chi-square 101.8*** 97.3*** 104.15***

DW statistics 1.79 1.71 1.81

N 148 148 148

Degrees of freedom 135 135 135

Constant 0.3832 (0.66) 0.3848 (0.72) 0.1339 (0.23)

X1 �0.6732 (�1.51) �0.7752* (�1.88) �0.5577 (�1.24)

X2 �16.1527*** (�3.24) �15.5828*** (�3.38) �15.7708*** (�3.14)

X3 3.4808*** (3.63) 3.2232*** (3.64) 2.9356*** (3.04)

X4 �0.4811 (�0.77) �0.3905 (�0.67) 0.0771 (0.12)

X5 0.3565 (0.71) 0.2837 (0.61) 0.2268 (0.45)

EPS 0.0016 (0.31) 0.0019 (0.40) �0.0038 (�0.74)

ROE 0.3090* (1.82) 0.2839* (1.81) 0.3540** (2.07)

Size �0.0776 (�0.52) �0.0439 (�0.32) �0.0779 (�0.52)

Local �0.4334*** (�2.94) �0.3511*** (�2.58) �0.3884*** (�2.62)

1999 0.1157 (0.67) 0.0739 (0.46) 0.2061 (1.19)

2000 0.0557 (0.33) 0.0104 (0.07) 0.0605 (0.36)

2001 1.4413*** (8.35) 1.2564*** (7.88) 1.5414*** (8.86)

Figures in parenthesis are t-ratios.

X1=ratio of loan loss provisions to gross loans, X2=ratio of cash and cash equivalent to total assets, X3=ratio of

equity to total assets, X4=ratio of retail deposits to total deposits, X5=ratio of net loans to total earning assets,

size=(smaller banks=1, larger banks=0), local=(domestic banks=1, foreign banks=0).

* Significant at 7–9% level (two-tailed t-test) that estimates are different from 0.

** Significant at 2–5% level (two-tailed t-test) that estimates are different from 0.

*** Significant at b1% level (two-tailed t-test) that estimates are different from 0.


The size dummy variable is insignificantly negative. This indicates that cost efficiency

is not related to size of banks in U.A.E. On the other hand, the type dummy variable is

significantly negative and supports the hypothesis that domestic banks are less cost-

efficient relative to their foreign counterparts. The implication is that managements in

domestic banks have the potential to further strengthen their cost-efficient operations

through adoption of effective ALM techniques, use of credit scoring models for

automating credit screening, and receivable securitization, which are currently scarcely

used by the domestic banks’ managements.

Only year 2001 dummy has significant positive coefficient. This supports the

hypothesis that, on an average, cost efficiency among U.A.E. banks was insignificantly

improving from 1998 to 2000. It however significantly improved in 2001. This is

consistent with our earlier discussion that the business environment of banks in 2001

experienced decline in capitalization, an improvement in loan quality through increased

provisioning for bad debt, decrease in net loans, decreased per employee cost, increase in

deposit costs, and decreased availability of retail deposits, all these suggests consolidation

of cost operations by the banks’ management.


5. Summary

This paper analyzed cost efficiencies of commercial banks using SFA during 1998–

2001 in U.A.E. a fast emerging economy and found that substantial cost inefficiencies

existed in U.A.E. banks ranging from 20.4% in translog form, 25.3% in flexible Fourier

form, and 10.4% in fixed-effect frontier form. Stage 1 analyzed the relationship between

costs and banks portfolios and found that net loans and investments influenced

significantly positively the total costs, while off-balance sheet activities influenced

significantly negatively the total costs. The implication is that banks could benefit by

diversifying from loans and investments to include off-balance sheet activities in their

portfolio. This strategy helps in generating substantial non-interest revenues at the banks.

The study findings also reveal that the banks could raise low cost deposit funds through

effective CRM. Interestingly, smaller banks experienced lower costs relative to larger

banks and domestic banks experienced lower costs relative to foreign banks.

Stage 2 analyzed the relationship of risk-return behavior of banks with cost-efficiency

estimates. The study reveals that higher liquidity (lower liquidity risk) at the banks

significantly increased the cost efficiencies at the banks, while the banks’ cost efficiencies

are found to be neither related to loan quality (default risk) nor portfolio mix of retail

deposits to total deposits and net loans to earning assets. Consistent with revised Basel

norms, the study revealed significant positive relationship between cost efficiencies and

capitalization risk implying that well-capitalized banks are less risky and are cost-efficient.

Interestingly, the study found that cost efficiency is not related to EPS, while cost efficiency

is positively influenced by ROE. Higher ROE implies competitive profit margin, improved

asset utilization, and safer leverage resulting in higher management efficiency at the banks.

Thus, investors may use ROE as criterion for their decisions on investments in U.A.E.

banks.

The study findings indicate that cost efficiency is not related to bank size, but domestic

banks are relatively cost-efficient than their foreign counterparts. The implication is that,

while smaller and domestic banks experienced lower total costs of operation, they could

become relatively more cost-efficient relative than foreign banks through adoption of

effective measures such as usage of ALM techniques, automating credit scoring

procedures, and receivable securitization, which are currently sparingly used by these

domestic banks.

Acknowledgements

The findings, interpretation, and views expressed in this paper are those of the author

only and do not necessarily represent the views of the institution where the author is

currently serving. Special thanks to anonymous referees of the earlier draft of the paper

and participants at the 2002 FMAI-European conference for their helpful comments. The

author sincerely acknowledges the critical comments and helpful suggestions of

unanimous referees of the journal. The author greatly acknowledges the support

provided by the current institution for the current research through granting of release

time.


References

Aigner, D., Lovell, K., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production

models. Journal of Econometrics, 6, 21–38.

Altunbas, Y., Gardner, E. P. M., Molyneux, P., & Moore, B. (2001). Efficiency in European banking. European

Economic Review, 45, 1931–1955.

Altunbas, Y., Liu, M. H., Molyneux, P., & Seth, R. (2000). Efficiency and risk in Japanese banking. Journal of

Banking and Finance, 24, 1605–1628.

Bauer, P. W., Berger, A. N., Ferrier, G. D., & Humphrey D.B. (1997). Consistency conditions for regulatory

analysis of financial institutions: A comparison of frontier efficiency methods, Federal Reserve Financial

Services, Working paper No. 02-97.

Beck, T., Levine, R., & Loayza, N. (2000). Finance and the sources of growth. Journal of Financial Economics,

58, 261–300.

Berg, S., Fbrsund, F., & Jansen, E. (1992). Malmquist indices of productivity growth during the deregulation of

Norwegian banking 1980–89. Scandinavian Journal of Economics, 94, 211–228.

Berger, Allen N., Demsetz, R. S., & Strahan, P. E. (1999). The consolidation of the financial services industry:

Causes, consequences, and implications for the future. Journal of Banking and Finance, 23, 135–194.

Berger, Allen N., & DeYoung, R. (1997). Problem loans and cost efficiency in commercial banks. Journal of


Berger, Allen N., & Humphrey, D. B. (1991). The dominance of inefficiencies over scale and product mix

economies in banking. Journal of Monetary Economics, 28, 117–148.

Berger, Allen N., & Humphrey, D. B. (1997). Efficiency of financial institutions: International survey and

directions for future research. European Journal of Operational Research, 1998, 175–212.

Berger, Allen N., Hunter, Williams C., & Timme, Stephen G. (1993). The efficiency of financial institutions: A

review and preview of research past, present, and future. Journal of Banking and Finance, 17, 221–249.

Berger, Allen N., & Mester, Loretta J. (1997). Inside the black box: What explains differences in the efficiencies

of financial institutions. Journal of Banking and Finance, 21, 895–947.

Berger, Allen N., & Mester, Loretta J. (1999).What explains the dramatic changes in cost and profit performance

of the U.S. banking industry? Finance and Economic Discussion Series, Federal Reserve Board, Washington

D.C., 1999.

Berger, Allen N., & Strahan, P. E., (1998). The consolidation of the financial services industry: Causes,

consequences, and the implications for the future, Working Paper, Federal Reserve Bank of New York.

Demirguc-Kunt, A., & Levine, R., (1999). Bank-based and market-based financial systems: Cross-country

comparisons, Working paper, World Bank.

Dietsch, M., Ferrier, G., & Weill., L. (1998). Integration and banking performance in the European Union:

Productivity, technical efficiency, scale efficiency, cost efficiency, and profit efficiency, Universite Robert

Schuman, Strasbourg (mimeo).

Gallant, A. Ronald (1981). On the bias in flexible function forms and an essentially unbiased form: The Fourier

flexible form. Journal of Econometrics, 15, 211–245.

Huang, C. J., Fu, T. -T., & Huang, M. Y. (1999). Cost efficiency of the farmers’ credit unions in Taiwan. In T. T.

Fu, C. J. Huang, & C. A. Knox Lovell (Eds.), Economic Efficiency and Productivity Growth in the Asia–

Pacific Region. Northampton, MA7 Edward Elgar Publishing.

Hughes, J. P., & Mester, L. J. (1993). A quality and risk-adjusted cost function for banks: Evidence on the btoo-big-to-failQ doctrine. Journal of Productivity Analysis, 4, 293–315.

International Monetary Fund (IMF). (2003). Country Report No. 03/67, March 2003.

Jondrow, James, Knox Lovell, C.A., Materov, I. S., & Schmidt, Peter (1982). On estimation of technical

inefficiency in the stochastic frontier production function model. Journal of Econometrics, 19, 233–238.

Leightner, J. E. (1999). The Achilles’ heel of Thailand’s financial market. In T. -T. Fu, C. J. Huang, & C. A. Knox

Lovell (Eds.), Economic Efficiency and Productivity Growth in the Asia–Pacific Region. Northampton, MA7

Edward Elgar Publishing.

Levine, R. (1999). Law, finance and economic growth. Journal of Financial Intermediation, 8, 8–35.

Levine, R., Loyayza, N., & Beck, T. (2000). Financial intermediation and growth: Causality and causes. Journal

of Monetary Economics, 46, 31–37.


Levine, R., & Zervos, S. (1998). Stock markets, banks and economic growth. American Economic Review, 88,

537–558.

Lovell, C. A. K. (1993). Production frontiers and productive efficiency. In H. O. Fried, C. A. K. Lovell, & S. S.

Schmidt (Eds.), The Measurement of Productive Efficiency: Techniques and Applications (pp. 3–67). New

York7 Oxford University Press.

McAllister, Patrick H., & McManus, Douglas (1993). Resolving the scale efficiency puzzle in banking. Journal of


Mester, L. J. (1996). A study of bank efficiency taking in to account risk preferences. Journal of Banking and

Finance, 20, 1024–1045.

Mitchell, Karlyn, & Onvural, Nur M. (1996, May). Economies of scale and scope at large commercial banks:

Evidence from the Fourier flexible functional form. Journal of Money, Credit, and Banking, 28(2).

Molyneux, P., Altunbas, Y., & Gardener, E. (1997). Efficiency in European Banking. West Sussex, UK7 John

Wiley & Sons.

Okuda, H., (2000). The production technology of Philippine domestic banks in the pre-Asian crisis period:

Estimation of the cost functions in the 1990–96 period. Working paper, Hitotsubashi University.

Rao, Ananth., (2000). Analysis of U.A.E. bank stocks. Economic horizon, The Federation of U.A.E. Chamber of

Commerce and Industry, Vol. 21, No. 82 – AH 1420–2000 (2), pp. 9–28

Ruthenberg, D., & Elias, R. (1996). Cost efficiencies and interest margins in a unified European banking market.

Journal of Economics and Business, 48, 231–249.

Sheldon, G. (1999). Costs, competitiveness and the changing structure of European banking, Working Paper,

Foundation Bank de France Pour la Recherche.

Vander Vennet, R. (1999). Cost and profit dynamics in financial conglomerates and universal banking in Europe.

University of Ghent (mimeo).

William, Greene H., LIMDEP Version 8. Econometric Software Inc, 2002, New York, USA.

Documents

Cost frontier efficiency and risk-return analysis in an emerging market