Management Research Review
Productivity and Spillover effect of merger and acquisitions in Malaysia
Nai Chiek Aik, M. Kabir Hassan, Taufiq Hassan, Shamsher Mohamed,
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MRR
38,3
Productivity and Spillover effect
of merger and acquisitions
in Malaysia
320
Received 25 July 2013
Revised 7 January 2014
14 April 2014
Accepted 17 April 2014
Nai Chiek Aik
Department of Economics, University Tunku Abdul Rahman,
Selangor, Malaysia
M. Kabir Hassan
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Department of Economics and Finance, University of New Orleans,
New Orleans, Louisiana, USA
Taufiq Hassan
Department of Accounting and Finance, University Putra Malaysia,
Selangor, Malaysia, and
Shamsher Mohamed
International Center of Education in Islamic Finance (INCEIF),
Kuala Lumpur, Malaysia
Abstract
Purpose – This paper aims to examine the productivity and spillover effect of Malaysian horizontal
merger and acquisition (M&A) activities in the long run.
Design/methodology/approach – In terms of analytical tools, economic value added (EVA) and
data envelopment analysis (DEA) are used.
Findings – The results of this study reveal that M&As in the absence of antitrust laws could be driven
by managerial self-interest to create market power instead of realizing synergistic gains. Also, in
Malaysia, the non-merging rival firms have significantly higher productivity improvement than the
control bidder firms, and therefore, this study has identified the spillover effect as a behavior of M&A
reaction.
Originality/value – This paper differs from previous studies in that it attempts not only to examine
the real long-term gains of horizontal M&A activities in Malaysia but also the spillover effects of M&A
activities on similar but non-merging firms.
Keywords Malaysia, DEA, EVA, Government policy, Merger
Paper type Research paper
Management Research Review
Vol. 38 No. 3, 2015
pp. 320-344
© Emerald Group Publishing Limited
2040-8269
DOI 10.1108/MRR-07-2013-0178
Introduction
In free-market economies, agents (management) of a firm often seek synergistic gains
from merger and acquisitions (M&As) to enhance the value or wealth of shareholders for
merging firms. The value that has been anticipated and created is predominantly in the
horizontal acquisition of a rival (Fee and Thomas, 2004; Shahrur, 2005). This horizontal
acquisition may accrue from greater realization of economies of scale, improved
JEL classification – G21, D24
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productive efficiency, more efficient management, elimination of overlapping facilities,
the exploitation of market power or any number of value enhancing mechanisms that
fall under the general rubric of corporate synergy (Bradley et al., 1988). The
concentration of the corresponding industry tends to rise if the M&A involves the larger
firms in the industry. In the absence of antitrust merger laws in an emerging market
such as Malaysia, direct rivals have to respond aggressively to the M&A transactions
whose probable anticompetitive consequences outweigh their likely benefits. The
extensive M&A literature has largely focused on how M&A events affect performance
of bidder and target firm share prices in the short-term. However, it has paid relatively
less attention to the long-term impact of M&As and also the impact on non-merging
rival firms, especially in the case of horizontal M&As. The documented evidence of
M&A studies in Malaysia has been conducted predominantly in bank mergers that were
“involuntary” in nature and implemented right after the Asian financial crisis (Fauzias
and Mohamed, 2003; Mansor and Yap, 2003; Fauzias, 2004; Krishnasamy et al., 2004;
Mahmood and Mohamad, 2004; Fauzias et al., 2005; Sufian and Ibrahim, 2005; Rasidah
et al., 2008). Unlike voluntary M&As, which are mainly market-driven, involuntary
mergers are the result of direct government intervention, and hence, findings thus far
may not validate evidence of M&A events.
Studies of M&A so far have relied mostly on the average abnormal stock market
reactions. which assume that an efficient capital market could be used to gauge the
impact of these events (Jensen and Ruback, 1983; Jarrell et al., 1988; Rhoades, 1994;
Pilloff and Santomero, 1997; Shinn, 1999; Andrade et al., 2001; Feroz et al., 2005). In a
related study, Hafizah (2007) analyzed the performance of bidders, both listed and
non-listed targets, and found that bidder firms in Malaysia earn significantly larger
abnormal returns around the announcement day, and the announcement effect appears
to persist over longer periods before and after the event. However, this abnormal return
approach in the event study contradicts the efficient market hypothesis. If the market is
efficient in incorporating the assessment of the expected future economic gains of the
firm, there should be no abnormal performance in the long-term. The purpose of this
paper is to examine the long-term performance of the merging firms and non-merging
rival firms listed on the Kuala Lumpur Stock Exchange (KLSE) for the period of
1994-2004[1]. This paper differs from previous studies in that it attempts not only to
examine the real long-term gains of horizontal M&A activities in Malaysia but also the
spillover effects of M&A activities on similar but non-merging firms.
This paper is organized as follows. Section 2 reviews the theoretical background and
empirical findings on M&As. Section 3 describes the data and methodology. Section 4
contains summary of the results and discussion, and Section 5 concludes the paper.
2. Literature review
Past studies on the success or failure of M&As have focused primarily (although not
exclusively) on developed countries, particularly the USA and European acquisitions,
covering various aspects in three major ways, namely, dynamic efficiency, operating
performance and event studies. Dynamic efficiency studies draw on the rapid evolution
of frontier methodologies that incorporate both parametric and non-parametric
approaches by examining whether M&As improve the efficiency of merging firms after
M&As, whereas operating performance studies draw mainly on the traditional financial
ratio analysis to determine any significant changes in mostly performance-related ratios
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before and after M&A events. In principle, the event study measures the abnormal share
price changes of merging firms around the M&A announcement as an indicator of the
perceived economic effects of the M&A.
Although accounting profits and share prices are useful performance indicators, they
have been criticized for not accurately reflecting real changes of a firm in the long run,
especially when accounting measures are subject to manipulation (Berger and
Humphrey, 1994; DeYoung, 1997; Bauer et al., 1998; Berger et al., 1999; Kohers et al.,
2000) and the assumptions of efficient markets are violated. Healy et al. (1992) argue that
capital market studies have not been able to identify whether equity gains from M&A
deals are due to market inefficiency or real economic gains. Pautler (2003) argues that
the conventional stock market event analysis measurements of the net returns to the
bidder and target firms provide a prediction of expected gains or losses to the
shareholders of the merging firms, rather than evidence that the gains (or losses)
actually occurred. Similarly, Sung and Gort (2006) argued that the analysis of share
prices is a test of the market’s confidence in gains from merger. In this regard, a positive
relationship between mergers and share prices would not be convincing evidence of
expectations of merger gains. The absence of abnormal returns beyond the very short
run after the merger, however, is fairly strong evidence that synergies that must be
translated into performance gains beyond those that are already expected are not
realized given additional time.
The rapid evolution of frontier efficiency methodologies, which incorporates both
parametric (econometric) and non-parametric (linear programming) methods, has
distorted the traditional techniques into becoming obsolete in the study of firm
performance. Cummins and Weiss (1998) justify the test of economic hypotheses such as
the effects of M&As will not be convincing unless the involvement of one or more
frontier-based performance measures. They reassert the frontier measures dominate
over financial ratio analysis because they develop meaningful and reliable measures of
firm performance in a single statistic (for a given type of efficiency) that controls for
differences among firms using a sophisticated multidimensional framework that has
been rooted in economic theory.
Using the stochastic frontier approach (SFA), Al-Sharkas et al. (2008) investigated
the cost and profit efficiency effects of bank mergers on the US banking industry. We
also use the non-parametric technique of data envelopment analysis (DEA) to evaluate
the production structure of merged and non-merged banks. The empirical results
indicated that mergers have improved the cost and profit efficiencies of banks. Further,
evidence shows that merged banks have lower costs than non-merged banks because
they are using the most efficient technology available (technical efficiency) as well as a
cost minimizing input mix (allocative efficiency). The results suggested that there was
an economic rationale for future mergers in the banking industry. Finally, mergers may
allow the banking industry to take advantage of the opportunities created by improved
technology.
There is, however, no consensus on the best method or set of methods for measuring
frontier efficiency. It is, however, not necessary to have a consensus on which is the best
single frontier approach for measuring efficiency. The efficiency estimates derived from
the different approaches should be consistent in their efficiency levels, rankings and
identification of best and worst firms; consistent over time and with competitive
conditions in the market; and consistent with standard non-frontier measures of
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performance. Operating performance measure is one of the most standard non-frontier
measures based on accounting numbers and generally permits past performance as the
benchmark for future performance. Undeniably, the validity of these accounting
measures is still very much debatable. However, the concept of economic value added
(EVA), which strips away all the accounting assumptions built into earnings effectively,
overcomes the suspicious aspect of using operating performance in M&A studies.
There are many reasons why a firm will merge with, acquire or be acquired by
another firm. These reasons range from those predicting that M&As increase firm value
to those arguing that M&As reduce firm value. Three of the generally accepted theory
types for M&A are efficiency, agency problem and hubris hypothesis. The efficiency
theories present the fundamentals for M&A as value-maximizing decisions, whereas
managerial self-interest theories, which include agency problem and hubris, are
non-value-maximizing decisions. In an M&A motivated by efficiency or synergy, it is
expected that shareholders of both target and bidder firms will gain from the expected
economic gains. However, in an M&A motivated by managerial motives, the bidder
management uses the target to extract value from their own shareholders and share
with the target shareholders. Because of management value extraction, it is expected
that there is a positive gain for target firm and a negative gain for bidder firm. The issue
of hubris, which leads to the winner’s curse forcing bidders to overpay, is expected to
produce similar results as wealth is transferred from bidders to targets. In many cases,
the rationales for M&A are not necessarily independent or mutually exclusive. Different
hypotheses may have similar implications for the effect of M&A, but work interactively
to bring about a transaction.
Empirical evidences on the efficiency gains from M&As are mixed. Many studies
(Berger and Humphrey, 1992; Shaffer, 1993; Focarelli and Panetta, 2003) suggest that
mergers have the potential to produce synergistic benefits; the evidence is in fact not
overwhelming. Ali and Gupta (1999) examined 45 pairs of successful takeovers of listed
firms that occurred in Malaysia during the period 1980 through 1993 and found that
bidder firms achieved larger size at the expense of reduced profits both for themselves
and the target firms. They also found the bidder firms in Malaysia have lower
profitability, higher risk and lower leverage vis-à-vis the control bidder firms, which is
quite the opposite in other countries. Using data from 2000 and 2001, Fauzias and
Mohamed (2003) found that mergers do not contribute to any significant increase in
technical efficiency of commercial banks in Malaysia. This finding is supported by the
results produced by Fauzias et al. (2005) who concluded that there is no significant
difference between the pre- and post-merger period levels of efficiency for the ten anchor
banks between 1995 and 2000. Using a sample of eight anchor banks from 1997 to 2002,
Mahmood and Mohamad (2004) find that even though the bank mergers in Malaysia are
“forced” in nature, it contributed to synergistic benefits and had a significant
post-merger improvement based on four accrual operating performance measures.
Mantravadi and Reddy (2008) studied the impact of merger on the operating
performance of acquiring firms in India. Study used several profitability ratios and
debt/equity ratio, and the results indicated that there were some differences in terms of
impact on operating performance following mergers in various industries in India.
Merger had a relatively positive impact on pharmaceutical, banking, textiles and
electrical equipment but negative impact on agri-product and chemical sectors.
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Ling and Petrova (2008) ran a study which used different methods and various ratios
and found that targets had lower leverage, less liquidity and lower profitability
compared to acquirers. Liu and Qiu (2010) used different measures, including
productivity, profitability, technology and size of the companies, to study the M&A in
the USA. Companies which attended in mergers were compared with non-participants
in this study. They found that acquirers had larger size, better technology, higher
productivity and higher profitability than targets during pre-merger period.
Netter et al. (2011) analyze a comprehensive set of M&As from SDC data from 1992
through 2009 without imposing common restrictions such as excluding private bidders,
small targets. The results show a broader scope of M&As activity than that implied in
the literature, which generally oversamples larger deals involving public firms. Further,
some of the results differ from the extant literature. For example, the finding that
mergers occur in waves is attenuated with a greater presence of smaller and/or
non-public firms. Also, acquirers gain in most takeovers, despite a threefold decline over
the sample period in acquirer returns.
Manns and Anderson (2013) investigate and designed a modified event study to test
whether markets respond to the details of the legal terms of acquisition agreements.
This study focused on a data set of cash-only public company mergers spanning the
decade from 2002 to 2011 to ensure that the primary influence on target company stock
prices is the expected value of whether a legal condition will prevent the deal from
closing. Results show that there is no economically consequential market reaction to the
disclosure of the details of the acquisition agreement. Markets appear to recognize that
parties publicly committed to a merger have strong incentives to complete the deal
regardless of what legal contingencies are triggered.
Although synergistic benefits and the maximization of shareholders’ wealth have
been extensively used to justify M&A activities, the predominant part of the existing
research is still unable to provide conclusive evidence that expected synergy would
indeed be realized and create value for the merging firms in the end. Moreover, each time
a bidder announces an M&A attempt, it conveys and signals information to the market.
Indeed, any bid by a firm may impact all firms or a specific firm in its industry (Song and
Walkling, 2005). To the best of our knowledge, only Ali and Gupta (1999) have included
the control firms to provide a benchmark measure of the pre- and post-takeover
performances. To attribute M&As to a firm’s performance, it is customary to compare
the treatment group (bidder and target firms) with the control group (matching firms
with similar characteristics to bidder firms that are not affected by M&As). Therefore,
any observed differences could, with some justifications, be largely attributed to M&A
activity. The dominance of industry-related M&As in the recent 1990s and 2000s merger
waves suggests a need for further research on the market-driven M&As in tandem with
the direction of merger programs undertaken by the Malaysian banking industry in the
past two decades.
3. Data and methodology
3.1 The data
The sample is drawn from all the publicly listed firms that have initiated and completed
horizontal M&As in Malaysia from 1994 to 2004. The sample period selected provides a
focus on M&As, as well as ensured that sufficient pre- and post-M&A data were
available to assess the performance of firms in this study[2]. Our initial M&A
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announcement list was compiled from Hafizah (2007) for 1985-2001, and this was
supplemented by announcements from the Investor Digest published by KLSE
company files for 2001-2004[3]. The list was then cross-checked with the company
announcement files, annual reports and the KLSE Annual Companies Handbooks to
confirm the bid outcome based on the following selection criteria:
(a) The bidder firms must be listed in the KLSE and acquire more than 33 per cent
voting rights of targets with the assumption that 33 per cent is sufficient to give
control or to result in a change in control as identified in Section 33(1) of the
Securities Commission Act 1993.
(b) The target firms can be listed or non-listed firms with the deal value exceeding
RM100 million[4].
(c) M&A deal restricted to horizontal type of M&As that occur in the same industry
among the companies that have the same or similar products, technologies and
markets.
(d) Bidder and target firms are Malaysian domiciled and not foreign companies.
(e) Acquisitions of subsidiaries (which bidders already own more than 50 per cent
stakes) or associate firms (which bidders already owned more than 20 per cent
stakes) are excluded, as these deals do not reflect a firm’s intention to pursue external
growth and including these types of acquisitions might contaminate the results
(Song et al., 2005).
(f) Acquisitions involving financial firms and investment trust are also excluded
because of their specific accounting and regulatory requirements and, thus, should be
treated separately.
(g) At least three years of pre- and post-M&A financial data are required for bidder and
target firms (excluding year of M&A)[5].
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Details of the relationship between the initial and final sample are provided in Table I.
Appendix 2 and 3 provide target and matching firms used in this study.
The selection criteria resulted in a final sample of 39 bidders that engaged in M&A
activities during 1994-2004. In addition, a set of matching (control) firms are generated
to examine the effect of M&A on non-merged firms. These firms are subject to the
following selection criteria:
(i) be a listed firm quoted in KLSE;
(ii) similar size to that of bidder firms in terms of market capitalization;
Total
Initial deals identified
Deals less than RM100 million
Non-horizontal deals
Cross-border deals
Subsidiaries/associates deals
Financial deals
Data availability
Final sample
69
18
3
0
2
6
1
39
Table I.
Selection criteria and
final sample included
in this study
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(iii) in a similar industry; and
(iv) similar year of establishment.
The final sample consists of 39 bidder firms matched with 39 control firms and 39 target
firms for 117 observations. A complete set of financial data for each sample firm is
obtained from the firms’ annual reports. Whenever the data are not available, it is
supplemented from the KLSE Annual Companies Handbook[6]. The sample selection
is truncated to 2004 to allow for three years post-performance data, and thus, the
financial data collected are up to 2007.
3.2 Methodology
This study uses four different measures to assess the changes in performance before and
after M&A. These measures are adequate to measure the achievement levels of desired
improvements in firms’ performance in the long run. The operating performance is
measured in terms of annual increases in EVA. The remaining measures are technical
efficiency, total factor productivity (TFP) and cost efficiency, which use the same set of
multiple inputs and multiple outputs, except that additional price information is needed
for cost efficiency (see Appendix 1 for different efficiency concepts used in the
literature).
3.2.1 Economic value added. Sirower and O’Byrne (1998) derived the fundamental
EVA equation which breaks total market value into its known and expected
components expressed as:
MV0 ⫽ Cap0 ⫹ EVA0 /c ⫹ [(1 ⫹ c)/c] *
兺
⬁
t⫽1
⌬EVAt /(1 ⫹ c)t
(1)
where MV0 is market value at the end of year 0, Cap0 is book capital at the end of year 0,
EVA0 is EVA for year 0, c is the weighted average cost of capital and ⌬EVAt is expected
EVA improvement in year t.
To measure the future growth value (FGV), which is the capitalized present value of
the expected annual EVA improvements in equation (1), the equation can be rewritten
as:
FGVt ⫽ MV0 ⫺ Cap0 ⫺ EVA0 /c
(2)
where MV0 is the sum of market value of equity, book value of preferred stock, minority
interest and interest bearing debt at the end of year 0, and Cap0 is the total assets minus
total non-interest bearing current liabilities at the end of year 0. The cost of capital (c) is
derived from:
c ⫽ [wd ⫻ kd(1 ⫺ T)] ⫹ (we ⫻ ke )
(3)
where wd is weight of debt, we is weight of equity, kd is cost of debt before tax, T is tax rate
and ke is cost of equity derived from the capital asset pricing model (CAPM). CAPM is
stated as:
ke ⫽ rf ⫹ (rm ⫺ rf )␤i
(4)
where rf is risk-free rate of interest, rm is return on Kuala Lumpur Composite Index
(KLCI)[7] and␤i is beta of ith firm.
Dimson-Fowler and Rorke’s (1983) method is used to correct the problem of
non-synchronous trading bias in the calculation of beta for listed firms. The adjusted
beta is given as:
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*␤ t1(DFR) ⫽ W2( ␤⫺2 ) ⫹ W1( ␤⫺1 ) ⫹ ␤t ⫹ W1( ␤⫹1 ) ⫹ W2( ␤⫹2 )
(5)
where W1 ⫽ (1 ⫹ 2␳ ⫹ ␳2 )/(1 ⫹ 2␳ ⫹ 2␳2 ) and W2 ⫽ (1 ⫹ ␳1 ⫹ ␳2 )/(1 ⫹ 2␳1 ⫹ 2␳2 ) in
which ␳1 is the first-order serial correlation coefficient in rm, and ␳2 is the second-order
serial correlation coefficient in rm.
The estimated beta for bidder firms and matching firms, which were in the same
industry, will be used as the comparable company because they are similar in business
risk. However, an unlevering process was important to remove the risk associated with
financial leverage. Following Bowman and Bush (2006), the model to unlever the equity
beta is:
␤e ⫽ ␤a[1 ⫹ (1 ⫺ T)(D/E)]
(6)
where ␤e is levered firm’s equity beta, ␤a is unlevered firm’s equity beta (asset beta), T is
tax rate, D is market value of debt andE is market value of equity.
EVA at the end of year 0 is calculated as:
EVA0 ⫽ NOPAT0 ⫺ c ⫻ Capt⫺1
(7)
where NOPAT0 is net operating profit after tax at the end of year 0, c is the weighted
average cost of capital and Capt⫺1 is book capital at the beginning of the year.
Sirower and O’ Byrne (1998) explains that constant EVA will only provide a cost of
capital return on current operation value. Hence, EVA improvement is needed to earn a
cost of capital return on FGV to get a cost of capital return on total market value. As
such, expected EVA improvement must satisfy the equation below:
⌬EVA1 ⫹ ⌬EVA1 /c ⫹ ⌬FGV1 ⫽ c ⫻ FGV0
(8)
where ⌬EVA1 is actual EVA improvement, ⌬EVA1/c is the capitalized actual EVA
improvement, ⌬FGV1 is the change in FGV and c⫻FGV0 is the cost of capital return on
FGV.
To provide a total value of c ⫻ FGV0, substantial ⌬EVA is required to satisfy the
following:
⌬EVA1 ⫻ (1 ⫹ c)/c ⫽ c ⫻ FGV0, or
⌬EVA1 ⫽ [(c ⫻ c)/(1 ⫹ c)] ⫻ FGV0
(9)
where ⌬EVA1 is actual EVA improvement, and 关(c ⫻ c)/(1 ⫹ c)兴 ⫻ FGV is the expected
EVA improvement. The actual EVA improvement is compared to the expected EVA
improvement to get the excess EVA improvement for post-M&A periods. Positive
excess EVA improvement indicates that the return is above what was expected in the
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operating performance of firms after M&As, whereas negative excess EVA
improvement indicates that the return is below what was expected.
3.2.2 Technical efficiency Farrell (1957) first introduced the concept of technical
efficiency by measuring the distance between the observed level of production and the
production frontier. Since then, a variety of methods have appeared in the literature,
among which a non-parametric programming approach and parametric statistical
estimation are the two pioneering contributions. To obtain robust estimates of efficiency
scores, this paper uses two of the most widely known techniques, non-parametric DEA
and parametric SFA.
This study is input-reducing focus and uses input-oriented DEA model following
Fare et al. (1994a, 1994b). Assuming constant returns to scale (CRS), technical efficiency
for a DMU j that produces m outputs using n different inputs is obtained by solving the
following model:
Mi n␭
(10)
␭z
subject to:
J
␮jm ⱕ
兺Z ␮
j
jm
m ⫽ 1, . . . , M
(11)
j⫽1
J
兺Z ␹
j
j⫽1
zj ⱖ 0
jn
ⱕ ␭␹jn
n ⫽ 1, . . . , M
(12)
j ⫽ 1, … , J
where ␭ is an efficiency measure to be calculated for each DMU j, ␮jm is quantity of
output m produced by DMU j, ␹jn is quantity of input n used by DMU j and zj is intensity
variable for DMU j
Equations (11) and (12) define the constraints of input and output. ␭ ⫽ 1 means that
a DMU is fully efficient. If ␭ ⬍ 1, then the DMU is inefficient.
To estimate the technical efficiency under SFA, this study applies the SFA model of
translog production function with truncated-normal distribution[8]. The model with
three inputs and a single output in this study may be expressed as follows:
ln yit ⫽ f(xit ; ␤ ) ⫹ (vit ⫺ uit )
⫽ ␤0 ⫹ ␤1lnx1it ⫹ ␤2lnx2it ⫹ ␤3lnx3it ⫹ ␤4(lnx1it )2 ⫹ ␤5(lnx2it )2 ⫹ ␤6(lnx3it )2
⫹ ␤7(lnx1it )(lnx2it ) ⫹ ␤8(lnx1it )(lnx3it ) ⫹ ␤9(lnx2it )(lnx3it ) ⫹ (vit ⫺ ␮it )
(13)
where yit is the output of the ith firm in the tth time period, and x1it, x2it, x3it are the input
items of the ith firm in the tth time period. vit is the classical disturbance term (noise
component) that is assumed to be iid N(0, ␴v2), and independent of the uit. uit ⫽ 兵exp
关 ⫺ ␩(t ⫺ T)兴其ui where the uit are non-negative random variables that are assumed to
account for time-varying technical inefficiency in production and are assumed to be iid
as truncations at zero of the N(␴u2) distribution and ␩ is an unknown scalar parameter to
be estimated.
The truncated-normal distribution of the technical inefficiency effect (uit) is E
关uitⱍ␧it 兴, which is the “mean productive inefficiency” for the ith firm at any time t. It is
represented by:
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␴␭
E [uit ⱍ ␧it ] ⫽
(1 ⫹ ␭ 2 )
冤
共 ␴␭ 兲 共 ␭
共 ␴␭ 兲 ␴
␾
␧it
⌽ ⫺
␧it
⫺
␧it
␮i
⫹
␴␭
兲
冥
329
(14)
where ␧it ⫽ vit ⫺ uit, ␴ ⫽ ( ␴u2 ⫹ ␴v2 )1/2, ␭ ⫽ ␴u/␴v, ␮i ⫽ ⫺ ␧i␴u2/␴ 2 and ⌽(.) and ␾(.) are the
standard normal cumulative distribution and density functions. Firm ith technical
efficiency at time tth is measured as:
TEit ⫽ e ⫺E[uit|␧it ]
(15)
This is an input-oriented measure, as it takes the output bundle as given and measures
(the inverse of) the maximum feasible contraction of input. As uit is bounded below by
zero, TEit lies between 1 for a full efficient firm (with uit ⫽ 0) and 0 for a full inefficient one
(with uit ¡ ⬁).
3.2.3 Cost efficiency. The cost efficiency is commonly measured by using the
parametric SFA method or the non-parametric DEA method. SFA is well-established in
the literature as an econometric method to examine costs, whereas DEA is a
mathematical programming approach that compares DMUs to the least cost DMU[9].
The DEA constructs a piecewise linear efficient frontier that serves as the reference
in the evaluation of efficiency. CRS cost efficiency can be calculated by eliminating the
last equality constraint in the following linear program:
min␭,␪CE ␪CE
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subject to:
␭ · Y ⱖ y0
␭ · C ⱕ ␪CE · c0
␭i ⱖ 0
␭i · i ⫽ 1
where Y is an n ⫻ m matrix of observed outputs, ␭ is a 1 ⫻ n vector of intensity
variables, c0 is a scalar representing a DMU’s cost level, i is a column vector of 1s and C
is the n ⫻ 1 matrix of observed costs.
Unlike the DEA, the specification of the functional form of the stochastic frontier is
assumed to contain an error term with two components. One component refers to cost
inefficiencies (␮i), and the other one refers to random disturbances (vi), reflecting the
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measurement errors. Aigner et al. (1977) demonstrated a stochastic cost frontier function
as:
TCi ⫽ Xi␤ ⫹ vi ⫹ ui
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330
i ⫽ 1, … , N
(17)
where TCi is total cost in logarithm form of firm i, Xi is a (k ⫻ 1) vector of outputs and
inputs prices in logarithm form, ␤ is unknown vector parameter, vi is a two-sided
classical random error term distributed independently of uit and ␮i is a one-sided
non-negative stochastic element, representing cost inefficiency.
Following Rao (2005), cost efficiency (Ci) with truncated-normal distribution of firm i
can be expressed as the expected value of ␮i conditional on ␧it. It is given by:
Ci ⫽ E [uit ⱍ ␧it ] ⫽
␴␭
(1 ⫹ ␭ 2 )
冤共
⌽
␾
共 ␴␭ 兲
␧i
␧i␭
␧i␭
⫹
␴
␴
兲
冥
(18)
where ␧it ⫽ vit ⫹ uit, ␴ ⫽ ( ␴u2 ⫹ ␴v2 )1/2, ␭ ⫽ ␴u/␴v, ⌽(.) is the cumulative standard normal
density function (cdf) and ␾(.) is the standard normal density function (pdf).
3.2.4 Productivity growth The growth of an economy is governed by two distinct
sources of growth: input-driven and productivity-driven. The input-driven growth is
achieved through the increase in factors of production that is inevitably subjected to
diminishing returns and is not sustainable in the long run, and the productivity-driven
growth is the growth in output that cannot be explained by the growth in total inputs.
The growth in productivity, which is also known as TFP growth, would bring the
economy to a higher production frontier with more efficient use of factor inputs.
Therefore, TFP is an important source of sustainable long-term economic growth.
To measure TFP change, this study follows Cummins and Xie (2008) in defining
input-based adjacent Malmquist productivity indices, which are more suitable for
studying M&As. For the Malmquist Productivity Index (MPI) to be consistent with the
average product notion of productivity requires a CRS technology (Odeck, 2008)[10].
This study, therefore, assumes a CRS when measuring the Malmquist index. The
Malmquist analysis based on input-oriented distance functions CRS model is expressed
as:
兵 关 兴
t
DCRS
(xiS, yiS ) ⫽ sup ␾iS:
xiS
␾iS
其
, yiS 僆 Vrt(yiS ) ⫽
1
inf兵␪iS: ( ␪iSxiS, yiS ) 僆 Vrt(yiS )其
(19)
t
where DCRS
(xiS,yiS ) is the input-oriented distance function for firm i in period s relative to
the production frontier in period t with CRS technology (note that s ⫽ t allows the
productivity changes of firm i over time to be estimated), and (xiS,yiS ) is the input-output
vector for firm i in time period s.
Apart from that, the input correspondence which transforms inputs into outputs by
production technology t is modeled by:
k
y S ¡ Vrt(y S ) 債 R⫹
(20)
k
where Vrt(y S ) is the subset of all input vectors x S僆R⫹
which yields at least y S by using t
n
with r, for any y S僆R⫹
and
k
and
n
are
dimensions
of the input and output vectors,
,
respectively.
According to Fare et al. (1994a, 1994b), an MPI can be defined relative to either the
t
t⫹1
t
technology in period t (MCRS
) or the technology in period t ⫹ 1 (MCRS
), where MCRS
measures productivity growth between periods t and t ⫹ 1 using the period t reference
t⫹1
technology, whereas MCRS
uses the period t ⫹ 1 reference technology. To avoid an
t
t⫹1
arbitrary selection of technology, MPI is defined as the geometric mean of MCRS
and MCRS
as follows:
1
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t
t⫹1
* MCRS
]2
MCRS(xAt⫹1, yAt⫹1, xAt, yAt ) ⫽ [MCRS
(21)
Further decompositions of MCRS(xAt⫹1,yAt⫹1,xAt,yAt ) into catching up index or efficiency
change (EC) that shows the relative change in efficiency between the periods and
frontier shift (FS) index, also known as the technical change, which shows the relative
distance between the frontiers, are given by:
EC ⫽
FS ⫽
关共
t
DCRS
(xAt, yAt )
(22)
t⫹1 t⫹1 t⫹1
DCRS
(xA , yA )
t⫹1 t⫹1 t⫹1
DCRS
(xA , yA )
t
DCRS
(xAt⫹1, yAt⫹1 )
兲共
t⫹1 t
DCRS
(xA, yAt )
t
DCRS
(xAt, yAt )
兲兴
1
2
(23)
Productivity declines if MCRS(xAt⫹1, yAt⫹1, xAt, yAt ) ⬍ 1, remains unchanged if MCRS
(xAt⫹1, yAt⫹1, xAt, yAt ) ⫽ 1 and improves if MCRS(xAt⫹1, yAt⫹1, xAt, yAt ) ⬎ 1. Similar interpretation
applies to EC and FS.
To measure TFP changes under SFA, this study fits a translog stochastic frontiers
production function with truncated-normal distribution. Following Coelli et al. (1998),
the technical efficiency of production for the ith firm at the tth year can be predicted as:
TEit ⫽ E [exp(⫺uit ⱍ ␧it )]
(24)
EC element of the Malmquist TFP index, ECi(t⫹1), which is the ratio of the two distance
functions, for time t ⫹ 1 and t, is then calculated as:
ECi(t⫹1) ⫽
TEi(t⫹1)
TEit
(25)
An index of FS (technical change, FSi(t⫹1)), between two adjacent periods, t ⫹ 1 and t, for
the ith firm, can be obtained from the estimated parameters of the stochastic production
frontier. This is done by estimating the partial derivatives of the production function for
time at t ⫹ 1 and t. Coelli et al. (1998) converted these into indices and calculated their
geometric means; the technological change index is then given by:
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FSi(t⫹1) ⫽
兵关
兴 关
⭸f(xi(t⫹1), (t ⫹ 1), ␤
⭸f(xit, t, ␤
1⫹
⫻ 1⫹
⭸(t ⫹ 1)
⭸t
兴其
1
2
(26)
The indices of EC and technical change obtained by using equations (25) and (26),
respectively, can be multiplied to obtain a stochastic, parametric Malmquist TFP index:
332
TFPit ⫽ ECit * FSit
(27)
which is equivalent to the decomposition of the deterministic, non-parametric
Malmquist index suggested by Fare et al. (1985).
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4. Analysis of empirical results
The general assertion is that firms that have merged perform better because of
synergies that are expected to arise as a result of the M&A. Presumably, M&A in an
industry may spur or motivate other non-merging firms, improving their efficiency as
well (spillover effect). The results (excluding period of Asian financial crisis) in Panel A
of Table II show that bidder firms are not able to realize the expected value creation in
operating performance after M&A activities. In fact, there is a real decline in operating
performance of bidder firms in the long run. Target firms, on the other hand, do not
document a real gain in operating performance after M&A activities. On the contrary,
Panel A: mean excess EVA results of
sample firms
Mean expected EVA Mean actual EVA Mean excess EVA
Bidder firms
Target firms
Matching firms
Table II.
EVA of mergers
⫺61,176
5,290
12,550
7,645
⫺2,688
6,634
Panel B: mean excess EVA results of sample firms
in different sub-periods
Bidder firms
One year after
Two years after
Three years after
Four years after
Five years after
All years after year 3
⫺1,47,924
⫺62,853
⫺44,859
⫺93,134
⫺42,949
⫺1,12,750*
Panel C: mean excess EVA results of sample firms
in different industries
Bidder firms
Manufacturing
Construction
Services
⫺46,437*
⫺53,633
⫺2,02,160
⫺68,821*
7,978
5,916*
Mean excess EVA
Target firms Matching firms
15,769
9,250
11,329
27,843
⫺4,588
⫺4,466
29,543*
6,688
12,533
2,669
⫺15,334
⫺21,180*
Mean excess EVA
Target firms Matching firms
⫺14,188
4,508
94,716
10,202
⫺20,978*
39,130
Notes: EVA reflects true economic value creation for firms; positive excess EVA indicates value
creation, whereas negative EVA indicates value destruction for firms after M&A activities; all values
are in RM thousand, * significant at the 5 % level, using a Mann–Whitney U-test
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matching firms are capable of realizing more than the expected improvement in
operating performance after M&A activities took place in the industry, indicating a
spillover effect. Further findings of sub-period analysis, however, reveal that M&A
activity improves the operating performance of matching firms only for a very short
run, specifically within a year after M&A (see Panel B of Table II). In fact, both bidders
and non-merging rivals are losing out in operating performance in the long run, most
notably, in all years after year three of M&As. These findings imply that horizontal
M&A activity in Malaysia does not lead to a synergy in operating performance of
merging firms in the long run. Instead, the M&A process diminishes the operating
performance of bidders and non-merging rivals in the long run, despite the fact that
M&A activity stimulated the rival firms in the industry to improve their operating
performance only for the short run. When the industry effect is considered based on the
grouping of Malaysian Standard Industrial Classifications, the findings reveal that
M&A activity diminishes operating performance of bidder firms in the manufacturing
industry and matching firms in the construction industry (see Panel C of Table II). These
findings, therefore, demonstrate that horizontal M&A activity in those industries that
rely heavily on manpower is particularly not advantageous to improve operating
performance of bidder firms and non-merging rival firms in Malaysia.
Horizontal M&A activities are largely justified on efficiency grounds, even though
the empirical evidence in the M&A literature is generally uncertain (Berger and
Humphrey, 1992; Vander, 1996; Rhoades, 1998). We find no significant change in
technical efficiency of the merging firms after M&A activities, which is consistent with
prior international M&A literature. The magnitude of insignificant declines
(improvements) in technical efficiency of bidder and matching (target) firms increases
(decreases) with the length of window when different window periods are considered in
the long run (see Panel B of Table III). These findings indicate that horizontal M&A
activities in Malaysia have neither an effect on real synergy in technical efficiency of
merging firms nor stimulate the non-merging rival firms to improve their level of
technical efficiency in substantiating their competitiveness in the long run. When the
industry effect is considered, the findings reveal that only M&A activity in a
fast-moving service industry, which requires a high degree of both speed and
operational flexibility, significantly improves technical efficiency of target firms in
Malaysia in the long run (see Panel C of Table III).
We also find that merging and non-merging rival firms do not experience a
significant change in cost efficiency after M&As, in spite of the fact that insignificant
declines in cost efficiency of target and rival firms are lower compared to bidder firms
(see Panel A of Table III). Further findings of sub-period analysis in Panel B of Table III
reveal that the pattern of declines in cost efficiency is similar to the findings in technical
efficiency. When the industry effect is considered in Panel C of Table III, the findings
reveal that M&A activity diminishes cost efficiency of bidder firms in the construction
industry. This result suggests a horizontal M&A activity in an industry, which relies
heavily on trained workers, is especially not good to improve cost efficiency levels of
bidder firms in Malaysia.
The average DEA scores for before and after M&As are calculated from all the
available data up to 2007. There is a minimum of three years of data before M&As for
the firms, and the average is calculated accordingly. However, the average for after
M&As is obtained from the first year after M&As to 2007. As a result, firms that
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334
Panel A: Average DEA efficiency results of sample firms
Frontier measure
Technical efficiency
Cost efficiency
Malmquist index
Efficiency change
Technical change
Firms
⫺0.06529
⫺0.04313
0.06336*
⫺0.06009
0.10368*
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Panel B: Average DEA efficiency results
of sample firms in different sub-periods
Firms
0.02920
⫺0.00122
0.08684*
0.10428*
⫺0.00144
Firms
⫺0.02111
⫺0.00134
0.12136*
⫺0.02626
0.24743*
Mean increase/decrease
Technical efficiency Cost efficiency Malmquist index
Bidder firms
Three years before and three years after
Five years before and five years after
Three years before and five years after
Three years before and all years after year 3
⫺0.03725
⫺0.03849
⫺0.00894
⫺0.03526
⫺0.03125
⫺0.02180
⫺0.01517
⫺0.03442
0.06092*
0.05674*
0.06037*
0.07066*
Target firms
Three years before and three years after
Five years before and five years after
Three years before and five years after
Three years before and all years after year 3
0.03591
⫺0.00885
0.01313
0.03983
0.00271
⫺0.00597
⫺0.00265
0.00326
0.07395*
0.08831*
0.07814*
0.07737*
Matching firms
Three years before and three years after
Five years before and five years after
Three years before and five years after
Three years before and all years after year 3
0.00144
⫺0.03744
⫺0.01825
0.01741
0.00638
⫺0.00317
0.00380
0.01392
0.12124*
0.11330*
0.12601*
0.13582*
Panel C: Average DEA efficiency results
of sample firms in different industries
Bidder firms
Manufacturing
Construction
Services
Target firms
Manufacturing
Construction
Services
Matching firms
Manufacturing
Construction
Services
Table III.
DEA efficiency
measures of mergers
Mean increase/decrease
Bidder
Target
Matching
Mean increase/decrease
Technical efficiency Cost efficiency Malmquist index
⫺0.06720
⫺0.08637
⫺0.01438
0.02117
0.00124
0.11006*
⫺0.03688
⫺0.02334
0.05545
⫺0.05001
⫺0.06240*
0.02703
0.09700*
⫺0.00927
0.05387
0.00238
⫺0.02891
0.03498
0.09038*
0.07237
0.09721*
⫺0.00160
⫺0.00981
0.01510
0.11846*
0.03652
0.28681*
Notes: Comparison is made on the average DEA efficiency of the sample firms before and after
M&As; * significant at the 5 % level, using a Mann–Whitney U-test
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completed M&As in the early years will have a longer window to calculate the average
after M&As.
Contrary to earlier findings in this study, the findings of productivity show
significant positive performance in productivity of merging firms and non-merging
rival firms in the long run. In fact, improvement in productivity of matching firms is
higher than the productivity growth of target firms, and the productivity growth of
bidder firms is lower as compared to the target firms (see Panel A of Table III). Similar
to the bidder firms, productivity growth of matching firms is mainly due to the technical
change (FS effect), despite the fact that the level of technological progress for matching
firms is higher as compared to the bidder firms. Productivity growth of target firms, on
the other hand, contributes to the EC (catching up effect). Further findings of sub-period
analysis in Panel B of Table III reveal that M&A activity improves the productivity of
merging and non-merging rival firms for all sub-periods in the long run. In fact, this
productivity growth achieves the highest level in a longer window period as opposed to
the shorter window period. When the industry effect is considered in the Panel C of
Table III, the findings reveal that the M&A process significantly improves the
productivity of merging and non-merging rival firms in the manufacturing industry,
and the target and matching firms in the services industry.
5. Summary and conclusions
We find that operating performance of bidder firms declines significantly in the long
run, but no significant operating synergy of target firms is seen during the same period.
In an unregulated market with no antitrust regulation, these results are not unexpected
because of non-value maximizing motivations, such as managerial hubris that
precludes an adequate due diligence process and lead bidder firms to ultimately
over-anticipate the synergies after M&A. This is consistent with an earlier study on
Malaysia that overwhelmingly argues for the validity of the managerial self-interest
theory in Malaysian M&As. There is also no significant difference in the technical and
cost efficiency of merging firms in the long run, which implies that merging firms have
only created or reinforced the market power, but there is no synergistic advantage
throughout the process of M&A.
On average, the effects of M&As show post-M&A operating performance of the
matching firms significantly higher than their corresponding pre-M&A levels and the
bidder firms. This may suggest that industry peers for the Malaysian market
are motivated to improve their operating performance significantly in response to the
industry shock, although only for a very short period of time after M&A. There is no
significant difference in the technical and cost efficiency of the matching firms during
the same period relative to bidder firms. However, the findings in the productivity
changes of this study reveal that the technological improvement has a significant role in
relative contributions to the productivity growth of major competitors (bidder and
matching firms), while positive change in efficiency also contributed to productivity
gain of target firms. These results show that rivals benefit from the M&A not because of
potential market power or collusion, but because M&A provides new information that
firms within an industry can utilize to become more efficient through consolidation. We
may conclude that spillovers occur in a peculiar environment of the Malaysian market
when horizontal M&As reduce the number of competing firms in the market (a higher
level of concentration). The findings in this study also suggest that manpower could be
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336
a limiting factor for the success of the horizontal M&A activity in Malaysia, especially
to major competitors in those industries that rely heavily on the foreign workers who
lack required skills.
The government and regulators should exert maximum due diligence to ensure
appropriate action is taken by bidder firms to exercise due diligence on potential target
firms. The government should establish a stringent antitrust environment with
competition and consumer protection laws to avoid anticompetitive and antiwelfare
M&A in Malaysia. The government should also accelerate the growth in skill-intensive
industries to shift away from industries intensive in natural resources that rely heavily
on unskilled labor. The government should liberalize the Malaysian economy further to
attract high valued-added companies with superior technological know-how.
In an unregulated market with no antitrust law, our results can reasonably be
expected as a result of non-value maximizing motivations. Such motivations include
various agency theory explanations such as managerial hubris that lead to over-bidding
of a target firm in the absence of due diligence process. We also show that in Malaysia,
the bidder firms are neither technically efficient nor cost-effective in the long-run. The
implication of our study is that M&A in Malaysia does not create synergy but only
increase market power. This may result in higher input prices and substantially reduce
consumer welfare with price increases after M&A. Our research may look into other
emerging market economies in their M&A experiences and draw policy and operational
guidelines to make them more consumer welfare-oriented.
Cross-border comparisons of M&As in emerging economies will lead to proper
benchmarking standards against which the benefits of M&As can be measured. In our
current study, we focus only on Malaysia with local benchmarking standard which may
not be appropriate for other emerging economies. Lack of pre-merger data does not
allow us to examine the impact of other firm-specific inputs and outputs in our frontier
analysis. A reasonable extension could be to estimate profit efficiency if data on output
quantity and prices were available.
Notes
1. Now known as Bursa Malaysia.
2. The reason for selecting deals during this period is the limitation of data availability. The
present study requires at least three years of pre- and post-M&As data. Therefore, the sample
study was restricted to horizontal M&As completed between 1994 and 2004, as this allows the
study to analyze the post-M&As performance over three years after the bid completion.
3. Investors Digest reports all important corporate announcements including acquisitions and
disposals by cash and the record of bonus and issues, schemes and acquisitions, etc. as
provided in the KLSE listing requirements.
4. Asquith et al. (1983) posit that performance measures of bidder firms may not be significant
when the investment in the target firms is relatively smaller to its bidder firms.
5. Sharma and Ho (2002) examine financial profiles of the bidder and target firms up to three
years after the acquisition, which is deemed to allow a time period that is sufficient for any
potential economic gains to be realized.
6. For the financial reports which are not available in KLSE library, mainly for delisted firms
and non-listed firms, data were obtained from Companies Commission of Malaysia.
7. Now known as FTSE Bursa Malaysia KLCI.
8. Berger and Mester (1997) finds that both translog and the Fourier flexible function form yield
essentially the same average level and dispersion of measured efficiency, whereas Altunbas
and Chakravarty (2001) identify limitations with the Fourier, suggesting that the translog is
the preferred model approach.
9. There are strengths and weaknesses to both approaches. Coelli et al. (1998) provide a
comprehensive discussion on the comparison of these approaches.
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10. Malmquist index in the presence of non-constant return to scale does not accurately measure
the productivity change, as it creates a systematic bias on the productivity measurement
derived, as has been shown by Grifell-Tatje and Lovell (1995).
11. Scale economies refer to the ratio of marginal cost to average cost, taken along a ray that holds
output mix constant, whereas scale efficiencies take into account the full difference in ray
average costs between the point of evaluation and the scale-efficient point (Berger and
Humphrey, 1994). For further discussions, see Berger (1993, 1995); Evanoff and Israilevich
(1991).
12. X-efficiency differs from scale and scope economies because it takes the output bundle as
given, whereas scale and scope economies try to determine least-cost scale and mix of the
output bundle, taking as given that the firms are on the efficient frontier (Berger and
Humphrey, 1994).
13. Farrell (1957) defined economic efficiency is the sum of technical and allocative efficiency.
14. Referred to hereafter as BHH.
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acquisitions in
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Eckbo, B.E. (1983), “Horizontal mergers, collusion, and stockholder wealth”, Journal of Financial
Economics, Vol. 11 No. 1, pp. 241-273.
Corresponding author
M. Kabir Hassan can be contacted at: mhassan@uno.edu
Effect of
merger and
acquisitions in
Malaysia
Appendix 1
Efficiency concept
Definition
Literature
Scale economies[11]
Examines whether costs per unit
can be reduced by increasing
output (Yao et al., 2007)
Examines whether costs per unit
can be lowered by joint
production (Yao et al., 2007)
Berger and Humphrey (1994);
Cummins and Zi (1998); Cummins
and Rubio-Misas (2006)
Hunter and Timme (1986); Elyasiani
and Mehdian (1990); McAllister and
McManus (1993); Rhoades (1994);
Berger and Humphrey (1994); Lang
and Welzel (1996)
Allen and Rai (1996); Vander (1996)
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Scope economies
Operational efficiency
Cost efficiency (input
X-efficiency)
Revenue efficiency (output
X-efficiency)
Standard profit efficiency
Alternative (non-standard)
profit efficiency
X-efficiency2 (managerial
efficiency)[12]
Optimization of the output mix
and input mix (Allen and Rai,
1996)
The ratio of the costs of a fully
efficient firm with the same
output quantities and input
prices to the given firm’s actual
costs (Cummins and RubioMisas, 2006)
The ratio of a given firm’s
revenues to the revenues of a
fully efficient firm with the same
input quantities and output
prices (Cummins et al., 1999)
Measures how close a firm is to
produce the maximum possible
profit given a particular level of
input prices and output prices
(Berger and Mester, 1997)
Measures how efficient a firm is
at earning its maximum
available profit given its output
levels rather than its output
prices (Berger and Mester, 1997)
Economic efficiency of any
single firm minus scale and
scope efficiency effects (BHT,
1993)[13]; Managerial ability to
control costs or to maximize
revenue (BHT, 1993); A
measurement composed of
technical efficiency and
allocative efficiency (Garden and
Ralston, 1999)
341
Berger and DeYoung (1997); Berger
and Mester (1997); Cummins et al.
(1999); Kwan and Wilcox (2002); Lin
(2002); Cummins and Rubio-Misas
(2006)
Cummins et al. (1999)
Akhavein et al. (1997); Berger and
Mester (1997)
Berger and Mester (1997);
Humphrey and Pulley (1997);
Lacewell et al. (2002); Hollo and
Nagy (2006)
Leibenstein (1966); Aly et al. (1990);
Berger and Humphrey (1992); BHT
(1993); Rhoades (1998); Avkiran
(1999); Garden and Ralston (1999);
Kohers et al. (2000); Hollo and Nagy
(2006)
(continued)
Table AI.
List of definition of
technical, cost and
other efficiency
concept: summary of
efficiency concepts
MRR
38,3
Efficiency concept
Definition
Literature
Technical efficiency
Measures the ability of a firm to
obtain maximal output for a
given set of inputs, and can be
decomposed into pure technical
efficiency and scale efficiency
(Cummins and Rubio-Misas,
2006)
Measures the ability of a firm to
use inputs in optimal
proportions given their prices
and the production technology
(Garden and Ralston, 1999
Measures how far off a
production unit is from the
production frontier to indicate
the potential reduction in inputs
a production unit can achieve by
adopting the best production
and/or management practices of
the best-performance production
unit (Dong and Featherstone,
2006)
Measures efficiency solely
associated with size and
indicates whether the production
unit is producing at the most
efficient size (Rhoades, 1998)
Proportionate overuse of all
inputs (Berger and Humphrey,
1992)
Suboptimal relative proportions
of inputs (Allen and Rai, 1996)
The failure to produce the
highest value of output for a
given set of input quantities and
output prices (Akhavein et al.,
1997)
Produces less of an output than
it would like (Berger and
Humphrey, 1994)
Reacts poorly to output prices in
choosing its output bundle
(Berger and Humphrey, 1994)
Aly et al. (1990); Lovell (1993);
Worthington (2004); Cummins and
Rubio-Misas (2006)
342
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Allocative efficiency
Pure technical efficiency
Scale efficiency
(Cost) technical
X-inefficiency
(Cost) allocative
X-inefficiency
Revenue X-inefficiency
(Revenue) technical
X-inefficiency
(Revenue) allocative
X-inefficiency
Table AI.
Source: Various literature
Aly et al. (1990); Lovell (1993);
Garden and Ralston (1999); Neal
(2004); Cummins and Rubio-Misas
(2006)
Aly et al. (1990); Miller and Noulas
(1996); Resti (1998); Worthington
(2001, 2004); and Cummins and
Rubio-Misas (2006)
Miller and Noulas (1996); Rhoades
(1998); Worthington (2001, 2004);
Cummins and Rubio-Misas (2006)
Berger and Humphrey (1992);
Berger et al. (1999)[14]; Berger and
Hannan (1998)
Berger and Humphrey (1992); BHH
(1993); Berger and Hannan (1998)
Akhavein et al. (1997)
BHH (1993); English et al. (1993)
BHT (1993); English et al. (1993)
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Appendix 2
No.
Target firms
Completed date
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
Malaysian Pacific Industries Bhd
Lingkaran Trans Kota Holdings Bhd
Woventex Corporation Bhd
Kedah Cement Holdings Bhd
Palmco Holdings Bhd
Metacorp Bhd
Tan & Tan Developments Bhd
Nissan-Industrial Oxygen Incorporated Bhd
Southern Steel Bhda
Powertek Bhd
Celcom (Malaysia) Bhd
Kuala Sidim Bhdb
Metrojaya Bhd
EPE Power Corporation Bhd
FFM Bhdc
Projek Penyelenggaraan Lebuhraya Bhd
ECI Integrated Holdings Sdn Bhd
Guolene Metal Can Sdn Bhd
Pacific Hypermarket Group Sdn Bhd
Gau Yang Plywood Sdn Bhd
Sepang Power Sdn Bhd
New Pantai Expressway Sdn Bhd
Timecel Sdn Bhd
SKS Power Sdn Bhd
Sapura Energy Sdn Bhd
Metropolitan TV Sdn Bhd
SR Technology Sdn Bhd
Cold Rolling Industry (Malaysia) Sdn Bhd
Pahang Cement Sdn Bhd
Rintisan Bumi Malaysia Sdn Bhd
Labur Bina Sdn Bhd
Oriental Extrusions Sdn Bhd
Teon Choon Realty Co Sdn Bhd
EAC Transport Agencies (Malaysia) Sdn Bhd
Rahman Hydraulic Tin Sdn Bhd
Metramac Corporation Sdn Bhd
Syarikat Bekalan Air Selangor Sdn Bhd
Sun Media Corporation Sdn Bhd
Syarikat Perumahan Pegawai Kerajaan Sdn Bhd
December 31, 1994
December 31, 1996
December 31, 1997
June 15, 1999
October 18, 2001
March 7, 2002
April 29, 2002
September 17, 2002
October 4, 2002
February 26, 2003
August 15, 2003
August 29, 2003
January 20, 2004
February 25, 2004
August 25, 2004
September 7, 2004
June 5, 2000
July 31, 2000
February 1, 2001
April 10, 2001
January 16, 2002
February 25, 2002
May 7, 2003
October 28, 2003
December 23, 2003
December 31, 2003
December 31, 2003
March 29, 2004
March 31, 2004
June 21, 2004
June 18, 2004
July 29, 2004
August 30, 2004
September 29, 2004
November 30, 2004
December 10, 2004
December 15, 2004
December 31, 2004
December 31, 2004
Notes: a Formally known as Southern Iron & Steel Works Ltd;
Rubber Co., Bhd; c formally known as Federal Flour Mills Bhd
b
formally known as Kuala Sidim
Effect of
merger and
acquisitions in
Malaysia
343
Table AII.
List of target firms
included in this
study
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344
Appendix 3
No.
Matching firms
Completed date
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
KESM Industries Bhd
IJM Corporation Bhd
Daibochi Plastic and Packaging Industries Bhd
Tasek Corporation Bhd
Kulim (Malaysia) Bhd
Malaysian Resources Corporation Bhd
Land & General Bhd
Petronas Gas Bhd
Ann Joo Resources Bhd
Sarawak Energy Bhda
DiGi.Com Bhd
Keck Seng (Malaysia) Bhd
Ngiu Kee Corporation (M) Bhd
YTL Power International Bhd
Malayan Flour Mills Bhd
WCT Engineering Bhd
Unisem (M) Bhd
Public Packages Holdings Bhd
Aeon Co. (M) Bhdb
Lingui Developments Bhd
Sarawak Energy Bhd
Zecon Bhd
DiGi.Com Bhd
Mega First Corporation Bhd
Dialog Group Bhd
Astro All Asia Networks plc
Globetronics Technology Bhd
Lion Corporation Bhd
Cement Industries of Malaysia Bhd
Timberwell Bhd
Asia Pacific Land Bhdc
UMW Holdings Bhd
Bina Darulaman Bhd
Integrated Logistics Bhd
Perusahaan Sadur Timah Malaysia (Perstima) Bhd
Plus Expressways Bhd
Kumpulan Perangsang Selangor Bhd
The New Straits Times Press Bhd
Damansara Realty Bhd
December 31, 1994
December 31, 1996
December 31, 1997
June 15, 1999
October 18, 2001
March 07, 2002
April 29, 2002
September 17, 2002
October 4, 2002
February 26, 2003
August 15, 2003
August 29, 2003
January 20, 2004
February 25, 2004
August 25, 2004
September 7, 2004
June 5, 2000
July 31, 2000
February 1, 2001
April 10, 2001
January 16, 2002
February 25, 2002
May 7, 2003
October 28, 2003
December 23, 2003
December 31, 2003
December 31, 2003
March 29, 2004
March 31, 2004
June 21, 2004
June 18, 2004
July 29, 2004
August 30, 2004
September 29, 2004
November 30, 2004
December 10, 2004
December 15, 2004
December 31, 2004
December 31, 2004
Table AIII.
List of matching
firms included in this Notes: a Formally known as Sarawak Enterprise Corporation Bhd;
Stores Bhd; c formally known as Mount Pleasure Holding Bhd
study
b
formally known as Jaya Jusco
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