Global Financial Crisis and Productivity Changes of Banks in UAE

This research evaluates the productivity change of the Emirati Banking sector for a balanced panel which covers 10 banks operating in UAE for the period 2006-2010, by estimating a non-parametric approach Data Envelopment Analysis. Input-oriented Malmquist indices of productivity change are estimated with DEA to measure total factor productivity (TFP) change. The TFP changes are decomposed into the product of technological change and technical efficiency change (catch-up).

International Journal of Business and Society, Vol. 18 S3, 2017, 437-448
GLOBAL FINANCIAL CRISIS AND PRODUCTIVITY
CHANGES OF BANKS IN UAE: A DEA-MPI ANALYSIS
Ammar Jreisat
Al Ain University of Science and Technology
Sameer Al-Barghouthi
Al Falah University
Amer Qasim
Al Ain University of Science and Technology
Khalil Nimer
American University of the Middle East
ABSTRACT
The primary objective of this research is to undertake in-depth evaluation and examination of the productivity
change of the Emirati Banking sector for a balanced panel which covers 10 banks operating in UAE for the
period 2006-2010, by estimating a non-parametric approach Data Envelopment Analysis. Input-oriented
Malmquist indices of productivity change are estimated with DEA to measure total factor productivity (TFP)
change. The TFP changes are decomposed into the product of technological change and technical efficiency
change (catch-up). The era of our sample is very rich with many aspects that influenced the UAE banking system
which cover the global financial crisis era. The empirical results are obtained by running an input-oriented DEA
model using the software package, DEAP Version 2.1 (Coelli, 1996). Our results reveal that the banking sector
in UAE shown a decline after the financial crisis in 2008.
Keywords: Two Stage Data Envelopment Analysis; UAE Banks; Malmquist Productivity Indices; Total Factor
Productivity; Global Financial Crisis.
1. INTRODUCTION
Financial system stability in any country is important for both the overall economic development and
the effectiveness of the central bank monetary policy. Over the last two decades, the UAE government
has undergone consistent and remarkable transformation from a socialist to a capitalist economy.
These changes were introduced mainly to improve the economic efficiency of UAE banking system
especially after the global financial crisis. The banking industry in the United Arab Emirates is one
of the major, and arguably the most important, industry in the United Arab Emirates after the oil and
gas industry. This is mainly due to its role as an intermediary and facilitator for the better allocation
of assets in a country that is seen as the regional hub for international finance, a free zone and
international trade hub, a leader in the development and sale of real estate mega-developments and
centre for a large concentration of net worth individuals with large multibillion dollar conglomerates.
Corresponding author: Dr. Ammar Jreisat, Department of Accounting, Finance and Banking, College of Business, Al Ain University
of Science and Technology, P.O. Box 112612, Abu Dhabi, UAE Email: [e-mail].

438 Global Financial Crisis and Productivity Changes of Banks In UAE: A DEA-MPI Analysis
All of these key industries in the United Arab Emirates require the presence of banks in order to allow
the right allocation of funds through financing the key projects and industries of the country.
In 2010 the UAE banking sector comprising of some 23 local and 26, the banking sector in the UAE
has for quite some time benefitted from being in a very sound and robust position. In the UAE, four
types of banks are seen to be operating simultaneously. They are conventional, Islamic and foreign
banks. Moreover, some commercial banks have started opening Islamic windows and Islamic units
for those clients who do not want to indulge in interest-based transactions. This conviction created an
increased demand for Islamic products in the field of financing, and gave birth to a market where
only Islamic products are acceptable. Thus, banks working under Islamic windows are established to
provide an additional service to Muslim clients or to offer a variety of products for general clientele.
The UAE was the first country in the world to establish an Islamic bank, namely Dubai Islamic Bank,
in 1975. Islamic banking is one of the fastest-growing segments in the financial sector globally.
Assets of UAE's Islamic banks reached $73.1bn at the end of 2010 according to UAE Central Bank
governor. Islamic banks in the UAE target all categories to broaden their reach, through innovative
product offerings including Islamic personal finance, Islamic credit cards and Islamic auto finance,
Shari’a-complaint mortgages, and a growing range of investment funds.
UAE bank shares were slammed by a bear market amid an epic fall in oil prices, a rise in the US
dollar and an exodus of global capital from emerging/frontier markets. The scale of selling on the
Dubai Financial Market and the Abu Dhabi Securities Exchange has eerie echoes with the autumn of
2008, when Lehman's failure led to a seizure in the global interbank and wholesale funding markets,
a $100 a barrel drop in Brent crude, panic flows into the US dollar and a free fall in Gulf property
markets.
The global financial crisis in autumn of 2008 has affected many countries globally and in particular
effect UAE economy in general. The UAE made various actions after the crisis to save their economy
from any impact may affect their economy. Firstly, UAE banks have vastly boosted their capital
cushions since 2008 and Basel Tier one capital for the banking sector is now 16.7 per cent, at least
500 basis points higher than on the eve of Lehman's failure. Secondly, six years of frenetic loan
growth had made loan/deposit ratios in UAE banking excessive at 108 per cent in fall 2008. Banking
sector leverage has also fallen to 7.6, 100 basis points lower than in 2008. Three, UAE banks have
also successfully raised the non-performing loan coverage ratios in the banking system to 113 per
cent, far higher than the coverage ratio during the 2008 global credit crisis. Fourth, the UAE banking
sector was dependent on fickle global wholesale funding markets, which froze after Lehman's failure
triggered an epic crisis of confidence in the international interbank market.
With the above information we should shed the light on measuring the productivity change of the
banking sector during the global financial crisis era, especially the domestic banks, to assess how
each bank performs during and after the global financial crisis. Measuring the efficiency of the United
Arab Emirates banks significant as the UAE has developed rapidly comparing with other Middle
Eastern countries and this will be important for analysts, practitioners and policymakers to be able to
understand the relative performance of banks benchmark the efficiency of the banks against each
other. (Jemrić and Vujčić, 2002).
The primary objective of this paper is to undertake and in-depth evaluation and examination of the
productivity growth in the UAE banking sector. Input-oriented Malmquist indices of productivity

Ammar Jreisat, Sameer Al-Barghouthi, Amer Qasim and Khalil Nimer 439
change are estimated with DEA to measure total factor productivity (TFP) change using a balanced
panel data containing 10 banks operating in UAE for the period 2006-2010. The study compares the
productivity change between the domestic banks during the sample period. The empirical results are
obtained by running an input-oriented DEA model using the software package, DEAP Version 2.1
(Coelli, 1996).
The rest of the paper is organised as follows. To put the study in perspective. Section 2 presents a
brief overview of existing literature on productivity changes in the banking industry. Section 3
presents the Malmquist Productivity Index (MPI) Measurement. The Data and the choice of variables
presented in Section 4. The drivers of productivity change are analysis in Section 5. Sections 6
summarises and brings together the main findings.
2. LITERATURE REVIEW
The literature on efficiency and productivity change of banks and how productivity influenced by
changes in regulations, innovation and technological processes and differences of productivity across
countries is vast. Various studies conducted in the US, Europe, Asia and a few in Africa have
measured efficiency and productivity change in banking sector.
Ferrier and Lovell (1990) and Grabowski et al. (1994) used the DEA approach to assess the
productive performance of US banks relative to the best practice frontier, and found that overall the
efficiency of the US banking industry ranges from 65% to 90%. Following this, Richard et al. (2002)
used the DEA model to evaluate the productive efficiency of US commercial banks from 1984–1998.
Strong and consistent relationships between efficiency and independent measures of performance
were found. Seiford and Zhu (1999) examined the performance of the top 55 US commercial banks
using DEA. They used a two-stage [1] production process to measure profitability and marketability,
with inputs and outputs in each stage consisting of eight factors. Their results indicated that relatively
large banks exhibited better performance on profitability, whereas smaller banks tended to perform
better with respect to marketability.
The Middle East studies on measuring efficiency and productivity change of banks are limited.
However, that are a few studies on measuring productivity and efficiency on the Middle East area.
For example, Al-Tamimi and Lootah (2007) investigated the operating and profitability efficiency of
15 branches of UAE-based commercial bank utilizing the DEA method. The results indicate that the
profitability efficiency appears to be higher than operational efficiency. Regarding the financial ratios
analysis, a consistent effect cannot be obtained and it cannot be determined which branch has an
overall position in terms of higher performance. In addition, management should consider major
operational improvement efforts to reduce employees’ expenses and other operating expenses
combined with an increase in the total loans portfolio. Moreover, both interest and non-interest
revenues require improvement to increase profitability efficiency of the whole branch network.
Using the data from the annual reports of individual banks published by Emirates Banks Association
for 1997- 2001, Al–Tamimi (2008) focussed on identifying the relatively best performing banks and
relatively worst performing banks in the United Arab Emirates (UAE). The study used DEA and
some traditional financial ratios such as returns on assets, returns on equity, ratio of loans to deposits
and ratio of loan to total assets to investigate efficiency of banks. The DEA model used interest
expense and non-interest expense as input variable; interest revenue and non-interest revenue as

440 Global Financial Crisis and Productivity Changes of Banks In UAE: A DEA-MPI Analysis
output variables. The study revealed that most of the UAE Commercial Banks were inefficient. The
national banks were relatively more efficient than the foreign banks. Two traditional ratios namely,
loans to deposits and loans to total assets indicated that the UAE Commercial Banks somehow did
not use the available resources efficiently.
Al-Muharrami (2007) used DEA techniques to estimate technical, pure technical, and scale
efficiency, using an input orientation for GCC banks for ten years. He highlighted several interesting
findings: First, smaller banks exhibited superior performance in terms of overall technical efficiency
than did larger ones. Second, big banks proved to be more successful in adopting the best available
technology, while medium banks proved to be more successful in choosing optimal levels of output.
Third, Islamic banks were more successful in both the adoption of the best available technology and
choosing optimal levels of output. Fourth, banks in Bahrain, Qatar, Oman, UAE, Kuwait and Saudi
Arabia ranked first to sixth, respectively, in terms of technical efficiency.
Miniaoui and Tchanetchan (2010) assessed technical efficiency of 44 top GCC banks over the period
2005-2008 using DEA approach. The results show that only 14 banks are rated as efficient under
CRS and/or VRS assumptions, and indicate that Islamic banks perform slightly better than the other
types of banks (conventional and windowing).
In a more recent , Al Suwaidi (2013), applied DEA method to evaluate and analyze the efficiency of
the national commercial banks in the United Arab Emirates by defining and using different
approaches of Data Envelopment Analysis in order to identify the relatively efficient and relatively
less efficient national commercial banks. This study concentrates on the main approaches of the
operating efficiency and the financial intermediary role efficiency. Through this study they observed
that over the period of 2008 – 2012 that (i) A general and consistent level of high operational
efficiency can be observed in the United Arab Emirates banking Sector; (ii) A general and consistent
level of high intermediary role efficiency can be observed in the United Arab Emirates banking Sector
with the presence of efficiency fluctuations in some banks; (iii) The age of individual banks had little
or no effect on the relative efficiency of the bank; (iv) Compared to previous studies we can observe
a general raise in operating efficiency levels among banks.
To the best of our knowledge, none of these studies have evaluate and analyse the productivity growth
covered pre and post global financial crisis era for the UAE banking sector. The present study
overcomes this limitation by encompassing the entire financial liberalisation period and investigating
the drivers of productivity change in UAE banks. The global financial crisis brought about significat
changes in practices of UAE banks from 2006 to 2010. The study undertaken in this paper will
provide a new perspective about the banking sector of UAE.
3. THE MALMQUIST PRODUCTIVITY INDEX (MPI): DECOMPOSITION AND
MEASUREMENT
The Malmquist TFP index was first introduced in two very influential papers by Caves, Christensen
and Diewert (1982a, 1982b). These authors define TFP index using Malmquist distance functions;
hence the resulting index is known as Malmquist TFP index or simply (MPI). One of the important
features of these distance functions is that these allow description of a multi-input, multi-output
production technology without the need to specifying a behavioural objective such as cost
minimisation or profit maximisation. Distance functions are of two types: the input distance functions

Ammar Jreisat, Sameer Al-Barghouthi, Amer Qasim and Khalil Nimer 441
and the output distance functions. Input distance functions look for a minimal proportional
contraction of an input vector, given an output vector; and output distance functions consider the
maximum proportional expansion of output with a given set of inputs. Since the banks have better
control over the inputs, we adopt an input-orientated approach for computing TFP.
Let M
t Ry  denotes an (Mx1) output vector, N
t Rx  an (Nx1) input vector, and L(y) denote the input
requirement set representing the set of all input vectors, x, which can produce the output vector, y.
Then the input distance function, which involves the scaling of input vector, is defined on input set,
L(y), as:
    yLxxyd ttttt
t
i  )/(:max,  (3)
where the subscript ‘i’ indicates ‘input oriented’ measure. The notation  tt
t
i xyd , stands for the
distance from the period t observation to the period t technology. In other words, this distance function
represents the largest factor, t , by which an input vector  tx is deflated to produce the output vector
under period t technology. Similarly,  tt
s
i xyd , would indicate distance from period t observation to
period s technology. An input distance function can be illustrated using an example where two
inputs, x1 and x2, are used to produce a given output vector, y. For a given output vector, the
production technology is represented by the isoquant, L(y) in figure 1. The value of the distance
function for the point, A, which defines the production point where the firm uses x1 of input 1 and x2
of input 2, to produce the output vector y, is equal to the ratio= OA/OB.
Figure 1: Input Distance Function and Input Requirement Set
Source: Coelli et. al (2005).
Based on input distance functions, the Malmquist TFP index can be constructed to measure
productivity change between periods s and t, based on period t technology,
   
 ss
t
i
tt
t
i
ttss
t
i xyd
xyd
xyxym ,
,
,,,  . (4)
x2
0 x1A
x2A A
B
Isoq-L(y)
X1

442 Global Financial Crisis and Productivity Changes of Banks In UAE: A DEA-MPI Analysis
A similar output oriented Malmquist index can be obtained based on period s technology as follows,
   
 ss
s
i
tt
s
i
ttss
s
i xyd
xyd
xyxym
,
,,
,
,  . (5)
Clearly, Equations (4) and (5) imply that estimation of TFP change between the two periods could
depend on the choice of technology. In order to avoid the effect of any arbitrarily chosen technology,
Färe et al (1994) suggests to estimate the output oriented TFP as the geometric mean of the indices
based on periods t and s technologies as given by equations (4) and (5), respectively. Hence we have
   
 
 
 
2
1
,
,
,
,
,,,





















ss
t
i
tt
t
i
ss
s
i
tt
s
i
ttssi xyd
xyd
xyd
xyd
xyxym . (6)
When the value of im exceeds unity this indicates a positive TFP growth from period s to period t
and a value of the index less than one indicates a decline in TFP growth. The Equation (6) can be re-
written as
   
   
   
 
2
1
,,,
,,,
,,, 
























ss
t
i
ss
s
i
tt
t
i
tt
s
i
ss
s
i
tt
t
i
ttssi xyd
xyd
xyd
xyd
xyd
xyd
xyxym . (7)
The ratio outside the square brackets measures the change in the input-oriented measure of technical
efficiency between periods, s and t. This efficiency change is equivalent to the ratio of the Farrell
technical efficiency in period t to the technical efficiency in period s. The remaining part of the index
indicates the shift in technology between the two periods. Thus, the Malmquist TFP index given by
equation (7) reveals shows that productivity change is the product of technical efficiency change
(catch-up) and technological change (shift in frontier). The fig. 2 below illustrates the decomposition.
The technologies for period t and period s (t >s) are represented by St and Ss showing technological
progress from period s to t. Both observations (yt, xt) and (ys, xs) are inefficient with respect to their
own frontier and (yt, xt) does not belong to (ys, xs). Our formula (7) of the Malmquist index can be
expressed in terms of distances along the x-axis. Thus we have
  2
1
/
/
,,, 

















 ob
oc
od
of
oboa
ofoe
xyxym ttssi (8)
To measure Malmquist TFP change between any two periods as defined in equation (7), four distance
functions have to be calculated. The decomposition of technical efficiency change into changes in
scale efficiency and pure teachnical efficiency components would requires the calculation of the
distance functions with VRS technology. The values obtained with CRS and VRS technology can
be used to calculate the scale efficiency change residually. The mathematics underlying the
estimation procedure is outlined in Fare and Grosskopf (1990) and Coelli et al (2005).

Ammar Jreisat, Sameer Al-Barghouthi, Amer Qasim and Khalil Nimer 443
Figure 2: Decomposition of Malmquist Productivity Index
Source: Färe et al (1990)
4. THE DATA AND THE CHOICE OF VARIABLES
The choice of input and output variables for the banking sector is very controversial. In the literature
one comes across three distinct approaches that are used for selecting inputs and outputs. These are:
the production approach, the intermediation approach, and the value-added approach. The first
approach views financial institutions as producers who use labour and capital to generate deposits
and loans. This approach is used, among others, by Sathye (2001) and Neal (2004). The
intermediation approach views financial institutions as intermediaries that convert and transfer
financial assets from surplus units to deficit units. In an another conceptualization of the intermediate
approach, Paul and Kourouche (2008) use interest expenses and non-interest expenses as inputs and
interest income and non-interest income as the outputs. Hence, in our paper we follow the paper done
by Paul and Kourouche (2008). The data used in this study covers 2006–2010 period and are taken
from, auditing annual report of individual banks in UAE. The data were collected from 10 banks
operating in UAE. We use the intermediation approach in which banks are viewed as financial
intermediaries employing inputs such as total deposit and labour to produce outputs such as total
loans and other investments. The variables are listed in Table 1.
Table 1: List of Inputs and Outputs
Inputs Outputs
Interest Expense on Customer Deposits (X1) Interest Income on Loans (Y1)
Other Interest Expense (X2) Other Interest Income (Y2)
The definitions of the variables used in DEA model are as a follows. Inputs are defined as interest
expenses and non-interest expenses. Interest expenses include expenses for deposits and other borrowed
money. Non-interest expenses include service charges and commissions, expenses associated with fixed
(yt , xt)
(ys ,xs )
St
Ss
y
x
c f b e d a0

444 Global Financial Crisis and Productivity Changes of Banks In UAE: A DEA-MPI Analysis
assets and general management affairs, salaries and other expenses. Outputs are defined as interest
income, and non-interest income. Interest income includes interest on loans and securities. Non-interest
income includes service charges on loans and transactions, commissions and other operating income.
The major limitations or obstacles faced by this study are the general lack of information available to
the public. Firstly, we faced the lack of publicly available information of the foreign banks as there are
no separate financial statements available to researches or investors for the United Arab Emirates’
activity. This means that all foreign banks had to be omitted from the study. To overstep these limitations
we concentrated on the study on the national banks without including the few national banks with
missing/no information. As the 10 national banks included in this study include the majority of major
banks in the United Arab Emirates banking industry we believe that they are sufficient to show a clear
picture of the banking industry as a whole.
A year by year analysis of the year end financial data provided from the Bank Scope Database was used.
The data were collected from 10 domestic banks operating in UAE. The banks studied here are listed in
Table 2. Based on their total assets size in 2010 measured in dirham.
Table 2: Assets of Domestic Banks in UAE, 2010
Bank Name City Total Assets
AED
National Bank of Abu Dhabi ABU DHABI 2,112,833,714
Emirates Bank International PJSC DUBAI 1,887,078,501
Abu Dhabi Commercial Bank ABU DHABI 1,781,498,424
First Gulf Bank ABU DHABI 1,406,621,794
Mashreqbank DUBAI 8,478,804,145
Commercial Bank of Dubai P.S.C. DUBAI 384,849,836
Bank of Sharjah SHARJAH 2,060,366,456
Arab Bank for Investment & Foreign Trade-Al Masraf ABU DHABI 1,342,215,668
National Bank of Umm Al-Qaiwain UMM AL-QAIWAIN 132,262,901
National Bank of Fujairah FUJAIRAH 1,290,780,706
Source: Bankscope.
5. EMPIRICAL RESULTS
We have used non-parametric data envelope approach to compute the input oriented Malmquist
indices of productivity change based on the panel data for 10 banks for the period 2006-2010. The
computer software DEAP (Coelli, 1996) is used to calculate these indices. The value of the MPI (i.e.
TFP) greater than one indicates positive productivity growth or productivity progress while a value
less than one productivity decline or productivity regress. Percentage change in productivity is given
by (productivity change – 1) x 100. Where mean aggregate indices are reported for the different
groups of banks, these are weighted geometric means using the shares of individual banks in the
group output as weights. Similarly, the indices aggregated over the period are also weighted
geometric means, where shares of yearly outputs in the total output for the period are used as weights.
As mentioned earlier the our approach is the same approach used by Paul and Kourouche (2008). The
sample period mean of TFP change and its components of technical efficiency change, pure technical
efficiency change, scale efficiency change and technological change indices for each bank are
presented in Table 3. The results reveal that the all the banks in our sample have shown productivity

Ammar Jreisat, Sameer Al-Barghouthi, Amer Qasim and Khalil Nimer 445
improvements over the years. The highest mean TFP growth per annum has been shown by
Mashreqbank 9.86% and lowest by the National Bank of Umm Al-Qaiwain. The observed
improvement in mean TFP is largely attributable to technological progress.
Table 3: Estimates of Malmquist TFP Change and its Components, UAE Banks
Bank TEC TC PTEC SEC TFPC
1 1.000 1.306 1.000 1.000 1.306
2 1.025 1.482 1.000 1.025 1.520
3 1.034 1.095 1.000 1.034 1.132
4 1.000 1.602 1.000 1.000 1.602
5 1.317 1.508 1.089 1.209 1.986
6 0.846 1.257 1.000 0.846 1.064
7 0.962 1.450 1.000 0.962 1.395
8 1.000 1.293 1.000 1.000 1.293
9 1.000 1.055 1.000 1.000 1.055
10 1.089 1.303 1.105 0.986 1.419
Source: Authors’ calculations.
Notes: TFP denotes total factor productivity, TEC is the technical efficiency change, PTEC is the pure technical efficiency
change, SEC is the scale efficiency change and TC denotes technological change.
The results reveals that TFP growth in the banking sector over the years from 2007 till 2010 presented
in Table 4. In 2007 it can be observed that there was a decline TFPC for the ours ample data. However,
in 2008 the banking sector have shown progress in TFP and that improvement before the global
financial crisis appear which started in late 2008. The banks TFPC have experienced a decline in
TFP growth in 2009 comparing with earlier year but still having progress in TFP. The reason may be
due to several factors affecting the banking performance, in particular, due to the global financial
crisis. The banking sector in UAE continue growing and having progress in TFP for the year 2010.
Our results reveal that the banking sector in UAE shown a decline after the financial crisis in 2008
and then they start improve their productivity for the year after, as the UAE government took action
for supporting their financial and banking sector. In addition, our results, consists with other study
done by (Al Suwaidi, 2013), as his study found decline in the efficiency and productivity for the
banks in UAE in 2009 and then having progress for the year after.
Table 4: Yearly Malmquist Indices of Productivity Change, 2006–2010
Year 2007 2008 2009 2010 Mean
TFC 1.016 0.944 1.142 1.022 1.028
TC 0.844 1.243 0.986 1.324 1.082
PTEC 0.996 0.985 1.028 1.019 1.007
SEC 1.021 0.958 1.111 1.003 1.022
TFPC 0.858 1.174 1.125 1.353 1.113
Source: Authors’ calculations.
Notes: TFP denotes total factor productivity, TEC is the technical efficiency change, PTEC is the pure technical efficiency
change, SEC is the scale efficiency change and TC denotes technological change.
6. CONCLUDING REMARKS
This paper has used DEA model to estimate input-oriented Malmquist indices to examine TFP
changes in the UAE banking sector during the global financial crisis era 2006-2010. The TFP changes

446 Global Financial Crisis and Productivity Changes of Banks In UAE: A DEA-MPI Analysis
were decomposed into the product of technological change and technical efficiency change (catch
up). The technical efficiency change is further decomposed into product of pure technical efficiency
change and scale efficiency change. To the best of our knowledge, this is the first attempt to examine
TFP changes in the UAE banking sector during the entire crisis era, not encompassed in the earlier
studies. The results reveals that over the sample period for the UAE banking sector as a whole shows
a productivity progress which is largely due to the technological change. Our results reveal that the
banking sector in UAE shown a decline after the financial crisis in 2008 and then they start improve
their productivity for the year after, as the UAE government took action for supporting their financial
and banking sector. In addition, our results, consists with other study done by (Al Suwaidi, 2013), as
his study found decline in the efficiency and productivity for the banks in UAE in 2009 and then
having progress for the year after.
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APPENDICES
Table A-5: Malmquist Indices of Productivity Change for Banks, Year 2007
Bank TEC TC PTEC SEC TFPC
1 1.000 0.606 1.000 1.000 0.606
2 0.727 0.877 0.771 0.943 0.637
3 0.821 0.763 1.000 0.821 0.626
4 1.364 0.886 1.000 1.364 1.208
5 1.169 1.136 1.000 1.169 1.328
6 1.000 0.843 1.000 1.000 0.843
7 1.000 0.691 1.000 1.000 0.691
8 0.917 0.953 1.000 0.917 0.874
9 1.000 1.016 1.000 1.000 1.016
10 1.345 0.797 1.243 1.082 1.073
Mean 1.016 0.844 0.996 1.021 0.858
Table A-6: Malmquist Indices of Productivity Change for Banks, Year 2008
Bank TEC TC PTEC SEC TFPC
1 0.763 1.103 1.000 0.763 0.841
2 0.680 1.205 0.678 1.003 0.820
3 0.929 1.143 1.000 0.929 1.062
4 0.707 1.149 1.000 0.707 0.813
5 0.845 1.297 1.000 0.845 1.096
6 1.000 1.635 1.000 1.000 1.635
7 0.915 1.215 1.000 0.915 1.112
8 1.521 1.446 1.000 1.521 2.199
9 1.000 1.180 1.000 1.000 1.180
10 1.401 1.149 1.270 1.103 1.610
Mean 0.944 1.243 0.985 0.958 1.174

448 Global Financial Crisis and Productivity Changes of Banks In UAE: A DEA-MPI Analysis
Table A-7: Malmquist Indices of Productivity Change for Banks, Year 2009
Bank TEC TC PTEC SEC TFPC
1 1.311 1.171 1.000 1.311 1.535
2 2.744 0.793 1.913 1.434 2.175
3 1.026 0.988 1.000 1.026 1.014
4 1.414 1.035 1.000 1.414 1.463
5 0.899 0.990 0.918 0.979 0.890
6 1.000 0.675 1.000 1.000 0.675
7 1.092 1.182 1.000 1.092 1.291
8 1.000 0.987 1.000 1.000 0.987
9 1.000 0.915 1.000 1.000 0.915
10 0.734 1.276 0.748 0.981 0.936
Mean 1.142 0.986 1.028 1.111 1.125
Table A-8: Malmquist Indices of Productivity Change for Banks, Year 2010
Bank TEC TC PTEC SEC TFPC
1 1.000 1.306 1.000 1.000 1.306
2 1.025 1.482 1.000 1.025 1.520
3 1.034 1.095 1.000 1.034 1.132
4 1.000 1.602 1.000 1.000 1.602
5 1.317 1.508 1.089 1.209 1.986
6 0.846 1.257 1.000 0.846 1.064
7 0.962 1.450 1.000 0.962 1.395
8 1.000 1.293 1.000 1.000 1.293
9 1.000 1.055 1.000 1.000 1.055
10 1.089 1.303 1.105 0.986 1.419
Mean 1.022 1.324 1.019 1.003 1.353
Source: Authors’ calculations.
Note: TFP denotes total factor productivity, TEC is the technical efficiency change, PTEC is the pure technical efficiency
change, SEC is the scale efficiency change and TC denotes technological change.