Efficiency and Productivity
of Greek Banks in the EMU Era
Georgios E. Chortareas†, Claudia Girardone* and Alexia Ventouri‡
Abstract
We provide a characterisation of the Greek banking system’s efficiency and productivity
under the new environment that the Economic and Monetary Union (EMU) participation
implies. We consider cost and profit efficiency as well as productivity change of
commercial banks using the non-parametric Data Envelopment Analysis (DEA) and the
Total Factor Productivity (TFP) Malmquist Index. The period under study is 1998-2003
covering Greece’s entry into the euro area in 2001 and the run-up to it. Moreover, enhanced
competition along with lower inflation and interest rates has further motivated financial
innovation and off-balance sheet (OBS) business. Our findings suggest that cost efficiency
has risen by 4.3% over the six years under study. Moreover, Greek banks seem to enjoy
relatively high profit efficiency (on average 75%) showing an increase by 93% over 19982003. Similarly, productivity seems to have risen by 15% and this was mainly driven by the
improvements in the performance of best-practice institutions. Our results do not show any
role for OBS activities in Greek banks’ efficiency. Finally, while the impact of profitability
and size on efficiency and productivity yields mixed results, our empirical findings seem to
corroborate previous studies in that controlling for risk preferences is important in
determining bank efficiency.
JEL classification: G21; D24.
Keywords: Greek Banks; Cost/Profit Efficiency; Data Envelopment Analysis; Malmquist
Total Factor Productivity Index; Off-Balance Sheet Business.
†
Department of Economics, University of Athens, 8 Pesmazoglou Street, 10559, Athens (Greece); Tel.:
+30(0)2103689805; Email: gchortar@econ.uoa.gr
* Corresponding author: School of Accounting, Finance and Management, University of Essex, Wivenhoe
Park, Colchester, CO4 3SQ (United Kingdom); Tel: +44(0)1206 874156; Email: cgirardone@essex.ac.uk
‡
School of Accounting, Finance and Management, University of Essex, Wivenhoe Park, Colchester, CO4
3SQ (United Kingdom); Email: avento@essex.ac.uk
1
Efficiency and Productivity
of Greek Banks in the EMU Era
1. INTRODUCTION
The banking sector in Greece has recently experienced major transformations,
extensive deregulation, and wide structural reforms. Over the 1990s the integration process
in the context of the run-up to the Economic and Monetary Union (EMU) and the
increasingly competitive euro-area environment has contributed to raise banks’ concerns
about their overall performance, efficiency and productivity.
As in other European countries, Greek banks have responded to these exogenous
pressures with a wave of consolidation and privatisation: between 1996 and 2003, 16
mergers and acquisitions took place, and 7 out of 10 state-owned banks were privatised
(Garganas, 2003). Furthermore, banks have strived to improve their customer base by
looking for new markets (e.g. in the Balkans) and providing a range of new, non-traditional
products and services, such as insurance, underwriting, asset management, and so on.
While seeking new sources of income, Greek commercial banks have expanded
significantly their Off-Balance Sheet (OBS) business, by around 45% over the studied
period.1
The literature on bank efficiency and, to a lesser extent productivity, is copious (see
Section 3) and the Greek banking sector has been investigated in several previous studies in
the context of cross-country analyses and, more often, in country-specific ones.2 This paper,
however, advances the existing literature in a number of ways. First, our analysis provides a
characterisation of the Greek banking sector’s efficiency and productivity using nonparametric methodology (Data Envelopment Analysis or DEA and Malmquist Total Factor
2
Productivity –TFP-- Index) for a recent banking data sample. It also covers a period
characterised by dramatic changes in the business and macroeconomic environment, largely
related to Greece’s entrance in the eurozone. Other key events include the significant fall in
bank share prices in the context of the wider stock market crisis in 1999, and the
implications of financing the public works in view of the 2004 Olympics for the banking
system. Second, while the existing work tends to focus on cost efficiency we explore both
cost and profit efficiency. This focus on the profit side appears warranted given that
exceptionally high profitability emerges as one of the key recent features of Greek banks.
Third, we take into account the role of OBS business on the efficiency estimation for Greek
banks. Finally, we test for the relationship between bank efficiency and productivity and
three fundamental aspects of banking business, namely size, profitability and risk.
During the run-up to the EMU Greece experienced a typical virtuous cycle between
banking system modernisation and liberalisation on one hand and changes in the
macroeconomic framework on the other (see, Stournaras, 2006).3 It has been initially
expected that the catch-up process in the new (EMU) era coupled with further liberalisation
and privatisation would enhance competition in the banking industry (e.g., Eichengreen and
Gibson, 2001). The profitability of Greek banks as shown by accounting indicators has
recently topped that of other euroarea countries (with the possible exceptions of Portugal
and Spain).4 At the same time various concentration indices such as the HerfindahlHirschmann (H-H) index have increased since 1999 (see for details Gibson, 2005).
In this context a number of interesting issues emerge such as the sources of high
profitability. A thorough assessment of Greek banks’ efficiency therefore is not a matter
with academic only implications but also allows a better understanding of bank profitability
than the typical reliance of accounting indices (e.g., Return On Assets; Return on Equity;
3
etc.). It also may provide indications in relation to market structure and potential anti-trust
measures.
The following section provides a brief literature review on Greek banks efficiency,
section 3 discusses the data and methodology, section 4 reports and discusses the results
and section 5 concludes.
2. BANK EFFICIENCY AND PRODUCTIVITY IN GREECE
An abundant international literature on cost efficiency in banking and, to a lesser
extent, productivity exists (see the extensive bank efficiency surveys by Berger and
Humphrey, 1997 and Goddard et al., 2001; and the works on productivity growth by
Grifell-Tatjé and Lovell, 1996; and Casu et al., 2004). The vast majority of efficiency
studies over the last decade or so have adopted frontier analysis to estimate alternative
efficiency measures (e.g. X- and scale efficiencies).5
We use the non-parametric DEA to investigate the Greek banking sector for two
reasons. First, the relatively small number of observations available excludes the possibility
that a parametric frontier may be employed. Secondly, the Malmquist Index for estimating
the evolution of productivity over time is conveniently implemented using DEA. Despite
the recent proliferation of parametric versions of the Malmquist and other productivity
indexes, the DEA Malmquist is still the most widely used TFP change measure in bank
efficiency and productivity literature. Besides, using DEA allows us to employ the same
methodological approach consistently throughout the paper.
Over the 1990s a number of efficiency and productivity studies have been carried
out on the Greek banking sector (see for an extensive review Chortareas et al., 2007). As
4
illustrated in Table 1 most studies tend to be country-specific and focus mainly on
efficiency rather than productivity.
<Insert Table 1 about here>
Overall, taking into account all studies reported in Table 1, the average inefficiency
level for Greek banks in the 1990s is around 20% and this is in line with the main evidence
from the international literature.6 DEA studies typically produce slightly lower inefficiency
levels compared with parametric approaches (88% against 76% respectively), as usually do
country-specific studies relative to pan-European ones. On the other hand, the productivity
growth has always been found positive and relatively high, with an average TFP of about
4% in 1993-97.
Various authors (see e.g. Clark and Siems, 2002; Tortosa-Ausina, 2003; Rime and
Stiroh, 2003; and Casu and Girardone, 2005) have indicated that omitting OBS items in the
output definition may result in a misspecification of what banks actually ‘produce’ and thus
may lead to incorrect conclusions. Only one recent attempts exists so far (Pasiouras,
forthcoming) investigating whether the inclusion of OBS items as variable in the output
definition changes the estimated efficiency scores of Greek banks. The finding suggests
that the inclusion of OBS items does not have an impact on the efficiency scores but the
statistical significance of this result is not tested.
Table 1 shows that in Greece the most widespread measurements of efficiency focus
on the cost side. One of the possible reasons for the lack of efficiency studies relating to the
profit side of Greek banks, is the unavailability or inaccurate price information which is
required for the estimation of profit efficiency. Notably, the existing international evidence
(see e.g. Berger and Mester, 2003 and Färe et al. 2004 for US studies and Maudos et al.,
5
2002; Maudos and Pastor, 2003; and Bos and Schmiedel, 2003 for EU studies) indicates
that on balance, profit efficiency levels are typically lower than those of cost efficiency.
Finally, in this paper we control for exogenously determined conditions that might
be responsible for observed differences in efficiency (see for example Casu and Molyneux,
2003, Ataullah and Le, 2006, Rezitis, 2006 and Pasiouras, forthcoming) and productivity
(Mukherjee et al., 2001 and Worthington, 1999) across banks. Following the prevalent
literature, we employ a two-stage method to investigate to what extent three bank-specific
variables explaining size, profitability and risks are associated with the estimated cost and
profit efficiency as well as productivity levels.
3. DATA AND METHODOLOGY
3.1 DATA AND INPUT/OUTPUT DEFINITION
We use annual individual bank data derived from non-consolidated banks’ financial
statements, derived from Bankscope by Bureau van Dijk. The time span considered is
1998-2003. Since a relatively homogeneous set of DMUs (Decision Making Units) is
required in DEA to minimise estimation bias, our sample consists of an unbalanced panel
of 85 commercial banks with an average of 14 observations per year.7 Our dataset is fairly
representative of the Greek banking sector as it constitutes 75% of the system’s total assets
on average. Table 2 illustrates selected descriptive statistics of the sample for 1998 and
2003, from which it is apparent that the average bank size has considerably increased
throughout the studied period (by 41%). Furthermore, on average all profitability ratios
(namely ROAA and ROAE) show an improvement over time, while the cost-to-income
ratio, a traditional accounting test for bank efficiency, remains virtually unchanged.
6
<Insert Table 2 about here>
This study adopts the intermediation approach (originally developed by Sealey and
Lindley, 1977) for determining the outputs of the banks’ production process, as it
incorporates all expenses (of which interest expenses are the most significant) and
recognises that deposits are more accurately inputs to the banking activities rather than
outputs. As a general rule of thumb the non-parametric DEA implies that the number of
observations in the dataset should be at least three times the sum of the number of input and
output variables (Cooper et al., 2000:252). In an attempt to satisfy this condition, given the
relatively small number of banks in Greece, we include one input (total cost figure) and two
outputs (total customer loans and total other earning assets). The total cost figure
incorporates both the cost of deposits (interest expenses) and the total operating expenses.8
The two outputs capture the traditional bank lending activity (total customer loans) and the
growing non-lending activities (total other earning assets). In addition, we estimate our
model twice in order to examine the importance of OBS business where off-balance sheet
items were aggregated as a single output. As in Casu and Girardone (2005), we use the
nominal value of banks’ OBS items as an output measure.9
When we turn our focus to the profit side, we rely on the “alternative” profit
efficiency measure which implies that firms are price setters rather than price takers and
include the following inputs and their prices: deposits and short-term funding; number of
employees; and total fixed assets.
The descriptive statistics for the input and output variables used for the calculation
of both cost and profit efficiency is reported in Table 2 for 1998 and 2003. The table shows
that on average total loans and OBS activities have increased by 159% and 45%,
7
respectively, while profits have increased fivefold over the studied period. On the other
hand the total cost figure has decreased only slightly.
3.2 METHODOLOGICAL ISSUES
3.2.1 Measuring Cost, Scale and Profit Efficiencies using the DEA Model
Originally proposed by Charnes et al. (1978) the DEA employs a mathematical
programming technique to measure the relative efficiency of multi-product DMUs (in our
case Greek commercial banks). The DEA efficient frontier is determined by connecting the
best-practice banks in the sample through piece-wise linear combinations that lie over the
observations, thereby ‘enveloping’ the data. The 100% technical efficient DMUs are
situated on the best-practice frontier and all the others are technically inefficient relative to
them.
In this paper we use the input-oriented10 DEA model with Variable Returns to Scale
(VRS) developed by Banker et al. (1984), which allows the possibility that the production
technology of DMUs may exhibit increasing, constant and decreasing returns to scale. The
VRS specification adds a convexity constraint to the original Charnes et al.’s Constant
Returns to Scale (CRS) model and should be calculated as:
min  , 
st
 y i  Y  0,
xi  X  0 ,
N1  1
  0,
(1)
8
where  is a scalar;  is an N  1 vector of constants; and N1  1 is the convexity
constraint, where N1 is an N  1 vector of ones. The linear programming model must be
solved N times, one for each DMU in the sample. The obtained value of  is the efficiency
score for the i-th firm and it satisfies   1 , with a value of 1 indicating a point on the
frontier, i.e. a technically efficient bank. Scale efficiencies are measured by running the
same dataset through both the CRS and VRS models. They are calculated by dividing the
CRS efficiency score by the corresponding score of the VRS model and retain values
between zero and one. Slack variables are also analysed. These indicate excess input
combinations in non-best practice banks relative to the benchmark efficient banks.
We then analyse the importance of including OBS business in the banks’
input/output definition for the estimation of cost efficiency. Two tests are carried out to
evaluate the significance of our results: the Efficiency Contribution Measure (ECM)
recently developed by Pastor et al. (2002) and the Mann-Whitney test.11
In addition, we calculate the ‘alternative’ profit efficiency to measure how close a
bank is to the point of generating the maximum profit given an amount of input prices and
output levels. 12 As discussed in the data section, input prices rather than levels are needed
for the estimation of profit efficiency with DEA to calculate input allocative inefficiency
(see for more methodological details Khumbakar and Lovell, 2000, p. 58).
To calculate the profit efficiency for firm j one starts by solving the following linear
programming problem:
MaxR j   w pj x pj
p
s.t. i Ri  Ri ,  i yiq  y jqq,  i xip  x jpp,
i

i
i
(2)
i
 1; i  0; i  1,..., N
i
9
The solution corresponds to the revenue Rj and input demand xj  ( xj1 ,..., x jp ) that
maximise profits, given the prices of the inputs w. The alternative profit efficiency for firm
j (APEj) can then be expressed as follows:
APE j 
Pj

j
AP

R j   w pj x pj
p
(3)
R   w pj x pj

j
p
That is, APE j represents the ratio between the observed profits ( Pj  R j   w pj x pj ) and
p
the maximum profits ( APj  R j   w pj x pj ) associated with the maximum revenue and the
p
input demand x j that maximise profits for bank j.
3.2.2 The Malmquist Total Factor Productivity (TFP) Index
The Malmquist index measures TFP change between two data points by calculating
the ratio of the distances of each data point relative to a common technology and it requires
the inputs and outputs from one time period to be mixed with the technology of another
time period.13 The Malmquist TFP index can be stated as follows (see e.g. Grifell-Tatjé and
Lovell, 1996):
D t 1 ( x t 1 , y t 1 )  D t ( x t 1 , y t 1 )
Dt (xt , y t ) 
t
t
t 1
t 1
M (x , y , x , y ) 
  t 1 t 1 t 1  t 1 t t 
Dt (xt , yt )
 D ( x , y ) D ( x , y ) 
1
2
(4)
10
where the notation Dt+1(xt, yt) is the distance between the period t observation and the
period t+1 technology. M can be decomposed into two components: technical efficiency
change (TEC), that is the term outside the brackets in equation (4), and technological
change (TC), that is the term inside the brackets. A value of M greater (or lower) than unity
indicates productivity growth (or decline).
3.2.3 Determinants of Bank Efficiency and Productivity using Tobit Regression Analysis
We employ a censored Tobit regression model to investigate whether and to what
extent bank size, risk and profitability are related to efficiency. Due to the small number of
observations per year, we pool together the data in order to gain extra degrees of freedom.
The model can be written as follows.
 i   0  1 ROAA   2 ln( TA)   3 ETA   i ,
(5)
where where  i is, alternatively, the cost efficiency, the profit efficiency and the
productivity change. ROAA is the Return On Average Assets and reflects how effectively a
bank management is using shareholders funds. ROAA is commonly employed as a measure
of performance in the banking literature and is expected to be positively related to
efficiency and productivity. However it is also plausible for a cost inefficient bank to be
profitable. ln(TA) is the natural logarithm of Total Assets and is included as a proxy for
scale economies. Following the literature this variable could take either positive or negative
signs. Finally, ETA is the equity-to-assets ratio and is a measure of the extent of risk taken
by bank managers, as higher leverage increases the risk of insolvency, which could result in
11
greater borrowing costs.
4. EMPIRICAL FINDINGS
4.1 BEST MODEL AND DEA COST AND PROFIT EFFICIENCY RESULTS
Table 3 shows that, contrary to recent US and EU empirical research, the inclusion
of OBS items in our standard model specification does not provide additional information
on the efficiency of Greek commercial banks. In a forthcoming study on the Greek banking
sector, Pasiouras obtains a similar result.
<Insert Table 3 about here>
The puzzling aspect of these findings is the relatively large volumes of OBS
activities of Greek banks relative to on-balance sheet ones over the studied period (see
Table 2). One possible explanation could be that a number of OBS activities correspond to
relatively new financial products and their recent introduction in the Greek banking system
did not give banks the opportunity to render them as profitable as it could be yet. If this
contention is correct then one should expect that the impact on financial costs derived from
banks undertaking this kind of non-traditional activities during a period of marked changes
as was 1998-2003 will be felt in the following years.14
Given the tests results described above, our analysis for Greek commercial banks
relies on the standard DEA model i.e. the one estimated without OBS. In that way our
results can be compared more directly to those of the already existing literature. The
average cost X-efficiency scores as well as scale efficiency measures are provided in Figure
12
1. Although not reported, the impact of slack variables on our empirical results is
negligible.15
<Insert Figure 1 about here>
Average cost efficiencies range between 82.6% and 91.1% and has increased by
4.3% over 1998-2003. The worst performing bank in the sample over the period scores
around 47% while by definition the best performing banks are 100% efficient and are
located on the best practice frontier. The average bank exhibit cost inefficiencies on the
order of 15%, a result that is quite in line with earlier international studies (e.g. Berger and
Humphrey, 1997) but seems at odds with previous studies on the Greek banking sector. For
example, Tsionas et al. (2003) and Rezitis (2006) report levels of X-efficiencies greater
than 90%. However our survey in Section 2 has indicated that these studies refer to
different time periods and although they use the intermediation approach to describe banks’
production process, their chosen input/output definitions are different from those used in
this paper. Besides, the study by Rezitis (2006) is not directly comparable because it
focuses on a limited number of banks (six banks over 1982-1997) and any idiosyncratic
features of those banks are more likely to be reflected in the results.
Such relatively low average efficiency scores may reflect the reshuffling of the
market and restructuring/reorganisation of the banks given the structural reforms that had
already taken place (e.g., EU directives for liberalising the banking system) and in view of
the anticipated changes during the studied period (e.g., introduction of euro, M&As, deeper
international integration) which probably took their toll on the Greek banking sector.
However, the efficiency scores seem to increase significantly over time for both models
indicating that the Greek banking sector is enhancing its cost effectiveness.
13
Several structural features specific to the Greek banking market may help explain
these results. The presence of foreign banks in Greece, for example, has been relatively
weak and as a consequence the competition has strong monopolistic features. Moreover, the
extensive fragmentation of the banking system may have not been in line with the needs of
a relatively small economy. In this sense, the increase in the efficiency over time probably
reflects – among other things – the efforts to consolidate the banking institutions. Another
possibility may have been that the strong prospects for revenue growth made the banks
focus on the revenue side. To the extent that the outlook for revenue growth gets tighter,
banks further want to focus on cost and operating efficiency.
As concerns the scale efficiency results, these remain relatively high (around 92%
on average) and stable over the examined period. This is again in line with US evidence
(e.g. Berger and Humphrey, 1997), which typically finds that scale and product mix
inefficiencies account for less than 5% of total costs. In other words, technical inefficiency
(failure to minimize costs for a given output vector) seems to be a much more serious
problem than scale inefficiency (failure to operate at the minimum efficient scale).
Therefore, in spite of the majority of the banks operating off their optimal
production scale, under-utilisation or input wasting seems to be a greater problem for Greek
banks. This is possibly because the fragmentation of the banking system prevents them
from achieving scale and other economies. In other words, there may be too many banks
(business groups) relative to the size of the Greek economy. Our findings of Greek
commercial banks operating at a scale above the optimal, especially in the last years of the
estimation period has strong implications for a number of current debates about the optimal
size, structure, and composition of the Greek banking system. If Greek banks operate at an
inefficiently large scale then the (popular for many analysts) argument for further
consolidation is weakened. For example, Kamberoglou et al. (2004) find that further
14
consolidation could lead to more efficient and competitive Greek banking institutions. One
more realistic possibility that reconciles the contrasting findings is that in the effort of
banks to become “one of the few” and win in the game of mergers they often have to
expand beyond the efficient levels. If this is the case then one would expect that when we
return to the steady state the scale of operations will revert to its efficient level. If this is not
the case, however, a number of questions are raised about the motives behind the Greek
bank mergers completed in previous years.
Table 4 reveals that the average profit efficiency for Greek commercial banks is on
the order of 75%, and it increases over the studied period by 93% (with an average yearly
increase of 18%).16 This suggests that Greek banks enjoy relatively high profit efficiency.
<Insert Table 4 about here>
This is especially true when our results are compared for example with those of
Maudos and Pastor (2003) for Spanish banks that using DEA find that profit efficiency is
about 50%. The same conclusion can be reached if our results are compared with those
derived from the application of parametric methods (although some caution should be taken
when making comparisons across studies using different approaches). For example, Vander
Vennet (2002) finds European banks’ profit efficiency to be around 70% on average.
Similarly, Bos and Schmiedel (2003) find that banks in eight European countries have a
profit efficiency of about 55% on average.
4.2 MALMQUIST TFP ESTIMATES
15
Table 5 presents the results of Malmquist TFP productivity change (see Färe et al.
(1994), and its decomposition into technical efficiency change (TEC) and technological
change (TC) on an annual basis for the period 1998-2003. The entries in each column are
annual geometric means of results of individual banks and the last row in the table reports
geometric means of the annual geometric means.
<Insert Table 5 about here>
Overall, the Greek banking sector seems to have experienced a significant
productivity growth by about 15% over the period. This result is in line with previous
Greek studies (e.g. Noulas, 1997; Tsionas et al., 2003) and confirms a positive trend that
started in the early 1990s and intensifies in the more recent periods. This may reflect not
only the effects of implementing a number of measures required by EU directives (that
aimed at the liberalisation and modernisation of the banking system) but also the
preparation of the Greek banking system for the challenges of competition in the new
environment.
Focusing on the components of the TFP index, our analysis reveals that the
improvement in productivity seems to have been caused by a positive technological change
(+14.4%), whereas there has been little catching up effect on the part of the remaining
institutions (+0.3%). In other words, our results suggest an improvement of the boundary of
the production over time (i.e. progress in technology), and only a negligible movement of
non-best practice banks towards the frontier (improvement in cost efficiency).
Finally, the results from the decomposition of technical efficiency change (TEC)
indicated that this marginal improvement in the cost efficiency levels is entirely due to an
increase in scale efficiency (+0.2%) on average over the period as there is no change in
16
pure technical efficiency. Overall, these findings suggest that best practice banks have
responded well to the difficulties associated with the consolidation process and investments
in technology in the Greek banking sector.
4.3 DETERMINANTS OF BANK EFFICIENCY AND PRODUCTIVITY
Table 6 reports the results of the censored Tobit regression model using a time
series cross sectional technique over the 1998-2003 period.
<Insert Table 6 about here>
We find a positive coefficient for ETA (equity-to-assets ratio) thus suggesting that
well capitalised Greek banks appear to be significantly cost and profit efficient. Although
the causality of this relationship might run both ways, most studies found that wellcapitalised banks are more efficient (Berger and Mester, 1997). However, this result should
not be interpreted as an indicator of should a bank increases its capital ratio, its efficiency
will increase (Mester, 1996). Possible explanations could be that higher capital ratios may
prevent moral hazard and may also reflect the shareholders incentives to monitor
management (Eisenbeis et al., 1999).
In line with the main literature, the relationship between the tested dependent
variables and bank size are mixed: it is positive and significant with efficiency (as in
Rezitis, 2006 and Pasiouras, forthcoming) and negative when the dependent variable is
productivity change. Finally, we find little evidence that profitability proxied as ROAA can
explain variations in Greek banks’ efficiency and productivity change.
17
5. CONCLUSION
We provide a characterisation of the Greek banking system’s efficiency and
productivity under the new environment that the EMU participation implies. In particular
we focus on analysing cost and profit efficiency as well as productivity change of Greek
commercial banks, using the non-parametric DEA technique and the Malmquist TFP
productivity index. While earlier research has provided empirical evidence on a number of
issues regarding efficiency another set of issues has remained relatively unexplored. Such
topics include productivity and its growth, profit efficiency, and the role of OBS activities.
Moreover, only little empirical evidence exists that covers the period 1998-2003.
The broader environment within which banks operate in Greece during this period is
characterised by significant changes. The most profound of these changes has been
Greece’s entry into the eurozone and the introduction of the euro in 2001, which resulted to
a new environment low inflation and low interest rates. Such developments coupled with
enhanced competition have fuelled the emergence of financial innovation and banks had to
look for new sources of income, including OBS activities, in order to boost their margins
and remain competitive. Other shocks to the banking system resulted from the fall in bank
share prices in the context of the wider stock market crisis in 1999, the subsequent
recovery, and the commercial banks’ role in financing the public works during the
preparation of the 2004 Olympics.
Our findings suggest that while Greek banks have suffered relatively high cost
inefficiency levels – on the order of 15%, cost efficiency has risen by 4.3% over the six
years under study. Moreover, Greek banks seem to enjoy relatively high profit efficiency
(on average 75%) showing an increase by 93% over 1998-2003. Similarly, productivity
seems to have risen by 15% over the period and this was mainly driven by the
18
improvements in the performance of best-practice institutions. Finally, in contrast to the
evidence produced for other countries, our analysis suggests that a model that includes OBS
activities as bank outputs in the calculation of efficiency is not successful in characterising
the efficiency performance of the Greek banking system. Finally, our findings for potential
correlates of efficiency reveal that while the impact of profitability and size on efficiency
and productivity yields mixed results, risk preferences are important determinants in
determining bank efficiency for Greek banks.
ACKNOWLEDGEMENTS
We thank two anonymous referees and P. Arestis, S. Carbó Valverde, B. Casu, D.
Chronopoulos, E. Desli, N. Moukas and Y. Stournaras for helpful comments on earlier
versions of this paper. For their many useful suggestions, we also thank conference
participants at the European Association of University Teachers in Banking and Finance
(the “Wolpertinger” Conference) held September 1-3, 2006 in Valladolid (Spain).
19
REFERENCES
Ataullah A., Le H., (2006), Economic Reforms and Bank Efficiency in
Developing Countries: The Case of the Indian Banking Industry, Applied
Financial Economics, 16, 653-663.
Banker, R.D., Charnes, A. and W.W. Cooper, (1984), “Some Models for Estimating
Technical and Scale Inefficiencies in Data Envelopment Analysis”, Management
Science, 30(9), 1078-1092.
Berger, A.N. (1993), “Distribution-Free Estimates of Efficiency in the US Banking
Industry and Tests of the Standard Distributional Assumptions,” Journal of
Productivity Analysis, 4, 261-292.
Berger, A.N. and D.B. Humphrey, (1997), “Efficiency of Financial Institutions:
International Survey and Directions for Further Research”, European Journal of
Operational Research, 98, 175-212.
Berger, A.N. and L.J. Mester, (1997), “Inside the Black Box: What Explains Differences in
the Efficiencies of Financial Institutions?”, Journal of Banking and Finance, 21, 895947.
Berger, A.N. and L.J. Mester, (2003), “Explaining the Dramatic Changes in Performance of
U.S. banks: Technological Change, Deregulation and Dynamic Changes in
Competition”, Journal of Financial Intermediation, 12(1), 57.
Bos, J.W.B. and Schmiedel, (2003), “Comparing Efficiency in European banking: A Meta
Frontier Approach”, De Nederlandsche Bank Research papers, 57.
Casu, B. and C. Girardone, (2005), “An Analysis of the Relevance of OBS Items in
Explaining Productivity Change in European Banking”, Applied Financial
Economics, 15, pp. 1053-1061.
Casu, B., Girardone, C. and P. Molyneux, (2004), “Productivity Change in European
Banking: A Comparison of Parametric and Non-Parametric Approaches”, Journal of
Banking and Finance, 28, 2521-2540.
Casu, B. and P. Molyneux, (2003), “A Comparative Study of Efficiency in European
Banking”, Applied Economics, 35(17), 1865-1876.
Charnes, A., Cooper, W.W. and E. Rhodes, (1978), “Measuring the Efficiency of Decision
Making Units”, European Journal of Operational Research, 2(6), 429-444.
Chortareas, Y., Girardone, C. and A. Ventouri, (2007) “Efficiency and Productivity Change
in Greek Banking: Methods and Recent Evidence”, in P. Molyneux and E. Vallelado,
Frontiers of Banks in a Global Economy, Palgrave Macmillan, 211-233
Christopoulos, D. and E.G. Tsionas, (2001), “Banking Efficiency in the Deregulation
Period: Results from Heteroscedastic Stochastic Frontier Models”, The Manchester
School, 69, 6, 656-676.
Clark, J.A. and T.F. Siems, (2002), “X-Efficiency in Banking: Looking Beyond the
Balance Sheet”, Journal of Money, Credit and Banking, 34(4), November, 987-1013.
Coelli, T.J. (1996) A Guide to DEAP Version 2.1: A Data Envelopment Analysis
Computer Program, Centre for Efficiency and Productivity Analysis Working Paper,
No. 8.
Coelli, T., Prasada Rao, D.S. O’ Donnell C.J. and G.E. Battese, (2005), An Introduction to
Efficiency and Productivity Analysis, 2nd Edition, Springer, New York.
Cooper, W.W., Seiford, L.M. and K. Tone, (2000), Data Envelopment Analysis, Boston:
Kluwer Academic Publishers.
20
Eichengreen, B. and H.D. Gibson, (2001), “Greek Banking at the Dawn of the New
Millennium, CERP Discussion Paper No. 2791, London.
Färe, R., Grosskopf, S., Norris, M. and Z. Zhang, (1994), “Productivity growth, Technical
progress and Efficiency changes in Industrialised countries”, American Economic
review, 84, 66-83.
Färe, R., Grosskopf, S. and W.L. Weber, (2004), “The effect of risk-based capital
requirements on profit efficiency in banking”, Applied Economics, 36, 1731-1743.
Farrell, M.J., (1957), “The Measurement of Productive Efficiency”, Journal of Royal
Statistical Society, Series A, 120(3), 253-290.
Garganas, N.C., (2003), “The Changing Structures of European Banking: The Greek
Experience”, speech of the Governor of the Bank of Greece at the Bulgarian Central
Bank, 14th February.
Gibson, H.D. (2005), “Developments in the Profitability of Greek Banks”. Bank of Greece
Economic Bulletin, No. 24, 7–28 (in Greek).
Giokas D.I., (2008), Cost efficiency impact of bank branch characteristics and location: An
illustrative application of Greek bank branches, Managerial Finance, 34(3), 172-185.
Goddard, J.A., Molyneux, P. and J.O.S. Wilson, (2001), European Banking: Efficiency,
Technology, and Growth. Chichester: John Wiley.
Grifell-Tatjé. and C.A.K. Lovell, (1996), “Deregulation and Productivity Decline, The Case
of the Spanish Saving Banks”, Economic Letters, 40, 1281–303.
Huizinga, H.P., Nelissen, J.H.M. and R. Vander Vennet, (2001), “Efficiency Effects of
Bank Mergers and Acquisitions in Europe”, Gent University Working Paper, No.
01/106.
Isik I., Hassan M.K., (2003), Efficiency, Ownership and Market Structure, Corporate
Control and Governance in the Turkish Banking Industry, Journal of Business
Finance and Accounting, 30 (9 & 10), 1363- 1421.
Kamberoglou, N.C., Liapis, E., Simigiannis, G.T. and P. Tzamourani, (2004), “Cost
Efficiency in Greek Banking”, Bank of Greece Working Paper, No. 9, January.
Kosmidou, K. (2008), The Determinants of Banks’ Profits in Greece During the Period of
EU Financial Integration, Managerial Finance, 34(3), 146-159.
Kumbakhar S. and C.A.K. Lovell (2000), “Stochastic Frontier Analysis”, Cambridge,
Cambridge University Press.
Leibenstein, H. (1966), “Allocative Efficiency vs. 'X-Efficiency'”, American Economic
Review, 56, 392-415.
Maudos, J. and J.M. Pastor, (2003), “Cost and Profit Efficiency in the Spanish Banking
sector (1985-1996): A non-parametric approach”, Applied Financial Economics, 13,
1-12.
Maudos, J., Pastor, J.M. and F. Perez, (2002), “Competition and Efficiency in the Spanish
Banking Sector: The Importance of Specialisation”, Applied Financial Economics,
12, 505-516.
Mukherjee K., Ray S.C., Miller S.M., (2001), Productivity growth in large US
commercial banks: The initial post-deregulation experience, Journal of
Banking and Finance, 25, 913-939.
Noulas, A.G., (1997), “Productivity Growth in the Hellenic Banking Industry: State versus
Private Banks”, Applied Financial Economics, 7, 223-228.
Noulas, A.G. (2001), “Deregulation and Operating Efficiency: The Case of the Greek
Banks”, Managerial Finance, 27, 8, 35-47.
21
Noulas, A.G., Glaveli, E., Kiriakopoulos, I., (2008), Investigating Cost Efficiency in the
Branch Network of a Greek Bank: An Empirical Study, Managerial Finance, 34(3),
160-171.
Pasiouras F., Estimating the Technical and Scale Efficiency of Greek
Commercial Banks: The Impact of Credit Risk, Off-Balance Sheet Activities,
and International Operations, Research in International Business and Finance,
Forthcoming.
Pasiouras F., Liadaki A., Zopounidis “C., Bank Efficiency and Share
Performance: Evidence from Greece”, Applied Financial Economics, Forthcoming.
Pastor, J.T., Ruiz, J.L. and I. Sirvent, (2002), “A Statistical Test for Nested Radial DEA
Models”, Operations Research, 50(4), 728-735.
Rezitis, A.N., (2006), “Productivity Growth in the Greek Banking Industry: A NonParametric Approach”, Journal of Applied Economics, IX, (1), May, 119-138.
Rime, B. and K.J. Stiroh, (2003), “The Performance of Universal Banks: Evidence from
Switzerland”, Journal of Banking and Finance, 27, 2121-2150.
Sealey, C.W. and J.T. Lindley, (1977), “Inputs, Outputs and Theory of Production and
Costs at Depository Financial Institutions”, Journal of Finance, 34, 1251-1266.
Schure, P., Wagenvoort, R. and D. O’ Brien, (2004), “The Efficiency and the Conduct of
European Banks: Developments After 1992”, Review of Financial Economics, 13,
371-396.
Shephard, R.W. (1970), Theory of Cost and Production Functions, Princeton N.J.,
Princeton University Press.
Staikouras C.K., Koutsomanoli-Filippaki A., Gerogiannaki A., (2006), Cost
and Profit Efficiency of the Greek banking system, 5th Annual Conference of
the Hellenic Finance & Accounting Association, Thessaloniki, December 15-16,
Available at: http://www.hfaa.gr/Docs/HFAA%202005/papers_5_HFAA.html
Stournaras, Y. (2006), “Interactions Between Financial Sector Developments and the
Macroeconomic Environment: The Greek Experience,” mimeo, University of Athens.
Tortosa-Ausina, E., (2003), “Nontraditional Activities and Bank Efficiency Revisited: A
Distributional Analysis for Spanish Financial Institutions”, Journal of Economics and
Business, 55, 371-395.
Tsionas, E.G., Lolos, S.E.G. and D.K. Christopoulos, (2003), “The Performance of the
Greek Banking System in view of the EMU: Results from a Non-Parametric
Approach”, Economic Modelling, 20, 571-592.
Vander Vennet, R. (2002), “Cost and Profit Efficiency of Financial Conglomerates and
Universal Banks in Europe”, Journal of Money, Credit and Banking, 34, 254-182.
Wagenvoort, J.L.M.,O’Brien, D. and Schure, P. (2001), “The Recursive Thick Frontier
Approach To Estimating Frontier Production Functions”, University of Victoria
Econometrics Working Papers, No. 0503.
Worthington A.C., (1999), Malmquist indices of productivity change in Australian
financial services, Journal of International Financial Markets, Institutions and
Money, 9, 303-320.
Weill, L., (2007), Is there a Lasting Gap in Bank Efficiency between Eastern and Western
European Countries?, Comparative Economic Studies 49, 101-127.
22
Table 1
Selected Greek studies on Bank X-Efficiency Using Alternative Frontier Methods
and the Malmquist Index of Productivity Growth
Main findings
s
Methodology
Period under
study
1991-1992
Technical/
Operating
Efficiencya
DEA
1993-1998
Noulas (2001)
Tsionas et al. (2003)
Kamberoglou et. al., (2004)
Schure et al. (2004)
Rezitis (2006)
Staikouras et al. (2006)
(averages for Greece)
Productivity
Malmquist
Overall
operating
efficiency
85%
Overall
Profit
efficiency
-
Productivity
growth
8.00%
SFA
-
83%
-
-
1993-1998
DEA
-
65%
-
-
1993-1998
1993-1999
1993-1997
1982-1997
1993-2005
DEA
DFA
RTFA
DEA
SFA
Malmquist
Malmquist
-
98%
67%
28%
91%
84%
45%
3.80%
2.40%
-
1996 & 2000
SFA
-
62%
-
-
Noulas et al. (2008)
Pasiouras (Forthcoming)
2000-2001
2000-2004
DEA
DEA
-
70%
95%
-
-
Pasiouras et al. (Forthcoming)
2000-2005
DEA
-
95%
-
-
Author (publication date)
Noulas (1997)
Christopoulos and Tsionas
(2001)
Weill (2007)
a
DEA= Data Envelopment Analysis; SFA= Stochastic Frontier Analysis; DFA= Distribution Free Approach; RTFA =
Recursive Thick Frontier Approach.
b
The Distribution Free Approach is a variation to the SFA that avoids imposing arbitrary distributional assumptions on the
composed error terms of econometric cost/profit functions to separate inefficiencies from random error (see Berger, 1993). The
Recursive Thick Frontier Approach is also a variation to the SFA approach that relies on an iterative procedure (see
Wagenvoort et al. 2001).
23
(
Table 2
Selected Descriptive Statistics of Dataset and Inputs and Outputs
Variable
Size
Total Assets
Ratios (%)
Ratio of Cost to Income
ROAA (Return on Average
Assets)
ROAE (Return on Average Equity)
Ratio of Equity to Total Assets
Cost and Profit Efficiency
Inputs
Total Cost
Operating Profits
x1=Total deposits
x2=Number of employees
x3=Total fixed assets
Input prices
w1=Interest expenses/ x1
w2=Personnel expenses/ x2
w3=Other operating expenses/ x3
Mean
Median
Standard Deviation
1998
2003
1998
2003
1998
2003
5,688,199
9,586,350
1,534,900
2,315,550
8,886,016
13,516,345
72.44
0.67
72.58
0.69
71.56
0.83
68.83
0.46
25.7403
1.1820
33.0644
1.2857
7.03
8.70
7.54
9.16
11.26
8.70
6.04
8.94
15.8469
4.0974
10.5649
2.9476
576,875.5
23,082
5,349,169
2,977
56,056
459,792.8
113,258
9,430,178
3,375
184,914
176,299
601
1,469,350
1,381
23,900
138,949.5
15,900
2,612,800
1,267
66,299
955,660.3
77,372
8,288,968
4,092
76,219
597,802.7
187,905
12,365,270
4,128
297,991
0.080
27.712
1.431
0.025
39.755
0.976
0.077
26.665
0.959
0.025
38.484
1.131
0.015
5.854
1.395
0.008
8.899
0.499
734,999
698,999
2,405,750
1,673,900
560,700
2,999,300
3,044,272
5,959,442
10,775,941
6,773,898
7,178,964
16,475,620
Outputs
Total Customer Loans
2,071,435 5,371,343
Total Other Earning Assets
3,351,917 3,918,518
OBS items
7,007,200 10,174,246
a
Unless otherwise stated, figures are in thousand Euros.
Source: Bankscope and own calculations.
24
Table 3
Statistical tests of equality of efficiency scores
Mann-Whitney Rank Sum Test
1998
t-statistic
Asymp. Sig.
(2-tailed)
H0
1999
(1-tailed)
Not Rejected
H0
Asymp. Sig.
(2-tailed)
0.927
(1-tailed)
t-statistic
Asymp. Sig.
(2-tailed)
t-statistic
Asymp. Sig.
(2-tailed)
t-statistic
Asymp. Sig.
(2-tailed)
H0
2003
0.871
Exact Sig.
H0
2002
Exact Sig.
70.000
H0
2001
115
t-statistic
H0
2000
Efficiency Contribution Measure
t-statistic
Asymp. Sig.
(2-tailed)
H0
Not Rejected
57
0.541
Not Rejected
57
0.369
Not Rejected
90
0.501
Not Rejected
111
0.709
Not Rejected
H0
Exact Sig.
0.488
Not Rejected
0.431
Not Rejected
0.431
(1-tailed)
H0
Exact Sig.
Not Rejected
0.002
(1-tailed)
H0
Exact Sig.
Rejected
0.512
(1-tailed)
H0
Exact Sig.
Not Rejected
0.463
(1-tailed)
H0
Not Rejected
Note: The hypothesis tested refers to the inclusion of an additional output describing non-traditional
banking business (the nominal value of OBS items) to the ‘standard’ 3-input/2-output DEA model.
25
Figure 1
DEA Cost Efficiency Estimates
96.0%
94.0%
92.0%
90.0%
88.0%
86.0%
84.0%
82.0%
80.0%
78.0%
76.0%
1998
1999
2000
2001
VRS Efficiency scores
2002
2003
Scale efficiency
26
Table 4
Profit Efficiency of Greek Commercial banks (1998-2003)
Years
1998
1999
2000
2001
2002
2003
Mean
Mean
% Change
0.398
0.728
0.843
0.900
0.854
0.770
0.749
83
16
7
-5
-10
+18
Standard
Deviation
0.597
0.436
0.545
0.347
0.364
0.435
27
Table 5
Malmquist Index Decomposition (Summary of Annual Means) a,b
Years
Technical
efficiency
change
(TEC)
Technological
change (TC)
Pure technical
efficiency
change (TE)
Scale
efficiency
change (SE)
Total Factor
Productivity
Change (TFP)
1998/1999
0.924
1.158
0.909
1.016
1.070
1999/2000
1.141
0.988
1.147
0.994
1.127
2000/2001
1.013
1.388
0.989
1.024
1.406
2001/2002
0.940
1.151
0.922
1.019
1.082
2002/2003
1.009
1.069
1.053
0.959
1.079
Mean
1.003
1.144
1.000
1.002
1.147
Note: All Malmquist index averages are geometric means
a
b
A number <1 indicates decline; a number >1 indicates growth
TE  SE  TEC; TEC  TC  TFP
28
Table 6
Determinants of Efficiency and Productivity: Censored Tobit Analysis (1998-2003)
Variables
_cons
ROAA
Log (TA)
E/TA
Log Likelihood
Pseudo R^2
Observations
Cost
Efficiency
Profit Efficiency
TFP Malmquista
-0.168
0.022
0.142***
0.014**
-6.548**
-0.145
1.064**
0.154**
-13.048
-0.033**
-10.790***
0.0530
-23.449
0.2242
85
-59.820
0.0782
80
-13.048
0.4633
50
Note: *,**,*** indicates significance at 10, 5 and 1 percent respectively
a
TFP productivity change is regressed against changes in the chosen independent
variables.
29
ENDNOTES
1
OBS activities include for instance financial derivatives, guarantees and lines of credit.
EU cross-country studies tend to focus specifically on the five largest economies, namely France,
Germany, Italy, Spain and the United Kingdom and even if they use enlarged samples they tend to exclude
the smallest banking markets.
3
Stournaras (2006) divides this period in two phases; one until 1998 that created the appropriate
environment and one from 1998 to Greece’s entry in the EMU during which a wave of economic and
financial reforms was implemented.
4
See Chortareas et al. (2007) for a more detailed discussion.
5
See Farrell (1957) and Shephard (1970). The term X-efficiency was coined by Leibenstein (1966) and
relates to efficiencies brought about by superior management and technology.
6
This figure excludes the work by Schure et al. (2004) which uses a variation to the parametric Thick
Frontier Approach and report some 72% average inefficiency levels for the Greek banking sector over
1993-97.
7
Bank holding companies, investment banks and securities houses, saving banks, real estate and mortgage
banks, non-banking credit institutions, and other specialised governmental credit institutions are excluded
from the sample. Moreover due to missing data the samples that includes OBS items was reduced to 77
observations and the one for the profit efficiency estimation to 80 observations.
8
Total operating expenses are inclusive of personnel expenses, general and administrative expenses,
depreciation of property and equipment and amortisation of goodwill and other intangible assets.
9
For the details of the alternative measures of a bank’s aggregate OBS see Clark and Siems (2002). The
data are not adjusted for the implementation of the International Accounting Standard (IAS) because the
years under investigation are prior to 2005. Besides, over a third of the banks included in our sample are
not publicly listed.
10
DEA models can be either input- or output- oriented according to whether the focus is on input
minimisation while keeping a given output level or output maximisation given levels of the inputs.
11
The ECM can be employed for comparing the efficiency measured by means of two DEA models that are
nested (Pastor et al. 2001). The Mann-Whitney test is commonly used to test for differences in scores from
DEA models because of the non-normality of DEA scores (Cooper et al., 2000).
12
The alternative profit efficiency measure is preferred to the ‘standard’ one for the Greek banking sector
because it assumes that banks have market power to set prices and do not behave as price-takers (see
Maudos and Pastor, 2003), Berger and Mester (1997) suggest that the alternative profit efficiency may
provide useful information because output prices cannot always be measured accurately, unmeasured
differences in the quality of banking services may exist, and output markets may not be perfectly
competitive. In addition, the alternative profit efficiency compares the ability of banks to generate profits
for the same level of outputs and thus reduce the scale bias that might be present when output levels are
allowed to vary freely.
13
The method of estimation used here to compute the distance functions is the Färe et al’s (1994). The
software used is the DEAP by Coelli (1996). Note that in order to apply this methodology data have to be
rearranged in the form of a balanced panel (for further details see also Coelli et al., 2005).
14
Another possibility is that the composition of OBS activities may not be identical with those of other
countries due to the existing accounting conventions. To further explore this avenue we would need
disaggregated data about the OBS activities. The collection of such data would be a research issue that goes
beyond the scope of the present paper.
15
We calculated the slack variables and found that (when present) their magnitude is significantly low.
16
We also balanced the sample to calculate the Pearson correlation coefficient between estimated cost and
profit efficient banks. We found that the coefficient is 0.449 and significant at the 1% level.
2
30