Silviase/jgov_v1.4_full_ann huggingface dataset:
• l−1 階層よりも上の市場に独占市場が存在するとき, 企業数に関わらず, 排他的取引契約により新規参入を阻止することが可能である。

・ｌ－１階層が独占市場である場合，排他的取引契約によって，新規参入企業を排除することができない。

• l 段階より上の階層に独占市場が存在しない場合，l-1 階層の企業数が， $ v-\sum_{i\in K}c_{I}^{i}-d_{I}-\epsilon/(d_{I}-d_{E}) $ 以下である場合，新規参入を防ぐことが可能である。

既存企業がすぐ下の階層（ $ l+1 $ 階層）に排他的取引契約を提示するとき：

• l 階層よりも上の市場に独占市場が存在するとき，企業数にかかわらず，既存企業の参入を市場への参入を妨げることが可能である。

• l 階層よりも上の市場に独占市場が存在しない場合， $ l+1 $ 階層が2社以上いる場合，排他的取引契約によって，新規参入を阻止することができない。一方 $ l+1 $ 階層に1社しかいない場合，排他的取引契約によって新規参入を阻止できる。
## 第4章 おわりに

これまでの章において，排他的取引契約を用いることにより，新規参入企業が排除される可能性があるかどうかを中心に考察をしてきた。基本的なモデルは Oki and Yanagawa (2011b) に依拠して行っている。特に，既存研究において，排他的取引契約を提示する相手は，その企業がいる下流の市場に対して行われた研究が中心であった。しかしながら，Oki and Yanagawa (2011b) では，流通業者から，自社より上流にある生産者に対して排他的取引契約を提示するモデルを考案した。ここでの研究は，そのような状況において，排他的取引契約が新規参入企業を排除する可能性を様々な観点から，考察を行った。以下で各章に関して，簡単に要約を行う。

第2章において，基本モデルである，Oki and Yanagawa(2011b)のモデルを簡単化して紹介し，既存研究との対比を明確にした。ここでの分析の仮定の範囲内では，生産者がその下流にある流通業者に対して排他的取引契約をオファーする場合よりも，流通業者が生産者に対して排他的取引契約をオファーする場合の方が，効率的な参入が阻止されにくいという結果を得た。もちろん，これは様々なモデルの中の仮定，特に卸売価格の決まり方及びそこに起因する垂直的な産業内での交渉力の配分が結果に大きく影響を与えている。排他的取引契約の違法性や規制，監視について考える場合には，どこに新規参入があり，またその産業内での交渉力の配分がどのようになっているのかを見極める必要があるだろう。

第3章において，多段階からなる産業構造を考え，その構造ごとに排他的取引契約による新規参入企業の排除可能である産業構造の特徴を調べている。最初に，単純な3段階の構造を考察した。このとき，中間的な市場において，既存企業が新規参入の脅威にさらさ
れ，排他的取引契約を用いてそれを排除しようとする場合，自分より上流の企業を囲い込むのか，下流の企業を囲い込むのかという点に着目した。特に興味深いものは，上流・下流の企業数，つまり競争の度合によって，排他的取引契約の成否，及び，成立した場合の既存企業の利得が異なるという点である。これは，2章で考察した交渉力の配分の問題にも関係している。産業の垂直的な構造によって，中間的な市場の企業が上流，又は下流にライバルの排除を目的とした排他的取引契約を提示するインセンティブは大きく影響される。この点は，排他的取引契約の反競争性を判断するひとつの指標となり得るだろう。言い換えると，ここでの考察結果は，産業構造全体を正確に把握することが競争政策上，重要であることを示唆している。

本報告書は排他的取引契約の反競争効果を中心に研究を行ってきた。投資等をモデルに組み入れ，競争促進効果に関する分析を行うことは将来の研究テーマとして残されている。
## 関連図書

Abito, J. M. and J. Wright (2008). “Exclusive dealing with imperfect downstream competition.” International Journal of Industrial Organization, 26 (1): 227–246.

Aghion, P. and P. Bolton (1987). “Contracts as a Barrier to Entry.” American Economic Review, 77 (3): 388–401.

Bork, R. H. (1978). The Antitrust Paradox: A Policy at War with Itself, New York Basic Books.

Fumagalli, C. and M. Motta (2006). “Exclusive Dealing and Entry, when Buyers Compete.” American Economic Review, 96 (3): 785–795.

Innes, Robert and Sexton, Richard J. (1994). “Strategic Buyers and Exclusionary Contracts.” American Economic Review, 84 (3): 566–584.

Landeo, C. M. and K. E. Spier (2009). “Naked Exclusion: An Experimental Study of Contracts with Externalities.” American Economic Review, 99 (5): 1850–1877.

Oki, R. and N. Yanagawa (2011a). “Exclusive Dealing and the Market Power of Buyers.” Asian Journal of Law and Economics, 2 (1): Article 1.

Oki, R. and N. Yanagawa (2011b). “Exclusive Dealing by Distributors.” CARF-F-243, Faculty of Economics University of Tokyo.
Patrick Rey, and Thibaud Vergé. (2008). “The Economics of Vertical Restraints.” in Handbook of Antitrust Economics, Paolo Bucci Rossi ed., MIT Press.

Posner, R. A. (1976). Antitrust Law: An Economic Perspective. Chicago, University of Chicago.

Rasmusen, E. B., J. M. Ramseyer, and J. W. J. S. (1991). “Naked Exclusion.” American Economic Review, 81 (5): 1137–1145.

Segal, I. and M. D. Whinston (2000). “Exclusive Contracts and Protection of Investments.” RAND Journal of Economics, 31 (4): 603–633.

Simpson, J. and A. L. Wickelgren (2007). “Naked Exclusion, Efficient Breach, and Downstream Competition.” American Economic Review, 97 (4): 1305–1320.

Spiegel, Y. (1994). “On the Economic Efficiency of Liquidated Damages.” Economics Letters, 45 (3): 379–383.

Spier, K. and M. Whinston (1995). “On the efficiency of privately stipulated damages for breach of contract: entry barriers, reliance, and renegotiation.” RAND Journal of Economics, 26 (2): 180–202.

Whinston, M. D. (2008). Lectures on Antitrust Economics, The MIT Press.

Wright, J. (2008). “Naked exclusion and the anticompetitive accommodation of entry.” Economics Letters, 98 (1): 107–112.

Wright, J. (2009). “Exclusive Dealing and Entry, when Buyers Compete: Comment.” American Economic Review, 99 (3): 1070–1081.
Yanagawa, N. and R. Oki (2010). “Exclusive Dealing and Inefficient Entry Threat.” Economics Bulletin, 30: 2478–2485.

松八重泰輔 (2009). “排他的取引契約の理論的進展.” CPDP 41-J.
## 1 重傷病給付金の支給対象期間等の在り方（調査結果の要旨）

調査対象:平成23・24年度重傷病給付金支給事案のうち治癒又は症状固定した事案(458件)

※ 平成23・24年度重傷病給付金支給事案のうち治癒又は症状固定していない（平成26年11月末時点）事案15件及び治癒又は症状固定しているが犯罪被害のみの治療期間が不明なため犯罪被害者負担額が不明な事案3件は除く

治癒又は症状固定までの期間

犯罪被害者負担額(治癒又は症状固定まで)

治癒までの療養費(犯罪被害者負担額)の総額(円)

※ 症状固定時点における療養費(犯罪被害者負担額)が120万円以下の事案
## 2 犯罪被害者に負担の少ない支給の在り方（調査結果の要旨）

重傷病給付金仮給付事案に係る平均的な流れ（平成23～27年度実績10件の中央値）

重傷者数と重傷病給付金支給者数の比較

年次数	H23	H24	H25	H26	H27
重傷者数(注1)	2,854	2,979	3,021	2,899	2,724
重傷病給付金支給者数(注2)	245(8.6)	243(8.2)	216(7.1)	127(4.4)	48(1.8)

(注 $ ^{1} $ )犯罪統計

(注2)他の給付等との調整により不支給となった者を含む。かっこ内は割合(%)

加療開始後各月ごとの犯罪被害者負担額の平均額（平成27年度裁定事案177件）

加療期間(か月目)

加療開始から7か月目以降1年以内の 犯罪被害者負担額(平成27年度裁定事案)
## 3 若年者の給付金の在り方（調査結果の要旨）

調査対象:平成23年～27年度の犯罪被害給付制度の支給裁定運用実績

18歳未満の生計維持関係遺族（遺児）がいる犯罪被害者（死亡）の年齢分布

18歳未満の生計維持関係遺族(遺児)と 犯罪被害者との関係及び年齢

18歳未満の生計維持関係遺族(遺児)がいる犯罪被害者（計92人）の理論上の支給額※1分布

※1 減額、損害賠償等との調整が行われず、満額支給された場合の額を算出したもの

※2 60歳以上の犯罪被害者については全て60歳として分布させた

※1,000万円以下の者については、平成20年改正前の制度が適用された者4名を含む
## 犯罪被害者生存の場合

## 4 親族間犯罪被害に係る給付金の在り方（調査結果の要旨）

調査対象: 平成26年1月1日～12月31日の間に加害者を検挙した事件(殺人168件、傷害致死30件、殺人未遂74件)

加害者から見た犯罪被害者の関係(272件)

犯罪被害者と加害者の同居・別居（事件時）（74件）

事件前後の同居等関係

原因動機(272件)

第一順位遺族と犯罪被害者の同居・別居(事件時)

(198件)

## 犯罪被害者死亡の場合

第一順位遺族と加害者の

同居・別居(事件時)

(198件)

事件前後の同居等関係

※ 事件後の関係が「不明」(17件)を除く。
# CPRC Discussion Paper Series

# Competition Policy Research Center Japan Fair Trade Commission

The Impact of Mergers on Profits, Share Value, Innovation, and Product Prices in Japan in the 2000s

Hiroyuki Odagiri

Faculty of Social Innovation

Seijo University, and CPRC

Koki Árai

Competition Policy Research Center,

Japan Fair Trade Commission

Noriyuki Doi

Graduate School of Economics

Kwansei Gakuin University, and CPRC

Yasushi Kudo

Competition Policy Research Center,

Japan Fair Trade Commission

Chiharu Yanagita

Competition Policy Research Center,

Japan Fair Trade Commission

CPDP-51-E July 2011

1-1-1, Kasumigaseki, Chiyoda-ku, TOKYO 100-8987 JAPAN

Phone:+81-3-3581-1848 Fax:+81-3-3581-1945

URL:http://www.jftc.go.jp/cprc.html

E-mail:cprcsec@jftc.go.jp
# The Impact of Mergers on Profits, Share Value, Innovation, and Product Prices in Japan in the 2000s

By Hiroyuki Odagiri $ ^{a,b} $ , Koki Arai $ ^{a} $ , Noriyuki Doi $ ^{a,c} $ , Yasushi Kudo $ ^{a} $ , and Chiharu Yanagita $ ^{a} $

July 2011

## Affiliation:

a: Competition Policy Research Center, Japan Fair Trade Commission, Tokyo, Japan

b: Seijo University, Tokyo, Japan

c: Kwansei Gakuin University, Nishinomiya, Hyogo, Japan

## Acknowledgement:

This paper is an outcome of a joint research project at Competition Policy Research Center (CPRC) of Japan Fair Trade Commission (JFTC). We wish to thank other members of CPRC for their assistance and comments, to Takuji Saito for his valuable advice on the use of propensity score method, and to Kuninobu Takeda for his comments on legal aspects. The views expressed in this paper are the authors' alone and do not represent that of CPRC or JFTC.
## Abstract

This paper investigates the extent of performance changes after mergers (including all forms of business combination) in Japan since 2000. The performance measure used is four-fold: profit rates, share prices (event study), innovation measures (R&D intensity and the number of patents), and product prices. To study the effects from diverse viewpoints, we adopt methodology and sample that would fit each performance measure best. The result produced little evidence to suggest that mergers improved the firms' performance. There are more cases of the performance worsening than improving after mergers, though hardly any results are statistically significant, because of the presence of large variance across cases.
### 1. Introduction

This paper purports to investigate the extent of performance changes after mergers (meaning all forms of business combination including not just mergers but also asset acquisitions, share acquisitions, and business integrations using holding companies) in Japan since 2000. The performance measure used is four-fold: profit rates, share prices, innovation measures (R&D intensity and the number of patents), and product prices. Our aim is to study the effects of mergers from diverse viewpoints. Thus, we adopt methodology and sample that would fit each measure of performance best, not always insisting uniformity across them. For instance, we focus on banks to study the effects on profitability, because several comparable mergers took place in this industry, while we focus on manufacturing to study the effects on innovation, because the R&D measure is most useful in this industry. More details will be discussed in the following sections.

Roughly speaking, we find little evidence to suggest that mergers improved the firms' performance. There are more cases of the performance actually worsening rather than improving after mergers, though hardly any results are statistically significant. The evidence, simply, is not strong enough to support the argument that mergers improve efficiency. Still, variance is large across cases, implying that some of the mergers might have been successful.

In the next section, we survey theoretical and empirical studies on mergers and discuss their relations to competition policy, taking the Japanese Anti-Monopoly Law as an example. In Sections 3 to 6, we discuss our studies on the above-mentioned four performance measures in turn, explaining the sample, methodology, and the results. In Section 7, we summarize and discuss implications.

### 2. Mergers and Efficiency

Japan, like many other countries, prohibits mergers, acquisitions, and other means of business combinations "when the effect of the merger may be substantially to restrain competition in a particular field of trade" (Anti-Monopoly Act, Section 15). Japan Fair Trade Commission (JFTC) has published “Guidelines to Application of the Anti-Monopoly Act Concerning Review of Business Combination” (Merger Guidelines hereafter) to clarify what they will investigate in their review of whether a particular merger is "substantially to restrain competition." $ ^{1} $

The question that has often arisen in this context is whether and to what extent the prospect of efficiency improvements should justify mergers that otherwise may harm competition. The Williamson trade-off argument is commonly used to argue that a merger that may raise the market price can nevertheless improve social welfare (i.e., the sum of consumers' and producers' surpluses) provided the merger-related efficiency improvements cause the marginal cost to be reduced sufficiently (Williamson, 1968). Business people and the advocates of industrial policy often try to justify mergers in
### 3. Effects on Profit Rates

#### 3.1. The Sample and the Choice of Control Group

The six merger cases to be studied are all between regional banks $ ^{5} $ .

According to the data compiled by a private consulting firm Recof, there were 1,000 to 2,000 mergers and acquisitions (including minority acquisitions) between domestic firms every year during 2000-2010 $ ^{6} $ . However, in order to make an accurate pre-merger and post-merger comparison, we need financial data for both of merging firms (i.e., both acquirer and acquired). We thus confined our analysis to mergers between public (i.e., listed) domestic firms, which occurred only in a handful of cases a year. And we wanted the merger to take place before 2006 so that we can have at least three-year post-merger financial statements. Furthermore, we decided to concentrate on a single industry (at basically the two-digit level) in order to eliminate industrial effects. We found that the banking industry had the largest number, albeit only six, of such cases. These are the merger cases we are going to analyze.

We intend to compare the changes in profit rate around the time of merger, relatively to that of a comparable firm (control firm). Most of the past studies selected control firms on the basis of similarity to merger firms in terms of business composition and size. Such a selection method is unsatisfactory, however, because banks of similar size may differ significantly in terms of their tendency to undertake mergers. As a result, one cannot say whether the observed difference between a merger firm and a control firm occurred as a consequence of the merger itself or that of the factors that prompted the merger firm to undertake that merger. To make this separation, we want to make a comparison between a merger firm and a firm with a similar likelihood of undertaking a merger but with no merger actually undertaken. The selection of such control firms can be made with the application of the propensity score matching method $ ^{7} $ .

Thus, as a first step, we used an unbalanced panel (pooled data) of all the regional banks during 1999-2005 (sample size = 773) to make a logit estimation of the probability of undertaking a merger. The independent variables are of two types. The first is the company financial data, such as ROE (the rate of return on equity, calculated as a ratio of current profits to net worth), its square, operating cost ratio (operating costs divided by net worth), capital asset ratio (net worth divided by total assets), bad loans ratio (the amount of insolvent debts divided by net worth), total assets in logarithm, and its square. The second is the variable for regional economic condition, measured by the rate of growth of products of the prefecture the bank is headquartered. To avoid reverse causality, all the independent variables were measured with one-year lag.

The estimated result is shown in Table 2. The result suggests that a bank with a higher
operating cost ratio, a lower capital asset ratio, a higher bad loans ratio, and bigger assets was more likely to undertake mergers during this period. That is, roughly speaking, a bigger and less healthy bank seems to have chosen to merge.

The probability of merger predicted from this estimated equation is the propensity score. We calculated the propensity score of each of the merger bank at the year of merger. Then, we chose as the control bank a bank that has the nearest propensity score in the same year. The result is in Table 3. Since Yachiyo Bank showed the nearest score to two of the merger banks, it appears twice in the list of control banks.

#### 3.2. Results

We employ ROE as a measure of corporate performance, and look at its changes as defined below.

$$ (1)\qquad\Delta ROE_{i,t}=ROE_{i,t}-ROE_{i,-1} $$

where $ ROE_{i,t} $ is ROE of i-th firm in t years after the merger $ (t=1,2,3) $ and $ ROE_{i,-1} $ is ROE of i-th firm one year before the merger. For merged banks, we calculated $ ROE_{i,-1} $ as that of a hypothetical combined bank by adding the two pre-merger firms' data and then making the ratio. Also, to control the effects of business fluctuation, we used year-adjusted ROE by subtracting from ROE the annual average across all the sample banks used in the logit analysis above.

The results are summarized in Table 4. For instance, one year before the merger, Shinwa Bank had its ROE 9.696 percent point higher than the average of the year, whereas the control bank, Chukyo Bank, 11.928 percent point higher. That is, both banks had above-average ROE but the control bank had a higher one. Kiyo Bank, on the other hand, had the ROE 14 percent point lower than the average whereas the control bank, Hokuto Bank, had an above-average ROE. Among the six cases, merged banks had higher ROEs in three cases and the control banks had higher ROEs in three cases.

The table also shows that ROE declined after the merger in four of the six cases. In comparison to control banks, ROE worsened in three of four cases one year after the merger, four of six cases two years after the merger, and three of six cases three years after the merger. On average, merger banks did worse than control banks; however, the difference is statistically insignificant because the variance across banks is large $ ^{8} $ . Therefore, we cannot conclude whether the mergers improved or worsened the ROE relatively to control firms. In other words, there is no evidence to support the argument that mergers generally improve the firms' profitability. However, the results vary significantly across cases.

### 4. Effects on Share Prices

#### 4.1. An Event Study Methodology

A frequent criticism to the pre- and post-merger comparison of profit rates is that it fails to capture the long-term consequence. It may take several years before the necessary re-organization is completed, the employees get used to new practices and corporate
culture and learn the better technologies and management method of the partners, and synergy effects take place. Until then, the financial indicators, such as profit rates, may not produce noticeable effects. Still, the shareholders would try hard to predict such long-run consequences to make buy-or-sell decisions. Of course, the information is imperfect and the prediction is bound to error. Still, investors, particularly institutional investors, tend to have substantial information on the internal and external conditions of the firm and the industry, as well as the history of mergers in the same industry, with which they will endeavor to predict the long-run profit stream of the merger firms. Such prediction will affect their buying/selling decisions and thus the share prices. It is for this reason that an event study methodology is commonly applied to study the changes in share prices around the time of merger announcement.

Let $ R_{i,t} $ be the rate of return to the share of i-th firm in day t. It is calculated as the rate of change in share price because there was neither dividend payment nor share split during the sample period $ ^{9} $ . Similarly $ R_{m,t} $ is the rate of return to market portfolio. Using the pre-merger time-series data, we first estimate the following equation:

$$ R_{i,t}=a_{i}+b_{i}R_{m,t}+u_{i,t} $$

where $ u_{i,t} $ is the error term. Using the estimated parameters, $ \hat{a}_{i,t} $ and $ \hat{b}_{i,t} $ , we calculate abnormal returns (AR) as the difference between the actual value and the predicted value, and the cumulative abnormal returns (CAR) as its cumulative value from day $ T_{1} $ to day $ T_{2} $ ; that is,

$$ CAR_{i}(T_{1},T_{2})=\sum_{t=T_{1}}^{T_{2}}\left[R_{i,t}-(\hat{a}_{i}+\hat{b}_{i}R_{m,t})\right] $$

If CAR thus calculated for the post-merger period is positive, then we can infer that the investors predicted that the merger would produce a higher stream of long-run profit, which resulted in the market share price being higher than that predicted by general market price movement alone.

#### 4.2. Sample and Period

The day of the event is the day when the merging firm(s) made their press release. As the actual merger always takes place several months after the press release, the share prices of both partners are available for both pre-merger and post-merger days. These share prices, of course, are available only for listed firms; thus the sample is confined to mergers between listed firms that occurred between 2000 and 2007. With this criterion, we selected 15 merger cases, of which 11 were between financial institutions (banks or insurance companies) and 4 were between manufacturing firms. With two mergers involving three banks each, we have the sample of 32 firms in total.

We estimated equation (2) using the daily share price data from one month before the announcement to two working days before. We then calculated CAR according to equation (3), where $ T_{1} $ is one working day before the announcement and $ T_{2} $ varies from 0
They argue that with the two together, they can exploit economies of scale and scope, taking advantages of larger and complementary resources and capabilities for innovation.

The existing literature is ambiguous on such effects. The so-called Schumpeterian hypothesis emphasizes the advantage of size in R&D and would predict an increase in R&D activity and/or R&D output after the merger. By contrast, the so-called quiet life hypothesis would argue that the management of a bigger and more monopolistic firm would indulge in a quiet and safe life, avoiding risky activity such as R&D. Also, the more monopolistic the firm is, it is more likely that its new product attracts customers only by having them switch from the existing products of the same firm. Because of this ‘replacement effect’, a bigger firm or a firm with more variety of products will have a smaller incentive to engage in R&D. These arguments suggest that the merger may result in lower innovation activity.

Empirical results are also mixed; however, there appear more cases suggesting a negative effect of merger $ ^{10} $ . For instance, Cassiman, et al. (2005) and Colombo and Garrone (2006) found that mergers, particularly horizontal ones, tend to reduce R&D intensity and this tendency is stronger when the merger partners' technologies are substitutable. All these results therefore suggest that the effects of mergers on innovation are neither straightforward nor universal, which is why we need to make an investigation in the Japanese cases.

#### 5.2. Sample and Variables

Our sample consists of 39 mergers of manufacturing firms. As in Section 3, we basically added the figures of merger partners for the pre-merger period. Two mergers, however, were the acquisitions by listed firms of smaller non-listed firms and, for these, we compared the pre-merger figure of the acquiring firm only to the post-merger figure, because the acquired firm was much smaller and adding its pre-merger figure was assumed to affect little. If anything, its neglect will overestimate the effect of a merger as regards the number of patents and, unless the smaller firm is more R&D intensive than the larger one, R&D intensity $ ^{11} $ .

We use both R&D input variable -- R&D intensity, measured by the ratio of R&D expenditures to sales -- and output variable – the number of published patent applications. In the Japanese patent regime, all applications are published after 18 months of application $ ^{12} $ . We counted the number of these published applications.

There is one important difference between the two variables because of data source. R&D intensity was calculated from consolidated financial statements; that is, both the numerator and the denominator include the figures of consolidated subsidiaries. By comparison, we counted the number of patents from the JPO (Japan Patent Office) database, based on the name of parent company only, because tracing all the subsidiaries and counting the patents filed under these names is very difficult and time-consuming. We trust that this difference between the two variables does not cause a serious bias as the