Economic Modelling 53 (2016) 156–165
Contents lists available at ScienceDirect
Economic Modelling
journal homepage: www.elsevier.com/locate/ecmod
The impact of earnings management on the performance of ASEAN banks
Yueh-Cheng Wu a, Irene Wei Kiong Ting b, Wen-Min Lu c,⁎, Mohammad Nourani d, Qian Long Kweh e
a
Department of Cultural Vocation Development, National Taipei University of Technology, 1, Sec. 3, Zhongxiao E. Rd., Taipei 10608, Taiwan
Department of Finance and Economics, College of Business Management and Accounting, Universiti Tenaga Nasional, Sultan Haji Ahmad Shah Campus, 26700 Muadzam Shah, Pahang, Malaysia
c
Department of Financial Management, National Defense University, No. 70, Sec. 2, Zhongyang North Rd., Beitou, Taipei 112, Taiwan
d
Faculty of Economics and Administration, University of Malaya, Jalan Universiti, 50603 Kuala Lumpur, Malaysia
e
Department of Accounting, College of Business Management and Accounting, Universiti Tenaga Nasional, Sultan Haji Ahmad Shah Campus, 26700 Muadzam Shah, Pahang, Malaysia
b
a r t i c l e
i n f o
Article history:
Accepted 20 November 2015
Available online xxxx
Keywords:
Performance evaluation
Data envelopment analysis
Earnings management
Liberalization
ASEAN
a b s t r a c t
Southeast Asian financial liberalization policies have enthused both performance evaluation (a pro) and earnings
management (a con). Using a sample of ASEAN commercial banks for the period 2007–2014, this study decomposes their banking performance into managerial and profitability efficiencies. An efficiency analysis reveals that
Singaporean banks obtained the highest overall and profitability efficiencies, while Bruneian banks had the lowest rates of banking performance. In the stage of managerial efficiency, the most inefficient banks are those of the
Philippines, whereas the greatest level is related to Malaysian banks. A frontier projection analysis suggests that
Singaporean banks and Malaysian banks are generally more efficient in managing their expenditures and longterm assets in generating income in the long run. With respect to the con, a regression analysis indicates that
loan loss provisions are negatively related to banking performance. Overall, it is advisable that policy makers
with oversight function should promote performance evaluation from a multidimensional perspective, and
keep an eye on estimates of loan loss provisions at banks over years because increases/decreases in loan loss provisions mean decreases/increases in net income or return on assets.
© 2015 Elsevier B.V. All rights reserved.
1. Introduction
According to a report by the ADB (2011), Asian emerging economies
hold approximately half of the world’s total exchange reserves, and the
whole region is also the major exporter and importer of capitals. This
phenomenon attests the underdeveloped intra-regional financial system to efficiently channel surplus funds. Consequently, effective regional coalition and cooperation are needful in dealing with this immaturity.
Among all, the Association of Southeast Asian Nations (ASEAN)1 has
succeeded to create a strong integration among its members of Southeast Asian nations. Since the 1970s, many emerging countries in Asia
have experienced substantial rectifications in their financial liberalization policies. Specifically, developing countries in Southeast Asia have
experienced various forms of financial liberalizations in promoting efficient allocation of resources to achieve greater economic growth during
⁎ Corresponding author.
E-mail addresses: ywu@ntut.edu.tw (Y.-C. Wu), irene@uniten.edu.my
(I. Wei Kiong Ting), wenmin.lu@gmail.com (W.-M. Lu), mohammad@nourani.net
(M. Nourani), qlkweh@gmail.com (Q.L. Kweh).
1
The ASEAN was established on August 1967 by the signing agreement of five countries, namely, Indonesia, Malaysia, the Philippines, Singapore, and Thailand. Since then,
membership has expanded to include Brunei, Cambodia, Laos, Myanmar, and Vietnam,
constituting the ten Member States of ASEAN.
http://dx.doi.org/10.1016/j.econmod.2015.11.023
0264-9993/© 2015 Elsevier B.V. All rights reserved.
the last few decades. Subsequently, a number of institutional reforms
occurred following Asian financial crisis and global financial crisis
erupted in 1997 and 2007–2008, respectively, to strengthen the regulatory and supervisory frameworks. As a result of financial liberalization
policies, the international capital mobility has increased between partner countries through different international agreements such as
ASEAN (Lim, 2005). However, it might be a blessing in disguise or a
curse.
On the one hand, substantial capital flows into the host countries. In
addition, the increased competition as the consequence of liberalization
policies stimulates firms to put more cautions on their activities such as
cost management, risk monitoring, and resource allocation (Gardener
et al., 2011). That is, opening up the economy to international investors
leads to higher efficiency of firms by means of intensifying the competition within a domestic market. According to the seminal works of
McKinnon (1973) and Shaw (1973), financial liberalization yields
higher economic growth through increasing interest rate level, which
enhances the competition among the market players, while the allocation of resources are well realized. Therefore, financial liberalization is
likely to increase higher savings, which eventually nurtures economic
growth by ameliorating the investment quantity and quality, i.e. efficient allocation of resources (Reinhart and Tokatlidis, 2003). This is
the reason why evaluating efficiency becomes very important in recent
Y.-C. Wu et al. / Economic Modelling 53 (2016) 156–165
years where many of the emerging countries have undergone full liberalization policies or are under the process of being liberalized, i.e. partial
liberalization.
On the other hand, liberalization enhances competition, which
dampens firms’ profitability. Therefore, underperforming firms will be
expelled from the marketplace because lower profitability increases
the risk of bankruptcy (Baik et al., 2011; Becchetti and Sierra, 2003;
Bolt et al., 2012). Put differently, this will create an incentive for managers to sham their corporate performance in order to attract investors.
In fact, past studies prove that pressurized firms with high chance
of bankruptcy are more inclined towards engaging “earnings
management2 (EM)” practices (Beneish et al., 2012; García Lara et al.,
2009). Therefore, the dramatic shift in liberalization policies in emerging countries has predisposed market players to manipulate the information about financial reporting. It has been repeatedly reported that
the intensified competition, as the result of liberalization and government deregulation, brings new opportunities for economic prosperity.
However, the flip side of the coin tells a different story. Internal managers are more prone towards hyperbolizing the firm’s performance to
market participants within a competitive environment. Therefore,
firms may report higher earnings compared to other rivals to attract investors. In a comprehensive review of the EM literature, Healy and
Wahlen (1999) conclude that “the evidence is consistent with firms
managing earnings to window-dress financial statements prior to public securities’ offerings, to increase corporate managers’ compensation
and job security, to avoid violating lending contracts, or to reduce regulatory costs or to increase regulatory benefits” (p. 368). In their review,
Healy and Wahlen (1999) emphasize that past studies have focused on
whether EM exists and why, yet the empirical issue about the effect of
earnings management practices on efficiency has not been well explored (p. 380).
In this vein, the banking sector has been the centerpiece in the eyes
of policy makers due to its invaluable importance to the economic development; hence, the sound and well-functioning banking sector is a
potent engine of economic growth. Given the above discussion, this
study aims to estimate the performance of ASEAN banking institutions
and address the relationship between EM practices and banking performance. To measure banking performance, frontier efficiency analysis,
which has received considerable attention from researchers, is more appropriate than a single-dimensional ratio analysis. Data envelopment
analysis (DEA), introduced by the influential work of Charnes et al.
(1978), has been coined as the most frequent frontier efficiency approach used by researchers, particularly among banking studies (Liu
et al., 2013). The value of DEA lies in its ability to transform various performance aspects into a single efficiency score (Yeh, 1996) through
evaluating the relative performance of a decision-making unit (DMU)
compared to its peers or competitors operating within the same group
(Liu et al., 2013). However, the traditional DEA models are not sufficient
to measure the banks’ complex production process because these
models assume the system as a single black box that converts inputs
to outputs (Chiu and Chen, 2009; Dong et al., 2014; Moradi-Motlagh
and Babacan, 2015; Wang et al., 2014). Accordingly, the detailed sources
of inefficiency could not be identified when applying the traditional DEA
models. Therefore, we adopt dynamic network DEA (DN-DEA) to deal
with inefficiencies of interacting divisions that are embedded inside
the banks’ production process.
The purpose of this study is twofold. First, we apply the newly developed DN-DEA model called dynamic network slacks-based measure
(DNSBM), formulated by Tone and Tsutsui (2014), which deals with
2
Earnings management is defined as the use of managerial judgment to manipulate the
financial reporting with the purpose of either influencing contractual outcomes which depend on the financial reports or misleading the stakeholders (and investors) about the
company’s performance (Healy and Wahlen, 1999).
157
multiple divisions connected by links of network structure within
each period vertically and also combines the network structure by
means of carry-over activities between two succeeding periods horizontally. The second important objective of the study is to investigate
the influence of controversial EM practices on the divisional efficiency
scores of ASEAN banking institutions. To measure EM practices, we follow Adams et al. (2009) to use the ratio of loan loss provisions to loans.
As a robustness check, we also include the ratio of loan loss reserves to
loans as another measure of EM practices. To the best of the authors’
knowledge, this study makes an early attempt by examining the effect
of EM on the efficiency of ASEAN banking institutions.
To articulate the contributions of our study, we would like to highlight two points. First, efficiency has become a contemporary major
issue to finance sectors due to the increment of competition, globalization, technological innovation and increased deregulation (DangThanh, 2012). According to Quiggin (2011), the global financial crisis
in 2007–2008 had a major impact on the financial systems of many
countries. Therefore, it is crucial for banking sectors to evaluate their efficiency level in order to compare competitiveness and further enhance
their productivity. As a result, this paper provides a direct economic
contribution by estimating the managerial and profitability efficiencies
of ASEAN commercial banks in order to speed up countries’ financial development and economic growth. Banks with well-functioning and efficient financial systems are less likely to be suffering financially during
financial crises (Moradi-Motlagh and Babacan, 2015). In contrast, it
calls for countries with banks’ low efficiency scores to increase banks’ financial autonomy in order to face economy challenges. We hope to
bridge the gap and shed some light on the literature, specifically in the
ASEAN context.
Second, banking institutions are not exempted from EM practices,
rather they are also more susceptible to this upshot compared to nonfinancial organizations (Grougiou et al., 2014) due to their wideranging financial products and complicated operation which lead to information opacity (Levine, 2004) and asymmetry (Mülbert, 2009).
Moreover, as highlighted by Greenawalt and Sinkey (1988), the higher
chance of earnings manipulation in banking institutions might be attributed to the subjective judgment that managers have to make in regard
to expected reserves for losses. Specifically, during the period of high
profit, banks’ managers incline to smooth the earnings by recording
more loan losses and vice versa (Cornett et al., 2009). While the theoretical and empirical literature supports the amplification in both firm efficiency and EM practices followed by liberalization, the question remains
that how EM could be observed from testing its relation to efficiency of
banks. Hence, this study makes another contribution by examining the
effects of earnings management on bank efficiency, which is lacking in
the literature.
The remainder of this paper unfolds as follows: Section 2 reviews the
extant literature on banking efficiency studies. Section 3 describes the
data collection and the methodology used. Section 4 presents the empirical findings and the discussion, and Section 5 concludes the paper.
2. Literature review
2.1. Efficiency studies in the ASEAN banking sector
In this section, we try to map out the studies addressing the efficiency analysis of banking institutions in the ASEAN alliance. We note that
the application of efficiency evaluation in ASEAN economies is very
scant at the moment. Hence, we also focus our attention to East Asian
studies of banking efficiency, where necessary, in order to provide a befitting background of the subject matter.
Laeven (1999) estimates the technical efficiency of East Asian banks
for the period from 1992 to 1996. The input variables include interest
expenses, labor expenses, other operating expenses and the output variables including loans and securities. The efficiency results were surprising since the scores were increasing or constant during the pre-crisis
158
Y.-C. Wu et al. / Economic Modelling 53 (2016) 156–165
period in which one might expect a declining efficiency trend. The author, therefore, tries to shed some light on the risk-taking behavior of
commercial banks. The findings assert that banks with low efficiency
scores have endured the crisis due to taking fewer loans and thus less
risk.
The first ASEAN banking efficiency paper appearing in the literature,
authored by Karim (2001), is the analysis of scale and cost efficiencies
using the stochastic cost frontier approach. The author uses the banking
data for four countries during 1989 to 1996, including Indonesia,
Malaysia, the Philippines, and Thailand, out of ten members due to
data unavailability for the remaining countries. Using the intermediation approach, three inputs (wages and salaries, land, buildings, and
equipment and interest on deposits) and five outputs (commercial
and industrial loans, other loans, time deposits, demand deposits, and
securities and investments) are selected for the efficiency analysis. His
findings on profit and cost efficiency suggest that Thai banks are the
least inefficient followed by Indonesian and Pilipino banks while
Malaysian banks perform the best. The author binds the inefficiency of
Indonesian and Pilipino banks to their restrictive regulatory systems.
His results also support the consolidation policy where the larger
banks incline towards higher cost efficiency.
Consistently, Williams and Nguyen (2005) examine the profit efficiency using stochastic frontier approach for the Southeast Asian commercial banks in the period of 1990 to 2003. The authors utilize the
unbalanced dataset of 231 commercial banks from five countries
(Indonesia, Korea, Malaysia, the Philippines, and Thailand) to create a
common frontier. This is disputatious, however, the efficiency results
can be controlled for significant cross-country differences. Williams
and Nguyen apply different country economic indicators to control for
cross-border differences. Their key findings support the policy of bank
privatization, which leads to higher profit efficiency.
Gardener et al. (2011) provide an empirical efficiency analysis of five
selected ASEAN banking institutions, including Indonesia, Malaysia, the
Philippines, Thailand, and Vietnam for the period of 1998 to 2004. The
authors estimate the technical and cost efficiencies using DEA with
two outputs (net loans and other earning assets) and three inputs
(fixed assets, deposits and personnel costs). Their key findings show
that the efficiency of banks over the sample period has reduced
interpreting the weak restructuring of post-1997 crisis. Malaysian and
Vietnamese banks perform better in terms of technical and cost efficiency while Indonesia and Thailand possess the least technical and cost-efficient banks in the post-crisis era. Furthermore, countries with higher
economic growth rates tend to be more efficient.
In summary, the above literature of efficiency studies in the East
Asian banking sectors put forward the importance of cross-country
comparison in which the efficiency scores differ between countries
and also before and after the crisis event. The restructuring of financial
institutions takes some time to be effectively implemented, and an immediate analysis might not be appropriate to judge the true influence of
restructuring policies. In addition, due to limitation in data, the above
studies fail to consider more countries into analysis in order to provide
a more inclusive picture of ASEAN alliance. Therefore, our study aims to
fill the gap by addressing a more recent period in efficiency analysis as
well as a broader group of ASEAN commercial banks.
2.2. Data envelopment analysis in the banking sector
There are many advantages for using DEA as a performance evaluation technique: (i) it handles multiple input and output variables at the
same time; (ii) it has unit invariance meaning that the units of input and
output variables do not influence the analysis; (iii) it does not require
the predetermination of the parameters; (iv) it provides the weights
for input and output variables using a mathematical method which
then suggests the areas for improvement; (v) it renders an objective
analysis (Cooper et al., 1999; Lu et al., 2015; Sueyoshi and Sekitani,
2009).
The literature on frontier efficiency methodology, particularly DEA,3
is fruitful with numerous research works focusing on methodological
development, application-centered and both theory and application
studies (see Cook and Seiford (2009) and Emrouznejad et al. (2008)
for methodological and theoretical developments and see Liu et al.
(2013) for a survey of application-embedded studies). Liu et al.
(2013), who review high-ranked DEA papers published during 1978
through 2010, indicate that application-embedded papers account for
nearly two-thirds of all published papers, and banking studies cover
10.3% of this category (the most popular field). Since the invention of
novel DEA by Charnes et al. (1978), the groundbreaking work of
Sherman and Gold (1985), where the authors examine the operating efficiency of bank branches, paved the way for the application of DEA in
banking sectors. Sherman and Gold’s argument about the uniqueness
of DEA technique embraced by a number of banking researchers
(Barth et al., 2013; Berg et al., 1993; Elyasiani and Mehdian, 1990;
Parkan, 1987; Pasiouras, 2008; Rangan et al., 1988). Berger and
Humphrey (1997), a survey-based study, and Thanassoulis (1999), an
informative study, motivate the researchers by providing the potential
areas that need to be addressed in the domain of banking efficiency
and the scope for enhancing the role of DEA in banking, respectively.
However, as mentioned before, the banks’ complex production process requires more sophisticated techniques to account for internal
structures within the black box. Fortunately, following the pioneering
work of Färe and Grosskopf (1996), who were the initiators to investigate the “black box”, many researchers developed new methodologies
to overcome the shortcomings. While a rising number of studies
pointing to the meaningfulness of decomposing the banks’ performance
into sub-divisions (Avkiran, 2009; Lin and Chiu, 2013; Lo and Lu, 2006;
Luo, 2003; Seiford and Zhu, 1999; Yang and Liu, 2012), the application
of DN-DEA in banking is still in its embryonic stage (Avkiran, 2014a;
Fukuyama and Weber, 2013; Kao and Liu, 2014; Wanke et al., 2014).
For example, Avkiran (2014a) assesses the dynamic efficiency of Chinese commercial banks combined with network structure; Wanke
et al. (2014) measure the efficiency of Brazilian banks using dynamic
SBM; Kao and Liu (2014) propose a relational network model applied
to a set of Taiwanese commercial banks; and Fukuyama and Weber
(2013) provide an example of dynamic network DEA using a large sample of Japanese banks. Hence, our study contributes to the scarce literature of DN-DEA in banking, and it is the first study to apply this
technique in a cross-country sample.
3. Research design
3.1. Descriptions of the DEA model and data
The efforts to test the hypothesis of this study focus on nine emerging economies: Brunei Darussalam, Cambodia, Indonesia, Lao People’s
Democratic Republic, Malaysia, the Philippines, Singapore, Thailand,
and Vietnam. These countries share a main trait, in which they are
members of the ASEAN. Therefore, we argue that the sample banks in
this study are by no means more influential in countries with larger
bank populations. Note that our data screening process leaves us with
no banks from Myanmar, which is also one of the ASEAN members.
This is not surprising because information is sometimes lacking in
emerging economies.
All data are extracted from the Bankscope database for the period
from 2007 to 2013. We collect financial data for as many commercial
banks as possible for each country and screen the initial dataset in the
following ways. Firstly, we focus only on commercial banks, which
have similar products and services, whereby each of the banks is treated
as a DMU for the DEA analysis. Secondly, we eliminate banks with no
3
According to a comprehensive survery of frontier efficiency analysis in financial institutions, mostly banking, by Berger and Humphrey (1997), DEA is the most frequently used
approach for efficiency evaluation.
Y.-C. Wu et al. / Economic Modelling 53 (2016) 156–165
complete financial data for the DEA analysis over the sample period. Finally, we remove banks with missing data for measuring the testing variable, viz. earnings management. As such, we have a balanced panel
dataset that is made up of 138 commercial banks in each year, in particular: Brunei Darussalam (1), Cambodia (9), Indonesia (55), Lao People’s
Democratic Republic (4), Malaysia (1), the Philippines (20), Singapore
(8), Thailand (20), and Vietnam (20). Relying on the Bankscope database for our dataset, we highlight that the final dataset for analysis
does not reflect a market concentration problem; instead, it is about
data screening based on data availability and selections of DEA and regression variables. In the selection of DEA variables, it should be noted
that we examine the operating processes of banks using intermediation
approach in line with prior studies4 (Avkiran, 2014a; Avkiran and
Thoraneenitiyan, 2010; Bhattacharyya et al., 1997; Miller and Noulas,
1996; Sturm and Williams, 2004). The intermediation approach stresses
on the situation whereby banks transform sources of production into
outputs such as earnings assets and ultimately income in the nature of
their business. The logical flow is that banks input staff (personnel expenses) who incurs other operating expenses to produce intermediates
including loans, deposits, and other earnings assets, which ultimately
generate net interest income to the banks. In the network production
process, fixed assets such as buildings and office equipment are used
over long-term periods by staff at work, while liquid assets are considered as another inputted carry-over item that will generate net interest
income together with the intermediates.
In regards of the DEA analysis, we present Table 1 to summarize the
descriptive statistics of variables used. Furthermore, we also perform
correlation analysis for the variables. Table 2 shows that all correlation
coefficients are significantly positive, indicating that the inputs, carryovers, intermediates, and outputs are isotonically related. In other
words, the selected variables are appropriate for further analysis using
the stipulated dynamic network DEA model. Finally, as the number of
banks meets the requirement that the number of DMUs should be larger
than double or triple the number of variables used for the DEA analysis,
we conclude that the developed DEA model has high construct validity.
The traditional DEA models assume a production process as a single
‘black box’ that transforms inputs to outputs. Therefore, every activity
has to be categorized as ‘input’ or ‘output’. This would create a problem
when there is a complex production process that requires multiple inputs and outputs. Accordingly, the network DEA models overcome the
abovementioned shortcoming by considering multiple divisions of production within the black box while evaluating the overall efficiency as
well. The network structure allows the evaluation of the connectivity
between inner linking activities (Kao, 2009; Tone and Tsutsui, 2009),
hence, it will enable us to build processes of banks’ inner business
activities.
This study also considers the linking activities between two
succeeding periods, which allow us to take the effect of time on performance measure into account. More specifically, we incorporate the time
effect by means of carry-over activities between the subsequent periods. As such, the idea of dynamic DEA (Tone and Tsutsui, 2010) observes the long-term fluctuated trends of banks through the years.
In addition to the above, in traditional DEA models, the relative efficiency for each DMU is measured under the assumption of the proportional changes of input and output variables, meaning that the models
are radial. In fact, radial models may lack objectivity in terms of
reflecting the real input/output conditions for each organization. Furthermore, these models assume that inputs and outputs can be adjusted
according to their ratios, which cannot be adopted under certain circumstances. As a solution, DNSBM, a model proposed by Tone and
Tsutsui (2014), is a non-radial model which takes the possibility of
4
In an investigation of major DEA applications in banking literature in top journals
across 2004–2009, Avkiran (2011) reaches the conclusion that “there is no clear agreement among the selection of inputs and outputs beyond the general observance of the intermediation approach to bank behavior” (Avkiran, 2011, p.326).
159
Table 1
Summary statistics of inputs, intermediates, and outputs.
Mean
Input
Personnel expenses (X2)
Other operating expenses (X3)
Input (carry-over)
Fixed assets (X1)
Liquid assets (X4)
Intermediate
Loans (Z1)
Other earning assets (Z2)
Deposits (Z3)
Output
Net interest income (Y1)
Standard
deviation
75.865
90.096
1st
Quartile
140.196
159.157
3rd
Quartile
7.000
8.000
89.986
189.728
7,422.737 18,799.629
66.750
82.750
6.000
67.750
398.000 5,060.750
5,516.260 14,150.085
296.550 3,591.500
5,347.262
9,516.799 2,556.250 4,101.750
7,424.737 18,799.629
400.000 5,062.750
277.247
535.101
31.700
227.000
Note:
1. All variables are denoted in USD million.
2. Definitions of the variables are as follows. Personnel expenses are total staff costs in year
t. Other operating expenses are total operating costs other than staff costs in year t. Fixed
assets are total tangible property, plant, and equipment in year t − 1. Liquid assets are resources that could be converted into cash quickly in year t − 1. Loans are temporary funds
provided to customers at interest in year t. Other earning assets include financial investments in stocks and bonds in year t. Deposits are monies kept by customers at banks in
year t. Net interest income refers to the excess income generated from loans and other
earnings assets over expenses associated with interests on deposits in year t.
non-proportional changes of inputs and outputs into account. This
model deals with slacks when estimating the divisional and overall
efficiencies. Considering differences of input and output slacks concurrently, this study uses non-oriented efficiency to estimate banks’ performance. The dynamic network process is shown in Fig. 1.
3.2. Modelling the dynamic network SBM
Consider the dynamic network processes presented in Fig. 1 that
deals with n banks (j = 1,…,n) consisting of k divisions (k = 1, …, K)
over T terms (t = 1, …, T). At each term, banks have common mkt inputs
(i = 1,b…, mkt ) consisting of k divisions and rkt outputs (i = 1,…, rkt )
consisting of k divisions. Let xkiot (i = 1,…, mkt ) and ykrot (i = 1,…, rkt ) denote the observed input and output values of bank j consisting of k dividenotes the continuity of link flows
sions at term t, respectively. z(k,h)
jt
(carry-overs) between terms t and t + 1. This study symbolizes the category link as Cbad. In order to identify them by term (t), bank (j), divi(p =
sions (k) and item (i), this study employs the notion Ck,bad
pot
; t = 1, …, T; k = 1, …, K) for denoting bad link values where
1, …, mk,bad
t
nbad is the number of bad links. These are all observed values up to the
denote the observed carry-over input values of DMU j
term T. Let mk,bad
t
consisting of k divisions at term t. Using these expressions for production, this study expresses the target banko (o = 1,…,n). Therefore, this
Table 2
Spearman correlation coefficients.
X2
Personnel expenses
(X2)
Other operating
expenses (X3)
Fixed assets (X1)
Liquid assets (X4)
Loans (Z1)
Other earning assets
(Z2)
Deposits (Z3)
Net interest income
(Y1)
X3
X1
X4
Z1
Z2
Z3
Y1
1.000
0.959 1.000
0.875
0.944
0.938
0.692
0.881
0.946
0.926
0.738
1.000
0.859 1.000
0.845 0.975 1.000
0.628 0.767 0.707 1.000
0.944 0.946 0.859 1.000 0.975 0.767 1.000
0.951 0.937 0.877 0.941 0.949 0.691 0.941 1.000
Note:
1. All of the coefficients are significant at the 1% significance level.
2. See Table 1 for the definitions of the variables.
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Y.-C. Wu et al. / Economic Modelling 53 (2016) 156–165
Fig. 1. The conceptual framework of the dynamic network production processes for banks.
study defines the non-oriented efficiency by solving the program as
follows:
−
for term T. This implies that the optimal slacks for term t in (3) are
all zero.
þ
Let ρ∗o denote the overall efficiency during the term T. Where skit , skrt ,
"
−
k ;bad
are slack variables denoting, respectively, input excess, outand spt
puts shortfall, link excess, and link deviation.
This objective function is an extension of the non-oriented SBM
model (Tone, 2001) and deals with excesses in both input resources
and undesirable (bad) links. The numerator is the average input efficiency and the denominator is the inverse of the average output efficiency. This study defines the non-oriented overall efficiency as a ratio
that ranges between 0 and 1, and attains 1 when all slacks are zero.
This objective function value is also units-invariant.
"
!#
−
k ;bad
Xmk sk− Xmk;bad spt
1 XT XK
1
t
k
it
t
w
1−
þ
i¼1 xk
p¼1
t¼1
k¼1 t
T
mkt þ mk;bad
C k;bad
iot
t
pot
"
!#
ρo ¼ Min
ð1Þ
kþ
X
X
X
k
1
1
srt
T
K
rt
k
w
1
þ
r¼1 yk
t¼1
k¼1 t
T
rkt
rot
s:t: X
−
n
xkiot ¼
xk λk þ skit
i ¼ 1; …; mkt ; t ¼ 1; …; T; k ¼ 1; …; K
j¼1 i jt jt
Xn
þ
yk λk −skrt r ¼ 1; …; r kt ; t ¼ 1; …; T; k ¼ 1; …; K
ykrot ¼
j¼1 r jt jt
Xn
λkjt ¼ 1ðt ¼ 1; …; T Þ
Xn ðk;hÞ k
Xnj¼1 ðk;hÞ h
z
λ jt ¼
z
λ jt ; ∀ ðk; hÞðt ¼ 1; …; T Þ
j¼1 jt
j¼1 jt
X
−
n
k;bad k
k ;bad
C k;bad
¼
C
λ
þ
s
p ¼ 1; …; mk;bad
; t ¼ 1; …; T; k ¼ 1; …; K
jt
pot
pt
t
j¼1 p jt
Xn
Xn
C k;bad λ jt ¼
C k;bad λ jtþ1 p ¼ 1; …; mk;bad
; t ¼ 1; …; T−1; k ¼ 1; …; K
t
j¼1 i jt
j¼1 i jt
−
−
þ
λkjt ≥0; skit ≥0; skrt ≥0; skpt ;bad ≥0
ð2Þ
If the optimal solution for Eq. (1) satisfies ρ∗o = 1, banko is called nonoriented overall efficient or briefly overall efficient.
XK
wk
k¼1 t
ρt ¼
"
1−
1
Xmk sk−
it
t
mkt þ mk;bad
t
"
XK
1
k
w 1þ k
k¼1 t
rt
T
i¼1
xkiot
−
þ
k ;bad
Xmk;bad spt
Xrk skþ
t
rt
r¼1 yk
rot
!#
t
p¼1
!#
C k;bad
pot
ð3Þ
In Eq. (3), ρo ¼ T1 ∑t¼1 ρt . If all optimal solutions of satisfy ρt = 1,
banko is called non-oriented term efficient or briefly term efficient
1−
ρkt ¼
−
k ;bad
Xmk sk− Xmk;bad spt
t
it
t
þ
i¼1 xk
p¼1
mkt þ mk;bad
C k;bad
iot
t
pot
"
!#
þ
1 Xrkt skrt
1þ k
r¼1 yk
rt
rot
1
!#
ð4Þ
In Eq. (4), ρt = ∑Kk = 1wkt ρkt . If all optimal solutions satisfy ρkt = 1,
banko is called non-oriented term efficient or briefly term efficient
with the divisions k at the term T. This implies that the optimal slacks
with the divisions k at term t in Eq. (4) are all zero.
3.3. Regression model
In a DEA-application involving multivariate analysis to find the effect
of contextual factors on performance measures, there are two main recent streams for a second-stage analysis. First approach suggests using a
maximum likelihood estimation of a truncated regression, proposed by
Simar and Wilson (2007). Second approach advocates the use of maximum likelihood estimation of ordinary least squares (OLS) or Tobit regression, proposed by Banker and Natarajan (2008). McDonald (2009)
asserts that DEA analysis followed by second stage-analysis involving
OLS could yield a valid estimation of the contextual factors, and it is
less restrictive as compared to the truncated model proposed by Simar
and Wilson (2007). Through OLS, Banker and Natarajan (2008) show
that consistent estimators of the regression coefficients could be obtained despite the fact that efficiency scores range between zero and one. As
mentioned by Liu et al. (2016), the status of development in a secondstage analysis for DEA studies has left practitioners with some confusion
about the true use of methodology. Although the abovementioned evidence suggests the use of OLS, many research works have also used
truncated regression with a bootstrapping approach for robustness
checks. Thus, we perform both approaches in our regression model.
In this study, the regression results are adjusted for year-specific and
country-specific effects. Specifically, we employ panel data estimation
procedures, which adjust for the time-series and cross-sectional effects.
Note that the Breusch-Godfrey serial correlation Lagrange multiplier
(LM) test suggests that panel data regression is a better estimation technique as compared to pooled regression in this study, while the
Y.-C. Wu et al. / Economic Modelling 53 (2016) 156–165
Hausman test indicates that fixed-effects model (FEM), instead of
random-effects model (REM), should be applied.
Therefore, we employ the fixed-effects panel data regression model
to examine the relationship between earnings management and overall
efficiency. In this study, the following regression models are run to test
the hypotheses:
X
OEit ¼ α 0 þ α 1 LLPLit þ α 2 LLRLit þ α 3 LNASSETSit þ α 4 GROW it þ α 5 LIABit þ þ
Yri
X
þ
Countryi þ εit
ð5Þ
where:
The non-oriented DNSBM overall efficiency score based on
variable returns to scale in year t.
Loan loss provisions scaled by loans in year t.
LLPLit
Loan loss reserves scaled by loans in year t.
LLRPit
LNASSETSit The natural logarithm of total assets in year t.
GROWit The growth rate of net income in year t.
Total liabilities scaled by total assets in year t.
LIABit
Year-specific effect dummy variables.
∑Yri
∑Countryi Country-specific effect dummy variables.
OEit
Based on Adams et al. (2009), we argue that earnings management
in banks commonly happens through loan loss provisions and loan
loss reserves due to the nature of their discretionary choices. The intuition is that increases in loan loss reserves and/or loan loss provisions
would mean decreases in net income and definitely the ratio of earnings
to assets. Therefore, the book value of equity is also reduced because
loan loss provisions and loan loss reserves flow through the financial
statements. LNASSETSit is used to control for the firm size of the sample
banks, while GROWit is a measure to control for the growth opportunities of banks. LIABit could serve as a proxy of individual bank risktaking. If bank managers engage in earning management, we would expect to have significantly negative coefficients on α1 and α2 because the
good-looking earnings today have to be paid off in the future, in which
case, it would be shown in this study through the dynamic performance
measure that is measured over a long-term period.
4. Empirical findings and discussion
4.1. Dynamic network performance analysis
Table 3 shows the results of the dynamic network DEA model for
banking institutions in the ASEAN region. Specifically, the table presents
both yearly and averages of overall, managerial, and profitability efficiency scores. While the overall efficiency of ASEAN member countries
seems to drop during 2008 to 2012 and then increases in 2013, the
Singaporean banking sector tends to swim against the tide. Also,
Singaporean banks appear to be more efficient in terms of overall efficiency followed by Cambodia and Malaysia, with average scores of
0.622, 0.511, and 0.421, respectively. The overall efficiency of the total
sample shows a monotonic decrease over the period with 62.8 per
cent room for improvement on average.
In addition, Table 3 also provides the breakdown of overall efficiency
into managerial and profitability efficiencies. In the first division, i.e.
managerial efficiency, we can see fluctuating trends in countries’ banking performance; however, the total sample average is again at declining trend. Although the managerial efficiency of ASEAN banking
institutions suggests the poor performance of banking sectors in the region, it happens to be more efficient when compared to profitability efficiency. The results indicate the slightly better performance of
managerial division, particularly in the last three years, pointing that
most countries have enhanced their capabilities to manage the human
resources but have failed to create salient profit-making capacities
using their managerial abilities. Nonetheless, there exists large room
for improvement in both managerial and profitability efficiencies. For
161
Table 3
Overall, managerial, and profitability efficiencies of banks in ASEAN countries for the period of 2008–2013.
Country
Overall efficiency
Brunei
Cambodia
Indonesia
Laos
Malaysia
Philippines
Singapore
Thailand
Vietnam
Total sample
Managerial efficiency
Brunei
Cambodia
Indonesia
Laos
Malaysia
Philippines
Singapore
Thailand
Vietnam
Total sample
Profitability efficiency
Brunei
Cambodia
Indonesia
Laos
Malaysia
Philippines
Singapore
Thailand
Vietnam
Total sample
2008
2009
2010
2011
2012
2013
Average
0.295
0.693
0.484
0.587
0.620
0.319
0.585
0.357
0.387
0.450
0.210
0.559
0.466
0.407
0.519
0.289
0.630
0.311
0.372
0.417
0.215
0.553
0.451
0.465
0.375
0.284
0.609
0.322
0.369
0.410
0.201
0.511
0.409
0.342
0.337
0.249
0.603
0.318
0.312
0.373
0.132
0.353
0.324
0.227
0.259
0.217
0.690
0.296
0.242
0.311
0.160
0.398
0.311
0.282
0.417
0.240
0.614
0.288
0.245
0.310
0.202
0.511
0.408
0.385
0.421
0.266
0.622
0.315
0.321
0.378
0.239
0.751
0.425
0.654
0.881
0.229
0.694
0.287
0.482
0.430
0.234
0.638
0.448
0.561
0.833
0.246
0.724
0.296
0.452
0.430
0.239
0.639
0.426
0.473
0.648
0.236
0.742
0.306
0.416
0.414
0.267
0.696
0.429
0.509
0.602
0.233
0.756
0.305
0.359
0.411
0.273
0.726
0.398
0.527
0.765
0.212
0.739
0.266
0.315
0.387
0.254
0.639
0.328
0.502
0.772
0.191
0.694
0.263
0.318
0.346
0.251
0.681
0.409
0.538
0.750
0.225
0.725
0.287
0.390
0.403
0.350
0.642
0.549
0.524
0.390
0.408
0.529
0.426
0.310
0.478
0.186
0.479
0.486
0.293
0.308
0.331
0.580
0.326
0.312
0.411
0.190
0.473
0.476
0.459
0.190
0.331
0.572
0.341
0.331
0.415
0.141
0.330
0.392
0.189
0.133
0.263
0.572
0.334
0.268
0.344
0.030
0.161
0.312
0.087
0.120
0.232
0.680
0.327
0.211
0.289
0.067
0.202
0.314
0.134
0.087
0.288
0.596
0.316
0.187
0.293
0.161
0.381
0.421
0.281
0.205
0.309
0.588
0.345
0.270
0.372
instance, bank institutions in ASEAN region can improve their managerial and profitability efficiencies by 59.7 per cent and 62.8 per cent, respectively, in order to be fully efficient. On average, the statistical
findings5 indicate the superior efficiency of Singaporean and
Malaysian banks; that is to say, the highest overall and profitability efficiencies are attributed to Singapore being at 62.2 per cent and 58.8 per
cent, respectively, and 75 per cent as the largest managerial efficiency
score is related to Malaysia. Yet Bruneian and the Philippine banking
sectors are ranked as the most inefficient members of ASEAN coalition
in which the former scored the least in overall and profitability efficiency and the latter scored the least in managerial efficiency.
The unreported results show that only one bank is found to be nonoriented efficient. This high level of inefficiency encourages us to report
the frontier projections. The potential improvements for inefficient
DMUs are determined based on the banks on the efficient frontier, i.e.
benchmark units (Avkiran, 2014b). Table 4 provides the average excess
and shortage of each variable for all member countries. A positive percentage implies the shortage of resources (inputs) and a negative percentage implies the excess of resources (outputs).
The findings in Table 4 suggest that the ASEAN members on average
have to cut their personnel expenses and other operating expenses by
59.4 per cent and 59 per cent, respectively. The carry-overs are approximately the same as the primary inputs for banks in which these two
input quantities have to be reduced by 60.7 per cent (fixed assets) and
59.9 per cent (liquid assets). The three intermediates act as dual-role
variables in production process; meaning that they are outputs for the
first division and inputs for the second division. Consequently, the
5
This study applied Kruskal–Wallis test, a non-parametric statistical analysis, to examine whether differences exist among efficiency performance of countries in the region. For
brevity purpose, we did not report the table. As the significance level of 1%, we prove that
there is a significant difference among ASEAN countries in terms of efficiency scores.
162
Y.-C. Wu et al. / Economic Modelling 53 (2016) 156–165
Table 4
Frontier projections for banks in ASEAN countries (%).
Input
Carry-over
Intermediate
Output
Country
X2
X3
X1
X4
Z1
Z2
Z3
Y1
Brunei
Cambodia
Indonesia
Laos
Malaysia
Philippines
Singapore
Thailand
Vietnam
Total sample
−75.2
−25.5
−60.5
−39.9
−39.6
−75.0
−31.6
−69.8
−60.9
−59.4
−73.7
−29.8
−56.6
−44.1
−12.4
−79.8
−32.7
−71.8
−60.3
−59.0
−75.8
−40.3
−60.2
−54.8
−22.9
−77.8
−18.2
−72.2
−61.6
−60.7
−83.4
−56.8
−55.6
−65.7
−68.5
−68.1
−30.5
−63.9
−69.5
−59.9
−60.9
−27.7
−37.1
−26.6
−30.7
−49.4
−9.6
−59.2
−48.2
−41.3
11.2
6.6
10.3
7.2
5.1
−25.7
−15.5
−29.3
−11.1
−5.6
−72.0
−23.2
−37.3
−25.5
−41.0
−61.0
−30.4
−54.7
−54.5
−44.4
8.8
21.3
19.3
32.0
69.1
7.1
101.3
6.9
18.5
21.1
Note:
Personnel expenses (X2); other operating expenses (X3); fixed assets (X1); liquid assets (X4); loans (Z1); other earning assets (Z2); deposits (Z3); net interest income (Y1).
Negative: Excess of resources.
Positive: Shortage of resources.
suggestions on potential improvements are mixed for these variables.
For instance, other earning assets have to be increased for Brunei,
Cambodia, Indonesia, Laos, and Malaysia while it has to be decreased
for the Philippines, Singapore, Thailand, and Vietnam. In summary,
ASEAN banks could be efficient if they can increase their net interest
income on average, as the only output, by 21.1 per cent while
performing the required changes in inputs, carry-overs, and intermediate variables.
In order to get more insights into the sources of inefficiencies,
Table 5 provides the frontier projections for banking sectors in each
Table 5
Frontier projections by year (%).
Panel A: Input and Output
Country
Brunei
Cambodia
Indonesia
Laos
Malaysia
Philippines
Singapore
Thailand
Vietnam
X2
X3
Y1
2008
2009
2010
2011
2012
2013
2008
2009
2010
2011
2012
2013
2008
2009
2010
2011
2012
2013
−76.7
−23.2
−58.8
−37.2
−35.8
−74.0
−35.6
−67.5
−52.7
−77.4
−31.6
−55.8
−45.3
−50.0
−73.4
−31.3
−66.4
−56.2
−81.2
−37.7
−60.6
−47.5
−41.8
−75.3
−27.6
−67.5
−60.8
−75.7
−24.2
−61.5
−40.1
−58.1
−75.8
−29.6
−70.4
−67.0
−69.7
−15.8
−60.5
−37.1
−27.1
−75.6
−28.1
−73.0
−68.5
−70.6
−20.3
−66.1
−32.0
−24.8
−76.0
−37.4
−73.7
−60.7
−72.0
−22.4
−57.8
−34.8
0.0
−79.9
−42.9
−76.1
−49.9
−76.2
−40.2
−56.2
−39.2
0.0
−80.0
−33.4
−75.8
−54.3
−76.7
−30.7
−55.8
−56.0
−42.1
−80.6
−31.9
−72.0
−57.7
−72.5
−30.1
−50.6
−49.1
−17.9
−76.9
−27.2
−65.5
−64.2
−70.4
−17.1
−53.1
−37.0
0.0
−78.9
−28.5
−70.9
−66.0
−74.2
−38.5
−65.9
−48.2
−14.5
−82.5
−32.5
−70.7
−69.8
0.0
5.1
50.3
2.5
13.7
6.1
46.6
1.8
15.6
0.0
0.4
3.9
26.8
48.5
0.0
37.3
0.0
12.1
0.0
3.8
2.7
6.7
47.9
0.1
118.6
6.7
17.5
12.2
2.7
2.1
8.2
30.5
7.1
167.4
5.4
3.9
40.3
92.8
43.7
100.2
266.3
29.1
114.9
17.0
48.6
0.0
23.1
12.9
47.5
7.4
0.0
123.0
10.4
13.1
2008
2009
2010
2011
2012
2013
2008
2009
2010
2011
2012
2013
−79.7
−29.1
−56.0
−31.9
0.0
−77.2
−13.4
−70.3
−52.9
−76.2
−36.8
−53.6
−47.3
0.0
−72.6
−18.1
−69.0
−53.8
−70.4
−40.0
−55.7
−54.7
−21.7
−73.3
−18.0
−68.6
−56.7
−71.7
−36.9
−59.1
−58.1
−43.3
−77.4
−16.2
−72.5
−61.1
−77.8
−49.4
−67.0
−67.8
−43.4
−82.1
−21.8
−76.2
−71.0
−79.2
−49.5
−69.7
−69.2
−29.2
−84.0
−21.9
−76.7
−74.2
−65.0
−32.3
−42.8
−46.4
−55.6
−58.3
−35.3
−56.7
−65.4
−81.4
−51.7
−49.9
−57.7
−54.3
−66.9
−37.4
−67.4
−65.0
−81.0
−51.6
−51.5
−51.0
−71.9
−66.8
−32.7
−63.9
−62.2
−84.2
−65.8
−60.2
−79.5
−82.6
−72.3
−26.9
−65.7
−72.7
−95.7
−66.7
−62.9
−80.6
−55.9
−73.1
−25.5
−63.1
−72.6
−93.4
−73.0
−66.6
−79.3
−90.7
−71.2
−25.3
−66.5
−79.1
2008
2009
2010
2011
2012
2013
2008
2009
2010
2011
2012
2013
2008
2009
2010
2011
2012
2013
−39.7
39.5
−20.3
19.2
−33.1
−38.0
−15.5
−55.5
−15.2
−47.8
−22.1
5.2
51.0
−18.3
−38.8
−24.5
−54.5
−50.1
−54.7
−13.8
−35.8
−36.8
−59.0
−41.0
−6.5
−46.2
−50.2
−38.7
−29.9
−45.0
−35.9
−65.5
−44.8
−8.3
−62.9
−44.6
−92.4
−56.0
−60.7
−66.8
33.7
−64.2
−9.5
−67.9
−58.3
−92.3
−83.7
−65.9
−90.4
−41.9
−69.3
6.5
−68.1
−70.5
−1.8
0.0
−2.9
−1.9
0.9
−27.6
−16.1
−30.2
−8.5
−1.1
0.1
−0.2
−2.1
2.1
−30.3
−19.0
−32.7
−9.5
2.1
0.5
7.4
−1.6
6.9
−30.9
−21.3
−27.9
−17.0
7.6
2.7
26.7
−1.0
4.9
−22.4
−16.5
−28.5
−18.1
28.9
17.7
17.9
20.2
5.7
−20.4
−10.8
−28.8
−9.3
31.4
18.4
12.9
29.5
10.2
−22.6
−9.4
−27.8
−4.4
−64.7
40.6
−28.7
−29.2
−27.8
−53.0
−41.4
−54.0
−50.3
−62.6
−8.0
−5.9
55.7
−33.7
−51.9
−34.4
−51.9
−40.1
−58.7
−29.3
−34.2
−7.9
−56.6
−54.8
−29.5
−28.3
−55.2
−72.6
−39.3
−49.1
−48.5
−75.6
−63.9
−28.0
−61.6
−59.9
−93.2
−54.1
−58.1
−66.9
−2.0
−70.7
−24.3
−66.4
−66.5
−80.1
−48.9
−47.5
−56.0
−50.4
−71.4
−25.1
−66.2
−55.2
Panel B: Carry−over
Country
Brunei
Cambodia
Indonesia
Laos
Malaysia
Philippines
Singapore
Thailand
Vietnam
X1
X4
Panel C: Intermediate
Country
Brunei
Cambodia
Indonesia
Laos
Malaysia
Philippines
Singapore
Thailand
Vietnam
Z1
Z2
Z3
Note:
Personnel expenses (X2); other operating expenses (X3); fixed assets (X1); liquid assets (X4); loans (Z1); Other earning assets (Z2); Deposits (Z3); Net interest income (Y1).
Negative, excess of resources; positive, shortage of resources.
Y.-C. Wu et al. / Economic Modelling 53 (2016) 156–165
country segregated by years. The table illustrates three panels including
input and output, carry-over and intermediate variables for the period
of 2008 to 2013. Singapore, a leading country in terms of overall efficiency score (Table 3), appears to be more efficiently using personnel
expenses along with Cambodia where the country stands second in
her overall efficiency score. The efficient usage of an input factor in performance analysis is one of the main factors to attain higher efficiency.
Malaysia has also shown tolerable usage of personnel expenses with
her overall efficiency score being ranked among the top three countries.
Indeed, Malaysia performed greater than other countries in consuming
other operating expenses as an input factor. Moreover, the carry-over
input (fixed assets) consumed by top three countries in terms of overall
efficiency scores seems to be efficiently managed as compared to other
countries. For example, Singaporean banks have to reduce their fixed
assets quantities by 21.9 per cent in 2013 while the Philippines has to
cut down this carry-over item by 84 per cent in the same year. Less efficient countries, namely, Brunei and the Philippines are suffering
from over-utilization of the two input factors as well as the carry-over
item in the first stage, dragging down both the overall efficiency and
managerial efficiency scores to be the least among other countries in
the region. Recall that the results of Table 3 ranked Malaysia,
Singapore and Cambodia as the leading countries, respectively, and
Brunei and the Philippines as being the low-performing nations in
terms of managerial efficiency. Apparently, the excess of the input factors and the carry-over item are in line with the managerial efficiency
rankings of the countries.
The intermediate variables are the outputs of the first stage and the
inputs of the second stage. Hence, the variations in terms of excess and
shortage of the factors are obtained to achieve the optimum solutions
for both managerial and profitability efficiency stages. In the majority
of cases, banking sectors have to reduce their loans and deposits quantities to attain higher efficiency; however, the results for other earnings
assets are mixed where, for example, Malaysian banking sector has to
target on increasing, and Singaporean banking sector has to focus on reducing this intermediate factor. The substantial excess in the usage of
the carry-over item in the second stage, liquid assets, discloses one of
the main causes for inefficiency of Malaysian banking sector in profitability efficiency obtained in Table 3. Singaporean banks, however, appeared to utilize the liquid assets more efficiently as compared to
other banks in the region and the trend shows improving throughout
the years. Finally, the results indicate the inefficiencies associated with
less efficient banking sectors in the sample, for example, Brunei and
the Philippines, are not induced by net interest income, the final output.
However, the Singaporean banking sector, among the leading sectors in
both managerial and profitability stages, need to substantially increase
her output quantity in order to be even more efficient.
4.2. The relationship between earnings management and efficiency
Banking institutions, which contribute much to economic developments, are susceptible to EM practices (Grougiou et al., 2014) due to
complicated operation that usually cause information opacity (Levine,
2004) and information asymmetry (Mülbert, 2009). Therefore, as mentioned earlier, this study examines the impacts of earnings management
on efficiency through regression analysis. Before we run any regression
analysis, we test the potential problem caused by multicollinearity. The
diagnostic test of variance inflation factors (VIF), which is not reported,
suggests that multicollinearity problems do not exist in this study,
whereby the centered VIF values are all less than 1.5 (Kennedy, 1998).
We also perform the diagnostic test of potential heteroskedasticity for
the regression residuals, and we find evidence of heteroskedasticity.
Therefore, the p-values in Table 6 are corrected using White crosssection standard errors. The F-statistics indicate that Eq. (1) is statistically significant.
The results in Table 6 indicate that loan loss provisions (LLPL) are significantly and negatively related to the dynamic overall efficiency of
163
Table 6
Regression results.
FEM
Truncated regression
Variable
Coefficient Standard P-value Coefficient Standard P-value
error
error
Intercept
LLPL
LLRP
LNASSETS
GROW
LIAB
Year dummies
Country
dummies
Adjusted R2
F-statistic
Log-likelihood
1.402***
−0.200***
0.243
−0.144***
0.011***
0.058
Yes
Yes
0.131
0.031
0.152
0.014
0.002
0.056
0.000
0.000
0.110
0.000
0.000
0.308
0.804***
−0.682*
0.493***
−0.028***
0.011***
−0.284***
0.063
0.378
0.100
0.006
0.003
0.057
Yes
Yes
0.000
0.071
0.000
0.000
0.000
0.000
0.838
31.229***
193.026
Note:
*, **, and *** denote the statistical significance at the 10%, 5%, and 1% level, respectively.
banks in ASEAN countries, suggesting that earnings management
today will be paid off in dynamic performance in the long term, consistent with our prediction. However, the positive coefficient on loan loss
reserves (LLRP) does not reach the conventional significance level. For
another sensitivity analysis, we estimate Eq. (1) using truncated regression, following Lu et al. (2014). The truncated regression results remain
almost qualitatively the same as those of the FEM. In summary, we find
that earnings management engaged by bank managers could be observed from loan loss provisions rather than loan loss reserves from
the perspective of dynamic performance.
4.3. Discussions
The main impetus of this study is financial liberalizations and institutional reforms in the ASEAN countries, which would help in promoting efficient allocation of resources in their banking sectors. This could
assist the sectors in achieving a greater economic growth through the
strengthened regulatory and supervisory frameworks. The borderless
business world has created the situation whereby ASEAN banks need
to be able not only to survive locally, but also to compete internationally.
In this regard, performance measurement of banks has since become an
important subject of study. Specifically, the relative efficiency of banks
in ASEAN countries is an important question to be answered. By
performing efficiency analysis on banks in ASEAN countries through
DEA, we are able to reveal their relative competitiveness from a multidimensional perspective. A clear implication of this assessment on applied
economics studies is that we scrutinize banking performance in the aspects of managerial efficiency and profitability efficiency over longterm periods. Before next initiatives to improve economic growth are
introduced, economists who are decision makers should first examine
how well the human capital in the countries performs in terms of managerial efficiency. They should next look at the individual but relative
performance of banks in the region before ultimately discussing economic growth. In other words, they should work on improving the fundamental issue of managerial and profitability efficiencies of ASEAN
banks.
The current paper finds that banks in certain countries should focus
on first improving their managerial efficiencies. The frontier projections
provide some ideas on how economists may look at the allocation of resources in their banking sectors, which would ultimately result in economic growth. It is also important to highlight the possibility of
window-dressed performance by the banks in ASEAN countries. As
discussed earlier, managers in the banking sector nowadays might
have greater tendency to dress up their corporate performance due to
the intensified competition in the region. Economists who intend to
promote economic prosperity in their countries may overlook this matter, namely, earnings management. A manipulated financial report
164
Y.-C. Wu et al. / Economic Modelling 53 (2016) 156–165
might, in the end, bring disastrous financial catastrophe to the company,
and if the company is large enough, the world economy will be affected.
The most relevant finding of this study is the significantly negative impacts of earnings management practices on the banking performance of
ASEAN banks.
Taken together, economists may consider using the DN-DEA model
to assess banking performance because it allows researchers to incorporate multiple variables in the performance measurement. Moreover, the
‘black-box’ of banking performance is revealed, while considering dynamism in banking performance over long-term periods.
5. Conclusions
This study aims to address two key questions in the literature of
bank efficiency. Firstly, we provide a unique example of the application
of the DN-DEA model in banking that is still in its embryonic stage. More
specifically, we decompose the efficiency of ASEAN banking institutions
into managerial efficiency and profitability efficiency using the newly
developed DN-DEA model, viz. DNSBM. Secondly, we provide an empirical answer to the important question that what the effect of any EM
practices is on the efficiency of firms.
For the first objective, the findings reveal that there exists large room
for improvement in the efficiency domain of ASEAN banks. This will accentuate the need for a better policy formulation in the region in boosting
the banking sectors upwards. Indeed, the role of ASEAN alliance could be
very influential to achieve this goal. Likewise, the monotonic decrease in
the overall and divisional efficiencies of the total sample is alarming.
Among all, the overall performance of Singaporean banking sector outstrips that of any other banking sectors in ASEAN region. In detail,
Singapore is ranked first in profitability division and second in managerial
division. Meanwhile, the poor performance of Malaysian banking sector
on profitability efficiency drags down its overall efficiency despite its
spectacular managerial efficiency scores. In order to determine the reasons behind the inefficiencies of ASEAN banks, we investigate the potential areas of improvement relevant to input and output variables. The
results suggest the equal attention should be given to carry-overs as
well as to inputs where the ASEAN banks have to reduce the input and
carry-over variables by approximately 60 per cent.
For the second objective, we find a significant negative relationship
between loan loss provisions and dynamic performance of ASEAN
banks. However, the loan loss reserves could not satisfy our proposed
hypothesis in determining the EM practices of banking institutions.
Therefore, we argue that loan loss provisions is an appropriate proxy
for banks’ EM practices, which significantly dampen the dynamic efficiency of ASEAN banks.
All in all, more research needs to be done to confirm the generalizability of our results. Indeed, the new approach developed in this
study on the relationship between EM practices and performance demands a global appeal. It can be applied to those banking sectors
where the sources of inefficiencies are unknown. We leave it to future
studies to examine further earnings management in banks through
other proxies when more data are available in the Bankscope database
to the public. Future studies should also include more bank-level and
country-level control variables. Dummies on country-level controls
may not be adequate to account for country-specific heterogeneity. Another important area for future research would be to compare various
methodologies developed in the domain of DN-DEA among different
banking institutions like Islamic banks. Particularly, rather than assuming a common frontier in cross-country samples, researchers may develop a meta-frontier DN-DEA to estimate the performance of ASEAN
banks or even world banks.
Acknowledgement
We sincerely thank two anonymous reviewers for their constructive
comments on an earlier version of this study.
References
Adams, B., Carow, K.A., Perry, T., 2009. Earnings management and initial public offerings:
the case of the depository industry. J. Bank. Financ. 33, 2363–2372.
ADB, 2011. Financial sector operational plan. ADB Administration and Governance. Asian
Development Bank.
Avkiran, N.K., 2009. Opening the black box of efficiency analysis: an illustration with UAE
banks. Omega 37, 930–941.
Avkiran, N.K., 2011. Association of DEA super-efficiency estimates with financial ratios:
investigating the case for Chinese banks. Omega 39, 323–334.
Avkiran, N.K., 2014a. An illustration of dynamic network DEA in commercial banking including robustness tests. Omega 55, 141–150.
Avkiran, N.K., 2014b. Probing organizational efficiency. Int. J. Organ. Anal. 22, 149–160.
Avkiran, N.K., Thoraneenitiyan, N., 2010. Purging data before productivity analysis. J. Bus.
Res. 63, 294–302.
Baik, Y.-S., Kwak, B., Lee, J., 2011. Deregulation and earnings management: the case of the
U.S. airline industry. J. Account. Public Policy 30, 589–606.
Banker, R.D., Natarajan, R., 2008. Evaluating contextual variables affecting productivity
using data envelopment analysis. Oper. Res. 56, 48–58.
Barth, J.R., Lin, C., Ma, Y., Seade, J., Song, F.M., 2013. Do bank regulation, supervision and
monitoring enhance or impede bank efficiency? J. Bank. Financ. 37, 2879–2892.
Becchetti, L., Sierra, J., 2003. Bankruptcy risk and productive efficiency in manufacturing
firms. J. Bank. Financ. 27, 2099–2120.
Beneish, M.D., Press, E., Vargus, M.E., 2012. Insider trading and earnings management in
distressed firms. Contemp. Account. Res. 29, 191–220.
Berg, S.A., Førsund, F.R., Hjalmarsson, L., Suominen, M., 1993. Banking efficiency in the
Nordic countries. J. Bank. Financ. 17, 371–388.
Berger, A.N., Humphrey, D.B., 1997. Efficiency of financial institutions: international survey and directions for future research. Eur. J. Oper. Res. 98, 175–212.
Bhattacharyya, A., Lovell, C.A.K., Sahay, P., 1997. The impact of liberalization on the productive efficiency of Indian commercial banks. Eur. J. Oper. Res. 98, 332–345.
Bolt, W., De Haan, L., Hoeberichts, M., Van Oordt, M.R., Swank, J., 2012. Bank profitability
during recessions. J. Bank. Financ. 36, 2552–2564.
Charnes, A., Cooper, W., Rhodes, E., 1978. Measuring the efficiency of decision making
units. Eur. J. Oper. Res. 2, 429–444.
Chiu, Y.-H., Chen, Y.-C., 2009. The analysis of Taiwanese bank efficiency: incorporating
both external environment risk and internal risk. Econ. Model. 26, 456–463.
Cook, W.D., Seiford, L.M., 2009. Data envelopment analysis (DEA)–thirty years on. Eur.
J. Oper. Res. 192, 1–17.
Cooper, W., Park, K., Pastor, J., 1999. RAM: a range adjusted measure of inefficiency for use
with additive models, and relations to other models and measures in DEA. J. Prod.
Anal. 11, 5–42.
Cornett, M.M., McNutt, J.J., Tehranian, H., 2009. Corporate governance and earnings management at large U.S. bank holding companies. J. Corp. Financ. 15, 412–430.
Dang-Thanh, N., 2012. Measuring the performance of the banking system: case of
Vietnam (1990–2010). J. Appl. Financ. Bank. 2, 289–312.
Dong, Y., Hamilton, R., Tippett, M., 2014. Cost efficiency of the Chinese banking sector: a
comparison of stochastic frontier analysis and data envelopment analysis. Econ.
Model. 36, 298–308.
Elyasiani, E., Mehdian, S.M., 1990. A nonparametric approach to measurement of efficiency and technological change: the case of large US commercial banks. J. Financ. Serv.
Res. 4, 157–168.
Emrouznejad, A., Parker, B.R., Tavares, G., 2008. Evaluation of research in efficiency and
productivity: a survey and analysis of the first 30 years of scholarly literature in
DEA. Socio Econ. Plan. Sci. 42, 151–157.
Färe, R., Grosskopf, S., 1996. Intertemporal production frontiers: with dynamic DEA.
Kluwer Academic Publishers, Boston.
Fukuyama, H., Weber, W.L., 2013. A dynamic network DEA model with an application to
Japanese Shinkin banks. Efficiency and Productivity Growth. John Wiley & Sons, Ltd,
pp. 193–213.
García Lara, J.M., Osma, B.G., Neophytou, E., 2009. Earnings quality in ex‐post failed firms.
Account. Bus. Res. 39, 119–138.
Gardener, E., Molyneux, P., Nguyen-Linh, H., 2011. Determinants of efficiency in South
East Asian banking. Serv. Ind. J. 31, 2693–2719.
Greenawalt, M.B., Sinkey Jr., J.F., 1988. Bank loan-loss provisions and the incomesmoothing hypothesis: An empirical analysis, 1976-1984. J. Financ. Serv. Res. 1,
301–318.
Grougiou, V., Leventis, S., Dedoulis, E., Owusu-Ansah, S., 2014. Corporate social responsibility and earnings management in U.S. banks. Account. Forum 38, 155–169.
Healy, P.M., Wahlen, J.M., 1999. A review of the earnings management literature and its
implications for standard setting. Account. Horiz. 13, 365–383.
Kao, C., 2009. Efficiency decomposition in network data envelopment analysis: a relational model. Eur. J. Oper. Res. 192, 949–962.
Kao, C., Liu, S.-T., 2014. Multi-period efficiency measurement in data envelopment analysis: the case of Taiwanese commercial banks. Omega 47, 90–98.
Karim, M.Z.A., 2001. Comparative bank efficiency across select ASEAN countries. ASEAN
Econ. Bull. 289–304.
Kennedy, P., 1998. A Guide to Econometrics. MIT Press, Cambridge.
Laeven, L., 1999. Risk and efficiency in East Asian banks. World Bank Policy Research
Working Paper No. 2255. World Bank, Washington, DC.
Levine, R., 2004. The corporate governance of banks: a concise discussion of concepts and
evidence. World Bank Publications.
Lim, S., 2005. Foreign capital entry in the domestic banking market of Korea. Korean Polit.
Sci. Rev. 39, 189–209.
Lin, T.-Y., Chiu, S.-H., 2013. Using independent component analysis and network DEA to
improve bank performance evaluation. Econ. Model. 32, 608–616.
Y.-C. Wu et al. / Economic Modelling 53 (2016) 156–165
Liu, J.S., Lu, L.Y., Lu, W.-M., Lin, B.J., 2013. A survey of DEA applications. Omega 41,
893–902.
Liu, J.S., Lu, L.Y.Y., Lu, W.-M., 2016. Research fronts in data envelopment analysis. Omega
58, 33–45.
Lo, S.-F., Lu, W.-M., 2006. Does size matter? Finding the profitability and marketability
benchmark of financial holding companies. Asia Pac. J. Oper. Res. 23, 229–246.
Lu, W.-M., Wang, W.-K., Kweh, Q.L., 2014. Intellectual capital and performance in the Chinese life insurance industry. Omega 42, 65–74.
Lu, W.-M., Kweh, Q.L., Nourani, M., Huang, F.-W., 2015. Evaluating the efficiency of dualuse technology development programs from the R&D and socio-economic perspectives. Omega, http://dx.doi.org/10.1016/j.omega.2015.08.011.
Luo, X., 2003. Evaluating the profitability and marketability efficiency of large banks: An
application of data envelopment analysis. J. Bus. Res. 56, 627–635.
McDonald, J., 2009. Using least squares and tobit in second stage DEA efficiency analyses.
Eur. J. Oper. Res. 197, 792–798.
McKinnon, R.I., 1973. Money and Capital in Economic Development. Brookings Institution, Washington DC.
Miller, S.M., Noulas, A.G., 1996. The technical efficiency of large bank production. J. Bank.
Financ. 20, 495–509.
Moradi-Motlagh, A., Babacan, A., 2015. The impact of the global financial crisis on the efficiency of Australian banks. Econ. Model. 46, 397–406.
Mülbert, P.O., 2009. Corporate governance of banks. Eur. Bus. Org. Law Rev. 10, 411–436.
Parkan, C., 1987. Measuring the efficiency of service operations: an application to bank
branches. Eng. Costs Prod. Econ. 12, 237–242.
Pasiouras, F., 2008. International evidence on the impact of regulations and supervision
on banks’ technical efficiency: an application of two-stage data envelopment analysis.
Rev. Quant. Finan. Acc. 30, 187–223.
Quiggin, J., 2011. What have we learned from the global financial crisis? Aust. Econ. Rev.
44, 355–365.
Rangan, N., Grabowski, R., Aly, H.Y., Pasurka, C., 1988. The technical efficiency of US banks.
Econ. Lett. 28, 169–175.
Reinhart, C.M., Tokatlidis, I., 2003. Financial liberalisation: the African experience. J. Afr.
Econ. 12, ii53–ii88.
Seiford, L.M., Zhu, J., 1999. Profitability and marketability of the top 55 US commercial
banks. Manag. Sci. 45, 1270–1288.
165
Shaw, E., 1973. Financial deepening in economic development. Oxford University Press,
New York.
Sherman, H.D., Gold, F., 1985. Bank branch operating efficiency: evaluation with data envelopment analysis. J. Bank. Financ. 9, 297–315.
Simar, L., Wilson, P.W., 2007. Estimation and inference in two-stage, semi-parametric
models of production processes. J. Econ. 136, 31–64.
Sturm, J.-E., Williams, B., 2004. Foreign bank entry, deregulation and bank efficiency: lessons from the Australian experience. J. Bank. Financ. 28, 1775–1799.
Sueyoshi, T., Sekitani, K., 2009. An occurrence of multiple projections in DEA-based measurement of technical efficiency: theoretical comparison among DEA models from
desirable properties. Eur. J. Oper. Res. 196, 764–794.
Thanassoulis, E., 1999. Data envelopment analysis and its use in banking. Interfaces 29,
1–13.
Tone, K., 2001. A slacks-based measure of efficiency in data envelopment analysis. Eur.
J. Oper. Res. 130, 498–509.
Tone, K., Tsutsui, M., 2009. Network DEA: a slacks-based measure approach. Eur. J. Oper.
Res. 197, 243–252.
Tone, K., Tsutsui, M., 2010. Dynamic DEA: a slacks-based measure approach. Omega 38,
145–156.
Tone, K., Tsutsui, M., 2014. Dynamic DEA with network structure: a slacks-based measure
approach. Omega 42, 124–131.
Wang, W.-K., Lu, W.-M., Liu, P.-Y., 2014. A fuzzy multi-objective two-stage DEA model for
evaluating the performance of US bank holding companies. Expert Syst. Appl. 41,
4290–4297.
Wanke, P., Barros, C.P., Faria, J.R., 2014. Financial distress drivers in Brazilian banks: a dynamic slacks approach. Eur. J. Oper. Res. 240 (1), 258–268.
Williams, J., Nguyen, N., 2005. Financial liberalisation, crisis, and restructuring: a comparative study of bank performance and bank governance in South East Asia. J. Bank.
Financ. 29, 2119–2154.
Yang, C., Liu, H.-M., 2012. Managerial efficiency in Taiwan bank branches: a network DEA.
Econ. Model. 29, 450–461.
Yeh, Q.-J., 1996. The application of data envelopment analysis in conjunction with financial ratios for bank performance evaluation. J. Oper. Res. Soc. 980–988.