September 7, 2009

Mergers, Innovation, and Productivity:
Evidence from Japanese Manufacturing Firms
Kaoru Hosono*
Miho Takizawa**
Kotaro Tsuru***
Abstract
We investigate the impacts of merger on innovation and efficiency using a
micro dataset of Japanese manufacturing firms including unlisted firms during themerger wave period of 1995-1999. To deal with the endogeneity bias arising fromthe merger decision, we adopt the propensity score matching method to select acontrol group. Our main findings are the followings. First, the acquirer’s total factor
productivity (TFP) decreases immediately after mergers and does not significantly
recover to the pre-merger level within three years after mergers. Second, the R&D
intensity does not significantly change after mergers despite a significant increasein the debt-to-asset ratio. Third, the recovery of TFP after mergers is significant forinter-industrial mergers or within-group mergers. Our results suggest that though
the costs of business integration are generally large and persistent, a synergy effectworks well and integration costs are small for inter-industrial or within-groupmergers.
Key Words: Merger, Research and Development, Total Factor Productivity* Gakushuin University. Email kaoru.hosono@gakushuin.ac.jp** Toyo University.
*** Research Institute of Economy, Trade and Industry

1Mergers and Innovation: Evidence from Japanese Manufacturing Firms
1. Introduction
There was a large wave of mergers and acquisitions (M&As) in Japan for morethan a decade from 1996. The number of M&As in which Japanese firms wereacquired by either domestic or foreign firms was 220 per year on average during theperiod of 1985-1995, jumped to 616 in 1996, then increased steadily, reaching thepeak of 2738 in 2007. Although such a rising trend stopped in 2008, when theworld-wide financial crisis hit the Japanese economy, there are continued interestsin the consequences of M&As. Do they improve the reallocation of capital in thesense that less efficient firms are taken over and improved by more efficient firms?
From the viewpoint of a longer horizon, do M&As promote or inhibit innovative
activities and how do they affect the productivity growth? These questions are ofgreat importance to understand the role of mergers in the reallocation of capital in amacroeconomy.

They also have implications to competition policy and hence

gained attention of both economists and regulators (e.g., Federal Trade Commission,1996).
The U.S. and European merger waves in the 1980s and 1990s have attracted
many researchers’ attention. Hall (1999) surveys literature examining the effects ofcorporate mergers and restructuring on productivity and technical change,summarizing that these transactions were followed by productivity improvements
in firms involved. However, despite a growing world-wide concern about theconsequences of M&As, literature on M&As outside the U.S. or Europe is still scarce.
We try to void this gap using a micro dataset of Japanese manufacturing firms
including unlisted firms during the period of 1995-1999. Though some precedingstudies focus on the recent Japanese M&A wave and find evidences that areconsistent with the role of M&As as capital reallocation (e.g., Arikawa andMiyajima, 2006), they do not focus on the dynamic aspects of M&As through theirimpact on the firms’ innovative activity. We investigate the impact of mergers on theacquiring firm’s R&D intensity and productivity.
We contribute to the literature on the consequences of mergers in three ways.
First, we deal with the endogeneity problem of the merger decision. If, forexample, a growing firm tends to be an acquirer, we may mistakenly attribute theimprovement of its performance to the effect of mergers. To lessen this type of bias,
many preceding studies select a control group to be compared with acquirers from2

those firms that are similar to acquiring firms in terms of the industry, size, or
profitability. To form a more suitable control group, we adopt the propensity scorematching method advocated by Rosenbaum and Rubin (1983). Thus we can evaluate
the effect of mergers by comparing the acquirers and the control group of firms that
had been most likely to be an acquirer but actually were not.
Second, our sample consists of small and medium-sized, unlisted firms as wellas listed firms while most of the preceding studies analyze mergers of listed firms.
In our sample, 47 percent of mergers were conducted by non-listed firms. Given thatsuch a high proportion of mergers conducted by unlisted firms, excluding unlisted
firms from the sample may be misleading when we analyze the restructuring
process at the industry level,
Finally, we take into consideration the heterogeneity of mergers. It is of great
importance to consider the possibility that different types of mergers have different
impacts on innovation and productivity. Specifically, we examine whether mergers
within an industry have different impacts from mergers across different industries.
Synergy effects and business integration costs may differ between intra-industrial
and inter-industrial mergers. In addition, we investigate the possibility thatmergers undertaken within a business group may have different impacts from theothers. Some preceding studies on mergers in Japan find competing results on therole of a business group in improving the efficiency of mergers (Yeh and Hoshino,
2002; Iwaki 2007). A parent firm may conduct a merger for the aim of rescuing afinancially distressed affiliated firm, which may result in poor merger performance.
On the other hand, if firms within a business group have much in common in
information system or business culture prior to mergers, they may successfully reap
the yields of mergers.
Our main findings can be summarized as follows. First, the acquirer’s totalfactor productivity (TFP) decreases immediately after mergers and does not
significantly recover to the pre-merger level within three years after mergers.
Second, the R&D intensity does not significantly change after mergers despite asignificant increase in the debt-to-asset ratio. Finally, the recovery of TFP after
mergers is significant for mergers across different industries or within the samebusiness group. Overall, our results suggest that the costs of business integration
are large and persistent but that a synergy effect works well and integration costs
are small for intra-industrial mergers or within-group mergers.
The rest of this paper consists of six sections. In Section 2, we present some3

hypotheses on the motives of mergers and their consequences on the innovative
activity and productivity. In Section 3, we review some related literature. Section 4
describes our dataset. In Sections 5 and 6, we present our empirical results for the
motives of acquiring firms and consequences on their R&D intensity andproductivity, respectively. In Section 7, we present evidences on how the pre-merger
characteristics of acquires are related to the post-merger performance. Section 8
concludes.
2. Hypotheses
In this section, we present three major hypotheses concerning the effects ofmergers on the acquirer’s productivity and innovative activities. Table 1
summarizes these hypotheses.
A. Value Creation
More productive firms acquire less productive firms to increase their profits byextending their superior technology or managerial skills to the acquired assets(Maksimovic and Phillips, 2002; Jovanovic and Rousseau, 2004). Though the profit
maximization motive suggests that the productivity of the acquired assets increases
after mergers, it is not clear whether the acquirers increase productivity. If there issome scale or scope economy or synergy effects, the acquirer’s productivity increases.
On the other hand, managers may find it more difficult to manage a large firm thana small one. In particular, if acquisitions of inefficient plants require substantial
management costs of integrating business, the acquirer’s’ productivity decreases.
The impact of mergers on R&D depends on technological aspects of acquired
assets. When mergers occur among firms that focus on the same or substitutable
technological areas, acquirers tend to reduce R&D activities to avoid the duplication
with acquired firms. In this case, the acquirer’s low R&D does not result in low
productivity. On the other hand, when mergers occur among the firms that operate
in complementary technological areas, acquirers tend to increase R&D to takeadvantage of the synergy effects associated with mergers.
B. Conflicts of Interest between Managers and Shareholders
Takeover decisions may be influenced by conflicts of interest between managers
and shareholders. Self-interested managers may undertake acquisitions for
empire-building or managerial entrenchment (e.g., Morck, Shleifer and Vishny,1990).
4

This view suggests that mergers tend to occur in matured or shrinking firmsthat have excess cash flow and in industries where returns to investment projects
are low (Jensen, 1986). In this case, no efficiency gains are realized by mergers.
If managers enjoy private benefits from R&D projects including unproductive
ones, there are some possible consequences of mergers on firm productivity andR&D intensity. They may increase R&D at the sacrifice of productivity after they
have established an empire through mergers. On the other hand, if mergers
substantially increase acquires’ indebtedness and probability of bankruptcy,
managers will reduce spending on unprofitable R&D projects to avoid bankruptcy
and maintain their positions (Hart and Moore, 1995). If such a disciplinary role ofdebt works effectively, the acquiring firm that used to be run inefficiently by
privately-motivated managers may decrease unproductive R&D and improve
efficiency after mergers.
C. External Finance Constraints
Mergers often increase acquirers’ leverage and reduce their cash holdings. If
acquirers cannot obtain sources of financing new R&D projects or have to pay highexternal financing costs due to financial market frictions, they have to give up theirprofitable R&D projects. Examples of financial market frictions are debt overhang
due to asymmetric information and myopic markets that do not value long-term
investment like R&D (Myers, 1977). In this case, acquirers will reduce productivity
due to low R&D intensity.
3. Literature
Many preceding studies obtained evidences supporting the value creation
hypothesis. Maksimovic and Philips (2001) examined the market for corporate
assets, i.e., partial asset sales and mergers, using the U.S. plant-level data for
manufacturing plants, and found that most transactions result in productivity gains
for the transferred asset, which is consistent with the profit-maximizing behavior.
Lichtenberg and Siegel (1987) find that plants changing owners had higher
productivity growth than those plants that did not change owners. Lang, Stultz andWalking (2002) show that the total stock market gains in tender offers are highest
when the bidder has a high Tobin’s q and the target has a low q. This is also
consistent with the value creation hypothesis. Servases (1991) obtained similar
results. Hall (1999) used the data of U.S. publicly traded manufacturing firms
during the period of 1976 to 1995 and found that R&D intensities and TFP
5

increased for the firms with a high probability of merging (i.e., with a large size, a
low R&D intensity, a high Tobin’s Q, and more cash flow).
There is also some evidence for self-interested managers’ motive for mergers.
Morck, Shleifer and Vishny (1990) find evidence that the stock market reacts
negatively to diversifying acquisitions and to acquisitions where the acquirers
perform poorly prior to the acquisitions. Maksimovic and Philips (2001) found thatfirm productivity declines in the case of mergers by acquirers with lower
productivity than that of the assets they purchase, which is consistent with theself-interested managerial behavior. McGuckin and Ngyen (1995) and Schoar (2000)
also find that the productivity of acquiring firms’ plants falls and that the
productivity of the targets’ plants rises following a takeover.
For financial constraint effects, Hall (1999) finds that R&D was frequently
reduced following a major increase in debt levels, whether or not accompanied bymergers.
Literature on the impacts of mergers of Japanese firms on R&D and productivity
is scarce. Yeh and Hoshino (2002) examined 86 Japanese mergers completed from1970 to 1994 and found that acquirers did not improve TFP but deteriorated
profitability. Fukao et al. (2006) analyze the post-acquisition performance of theacquired Japanese firms and find that those firms acquired by foreign firms
improve TFP and labor productivity while those acquired by domestic firms do not.
Iwaki (2006) analyzes mergers and acquisitions by Japanese publicly-listed firms
completed from 1980 to 2004１ and finds that mergers increase labor productivity
and that the positive effect of mergers is stronger for within-group mergers thanothers. However, she does not distinguish acquirers from targets. None of thesepreceding studies control for the endogeneity of mergers in selecting control
samples with which acquiring or acquired firms are to be compared, which may
cause a bias in estimating the effects of mergers, as is discussed in the introduction.
As a related issue, Hosono, Tomiyama and Miyagawa (2004) examined the Japanesemanufacturing firms and showed that R&D tended to increase the firm value in thelatter half of the 1990s, against the view that R&D is a managerial perquisite.
Concerning the role of a business group in Japan, there are competing
evidences. Some suggest that a close relationship within a business group worsens
the conflicts of interests between managers and shareholders by strengthening themanagerial entrenchment. Yeh and Hoshino (2002) found that mergers by firms
within the same Japanese business group, keiretsu, worsened the acquiring firm’s
profitability, suggesting that mergers within a keiretsu were often conducted for the6

purpose of bailing out a financially distressed affiliated firm. Others suggest that aclose relationship within a business group help mitigate the information problem
and create higher values through mergers. Iwaki (2006) found that mergers withina business group tend to increase labor productivity and return on assets (ROA),
suggesting that pre-merger information sharing through the dispatch of directors
from the parent company to its subsidiary and clear power balance between theacquiring and acquired firms within the business group help improve post-merger
efficiency.
4. Data
We construct the dataset of manufacturing firms by matching firms containedin Basic Survey of Japanese Business Structure and Activities (JBSA) and firms in

MARR M&A Data (MARR) published by RECOF.
JBSA covers all the firms with 50 or more employees and with 30 million yens
or

more

of

equitycapitalbelongingtomining,manufacturingandnonmanufacturing industries except for finance and some other service industries.
The number of sample firms amounts to about 30 thousand each year. JBSA
contains information on key financial indicators, research and development
expenditures, and other detailed information on business activities. MARR contains
mergers and acquisitions (M&As) involving Japanese firms, dates on which M&A
information was released, M&A type (mergers, acquisitions, transfer of business,increase in capital investment, and others), industries and nationalities of firms
involved, and information on whether M&As were undertaken within the samebusiness group or not.
Our dataset covers mergers completed from fiscal year 1995 to fiscal year 1999.
We decide the beginning year considering that JBSA has been published annually
since 1994 and that we use one-year pre-merger characteristics of firms. The ending
year is set so that we use JBSA of fiscal year 2002 and examine the post-merger
performance up to three years after mergers.
We could match JBSA and MARR for 141 mergers for which acquirers’ data
were available for one year before mergers and three years after mergers. On theother hand, the number of acquired firms for which we could match JBSA and
MARR was as small as 55. Consequently, we focus only on the acquiring firms inthis paper.
Tables 2A and 2B show the numbers of mergers by year and by industry,
respectively. Table 2C classifies merger types into four. Theoretically, mergers can7be classified into horizontal, vertical, and diversifying ones. Unfortunately, we
cannot distinguish the vertical and diversifying mergers given that only theindustries of the acquiring and acquired firms are available. Mergers in which
acquiring and acquired firms belong to the same industry are classified as
intra-industrial mergers and others as inter-industrial mergers here. Using theindustry classification adopted by MARR (40 industries), we find that 56 percent ofmergers are intra-industrial. We also classify mergers according to whether they
are within the same business group or not. We define the within-group merger,
following the definition of MARR, as a merger between a parent company and its
subsidiary or between the largest shareholder and its affiliated companies.２ We
find that 91 percent of mergers occurred within the same business group.
5. Acquirers’ Pre-merger Characteristics
In this section, we investigate the pre-merger characteristics of acquirers. The
main purpose of this analysis is to obtain a suitable control group to be comparedwith acquirers, which we use in the following sections. It will also be useful to
examine the validity of the hypotheses presented in Section 2. Specifically, we
perform the following logistic regression: p ln  i ,t    ' X i ,t 1  Industryi  Yeart   i ,t
 1  pi ,t 

(1),where pi ,t is the probability of a firm i’’s being an acquirer in year t. X i ,t 1 is avector of the firm i’’s characteristics in year t-1. We add industry and year dummies.
As the firm characteristics, we choose total factor productivity (TFP), R&D
expenditures as a proportion of sales (R&D intensity, hereafter), cash flow-to-asset
ratio, logarithm of total assets, logarithm of firm age, cost-to-asset ratio, anddebt-to-asset ratio. See Appendix for the construction of the TFP measure. The
sample firms are all manufacturing firms compiled in JBSA.
Table 3 shows the estimation result. The coefficient on TFP is positive andsignificant at the 10 percent level, which is consistent with the value-creating
hypothesis positing that efficient firms acquire inefficient firms to spread theirsuperior management skills,. The cost ratio has a positive and significant coefficient,while the cash flow ratio has a negative and significant coefficient. Though theseresults do not seem to be consistent with the value-creating hypothesis, inefficientfirms may conduct mergers to improve their efficiency by taking advantage of the8targets resources or by reaping the gains from synergy effects.

The negative

coefficient on cash flow is not consistent with the free cash flow hypothesis (Jensen,
1986) or the financial constraint hypothesis, either. The coefficient on the logarithmof total assets is positive and significant. A larger firm may acquire a smaller one togain control of the integrated firm and to minimize the integration costs. The
coefficients on the R&D intensity and the debt-to-asset ratio are insignificant.
6. Post-merger Performance
6.1 Approach
To examine the effects of mergers, we have to avoid the endogeneity problem of
the merger decision. If, for example, a growing firm tends to be an acquirer, we may
mistakenly attribute the improvement of its performance to the effect of mergers. To
deal with this problem, we select a control group adoping the propensity scorematching method. Specifically, we adopt the following two steps procedures. First,
based on the result of Table 3, we select a control sample using the one-to-one
nearest matching. Second, we estimate the effects of mergers on the acquirer’s TFP
and

R&D

for

up

to

three

years

after

mergerusing

thefollowing

difference-in-difference approach:̂ ATT ,s 









1 n treated
1 n,yts  ytcontrol 1 yttreated ytcontrol

s
1
1
1
n
n

s = {1, 2, 3},(2),, where n is the number of observations, i.e., acquirers, t is the acquisition year, s isa post-merger year, and y is a measure of firm performance. We look at the
post-merger performance up to three years after mergers. As measures of firm
performance, we choose TFP and R&D intensity. In addition, we also choose returns
on assets (ROA) and debt-to-asset ratio to examine the post-merger changes in
profitability and leverage. The superscripts “treated” and “control” denote acquirers
and their matched firms, respectively.
Though ̂ ATT is an appropriate measure of the effects of mergers, it contains
some impacts of acquired firms at least in the short run. For example, if theperformance of the acquired firm was poor prior to mergers, it may take aconsiderable time to integrate the business in an efficient way. In this case, ̂ ATT
shows a negative effect of the merger, even if the merger has synergy effects in thelong run. To eliminate the short-run impact of the acquired firm, we also measurechanges̂ ATT ,s 

in

TFP



and

R&D

intensity





from



1 n treated
1 n,yts  ytcontrol 1 yttreated ytcontrol

s
1
1
1
n
n
9

one

year

after

s = {2, 3},mergers:(3).

6.2 Baseline Results
Table 4A presents the estimates of ̂ ATT , changes from the pre-merger level. It
shows that TFP decreases significantly just after mergers and does not recover tothe pre-merger level up to three years after mergers. On the other hand, R&D
intensity does not show a significant change up to three years after mergers. The
increased leverage after mergers does not significantly constrain R&D. A decreasein TFP may suggest that some managerial costs of business integration are required
to acquire firms. A temporary decrease in ROA, which is negative and significant
one year after mergers, also suggests some managerial costs of integration.
Table 4B presents the estimates of ̂ ATT , changes from one year after mergers.
It shows that the change in TFP is not significant. Scale or scope economy is notworking at least up to three years after mergers. The change in the R&D intensity
is not significant, either. Yeh and Hoshino (2002), who examined the productivity ofthe merging firms that completed mergers from 1970 to 1995 using the industry
median as a control group, also found no significant recovery in the post-merger
years (Table 4, pp. 355), though their sample period and control group are different
from ours. .
What do the results concerning the post-merger performance suggest concerning
the hypotheses in Section 2. First, considering a significant post-merger decrease in
TFP, we may safely say that integration costs are huge and persistent at least up to
three years, even though mergers may bring about an efficiency gain on acquired
assets. Second, given that R&D did not significantly increase after mergers,
managers did not conduct mergers to expand their private benefits from
nonproductive R&D. Finally, judging from the fact that R&D did not significantly
decrease after mergers, we may conclude that external finance did not constrain
R&D activities, though firms significantly increased leverage after mergers..
6.3 Results by Merger TypeThe post-merger performance may depend on various merger types: whether
merging firms are within the same industry (intra-industry) or across different
industries (inter-industry), or whether merging firms are within the same businessgroup or not. Intra-industrial mergers may be easier to save the duplication of R&D
investment. Efficiency improvement through synergy effects may also be different
10between intra-industrial mergers and inter-industrial mergers. Merging firms
within a business group may share sufficient information prior to mergers and an
unambiguous power balance between the acquiring and acquired firms, which may
contribute to the reduction in the conflicts of interests and integration costs (Iwaki,2006). On the other hand, A firm affiliated with a business group may tend to
bailout financially distressed firms within the group, and hence its post-merger
productivity may be negatively affected by the merger (Yeh and Hoshino, 2002).
Table 5A shows the post-merger performance of mergers for the intra-industrial
and inter-industrial mergers. Looking at the changes from one-year post-merger, we
see that a significant recovery of TFP after mergers can be observed only forinter-industrial mergers. ROA also increases during the two years beginning fromone-year post-merger in the case of inter-industrial mergers. Synergy effects seemto be significant for inter-industrial mergers. The changes in R&D are notsignificant either for intra-industrial mergers or for inter-industrial mergers.
Table 5B shows the post-merger performance of mergers for the within-groupmergers and non-within-group mergers. The recovery of TFP after mergers is
significant only for within-group mergers. This is consistent with Iwaki (2006),
suggesting that the integration costs are relatively small in the case of mergers
within a business group. The changes in R&D intensity are not significant for either
type of mergers. The change of debt-to-asset ratio from the one-year pre-merger ispositive and significant only for within-group, which may suggest that merges areundertaken to bailout a financially distressed firm within the same group. However,a financial constraint effect on R&D investment cannot be observed.
7. Pre-merger Characteristics, Merger Type, and Post-merger Performance
7.1 Approach
In this section, we investigate how pre-merger characteristics of an acquirer
and merger types have impacts on its post-merger performance. Specifically, we
first measure the effects of mergers on TFP and R&D intensity as yi ,t 3  yttreated ytcontrol
 yttreated ytcontrol
3
3
1
1



(4),

, where y denotes either TFP or R&D intensity. Note that we adopt a four-year
window. Next, we regress the change in TFP or R&D intensity on the pre-merger
characteristics of the acquirer.

yi ,t 3  1 ' X i ,t 1   2 ' Typei   i ,t

(5),

11

where X i ,t 1 is the vector of the pre-merger characteristics of acquirer i and

Typei is the vector of merger types. X i ,t 1 includes the TFP level, ROA, R&D
intensity, debt-to-asset ratio, and the average age of the industry the acquirer
belongs to. We try to capture the maturity of the industry by the industry-level age.
If the free cash flow hypothesis is valid, mergers in matured industries do not
improve TFP. Typei consists of the two variables. One is the intra-industry merger
dummy and the other is the within-group merger dummy.
7.2 Results
The first two columns of Table 6 show the estimation results of TFP and the lasttwo columns show those of R&D intensity.
Looking at the results of TFP, we find that the initial R&D intensity has apositive and marginally significant effect on the change in TFP. The finding thatmergers by an R&D intensive acquirer are likely to improve its TFP is consistent
with the value maximization hypothesis. The initial TFP has a negative impact on
the change in TFP, suggesting its mean reverting property. The industry-level age is
not significant. Column 1, with only the intra-industry dummy and the groupdummy, shows that neither of the merger type dummies is significant. In Column 2,
we classify mergers into four types: intra-industry and within-group (type A),
intra-industry and non-within-group (type B), inter-industry and within-group(type C), and inter-industry and non-within-group (type D). Given type D as abenchmark, all the other three type dummies are positive and significant withalmost the same coefficients, suggesting that type D underperforms the other three
types in terms of TFP. Type D may involve quite a high managerial cost for businessintegration.
Turning to the results of R&D intensity, we find that the initial R&D intensity is
negative and significant, suggesting its mean reverting movement.

Thedebt-to-asset ratio is not significant. If mergers tightened the financial constraint
faced by the acquirer, such effects would be stronger for the acquirers whose
pre-merger debt level was high. Our result suggests that the financial constraint
does not have a negative impact on the post-merger R&D intensity. The
industry-level age dummy is positive but not significant. The managerial private
motive hypothesis is not supported. Column 3, including two merger types of12dummies, the intra-industry dummy and the group dummy, shows that the groupdummy is negative and significant at the 10 percent level. The finding thatwithin-group mergers reduce R&D intensity but not TFP level suggests that firms
within a group may have substitutable technology and therefore acquirers can save
duplication of R&D within-groups, though this effect is only marginally significant.
In column 4, we include four merger type dummies and find that none of the merger
type dummies is significant.
8. Conclusion
We investigate the impact of merger on innovation and efficiency using a
micro dataset of Japanese manufacturing firms including unlisted firms during themerger wave period of 1995-1999. We find that the acquirer’s total factor
productivity (TFP) decreases immediately after mergers and does not significantly
recover to the pre-merger level within three years after mergers. We also find that
the R&D intensity does not significantly change after mergers despite a significant
increase in the debt-to-asset ratio. Our results suggest that the costs of businessintegration are large and persistent.
Considering a large integration cost, we also analyze the post-merger
performance from one year after mergers, finding no significant increase in TFP or
R&D intensity up to three years after mergers.
To take into consideration the heterogeneity of mergers, we analyze thepost-merger performance by classifying merger types. We find that the recovery of
TFP after mergers is significant for mergers across different industries or within
the same business group, suggesting that a synergy effect works well andintegration costs are small for those types of mergers
Though our sample is broad in that it includes both listed and non-listed firms,
our sample period, fiscal year 1995 to1999, is relatively short, which coincides withthe severe banking crisis in Japan and a major part of which covers the downturn ofbusiness cycle (1997:II through 1999:I), mainly due to the limited availability ofdataset. Would the results change if we could extend the sample period to cover amore “normal” period? We conjecture that our results that mergers within a groupor across industries relatively perform better than other types would still hold. On
the other hand, the overall post-merger performance might depend on how many
mergers are for the purpose of rescuing distressed firms and how many are for morestrategic business purposes. The exact answer to the question has to be waited until
13more recent data becomes available.

14Appendix: Construction of the TFP measure
The dataset employed in this paper was obtained from Basic Survey ofJapanese Business Structure and Activities for the period from 1994-2002, which is
conducted annually by the Ministry of Economy, Trade and Industry (METI).
We define the productivity level of firm i in year t in a certain industry incomparison with the productivity level of a hypothetical representative firm in baseyear 0 in that industry. This TFP measure was developed by Caves, Christensen,
and Diewert (1982) and extended by Good, Nadiri, Roller, and Sickles (1983).
The TFP level is defined as follows:n 1
ln TFPi ,t  (ln Qi ,t  ln Qt )   f 1 ( S f ,i ,t  S f ,t )(ln X f ,i ,t  ln X f ,t )
2
t
t
n 1
 s 1 (ln Qs  ln Qs 1 )  s 1  f 1 ( S f , s  S f , s 1 )(ln X f , s  ln X f , s 1 )]2(A1)

where Qi, t, Sf, i, t, and Xf, i, t denote the output of firm i in year t, the cost share offactor f for firm i in year t, and firm i’s input of factor f in year t, respectively.
Variables with an upper bar denote the industry averages of those variables.
We drop from our sample all firms for which data on total sales, total numberof workers, tangible fixed assets, total wages and intermediate inputs are zero or
negative value at least for one year. If firm i switches industries, we classify firm iinto an industry by entry date.
OutputExcept for the commerce sector, gross output is defined as firms’ total sales. For thecommerce sector, gross output is measured as sales minus expenses for purchasedmaterials. Gross output is deflated by the output deflator derived from Japan

Industrial Productivity Database 2006 (JIP 2006) compiled by Research Institute of
Economy, Trade and Industry.
Intermediate inputs
For the commerce sector, intermediate inputs are calculated as (Cost of sales +Operating costs) – (Wages + Depreciation costs + Expenses for purchased materials).
The intermediate inputs of other sectors are defined as (Cost of sales + Operating
costs) – (Wages + Depreciation costs). Intermediate inputs are deflated by theintermediate input deflator provided by JIP 2006.
15

Labor input
As labor input, we used each firm’s total number of workers multiplied by thesectoral working hours from JIP 2006.
Capital Stock
For capital stock, the only data available are the nominal book values of tangible
fixed assets. Using these data, we calculated the net capital stock of firm i inindustry j in constant 1995 prices as follows:

Kit  BVit  ( INK jt / IBV jt )where BVit represents the book value of firm i’s tangible fixed capital in year t, INKjt
stands for the net capital stock of industry j in constant 1995 prices, and IBVjt
denotes the book value of industry j’s capital. INKjt was calculated as follows. First,
as a benchmark, we took the data on the book value of tangible fixed assets in 1975
from the Financial Statements Statistics of Corporations published by MOF. We
then converted the book value of year 1975 into the real value in constant 1995
prices using the investment deflator provided by JIP 2006. Second, the net capitalstock of industry j, INKjt, for succeeding years was calculated using the perpetualinventory method. We used the investment deflator in JIP 2006. The sectoral
depreciation rates used are taken from JIP 2006.
Cost Shares
Total cost of labor is measured as total wages. We used nominal intermediate input
as the intermediate input cost. Capital cost was calculated by multiplying the real
net capital stock with the user cost of capital. The latter was estimated as follows:

ck 

p
1 z
p k {r  (1  u )(1   )i   i  ( k )}1 u
pk

where pk , i,  , u,  and z are the price of investment goods, the interest rate
(newly issued government bonds yield (10 years)), the depreciation rate, thecorporate tax rate, the equity ratio, and the present value of depreciation deduction
on a unit of nominal investment, respectively. Data on investment goods prices,interest rates, and corporate tax rates were taken from JIP 2006, the Bank ofJapan’s website, and the Ministry of Finance Statistics Monthly, respectively. The
16

depreciation rate for each sector (  i ) was taken from JIP 2006. We calculated thecost shares of each factor by dividing the cost of each factor by total costs, which
consist of the sum of labor costs, intermediate inputs costs, and capital costs. z
was estimated as follows:z  (u i ) /[{r  (1  u)(1   )i}   i ] .

17

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19

Table 1. Hypotheses of the Characteristics and Performance of Acquirers
MotivesValue-Creation

Managerial Private Motive

Financial Constraint

Pre-merger Characteristics Post-Merger Performance
TFP
R&D
Change in TFP
Change in R&D
HighIncrease
Increase(Synergy)(Complementary)
Decrease
Decrease(Integration Costs) (Substitutable)
Low
High
Decrease
Increase(Empire-Building)(Empire-Building)
Increase
Decrease(Discipline by Debt) (Discipline by Debt)
Decrease
Decrease(Debt Overhang)

20

Table 2. Number of Mergers
A. By Year
Fiscal Year
1995
1996
1997
1998
1999
Total

1
26
26
41
46
140

B. By Industry of Acquirers
Food products and beverages
TextilesPulp ,paper and paper products
ChemicalsPetroleum and coal products
Non-metallic mineral products
Iron, steel, and non-ferrous metals
Fabricated metal products
Machinery
Electrical machinery ,equipment and supplies
Transport equipment
Precision instruments
Others
Total

10
4
2
17
1
9
19
11
19
16
15
6
11
140

C. By Type
Within Group
Within Industry
Across Industries
Total

Others
68
59
127

21

Total
11
2
13

79
61
140

Table 3. Decision of Being an Acquirer
TFP
Cash Flow / Asset
Log (Asset)
Log (Age)
R&D / Sales
Cost / Asset
Debt / Asset
Constant
No. of Obs.
Pseudo R2

0.736 *
(1.71)-0.395 ***(-4.24)0.542 ***
(16.25)-0.080(-0.53)-1.463(-0.62)0.176 ***
(3.16)-0.156(-0.63)-9.570 ***(-12.97)
62072
0.095

1 Logit estimate for the probability of being an acquirer is presented.
2. All the explanatory variables are one-year lagged variables.
Year dummies and industry dummies are included in the explanatory variables.
3. Numbers in the parentheses are z-values.
4. ***, **, * stand for the significance levels of 1%, 5% and 10%, respectively.

22

Table 4. Post-merger Performance
A. Changes from one year before mergers
years after mergers

TFP

1 year

-0.025(214)-0.026(199)-0.031(140)

2 years
3 years

***
***
***

R&D
Intensity

ROA-0.001
(214)
0.000
(199)
-0.002(140)-0.009(214)-0.007(199)-0.005(140)**

Debt-toAsset
Ratio
0.012
*
(214)
0.014
*
(199)
0.032
**
(140)

B. Changes from one year after mergers
years after mergers

TFP

R&D
Intensity

ROA

2 years-0.001
(183)
0.011
(122)

0.000
(183)
-0.003(122)-0.001
(183)
0.002
(122)

3 years

1. Changes from one year after mergers are shown.
2. Numbers in the parentheses are numbers of observations.
3. ***, **, * stand for the significance levels of 1%, 5% and 10%, respectively.

23

Debt-toAsset
Ratio
0.001
(183)
0.003
(122)

Table 5. Post-merger Performance by Merger Type
A. Within-industry versus across-industry
Within-industry
Changes

TFP

from (t-1) to (t+3)-0.037
(79)
0.001
(68)

from (t+1) to (t+3)

***

R&D
Intensity

ROA-0.002(79)-0.003(68)-0.015(79)-0.006(68)**

Debt-toAsset
Ratio
0.037 **
(79)
0.011
(68)

Across-industry
Changes

TFP

R&D
Intensity

ROA

from (t-1) to (t+3)-0.024
(61)
0.024
(54)

-0.002(61)-0.004
(54)

0.007
(61)
0.011
(54)

R&D
Intensity

ROA-0.002(127)-0.003(110)-0.003
(127)
0.003
(110)

R&D
Intensity

ROA

0.002
(13)
-0.002(12)-0.026(13)-0.010(12)

from (t+1) to (t+3)***

Debt-toAsset
Ratio
0.025
(61)
-0.007(54)

B. Within-group versus Not-within group
Within-group
Changes

TFP

from (t-1) to (t+3)-0.027
(127)
0.017
(110)

from (t+1) to (t+3)***

Debt-toAsset
Ratio
0.031 **
(127)
0.006
(110)

Non-within-group
Changes

TFP

from (t-1) to (t+3)-0.075(13)-0.035(12)

from (t+1) to (t+3)**

1. Year t denotes the merger year.
2. Changes from one year after mergers are shown.
3. Numbers in the parentheses are numbers of observations.
4. ***, **, * stand for the significance levels of 1%, 5% and 10%, respectively.

24

Debt-toAsset
Ratio
0.046
(13)
-0.031(12)

Table 6. Pre-merger Characteristics and Post-merger Performance
Dependent Variable
Column No.
TFP level
ROA
R&D intensity
Debt-to-asset ratio
Intra-industry dummyGroup dummy

TFP
1-0.678 ***(-8.69)0.293(1.28)
0.780 *
(1.85)-0.019(-0.43)-0.002(-0.07)0.029(0.73)

TFP
2-0.705 ***(-9.05)0.328(1.46)
0.775 *
(1.87)-0.015(-0.35)TypeA dummy(within-industry and within-group dummy)
TypeB dummy(within-industry and non-within-group dummy)
TypeC dummy(across-industry and within-group dummy)
TypeD dummy(across-industry and non-within-group dummy)
Industry-level age
ConstantObs.
R-squared

R&D intensity
3-0.028(-1.06)-0.755 ***(-14.90)-0.003(-0.64)-0.003(-1.19)-0.008 *(-1.66)0.190 **
(2.11)
0.202 **
(2.07)
0.204 **
(2.26)(dropped)
0.001(0.50)-0.114(-0.94)
128
0.399

0.002
(0.61)-0.294 **(-2.01)128
0.421

1 Dependent variables are changes from one year before mergers to three years after mergers.
2. All the explanatory variables are one-year before mergers.
3. Numbers in the parentheses are t-values.

25

R&D intensity
4-0.027(-1.03)-0.756 ***(-14.89)-0.003(-0.60)-0.004(-0.35)0.006(0.49)
0.000(0.00)(dropped)
0.001(1.47)-0.013(-0.91)
128
0.682

0.001
(1.49)-0.021(-1.20)128
0.683

Footnotes

Iwaki (2007) excludes the so-called “equal mergers” from her sample firms.
As for the definition of the Japanese business group, diversified groups consistingof a bank and a range of large manufacturing firms that cross-share stocks andoften accept directors from the bank is defined as keiretsu (e.g., Hoshi et al., 1990),
Keiretsu has sometimes another meaning of a vertical grouping of manufacturingfirms. Our definition does not necessarily imply such a bank-centered group or avertical tie.
１
２

26