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Estimating the technical and scale efficiency of Greek commercial banks: the impact of credit risk, off-balance sheet activities,
and international operations
Fotios Pasiouras University of Bath
School of Management Working Paper Series
2006.17 This working paper is produced for discussion purposes only. The papers are expected to be published in due course, in revised form and should not be quoted without the author’s permission.
University of Bath School of Management Working Paper Series
School of Management
Claverton Down Bath
BA2 7AY United Kingdom
Tel: +44 1225 826742 Fax: +44 1225 826473
http://www.bath.ac.uk/management/research/papers.htm
2006
2006.01 Neil Allan and Louise Beer
Strategic Risk: It’s all in your head
2006.02 Richard Fairchild Does Auditor Retention increase Managerial Fraud? - The Effects of Auditor Ability and Auditor Empathy.
2006.03 Richard Fairchild Patents and innovation - the effect of monopoly protection, competitive spillovers and sympathetic
collaboration.
2006.04 Paul A. Grout and Anna Zalewska
Profitability Measures and Competition Law
2006.05 Steven McGuire The United States, Japan and the Aerospace Industry: technological change in the shaping of a political
relationship
2006.06 Richard Fairchild & Yiyuan Mai
The Strength of the Legal System, Empathetic Cooperation, and the Optimality of Strong or Weak
Venture Capital Contracts
2006.07 Susanna Xin Xu, Joe Nandhakumar and Christine Harland
Enacting E-relations with Ancient Chinese Military Stratagems
2006.08 Gastón Fornés and Guillermo Cardoza
Spanish companies in Latin America: a winding road
2006.09
Paul Goodwin, Robert Fildes, Michael Lawrence
and Konstantinos Nikolopoulos
The process of using a forecasting support system
2006.10 Jing-Lin Duanmu An Integrated Approach to Ownership Choices of MNEs in China: A Case Study of Wuxi 1978-2004
2006.11 J.Robert Branston and James R. Wilson
Transmitting Democracy: A Strategic Failure Analysis of Broadcasting and the BBC
2006.12 Louise Knight & Annie Pye
Multiple Meanings of ‘Network’: some implications for interorganizational theory and research practice
2006.13 Svenja Tams Self-directed Social Learning: The Role of Individual Differences
2006.14 Svenja Tams Constructing Self-Efficacy at Work: A Person-Centered Perspective
2006.15 Robert Heath, David Brandt & Agnes Nairn
Brand relationships: strengthened by emotion, weakened by attention
2006.16 Yoonhee Tina Chang Role of Non-Performing Loans (NPLs) and Capital Adequacy in Banking Structure and Competition
2006.17 Fotios Pasiouras Estimating the technical and scale efficiency of Greek commercial banks: the impact of credit risk, off-balance
sheet activities, and international operations
1
Estimating the technical and scale efficiency of Greek commercial banks: the impact of credit risk, off-balance sheet
activities, and international operations
Fotios Pasiouras*
School of Management, University of Bath, Bath, BA2 7AY, UK
Abstract This paper uses data envelopment analysis (DEA) to investigate the efficiency of the
Greek commercial banking industry over the period 2000-2004. We examine the impact
of credit risk and off-balance sheet activities, an approach not undertaken in previous
studies in Greece, by using loan loss provisions and off-balance sheet items as
additional inputs/outputs. We also compare the traditional intermediation approach used
in previous studies with the recently proposed (in the context of DEA) profit-oriented
approach. Furthermore, we compare banks that undertake only domestic activities with
the ones that have expanded their operations abroad. Finally, we use Tobit regression to
explain the efficiency of banks. Our results indicate that the inclusion of loan loss
provisions as an input increases the efficiency scores, while off-balance sheet items do
not have a significant impact. The differences between the efficiency scores obtained
through the profit-oriented model and the ones developed through the intermediation
approach are in general small. Banks that have expanded their operations abroad appear
to be more efficient than the ones operating only at a national level. Higher
capitalization, loan activity, and market power increase the efficiency of banks. The
number of branches also has a positive significant impact on efficiency, whereas the
number of ATMs does not appear to influence efficiency. The results are mixed with
respect to variables indicating whether the banks are operating abroad through
subsidiaries or branches.
Keywords: Banks, DEA, Efficiency, Greece
JEL: G21, C24, C67, D61 * © Fotios Pasiouras, 2006, Tel: +44 1225 384 297, E-mail: [email protected]
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1. Introduction
Over the last years, the Greek banking sector has experienced a major restructuring.
Important changes that are frequently highlighted by both academics and practitioners
are the establishment of the single EU market, the introduction of the euro, the
internationalization of competition, the interest rate liberalization, the deregulation, and
the recent wave of mergers and acquisition.
The Greek banking has also experienced considerable improvements in terms of
communication and computing technology, as banks expanded and modernized their
distribution networks, which apart from the traditional branches and ATMs, now
include alternative distribution channels such as internet banking. As mentioned in the
annual report of the Bank of Greece (2004), in recent years, Greek banks have also
taken major steps towards upgrading their credit risk measurement and management
systems, by introducing credit scoring and probability default models. Furthermore,
they expanded their product/service portfolio to include activities such as insurance,
brokerage and asset management, and at the same time increased their off-balance sheet
operations and non-interest income.
Finally, another attribute that is worthwhile mentioning is the increased trend
towards globalization that focused on the wider market of Balkans (e.g Albania,
Bulgaria, FYROM1, Romania, Serbia) and added to the previously limited international
activities of Greek banks in Cyprus and USA. The performance of the subsidiaries
operating abroad is expected to have an impact on the performance of parent banks and
consequently on future decisions for further internationalisation attempts.
The purpose of the present study is to employ data envelopment analysis (DEA)
and re-investigate the efficiency of the Greek banking sector, while considering several
1 Former Yugoslav Republic Of Macedonia
3
of the issues discussed above. We therefore differentiate our paper from previous ones
that focus in the Greek banking industry2 and add insights in several respects, discussed
below.
First of all, we examine for the first time the impact of credit risk on the
efficiency of Greek banks by including loan loss provisions as an additional input as in
Charnes et al. (1990), Drake (2001), Drake and Hall (2003), and Drake et al. (2006)
among others. As Mester (1996) points out “Unless quality and risk are controlled for,
one might easily miscalculate a bank’s level of inefficiency; e.g. banks scrimping on
credit evaluations or producing excessively risky loans might be labelled as efficient
when compared to banks spending resources to ensure their loans are of higher quality”
(p. 1026). We estimate the efficiency of banks with and without this input to adjust for
different credit risk levels and examine its impact on efficiency.
Second, unlike previous studies in the Greek banking sector, we consider off-
balance sheet activities during the estimation of efficiency measures. Several recent
studies that examine the efficiency of banks, with data envelopment analysis or
stochastic frontier techniques, acknowledge the increased involvement of banks in non-
traditional activities and include either non-interest (i.e. fee) income (e.g. Lang and
Welzel, 1998; Drake, 2001; Tortosa-Ausina E., 2003) or off balance sheet items (e.g.
Altunbas et al., 2001; Altunbas and Chakravarty, 2001; Isik and Hassan, 2003a,b; Bos
and Kolari, 2005; Rao, 2005) as an additional output. However, despite their increased
importance for Greek banks, such activities have not been considered in the past. Again,
we estimate the efficiency of the banks in our sample with and without off-balance sheet
activities to observe whether it will have an impact on efficiency.
2 Previous studies that focus on the efficiency of the Greek banking sector are: Karafolas and Mantakas (1996), Noulas (1997), Christopoulos and Tsionas (2001), Christopoulos et al. (2002), Tsionas et al. (2003), Halkos and Salamouris (2004), Apergis and Rezitis (2004), Rezitis (2006). These studies are discussed in more detail in the next section.
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Third, we compare the results obtained from the intermediation approach that
has been followed in most recent studies in banks’ efficiency with a profit-oriented
approach that was recently proposed by Drake et al. (2006) in the context of DEA and is
in line with the approach of Berger and Mester (2003) in the context of their stochastic
frontier approach. This allows us to observe if different input/output definitions affect
efficiency scores.
Fourth, we compare the efficiency scores of Greek banks that have expanded
their operations abroad (i.e. international Greek banks, hereafter IGBs), with the ones of
Greek banks whose operations are limited in the domestic market3 (i.e. purely domestic
banks, hereafter PDBs). To the best of our knowledge, no study has undertaken such an
analysis in Greece. However, in a study of the Turkish banking sector, Isik and Hassan
(2002) found evidence that multinational domestic banks are superior to purely
domestic banks in terms of all efficiency measures (i.e. cost efficiency, allocative
efficiency, technical efficiency, pure technical efficiency) except for scale efficiency.
The conclusions drawn from our study could be useful to the managers of other Greek
banks or other medium-sized banking sectors in general, considering the
internationalization of their operations.
Fifth, we run a regression to explain the efficiency of banks, an approach that
has been followed only in two of the past studies in Greece (Christopoulos et al., 2002;
Rezitis, 2006). However, in our case we examine a most recent period that is after the
numerous changes outlined above. 3 One could argue that the IGBs are the large Greek banks, and we therefore actually comparing large versus small banks. While this argument would have a basis, this is obviously the case in numerous studies that compare various groups of banks either in terms of ownership such as state/private (e.g. Noulas, 1997), and foreign/domestic (e.g. Sturm and Williams, 2004; Kasman and Yildirim, 2006) or in terms of specialization such as commercial, savings, cooperative (e.g. Altunbas et al., 2001; Girardone et al., 2004). For example, domestic banks are in most cases quite larger than foreign banks operating in a country (i.e. subsidiaries), as commercial banks are usually larger than cooperative and savings banks. Noulas (1997) also mentions that the private banks in his sample are of much smaller size than the state ones. Hence, while one could kept in mind this note while interpreting the results, we do not believe that it reduces they usefulness of the study.
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The rest of the paper is as follows: Section 2 reviews the literature that focuses
on the efficiency of the Greek banking sector. Section 3 provides a brief discussion of
DEA. Section 4 presents the data and variables. Section 5 discusses the empirical
results, and Section 6 concludes the study.
2. Literature Review
Karafolas and Mantakas (1996) use a second-order translog cost function to estimate for
the first time an econometric form of the costs in the Greek banking and investigate
economies of scale. Using data for eleven banks from the period 1980-1989, they find
that although operating-cost scale economies do exist, total cost scale economies are not
present. Participation of the dataset in sub-samples by banks’ size (i.e. large and small
banks) and time periods (i.e. 1980-84, 1985-89) has not altered the results. Finally, the
results indicate that technical change has not played a statistically significant role in the
decrease of average cost.
Noulas (1997) examines the productivity growth of ten private and ten state
banks operating in Greece during 1991 and 1992, using the Malmquist productivity
index and the DEA method to measure efficiency. The author follows the
intermediation approach and finds that productivity growth averaged about 8%, with
state banks showing higher growth than private ones. The results also indicate that the
sources of the growth differ across the two types of banks. State banks’ productivity
growth is a result of technological progress, while private bank’s is a result of increased
efficiency.
Christopoulos and Tsionas (2001) estimate the efficiency in the Greek
commercial banking sector over the period 1993-1998 using homoscedastic and
heteroscedastic frontiers. They find an average technical efficiency about 80% for the
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heteroscedastic model and 83% for the homoscedastic one. They also find that both
technical and allocative inefficiencies decrease over time for smaller as well as larger
banks. The regression of inefficiency measures against a trend indicates that the
improvement in technical and allocative inefficiencies for small banks equal 19.7% and
39.1%, accordingly. The corresponding figures for large banks are 10.4% and 21.1%.
Christopoulos et al. (2002) examine the same sample with a multi-input, multi-
output flexible cost function to represent the technology of the sector and a
heteroscedastic frontier approach to measure technical efficiency. Regression of the
efficiency measures over various bank characteristics indicates that larger banks are less
efficient than smaller ones, as well as that economic performance, bank loans and
investments are positive related to cost efficiency.
In a latter study, Tsionas et al. (2003) use the same sample as in Christopoulos
and Tsionas (2001) and Christopoulos et al. (2002) but employ DEA to measure
technical and allocative efficiency, and the Malmquist Total Factor Productivity
approach to measure productivity change. The results indicate that most of the banks
operate close to the best market practices with overall efficiency levels over 95%. Large
banks appear to be more efficient than smaller ones, while allocative inefficiency costs
seem to be more important than technical inefficiency costs. They also document a
positive but not substantial technical efficiency change which is mainly attributed to
efficiency improvement for medium-sized banks and to technical change improvement
for large banks.
Halkos and Salamouris (2004) also use DEA but follow a different approach
than previous studies by using financial ratios as output measures and with no use of
input measures. The sample ranges between 15 and 18 banks depending on the year
under consideration. The results indicate a wide variation in average efficiency through
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the 1997-1999, and a positive relationship between size and efficiency. Furthermore,
there is non-systematic relationship between transfer of ownership through privatization
of public banks and last’s period performance.
Apergis and Rezitis (2004) specify a translog cost function to analyze the cost
structure of the Greek banking sector, the rate of technical change and the rate of growth
in total factor productivity. They use both the intermediation and the production
approach and a sample of six banks over the period 1982-1997. Both models indicate
significant economies of scale and negative annual rates of growth in technical change
and in total factor productivity.
Rezitis (2006) uses the same dataset but employs the Malmquist productivity
index and DEA to measure and decompose productivity growth and technical
efficiency, respectively. He also compares the 1982-92 and 1993-97 sub-periods, while
Tobit regression is employed to explain the differences in efficiency among banks. The
results indicate that the average level of overall technical efficiency is 91.3%, while
productivity growth increased on average by 2.4% over the entire period. The growth in
productivity is higher in the second sub-period and is being attributed to technical
progress, in contrast to improvements in efficiency that was the main driver until 1992.
Furthermore, during the second sub-period pure efficiency is higher, and scale
efficiency is lower, indicating that although banks achieved higher pure technical
efficiency, they moved away from optimal scale. The regression results indicate that
size and specialization have a positive impact on both pure and scale efficiency.
3. Methodology
From a methodological perspective, there are several approaches that can be used to
examine the efficiency of banks, such as Stochastic Frontier Analysis (SFA), Thick
8
Frontier Approach (TFA), Distribution Free Approach (DFA), and DEA. Berger et al
(1993), Berger and Humphrey (1997) and Goddard et al. (2001) provide key discussions
and comparison of these methods in the context of banking.
In the present study, following several recent studies we use DEA to estimate
the efficiency of banks4. We only briefly outline DEA here, while more detailed and
technical discussions can be found in Coelli et al. (1999), Cooper et al. (2000), and
Thanassoulis (2001).
DEA is a mathematical programming approach for the development of
production frontiers and the measurement of efficiency relative to the developed
frontiers (Charnes et al., 1978). The best-practice production frontier for a sample of
decision making units (DMUs) is constructed through a piecewise linear combination of
actual input-output correspondence set that envelops the input-output correspondence of
all DMUs in the sample (Thanassoulis, 2001). In their seminal study Charnes et al.
(1978) proposed a model that had an input orientation and assumed constant returns to
scale (CRS). However, CRS is only appropriate when all firms are operating at an
optimal scale. Nevertheless, as firms may not be operating at optimal scale due to
imperfect competition or constraints in finance, Banker et al. (1984) suggested the use
of variable returns to scale (VRS) that allows the calculation of technical efficiency
(TE) devoid of scale efficiency (SE) effects.
The DEA model can be either input or output-oriented. As Coelli et al. (1999)
point out the input-oriented technical efficiency measures address the question: “By how
much can input quantities be proportionally reduced without changing the output
quantities produced?” (p. 137). In contrast, the output-oriented measures of technical
efficiency address the question: “By how much can output quantities be proportionally 4 Examples of recent studies that use DEA are among others Haslem et al. (1999), Maudos et al. (2002a), Casu and Molyneux (2003), Drake and Hall (2003), Luo (2003), Ataullah et al. (2004), Hauner (2005), Ataullah and Le (2006), Casu and Girardone (2006), Drake et al. (2006).
9
expanded without altering the input quantities used?” (p. 137). It should be mentioned
that the two measures always provide the same value under CRS but are unequal when
VRS is assumed. However, Coelli et al. (1999) mention that since linear programming
does not suffer from statistical problems as simultaneous equation bias, the choice of an
appropriate orientation is not as crucial as it is in the case of econometric orientation,
and it many instances, the choice of orientation has only a minor influence upon the
scores obtained (Coelli and Perelman, 1996). Most of the studies in banking, including
the present one, follow the input-oriented approach, although some studies adopt the
output-oriented approach (e.g. Ataullah et al., 2004; Ataullah and Le, 2006) or report
the results from both (e.g. Casu and Molyneux, 2003; Beccali et al., 2006).
One of the well-known advantages of DEA, which is relevant to our study, is
that DEA works particularly well with small samples. As Maudos et al. (2002a) point
out, “Of all the techniques for measuring efficiency, the one that requires the smallest
number of observations is the non-parametric and deterministic DEA, as parametric
techniques specify a large number of parameters, making it necessary to have available
a large number of observations.” (p. 511). Other advantages of DEA are that it does not
require any assumption to be made about the distribution of inefficiency and that it does
not require a particular functional form on the data in determining the most efficiency
decision making units (DMUs). One the other hand, the shortcomings of DEA are that it
assumes data to be free of measurement error and is sensitive to outliers5.
5While many studies that use DEA do not address the issue of sensitivity to outliers, others choose either to perform the analysis with and without the potential outliers and compare the results (e.g. Casu and Molyneux, 2003) or delete observations that are considered outliers (Isik and Hassan, 2002). For example, Havrylchyk (2006) deleted banks whose prices were below or above 1% or 99%. In the present study, considering the small number of observations, we decided to smooth figures above and below the 99% and 1% percentiles respectively, hence reducing the impact of outliers while retaining all observations in sample.
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4. Data and variables
Our sample consists of the universe of commercial banks6 with financial statements
available in Bankscope database of Bureau van Dijk’s company, operating in Greece
between 2000 and 20047. Supplementary data for the banks (e.g. staff number, number
of ATMs) were collected from the Hellenic Bank Association. The sample ranges
between 12 and 18 banks per year and consists of 78 observations in total.
As mentioned in several studies, there is an on-going debate in the banking
literature relative to the proper definition of inputs and outputs. The two main
approaches are the “production approach” and the “intermediation approach” (Berger
and Humphrey, 1997). The production approach assumes that banks produce loans and
deposit account services, using labour and capital as inputs, and the number and type of
accounts measure outputs. The intermediation approach views banks as financial
intermediates that collect purchased funds and use labour capital to transform these
funds to loans and other assets. Berger and Humphrey (1997) point out that neither of
these two approaches is perfect because they cannot fully capture the dual role of
financial institutions as providers of transactions/document processing services and
being financial intermediaries. They point out that the production approach may be
somewhat better for evaluating the efficiencies of branches of financial institutions and
the intermediation approach may be more appropriate for evaluating entire financial
institutions. Most recently, Drake et al. (2006) proposed the use of a profit-oriented
approach in a DEA context that is in line with the approach of Berger and Mester (2003)
in the context of their stochastic frontier approach. They point out that their results
6 On the basis of the classification available in Bankscope. 7 The study begins in 2000 for various reasons. First of all, this is the earliest year for which data were available in the online version of Bankscope to which we had access. Second, prior to 2000 the Greek banking industry witnessed a number of M&As that could complicate our analysis. Third, existing studies already provide evidence for various periods up to 1999. Data for 2005, that was the most recent year with available data, were not considered as the EU imposed the use of International Accounting Standards, and the data would not be comparable across the period of our analysis.
11
support the argument of Berger and Mester (2003) that a profit-based approach is better
able to capture the diversity of strategic responses by financial firms in the face of
dynamic changes in competitive and environmental conditions.
In the present study, following most of the recent studies we adopt the
intermediation approach. However, we also compare the obtained results with the ones
of the profit-oriented approach suggested by Drake et al. (2006). We estimate five DEA
models in total (Table 1).
[Insert Table 1 Around Here]
Models 1 to 4 are based on the intermediation approach but different
inputs/outputs combinations are examined so as to explore the impact of credit risk and
off-balance sheet activities on bank efficiency. In Model 1, we select the following three
inputs: fixed assets, customer deposits & short term funding, and number of employees.
The two outputs of Model 1 are loans and other earning assets. Hence, this is a classical
model under the intermediation approach found in most studies. In Model 2, we
introduce off-balance sheet items as an additional output, to account for the fact that in
recent years banks are heavily involved in off-balance sheet activities. Model 3 is a re-
estimation of Model 1 but following Charnes et al. (1990), Drake (2001), Drake and
Hall (2003), and Drake et al. (2006) among others, includes loan loss provisions as an
additional input in the DEA model to account for credit risk8. Finally, Model 4 is a re-
8 Mester (1996), Altunbas et al. (2000) and Drake and Hall (2003) among others point out that failure to adequately account for risk can have a significant impact on relative efficiency scores. Berg et al. (1992) made the original observation and included nonperforming loans in a nonparmetric study of bank production, whereas Hughes and Mester (1993) applied the concept to parametric estimations (Berger and DeYoung, 1997). Some other studies use equity capital as a control for risk (e.g. Altunbas et al., 2001; Maudos et al., 2002b; Akhigbe and McNulty, 2003; Kasman and Yildirim, 2006). However, Laeven and Majnoni (2003) mention that risk should be incorporated into efficiency studies via the inclusion of loan loss provisions that is actually a cost required to build up loan loss reserves. Altunbas et al. (2000) and
12
estimation of Model 1 that includes both off-balance sheet items and loan loss
provisions, to simultaneously account for off-balance sheet activities and credit risk.
Model 5 is the profit-oriented one, in which following Drake et al. (2006) revenue
components are defined as outputs and cost components as inputs. The three inputs
specified are employee expenses, non-interest expenses, and loan loss provisions. The
three outputs are net interest income, net commission income and other income. As
Drake et al. (2006) point out “from the perspective of an input-oriented DEA relative
efficiency analysis, the more efficiency units will be better at minimizing the various
costs incurred in generating the various revenue streams and, consequently, better at
maximizing profits” (p. 1451).
5. Empirical results
The discussion of the empirical results on the efficiency of banks in Greece is structured
in three parts. First, we discuss the efficiency of the full sample of banks obtained
through an input-oriented approach with VRS and the various inputs/outputs
combination discussed above9. Then we focus on the specific issue of the relative
efficiency of IGBs versus PDBs. Finally, we investigate the determinants of efficiency
using Tobit regression10.
5.1. Efficiency estimates - full sample
Table 2 presents the results from the four models that correspond to input/outputs
selected on the basis of the intermediation approach. Table 3 reports the results of
Model 5 that corresponds to the profit oriented approach.
Pastor and Serrano (2005) have used loan loss provisions in a stochastic frontier context as have the few recent studies in a DEA context mentioned in the text. 9 Efficiency scores were estimated with DEAP 2.1 discussed in Coelli (1996). 10 Tobit analysis was performed with E-views 5.1.
13
[Insert Tables 2 and 3 Around Here]
The average TE obtained by Model 1 ranges between 0.882 (2004) and 0.977 (2000),
with an overall mean11 over the entire period equal to 0.950, while the corresponding
figures for SE are 0.938 (2003), 0.991 (2001) and 0.966 (overall mean) respectively.
Hence, between 2000 and 2004 banks could improve technical efficiency by 5% and
scale efficiency by 3.4% on average. These figures increase only slightly when we
consider off-balance sheet items as an additional output and equal 0.952 (TE) and 0.971
(SE). However, when we consider loan loss provisions the overall mean technical
efficiency increases by almost 1.5%. Thus, controlling for credit risk appears to have
some impact on the efficiency scores. This is supported further by the only marginal
increase by 0.001 in Model 4 where off-balance sheet items and loan loss provisions are
simultaneously considered, indicating that the increase from the base Model (ie. Model
1) is due to loan loss provisions. Our results are similar to the ones obtained in previous
studies in Greece that employ DEA and follow the intermediation approach. For
example, Rezitis (2006) reports pure technical efficiencies between 0.977 and 0.994,
and scale efficiencies between 0.918 and 0.934 depending on the period under
consideration, while Tsionas et al. (2003) also report an overall technical efficiency
equal to 0.984.
Turning to the results obtained from the profit-oriented model (i.e. Model 5) we
observe that TE is between 0.924 (2004) and 0.975 (2003) with an overall mean equal
to 0.950. The corresponding figures for SE are 0.942 (2002), 0.978 (2004) and 0.960
(overall mean). The contrast between these results and the ones obtained from the
11 This overall mean corresponds to the average calculated by pooling the efficiency scores calculated by year, and not to a model estimated with panel data.
14
intermediation approach are mixed. We only partially support the results of Drake et al.
(2006) indicating that technical efficiency is generally higher under the intermediation
approach than under the profit approach. In our study, this is not always the case and
depends upon the models that are compared and the year of observation. Most detailed,
we observe that compared to Models 1 and 2 technical efficiency is higher under the
profit-oriented approach during 2003 and 2004 and lower over the period 2000-2002.
Compared to Models 3 and 4 technical efficiency is higher under the profit-oriented
approach only during 2003. However, it should be mentioned that the intermediation
oriented model estimated in Drake et al. (2006) is most closely related to Model 4 of the
present study12.
Looking at the overall mean now, we observe that the profit-oriented approach
indeed provides lower efficiency scores than all the models estimated under the
intermediation approach. Nevertheless, in any case the differences between the two
approaches are much smaller than the ones reported in Drake et al. (2006). Another
interesting point that emerges from the contrast of the results obtained by the two
approaches is that the range in the efficiency scores is smaller when the profit-oriented
approach is used. That is, the average efficiency scores for Model 1 range between
0.882 and 0.992, and those of Model 2 range between 0.887 and 0.992. The
corresponding figures for Model 3 are 0.927 and 0.992 and those of Model 4 are 0.930
and 0.992. In contrast, Model 5 the efficiency scores of Model 5 range only between
0.924 and 0.975. This could be in part attributed to the argument of Drake et al. (2006)
that “…the profit approach will capture the full impact of any adverse environmental
factors on revenues as well as costs, while the intermediation approach tends to focus
on the technical efficiency of the financial intermediation approach” (p. 1462). 12 Drake et al. (2006) use personnel expenses as input whereas we use the number of staff members. They also use non-interest income rather than off-balance sheet items as an output for off-balance sheet activities.
15
However, in any case the differences among the models are in general quite small and
do not allow us to conclude whether the profit-oriented approach provides more reliable
efficiency scores or not.
5.2. International versus purely domestic banks
We now turn to the efficiency of IGBs as opposed to the efficiency of PDBs. Morck and
Yeung (1991) provide some empirical evidence of the multinational advantage based on
the transfer of intangible assets such as technology and reputation from home country so
subsidiaries. Furthermore, operating abroad gives banks the opportunity to follow their
customers and consequently retain them13 (i.e. defensive expansion theory, see
Williams, 2002). This is obviously the one of type of transfer in the firm, while the
opposite or the transfer from one subsidiary to another is possible as well. Hence, banks
that operate abroad might be able to transfer resources such as technology or employees
with increased skills and experience in terms of risk management, regulatory and
reporting practices, gained from working in more sophisticated and advanced
environments (e.g. UK, USA). In that case, the efficiency of IGBs will be higher than
the one of PDBs. On the other hand, IGBs will have to transfer efforts and resources to
the subsidiaries that would otherwise be available to compete in the domestic market,
and this might have a negative effect on their efficiency relative to PDBs.
Six of the banks in the sample (i.e. Alpha Bank, EFG Eurobank Ergasias,
Egnatia Bank, Emporiki Bank of Greece, National Bank of Greece, Piraeus Bank) had
subsidiaries abroad over the entire period of our analysis. Most of these banks as well as
Agricultural Bank of Greece had also branches abroad, while Novabank had branches in
2002 and switched to an international presence through subsidiaries in 2003 and 2004.
13 It is possible that otherwise these customers would switch to another bank that provides services both abroad as well as in the home market.
16
We therefore adopt two definitions for IGBs. First, we consider as IGBs only those
banks that are operating abroad through subsidiaries (SIGBs). Then, we consider as
IGBs those banks that have an international presence either through subsidiaries or
branches (SBIGBs). Table 4 reports the average efficiency scores for the two types of
banks estimated by Models 414 and 5, while distinguishing between PDBs and SIGBs
(Panel A) and PDBs and SBIGBs (Panel B).
[Insert Table 4 Around Here]
The contrast of the overall mean efficiency scores obtained from the pooled sample
indicates that IGBs are more efficient than PDBs in all cases. The largest difference is
observed in the case of TE while comparing SIGBs and PDBs under the profit-oriented
approach (0.045). That is, SIGBs can improve technical efficiency by 2.3% whereas
PDBs can improve it by 6.8%. In contrast, the smallest difference is obtained in the case
of SE while comparing SBIGBs and PDBs under the intermediation approach and
equals 0.005.
The comparison of the efficiency scores of PDBs and SIGBs by year indicates
that under the intermediation approach PDBs have higher SE only in 2000. This finding
holds under the profit-oriented approach as well. However, under the latter approach
PDBs appear to have a higher TE in 2003 as well. Inclusion of the banks that operate
through branches in the group SIGBs does not alter the results, the only difference being
that PDBs are now slightly more efficient, in terms of scale efficiency estimated by
Model 4, in 2002 as well.
14From this point and for the rest of our analysis we select Model 4 as representative of the intermediation approach, assuming that according to numerous recent studies it represents a more appropriate combination of inputs and outputs that considers off-balance sheet items and credit risk.
17
To examine whether the differences between the groups of interest are
statistically significant, we perform a Kruskal-Wallis (K-W) non-parametric test. Due to
the small number of observations from each group by year, the test is performed on the
scores of the pooled sample of the 78 observations. The results of the K-W test indicate
that under the intermediation approach IGBs, both SIGBs and SBIGBs, are more
efficient than PDBs, in terms of TE that is statistically significant at the 10% and 5%
level respectively15. Hence, our results appear to be in line with the ones of Isik and
Hassan (2002) in the Turkish banking sector. There are several possible explanations for
these findings. First, these banks may transfer resources such as technology or
employees with increased skills gained abroad to the home market, hence increasing
their technical efficiency. They can also retain their customers by following them
abroad or experience gains due to increased reputation and diversification that reduces
their risk. As for the insignificant differences in the case of scale efficiency, Isik and
Hassan (2002) mention that exploitation of scale economics cannot be the motivation
for these large banks’ foreign expansion as multinational banks would have exhausted
their scale economies when they were small, pure domestic banks.
5.3. Stage 2 – Tobit analysis
In order to investigate the determinants of efficiency we construct an econometric
model with technical and scale efficiency as dependent variables. As in previous
studies, due to the limited nature of our efficiency measure (i.e. ranges between 0 and 1)
we use Tobit analysis. As Saxonhouse (1976) points out, heteroscedacity can emerge
when estimated parameters are used as dependent variables in the second stage analysis.
15 In the first case, the chi-square equals 3.612 whereas in the later case it equals 5.092. Differences in SE were not significant for none of the two models, as they were not significant in the case of TE obtained from Model 5. The chi-square and p-values are not reported here but are available from the author upon request.
18
Hence, following Hauner (2005), QML (Huber/White) standard errors and covariates
are calculated.
We examine the effect of two groups of factors on efficiency. First, we analyze
the influence of various bank financial characteristics. We follow previous studies and
examine the following variables: equity to assets (EQAS), return on average assets
(ROAA), loan to assets (LOANS), and market power (POWER) as measured by the
relative size of bank (i.e. market share in terms of assets).
Second, we examine the influence of bank’s strategies in terms of investments in
technology (i.e. ATMs and branches) and internationalization of operations. We include
the number of ATMs (ATMs), the number of branches (BRANCH), and two dummy
variables indicating whether banks are offering their services abroad through
subsidiaries (SUB_ABR, that takes the value of 1 if yes and 0 otherwise) or branches
(BR_ABR, that takes the value of 1 if yes and 0 otherwise).
Considering the small number of observations in our sample, we estimate two
specifications of the Tobit model with each one of the two sets of variables examined
sequentially. We follow this approach in order not to overload the regressions. The
findings are reported in Table 5. Panel A presents the results of the regressions with the
bank financial characteristics, while Panel B presents the ones with the variables that
proxy for the strategic decisions of the banks.
[Insert Table 5 Around Here]
EQAS is statistically significant and positively related to efficiency in all our
specifications. Hence, well-capitalized banks are also more efficient, both in terms of
technical and scale efficiency. These results are in line with Isik and Hassan (2003a) in
19
Turkey, Casu and Giradone (2004) in Italy, Rao (2005) in United Arab Emirates and
Kwan and Eisenbeis (1997) in the US among others, all reporting a positive relation
between capitalization and various measures of efficiency. One potential explanation for
these findings is that since EQAS reflects the degree to which shareholders have their
own capital at risk in their institution it also reflects their incentives to monitor
management and assure that the bank operates efficiently (Eisenbeis et al., 1999).
Hence, as Isik and Hassan (2003a) mention these results are in favour of the conjectures
of moral hazard theory.
ROAA is positively related to the efficiency measures in all cases however, it is
statistically significant only in the case of the profit-oriented approach (i.e. Model 5).
Even in that case, it has only a marginal impact (i.e. 10% level) on SE. Although
Christopoulos et al. (2002) report a positive and significant relationship between
profitability and efficiency in the Greek banking sector between 1993-1998, the results
from studies in other countries are mixed. For instance, Ataullah and Le (2006) report
both negative and positive statistically significant impacts of return on assets on
efficiency measures in India depending on the specification of the model. Casu and
Molyneux (2003) examine a sample of banks from the principal EU banking sectors16
and find a positive relationship between profitability efficiency, which is however
statistically significant in only two of the five years of the analysis. Isik and Hassan
(2002a) report a positive and significant correlation between both return on equity and
return on assets and efficiency in Turkey. However, Casu and Girardone (2004) report a
negative and statistically significant relationship in Italy.
LOANS carries a positive sign that is statistically significant in all cases and is
consistent with Isik and Hassan (2003a). Casu and Giradone (2004) also report a
16 The principal EU banking sectors are: France, Germany, Italy, Spain, UK.
20
positive relationship although not statistically significant. Isik and Hassan (2003a) argue
that the positive relationship between loan activity and efficiency can be attributed to
the ability of relatively efficient banks to manage operations more productively, that
enables them to have lower production costs and consequently to offer more reasonable
loan terms allowing them to gain larger share in the loan market segment. In contrast to
the above studies, Havrylchyk (2006) finds a negative relationship between the loans to
assets ratio and efficiency, which however becomes positive once the off-balance sheet
items are omitted from outputs.
POWER is also statistically significant and positively related to TE and SE in
both models (i.e. 4 and 5). Since this variable reflects the relative size of the bank and
its market power, our findings seem to support the arguments in favour of size as well
as market share. While our results contradict the ones of Christopoulos et al. (2002) that
report a negative relationship between size and efficiency, they are in line with the
studies of Halkos and Salamouris (2004) and Apergis (2006) in Greece and Berger et al.
(1993) and Miller and Noulas (1996) in the US that report a positive relationship
between size and efficiency. When we interpret our variable as an indicator of market
power rather than size, we tend to support the efficient structure hypothesis. As Isik and
Hassan (2003a) explain due to their low costs of production, relatively efficient firms
might have competed more aggressively, made higher profits and ultimately gained
larger market share.
Turning to the variables related to the strategic choices of the banks, the results
indicate that only BRANCH is significant in all cases (although significant only at the
10% level in the case of TE estimated with model 4). On the other hand, ATM does not
have an impact on efficiency in any of our specifications. One potential explanation is
that the Greek banking system relies heavily on branches as a distribution network with
21
the number of branches increasing from year to year contrary to other EU countries,
where a declining trend takes place. As mentioned in the summary of the Annual Report
of the Bank of Greece (2005) the continued increase in the number of bank branches in
Greece is associated with the fast growth of retail banking and Greek customer’s
continued preference for transaction through branches. While the number of ATMs also
increases (e.g. 5,468 in 2003; 5,787 in 2004) such channels are supplementary to
branches, which retain their key role as points of sale (Annual Report of Bank of
Greece, 2004). Attracting new customers and maintaining the existing ones is a main
strategic choice for banks, which as mentioned in the 2004 annual report of the Bank of
Greece, can be achieved more effectively through personal contact at branches.
Furthermore, an extensive branch network also supports the expansion of Greek banks
via cross-selling such as bankassurance products (Summary of Annual Report of Bank
of Greece, 2005). These characteristics of the Greek banking sector obviously explain
why the number of branches has a positive impact on the efficiency of banks rather than
alternative distribution networks such as ATMs.
Finally, with respect to the variables that are related to the international presence
of banks, the results are mixed. Operating abroad through branches appears to be
negatively related to the efficiency of banks, which is however significant only in the
case of TE estimated with Model 5. In contrast, operating abroad through subsidiaries
appears to have a positive impact on both technical and scale efficiency although this is
statistically significant only for Model 4.
6. Conclusions
In the present study we have estimated the technical and scale efficiency of Greek banks
over the period 2000-2004. We used in-put oriented data envelopment analysis with
22
variable returns to scales and estimated five models to examine several issues not
considered in the study of the Greek banking sector in the past.
More detailed, we estimated the efficiency of the banks in our sample with and
without off-balance sheet items and loan loss provisions to account for different levels
of off-balance sheet activities and credit risk. In all cases, the models were estimated
following the traditional intermediation approach and the recently proposed (in the
context of DEA) profit-oriented approach. We also compared the efficiency of banks
that have an international presence with the ones that offer their services only in the
domestic market, an issue that has received relatively small attention in the bank
efficiency literature. Finally, we used Tobit analysis to regress the efficiency scores
obtained from the first stage over several variables reflecting bank financial
characteristics and strategic decisions.
The results indicate that the inclusion of off-balance sheet items in the outputs
does not have an impact on the efficiency scores, while the inclusion of loan loss
provisions in the inputs increases the efficiency scores. The contrast of the scores
obtained from the models estimated through the intermediation approach with the ones
obtained from the profit-oriented approach, by year, provided mixed results. However,
we found that in terms of the overall mean, the profit-oriented model provided lower
efficiency scores than all the models estimated under the intermediation approach.
Nevertheless, the differences between the two approaches were much smaller than the
ones reported in Drake et al. (2006). Banks with international operations appeared to be
more efficient than the ones operating only at the national level, consistent with Isik and
Hassan (2002). We obtained similar results whether we defined as international, banks
that offer their services abroad either through subsidiaries or both subsidiaries and
branches. However, the differences were statistically significant only in the case of
23
technical efficiency estimated under the intermediation approach. We finally regressed
the scores obtained from the profit-oriented model and the full intermediation model
over banks’ financial characteristics and variables reflecting strategic decisions.
Capitalization, loan activity and market share in terms of total assets were statistically
significant and positively related to the efficiency measures in all cases. Profitability
was positively related to the efficiency measures in all cases however, was statistically
significant only in the case of the profit-oriented approach. Turning to the variables
related to the strategic choices of the banks, the results indicated that the number of
branches was significant in all cases, while the number of ATMs did not had an impact
in any of our specifications. Finally, with respect to the dummy variables indicating
whether the banks were operating abroad through branches or subsidiaries the results
were mixed.
Future research could extend the present study towards numerous directions.
First, commercial banks could be compared with cooperative banks, as the later ones
have not received any attention in past studies in Greece. Second, domestic banks could
be compared with foreign banks, which have received only limited attention and not in
the context of efficiency. Finally, it would be worthwhile to consider a longer time
period and examine the impact of environmental factors such as GDP, inflation and
stock market capitalization on the efficiency of the Greek banking sector.
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Table 1 - Combination of inputs/outputs
Intermediation approach Profit oriented approach Model 1 Model 2 Model 3 Model 4 Model 5 Inputs Fixed assets Fixed assets Fixed assets Fixed assets Employee expenses
Customer deposits & short term funding
Customer deposits & short term funding
Customer deposits & short term funding
Customer deposits & short term funding
Other Non interest expenses
Number of employees Number of employees Number of employees Number of employees Loan loss provisions Loan loss provisions Loan loss provisions Outputs Loans Loans Loans Loans Net interest income Other earning assets Other earning assets Other earning assets Other earning assets Net commission income Off-balance items Off-balance items Other operating income
30
Table 2 – DEA results with intermediation approach (Models 1-4)
TE (VRS)
SE TE (VRS)
SE
Year Mean Mean Mean Mean Model 1 Model 2 2004 (N =18) 0.882 0.974 0.887 0.977 2003 (N =17) 0.938 0.938 0.938 0.938 2002 (N = 17) 0.980 0.981 0.980 0.981 2001 (N = 14) 0.992 0.991 0.992 0.991 2000 (N = 12) 0.977 0.978 0.979 0.979 Overall (2001-2004; N = 78) 0.950 0.966 0.952 0.971 Model 3 Model 4 2004 (N =18) 0.927 0.992 0.930 0.994 2003 (N =17) 0.954 0.954 0.954 0.955 2002 (N = 17) 0.980 0.981 0.908 0.981 2001 (N = 14) 0.992 0.991 0.992 0.991 2000 (N = 12) 0.977 0.978 0.979 0.979 Overall (2001-2004; N = 78) 0.964 0.975 0.965 0.979 Notes: TE: technical efficiency, SE: scale efficiency, VRS: Variable returns on scale; Model 1 is estimated with fixed assets, customer deposits & short term funding, and number of employees as inputs, and loans and other earning assets as outputs; Model 2 is estimated as Model 1 but with off-balance sheet items as an additional output; Model 3 is estimated as Model 1 but with loan loss provisions as an additional input; Model 4 is estimated as Model 1 but with off-balance sheet items as an additional output and loan loss provisions as an additional input.
31
Table 3 – DEA results with profit oriented approach (Model 5)
TE (VRS) SE Mean Mean 2004 (N =18) 0.924 0.978 2003 (N =17) 0.975 0.976 2002 (N = 17) 0.944 0.942 2001 (N = 14) 0.946 0.945 2000 (N = 12) 0.968 0.966 Overall (2001-2004; N = 78) 0.950 0.960 Notes: TE: technical efficiency, SE: scale efficiency, CRS: constant return on scale, VRS: Variable returns on scale; Model 5 is estimated with employee expenses, other non interest expenses and loan loss provisions as inputs, and net interest income, net commission income and other operating income as outputs
32
Table 4 –Purely domestic versus international Greek banks
N PDBs IGBs Model 4 Model 5 Model 4 Model 5 TE (VRS) SE TE
(VRS) SE TE (VRS) SE TE (VRS) SE
Mean Mean Mean Mean Mean Mean Mean Mean Panel A: operations abroad through subsidiaries (SIGBs)
2004 11 PDBs / 7 SIGBs 0.924 0.992 0.914 0.973 0.941 0.998 0.941 0.985 2003 10 PDBs / 7 SIGBs 0.929 0.967 0.978 0.967 0.991 0.979 0.971 0.999 2002 11 PDBs / 6 SIGBs 0.970 0.954 0.917 0.911 0.999 0.966 0.993 0.974 2001 8 PDBs / 6 SIGBs 0.987 0.993 0.916 0.900 1.000 1.000 0.987 0.971 2000 6 PDBs / 6 SIGBs 0.959 0.981 0.936 0.989 0.999 0.968 1.000 0.969 Overall (2001-2004)
46 PDBs / 32 SIGBs 0.951 0.976 0.932 0.946 0.985 0.982 0.977 0.980
Panel B: Operations abroad through subsidiaries and branches (SBIGBs)
2004 10 PDBs / 8 SBIGBs 0.916 0.991 0.905 0.970 0.948 0.998 0.949 0.987 2003 9 PDBs / 8 SBIGBs 0.937 0.964 0.976 0.963 0.974 0.981 0.975 0.999 2002 9 PDBs / 8 SBIGBs 0.964 0.959 0.938 0.908 0.999 0.957 0.951 0.962 2001 7 PDBs / 7 SBIGBs 0.985 0.992 0.903 0.886 1.000 1.000 0.988 0.975 2000 5 PDBs / 7 SBIGBs 0.950 0.977 0.923 0.986 0.999 0.972 1.000 0.973 Overall (2001-2004)
40 PDBs / 38 SBIGBs 0.948 0.976 0.930 0.942 0.983 0.981 0.971 0.979
Notes: PDBs: Purely domestic banks, IGBs: International Greek Banks; TE: technical efficiency, SE: scale efficiency, VRS: Variable returns on scale; Model 4 is estimated with fixed assets, customer deposits & short term funding, number of employees and loan loss provisions as inputs, and loans, other earning assets and off-balance sheet items as outputs; Model 5 is estimated with employee expenses, other non interest expenses and loan loss provisions as inputs, and net interest income, net commission income and other operating income as outputs
33
Table 5 – Tobit censored regression results
Model 4 Model 5 TE SE TE SE Coef. p-value Coef. p-value Coef. p-value Coef. p-value
Panel A: TE & SE regressed over bank financial characteristics EQAS 0.065537 0.0000 0.030122 0.0000 0.049830 0.0000 0.019114 0.0007ROAA 0.005145 0.8566 0.010771 0.7022 0.120047 0.0011 0.071777 0.0567LOANS 0.006359 0.0031 0.011457 0.0000 0.009719 0.0000 0.013829 0.0000POWER 0.038415 0.0000 0.021705 0.0000 0.025861 0.0000 0.015411 0.0000 Panel B: TE & SE regressed over strategic decisions related variables ATM 0.001783 0.2603 0.000312 0.7624 0.001306 0.3930 -0.000336 0.7571BRANCH 0.005875 0.0805 0.004469 0.0017 0.010266 0.0083 0.006977 0.0051BR_ABR -0.641537 0.4266 -0.363895 0.5715 -1.715891 0.0309 -0.489866 0.4573SUB_ABR 0.768489 0.0285 0.725462 0.0007 0.711865 0.1073 0.330059 0.1962Notes: N=78 observations; TE: technical efficiency, SE: scale efficiency; Model 4 is estimated with fixed assets, customer deposits & short term funding, number of employees and loan loss provisions as inputs, and loans, other earning assets and off-balance sheet items as outputs, Model 5 is estimated with employee expenses, other non interest expenses and loan loss provisions as inputs, and net interest income, net commission income and other operating income as outputs; EQAS: equity to assets, ROAA: return on average assets, LOANS: loans to assets, POWER: market share in terms of total assets, ATM: the number of bank’s ATMs, BRANCH: the number of bank’s branches, BR_ABR: dummy variable that equals 1 if the bank has branches abroad and 0 otherwise, SUB_ABR: dummy variable that equals 1 if the bank has subsidiaries abroad and 0 otherwise; QML (Huber/White) standard errors and covariates have been calculated to control for heteroscedacity
34
University of Bath School of Management Working Paper Series
Past Papers
School of Management Claverton Down
Bath BA2 7AY
United Kingdom Tel: +44 1225 826742 Fax: +44 1225 826473
http://www.bath.ac.uk/management/research/papers.htm
2005
2005.01 Bruce A. Rayton Specific Human Capital as an Additional Reason
for Profit Sharing
2005.02
Catherine Pardo, Stephan C. Henneberg, Stefanos
Mouzas and Peter Naudè
Unpicking the Meaning of Value in Key Account Management
2005.03 Andrew Pettigrew and Stephan C. Henneberg
(Editors)
Funding Gap or Leadership Gap – A Panel Discussion on Entrepreneurship and Innovation
2005.04 Robert Heath & Agnes Nairn
Measuring Affective Advertising: Implications of Low Attention Processing on Recall
2005.05 Juani Swart Identifying the sub-components of intellectual capital: a literature review and development of measures
2005.06 Juani Swart, John Purcell and Nick Kinnie
Knowledge work and new organisational forms: the HRM challenge
2005.07 Niki Panteli, Ioanna Tsiourva and Soy Modelly
Intra-organizational Connectivity and Interactivity with Intranets: The case of a Pharmaceutical Company
2005.08 Stefanos Mouzas, Stephan Henneberg and Peter
Naudé
Amalgamating strategic possibilities
2005.09 Abed Al-Nasser Abdallah Cross-Listing, Investor Protection, and Disclosure: Does It Make a Difference: The Case of Cross-Listed
Versus Non-Cross-Listed firms
2005.10 Richard Fairchild and Sasanee Lovisuth
Strategic Financing Decisions in a Spatial Model of Product Market Competition.
2005.11 Richard Fairchild Persuasive advertising and welfare in a Hotelling market.
2005.12 Stephan C. Henneberg, Catherine Pardo, Stefanos Mouzas and Peter Naudé
Dyadic ‘Key Relationship Programmes’: Value dimensions and strategies.
35
2005.13 Felicia Fai and Jing-Lin Duanmu
Knowledge transfers, organizational governance and knowledge utilization: the case of electrical supplier
firms in Wuxi, PRC
2005.14 Yvonne Ward and Professor Andrew Graves
Through-life Management: The Provision of Integrated Customer Solutions By Aerospace Manufacturers
2005.15 Mark Ginnever, Andy McKechnie & Niki Panteli
A Model for Sustaining Relationships in IT Outsourcing with Small IT Vendors
2005.16 John Purcell Business strategies and human resource management: uneasy bedfellows or strategic partners?
2005.17 Richard Fairchild Managerial Overconfidence, Moral Hazard, and Financing and Investment Decisions
2005.18 Wing Yee Lee, Paul Goodwin, Robert Fildes,
Konstantinos Nikolopoulos, & Michael
Lawrence
Providing support for the use of analogies in demand forecasting tasks
2005.19 Richard Fairchild and Sasanee Lovisuth
Product Differentiation, Myopia, and Collusion over Strategic Financing Decisions
2005.20 Steven Brammer, Andrew Millington & Bruce
Rayton
The Contribution of Corporate Social Responsibility to Organisational Commitment
2005.21 Richard Fairchild and Ganggang Zhang
Repurchase and Dividend Catering, Managerial Myopia, and Long-run Value-destruction