公司財務決策論文兩篇:跨國購併目標公司之選擇以及聯貸市場參貸銀行的選擇 - 政大學術集成

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(1)國立政治大學財務管理研究所博士論文. 論文題目政治. 大. ‧. ‧ 國. 學. Two Essays on立Corporate Financial Decisions: Choices of Target Firms in Cross-Border M&As and Choices of Participant Banks in Syndicated Loan Market. n. er. io. sit. y. Nat. al. Ch. engchi. i n U. v. 指導教授：張元晨博士岳夢蘭博士研究生：謝依婷. 撰. 中華民國一○四年六月.

(2) 謝辭. 細數過去二千五百多個日子，該是離開政治大學的時刻了。在博士班學習的軌道上，從不斷克服壓力中所成長，在學業即將畫下句點的同時，回顧起一路走來的歲月，那飛逝的青春在我人生中刻下了痕跡。回想起剛剛起步的自己，感謝這些日子以來支持我的師長與親友，力挺我到最後一刻。首先最要感謝的是我的指導教授，張元晨博士及岳夢蘭博士，在我進行博士班研究的過程中，不斷地給予我學業及研究上的指導與充份的支援。透過一次次的討論，逐步調整研究方向，不斷地擬定及延伸新的思路，讓我學術研究的過程中時時刻刻都在進步。也感謝指導老師們的提攜，讓我有機會與國際知名學者們接觸與合作，讓我的學術視野變得更廣更國際化。除了學術研究外，張老師及岳老師總是在我面臨困惑迷惘時，拉我一把，給予我適時的鼓勵以及精神喊話，在失意之時找到自我。在此，謹向老師表達最誠摰的謝意。. 立. 政治大. ‧ 國. 學. 感謝我學術生涯的啟蒙老師，英國曼徹斯特大學的 Professor Edward Lee，是我學術生涯剛起步時的嚮導，帶著我一步一步瞭解學術研究，告訴我用正面思. ‧. 考的面向解決問題，引導我如何使用跨領域的思維穿梭在學術研究中，建立多方面的想像空間，並強調，堅持自己信念，目標終會達成。同時也感謝，亦師亦友的詹凌菁老師，總是在輕鬆愉悅的氛圍下，引領學生，不時提供不同的觀點給予啟發，也在茶餘飯後之時，給予學生適時的紓解。. sit. y. Nat. n. al. er. io. 感謝博士班求學過程中所有的任課老師，李志宏老師、周行一校長、周冠男老師、姜堯民老師、徐燕山老師、屠美亞主任、湛可南老師、盧敬植老師、顏錫銘老師、江彌修老師等，在老師們的專業培訓過程下，不論在基礎專業課程上的提供學術根基，引領國內外知名學者的造訪演說，讓學生的學術研究基礎更加扎實，因為有你，讓學生的學術研究過程更加精彩。. Ch. engchi. i n U. v. 特別感謝博士論文的口試委員，林修葳老師、張士傑老師、陳思寬老師、以及盧秋玲老師。在博士論文的審核過程中，對於論文的架構給予詳細的建議，也在口試過程中，不斷地提出不同的觀念以及新的思維，對學生的啟發甚大。感謝口試委員對於學生論文的指導與協助，從不斷的解決問題中，以及釐清可能問題下，使得這本論文更加精彩。在此，致上最深的謝意。博士班的研究過程中，因為有學長姐以及學弟妹們的陪伴讓我研究過程中不孤單。感謝采彤助教給予我適時的提醒與協助，成為我忙碌時的備忘欄。感謝湘萍學姐和文謙學長的鼓勵與協助，不僅在學術研究上提供建議也在各方便給予開導，這些支持都讓我點滴在心頭並且感激不已。感謝同儕之間相互打氣，讓我倍. i.

(3) 感溫情，感謝你們。感謝學弟妹的意見交流資訊提供，讓學術研究不再乏味，謝謝你們。感謝政治大學財務管理學系的每一份子，謝謝。感謝家人，爸爸、媽媽、姐姐、弟弟，以及近幾年加入的新成員姐夫和外甥，在我的研究過程不斷給予我適時的打氣與經驗分享，並且提供學業以外的紓解管道，讓我學術之路充滿歡樂以及幸福感，成為我最堅強後盾，感謝有您們在我的身旁，謝謝。最後，感謝我學術研究上最重要的人，陪伴我碩士班及博士班九年的男朋友，信豪。在我每次快要倒下時，都會拉我一把；在我課業出現危機時，給予我支援；在我生活處事方面出現瓶頸時，給予我最佳建言。有你我才能無憂無慮的進行學術研究，有你我才能更堅定的走我想走的路，謝謝你的陪伴，希望我們之後也可以完成驚世鉅作。謝謝你。. 政治大. 學術研究是漫長且無際的抗戰，希望在未來我能堅持到底，達成自己的理想目標。本篇論文致我最親愛的師長親友們以及所有關心我的人，謝謝。. 立. ‧. ‧ 國. 學謝依婷謹識於. 國立政治大學財務管理學系. y. Nat. n. al. er. io. sit. 二○一五年六月三十日. Ch. engchi. ii. i n U. v.

(4) 中文摘要. 本論文主要由兩篇文章所組成，探討有關跨國購併活動中目標公司的選擇，以及聯貸市場中參貸銀行的選擇。第一部份旨在分析市場集中程度與跨國購併在垂直相關產業的議題。Beladi, Chakrabarti and Marjit (2013)建立一般均衡寡佔模型，連結當地國的市場競爭力和跨國購併在垂直相關產業的論點。他們模型認為當地國家的垂直整合程度會改變國外主併者策略優勢。我們使用 1990 年至 2012 年涵蓋 86 個國家，之全球購併活動案件，我們衡量當地國家的市場競爭與垂直整合程度，呈現當地國家前期的市場競爭力將會誘發國外市場主併者進入，以垂直購併的方式購併當地國家之目標公司。本研究結果提供了實證性的結果來支持過去理論之發現，認為產業的集中程度會影響跨國購併。. 政治大. 本論文的第二部份，在研究知識技能互補和銀行商譽在參貸銀行的選擇，觀察主貸銀行該如何選擇參貸銀行之決策分析。延伸 Diamond (1991)的商譽建立假說，透過主貸銀行本身的特性因子與工作經驗，來探討主貸銀行選擇參貸銀行的誘因動機。研究結果發現，當主貸銀行具有較高的自身商譽佳、經營及投資具效率性、內部監理機制較佳、且市場經驗較為豐富時，會誘使主貸銀行減少對高. 立. ‧ 國. 學. ‧. 商譽參貸銀行的需求。呈現知識技能互補的現象於主貸銀行選擇參貸銀行的決策。本研究結果可提供我們對聯貸銀行團商譽互補現象及分析。. y. Nat. sit. n. al. er. io. 關鍵字：公司財務決策、市場集中度、跨國購併、垂直整合、比較利益、聯貸市場、參貸者商譽、建立商譽的互補現象. Ch. engchi. iii. i n U. v.

(5) 英文摘要. Two essays are comprised in this dissertation to study on choices of target firms in cross-border M&As and choices of participant banks in the syndicated loan market. In the first essay, cross-border mergers and market concentration in a vertically related industry, we examine the relationship between market concentration and cross-border M&A. Beladi, Chakrabarti and Marjit (2013) present an oligopoly in general equilibrium model to identify the linkages between local market competition and cross-border mergers in a vertically related industry. Their model predicts that a vertical integration at home changes the strategic advantage for foreign acquirers. Using firm-level data from 86 countries between 1990 and 2012, we calculate proxies for local market competition and show that lower (higher) pre-merger local competition at home country will increase (decrease) mergers between a foreign firm and a vertically integrated home firm. These findings provide empirical supports for the significant impact of industry concentration on the decisions of cross-border M&A.. 立. 政治大. ‧. ‧ 國. 學. In the second essay, the effects of knowledge complementarities and bank reputation on participant banks choices, we focus on the decision of lead arrangers on participant bank choices in the syndicated loan market. We extend reputation building theory (Diamond, 1991) and model the lead arranger’s partner choice problem through the effect of self-related and task-related factors. Our paper show that when lead arrangers have higher reputation, operating efficiency, and market experience, lead arrangers tend to choose less reputable partners. These results help to explain how lead arrangers, through their partner selection decisions, manage the reputation pool among banks in the syndicated loan market.. n. er. io. sit. y. Nat. al. Ch. engchi. i n U. v. Keywords: Corporate Financial Decisions, Market Concentration, Cross-border Merger, Vertical Integration, Comparative Advantage, Syndicated Loan Market, Participant Bank Reputation, Complementarity in reputation Building.. iv.

(6) 目錄 Introduction ................................................................................................................. 1 【第一篇論文】 Cross-Border Mergers and Market Concentration in a Vertically Related Industry .................................................................................................. 2 1. Introduction ................................................................................................. 2 2. Literature review: Cross-Border Mergers and Market Concentration 3 4. 5. 6. 7.. Hypotheses development: Determinants of cross-border M&As........ 5 Sample and descriptive statistics on Cross-border M&As and market concentration ............................................................................................... 6 Empirical Results ...................................................................................... 10 Robustness ................................................................................................. 15 Conclusion ................................................................................................. 20. 立. 政治大. 學. 【第二篇論文】. ‧ 國. 3.. 銀行的商譽與參貸銀行的選擇：商譽互補效果...................................................... 32 前言.............................................................................................................. 32 研究架構：參貸銀行的選擇與商譽互補誘因.......................................... 35 2.1. 背景：全球聯合貸款市場.................................................................. 35 2.2. 商譽互補：參貸銀行的商譽(Big3Global) ......................................... 37 2.3. 選擇參貸的潛在誘因：銀行自身特性與工作經驗.......................... 37 2.3.1 自身特性相關因子(Self-related)的衡量 .............................. 39 2.3.2 銀行工作經驗(Task-related) ................................................ 40 高商譽參貸的選擇模型：樣本描述及敘述統計...................................... 41 3.1. 樣本篩選與資料來源.......................................................................... 41. ‧. 1. 2.. n. er. io. sit. y. Nat. al. 3.. Ch. engchi. i n U. v. 5.. 3.2. 敘述統計.............................................................................................. 42 參貸銀行選擇上的互補誘因...................................................................... 48 4.1. 自身相關特性(Self-related)與參貸選擇 ........................................... 49 4.2. 工作經驗(Task-related)與參貸選擇 ................................................. 52 進一步的研究.............................................................................................. 54 5.1. 工作經驗與參貸銀行商譽選擇，以條件性邏輯斯迴歸(Conditional logit regression)再驗證 ..................................................................... 54 5.2. 銀行商譽程度的高低與主貸銀行選擇的影響.................................. 55 5.3. 學習誘因與參貸銀行商譽的選擇...................................................... 56. 6.. 結論及後續研究建議.................................................................................. 60. 4.. v.

(7) 表目錄 Table 1.1 Summary Statistics of M&A sample. ....................................................... 7 Table 1.2 Number of M&A in the Oligopoly Industry. ......................................... 9 Table 1.3 Determinants of Cross-Border M&As in Oligopoly Industry. .......... 12 Table 1.4 Determinant of Cross-Border Home Vertical M&As in Oligopoly Industry ...................................................................................................... 14 Table 1.5 Robustness check: SIC-Based Classification of the Determinant of Cross-Border M&As in Oligopoly Industry. ......................................... 16 Table 1.6 Robustness check: SIC-Based Classification of the Determinant of Cross-Border Home Vertical M&As in Oligopoly Industry. .............. 17 Table 1.7 Robustness check: Additional tests on the Determinant of Cross-Border M&As in Oligopoly Industry. ......................................... 18 Table 1.8 Robustness check: Additional tests of the Determinant of Cross-Border Home Vertical M&As in Oligopoly Industry. .............. 19 表 2.1 全球聯貸銀行的排名表.................................................................................. 36 表 2.2 資料來源與預期方向...................................................................................... 38 表 2.3 敘述統計：參貸銀行的選擇.......................................................................... 43. 立. ‧ 國. 學. ‧. 選擇高商譽參貸 vs. 選擇非高商譽參貸：主貸銀行特性之比較.............. 47 概率模型結果：主貸銀行商譽、營運及投資多角化與參貸銀行的選擇.. 50 概率模型結果：主貸銀行內部監控機制與參貸銀行的選擇...................... 51 概率模型結果：主貸銀行工作經驗與參貸銀行的選擇.............................. 53 穩健性檢定-條件式邏輯斯迴歸模型：主貸銀行的工作經驗與參貸銀行的選擇.................................................................................................................. 57 表 2.10 穩健性檢定-多元邏輯斯檢定：商譽特性與資訊不對稱 .......................... 58 表 2.11 穩健性檢定：學習誘因與參貸銀行的商譽................................................ 59. n. al. er. io. sit. y. Nat. 表 2.5 表 2.6 表 2.7 表 2.8 表 2.9. 政治大. Ch. engchi. vi. i n U. v.

(8) 圖目錄 Figure 1.1 Herfindahl-Hirschman Index of M&A sample. ................................... 8 圖 2.1 全球聯貸時間趨勢圖與高商譽的持有份額.................................................. 35. 立. 政治大. ‧. ‧ 國. 學. n. er. io. sit. y. Nat. al. Ch. engchi. vii. i n U. v.

(9) 附件目錄 Appendix 1.A Variable definitions and data sources for Chapter II ................. 24 Appendix 1.B Model and Propositions .................................................................. 25 附件 2.A 面對不同的參貸選擇下主貸銀行的宣告效果 ......................................... 70 附件 2.B 變數定義與資料來源 for Chapter III...................................................... 71. 立. 政治大. ‧. ‧ 國. 學. n. er. io. sit. y. Nat. al. Ch. engchi. viii. i n U. v.

(10) 文獻目錄 Chapter II References ............................................................................................... 21 Chapter III 參考文獻 ................................................................................................ 61. 立. 政治大. ‧. ‧ 國. 學. n. er. io. sit. y. Nat. al. Ch. engchi. ix. i n U. v.

(11) Introduction Cross-border M&A is an important driving force of entrepreneurship throughout the world. The volume of cross-border M&As has been growing worldwide, from 29% of total merger volume in 2002 to more than 40% in 2015. Motivation behind firm’s decisions to engage in cross-border M&A are complex. Neary (2003, 2007) developed an oligopolistic general equilibrium model model to explore strategic interactions among oligopolistic firms in cross border M&As. Beladi et al. (2013) extend this theory and examine the linkages between local market competition and cross-border mergers in a vertically related industry. They suggest that comparative advantage is an important factor behind target firm choices in cross-border M&As. The first essay of this dissertation evaluates this prediction and provides empirical evidence on the determinants of target firm choices in cross-border M&As. Logistic regressions of cross-border M&A confirm that international factors and market concentration are important determinants of cross-border M&A. These results are consistent with recent empirical findings (Alfaro and Charlton, 2009) and theoretical predictions from the oligopoly in general equilibrium model proposed by Neary (2007) and Beladi et al. (2013).. 立. 政治大. ‧ 國. 學. ‧. Syndicated loan market financing is a topic that has become an object of major attention in the world economy. A syndicated loan is originated by a lead arranger which sells shares of loan to other financial institutions by seeking syndicate partners after the deal is signed. The second part of this dissertation ask the following question: How lead arrangers choose partners with different reputations in syndicated loan market? This is an important question, given the varying degree of bank reputation and experience in syndicated loan contracts.. n. er. io. sit. y. Nat. al. i n U. v. In the second essay, we investigate how lead arrangers select their participant banks. Using the concept of complementarity in reputation building, we examine the issue of how financial intermediary manage their reputation pool in syndicated loan market. We find evidence that, lead arrangers with high reputation, efficiency and experience, and tend to choose less reputable participant banks.. Ch. engchi. The remainder of this dissertation is organized as follows. Chapter II explores the effects of market concentration on the cross-border M&As decisions. Chapter III examines the theory of complementarity in reputation building on the syndicated loan partner choices.. 1.

(12) Cross-Border Mergers and Market Concentration in a Vertically Related Industry 1. Introduction Is there a link between market concentration and cross-border M&A? Does the vertical structure of an industry affect such linkages? Beladi et al. (2013) use a two-country model of oligopoly in general equilibrium show that local competition matters for cross-border M&A in a vertically related industry. Market competition of various types can create incentives for firms to engage in cross-border M&A, especially for firms with strategic motives. Theoretical studies have shown that competitive advantage interacts with comparative advantage to determine cross-border M&A (Neary, 2007 and Beladi et al., 2013). On the other hand, empirical studies have focused primarily on international factors such as geography, culture, accounting disclosure and stock market performance in motivating cross-border M&A (Erel et al., 2012 and Ahern et al., 2013). This paper stems from a direct comparison of the pattern of specialization and the incentives for cross-border mergers with and without the possibility of vertical integration, include the identification of a) potential gains from cross-border mergers in the presence of vertical integration and b) conditions for a relatively efficient firm’s choice between merging, across the border, with a disintegrated firm or an integrated firm. Our empirical exercise also assesses geography, culture, distance, and market concentration as factors that can jointly affect the likelihood of cross-border M&A.. 立. 政治大. ‧. ‧ 國. 學. y. Nat. sit. n. al. er. io. This millennium’s wave of cross-border mergers was preceded by five successive waves in close proximity. We retrieve cross border M&A observations on individual firms and then augment this data with detailed information on competition measures, which theory suggests should influence firms’ cross-border M&A decisions. Logistic regression analyses of cross-border M&A are performed on a panel of firm-level data spanning 86 countries over the years 1990 – 2012. Our results confirm that market concentration is an important determinant of cross-border M&A. The empirical evidence supports the conjectures of theoretical predictions from Neary (2007) and Beladi et al. (2013) and empirical findings of Alfaro and Charlton (2009).. Ch. engchi. i n U. v. In effect, our paper makes three important contributions to the existing literature. First, this paper is the first to explore the relationship between market concentration and cross-border M&A. Second, we show how vertical integration of target firms acts as a mechanism by which geography, culture, distance, and market concentration interact as factors jointly affecting the likelihood of cross-border M&A. Third, we use various measures of vertical integration for target and acquirer firms to capture industry commodity flows information, while most previous studies focus on a single dimension (e.g., SIC code). 2.

(13) The rest of our paper is organized as follows. In the next section, we place our work in the context of the relevant literature. Section 3 provides testable hypotheses. Section 4 describes the data and methodology. In Section 5, logistic regressions are used to assess the empirical link between the probability of cross-border M&A and market concentration. Section 6 offers a summary of our findings and conclusions.. 2. Literature review: Cross-Border Mergers and Market Concentration The literature on cross-border mergers, by any standard, is still at its infancy. Notwithstanding the fact that a third of worldwide mergers involve firms from different countries, the vast majority of the academic literature on mergers has been primarily limited to intra-national mergers. Among notable theoretical contributions are the works of Long and Vousden (1995), Head and Ries (1997), Falvey (1998), Reuer et al. (2004), Nocke and Yeaple (2007), Neary (2007), and Beladi et al. (2013). Long and Vousden (1995) analyzed the effects of tariff reductions on horizontal mergers in a Cournot oligopoly. They showed that unilateral tariff reductions encourage cross-border mergers which concentrate market power at the expense of mergers which reduce cost, while bilateral tariff reductions have the opposite effect, encouraging mergers which significantly reduce cost. Head and Ries (1997) investigated the welfare consequences of horizontal mergers between firms based in different nations. They demonstrated that when mergers do not generate costs saving, it will be in the national interest for existing competition agencies to block most world welfare-reducing combinations. When mergers generate cost savings, national welfare-maximizing regulators cannot be relied upon to prevent mergers that lower world welfare. Falvey (1998) showed how the rules for approving an international merger should be adapted to account for the fact that the regulator is only concerned with domestic welfare i.e. ignores the effect of the merger on foreign firms and consumers. Reuer et al. (2004) have analyzed the role of sector-specific contractual heterogeneity of cross-border mergers in mitigating the problem of adverse selection. They pointed out that, in the case of international mergers, a key contractual variable is whether the parties agree to a performance-contingent payout structure which can mitigate the risk of adverse selection. Bertrand and Zitouna (2006) examined policy designs for international mergers. They showed that the effect of trade liberalization on merger incentives depends on the technological gap: for low and high (medium) gap, there is an inverted U- (W-) shaped relation between trade costs and incentives to merge. Nocke and Yeaple (2007) modeled cross-border mergers as international purchases and sales of country-specific firm capabilities. They demonstrated that the degree to which firms differ in their mobile and non-mobile capabilities plays a crucial role for the international organization of production: depending on whether firms differ in their mobile or immobile capabilities, cross-border mergers may involve. 立. 政治大. ‧. ‧ 國. 學. n. er. io. sit. y. Nat. al. Ch. engchi. 3. i n U. v.

(14) the most or the least efficient active firms. Neary (2007) constructed the first analytically tractable general equilibrium model of cross-border mergers where he showed how trade liberalization can trigger international merger waves through bilateral mergers in which it is profitable for low-cost firms to buy out higher-cost foreign rivals. Beladi et al. (2013) argue that the vertical structure introduces a distinction between the foreign and domestic firm even in the absence of transport costs since mergers can affect competition in input markets creating, in addition to the usual market power motive, an input-market concentration effect. While each of these studies has pushed the boundaries of our understanding of mergers across borders, all of them have focused on the horizontal aspects of such mergers. Our work complements this literature in recognizing the importance of the role played by the vertical structure of these industries in which cross-border mergers have been initiated. In particular, the significance of the vertical dimension of horizontally merging firms is evident from the initiatives taken by suppliers (of large retail chains) seeking to control the costs of purchasing and carrying inventories by cutting down on the number of vendors. A cross-border horizontal merger involves firms producing substitutes in two distinct countries with the consequence that such a merger will remove direct competitive pressures absent other constraining factors or offsetting efficiencies. When cross-border horizontal mergers take place in industries that are vertically related, they present a greater challenge for competition authorities since the vertical structure itself affects the intensity of competition. We analyze cross-border horizontal mergers in a vertically related oligopolistic industry capturing the incentives for and implications of cross-border mergers apparent in international data. From an analytical perspective, the vertical structure injects a distinction between the foreign and domestic firm even in the absence of transport costs since mergers can affect competition in input markets creating, in addition to the usual market power motive, an input-market concentration effect. We argue that vertical integration can act as a foreclosing device reducing upstream competition and hence raising the input price for the disintegrated downstream rivals. In particular, we try to capture the possibility of augmenting the gains from cross-border horizontal mergers when a vertically integrated production structure exists. We are also able to highlight the importance of the interaction between efficiency and concentration since a merger between a high-cost and a low-cost firm increases efficiency by eliminating the high-cost firm and raises price by increasing concentration.. 立. 政治大. ‧. ‧ 國. 學. n. er. io. sit. y. Nat. al. Ch. engchi. i n U. v. The existing empirical literature documents many potential factors that are associated with cross-border M&A. Rose (2000) argue that physical distance can increase the cost of cross border M&A and the level of market development and corporate governance are also likely to affect cross border M&A. Using a large panel data set of cross-border M&A deals for the period 1990–1999, Giovanni (2005) show that the size of financial markets has a strong positive association with domestic firms investing abroad. Jovanovic 4.

(15) and Rousseau (2008) find that mergers play an important role in reallocating assets toward an economy’s more efficient firms. Chari, Ouimet and Tesar (2009) show that acquirer from developed markets benefit more from weaker governance environments in emerging markets. Alfaro and Charlton (2009) assess the importance of comparative advantage considerations in the determination of FDI. They show that trade costs and an increase in the subsidiary country skill level have negative and significant effects on the level of multinational activity. The interaction term of country skill abundance and industry skill intensity is positively related to FDI. They also show that intra-firm FDI between rich countries in high skill sectors is consistent with the notion that firms in high institution countries with sophisticated inputs engaging more in FDI. Erel, Liao and Weisbach (2012) analyze cross-border mergers in 48 countries between 1990 and 2007. They find that geography, the quality of accounting disclosure and bilateral trade increase the likelihood of mergers between two countries. Ahern, Daminelli and Fracassi (2013) find that the volume of cross-border M&A is affected by national culture characteristics such as trust, hierarchy and individualism. Weinberg and Hosken (2013) use a static Bertrand model to directly estimate the price effects of two mergers. Bernile, Lyandres and Zhdanov (2012) show that the U-shaped relation between the state of demand and the propensity of firms to merge is driven by horizontal mergers in industries that are more concentrated and characterized by relatively strong competitive interaction among firms. We draw on this body of work when testing the predictions of our theoretical construct.. 立. 政治大. ‧. ‧ 國. 學. er. io. sit. y. Nat. 3. Hypotheses development: Determinants of cross-border M&As. al. n. v i n C h from the theoretical Our hypotheses are derived results which shown U i e h n that competitive advantage interacts g cwith comparative advantage to. determine cross-border M&A (Neary, 2007 and Beladi et al., 2013). Beladi et al., (2013) show that vertical integration at home increases the gains from cross-border mergers. We argue that when an upstream firm is integrated with a downstream firm at home, a relatively efficient foreign firm will have an incentive to acquire a disintegrated home firm. In other words, the incentives for cross-border mergers rise with vertical integration when the pre-merger market competition at home is sufficiently low relative to the competition in the foreign country. Intuitively, this follows from an interaction between efficiency and concentration as a merger between a high-cost and a low-cost firm increases efficiency by eliminating the high-cost firm. The larger the cost differential, the greater is the gain from a low cost firm taking over a high cost firm. Vertical integration affects relative costs through its effects on competition in the input market. The profits of the downstream unit of the integrated firm increase with a rise in its rivals’ costs and the integrated firm is better off withdrawing from the input market. The 5.

(16) withdrawal of a vertically integrated firm from the input market weakens upstream competition. This raises the input price which, in turn, leads to a higher cost for the non-integrated downstream firms. The profits of a low-cost foreign firm must increase significantly to justify its taking over a high-cost home firm. When this cost differential is sufficiently large, the foreign firm has a greater incentive to merge with the integrated home firm than to merge with a disintegrated home firm. Vertical integration at home makes foreign downstream firms more competitive and the disintegrated domestic downstream firms less competitive. As such, a vertical integration at home changes the strategic advantages for foreign firms. With the vertically integrated home-firm operating at a lower cost relative to the disintegrated home firms, the increase in the profits of a foreign firm from a merger with an integrated home firm exceeds the increase in its profits from a merger with a disintegrated home firm when there are fewer disintegrated firms at home in the pre-merger equilibrium. In other words, the incentives for a merger between a foreign firm and a vertically integrated home firm will be higher (lower) than that for a merger between a foreign firm and a disintegrated home firm, when the pre-merger competition at home is high (low) relative to the pre-merger competition in the foreign country.. 立. 政治大. ‧ 國. 學. ‧. 4. Sample and descriptive statistics on Cross-border M&As and market concentration. n. al. er. io. sit. y. Nat. Our merger sample is taken from Security Data Corporation’s (SDC) Mergers and Corporate Transactions database. We include both completed and incomplete merger announcements between 1990 and 2012. We exclude LBOs, spinoffs, recapitalizations, self-tender offers, exchange offers, repurchases, partial equity stake purchases, acquisitions of remaining interest, and privatizations, and deals in which the target or the acquirer is in the financial or utilities industries. Both acquirers and targets must have IO-Table and total sales information from WorldScope database. These restrictions yield a sample of 90,614 mergers with firms from 86 countries and a total transaction value of $10.49 trillion, 22,600 of which are cross-border mergers with a total transaction value of $3.15 trillion.. Ch. engchi. i n U. v. We also retrieve the nationalities and primary industries of target and acquirer firms from the SDC database. To examine the impact of market concentration on cross-border M&A in a vertically integrated industry, we distinguish patterns of vertical and horizontal M&A based on both SIC and IO (Input and output) specifications. For the SIC-based classification, we define “Pure Horizontal” as mergers between firms with the same two, three, and four-digit SIC codes and “Pure Vertical” as mergers between firms in different two, three, and four-digit SIC codes. For the IO-based classification, we use the input-output matrix from the BEA (Bureau of Economic Analysis) to construct the coefficients of inter-industry vertical relatedness. This matrix 6.

(17) provides a vector of coefficients with which we can determine which industries are connected through an input relationship. Following McGuckin, Nguyen, and Andrews (1991) and Fan and Goyal (2006), we select a threshold of 1% and 5% to determine the strength of vertical integration. Based on the looser (stricter) criteria, we classify a merger as vertically integrated if its associated vertical relatedness coefficient is greater than 1% (5%). We also classify vertically related mergers within an industry as “Mixed” and mergers that are cross-industry and vertically unrelated as “Conglomerate”.. Table 1.1 Summary Statistics of M&A sample. This table presents the number of mergers by different market concentration types. Panels A and B show the results of different market concentration type for the full and cross border samples between 1990 and 2012. We retrieve total sales data of the acquirer and target firms and calculate market concentration ratio (HHI) according to the horizontal merger guidelines issued in 2010 by the U.S. Department of Justice. Industries with HHI Indices below 1,500 are defined as low concentration type, while industries with HHI indices between 1,500 and 2,500 are defined as moderate concentration type, and industries with indices above 2,500 are treated as high concentration type. The percentages of concentration type are defined as the ratio of merger type (High, Modest and Low) to the total number of merger cases.. 立. 政治大. ‧ 國. 學. Panel A: Full Sample. ‧. Acquirer Market Concentration Type High Moderate Low. Target Market Concentration Type Number of cases High Moderate Low Total. Ch. Total 46,697 18,940 24,977 90,614. 5.97 3.86 18.57 28.40. 51.53 20.90 27.56 100.00. Acquirer Market Concentration Type High Moderate Low. Total. 40.63 5.16 5.67 51.45. engchi. y. 5,411 3,498 16,823 25,732. n. Panel B: Cross-Border M&As. Percentage High Moderate Low Total. al. 4,470 10,769 3,019 18,258. sit. io. Percentage High Moderate Low Total. 36,816 4,673 5,135 46,624. er. Nat. Target Market Concentration Type Number of cases High Moderate Low Total. i n U 4.93 11.88 3.33 20.15. v. 10,159 1,759 1,771 13,689. 1,973 1,208 672 3,853. 2,355 868 1,835 5,058. 14,487 3,835 4,278 22,600. 44.95 7.78 7.84 60.57. 8.73 5.35 2.97 17.05. 10.42 3.84 8.12 22.38. 64.10 16.97 18.93 100.00. 7.

(18) We obtain total sales for target and acquirer firms and calculate market concentration ratio (HHI) during 1990 to 2012 following the horizontal merger guidelines issued in 2010 by the U.S. Department of Justice. Industries with HHI Indices below 1,500 are defined as low concentration, while industries with HHI indices between 1,500 and 2,500 are defined as moderately concentrated, and industries with indices above 2,500 are treated as highly concentrated. The frequency of mergers among these market concentration ratios are summarized in Table 1.1. Panels A and B in Table 1.1 show the results for the full sample and cross border sample, respectively. It is seen that the majority of cross-border M&A activity involving deals in highly concentrated industries. The percentage for acquirer in highly concentrated industries to merge with firms in highly concentrated industries is 40.63% for the full sample. The percentage is even higher for cross-border sample (44.95%). Since our theoretical model focuses on firm behavior in an oligopoly industry, we retrieve observations with HHI indices above 1,500 for target and acquirer firms, which correspond to high and moderate market concentration types. Results include low market concentration type are similar and available upon request.. 立. 政治大. ‧. ‧ 國. 學. n. er. io. sit. y. Nat. al. Ch. engchi. i n U. v. Figure 1.1 Herfindahl-Hirschman Index of M&A sample. This figure presents the mean level of Herfindahl Hirschman Index for target-acquirer industry in the sample. The vertical axis is the level of HHI. The cross-border merger sample is in the left hand side and home market merger sample is in the right hand side.. The HHI Indices of the target and acquirer firms for cross-border and domestic M&As are shown in Figure 1.4. There are three key patterns in this figure. First, the HHI indices are higher for cross-border M&A than domestic M&A. Second, it is seen that the gaps between target and acquirer HHI 8.

(19) indices for the cross-border M&A are larger than domestic M&A. Third, the HHI Index of target firms is, on average, higher than acquirer firm for the cross border cases.. Table 1.2 Number of M&A in the Oligopoly Industry. This table presents different patterns of mergers acquisitions based on SIC code and IO specifications. Panels A and B show results for the full and cross border in oligopoly samples, respectively. For the SIC-based classification, we identify “Pure Horizontal” as those mergers between firms belonging to the same two and three -digit SIC industries. “Pure Vertical” as those mergers that take place between firms in different two and three -digit SIC industries. For the IO-based classification, we report the fraction of “Pure Horizontal”, “Pure Vertical”, “Mixed”, and “Conglomerate” mergers based on a 1% and 5% vertical relatedness cutoff points. A merger is purely horizontal if it is within an industry but vertically unrelated. Pure vertical mergers are between firms that are vertically related but belong to different industries on the basis of IO codes. We classify vertically related mergers within an industry as mixed. Conglomerate mergers are cross-industry and vertically unrelated.. 政治大. n. IO Based. Two-digit Three-digit. 69.23 61.97. 30.77 38.03. 1% cutoff 5% cutoff. 7.73 23.00. 23.75 5.74. Conglomerate. 9,919 20,518. y. sit. io. Percentage SIC Based. engchi. 53.14 34.59. iv n U Mixed. Panel B: Cross-Border M&As in Oligopoly Industry Pure Horizontal Pure Vertical Number of Cross-Border Deals in Oligopoly (N=18,322) SIC Based Two-digit 12,684 5,638 Three-digit 11,354 6,968 IO Based 1% cutoff 1,417 4,351 5% cutoff 4,214 1,051. Ch. 34,882 22,705. er. Nat. al. Mixed. ‧. ‧ 國. 立. 學. Panel A: Oligopoly Industry Sample Pure Horizontal Pure Vertical Number of M&A Deals in Oligopoly (N=65,637) SIC Based Two-digit 46,644 18,993 Three-digit 41,696 23,941 IO Based 1% cutoff 6,751 14,085 5% cutoff 18,928 3,486 Percentage SIC Based Two-digit 71.06 28.94 Three-digit 63.53 36.47 IO Based 1% cutoff 10.29 21.46 5% cutoff 28.84 5.31. 15.11 31.26. Conglomerate. 9,787 6,990. 2,767 6,067. 53.42 38.15. 15.10 33.11. Table 1.2 presents different patterns of mergers acquisitions based on SIC code and IO specifications for the full and cross border samples. Panel A in Table 1.1 shows that the vertical relatedness for the full sample M&A is 9.

(20) 28.94% and 36.74% for 2 and 3 digit SIC codes. Results based on IO tables suggest that the vertical relatedness for M&A activities are 21.67% for the 1% cut-off point but the percentage drop to 5.31% for the 5% cut-off point. Similar results for cross-border M&A are presented in Panel B of Table 1.2. These results indicate that deals that are considered to be pure vertical are much higher at 1% cut-off point than those at 5% cut-off point. These findings are consistent with previous empirical literature (Ravenscraft and Scherer, 1987; Markids, 1995; Fan and Goyal, 2006; Herger and McCorriston, 2012). Fan and Goyal (2006) show that IO method provides a more sophisticated measure of vertical integration using industry commodity flows information; therefore, we use the IO method to distinguish vertical and horizontal mergers in the following empirical sections. Additional results based on SIC codes are provided in the robustness section.. 5. Empirical Results. 立. 政治大. ‧ 國. 學. To explore the link between cross-border mergers and market concentration in a vertically related industry, we begin by estimating the following equation:. ‧. CrossBorderit =α+β1 RelativeHHIi.t +β2 HomeVerticalTarget ,i.t t-1 +β3 RelativeHHIi.t × HomeVerticalTarget ,i.t t-1 +β4 LogDistancei.t +β5 LogMktSize_Ratioi.t +β6 CountrySkill_Ratioi.t +β7 IndustrySkill_Ratioi.t +β8 CountrySkill_Ratioi.t × IndustrySkill_Ratioi.t + 𝜀𝑖,𝑡. n. er. io. sit. y. Nat. al. (1.1). v. where subscripts i and t index deal and year, and subscript Target the target of M&A. We use a dummy variable, CrossBorder , to measure cross-border merger activity. RelativeHHI , the relative market concentration ratio, is measured by the ratio of target firm’s HHI divided by acquirer firm’s HHI. We analyze the target’s pre-merger activities by a dummy variable, HomeVerticalTarget , which is equal to one when target firms had. Ch. engchi. i n U. t-1. previously been vertically merged at home market and zero otherwise. Proposition I suggests that the incentives for cross-border mergers rise with vertical integration when the pre-merger HHI at home is sufficiently low relative to the HHI in the foreign country. We test this proposition using the interaction term between RelativeHHI and HomeVerticalTarget in equation t-1. (1.1). Columns (1) in Table 1.3 shows that the coefficient of RelativeHHI is positively significant for the 1% cut off point case, which indicates that higher industry competition ratio of target firm to acquirer firm is associated with higher probability of cross border M&A. The interaction term RelativeHHI × HomeVerticalTarget ) in column (1) is significantly negative at the one percent t-1. 10.

(21) level, which supports proposition I 1 that the incentives for cross-border mergers rise with vertical integration when the pre-merger HHI at home is sufficiently low relative to the HHI in the foreign country. Following Alfaro and Charlton (2009), we also control for international factors such as macroeconomic, country, and industry characteristics in equation (1.1). We proxy trade costs using bilateral distance between target and acquirer countries, Distance. LogMktSize_Ratio is the ratio of GDP per capita of target and acquirer countries. Proxy variable for a country’s unit cost of production is given by the ratio of average years of schooling of target and acquirer countries, CountrySkill_Ratio . The industry-level skill ratio, IndustrySkill_Ratio, is the logarithm of total workers in a given industry. The interaction term between country-level and industry-level skill, CountrySkill_Ratio × CountrySkill_Ratio , is used to proxy for the comparative advantage of target and acquirer countries. Results show that both Distance and CountrySkill_Ratio have positive and significant effects on the probability of cross-border merger. These results are consistent with Alfaro and Charlton (2009). We also control for proximity between target and acquirer firms, which equals to the ratio of direct to total requirements coefficients (Proximity) and absolute difference between the four-digit SIC codes between target and acquirer (Closeness) used in the previous literature.. 立. 政治大. ‧. ‧ 國. 學. n. er. io. sit. y. Nat. al. 1. Ch. engchi. See Appendeix 1.B 11. i n U. v.

(22) Table 1.3 Determinants of Cross-Border M&As in Oligopoly Industry. We estimate the following logit regression model: CrossBorderit =α+β1 RelativeHHIi.t +β2 HomeVerticalTarget ,i.t t-1 +β3 RelativeHHIi.t × HomeVerticalTarget ,i.t t-1 +β4 LogDistancei.t +β5 LogMktSize_Ratioi.t +β6 CountrySkill_Ratioi.t +β7 IndustrySkill_Ratioi.t +β8 CountrySkill_Ratioi.t × IndustrySkill_Ratioi.t + 𝜀𝑖,𝑡. (1.1) This table presents marginal effects for the logit model of Equation (1.1). The dependent variable CrossBorder equals one for cross-border deal and zero otherwise. Columns (1) and (2) use IO-based 1% cut-off point, while columns (3) and (4) use IO-based 5% cut-off point. HomeVertical equals one for targets that were vertical merged at domestic market and zero otherwise. RelativeHHI is defined as the ratio (Target_HHI/Acquirer_HHI). Target_HHI is the Herfindahl Index of target’s industry. Acquirer_HHI is the Herfindahl Index of acquirer’s industry. Our Control variables in this model are: Distance is the bilateral distance between target and acquirer country. MarketSize is the GDP per capita in U.S. dollars. MarketSize_Ratio is defined as the ratio (Target_LogMarketSize/Acquirer LogMarketSize). CountrySkill is the high school enrollment years of schooling per worker. CountrySkill_Ratio is defined as the ratio (TargetCountry_Skill/AcquirerCountry_Skill). IndustrySkill is the number of workers in the industry (Unit: Thousand). IndustrySkill_Ratio is defined as the ratio (TargetIndustry_Skill/AcquirerIndustry_Skill). Country_Skill_Ratio× IndustrySkill_Ratio is defined as the interaction term between CountrySkill and IndustrySkill ratio. Proximity is a ratio of the direct to the total inputs used by the firm. Closeness is the absolute difference if four-digit SIC between target and acquirer. The data sources and definitions of the variables are provided in Appendix 1.A. The marks a, b, and c indicate significance at the 1%, 5%, and 10% level, respectively.. 政治大. 立. ‧. ‧ 國. 學. y. Nat. al. n. HomeVertical. sit. IO-Based 1% cutoff Column (1). IO-Based 5% cutoff. Column (2). er. io Independent variables. Dependent variable: CrossBorder Column (3). i n U. v. Column (4). Coeff. p-val.. Coeff. p-val.. Coeff. p-val.. Coeff. p-val.. 0.067. (0.689). 0.113. (0.497). -0.255. (0.430). -0.234. (0.472). (0.000). 0.128a. (0.000). 0.065b. (0.016). 0.082a. (0.003). (0.006). -0.223a. (0.006). 0.003. (0.984). -0.008. (0.960). Ch. engchi. RelativeHHI. 0.111a. HomeVertical×RelativeHHI. -0.224a. LogDistance. 0.981a. (0.000). 0.983a. (0.000). 0.987a. (0.000). 0.988a. (0.000). LogMaketSize_Ratio. 1.040. (0.259). 1.126. (0.234). 1.038. (0.263). 1.121. (0.238). CountrySkill_Ratio. 1.076b. (0.010). 0.967b. (0.022). 1.181a. (0.005). 1.059b. (0.011). IndustrySkill_Ratio. 0.010. (0.223). 0.008. (0.327). 0.010. (0.208). 0.008. (0.313). CountrySkill_Ratio× IndustrySkill_Ratio. -0.007. (0.363). -0.005. (0.497). -0.007. (0.347). -0.006. (0.484). 1.647a. (0.000). 1.717a. (0.000). -0.164. (0.059). -0.171b. (0.047). -10.26a. (0.000). -10.35a. (0.000). Proximity Closeness Constant. -10.18a. (0.000). Observations. 3,655. 3,655. 3,655. 3,655. Pseudo R2. 0.1627. 0.1673. 0.1593. 0.1644. 12. -10.30a. (0.000).

(23) Column (2) in Table 1.3 shows that the interaction term of RelativeHHI and HomeVerticalTarget is still significant when we add these control t-1. variables in equation (1.1). Columns (3) and (4) in Table 1.3 provide results for equation (1.2) based on IO specifications at the 5 percent cut-off point. Columns (3) and (4) in Table 1.3 show that the coefficients of RelativeHHI are positively significant; however, the parameters of the term between RelativeHHI and HomeVerticalTarget are negative but insignificant at the five t-1. percent level. Proposition II 2 indicates that a relatively efficient foreign firm has incentive to acquire an integrated domestic firm when the cost differential is sufficiently large. To test this proposition, we estimate models of the following form: CrossBorder × HomeVerticalTarget ,i.t t-1 =α+𝛾1 RelativeHHIi.t +𝛾2 LogDistancei.t +𝛾3 LogMktSize_Ratioi.t +𝛾4 CountrySkill_Ratioi.t +𝛾5 IndustrySkill_Ratioi.t +𝛾6 CountrySkill_Ratioi.t × IndustrySkill_Ratioi.t + 𝜀i,𝑡 (1.2). 立. 政治大. ‧ 國. 學. The dependent variable (RelativeHHI × HomeVerticalTarget ) in equation t-1. ‧. (1.2) is equal to one when target firms had previously been vertically merged at home market by a foreign firm and zero otherwise. Columns (1) and (3) in Table 1.4 show that the coefficients of RelativeHHI are positively significant for the 1% and 5% cut off point cases, which provides empirical support for proposition II. Columns (2) and (4) in Table 1.4 show that the significant results for the RelativeHHI still hold when we include the proximity variable in equation (1.2).. n. er. io. sit. y. Nat. al. 2. Ch. engchi. See Appendeix 1.B 13. i n U. v.

(24) Table 1.4 Determinant of Cross-Border Home Vertical M&As in Oligopoly Industry We estimate the following logit regression model: CrossBorder × HomeVerticalTarget ,i.t t-1 =α+𝛾1 RelativeHHIi.t +𝛾2 LogDistancei.t +𝛾3 LogMktSize_Ratioi.t +𝛾4 CountrySkill_Ratioi.t +𝛾5 IndustrySkill_Ratioi.t +𝛾6 CountrySkill_Ratioi.t × IndustrySkill_Ratioi.t + 𝜀i,𝑡. (1.2) This table presents marginal effects for the logit model of Equation (1.2). The dependent variables CrossBorder× HomeVertical is the interaction term between CrossBorder and HomeVertical. CrossBorder is a dummy variable, which equals to one if the M&A is cross-border deal and zero otherwise. HomeVertical is a dummy variable, which equals to one if the target has been vertical integrated at home market and zero otherwise. Columns (1) and (2) use IO-based 1% cut-off point, while columns (3) and (4) use IO-based 5% cut-off point. RelativeHHI is defined as the ratio (Target_HHI/Acquirer_HHI). Target_HHI is the Herfindahl Index of target’s industry. Acquirer_HHI is the Herfindahl Index of acquirer’s industry. Our Control variables in this model are: Distance is the bilateral distance between target and acquirer country. MarketSize is the GDP per capita in U.S. dollars. MarketSize_Ratio is defined as the ratio (Target_LogMarketSize/Acquirer LogMarketSize). CountrySkill is the high school enrollment years of schooling per worker. CountrySkill_Ratio is defined as the ratio (TargetCountry_Skill/AcquirerCountry_Skill). IndustrySkill is the number of workers in the industry (Unit: Thousand). IndustrySkill_Ratio is defined as the ratio (TargetIndustry_Skill/AcquirerIndustry_Skill). Country_Skill_Ratio× IndustrySkill_Ratio is defined as the interaction term between CountrySkill and IndustrySkill ratio. Proximity is a ratio of the direct to the total inputs used by the firm. Closeness is the absolute difference if four-digit SIC between target and acquirer. The data sources and definitions of the variables are provided in Appendix 1.A. The marks a, b, and c indicate significance at the 1%, 5%, and 10% level, respectively.. 政治大. 立. ‧. ‧ 國. 學. n. IO-Based 1% cutoff. Ch. Column (1). er. io. sit. y. Nat. al. Dependent variable: CrossBorder × HomeVerticalTarget. i n U. Column (2). Independent variables. Coeff. RelativeHHI. 0.277a. engchi. LogDistance. v. t-1. ,i.t. IO-Based 5% cutoff. Column (3). Column (4). p-val.. Coeff. p-val.. Coeff. p-val.. Coeff. p-val.. (0.000). 0.290a. (0.000). 0.294a. (0.000). 0.307a. (0.000). 0.995a. (0.000). 0.989a. (0.000). 0.992a. (0.000). 0.986a. (0.000). LogMaketSize_Ratio. -0.951a. (0.008). -0.953a. (0.008). -1.033a. (0.004). -1.040a. (0.004). CountrySkill_Ratio. 2.478a. (0.000). 2.478a. (0.000). 2.515a. (0.000). 2.517a. (0.000). IndustrySkill_Ratio. -0.005. (0.358). -0.005. (0.368). -0.005. (0.363). -0.005. (0.374). CountrySkill_Ratio×IndustrySkill_Ratio. 0.005. (0.347). 0.005. (0.358). 0.005. (0.358). 0.005. (0.368). Proximity. 1.289a. (0.000). 1.313a. (0.000). Closeness. 0.0108. (0.772). 0.012. (0.741). -9.649a. (0.000). -9.578a. (0.000). Constant. -9.592a. Observations. 3,655. (0.000). 3,655. 3,655. 3,655. Pseudo R2. 0.1775. 0.1792. 0.1786. 0.1804. 14. -9.522a. (0.000).

(25) 6. Robustness In this section, we check the robustness of our empirical results based on 2 and 3 digit SIC codes for equations (1.1) and (1.2). Results are shown in Tables 1.5 and 1.6, which confirm the findings in Tables 1.3 and 1.4. The interaction term RelativeHHI × HomeVerticalTarget , in columns (1) to (4) of t-1. Table 1.5, are significantly negative at the one percent level, which provides further empirical support of proposition I that the incentives for cross-border mergers rise with vertical integration when the pre-merger HHI at home is sufficiently low relative to the HHI in the foreign country. Columns (1) to (4) in Table 1.6 show that the coefficients of RelativeHHI are positively significant. We also check the robustness of our empirical results using different specifications of target and acquirer market concentration measures in equations (1.3) and (1.4). Instead of using the relative HHI of target and acquirer firms, we use the levels of HHI of target and acquirer firms. Results are shown in Tables 1.7 and 1.8. Results are consistent with what we find in Tables 1.3 and 1.4 using RelativeHHI ratio.. 立. 政治大. ‧. ‧ 國. 學. Column (1) in Table 1.7 shows that the interaction term between HomeVertical and AcquirerHHI indicate that the incentives for cross-border mergers rise with vertical integration when the pre-merger HHI in the foreign country is high. The findings in columns (1) to (4) are consistent with results in Table 1.4 and provide further empirical support for proposition II.. n. al. er. io. sit. y. Nat. CrossBorderit =α+𝛿1 HomeVerticalTarget ,i.t +𝛿2 TargetHHI𝑖.𝑡 +𝛿3 AcquirerHHI𝑖.𝑡 t-1 +𝛿4 HomeVerticalTarget ,i.t × TargetHHI𝑖.𝑡 t-1 +𝛿5 HomeVerticalTarget ,i.t × AcquirerHHI𝑖.𝑡 t-1 +𝛿6 LogDistancei.t +𝛿7 LogMktSize_Ratioi.t +𝛿8 Target_CountrySkilli.t +𝛿9 Target_IndustrySkilli.t +𝛿10 Target_CountrySkilli.t × Target_IndustrySkilli.t +𝛿11 Acquirer_CountrySkilli.t +𝛿12 Acquirer_IndustrySkilli.t +𝛿13 Acquirer_CountrySkilli.t × Acquirer_IndustrySkilli.t + 𝜀𝑖,𝑡 (1.3). Ch. engchi. i n U. v. CrossBorder × HomeVerticalTarget ,i.t =α+𝜃1 TargetHHI𝑖.𝑡 +𝜃2 AcquirerHHI𝑖.𝑡 t-1 +𝜃3 LogDistancei.t +𝜃4 LogMktSize_Ratioi.t +𝜃8 Target_CountrySkilli.t +𝜃9 Target_IndustrySkilli.t +𝜃10 Target_CountrySkilli.t × Target_IndustrySkilli.t +𝜃11 Acquirer_CountrySkilli.t +𝜃12 Acquirer_IndustrySkilli.t +𝜃13 Acquirer_CountrySkilli.t × Acquirer_IndustrySkilli.t + 𝜀𝑖,𝑡 (1.4). 15.

(26) Table 1.5 Robustness check: SIC-Based Classification of the Determinant of Cross-Border M&As in Oligopoly Industry. We estimate the following logit regression model: CrossBorderit =α+β1 RelativeHHIi.t +β2 HomeVerticalTarget ,i.t t-1 +β3 RelativeHHIi.t × HomeVerticalTarget ,i.t t-1 +β4 LogDistancei.t +β5 LogMktSize_Ratioi.t +β6 CountrySkill_Ratioi.t +β7 IndustrySkill_Ratioi.t +β8 CountrySkill_Ratioi.t × IndustrySkill_Ratioi.t + 𝜀𝑖,𝑡. (1.1) This table presents marginal effects for the logit model of Equation (1.1). The dependent variable CrossBorder equals one for cross-border deal and zero otherwise. Columns (1) and (2) use SIC-Based 2-digit classification, while columns (3) and (4) use SIC-Based 2-digit classification. HomeVertical equals one for targets that were vertical merged at domestic market and zero otherwise. RelativeHHI is defined as the ratio (Target_HHI/Acquirer_HHI). Target_HHI is the Herfindahl Index of target’s industry. Acquirer_HHI is the Herfindahl Index of acquirer’s industry. Our Control variables in this model are: Distance is the bilateral distance between target and acquirer country. MarketSize is the GDP per capita in U.S. dollars. MarketSize_Ratio is defined as the ratio (Target_LogMarketSize/Acquirer LogMarketSize). CountrySkill is the high school enrollment years of schooling per worker. CountrySkill_Ratio is defined as the ratio (TargetCountry_Skill/AcquirerCountry_Skill). IndustrySkill is the number of workers in the industry (Unit: Thousand). IndustrySkill_Ratio is defined as the ratio (TargetIndustry_Skill/AcquirerIndustry_Skill). Country_Skill_Ratio× IndustrySkill_Ratio is defined as the interaction term between CountrySkill and IndustrySkill ratio. Proximity is a ratio of the direct to the total inputs used by the firm. Closeness is the absolute difference if four-digit SIC between target and acquirer. The data sources and definitions of the variables are provided in Appendix 1.A. The marks a, b, and c indicate significance at the 1%, 5%, and 10% level, respectively.. 政治大. 立. ‧. ‧ 國. 學. al. n Independent variables. Column (1). Ch. Coeff. HomeVertical. -0.311b. RelativeHHI. 0.125a. HomeVertical×RelativeHHI LogDistance. i n U. Column (2). p-val.. Coeff. engchi. er. io. sit. y. Nat. Dependent variable: CrossBorder SIC-Based 2 digit p-val.. v. SIC-Based 3 digit. Column (3). Coeff. p-val.. Column (4) Coeff. p-val.. (0.015). -0.272b. (0.036). -0.242. (0.051). -0.210. (0.095). (0.000). 0.136a. (0.000). 0.158a. (0.000). 0.167a. (0.000). -0.164a. (0.006). -0.165a. (0.006). -0.223a. (0.000). -0.224a. (0.000). 0.978a. (0.000). 0.979a. (0.000). 0.975a. (0.000). 0.976a. (0.000). LogMaketSize_Ratio. 1.244. (0.187). 1.309. (0.174). 1.307. (0.171). 1.371. (0.162). CountrySkill_Ratio. 1.238a. (0.002). 1.154a. (0.005). 1.211a. (0.003). 1.133a. (0.006). IndustrySkill_Ratio. 0.007. (0.342). 0.006. (0.374). 0.007. (0.342). 0.006. (0.368). CountrySkill_Ratio× IndustrySkill_Ratio. -0.005. (0.472). -0.004. (0.529). -0.005. (0.465). -0.005. (0.516). Proximity. 1.648a. (0.000). 1.663a. (0.000). Closeness. -0.076. (0.368). -0.054. (0.522). -10.53a. (0.000). -10.57a. (0.000). Constant. -10.42a. (0.000). -10.45a. (0.000). Observations. 3,895. 3,895. 3,895. 3,895. Pseudo R2. 0.1685. 0.1722. 0.1718. 0.1754. 16.

(27) Table 1.6 Robustness check: SIC-Based Classification of the Determinant of Cross-Border Home Vertical M&As in Oligopoly Industry. We estimate the following logit regression model: CrossBorder × HomeVerticalTarget ,i.t t-1 =α+𝛾1 RelativeHHIi.t +𝛾2 LogDistancei.t +𝛾3 LogMktSize_Ratioi.t +𝛾4 CountrySkill_Ratioi.t +𝛾5 IndustrySkill_Ratioi.t +𝛾6 CountrySkill_Ratioi.t × IndustrySkill_Ratioi.t + 𝜀𝑖,𝑡. (1.2) This table presents marginal effects for the logit model of Equation (1.2). The dependent variables CrossBorder× HomeVertical is the interaction term between CrossBorder and HomeVertical. CrossBorder is a dummy variable, which equals to one if the M&A is cross-border deal and zero otherwise. HomeVertical is a dummy variable, which equals to one if the target has been vertical integrated at home market and zero otherwise. Columns (1) and (2) use SIC-Based 2-digit classification, while columns (3) and (4) use SIC-Based 2-digit classification. RelativeHHI is defined as the ratio (Target_HHI/Acquirer_HHI). Target_HHI is the Herfindahl Index of target’s industry. Acquirer_HHI is the Herfindahl Index of acquirer’s industry. Our Control variables in this model are: Distance is the bilateral distance between target and acquirer country. MarketSize is the GDP per capita in U.S. dollars. MarketSize_Ratio is defined as the ratio (Target_LogMarketSize/Acquirer LogMarketSize). CountrySkill is the high school enrollment years of schooling per worker. CountrySkill_Ratio is defined as the ratio (TargetCountry_Skill/AcquirerCountry_Skill). IndustrySkill is the number of workers in the industry (Unit: Thousand). IndustrySkill_Ratio is defined as the ratio (TargetIndustry_Skill/AcquirerIndustry_Skill). Country_Skill_Ratio× IndustrySkill_Ratio is defined as the interaction term between CountrySkill and IndustrySkill ratio. Proximity is a ratio of the direct to the total inputs used by the firm. Closeness is the absolute difference if four-digit SIC between target and acquirer. The data sources and definitions of the variables are provided in Appendix 1.A. The marks a, b, and c indicate significance at the 1%, 5%, and 10% level, respectively.. 政治大. 立. ‧. ‧ 國. 學. er. io. sit. y. Nat. Dependent variable: CrossBorder × HomeVerticalTarget. n. al. Ch. SIC-Based 2 digit. v ni. t-1. ,i.t. SIC-Based 3 digit. Independent variables. Coeff. engchi U p-val.. Coeff. p-val.. Coeff. p-val.. Coeff. p-val.. RelativeHHI. 0.261a. (0.000). 0.279a. (0.000). 0.277a. (0.000). 0.290a. (0.000). LogDistance. 1.002a. (0.000). 0.997a. (0.000). 0.995a. (0.000). 0.989a. (0.000). LogMaketSize_Ratio. -0.857b. (0.015). -0.864b. (0.015). -0.951a. (0.008). -0.953a. (0.008). CountrySkill_Ratio. 2.488a. (0.000). 2.487a. (0.000). 2.478a. (0.000). 2.478a. (0.000). IndustrySkill_Ratio. -0.004. (0.465). -0.004. (0.478). -0.005. (0.358). -0.005. (0.368). CountrySkill_Ratio×IndustrySkill_Ratio. 0.004. (0.453). 0.004. (0.472). 0.005. (0.347). 0.005. (0.358). Proximity. 1.320a. (0.000). 1.289a. (0.000). Closeness. -0.008. (0.818). 0.011. (0.772). -9.813a. (0.000). -9.649a. (0.000). Column (1). Column (2). (0.000). Column (3). Constant. -9.765a. Observations. 3,895. 3,895. 3,895. 3,895. Pseudo R2. 0.1777. 0.1796. 0.1775. 0.1792. 17. -9.592a. (0.000). Column (4).

(28) Table 1.7 Robustness check: Additional tests on the Determinant of Cross-Border M&As in Oligopoly Industry. We estimate the following logit regression model: CrossBorderit =α+𝛿1 HomeVerticalTarget ,i.t +𝛿2 TargetHHI𝑖.𝑡 +𝛿3 AcquirerHHI𝑖.𝑡 t-1 +𝛿4 HomeVerticalTarget ,i.t × TargetHHI𝑖.𝑡 t-1 +𝛿5 HomeVerticalTarget ,i.t × AcquirerHHI𝑖.𝑡 t-1 +𝛿6 LogDistancei.t +𝛿7 LogMktSize_Ratioi.t +𝛿8 Target_CountrySkilli.t +𝛿9 Target_IndustrySkilli.t +𝛿10 Target_CountrySkilli.t × Target_IndustrySkilli.t +𝛿11 Acquirer_CountrySkilli.t +𝛿12 Acquirer_IndustrySkilli.t +𝛿13 Acquirer_CountrySkilli.t × Acquirer_IndustrySkilli.t + 𝜀𝑖,𝑡. (1.3) This table presents marginal effects for the logit model of Equation (1.3). The dependent variables CrossBorder× HomeVertical is the interaction term between CrossBorder and HomeVertical. CrossBorder is a dummy variable, which equals to one if the M&A is cross-border deal and zero otherwise. HomeVertical is a dummy variable, which equals to one if the target has been vertical integrated at home market and zero otherwise. Columns (1) and (2) use IO-based 1% cut-off point, while columns (3) and (4) use IO-based 5% cut-off point. HomeVertical equals one for targets that were vertical merged at domestic market and zero otherwise. RelativeHHI is defined as the ratio (Target_HHI/Acquirer_HHI). Target_HHI is the Herfindahl Index of target’s industry. Acquirer_HHI is the Herfindahl Index of acquirer’s industry. Our Control variables in this model are: Distance is the bilateral distance between target and acquirer country. MarketSize is the GDP per capita in U.S. dollars. MarketSize_Ratio is defined as the ratio (Target_LogMarketSize/Acquirer LogMarketSize). CountrySkill is the high school enrollment years of schooling per worker. CountrySkill_Ratio is defined as the ratio (TargetCountry_Skill/AcquirerCountry_Skill). IndustrySkill is the number of workers in the industry (Unit: Thousand). IndustrySkill_Ratio is defined as the ratio (TargetIndustry_Skill/AcquirerIndustry_Skill). Country_Skill_Ratio× IndustrySkill_Ratio is defined as the interaction term between CountrySkill and IndustrySkill ratio. Proximity is a ratio of the direct to the total inputs used by the firm. Closeness is the absolute difference if four-digit SIC between target and acquirer. The data sources and definitions of the variables are provided in Appendix 1.A. The marks a, b, and c indicate significance at the 1%, 5%, and 10% level, respectively.. 立. 政治大. ‧. ‧ 國. 學. n. er. io. sit. y. Nat. al. Independent variables HomeVertical TargetHHI AcquirerHHI HomeVertical × TargetHHI HomeVertical × AcquirerHHI LogDistance Target_LogMaketSize Acquirer_LogMaketSize Target_CountrySkill Target_IndustrySkill Target_(CountrySkill× IndustrySkill) Acquirer_CountrySkill Acquirer_IndustrySkill Acquirer_(CountrySkill× IndustrySkill) Proximity Closeness Constant Observations Pseudo R2. Ch. engchi. i n U. v. Dependent variable: CrossBorder IO-Based 1% cutoff IO-Based 5% cutoff Column (1) Column (2) Column (3) Column (4) Coeff p-val. Coeff p-val. Coeff p-val. Coeff p-val. -0.648a (0.002) -0.604a (0.004) -0.574 (0.285) -0.598 (0.271) 1.526a (0.000) 1.556a (0.000) 1.293a (0.000) 1.325a (0.000) 0.628b (0.016) 0.617b (0.019) 1.061a (0.000) 1.038a (0.000) -0.496 (0.267) -0.521 (0.246) 0.681 (0.516) 0.616 (0.562) 1.214a (0.005) 1.223a (0.005) 0.536 (0.617) 0.644 (0.549) 1.347a (0.000) 1.343a (0.000) 1.357a (0.000) 1.353a (0.000) 0.104 (0.478) 0.112 (0.453) 0.0769 (0.603) 0.091 (0.542) 0.111 (0.424) 0.109 (0.441) 0.127 (0.363) 0.123 (0.384) 0.803a (0.000) 0.796a (0.000) 0.815a (0.000) 0.806a (0.000) 0.0106 (0.646) 0.0118 (0.610) 0.011 (0.638) 0.012 (0.596) -0.002 (0.555) -0.002 (0.516) -0.002 (0.555) -0.002 (0.516) 0.894a (0.000) 0.890a (0.000) 0.901a (0.000) 0.897a (0.000) 0.036b (0.019) 0.035b (0.020) 0.035b (0.019) 0.035b (0.020) -0.005b (0.031) -0.005b (0.032) -0.005b (0.032) -0.005b (0.032) 1.316a (0.008) 1.488a (0.003) -0.035 (0.719) -0.087 (0.363) -24.10a (0.000) -24.16a (0.000) -24.37a (0.000) -24.43a (0.000) 3,655 3,655 3,655 3,655 0.3127 0.3144 0.3077 0.3102. 18.

(29) Table 1.8 Robustness check: Additional tests of the Determinant of Cross-Border Home Vertical M&As in Oligopoly Industry. We estimate the following logit regression model: CrossBorder × HomeVerticalTarget ,i.t =α+𝜃1 TargetHHI𝑖.𝑡 +𝜃2 AcquirerHHI𝑖.𝑡 t-1 +𝜃3 LogDistancei.t +𝜃4 LogMktSize_Ratioi.t +𝜃8 Target_CountrySkilli.t +𝜃9 Target_IndustrySkilli.t +𝜃10 Target_CountrySkilli.t × Target_IndustrySkilli.t +𝜃11 Acquirer_CountrySkilli.t +𝜃12 Acquirer_IndustrySkilli.t +𝜃13 Acquirer_CountrySkilli.t × Acquirer_IndustrySkilli.t + 𝜀𝑖,𝑡. (1.4) This table presents marginal effects for the logit model of Equation (1.4). The dependent variables CrossBorder× HomeVertical is the interaction term between CrossBorder and HomeVertical. CrossBorder is a dummy variable, which equals to one if the M&A is cross-border deal and zero otherwise. HomeVertical is a dummy variable, which equals to one if the target has been vertical integrated at home market and zero otherwise. Columns (1) and (2) use IO-based 1% cut-off point, while columns (3) and (4) use IO-based 5% cut-off point. HomeVertical equals one for targets that were vertical merged at domestic market and zero otherwise. RelativeHHI is defined as the ratio (Target_HHI/Acquirer_HHI). Target_HHI is the Herfindahl Index of target’s industry. Acquirer_HHI is the Herfindahl Index of acquirer’s industry. Our Control variables in this model are: Distance is the bilateral distance between target and acquirer country. MarketSize is the GDP per capita in U.S. dollars. MarketSize_Ratio is defined as the ratio (Target_LogMarketSize/Acquirer LogMarketSize). CountrySkill is the high school enrollment years of schooling per worker. CountrySkill_Ratio is defined as the ratio (TargetCountry_Skill/AcquirerCountry_Skill). IndustrySkill is the number of workers in the industry (Unit: Thousand). IndustrySkill_Ratio is defined as the ratio (TargetIndustry_Skill/AcquirerIndustry_Skill). Country_Skill_Ratio× IndustrySkill_Ratio is defined as the interaction term between CountrySkill and IndustrySkill ratio. Proximity is a ratio of the direct to the total inputs used by the firm. Closeness is the absolute difference if four-digit SIC between target and acquirer. The data sources and definitions of the variables are provided in Appendix 1.A. The marks a, b, and c indicate significance at the 1%, 5%, and 10% level, respectively.. 政治大. 立. ‧. ‧ 國. 學. n. er. io. sit. y. Nat. al. Ch. engchi. i n U. v. Dependent variable: CrossBorder × HomeVerticalTarget. IO-Based 1% cutoff. t-1. ,i.t. IO-Based 5% cutoff. Column (1) Coeff p-val.. Column (2) Coeff p-val.. Column (3) Coeff p-val.. Column (4) Coeff p-val.. TargetHHI AcquirerHHI LogDistance Target_LogMaketSize Acquirer_LogMaketSize Target_CountrySkill Target_IndustrySkill Target_(CountrySkill× IndustrySkill) Acquirer_CountrySkill Acquirer_IndustrySkill Acquirer_(CountrySkill× IndustrySkill) Proximity Closeness Constant. 1.454a 0.600a 1.378a -0.223a 0.310a 0.811a -0.012c 0.002 0.713a 0.020a -0.003a. (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.093) (0.119) (0.000) (0.003) (0.003). 1.455a 0.575a 1.377a -0.244a 0.321a 0.811a -0.011 0.001 0.711a 0.019a -0.003a. (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.114) (0.144) (0.000) (0.006) (0.007). -21.98a. (0.000). 1.455a 0.613a 1.369a -0.223a 0.308a 0.808a -0.012c 0.002 0.705a 0.021a -0.003a 1.011a 0.076c -21.95a. -21.85a. (0.000). 1.457a 0.589a 1.367a -0.244a 0.319a 0.808a -0.011 0.002 0.703a 0.019a -0.003a 1.049a 0.077c -21.82a. Observations Pseudo R2. 3,655 0.2897. Independent variables. 3,655 0.2906. 19. (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.089) (0.112) (0.000) (0.003) (0.003) (0.000) (0.066) (0.000). 3,655 0.2907. 3,655 0.2917. (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.112) (0.139) (0.000) (0.007) (0.007) (0.000) (0.067) (0.000).

(30) 7. Conclusion Cross-border M&A have increasingly evolved into an effective strategy used by a large number of companies with global presence. Over the last couple of decades, cross-border M&A have continued to intensify significantly as capital reallocation between firms has increasingly been facilitated through interactions between financial liberalization policies and regional agreements. Our paper contributes to the growing interest around this trend which is important but much less understood since the driving forces underlying the patterns of cross-border M&A are complex in nature. Using a general equilibrium model of oligopoly, we show that local competition matters for cross-border M&A in a vertically integrated industry. In particular, we show that the incentives for cross-border mergers rise with vertical integration in an industry when the pre-merger concentration in that industry is sufficiently high relative to the concentration in the same industry in a foreign country. We also show that the incentives for a merger between a foreign firm and a vertically integrated home firm will be higher than that for a merger between a foreign firm and a disintegrated home firm, when the pre-merger concentration at home is low relative to the pre-merger concentration in the foreign country.. 立. 政治大. ‧ 國. 學. ‧. Logistic regression analyses of cross-border M&A observations, on firms from 86 countries over the years 1990 – 2012, provide strong empirical support for the significant impact of industry concentration on firms’ cross-border M&A decisions. We believe that the relevance of our findings will capture the attention of future researchers with the growing consensus that “a new wave” of cross-border mergers is imminent. Some interesting extensions, of our work, may involve the allowance for technology transfer á la Mukherjee and Pennings (2006), greenfield foreign direct investment á la Mukherjee and Suetrong (2009), and urban unemployment á la Oladi and Gilbert (2011).. n. er. io. sit. y. Nat. al. Ch. engchi. 20. i n U. v.

(31) Chapter II References Ahern, K. R., Daminelli, D., and Fracassi, C., 2013. “Lost in Translation? The Effect of Cultural Values on Mergers around the World,” Journal of Financial Economics, forthcoming. Alfaro, L. and Charlton A., 2009. “Intra-industry Foreign Direct Investment,” American Economic Review, 99, 2096-2119. Beladi, H., Chakrabarti, A., and Marjit, S., 2013. “Cross-border mergers in vertically related industries,” European Economic Review 59, 97-108. Bernile G., Lyandres E., Zhdanov A., 2012. "A Theory of Strategic Mergers", Review of Finance, 16, 517-575.. 政治大. Chari, A., Ouimet, P., and Tesar, L. L., 2010. “The Value of Control in Emerging Markets,” Review of Financial Studies, 23, 1741-1770.. 立. ‧ 國. 學. Erel, I., Liao, R. C., and Weisbach, M. S., 2012. “Determinants of Cross-border Mergers and Acquisitions,” Journal of Finance, 67, 1045-1082.. ‧. Falvey, R., 1998. “Mergers in Open Economies”, The World Economy 21, 1061-1076.. io. sit. y. Nat. Fan, J. P. H., and Goyal, V., 2006. “On the Patterns and Wealth Effects of Vertical Mergers,” Journal of Business, 79, 877-902.. n. al. er. Finkelstein, S., 1999. “Safe Ways to Cross the Merger Minefield” in Tim Dickson ed., Financial Times Mastering Global Business: The Complete MBA Companion in Global Business, London: Financial Times Pitman Publishing, 119-123.. Ch. engchi. i n U. v. Giovanni J. D., 2005, “What Drives Capital Flows? The Case of Cross-border M&A Activity and Financial Deepening”, Journal of International Economics, 65, 1, 127-149. Head, K. and J. Ries, 1997. “International Mergers and Welfare under Decentralized Competition Policy”, Canadian Journal of Economics, 30, 1104-1123. Jovanovic, B., and Rousseau. P. L., 2008, "Mergers as Reallocation." The Review of Economics and Statistics, 90, 765-776. La Porta, R., Lopez-de-Silanes, F., Shleifer, A., and Vishny, R. W., 1997. “Legal Determinants of External Finance,” Journal of Finance, 52, 1131-1150. 21.