Measuring and Explaining the Efficiencies of the United Arab

Emirates Banking System

By

Fatima S. Al Shamsi Hassan Y. Aly Mohamed Y. El-Bassiounii

UAE University The Ohio State University UAE University

Accepted and forthcoming in Applied Economics

i The authors would like to thank a reviewer for his/her helpful comments and would like to acknowledge with gratitude the financial support provided by the UAE University (interdisciplinary research grant # 01-4-12/02).

Measuring and Explaining the Efficiencies of the United Arab

Emirates Banking System

Abstract

Using a newly collected data from a survey distributed to all banks in the

United Arab Emirates (UAE), this paper measures economic efficiency in the banking

industry, namely allocative, technical, pure technical and scale efficiency. Employing

a non-parametric -DEA- approach, the study estimates the efficiency for a cross

section of the UAE banks in 2004. The results indicate that the dominant source of

inefficiency in the UAE banking is stemming from allocative inefficiency rather than

technical inefficiency. Furthermore, the main source of the relatively small size,

technical inefficiency in the UAE banking industry is not the scale inefficiency but

rather the pure technical inefficiency. The results further indicate that the UAE banks

are able to use their input resources more efficiently when they have more branches,

and that newer banks are performing better than older banks on average. Moreover,

the results also show that short experiences of employees affect efficiencies

negatively and government ownership tends to reduce efficiency (as the government

shares increases in the bank, the efficiency scores get lower). Finally, the most

interesting results have to do with finding higher average efficiencies in banks that

employ more women, more mangers, and less national citizens of the UAE.

I. Introduction

The banking industry worldwide is facing competitive pressure as the world

financial structure is changing rapidly due to the ramifications of the establishment of

the World Trade Organization. Deregulating the financial sectors and financial

services, the increasing use of information technology, and the huge speed of

dispensing financial information are also among the factors leading to reevaluating

and restructuring of financial institutions worldwide. In such conditions, bank

regulators, managers, investors as well as governments are concerned about how

efficiently banks transform their inputs into various financial products and services or

in simple wording how efficiently banks performs their functions. Thus, in order for

financial institutions to survive the upcoming battles created by the expected stiff

worldwide competition, as by product of the enactment of WTO regulations, it is

imperative that these institutions be subjected to measurement and evaluation of its

own economic efficacies and performance.

The UAE banking sector is no exception. Having served as a financial center

for the Middle East region, the UAE banking institutions need to revaluate their

performance. Moreover, the financial sector needs to compare its economic

efficiencies with the competitors from around the world who soon might be able to

enter the UAE market and seize part of the local share.

It is a matter of vital importance for bank managers, bank regulators, and the

Central Bank authority of the UAE to get full information about the bank’s economic

efficiencies (technical, allocative and scale efficiencies). To this end, the current study

provides estimates of the various efficiency scores for the UAE banking sector,

investigates the types of inefficiencies (if any) and identifies their sources.

However, while the measurement and the comparisons of efficiencies are

significant and vital objectives for this study, explaining the efficiencies and

attempting to analyze them is more important than ever. Accordingly, additional

demographic, social and technical data were collected and used to go one step further

beyond the classical analysis and explain why the efficiency scores are what they are

and what are the factors that could help enhance them and finally how governmental

regulations may help their improvement.

The main findings of the study indicate that UAE banks are generally

technically efficient but allocatively less efficient. Also, the results show real small

scale inefficiency, if any, exist in the UAE banking sector. In explaining these results,

the study confirms that factors such as branching, having more women employees as

well as more managers, and having longer experience for employees do positively

impact average efficiencies. On the other hand, government ownership and having

more national citizens as employees do negatively affect the average efficiencies

In section II a background of the UAE banking industry is provided whereas

a pertinent review of literature is presented in Section III with special attention to

studies conducted on MENA (Middle East and North African) countries or

comparable economies. Section IV outlines the methodology used in this study in

addition to identifying the models, variables and data sources. Section V presents the

empirical DEA results (efficiency scores), while Section VI explains these results in

light of the explanatory variables (demographic, social, regulatory, and economic)

that are used for the first time in such studies. Section VII concludes by policy

implications, limitations, and a brief summary.

II. Background of the UAE Banking Industry

The UAE banking sector consists of the UAE Central Bank, the commercial

banks (local and foreign) and other specialized banks and financial institutions. The

Central Bank of the UAE was formed in 1980. Its duties include advising the

government on monetary and financial issues, issuing currency, maintaining gold and

foreign currency reserves, formulating credit policy and providing regulation and

supervision. Total assets of the UAE Central Bank (CB) reached AED 67.6 billion in

2004 and its net earnings during 2004 increased by 34.4% from AED 559.2 million to

AED 801.9 million (MENAFN.Com, 2005).

Banks play a critical role in the UAE economy, and the banking system is

strong and developed, technologically advanced and more integrated into the world

economy. According to the IMF, the banking system is broadly profitable, well

supervised, and capitalized (IMF public information notice, March 2003). Benefiting

from relatively low operating costs and widening margins between deposit and

lending interest rates, combined net profits of UAE banks rose by 39.55% in 2004

(MENAFN.Com, 2005). The ratio of gross nonperforming loans to total loans

continued to decline, but remains high at 14% compared to other GCC countries

(Kuwait is 6%), while capital adequacy ratios remained significantly above

international standards. Bank assets constitute around 184% of non-oil GDP and

125% of total GDP in 2003 (Central Bank, 2004). The growth in the banking GDP is

attributed to the high credit and deposit growth on the back of low interest

environment. Consolidated assets of the banking sector grew by 11.3% to AED 413.7

billion due to the growth of total credit by 17.3% to AED 238.2 billion in 2004

(Central Bank, 2004). Some selected monetary and banking indicators are given in

Table 1.

Table 1. UAE Selected Monetary and Banking Indicators Indicator 1990 1995 1999 2000 2001 2002 2003 2004CB assets (billion AED*)

21.1 27.2 39.2 45.8 52.5 55.3 54.5 59.9

CB assets to equity ratio

20.5 20.2 20 18.9 18.2

Banks assets (billion AED*)

153.9 180.9 251.1 264.2 235.1 331.6 366.9 394.1

Gross non performing loan/total lending

15.2 14.4 13.6 12.7 15.7 15.3 14.3

Return on assets 1.9 2.0 1.5 1.8 2.6 2.2 2.3 Return on equity 17.1 18.3 12.8 14.9 16.7 15.6 16.4 Average interest rate spread (%)

4.4 4.5 5.8 3.7 4.7 5.6 4.7

UAE Banks** (No.) 207 241 295 370 324 345 367 377F. Banks** (No.) 119 119 110 109 109 112 112 112Labor (No.) 9,677 10,916 14,274 14,385 15,191 16,080 17,229 18,381GDP*** share (%) 4.2 4.9 6.4 5.8 6.6 6.6 6.3 Non-oil GDP*** share (%)

8.7 9.6 9.5 9.5

Notes: * 1 AED = US$ 3.668 ** includes head offices and their branches *** For all financial institutions and insurance

Sources: Central Bank of the UAE, Statistical Bulletin volume 24, No.3 September 2004 and IMF (2004), Country report No. 04/ 175, June 2004.

The UAE commercial banks market is quite large compared to other Gulf

States. At the end of the 2004, there were 46 banks, with 489 branches, serving a

population of around 3.6 million compared to 14 banks in Saudi Arabia serving some

23.5 million inhabitants (Central Bank & Global Investment House, 2004). Of the

UAE commercial banks, 21 were locally incorporated, with a total of 377 branches

and 70-100% UAE ownership. The remaining 25 banks, with 112 branches, are

foreign owned. It is worth mentioning here that foreign banks are restricted to no

more than five branches. The total assets of the commercial banks have expanded

significantly during the past years reaching AED 413.7 billion in 2004 (of which more

than 27% are foreign assets). The UAE banking sector is the second after Saudi

Arabia within the GCC in term of asset size. Asset size of national banks increased

by 11.5% to AED 314.3 billion in 2004, while that of foreign banks increased by a

similar rate (11.2%) to AED 99.5 billion for the same year (Central Bank, 2004). The

financial institutions’ GDP share reached 6.3% in 2003 and their share of non-oil

GDP reached almost 10% for the same year, while the total workforce in the banking

sector reached 18,381 workers in 2004 (of whom less than 2% are nationals). The top

five banks dominate the industry, accounting for roughly 50% of all assets and 75%

of all deposits. They are partly owned by the governments of Abu Dhabi and Dubai.

Among the local banks, there are four Islamic banks that follow the Islamic banking

principle.

The UAE commercial banks provide a full range of financial services;

covering retail and investment banking, and their commercial lending are dominated

by short- and medium-term loans. Total bank credit extended to residents and non-

residents reached AED 238.2 billion in 2004, a rise of 17% from 2003. An analysis

of the components of bank credit according to economic activities reveals that the

largest share of bank credit was extended to the trade sector which attains an average

share of 29% for the last three years. The second sector in term of acquired credit is

the construction sector with a share of 14.5% on average for the period from 2002-

2004 (see Table 2). The distribution of credit by economic activities shows an

increase in the credit extended to the government in the last three years by 22.5% in

2003 and 32.5% in 2004, which increased the government share in total bank credit

from 9.2% in 2002 to 12.8% in 2004.

Table 2. Bank Credit by Economic Activity 2002-2004 (Million AED) Activity 2002 % 2003 % 2004 %Trade 48870 29.5 57053 28.9 69083 29.0Construction 27063 16.3 26845 13.6 31856 13.5personal loans for Business 20716 12.5 23965 12.2 30392 12.8personal loans for consumption 17704 10.7 21443 10.9 23195 9.7Government 15222 9.2 19650 9.9 29134 12.2manufacturing 9901 5.9 11082 5.6 13717 5.8Transport, communication 5127 3.1 6325 3.2 6573 2.8Electricity, water and gas 3219 1.9 11110 5.6 9113 3.8Mining and quarrying 2213 1.3 2077 1.1 2497 1.0Agriculture 1154 0.7 830 0.4 880 0.4

Non-banking financial institution 1903 1.1 2272 1.2 3745 1.6Other 12512 7.8 14254 7.4 18023 7.4Total 165604 100 196906 100 238208 100Source: Central Bank of the UAE, Statistical Bulletin volume 24, No.3 September 2004.

In compliance with the Central Bank regulation, all 46 banks operating in the

UAE met the 10% capital asset ratio. Bank lending has grown substantially,

increasing at an annual rate of 17% during 2001-2003.

Securities markets are a new development in the UAE financial systems that

were launched in 2000. There are two exchanges in the UAE, Abu Dhabi securities

market (ADSM) and Dubai financial market (DFM). ADSM is the third stock market

by capitalization in the Arab world after Saudi Arabia and Kuwait which was

estimated to be at more than AED 111 billion at the end of 2003 and DFM has a

market capitalization at about AED 60 billion. The transaction volume of the UAE

securities markets increased by 40% in 2003; however, market liquidity remains low

and speculation is restrained. The general market index grew at 32% in 2003, "among

the lowest appreciation in the GCC market" (IMF, 2004, p47).

Dubai International Financial Center (DIFC) is another new development that

added to UAE financial system in 2004. DIFC was initiated by Dubai government as

a free zone financial center and is expected to encompass a comprehensive set of

international financial functions. DIFC activities will include institutional and

investment banking, insurance and re-insurance, asset management, Islamic financial

services, back office operation and an international exchange that will trade a full

range of financial instrument (IMF, 2004, p49). DIFC is intended to be segregated

from the UAE financial sector and its banking operation is confined to institutional

wholesale banking. The launch of DIFC is expected to give the country an

opportunity to be a "universally recognized hub for institutional financial services and

the regional gateway for capital and investment " (Global Investment House, 2004,

p.3).

III. Review of Pertinent Literature

There are two major approaches for measuring efficiency that have been used

in all studies; the parametric approach and the non-parametric approach. In general,

the parametric approaches specify a functional form for the cost, revenue, profit, or

production relationship among inputs, outputs and others (for instance, environmental

factors), and allow for random error. The most famous technique used in the

parametric approach is Stochastic Frontier Approach (SFA).

On the other hand, the nonparametric approach most well-known technique is

the Data Envelopment Analysis (DEA). The DEA- to be used in this study- computes

the relative efficiency of each bank by using multiple inputs and multiple outputs.

Linear programming techniques allow for the construction of best practice cost and

production frontiers from these data and the performance of a particular bank is then

judged relative to this frontier. The specific efficiency measures calculated can be given

fairly simple interpretations. The technical efficiency measure gives the proportional

reduction in input usage, which could have been achieved if the firm operated on the

efficient frontier. The technical efficiency can be decomposed into the proportional

reduction in input usage, if inputs were not wasted (pure technical efficiency) and that

reduction if there existed constant returns to scale (scale efficiency). As such, pure

technical inefficiency reflects excess input levels for a given level of output. This

inefficiency is unique in that it is caused and could be corrected by proper management.

From the economics’ standpoint, firms that operate at constant returns to scale represent

the long run socially efficient level of operation. Therefore, choosing non-constant scale

of operation also constitutes inefficiency.

In this section a review of the DEA literature limited to recent studies that

applied the technique to MENA (Middle East and North African) countries or

comparable emerging economies or made international comparisons between different

countries is presentedii.

First, a comprehensive study by Berger and Humphrey (1997) surveyed over

125 studies that applied frontier approaches to measure the performance and

efficiency of financial institutions in more than 20 different countries. Most of the

studies are conducted in the U.S. banking industry between 1990 and 1998. Very few

studies were done outside the US and a call for the need to examine the efficiency of

banks outside the United States was emphasized. In their article, Berger and

Humphrey, illustrated differences and dispersion in efficiency estimates between

nonparametric and parametric frontier techniques. They reviewed and critically

analyzed empirical estimates of financial institution efficiency in order to address the

implications of efficiency results in the areas of government policy, research, and

managerial performance.

Mohd Zaini Abd Karim (2001) investigates whether there are significant

differences in bank efficiency across select ASEAN countries (Indonesia, Malaysia,

Philippines, and Thailand). The study indicates that the substantial proportion of a

total variability is associated with inefficiency of input used. It also highlights the fact

ii For more details on the issue of measuring bank efficiency, using the non-parametric DEA

approach and its comparability across different countries, regions of the world and under various operational and environmental working conditions, the interested reader is referred to Drake 2001, Devaney and Weber 2002, Kong and Tongzon 2006, Steinmann and Zweifel 2003, Sathye 2001, Miller and Noulas, 1996, Hasan and Marton 2003, Drake and Hall 2003, Akhigbe and McNulty, 2003, Esho 2001, Santomero and Seater, 2000, De Young, Robert 1997, Barnum and Gleason 2006, Allen and Anoop 1996, Lozanzo-Vivas et al. 2002, Orea 2002, Staat, Matthias 2006, Cook 2000, Clark and Siems 2002, Vennet, 2002, Von Hirschhausen et al 2006, Shujie Yao 2006, Wheelock and Wilson 1999, Athanassopoulos 1998, Peristiani 1997, Clark 1996 and Allen N. Berger and Loretta J. Mester (1997). These studies may be considered examples and are not intended to be an exhaustive survey.

that inefficiency tends to decrease with bank size and increase with government

ownership.

David A. Grigorian and Vlad Manole (2002), using both cross-country and

cross-regional settings, and applying the DEA approach, tried to calculate an

appropriate measure of commercial bank efficiency in a multiple input/output

framework for transition economies, and to identify the effects of policy framework

on the performance of commercial banks. The results of the study illustrated that

banks with a larger market share and a larger controlling foreign ownership are likely

to be more efficient than those with a smaller market which was attributed to their

better risk management and operational techniques. They also discovered that banks

in higher per capita income countries are more efficient in terms of attracting more

deposits and generating stronger cash flows than banks in low income countries. It is

also indicated that while privatization of state-owned enterprises, enterprise

competition and corporate governance related improvements are important in

boosting commercial bank efficiency, the securities market and non-bank financial

institutions development hinders the efficiency of banks.

Ihsan Isik, M.Kabir Hasssan (2002) estimated allocative efficiency, scale

efficiency and overall cost efficiency of Turkish commercial banks. The study

correlated 4 measures of financial performance with the 5 measures of cost efficiency

and investigated whether higher performance impacts bank cost efficiency. They

discovered that the efficiency of the Turkish commercial banks had deteriorated in the

90’s and the dominant source of the cost inefficiency is technical, which they

suggested might be attributable partly to the recent abnormal growth of small and

medium banks, and partly to the recent heavy investment in expensive

computerization and automation projects and consequently idle capacity. They

examined the relationship between the X-efficiency measures and the proxy measures

of performance to reveal that there is a statistically significant relationship. The study

also suggests that strong competition might have induced more market discipline on

small banks, leading to greater cost efficiency. The study also indicates that foreign

banks are significantly more efficient than their domestic peers and private banks are

more efficient than public banks. Their study suggested that publicly traded banks are

more technically efficient than privately held banks, and banks under a holding

company structure are more efficient than independent banks.

In their study, Ali F. Darrat et.al (2002), investigated the efficiency of banks in

the MENA region. The study uses DEA and Malmquist total factor of productivity

index to evaluate the performance of 8 locally incorporated Kuwaiti banks, in terms of

their efficiency, productivity growth and technological change over the period 1994-

1997. The study indicates that small banks are more efficient and there is an upward

trend in cost efficiency of Kuwaiti banks. The study also suggests that while technical

efficiency of Kuwaiti banks is consistently higher than allocative efficiency, scale-

efficiency is also persistently higher than pure-technical efficiency over the estimation

period. Market power plays an important role in cost and allocative efficiencies and

capitalization of Kuwaiti banks positively impacts their cost efficiency. It is also

suggested that Kuwaiti banks experienced productivity growth over the sample period

from becoming more technologically advanced rather than being more technically

efficient.

Claudia Girardone et.al (2004) examined the determinants of Italian banks’

cost efficiency over the period 1993-1996 by employing a Fourier-flexible stochastic

cost frontier in order to evaluate X-efficiency and scale economies and to identify the

main characteristics of efficient banks. The important findings of the study include:

deregulation has a positive impact and X-efficiency levels seem to decrease over time,

for all sizes of banks, the smallest banks appear to be less inefficient than their larger

counterparts, the main differences between the most efficient and the least efficient

bank seem to be mainly related to staff expenses, efficient banks always appear to

have lower levels of equity/assets and higher levels of non-performing loans,

inefficiencies appear to be inversely correlated with capital and positively related to

the level of non-performing loans, inefficient banks tended to have (on average) a

greater retail banking orientation, higher interest margins and more branches

compared with their efficient counterparts, no clear relationship between assets size

and bank efficiency.

Finally Berger (2007) conducted a comprehensive survey article where he

reviewed and critiqued over 100 studies that compare bank efficiencies across nations.

His goal was explaining the consolidation pattern among financial institutions in

developed versus developing countries. He grouped these studies into three distinct

categories: (1) studies that make the comparisons of bank efficiencies in different

nations based on the use of a common efficient frontier, (2) other that make the

comparisons of bank efficiencies in different nations using nation-specific frontiers,

and (3) finally the ones that compare the efficiencies of foreign-owned versus

domestically owned banks within the same nation using the same nation-specific

frontier. He used the results to explain the consolidation patterns in different part of

the world and he concluded that the efficiency disadvantages of foreign-owned banks

relative to domestically owned banks tend to outweigh the efficiency advantages in

developed nations on average, and this situation is generally reversed in developing

nations.

IV. Methodology and Data

From section III, we can clearly conclude that each approach of measuring

efficiency (parametric or non-parametric) has its own advantage and shortcomings.

The main disadvantage of the parametric approach is the imposition of certain

functional forms on the technology used in production. The approach does dictate the

type of technology used by utilizing certain functional forms and not others. Some of

these restrictions on the technology could be very severe depending on the flexibility

of the functional form (or lack thereof). While the non-parametric approach does not

impose any restrictions on the technology, it does not allow for any stochastic random

disturbances. Variations in efficiency might be due to random shocks stemming from

external factors like weather changes or the like.

The approach selected for use in this study is the non-parametric approach.

The random disturbance in the banking industry is insignificant in comparison to

another industry (e.g., agriculture) where weather and external factors could have

great impact on productivities. Also, the benefits from using no restricting technology

could be greater in the banking industry compared to others, due to the varied

methods of producing financial services among different financial institutions. In

addition, it is clear from the review of relevant literature (Berger and Humphrey,

1997, cited earlier) that efficiency estimates from nonparametric studies are very

similar to those from parametric frontier models; but nonparametric methods

generally yield slightly lower mean efficiency estimates. Also, since most of the

studies conducted on the banking sectors in other countries have used the non-

parametric approach, for the sake of consistency in comparison, this study uses the

same approach.

The specific technique to be used to measure the overall technical efficiency

index of a bank is the solution of a group of linear programming problems that are

very well known by now. They can be illustrated by:

Min λ such that

(1) L1Z1 + L2Z2 + . . . + LnZn ≤ λLn

K1Z1 + N2Z2 + . . . + KnZn ≤ λKn

Y1Z1 + Y2Z2 + . . . + YnZn ≥ Yn

Zi ≥ 0 , i = 1, . . ., n ,

where n denotes the number of observations, λ represents the overall technical

efficiency index and Z is a variable measuring the intensity of production, Y denotes

output and L and K denote labor and capital as defined below. Note that the condition

Zi ≥ 0 imposes constant returns to scale in the underlying technology.

In order to relax the assumption of “constant returns to scale” (CRS) and

separate λ into “pure technical efficiency” (PTE) and “scale efficiency” (SE), an

additional linear programming problem suggested by Färe, Jansson and Lovell (1985)

is constructed and solved as follows:

(2) Min β such that

L1Z1 + L2Z2 + . . . + LnZn ≤ βLn

K1Z1 + K2Z2 + . . . + NnZn ≤ βKn

Y1Z1 + Y2Z2 + . . . + YnZn ≥ Yn

∑n Zi = 1,

where β is a measure of pure technical efficiency. Having calculated β, a measure of

scale efficiency (SE) can be computed as follows:

(3) SE = λ/β.

The efficiency scores of all Decision Making Units (banks in this case) are

bounded between 0 and 1, with the most efficient banks receiving an efficiency score

of unity. The CRS assumption is appropriate if all banks are operating with optimal

scales. Otherwise, technical efficiency scores could be confounded with scale

efficiency. For more details on the specificities of the techniques see among others

Aly et al. 1988 and 1990.

Now, we turn to the data and definition of input and output variables. That

part of the required data is collected from balance sheets and income statements from

most of the commercial bank, operating in the UAE for the last five years. The three

major inputs targeted by this study are labor, capital and deposits, and the two outputs

are loans and investments. The choice of inputs and outputs for banks has received

much attention in literature due to the unique nature of the bank’s production process.

There are two approaches in the literature. The production approach and

intermediation approach. In the production approach, banks are considered as units

producing services for clients such as performing transactions and processing

documents. Therefore, inputs are measured by physical units, and outputs are

measured by the number and type of transactions or documents processed over a

given time period. Under the other intermediation approach, banks are viewed as

channeling funds between depositors and borrowers. Thus, banks sustain labor,

capital and loanable funds expenditures to transfer funds from those with surplus of

funds to those with shortage of funds. Thus, total costs should include interest

expenses as well as operating costs (Topuz and Isik, 2004). The study follows the

second approach.

Labor (L) is measured by the total number of employees, capital (K) by the

book value of fixed assets and premises, and deposits (D) by the sum of demand and

saving deposits. The analysis also includes input prices in order to measure cost

efficiency. The unit price of labor (WL) is the total cost of all bank employees (i.e.,

salaries, employee benefits, etc) divided by the total number of employees. The unit

price of capital (RK) is measured by the total expenditure on fixed assets and

premises divided by the book value of fixed assets and premises. The unit price of

deposits (PD) is computed by the total interest expenses of deposits divided by the

sum of demand and saving deposits. As to the outputs, loans (L) include all types such

as real estate loans, commercial and industrial loans, and consumer loans. Investments

(I) reflect the value of all securities other than those held in the bank’s trading

accounts.

It is important to mention here that the appropriate number of inputs and

outputs is determined based on the available data. As a general rule, the product of

inputs times outputs in a DEA approach should optimally be less than the sample size

in order to effectively differentiate among banks. In our case, the general rule is

satisfied since the total number of valid and non missing observations is 22 (twenty

two banks) and the restriction based on the general rule requires only 6 (six banks).

In addition, data on the education, gender, ethnicity and race mix of the

employees; number of branches; size of foreign transactions; proportion of different

types of loans; and number of financial products offerings are needed for explaining

the efficiency scores as mentioned earlier.

This major part of the data (demographic, social, and technical variables) was

collected through a survey instrument (see Appendix I) that was designed and tested

on two banks before it was distributed to all commercial banks operating in the UAE

in the year 2004. The Central Bank assisted in collecting the data by providing an

attached letter encouraging the banks to respond to the survey. However, the response

rate is about 54% (25 banks out of 47) and the usable observation is 22 banks. While

the collection was conducted in the Spring and Fall of 2004, the data were collected

for the year ending in December of 2002, the last year that banks had full information

for the required data.

To explain the efficiency scores based on socioeconomic, regulatory, and

demographic variables, we used the regular ordinary least square method as well as a

logistic regression approach due to the nature of the dependent variable which lies in

the interval [0,1]. The logistic regression analysis was performed through the

implementation of Weighted Least Square (WLS), with a backward stepwise

elimination option, to relate the dependent variable: efficiency score (y), and the

independent variables: number of years in operation (X1), the percentage of

government participation (X2), the number of branches (X3), the number of IT

employees (X4), the percentage of males (X5), the percentage of managerial

employees (X6), the percentage of national employees (X7), the percentage of

employees with higher education; university plus (X8) and the percentage of

employees with short experience; less than 5 years (X9). The logit transformation was

used to obtain the logistic regression model

(4) X...XXy1yln 9922110 ββββ ++++=⎟⎟

⎠

⎞⎜⎜⎝

⎛−

.

The weights which are inversely proportional to the variances of the logits were

approximated by (yi(1-yi)).

The descriptive statistics on the variables used in this study are reported in

Table 3.

Table 3. Descriptive Statistics for the UAE Banking Data Item Mean Std.

deviationMin Max

Total wages** 48.35 45.75 3.92 148.26No. of employees (Labor) 486.05 431.13 39 1386Wages per capita** (Unit price of labor) 0.11 0.03 0.07 0.18Book value of assets and premises** 138.11 151.87 4.00 547.00Total spending on fixed assets and premises**

36.82 67.24 0.00 274.60

Sum of current accounts and savings deposits**

2333.54 3775.54 111.85 16507.00

Sum of commercial loans** 1469.98 2593.60 0.37 12131.65Sum of industrial loans** 1032.23 1959.31 0.00 8917.29Sum of consumer loans** 1448.74 2275.17 7.98 8262.31Sum of other loans (if any) 719.42 1971.31 0.00 9088.00Total interest on current accounts and savings deposits**

406.50 1080.52 0.00 5054.00

Sum of real estate loans** 779.66 1591.10 0.00 6678.80Total loans** (L) 2957.70 4148.52 217.09 17815.00Total investments** (I) 740.83 1123.20 0.00 3579.36Unit price of capital** 0.62 1.18 0.00 5.163Unit price of deposit** 1.93 3.62 0.00 16.58** in million AED V. DEA Results

The DEA analysis produced the various efficiency scores in Table 4. The

average overall cost efficiency is a bit low (55%). This means that the average UAE

bank could have produced the same level of output using only 55% of the resources

actually employed had it been producing on the frontier rather than at its current level.

Another way to explain this is to say that the UAE banks do have potentials to

increase their level of output by another 45% using the same level of input they

actually have.

Also, such an overall average of cost efficiency (55%) is a bit lower than those

typically reported for developed countries. Here, we make the comparison between

the efficiencies of banks in different nations, with banks from each nation measured

against their own nation-specific frontier not against a common frontieriii. For

iii In this case, the standardization of the DEA methodology seems to tolerate such comparisons of the size of the efficiency coefficients. However, one has to bear in mind the national differences in regulations, legal system, economic and financial markets conditions in the different countries, as well as the differences in the time period the research covers - that will surly results in different frontiers (see Berger, 2007).

example, Aly et al. (1990) reported overall efficiency of 65% for US banks, Berger et

al. (1993) estimated cost efficiency at 80% for U.S. banks, and Altunbas et al. (1994)

estimated it at about 95-90% for British banks. In another Gulf country (Kuwait)-

where the legal, economic, financial, and social conditions are almost the same-

Darrat et al. 2003 reported banks to have cost efficiency of 67%.

Table 4. Efficiency Scores for the UAE Banks in 2002

Bank* CE AE TE PTE SE RTSBank 1 0.45 0.45 1.00 1.00 1.00 CRSBank 2 0.30 0.42 0.71 0.71 1.00 CRSBank 3 0.03 0.82 0.04 0.04 1.00 CRSBank 4 0.45 0.94 0.48 0.48 0.99 DRSBank 5 0.22 0.38 0.59 0.59 1.00 CRSBank 6 0.89 0.92 0.97 0.97 1.00 CRSBank 7 1.00 1.00 1.00 1.00 1.00 CRSBank 8 0.79 0.79 1.00 1.00 1.00 CRSBank 9 0.87 1.00 0.87 0.87 1.00 CRSBank 10 0.37 0.43 0.85 0.88 0.96 DRSBank 11 0.22 0.28 0.77 0.78 0.98 IRSBank 12 0.57 0.57 1.00 1.00 1.00 CRSBank 13 1.00 1.00 1.00 1.00 1.00 CRSBank 14 0.81 0.81 1.00 1.00 1.00 CRSBank 15 0.31 0.31 1.00 1.00 1.00 CRSBank 16 0.68 1.00 0.68 0.68 1.00 CRSBank 17 0.30 0.36 0.83 0.83 1.00 CRSBank 18 0.53 0.54 0.98 0.98 0.99 DRSBank 19 0.07 0.57 0.12 0.12 1.00 CRSBank 20 1.00 1.00 1.00 1.00 1.00 CRSBank 21 0.75 0.75 1.00 1.00 1.00 CRSOverall Average 0.55 0.68 0.80 0.80 0.99 CE: Cost Efficiency RTS: Returns to scale AE: Allocative Efficiency CRS: Constant returns to Scale TE: Technical Efficiency DRS: Decreasing returns to scale PTE: Pure Technical Efficiency IRS: Increasing returns to scale SE: Scale Efficiency * Due to a confidentiality clause in the survey we used to collect the data, the names of the banks are not revealed in this research project and will be provided to the participating banks upon request.

The results also reveal several other very important inferences. Firstly, the low

overall cost efficiency stems from allocative inefficiency rather than technical

inefficiency. Actually, the technical efficiency (.80) of UAE banks is consistently

higher than the allocative efficiency (.68) for most of the banks included in the

sample. Whether small or large, local or foreign, banks in the UAE seem to be doing

a better job technically than allocatively. This indicates that the leading source of cost

inefficiencies in the UAE banking industry is likely to be regulatory (rather than

managerial) in nature. The results imply that UAE banks do a better job utilizing

available inputs than choosing the proper input combination given the input prices.

Secondly, the main source of the relatively small size technical inefficiency in

the UAE banking industry is not the scale inefficiency (operating on non-optimal

scale) but rather the pure technical efficiency (resources being underutilized or merely

wasted). Actually, the UAE banks in the sample have consistently higher scale

efficiency than pure-technical efficiency. This happened without exception over every

single bank in the sample. Surprisingly, the UAE banks have almost perfect scores

when it comes to scale efficiency. Only 1% of the technical inefficiency in the UAE

could be attributed to scale. This is probably the lowest scale inefficiency in

comparison to all previous studies done all over the world.

VI. Regression Results

In this section the more interesting part of the study, namely explaining the

efficiency scores based on socioeconomic, regulatory, and demographic variables will

be attempted. We have used both the regular ordinary least squares and the logistic

regression methods, but we put more emphasis on the logistic approach due to the

nature of the dependent variable which lies in the interval [0, 1]. Due to the

importance of the logistic approach, its results for cost efficiency are detailed in

Appendix II.

Some of the interesting results obtained from the attempts above include:

Firstly, the fact that only technical and pure technical efficiency scores seemed to be

partially explainable by the number of branches. The coefficient of this variable

(branching) is positive and statistically significant (under 95% percent level of

confidence with respect to technical efficiency scores). This means that the more

branches the bank has, the higher its technical and pure technical efficiency scores.

This result was confirmed by both logistic and OLS regressions. Overall, it matches

other studies where banks are able to use their input resources more efficiently when

they have more branches.

Secondly, it is also interesting and surprising to see that the variable reflecting

the bank’s number of years in business is negatively impacting all the efficiency

scores, but at a statistically lower level of confidence (90% on average). This implies

that newer banks are performing better than older banks on average. While this is

surprising since experienced banks are expected to perform better, it may have to do

with newer and more recent banks adopting newer and more modern technology. This

interpretation is supported by a negative correlation of -0.686, with an associated p-

value of 0.007, indicating a highly significant inverse relationship between the bank’s

number of years in operation and the IT share (percentage) of its budget. Also, newer

banks are always able to attract experienced employees from existing and older banks.

Thirdly, while the variable representing private versus government ownership

(percentage of government ownership) is not statistically significant for any efficiency

score (only at 75% level of confidence on average), it is interesting to see a negative

sign in all regressions and with all types of efficiency scores. This indicates that as

the government shares increases in the bank, the efficiency scores get lower. This

may lead us to trust that privatization might be an appealing option for government

owned banks that are under performing in terms of efficiency scores.

Fourthly, an easy variable to explain is the short experience variable

(percentage of employees with less than five years of experience to total employees).

This variable seems to be negative and statistically significant at all levels. Thus,

banks with higher percentage of employees with short experience are definitely less

efficient than its counter parts. This will give credence to human resource mangers

as they always opt to hire employees with more experience.

Fifthly, a very interesting variable to explain is the percentage of male

employees to total employees. The logit analysis indicates that this variable is

negative and statistically significant. This is good news to the women labor force in

the UAE, as it signifies that banks with higher percentage of women are more

efficient than their counter parts. While this result is hard to explain, the connotation

is to give more attention to hiring more women in the banking industry if the bank

wants to be more efficient. Definitely, more studies need to be done here to explain

why women might have a comparative advantage in this industry. Also, the variable

of percentage of mangers to total employees, having statistically positive impact on

efficiency, needs a more careful look on why it is conducive to enhancing efficiency.

Finally, the most interesting and important findings here have to do with the

variable representing the percentage of nationals (citizens) to the total labor force.

This variable is found to be negative and highly statistically significant in the logit

analysis. This means banks with higher percentages of UAE nationals are under

performing in terms of overall efficiency in comparison to their counter parts. This

result is very important due to the ongoing efforts to nationalize the banking industry.

The UAE established a law requiring banks to hire at least 4% of its labor force from

the local nationals and has given a lot of consideration to this issue.

This last result coupled with the allocative (regulatory) inefficiency being the

major source of inefficiency, as shown in Table 4, should call attention to the wisdom

of enforcing the nationalization policies on the UAE banking sector.

VII. Summary and Policy Implications

The descriptive analysis of the UAE banking system shows that it is strong

and highly developed in structure and size, technologically advanced and more

integrated into the world economy. This superior picture may be attributed to many

factors. The most important is an overall improvement in the country’s economic

conditions that have enhanced the provision of the financial services to all sector of

the economy. Also, there is always government intervention and support for the

country financial sector. "the UAE has used its wealth to cushion financial sector

shocks and has the authority to recapitalize systematically important financial

institutions" (IMF: 2003). In addition, there is, to some extent, an insufficient

competition from foreign banks.

However, this rosy picture is not supported by the empirical results of

efficiency measures, as highlighted in Table 4. The results indicate that the UAE

banks' overall average of cost efficiency is a bit lower than those reported for

developed countries. It is also lower than that of another GCC country, Kuwait. The

study also indicates that the low overall cost efficiency of UAE banking stems from

allocative inefficiency rather than technical inefficiency. Furthermore, the main

source of the relatively small size, technical inefficiency in the UAE banking industry

is not the scale inefficiency but rather the pure technical efficiency. It is also

suggested that the UAE banks are able to use their input resources more efficiently

when they have more branches and that newer banks are performing better than older

banks on average. The results also show that short experiences of employees affect

efficiencies negatively and government ownership may tend to reduce efficiency (as

the government shares increases in the bank, the efficiency scores get lower).

The most interesting results have to do with finding higher efficiencies in

banks that employ more women, more mangers, and less national citizens of the UAE.

These last results should be investigated further in order to determine what can

be done about it. The results go against the current government policies of

nationalization and should be looked upon more carefully. Other means of employing

nationals without imposing a restriction on banks in terms of employment policies

should be considered. It goes against efficiency optimization to ask a bank to employ

certain resources without given much attention to the bank’s own profit maximization

policy. Perhaps, a direct subsidy to the employees who are working in the banking

sector could be an alternative approach.

The results of the study are very much consistent with previous studies

(especially in the Gulf). Banks are overall more technically efficient than allocatively

efficient.

It should be realized that all across the globe, a great force of change is

sweeping the banking industry, forcing radical adjustments to new business

conditions. As the UAE is entering a new era of structure change and development, it

is essential to prepare its domestic banking system for global competition, and it has

no choice but to integrate into the global economy. Such development will require

careful handling of the existing structural weaknesses and adopting bank policies that

keep pace with the new evolution and development in the world financial sector.

Thus, attention to the results of this study might prove useful in this regard.

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Appendix I

UAE Banking Sector Survey 2004

Serial Number: ……… Interviewer code: …………… Date: … / … / 2004

Bank Title: …………………………………………………………………………

Number of years in operation

Years

Ownership Structure (% government participation)

%

Number of branches

Branches

Number of Employees by Gender

Gender / Employees Managerial Rest Males Females

Number of Employees by Ethnicity

Ethnicity / Employees Managerial Rest National Arab Asian Western Others

Number of Employees by Education

Education / Employees Managerial Rest No education Primary (grades 1-6) Preparatory (grades 7-9) Secondary (grades 10-12) Two-years college University Higher degree (e.g., diploma, Master, Ph. D.)

Number of Employees by Experience

Experience / Employees Managerial Rest Lass than 5 years 5 to less than 10 years 10 to less than 15 years 15 years and more

Total Wages

Employees Total wages Managerial Rest

Department of Information Technology (IT)

Number of employees in IT department Percentage of IT department’s operating budget to total operating budget

Financial Services Offered

Services Yes / No Branch banking ATM EFTPOS Credit cards Telephone banking PC banking Internet banking Others (specify)

Financial Products Offered (to attract new deposits)

Products Value Special accounts Certificate of Deposits (CD) Others (specify)

Bank Main Activities

Item Value Book value of fixed assets and premises Total spending on fixed assets and premises Sum of current accounts and savings deposits Total interests on current accounts and savings deposits Sum of real estate loans Sum of commercial loans Sum of industrial loans Sum of consumer loans Sum of other loans (if any) Total investments (all security other than those held in trading accounts)

Percentage of foreign transactions to total transactions Percentage of foreign assets to total assets

Off Balance Sheet Activities

Item Value Swaps Future contracts Others (specify)

Thank you very much for your time

Appendix II Regression Model 1

A logistic regression analysis was performed, through a backward stepwise

elimination option, to relate the dependent variable: cost efficiency (y) and the

independent variables in equation (4). The following model was obtained:

X814.14X286.19X692.59X286.0X337.0111.51y1

yln 96531 −+−+−=⎟⎟⎠

⎞⎜⎜⎝

⎛−

.

For Model 1, R2 = 0.784 and the standard error of the estimate = 0.284. The

regression coefficients, their standard errors and the corresponding t and p-values are

given in the following table:

Table II-1: Logistic Regression Coefficients for Model 1 Variable Coeff. Std.

ErrorStd.

Coeff. t p-value

Constant 51.111 14.777 3.459 .013

Numbers of years in operation (X1) -.337 .078 -1.925 -4.336 .005

Number of total branches (X3) .286 .078 1.892 3.681 .010

Percentage of males (X5) -

59.692 17.525 -2.061 -3.406 .014

Percentage of managerial employees (X6)

19.286 7.613 .818 2.533 .044

Percentage of employees with short experience (X9)

-14.814 4.458 -1.909 -

3.323 .016

Regression Model 2

A second logistic regression analysis was performed through a forward

stepwise option, to relate the dependent variable: cost efficiency (y) and the

independent variables in equation (4). The following model was obtained:

X295.13X91.0305.5y1

yln 71 −−=⎟⎟⎠

⎞⎜⎜⎝

⎛−

.

For Model 2, R2 = 0.563 and the standard error of the estimate = 0.330. The

regression coefficients, their standard errors and the corresponding t and p-values are

given in the following table:

Table II-2: Logistic Regression Coefficients for Model 2 Variable Coeff. Std. Error Std. Coeff. t p-valueConstant 5.305 1.541 3.444 .007Percentage of nationals (X7) -13.295 4.258 -.723 -3.122 .012Numbers of years in operation (X1) -.091 .041 -.521 -2.247 .051