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JIBS-2009-Aybar and Ficici-Cross-border a value, an analysis of emerging-market multinationals


Journal of International Business Studies (2009) 40, 1317–1338

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Cross-border acquisitions and firm value: An analysis of emerging-market multinationals

Bu ¨ lent Aybar and Aysun Ficici
School of Business, Southern New Hampshire University, Manchester, NH, USA Correspondence: B Aybar, School of Business, Southern New Hampshire University, Manchester, NH 03106, USA. Tel: ? 1 603 644 3116; Fax: ? 1 603 645 9737

Abstract The primary objective of this study is to examine the value implications of crossborder acquisitions of emerging-market multinationals (EMMs). We examine 433 mergers and acquisitions announcements associated with 58 EMMs during the sample period 1991–2004. The mergers and acquisitions announcements data come from the Thomson SDC Platinum database. We employ event study methodology to explore the impact of the announcements on the value of acquiring firms. The results show that, on average, cross-border expansions of EMMs through acquisitions do not create value, but point to value destruction for more than half of the transactions analyzed. To explore the factors influencing the direction and magnitude of market reaction, we analyze a cross-sectional sample of firms. While we find that target size, ownership structure of the target (private vs public), and structure of the bidder (diversified vs non-diversified) positively affect the bidder value, high-tech nature of the bidder and pursuit of targets in related industries negatively affect the bidder value. Our empirical findings provide some support for the positive impact of the stake pursued in the target firm and cultural distance, but not for the international experience and enhanced corporate governance. Journal of International Business Studies (2009) 40, 1317–1338. doi:10.1057/jibs.2009.15
Keywords: internationalization; emerging-market multinationals; foreign direct investment; cross-border mergers and acquisitions; international investments; firm value

Received: 17 January 2006 Revised: 2 November 2008 Accepted: 21 November 2008 Online publication date: 30 April 2009

INTRODUCTION The internationalization of companies originating from the emerging economies is not a new phenomenon. Increasingly outward-oriented postures by emerging-market companies parallel their home countries’ integration into the world economy. Such a pattern has intensified during the early 1990s, and a group of emerging-economy companies embarked on myriad international feats to take advantage of regional and global business opportunities. Broadly referred to as emerging-market multinationals (EMMs), this new breed of companies faced increasing competition from their domestic rivals, and aggressive outward expansion by foreign internationals into their markets. In response, the EMMs sought value by adopting an outward strategic orientation. In this study we explore whether such value manifests itself in cross-border company acquisitions by EMMs. Despite EMMs’ growing regional and global importance, our knowledge of their various attributes is limited. For this reason, as we study the impact of cross-border expansion on shareholder

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wealth, we focus not only on the players but also on the process of outward posturing by EMMs. The analysis is based on 433 cross-border M&A expansion announcements associated with 58 bidding firms between 1991 and 2004. We use standard event study methodology to capture the impact of each announcement on firm value around the announcement date. Our findings indicate that, on average, crossborder expansions of EMMs through acquisitions do not create value; rather, they point to value destruction for more than half of the transactions analyzed. To explore the factors influencing the direction and magnitude of market reaction, we analyze a cross-sectional sample of firms. While we find that target size, ownership structure of the target (private vs public), and structure of the bidder (diversified vs non-diversified) positively affect bidder value, the high-tech nature of the bidder and the pursuit of targets in related industries have negative effects on bidder value. Our empirical findings provide support for the positive impact of the extent of the stake pursued in the target firm and cultural distance, but not for international experience or enhanced corporate governance. The paper continues as follows. In the next section we provide a brief theoretical and conceptual background for our inquiry, and review the evidence in the literature. This is followed by our proposed research hypotheses to be tested. We then discuss our data and methodology, and follow this with a presentation of our findings. The final section concludes the study.

INTERNATIONAL EXPANSION AND FIRM VALUE: THEORETICAL ISSUES The internalization framework in the literature favors the contention that firms extract abovenormal returns from cross-border investments by internalizing host-country market imperfections when their firm-specific assets cannot find comparable value elsewhere (e.g., Buckley & Casson, 1976; Caves, 1971, 1998; Hymer, 1976; Morck & Yeung, 1991, 1992; Williamson, 1979). The resulting rents derived from internalization are expected to be capitalized into a higher value of the firm. Such an effect, under the multinational network hypothesis, is multiplied by positive network externalities so that investment decisions improve the expanding firm’s ability to benefit from the systemic advantages inherent in a multinational network. This finding is true because, as options increase, the

value of the firm should increase to reflect the incremental value of these options, as long as they remain non-imitable (Doukas & Travlos, 1988; Errunza & Senbet, 1981, 1984). The valuation effects of strategic actions leading to the creation of a multinational network stem from the firm’s ability to arbitrage across institutional environments, the informational externalities captured by the firm, and the cost savings gained by economies of scale in production, marketing and finance. Cross-border acquisitions may also increase the operational flexibility of the firm by giving it the opportunity to exploit market conditions (Kogut, 1983). A similar argument can be made for average output prices in international markets when demand shocks are not perfectly correlated. As long as the costs of creating and maintaining a diversified corporate network are not excessive, presence in multiple markets can yield additional value to the firm because of its ability to exploit more diverse conditions. So international expansion through acquisitions offers significant value-creation opportunities for firms; but it also presents significant challenges that jeopardize the potential hypothesized gains. For example, an often-cited complexity in cross-border acquisitions is the difficulties associated with postacquisition integration of the acquired company. In this context various researchers highlight risks such as ‘‘liability of foreignness’’ and ‘‘double-layered acculturation’’ (Barkema, Bell, & Pennings, 1996; Eden & Miller, 2004). Such risks pertain to the differences in natural culture, customer preferences, business practices, and institutional forces; and they are exacerbated impediments to the complete realization of strategic objectives.1 Lack of experience in the acquiring firm of executing acquisitions, organizational inertia in absorbing the target, and prior absence in the country of the target company may inhibit the benefits of acquisition for firm value. Additionally, complications in target assessment, misidentification of asset complementarities, informational asymmetries, and high premiums paid for the targets may also have adverse effects on the value of acquiring firms (Hitt, Hoskisson, & Ireland, 2001a; Hitt, Ireland, Camp, & Sexton, 2001b; Kissin & Herrera, 1990). Although it is reasonable to expect value creation from EMMs’ foreign acquisitions, there are opposing arguments about the impact of such expansions. For example, evidence from the recent literature on industrial diversification provides insights into the potential value-destructive effects

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of cross-border horizontal expansions. Denis, Denis, and Yost (2001) propose that global diversification can lead to the inefficient cross-subsidization of less profitable business units.2 Similarly, another group of scholars, adopting the agency cost framework, suggest that managers’ self-serving goals and incentives in value-reducing diversification strategies may not be entirely consistent with shareholder wealth creation (Denis et al. 2001). Roll’s hubris hypothesis and Stulz’s empire-building motives (e.g., Roll, 1988; Stulz, 1990) are along these lines. This view is justified on the grounds that the cash flows of global segments are imperfectly correlated, while global diversification reduces the risk of the manager’s relatively undiversified personal portfolio (Ahimud & Lev, 1981). In summary, the literature offers conflicting evidence about the effects of international expansion on firm value. The opposing views, and the variation in empirical results, are not surprising, given that the M&A literature supports a complex interplay of firm-specific, industry-specific, and country-specific factors in the process of crossborder acquisition. Our study applies a lens to the impact and significance of these factors as it analyzes possible value creation by internationally expanding EMMs.

The regional domicile hypothesis in this study follows previous studies that consider geographic influence on the performance of acquiring firms, and the way in which markets react to their strategic activities (Brouthers & Brouthers, 2000; Krugman, 1991; Penrose, 1959; Shrivastava, 1986). The investment size hypothesis is based on the classical argument that firms can achieve operating economies, leading to economies of scale in management, marketing, production, or distribution. Like their counterparts in developed countries, EMMs may accrue significant benefits from more efficient use of fixed capital, and extended global market presence: hence ultimately higher profitability for EMMs. The increase in size through successful cross-border acquisitions might lead to a combined value of the two companies that is higher than their standalone value (Lamacchia, 1997). In contrast, studies focusing on the value-destructive aspects of acquisitions point to misidentified complementarities, asymmetric information in target assessment and valuation, and challenges in post-acquisition integration of the target, particularly in cross-border transactions. Additionally, if the transaction process takes longer than anticipated, negative market reaction could be observed (Mulherin & Boone, 2000). Level of control in target is another factor. Earlier studies consider this factor as being related to opportunistic behavior of the venture partners (Beamish & Banks, 1987; Geringer & Hebert, 1989; Hanvanich & Cavusgil, 2001). Indeed, Chari, Ouimet, and Tesar (2004) find evidence on the significance for acquirer value of gaining a controlling stake in the acquisition of emerging-market targets by developed-country multinationals. The target status hypothesis is based on the notion that acquirers earn significantly negative returns when buying public targets, and earn significantly positive returns when buying private or subsidiary targets (Fuller, Netter, & Stegemoller, 2002). This finding is largely attributable to the complexity of the ownership structure in a public company, which increases the possibility that the transaction price will be increased to satisfy the interests of a diverse group of shareholders as well as stakeholders of a target firm (Choi & Russell, 2004). However, EMMs’ inexperience in sophisticated cross-border acquisitions may also lead to significantly higher premiums paid for

HYPOTHESES DEVELOPMENT In this paper we consider an array of firm-specific, industry-specific, and target-country-specific factors in international acquisitions of EMMs.
Firm Characteristics and Value Implications Here we set out to discern the target and acquirer characteristics that are likely to have an impact on the value of the expanding firm. The international business and finance literature provides ample evidence for a large array of factors that have an impact on the value captured by the acquirers in international expansions. The list includes firm size, leverage, acquirer’s international experience, prior presence in the host country, relative size of the target, the stake acquired in the target (controlling or non-controlling), and acquirer’s corporate governance structure. In addition to these factors, we also explore the significance of the regional domicile of EMMs, as the regional characteristics of Asian, Latin American, and Eastern European EMMs may lead to discernible patterns.3 Below we develop our first set of hypotheses involving the six factors:

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public targets to reduce the resistance by current shareholders. The level of international experience, both general and target-country specific, is a salient factor in crossborder expansions, and it is widely discussed in the literature (Barkema & Vermeulen, 1998; Brouthers & Brouthers, 2000; Kogut & Singh, 1988; Markides & Ittner, 1994). Previous studies suggest that experience in the international market is a source of sustainable advantage for investing firms, and is associated with positive abnormal returns generated around acquisitions (Harzing, 2002). Doukas and Travlos (1988) show that the acquisition announcements of firms with an established presence in the target country generate positive and statistically significant abnormal returns. It is plausible to argue that firms with a local presence are better positioned to identify investment opportunities in the host market, and are less likely to incur high premiums, than firms with no prior local presence. In addition, EMMs’ familiarity with the local environment may reduce the post-acquisition integration costs, since such acquisitions are less risky than acquisitions in environments with no prior presence. In other words, information asymmetries and the liability of foreignness are reduced in these cases (Martin, Swaminathan, & Mitchell, 1998). Good corporate governance on the acquirer side is expected to contribute to acquirer value. This factor is particularly important, because poor corporate governance practices in emerging markets, and their implications, have been well documented in the literature. Lax disclosure requirements, the lack of effective monitoring systems, and the underdeveloped nature of local equity markets increase managerial discretion, and create incentives for value appropriation at the expense of minority shareholders. In light of these emerging-market regularities, shareholders might approach foreign acquisitions of EMMs with suspicion, and perceive such strategies as an integral part of empire building or value appropriation efforts. To capture this effect, we use Level II and Level III ADR issuance by the EMMs as governance proxy. While we contend that these two types of ADR issuance by EMMs are important steps towards good corporate governance, a proxy based on these initiatives has limitations.4 In the light of the above discussion we offer the following two hypotheses on firm-specific effects.

Hypothesis 1a: Regional domicile, effectiveness of corporate governance, investment size, level of control, experience, and target charter status are significant factors in affecting bidder value in cross-border expansions. Hypothesis 1b: The lower level of control and public status of the target company has a negative impact on acquirer/bidder value.

Industry-Specific Factors and Firm Value in Cross-Border Acquisitions of EMMs Evidence reported in the extant literature suggests that both the type of industry and the structure of the firm affect the expansion decisions, the type of expansion activity, and the value implications of expansions (Brouthers & Brouthers, 2000; Markides & Ittner, 1994; Shimizu, Hitt, Vaidyanath, & Pisano, 2004). In this study, we focus on the hightech/non-high-tech dichotomy by industry type, and therefore evaluate the impact of the firm being embedded in a high-tech industry. While acquisitions in high-tech industries may bring significant product and process technologies to EMMs, and propel their product development and efficiency enhancement efforts, the informational asymmetries associated with the assets acquired and their compatibility, as well as the ensuing premiums associated with them, may altogether lead to value destruction. On the other hand, theoretical arguments suggest that diversification into related and unrelated businesses affects firm value. For example, the internal capital markets show a higher degree of independence from specific industry segments than external capital markets: therefore we expect resource allocation to be more efficient in diversified firms than in non-diversified firms (Matsusaka & Nanda, 1996; Rieck, 2002; Stein, 1997). Diversified firms have additional advantages from what is referred to as the co-insurance effect, as their combined cash flow will be less unstable than that of non-diversified firms of similar size. In this respect, diversified cross-border expansions may lead firms to balance gains and losses from different segments (Stulz, 1990). Diversified firms may also have a higher degree of conglomerate power by engaging in cross-subsidization. In contrast, however, diversification can decrease firm value, since it can cause an increase in the cross-subsidization of failing business segments and an increase in the agency costs of firms (Denis et al. 2001; Jensen, 1986; Rieck, 2002; Stein, 1997; Stulz, 1990).

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Accordingly, the following hypothesis addresses the impact of the acquiring company’s structure on the value of the acquirer: Hypothesis 2: EMMs’ industry characteristics (high-tech or non-high-tech) strategic focus of EMMs (diversified vs non-diversified), and the type of expansion (through acquisition of a related or an unrelated company) are associated with bidder value in cross-border acquisition announcements of EMMs.

Target Country Characteristics and Firm Value in Cross-Border Acquisitions We consider two sets of target-country characteristics to evaluate their impact on the value captured by the acquirer: geographic and cultural proximity to the acquirer’s home country, and the level of economic development (developed vs developing) and the institutional infrastructure.
Geographic and cultural proximity is a long-studied effect. While earlier studies provide evidence that expansion into new geographical and economically dissimilar areas increase shareholders’ wealth (e.g., Doukas & Travlos, 1988), more recent studies demonstrate the benefits of proximity. Barkema et al. (1996) argue that foreign acquirers are more likely to fail in the cultural adjustment process than local acquirers ‘‘whenever acculturation involved is more demanding.’’ Brock (2005) emphasizes the impact of cultural distance in the context of mergers and acquisitions, and how it hinders the realization of intended synergies through its impact on the integration process, managerial commitment, and ease of resource sharing. Coval and Moskowitz (2001) propose that geographic and cultural proximity sharply reduces information acquisition costs. Ghemawat (2001) identifies four dimensions of distance – cultural, administrative, geographic, and economic – and argues that technological innovations have not eliminated the very high costs of distance. In addition to the taxing effect of cultural distance, there is ample evidence suggesting that geographic distance raises the cost of transferring knowledge and technology, and dramatically reduces the effectiveness of knowledge-sharing (e.g., Almeida & Kogut, 1999; Branstetter, 2001; Keller, 2002; Storper & Venables, 2004). Institutional infrastructure is another substantial factor in the development of an outward posture

in an underdeveloped market. These are markets bearing higher levels of operational and investment risk due to inefficient and corrupt legal infrastructures, insufficient property rights protection, dysfunctional financial systems, restrictive and volatile regulatory regimes, and external investment and trade barriers (Brouthers, 2002). While investing firms may be able to take advantage of market imperfections, they may also have to deal with the excessive costs of uncertainty and government discretion. La Porta, Lopez de Silanes, Shleifer, and Vishny (1998) argue that in countries with less political and economic freedom, business opportunities are also undermined. In an effort to evaluate the impact of institutional infrastructure on the value captured by the acquirers, we use an economic freedom index for each target country. Our hypotheses are formulated as follows: Hypothesis 3a: Geographic and cultural proximity of the target country may be an influential factor affecting bidder value in cross-border acquisition announcements of EMMs. Hypothesis 3b: The poor institutional infrastructure of the target country may have a negative impact on bidder value in cross-border acquisition announcements.

DATA AND METHODOLOGY
Data The cross-border announcements we analyze in this study are associated with 58 EMMs compiled from various issues of the World Investment Report (United Nations, 1999–2002). This is published by UNCTAD annually: it provides a list of the top emergingmarket and transition-economy transnational corporations. Mergers and acquisitions announcements data for the period 1991–2004 are extracted from the Thomson SDC Platinum database. The information on transaction value, shares acquired by the bidder, and shares owned by the bidder after the transaction also come from the Thomson SDC Platinum database.5 Equity prices and company accounts data are compiled from DataStream International. Information on company foreign sales and foreign employees comes from UNCTAD’s World Investment Report, individual company annual reports, and mandatory filings. Our initial roster of announcements was filtered for other major corporate events associated with the acquiring firms to control for confounding

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events that could otherwise have had an impact on firm value. We also screened the initial roster for data availability to conduct event study and crosssectional analysis. After controlling for confounding events, we compiled a workable sample of 433 cross-border merger and acquisition announcements associated with 58 firms. Our sample period covered the time between 1991 and 2004. The sample firms operate in a range of industries, and originate mainly from Latin America6 (Argentina, Brazil, Colombia, Chile, and Mexico) and Asia (Hong Kong, India, Malaysia, Philippines, South Korea, and Singapore). Two firms originate from Hungary and three from South Africa. A large percentage of the transactions (78.9%) covered in our analysis were initiated by Asian EMMs. The EMMs from Latin America account for 15% of the transactions. Approximately 30% of the transactions are accounted for by 11 high-tech firms in our sample. About 38% of transactions are attributed to 13 diversified conglomerates. While, on average, 52% of the targets pursued by EMMs are located in

culturally proximate environments, 39% acquisition announcements involved located in developed markets. We provide on sample characteristics in Tables 1, 2 and

of the targets details 3.

Methodology Event study methodology in the finance literature has become a standard in evaluating the stock price reaction to a specific event. This approach has also been used to identify the organizational and public policy implications of both endogenous and exogenous corporate events (McWilliams & Siegel, 1997). Event study allows researchers to conclude whether an event had a positive or negative effect on shareholder wealth. Traditionally, the ‘‘market model’’ is assumed to be the underlying return process.7 The market model assumes a linear relationship between the return of a security and the return of the market portfolio. For each security i, the market model assumes that the returns generated are given by
Rit ? ai ? bi Rmt ? eit ?1 ?

Table 1

Sample firms by industry, country, region, and listing market

Company name Acer Amsteel Asia Pacific Brews Ltd Barloworld Bavaria SA Berjaya Cathay Pacific Airways Cemex CITIC Pacific CLP Holdings Ltd Creative Technology Ltd Empresas ICA Sociedad Control Enersis SA Evergreen Marine First Pacific Formosa Fraser & Neave Ltd Genting Gerdau Gruma Guangdong Investment Guangzou Investment Hong Leong Hutchison Whampoa Hyundai Keppel Corporation Ltd

Country Taiwan Malaysia Singapore South Africa Colombia Malaysia HK Mexico HK HK Singapore Mexico Chile Taiwan HK Taiwan Singapore Malaysia Brazil Mexico HK HK Malaysia HK South Korea Singapore

Region Asia Asia Asia Africa Asia Asia Asia LA Asia Asia Asia LA LA Asia Asia Asia Asia Asia LA LA Asia Asia Asia Asia Asia Asia

Industry Computer hardware Steel Brewers Diversified Brewers Diversified Airlines and airports Building materials Diversified Electricity Computer hardware Other construction Electricity Shipping and ports Diversified Chemicals and advanced materials Soft drinks Hotels Steel Food processing Water Diversified Diversified Diversified Diversified Engineering/general

Listing market LSE/Taiwan Kuala Lumpur/Singapore Singapore LSE/JSE Colombia HK HK NYSE/Mexico LSE/HK HK/Shenzen Singapore NYSE/Mexico NYSE/Santiago LSE/Taiwan LSE/HK LSE/Taiwan Singapore Kuala Lumpur/Singapore NYSE/Brazil NYSE/Mexico HK HK/Shanghai Singapore HK KSE LSE/Singapore/HK

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Table 1 Continued Company name LG Corporation Magyar Olaj Gazi (MOL) Malaysian International Shipping Mosel Vitalic Natsteel Ltd Neptune Orient Airlines Ltd New World Development Petronas Posco Ranbaxy Reliance Industries Ltd Samsung Electronics San Miguel Corporation Sappi Ltd Savia SA de CV Sime Darby Singapore Airlines Singapore Telecom Ssyangyong Cement Swire Pacific Taiwan Semiconductor Tata Telekom Malaysia ? t Rt Tiszai Vegyi Kombina Tong Yang United Microelectronics Varig Vitro Want Want Holdings Wing On YPF Country South Korea Hungary Malaysia Taiwan Singapore Singapore HK Malaysia South Korea India India South Korea Philippines South Africa Mexico Malaysia Singapore Singapore South Korea HK Taiwan India Malaysia Hungary South Korea Taiwan Brazil Mexico Singapore HK Argentina Region Asia E. Europe Asia Asia Asia Asia Asia Asia Asia Asia Asia Asia Asia Asia LA Asia Asia Asia Asia Asia Asia Asia Asia Eastern Europe Asia Asia LA LA Asia Asia LA Industry Diversified Oil Integrated Shipping and ports Semiconductors Steel Shipping and ports Diversified Oil integrated Steel Pharmaceuticals Oil integrated Semiconductors Brewers Paper Farming and fishing Diversified Airlines and airports Telecom Building Materials Diversified Semiconductors Diversified Telecom Chemicals/commodity Building Materials Semiconductors Airlines and airports Glass Food processing Retailers Oil and gas exploration and production Listing market KSE Frankfurt/Budapest Kuala Lumpur Taiwan Singapore Singapore HK Kuala Lumpur/HK NYSE/KSE Bombay Bombay LSE/KSE Philippines NYSE/JSE Mexico Kuala Lumpur/HK Singapore Singapore KSE HK NYSE Bombay Kuala Lumpur Budapest KSE NYSE Brazil Mexico Singapore HK NYSE/Buenos Aires

Table 2

Average assets and sales of the sample firms (Millions of USD)

Mean Total assets ($) Foreign assets ($) FA/TA (%) Total sales ($) Foreign sales ($) FS/TS (%) 7,803.75 2,370.38 19.54 4,221.39 1,526.47 23.84

Median 5,677.72 1,362.40 18.55 2,426.50 1,242.19 21.89

St. dev. 7,342.8901 2,955.6298 0.1649114 4,738.7111 1,588.0905 0.1828255

Kurtosis 0.9382808 2.4534347 2.9922848 5.3600439 3.4174761 0.8710406

Skewness 1.2976253 1.8717439 1.5891776 2.3332731 1.8153563 0.9570968

where Rit is the return on security i at time t. The subscript t indicates the time, the subscript i indicates a specific security, and the subscript m indicates the market. Rmt is the return on the market portfolio during period t. Under the assumption of linearity and normality of returns, et is a random error term for security i at time t, and bi is a firm-specific coefficient, to be estimated

from the market model regressions. The market model expressed in Eq. (1) is used to compute the return on the stock that would have been expected on the day of the event, or during a selected event window if the event had not occurred. Equation (1) is estimated by using a 255-day estimation period from t??11 to t??265, where t?0 is the event day.

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Table 3

Average assets and sales by industry (Millions of USD)

Industry

No. of transactions 21 18 42 11 1 8 168 3 3 18 2 2 3 5 17 2 6 1 1 2 30 7 6 27 22 7

Total assets ($) 2,504.08 3,053.03 7,623.25 596.47 476.50 1,657.12 11,592.95 8,058.50 NA 12,161.56 4,121.50 1,019.38 2,486.45 9,349.47 11,677.63 816.00 2,953.03 1,247.40 1,460.13 12,684.00 6,665.99 1,076.45 1,774.13 1,220.52 5,779.02 2,589.73

Foreign assets ($) 484.78 524.03 4,597.78 17.40 81.75 431.73 3,595.93 848.50 NA 1,423.49 1,129.00 733.13 715.75 2,129.88 1,851.13 80.25 2,222.51 – 583.00 2,114.00 556.97 643.00 574.75 348.07 1,861.02 1,844.36

FA/TA (%) 7.16 30.51 37.26 3.78 4.29 19.43 18.38 5.26 NA 9.20 40.06 81.76 28.90 17.40 6.10 2.46 43.25 0.00 39.73 16.68 2.73 20.87 15.67 17.06 17.91 68.64

Total sales ($) 1,397.54 1,314.53 3,022.06 398.96 372.75 1,657.95 6,803.46 1,703.00 NA 1,766.96 1,763.00 767.25 905.65 4,430.29 5,870.34 346.50 2,195.49 781.40 380.15 5,959.00 6,419.30 1,055.29 839.88 898.00 2,030.31 804.23

Foreign sales ($) 775.77 410.69 1,649.69 138.97 58.25 1,004.18 2,369.73 153.00 NA 235.73 729.00 506.93 183.75 867.94 2,461.81 23.75 1,566.60 40.00 53.65 3,440.00 1,925.45 650.98 525.50 197.96 102.45 614.59

FS/TS (%) 18.13 32.33 33.33 28.57 15.63 35.32 26.13 4.49 NA 8.89 60.96 76.76 21.59 15.60 21.15 0.00 40.45 0.86 14.11 57.73 9.51 24.08 24.27 10.51 2.56 77.58

Airlines and airports Brewers Building materials Chemicals, commodity Chemicals and advanced materials Computer hardware Diversified industry Electricity Electronic equipment Engineering, general Farming and fishing Food processors Hotels Oil and gas exploration and production Oil integrated Other construction Paper Pharmaceuticals Retailers, multi-department Security and alarms Semiconductors Shipping and ports Soft drinks Steel Telecom fixed line Water

The abnormal return (AR) due to the announcement on any given day therefore equals the actual return minus the predicted normal return: ARit ? Rit ? ?ai ? bi Rmt ? ?2 ?

Daily abnormal returns are then computed for each day t for each firm i. To obtain a general insight into the abnormal return observations for a sample of N firms, abnormal returns (AR) for each day t are averaged as follows: ARt ?
N 1 X ARit N i? 1

compared with the actual returns observed on each day within the event window. The difference between the predicted return and the actual return for a period such as event window is called the cumulative abnormal return and is calculated as follows: CARi ?
T X t ?1

ARit

?4 ?

?3 ?

More specifically, the cumulative abnormal return during the event window (T1, T2), CARi (T1, T2)?CARi?EW, is given as CARi?EW ?
T2 X t ?T 1

Since the full impact of an event on firm value may not be felt on a single day, event studies often examine the returns for periods around an event, called the event window. In our study, we define the event window as the period between 10 days prior to the event and 10 days after the event. The expected returns on the stock calculated from model (1) for the security during the event window (?10, ? 10) are

ARit

?5 ?

When CARs differ from zero, parametric tests can be performed to see whether this deviation is statistically significant. Coutts, Mills, and Roberts (1995) suggest using standardized cumulative abnormal returns (SCARs) for longer event windows to correct

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for serial correlation of daily event period abnormal returns for the same firm.8 Each firm’s cumulative abnormal return is standardized according to CARi ?T1 ; T2 ? CARi?EW ? SDi SDi where SDi is given as SCARi ?T1 ; T2 ? ?
v???????????????????????????????????????????????????? u k P u m ?2 u Rmt ? k?R u k t ? 1 SDi ? Si u uk ? T ? P T t m ?2 ?Rmt ? R
t ?1

where: SIZE? TYPE? log of bidder firm total assets. a dummy variable, taking the value 1 if the target firm is in a related industry, and 0 otherwise. The dummy variable is assigned after comparing the four-digit SIC codes for the acquirer and the target: it takes the value 1 for matching SIC codes, and 0 otherwise. target status: a dummy variable taking the value 1 if the target is privately owned, and 0 otherwise. Only 23 of 433 transactions in our sample involved publicly owned targets. level of control (percentage stake pursued by the bidder). We use the percentage of shares acquired, as reported by the Thomson SDC Platinum database. ratio of the dollar value of the transaction to the bidder’s market value. The dollar value of the transaction was gathered from the Thomson SDC Platinum database, and bidder value was compiled from DataStream International.9 level of development of the institutional infrastructure. We use two proxies to measure this. The first is based on the Fraser Institute’s World Economic Freedom Index: the index takes values between 1 (non-market economy) and 10 (fully functional market economy).10 The second is based on income level: a country is considered developed if it is a highincome OECD economy (we exclude the Czech Republic and South Korea because of some institutional weaknesses in these countries). By this measure 39.4% of our cross-border M&A expansion announcements involve targets located in a developed economy over the period 1991–2004. geographic and/or cultural proximity. We use two different proxies

?6a?

?6b? TSTATUS?

where Si is the standard error of the market model regression, T is the number of observations in the estimation period, Rmt is the return on the market ?m is the average return of the portfolio for day t, R market portfolio for the estimation period, and k is the number of days in the event window. A Z statistic is calculated according to N 1 X SCARi ?7 ? Z ? p???? N i? 1 Under the null hypothesis of no stock price effect, this statistic will have approximately a standard normal distribution. As briefly discussed above, this method is preferred because it accounts for possible serial correlation among the abnormal returns within the event window. We report the SCARs for the following event windows: SCAR(?10, ? 10), SCAR(?5, ? 5), SCAR (?10, ? 5), SCAR(?5, ? 1), SCAR(?2, ? 1), SCAR(?1, ? 1), and SCAR(?1, 0). The SCARs calculated in the event study are utilized as dependent variables in the multivariate and logistics regression analyses.

CONTROL?

INVSTSIZE?

INSTITUTION?

Cross-Sectional Analysis of Cumulative Abnormal Returns As discussed earlier, value captured in an acquisition depends on a range of firm-, industry-, and country-specific factors. To explain the crosssectional variation in the cumulative abnormal returns, we use the following multivariate model:
SCAR?T1 ; T2 ? ? b0 ? b1 ?SIZE? ? b2 ?TYPE? ? b3 ?TSTATUS? ? b4 ?CONTROL? ? b5 ?INVSTSIZE? ? b6 ?INSTITUTION? ? b7 ?PROXIMITY? ? b8 ?INTEXPR? ? b9 ?PRIORPRES? ? b10 ?HITECH? ? b11 ?GOVERN? ? b12 ?STRUCTURE? ? b13 ?REGION 1? ? b14 ?REGION 2? ? e

PROXIMITY?

?8 ?

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INTEXPR?

PRIORPRES?

HITECH?

GOVERN?

to measure cultural distance. Our first proxy combines cultural and geographical distance (Geoculprox). By this measure, 52.4% of our cross-border M&A expansion announcements involve culturally proximate targets over the period 1991–2004. Our second cultural distance measure is a cultural distance index based on Hofstede’s cultural dimensions. The CDI index takes values between 1 and 0: a score close to 1 implies significant cultural distance; a score close to 0 implies significant cultural proximity. Our median sample CDI score is 0.41 (see the Appendix for a detailed discussion of the proxies used for proximity). extent of international experience of the bidder. We use two ratios (foreign sales/total sales and foreign assets/total assets) as a proxy for international experience. These variables were compiled from data provided in UNCTAD’s World Investment Report, company annual reports, and DataStream International. prior presence in target market: a dummy variable, taking the value 1 if the acquiring firm has prior presence in the target market, and 0 otherwise. In order to identify companies with prior presence in the target market we screened a variety of sources, including the Thomson SDC Platinum database, company annual reports, company websites, and news articles compiled from database searches with key words. In 58% of the transactions, the acquiring firm had a prior presence in the target country. acquirer industry: a dummy variable, taking the value 1 if the acquirer is in a high-tech industry, and 0 otherwise. a dummy variable, taking the value 1 if the EMM has issued Level II or LEVEL III ADRs, and

STRUCTURE?

REGION 1?

REGION 2?

otherwise; As it was discussed earlier, companies with Level II and Level III ADRs are considered to have enhanced corporate governance structures. In about 13.8% of the transactions the acquirer has an outstanding Level II or Level III ADR. Strategic Orientation and Structure of the Bidder (Dummy variable takes value of 1, if the bidder is a diversified conglomerate, 0 otherwise). regional domicile: a dummy variable, taking the value 1 if the acquirer comes from Asia, and 0 otherwise. regional domicile: a dummy variable, taking the value 1 if the acquirer comes from Latin America, and 0 otherwise.

To check the robustness of our multivariate regression results, we also employ binary logistic regression analysis.11 In our logistic regression model, we define the dependent variable DSCAR as a dichotomous variable, designated to a value of 1 if it is positive and 0 otherwise. We use the same set of independent variables as in the multivariate regression model (Eq. (8)). The resulting logistic regression model is specified as follows:
DSCAR?T1 ; T2 ? ? b0 ? b1 ?SIZE? ? b2 ?TYPE? ? b3 ?TSTATUS? ? b4 ?CONTROL? ? b5 ?INVSTSIZE? ? b6 ?INSTITUTION? ? b7 ?PROXIMITY? ? b8 ?INTEXPR? ? b9 ?PRIORPRES? ? b10 ?HITECH? ? b11 ?GOVERN? ? b12 ?STRUCTURE? ? b13 ?REGION ? 1? ? b14 ?REGION ? 2? ? e

?9 ?

ANALYSIS AND RESULTS We analyze SCARs for varying event windows. Our results, reported in Table 4, show that announcements of international acquisitions of EMMs are, on average, associated with negative abnormal returns. While the mean (median) cumulative abnormal returns immediately prior to and after the announcement (two- and three-day event windows) are negative and statistically significant at the 10% level, mean (median) cumulative abnormal returns in wider event windows are statistically insignificant. Positive market reactions

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Table 4
Interval

Daily and standardized cumulative abnormal returns from cross-border expansion announcements
Mean Mean Z-value Median ?0.06398 ?0.0626 ** ?0.05582* ?0.06612* ?0.05618** ?0.06023** ?0.06228*** ?1.23121 ?1.71485 ?1.61235 ?1.59322 ?1.66743 ?2.25568 ?2.73531 207:226 206:227 207:226 198:235** 198:236** 195:238** 195:239** ?0.91308 ?1.0092 ?0.91308 ?1.77811 ?1.77811 ?2.06645 ?2.06645 433 433 433 433 433 433 433 47.81 47.58 47.81 45.73 45.73 45.03 45.03 WSR Z-value Positive: negative Doukas’ Z for positive:negative Total no. of transactions Positive market reaction (%)

(?10, +10) (?10, +5) (?5, +5) (?5, +1) (?2, +1) (?1, +1) (?1, +0)

?0.04843 ?0.05759 ?0.05384 ?0.04962 ?0.05442 ?0.09133 ** ?0.12158***

?1.11716 ?1.33182 ?1.28754 ?1.13237 ?1.22589 ?1.95378 ?2.76804

***, **, and * denote statistical significance at the 1, 5, 10% levels, respectively. The table presents the daily standardized abnormal returns (SCARs) of 433 cross-border M&A expansion announcements by emerging-market multinationals (EMMs) over the period 1991–2004. Daily standardized abnormal returns (SCARs) are computed from the market model as prediction errors. Day 0 refers to the announcement day of acquisitions as reported in the SDC Database. Z-statistics (Wilcoxon signed-rank test) are used to test for the statistical significance of mean SCARs. The statistical significance of the mean (median) difference between groups is computed by the Mann–Whitney test for unmatched pairs. Z statistics (Doukas’ test) are used to test for the statistical significance of positives/negatives.

with varying event windows range from 45.03 to 47.81%. Doukas’ positives/negatives test shows significant Z values at (?5, ? 1), (?2, ? 1), (?1, ? 1), and (?1, 0) at the 5, 10, 5, and 5% levels, respectively, and confirm the dominance of the negative reactions. The negative two- and three-day cumulative abnormal returns suggest that the potential benefits expected from cross-border expansion are offset by various costs associated with the acquisition of the targets. Our results indicate that overall investor sentiment with reference to the EMMs’ international expansions through acquisitions is not positive. In other words, investors do not perceive EMMs’ cross-border acquisitions as value-creating strategic initiatives. These findings contradict the positive returns reported for similar event windows in earlier studies focused on international acquisitions (Doukas & Travlos, 1988; Morck & Yeung, 1992), but are in line with the hypothesized value destruction elaborated in studies by Click and Harrison (2000), Hitt et al. (2001a, 2001b) and Kissin and Herrera (1990). The negative valuation effect of international acquisitions of EMMs documented here also appears to be consistent with the value-reducing diversification of US multinationals reported by Denis et al. (2001), Jensen’s hubris hypothesis and Stulz’s empire-building motives (see Jensen, 1986, as well as Stulz, 1990). However, it is plausible to suggest that firm characteristics, the nature of the investment, strategic fit, and target market conditions might be influential factors in investor reactions. In the following section we discuss these factors.

Firm-Specific Factors We consider several firm-specific factors, ranging from regional domicile to prior presence of the acquiring firm in the target firm’s country, to explain the cross-sectional variations in SCARs. The classification of transactions based on the regional origin of the acquirer indicates that failure to create value is a commonly observed outcome of cross-border EMM acquisitions, regardless of the regional domicile of the acquirer. We report SCARs classified by region in Table 5. An initial review of the SCARs points to some differences in SCARs across different event windows. Wider event windows of (?10, ? 10) and (?10, ? 5) suggest that EMMs from South Africa and Hungary experience more acute value destruction than their Asian and Latin American counterparts. In contrast, for narrower windows, mean SCARs suggest higher-level value destruction for Asian EMMs. However, mean SCAR differences are not statistically significant at any of the event windows that we analyze. Our parametric and non-parametric tests reported in Panels D and E of Table 5 indicate that differences of mean and median SCARs are not statistically significant. Although they are not reported here, pairwise differences of means tests also confirm these findings. The insignificance of regional origin is also confirmed in our cross-sectional analysis reported in Panels A and B of Table 6. Although the coefficient signs are consistent with the univariate findings, the region dummies are insignificant in both linear and binary logistic regression models.12

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Table 5
Interval

Daily and standardized cumulative abnormal returns of cross-border expansion announcements
Mean Mean Z-value Median WSRT Z for median Positive: negative Doukas Z for positive:negative Total no. of events Positive market reaction (%)

Panel A EMMs from Asia (?10, 10) ?0.0124 (?10, +5) ?0.02443 (?5, +5) ?0.02771 (?5, +1) ?0.0385 (?2, +1) ?0.06024 (?1, +1) ?0.10273** (?1, +0) ?0.1345*** Panel B EMMs from Latin America (?10, +10) ?0.15316* (?10, +5) ?0.17695* (?5, +5) ?0.14748 (?5, +1) ?0.10433 (?2, +1) ?0.01311 (?1, +1) ?0.05229 (?1, +0) ?0.05137 Panel C Other EMMs (South Africa (?10, +10) ?0.26489 ?0.19602 (?10, +5) (?5, +5) ?0.16411 (?5, +1) ?0.05722 (?2, +1) ?0.08383 (?1, +1) ?0.0385 (?1, +0) ?0.1304

?0.25883 ?0.50945 ?0.60405 ?0.78602 ?1.17211 ?1.8883 ?2.64059

?0.01577 0.019907 0.01056 ?0.03909 ?0.05249* ?0.07362*** ?0.08403***

?0.45415 ?0.58231 ?0.58777 ?0.84955 ?1.46821 ?2.46313 ?2.73284

170:172 175:167 173:169 165:177 159:183* 149:193*** 152:190**

?0.10815 0.43259 0.216295 ?0.64889 ?1.29777 ?2.37925 ?2.0548

342 342 342 342 342 342 342

49.71 51.17 50.58 48.25 46.49 43.57 44.44

?1.34307 ?1.59262 ?1.21587 ?0.8692 ?0.13056 ?0.51824 ?0.52941 and Hungary) ?1.30436 ?0.94716 ?0.98196 ?0.34399 ?0.50535 ?0.21664 ?0.77655

?0.16887 ?0.3164** ?0.23063* ?0.13587** ?0.07726 ?0.03514 ?0.00744

?1.0732 ?1.94836 ?1.62257 ?1.69923 ?0.6963 ?0.37051 ?0.35454

29:37 24:42** 26:40** 24:42** 28:38 31:35 32:34

?0.98473 ?2.21565 1.72328 ?2.21565 ?1.23091 ?0.49237 ?0.24618

66 66 66 66 66 66 66

48.00 40.00 48.00 40.00 48.00 56.00 44.00

?0.24463 ?0.14942 ?0.23025 ?0.14342 ?0.05532 0.084645 ?0.09589

?1.20464 ?0.72202 ?1.37776 ?0.8622 ?0.33347 0.476275 ?0.57108

8:17* 7:18** 8:17* 9:16 11:14 15:10 15:10

?1.80 ?2.20 ?1.80 ?1.40 ?0.60 1.00 1.00

25 25 25 25 25 25 25

32.00 28.00 32.00 36.00 44.00 60.00 44.00

Event window Asia

Regions LA

Other

F-statistic

p-value

Panel D Testing regional differences: one-way ANOVA (?10, +10) ?0.00012 ?0.00153 (?10, +5) ?0.00024 ?0.00177 (?5, +5) ?0.0003 ?0.0015 (?5, +1) ?0.0004 ?0.0010 (?2, +1) ?0.0006 ?0.0001 (?1, +1) ?0.0010 ?0.0005 (?1, +0) ?0.0013 ?0.0005 Event window Panel E Testing regional differences: Kruskal–Wallis test (?10, +10) (?10, +5) (?5, +5) (?5, +1) (?2, +1) (?1, +1) (?1, +0) Chi-square statistic

?0.00265 ?0.00196 ?0.0016 ?0.0006 ?0.0008 ?0.0004 ?0.0013

1.440 1.109 0.736 0.145 0.085 0.113 0.229

0.238 0.331 0.480 0.865 0.918 0.893 0.795 p-value

2.618 4.160 3.408 1.470 0.003 1.363 0.553

0.270 0.125 0.182 0.479 0.998 0.506 0.758

***, **, and * in Panel A denote statistical significance at the 1, 5 and 10% levels, respectively. ** and * in Panel B denote statistical significance at the 5 and 10% levels, respectively. ** and * in Panel C denote statistical significance at the 5 and 10% levels, respectively. The table presents the daily and standardized cumulative abnormal returns (SCARs) of 342 cross-border MA expansion announcements by emergingmarket multinationals (EMMs) originating from Asia over the period 1991–2004. Daily standardized cumulative abnormal returns (SCARs) are computed from the market model as prediction errors. Day 0 refers to the announcement day of acquisitions as reported in the SDC Platinum database. Z-statistics (Wilcoxon signed-rank test) are used to test for the statistical significance of mean SCARs. The statistical significance of the mean (median) difference between groups is computed by the Mann–Whitney test for unmatched pairs. Z statistics (Doukas’ test) are used to test for the statistical significance of positives/negatives.

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A comparative analysis of EMMs’ acquisition of small and large foreign targets indicates that relative size (measured as the ratio of the dollar value of the acquired stake to the bidder’s market value) is a significant factor for discerning investor reaction to cross-border acquisition announcements. Whereas mean SCAR differences are negative for wider event windows, they are positive for narrow windows. The significance of the positive differences of mean SCARs can be confirmed at the 10% level for (?1, ? 1) and (?1, 0) event windows.13 In our cross-sectional linear regression model we find that relative target size is significant in three event windows – (?5, ? 1), (?2, ? 1) and (?1, ? 1) – at the 1% significance level. In our binary logistic regression model, relative investment size is significant only in the (?1, ? 1) event window at the 10% significance level. A surprising finding in the univariate and multivariate models is the positive sign of the coefficient in the significant event windows, which is contrary to what we expected to find. While the negative impact of the large acquisitions on the bidder value is widely reported in the literature, our findings suggest that, for EMMs, investors perceive better prospects when the acquirer bids for larger targets relative to its assets. In our univariate analysis we compare the market reactions to transactions involving bids for 50% or higher percentage of the target shares against bids involving less than 50% of the target shares. In 134 transactions bidders contemplated 50% or more of the target shares. The results suggest that the impact on acquirer value of the level of control gained by the acquirer is positive for five out of the seven event windows, and significant for at least one event window. In contrast, we fail to confirm the significance of the level of control in our multivariate analysis.14 In our linear regression model, the sign varies across the event windows, but the coefficient remains insignificant. In our binary logistic regression analysis, the sign is consistently negative across the event windows. Our analysis of the effect of target status on the acquiring firm value indicates that, on average, EMMs experience a higher percentage of positive reactions for all event windows when announcements involve private targets. Regardless of the target status, SCARs reported for narrow windows are negative and statistically significant for both private and public targets.15 The parametric and non-parametric tests of the mean SCAR differences show that the differences are statistically insignif-

icant for all event windows. Here, our multivariate analysis results contradict the results of the univariate analysis. In our linear regression analysis, target status proves to be a significant factor, explaining variation in SCARs at the 10% significance level for all event windows. In contrast, in our binary logistic regression analysis, significance can be confirmed only for event window (?5, ? 5) at the 5% significance level. The sign of the coefficient is consistent with our expectations in five out of seven event windows, indicating that the acquisition of private targets is not as value destructive as the acquisition of public targets. A caveat to these findings is the existence of a relatively small number of ‘‘publicly’’ owned targets in the sample. We have only 23 transactions involving publicly owned targets vs 410 transactions involving privately owned targets. We expected international experience in general and knowledge of the target market in particular to improve EMMs’ ability to capture value from international acquisitions on familiar turf. Yet our univariate analysis results indicate that prior presence in the target market does not have a significant or consistent impact on bidder value. The mean differences of SCARs are between two groups of acquirers: those that have prior presence in the target market, and those that do not reveal varying signs depending on the event window. We find weak support for the significance of prior presence in one of two multivariate models at the event window (?5, ? 5). Overall, in light of our empirical findings, we fail to find strong support for the significance of this factor. Consideration of two commonly used proxies for international experience (or the extent of internationalization of the firm) leads us to a similar conclusion. In other words, we cannot verify with confidence the proposition that more experienced firms are more likely to capture value from crossborder acquisitions. In contrast, we obtain an opposite sign for the mean differences and the coefficient of the international experience proxy in univariate and multivariate analyses, respectively. In our univariate analysis, mean SCAR differences for event window (?2, ? 1) are negative and significant, suggesting deeper value destruction for experienced bidders. We find a similar result for the event window (?10, ? 10) in our linear regression analysis (see Table 6, Panel A). For all other windows, mean SCAR differences and coefficients prove to be insignificant. Although we do not report the multivariate specifications with

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Table 6

Cross-sectional regressions: standardized cumulative abnormal returns of emerging-market multinationals (EMMs)
SCAR (?10, +10) SCAR (?10, +5) SCAR (?5, +5) SCAR (?5, +1) SCAR (?2, +1) SCAR (?1, +1) SCAR (?1, 0)

Dependent variable Independent variable Panel A Linear regression Intercept SIZE TYPE TSTATUS CONTROL INVSTSIZEa INSTITUTIONb PROXIMITYc INTEXPRd PRIORPRES HITECH GOVERN STRUCTURE REGION 1 (Asia)

0.0251 0.0068 ?0.000036 0.9722 0.0014 0.4076 ?0.0141*** 0.0000 ?0.0024 0.3151 0.0041 0.6722 ?0.0019** 0.0482 0.0072 0.2498 ?0.0166** 0.0175 0.0015 0.4293 ?0.0042* 0.0581 0.0022 0.5524 0.0021 0.3380 0.0023 0.4487

0.0275 0.0100 0.000699 0.4959 0.0023 0.1452 0.0097*** 0.0003 ?0.0012 0.5797 0.0015 0.8678 ?0.0019** 0.0280 0.0028 0.6306 ?0.0181* 0.0853 0.0027 0.1609 ?0.0025 0.2269 0.0016 0.6982 0.0032 0.1341 0.0006 0.8206

0.0209 0.0247 0.000555 0.5628 0.0017 0.2180 0.0135*** 0.0000 ?0.0004 0.8357 0.0128 0.1941 ?0.0011 0.1396 0.0022 0.6669 ?0.0131 0.1837 0.0036** 0.0380 ?0.0026 0.1936 0.0006 0.8525 0.0021 0.2468 0.0026 0.2092

0.0198 0.0279 0.000623 0.5375 0.0014 0.4024 0.0160*** 0.0000 0.0014 0.5060 0.0303*** 0.0001 ?0.0003 0.6961 ?0.0008 0.8651 ?0.0114 0.1738 0.0021 0.1982 ?0.0002 0.9150 0.0014 0.6804 0.0040* 0.0542 ?0.0011 0.6673

0.0053 0.5101 0.000510 0.6136 0.0020 0.2349 ?0.0062** 0.0293 0.0013 0.6061 0.0285*** 0.0094 0.0003 0.6503 0.0017 0.7281 ?0.0021 0.5313 0.0001 0.9308 ?0.0014 0.4731 ?0.0030 0.2833 0.0029 0.1384 ?0.0004 0.8511

0.0050 0.5576 0.000257 0.7986 0.0010 0.5562 0.0109*** 0.0004 ?0.0007 0.7927 0.0302*** 0.0207 0.0008 0.2752 0.0038 0.4992 ?0.0015 0.7710 ?0.0013 0.4778 ?0.0034 0.1096 ?0.0065* 0.0687 0.0020 0.3175 ?0.0038 0.1681

0.0018 0.8173 0.000190 0.8511 0.0016 0.3206 0.0078*** 0.0089 0.0027 0.2725 0.0035 0.6694 0.0004 0.5491 0.0061 0.2243 0.0025 0.5068 0.0003 0.8377 0.0002 0.9027 ?0.0026 0.3982 0.0015 0.4251 ?0.0030 0.2570

Panel B Binary logistic regression Intercept 1.4853 0.3249 TYPE ?0.4924 0.1363 TSTATUS ?0.9223 0.4137 CONTROL ?0.5448 0.2651 INVSTSIZEa 0.3998 0.8445 INSTITUTIONb ?0.1735 0.2809 PROXIMITYc 2.3239** 0.0429 d ?0.5437 INTEXPR 0.5693 PRIORPRES ?0.2229 0.4875 HITECH ?0.7909** 0.0355 GOVERN ?0.0648 0.9114 STRUCTURE 0.1128 0.7630 REGION 1 (Asia) 0.6956 0.2014
a b c

0.3197 0.8348 ?0.2034 0.5262 ?1.1269 0.3406 ?0.3833 0.4089 0.4697 0.8223 ?0.0383 0.8063 1.9377* 0.0783 0.7671 0.4580 ?0.3229 0.7010 ?0.2112 0.5617 ?0.1795 0.7492 0.1369 0.7047 0.7010 0.2014

0.0000 0.0000 ?0.0937 0.7687 ?1.8638** 0.0195 ?0.5314 0.2508 1.3451 0.5300 0.0814 0.5216 1.7528 0.1121 ?0.0031 0.9973 ?0.1348 0.6752 ?0.3500 0.3376 0.0924 0.8699 0.2331 0.5214 0.8202 0.1122

?1.2020 0.4410 ?0.2118 0.5165 ?1.3939 0.1811 ?0.0102 0.9821 4.1754 0.1406 0.2523 0.1036 0.7211 0.5174 1.6610 0.1048 0.0713 0.8241 ?0.2385 0.5347 ?0.7903 0.1739 0.4102 0.2594 ?0.0623 0.9108

?0.7036 0.6550 ?0.5503* 0.0805 ?0.4160 0.7033 ?0.0770 0.8655 2.2106 0.3664 0.1022 0.5037 1.4907 0.1522 0.6410 0.4934 0.0581 0.8545 ?0.1548 0.6742 ?0.0749 0.8969 0.0811 0.8226 ?0.3720 0.4992

0.9082 0.5984 ?0.2258 0.4976 ?0.3258 0.7375 ?0.2623 0.5569 5.6872* 0.0981 ?0.1421 0.3776 2.1759* 0.0540 0.6924 0.5258 0.0316 0.9224 ?0.1761 0.6391 ?0.6196 0.3668 0.1536 0.6728 ?0.7620 0.2530

?0.8830 0.5719 0.0719 0.8232 ?0.6943 0.5160 0.0067 0.9881 3.0402 0.1844 ?0.0437 0.7773 2.6807** 0.0191 0.8279 0.3938 0.3577 0.2816 0.0656 0.8583 0.3243 0.5775 0.3497 0.3346 ?0.2433 0.6632

The variable used in the model is relative investment size (ratio of acquired stake to bidder’s market value). The variable used in the model is the economic freedom index. The variable used in the model is the CDI index, based on Hofstede’s cultural dimensions. d The variable used is foreign assets/total assets ratio. p-values are reported in italics below the coefficient estimates. ***, **, and * denote statistical significance at the 1, 5, and 10% levels, respectively. The dependent variable in the regressions is the standardized cumulative abnormal return (SCAR) of EMMs engaged in cross-border acquisitions over the period 1991–2004. SCARs are defined over various event windows around the acquisition announcement.

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foreign sales ratio, it is crucial to mention that coefficients are insignificant for all event windows, and that coefficient signs do not deviate from the findings reported for foreign asset ratio. We also evaluate the impact of good corporate governance on acquirer value in cross-border acquisitions. This consideration could be a significant issue, as investors of EMMs may perceive foreign acquisitions as efforts to appropriate value from current shareholders. Earlier, in the theory section of this paper, we conjectured that ADR issuance by EMMs can be interpreted as a signal for commitment to good corporate governance, as EMMs often choose voluntarily to meet higher disclosure and reporting requirements than home markets. Although voluntary adherence to higher regulatory standards does not necessarily guarantee good governance, one might argue that, from the EMMs’ perspective, this practice can be an important step towards moderation of informational asymmetry. However, our empirical results do not provide strong support for this argument: therefore we are unable to verify the impact of good corporate governance on bidder value. Yet our univariate analysis displays negative mean SCAR differences, which suggests value destruction for bidders with enhanced governance. The statistical significance of the negative impact can be verified only for the event window (?5, ? 5). In our multivariate analysis we report a similar finding for the event window (?1, ? 1), explicitly a negative and significant coefficient, thereby suggesting value destruction for bidders with outstanding Level II and Level III ADR issues. The logistic regression results, on the other hand, indicate positive coefficients for larger event windows (up to (?5, ? 1)) and negative coefficients for narrower event windows.

Industry-Specific Factors We find generally negative market reactions to high-tech EMMs’ cross-border acquisitions. The mean SCAR differences are significant, with the exception of the (?5, ? 1) and (?2, ? 1) event windows. In our cross-sectional analysis we can confirm the negative impact of being a high-tech bidder; however, the coefficient is statistically significant only for the (?10, ? 10) window. Cross-sectional linear and binary logistic regression results appear in Table 6, Panels A and B. Generally, the sign of the coefficient is consistent with our expectations, namely that the acquisition of hightech targets may have some value-reducing attri-

butes, such as incompatibility of the acquired assets due to informational asymmetries, and high premiums paid for the targets. Our analysis of the impact of the structure of EMMs on market reaction reveals that mean SCAR differences are negative, with the exception of event window (?10, ? 5), which implies that diversified conglomerate-type EMMs experience less value destruction than non-diversified EMMs. Our univariate analyses also concur with the positive to negative market reaction ratios. While, on average, non-diversified EMMs experience a 39.5–46.5% positive market reaction across various event windows, the corresponding range for the diversified EMMs is 44.6–54.89%. The multivariate cross-sectional analyses do not provide strong support for our findings. Although the binary logistic regression results reported in Table 6, Panel B, produce the expected sign for the diversification dummy (i.e., positive), coefficients are not significant across the event windows. We can verify statistical significance only for event window (?5, ? 1) in our linear regression analysis (see Table 6, Panel A). Overall, our empirical findings provide some support for the hypothesized substitution of institutional deficiencies through the creation of internal markets. Finally, we find some weak evidence that bidders pursuing related targets experience deeper value destruction than those attempting diversification through unrelated targets. Although our univariate analysis results point to significant value destruction in the pursuit of related targets in three out of seven event windows, we can verify this finding for only one event window in our binary logistic regression analysis. This finding evidently contradicts the widely reported results in favor of focused strategies in developed country settings, yet it is consistent with the benefits attributed to diversification in emerging markets.16

Target Country Characteristics In this section, we discuss our findings on two specific country characteristics: geographical-cultural proximity between the target and bidder countries, and the development level of the market institutions in a given target country.17 The measurement of cultural distance is a challenging process, and inevitably methods used to capture cultural distance are open to criticism. While the measure suggested by Kogut and Singh (1988) has been widely embraced in the IB literature, it has also been criticized for conceptual and theoretical

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weaknesses.18 In our study, we adopt two alternative measures to capture cultural distance. As presented in the methodology section, our first measure combines geographic and cultural distance measures into a single indicator based on the distance scores of the bidder and target countries. Our analysis based on this measure suggests that cultural proximity does not enhance a bidder’s prospect of success, as hypothesized in our theoretical discussion and in the related literature. On the contrary, we find that average SCARs for geographically and culturally proximate targets are dominantly negative and statistically significant. For various event windows we find that only 38.8– 48.6% of transactions between proximate bidders and targets produce a positive investor reaction. The positive reaction percentage is higher for transactions between distant bidders and targets (37.8– 58.44% for various event windows). The univariate analysis results show that the mean SCAR differences between these two groups (i.e., the difference between the mean SCARs of proximate targets and distant targets) are not statistically significant. Although consideration of this particular measure of cultural distance suggests that proximity leads to increased value destruction, as suggested by the negative mean SCAR differences on all event windows, we cannot verify this finding statistically. We find a similar pattern when we consider our alternative cultural distance measure based on Hofstede’s cultural dimensions (see Hofstede, 1980, 1984). Although we cannot verify statistical significance, we find that the impact of cultural distance is positive for narrow event windows. In other words, the higher the cultural distance, the lower is the value-destructive impact of the announcement. Our binary logistic regression analysis provides some support for the significance of the positive impact of cultural distance on bidder value. The coefficient estimate for the cultural distance variable is positive for all event windows, and statistically significant for the intervals (?10, ? 10), (?1, ? 1), and (?1, 0). Contrary to our expectations, multivariate cross-sectional regression analysis shows that higher cultural distance is associated with higher cumulative abnormal returns for a number of event windows. Our linear regression analysis produces similar results, but does not portray significant estimated coefficients. Based on our empirical findings, we conclude that investors do not perceive value-creative strategic benefits in the acquisition of culturally proximate targets.

In this study, we also propose that the economic development level of the target country is an important determinant of the benefits that can be gained from a strategic cross-border expansion. As we show, in an institutionally underdeveloped environment acquirers face tradeoffs between the ability to take advantage of market imperfections and the excessive costs of uncertainty and government discretion. However, in institutionally developed environments EMMs are likely to encounter reduced uncertainty and limited government discretion in exchange for a highly complex and fiercely competitive marketplace. While the perceived benefits might depend on particular characteristics of the target, we have the opportunity to evaluate the impact of the institutional characteristics of the target domain on the acquirer value as perceived by investors. In our sample we have 171 transactions where targets are located in developed economies as defined by the World Bank. We should point out here that, usually, a higher economic development level also implies a more advanced institutional infrastructure. We find a higher percentage of positive reactions to acquisition announcements when the target is located in a developed economy than when it is located in an emerging economy. Furthermore, we discover that transactions involving targets in developed economies tend to produce higher cumulative abnormal returns for all event windows. Additionally, our univariate analysis of mean SCAR differences suggests that mean SCARs for targets located in developed economies are higher, and hence we are able to confirm the statistical significance of the mean differences in four out of seven event windows. In our cross-sectional multivariate analysis we use an alternative measure of the level of institutional development – the economic freedom index score. When we apply the economic freedom index as a proxy for the level of development, we obtain mixed results, because the coefficient for this variable does not have a consistent sign across the event windows. In our linear regression analysis, however, the sign of the coefficient is negative and significant in wider event windows, suggesting value destruction for targets located in more developed institutional settings, but it is positive and insignificant for narrow event windows. In our binary logistic regression analysis we encounter a similar inconsistency in the sign of the coefficient, but coefficients remain insignificant in all event windows. Our cross-sectional linear and logistics

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regression results appear in Panels A and B of Table 6. Overall, we cannot verify the impact of level of economic development on acquirer value by using the economic freedom index, but a simple dichotomous separation of the sample transactions, depending on the target location, suggests that investors perceive value creation opportunities when EMMs pursue targets in developed economies.

CONCLUDING REMARKS In this study, we examine the value implications of cross-border acquisitions of EMMs during the 1991– 2004 time frame. Our sample of 433 acquisition announcements originates from a variety of countries across Latin America, Eastern Europe, Asia, and Africa. An initial screening of the sample reveals that while 60.51% of EMMs’ acquisition targets are located in emerging-market economies, the reminder of the targets come from developed economies. Our findings indicate that the equity markets react negatively to the cross-border acquisition announcements of EMMs. The average abnormal return on the day of the announcement is ?1.38% (see Table 7), which is significant in both economic
Table 7
Day

and statistical terms. Furthermore, cumulative abnormal returns surrounding the announcement day also suggest that acquisition announcements of EMMs, on average, are perceived by investors as value destructive. In this study, cross-sectional analysis results indicate that the relative size of the target, bids for privately owned targets, and a diversified corporate structure positively influence the abnormal returns around the expansion announcement. In contrast, we find that acquisition announcements of high-tech EMMs and acquisition announcements of related targets are associated with value destruction. We find some support for the positive influence of the extent of control pursued by the bidder and the negative impact of cultural proximity. Despite the expectation of positive influence of cultural proximity on cumulative abnormal returns, the findings show the opposite. Although it is sensitive to the selection of the proxy for level of institutional development, our results point to the influence of the level of institutional development of the target country. The use of a simple dummy variable separating developed and emerging markets proved to be more effective in differentiating value-creative and

Bidders’ daily abnormal returns (SARs) M&As
Mean Mean Z-value Median 0.015553 ?0.03562 ?0.01913 ?0.02268 ?0.00597 ?0.03242 ?0.00569 ?0.00267 ?0.01601 ?0.03562** ?0.02989** ?0.0317 ?0.04424** ?0.02516 ?0.01185 ?0.04421* ?0.00655 ?0.04424* ?0.02176 ?0.02803 ?0.0085 0.993685 ?1.34442 ?1.10365 ?0.56378 ?0.60432 ?0.2555 ?0.36546 ?0.14305 ?0.12813 ?2.29967 ?1.86861 ?0.60948 ?1.93994 ?0.28361 ?0.71314 ?1.37578 ?0.1375 ?1.52935 ?0.35647 ?0.91604 0.052782 226:206 197:236** 208:225 203:230 215:218 200:232* 213:220 213:220 207:226 199:234* 192:241*** 206:227 193:240** 203:230 209:224 190:243*** 210:223 197:236** 209:224 202:231* 209:224 0.96225 ?1.87422 ?0.81697 ?1.29754 ?0.14417 ?1.5396 ?0.3364 ?0.3364 ?0.91308 ?1.68199 ?2.35479 ?1.0092 ?2.25868 ?1.29754 ?0.72085 ?2.54702 ?0.62474 ?1.87422 ?0.72085 ?1.39365 ?0.72085 432 433 433 433 433 432 433 433 433 433 433 433 433 433 433 433 433 433 433 433 433 52.31 45.50 48.04 46.88 49.65 46.30 49.19 49.19 47.81 45.96 44.34 47.58 44.57 46.88 48.27 43.88 48.50 45.50 48.27 46.65 48.27 WSRT-Z for median Positive: negative Doukas’ Z for positive:negative Total no. of transactions Positive market reaction(%)

SAR SAR SAR SAR SAR SAR SAR SAR SAR SAR SAR SAR SAR SAR SAR SAR SAR SAR SAR SAR SAR

?10 ?9 ?8 ?7 ?6 ?5 ?4 ?3 ?2 ?1 0 1 2 3 4 5 6 7 8 9 10

0.0453899 ?0.0292348 ?0.0386052 ?0.007518 ?0.0220278 0.014045 ?0.0376143 0.0013026 0.0491559 ?0.1059454*** ?0.0659145* 0.0135414 ?0.0518635 0.035915 0.0003904 ?0.0317581 0.0135948 ?0.0479753 0.0387834 ?0.0219282 0.0263181

1.001728 ?0.61631 ?0.82345 ?0.15965 ?0.43639 0.320164 ?0.70562 0.028064 1.059238 ?2.38393 ?1.38357 0.241329 ?1.05499 0.746715 0.008847 ?0.74269 0.249657 ?0.88633 0.819585 ?0.47266 0.546626

***, **, and * denote statistical significance at the 1, 5, and 10% levels, respectively. The table presents the daily standardized abnormal returns (SARs) of 433 cross-border M&A expansion announcements by emerging-market multinationals (EMMs) over the period 1991–2004. Daily standardized abnormal returns (SARs) are computed from the market model as prediction errors. Day 0 refers to the announcement day of acquisitions as reported in the SDC database. Z-statistics (Wilcoxon signed-rank test) are used to test for the statistical significance of mean SARs. The statistical significance of the mean (median) difference between groups is computed by the Mann–Whitney test for unmatched pairs. Z-statistics (Doukas’ test) are used to test for the statistical significance of positives and negatives.

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value-destructive transactions than the use of the economic freedom index. By using this simple measure we could verify the significance of the institutional infrastructure factor more decisively. Other factors, such as the bidder’s prior presence in the target market, international experience and enhanced governance, proved to be insignificant in determining cumulative abnormal returns. A caveat is the imperfection of our ‘‘good governance’’ proxy. As we discussed in our hypotheses development section, various levels of ADR issuance might not fully capture the nature of governance. Hence we cautiously interpret the insignificance of the governance factor. The recent surge in outward foreign direct investment from emerging-market economies, and projected trends, suggest that EMMs will continue in their efforts to catch up with established players by accessing strategic assets, new technologies, and markets (e.g., Sauvant, 2008). These trends confirm the relevance of our empirical findings, and provide insights for investors and managers of EMMs. While our research presents a rigorous attempt to explore the international expansion–firm value nexus, its limitations should be noted. First, despite its statistically desirable properties, our event study methodology is based on the assumption that the market response to public information about the strategic event is instantaneous, complete, and unbiased, based on the semi-strong form of the efficient market hypothesis. Hence the value created or destroyed by the international acquisition announcements should be interpreted cautiously, as it reflects the market’s evaluation of complex, and in some cases infrequent, strategic initiatives. It is plausible that the performance implications of such complex strategic ventures are not fully understood by market participants, and may be prone to heuristic biases. In light of this methodological constraint, the use of long-term performance measures to supplement future event studies may provide a robustness check on the event study results. The second key limitation of the study is the regional concentration of the parent companies in Asia and Latin America, which warrants caution in generalizing the results across the emergingmarkets universe. However, the recent surges in outward investment from Russia, India, and China, as well as smaller emerging-market economies in eastern and south-eastern Europe, offer the potential to design studies with more diverse samples.

Finally, it is important to emphasize that our analyses focus on value creation or destruction from the bidder’s perspective. While we report value destruction from bidder’s standpoint, the combined value for the bidder and target might be positive. To the best of our knowledge this study constitutes one of the rare multi-country studies of EMMs focusing on the value implications of internationalization through acquisitions. Our conclusion that EMMs’ cross-border acquisitions are, on average, value destructive agrees with various findings reported in the contemporary literature focusing on the value of multinational firms. Our research supports the view that EMMs will continue to seek value in cross-border acquisitions as their domestic environment pressures them to expand. While empirical studies with a larger sample size and a more balanced geographic diversification are likely to expand our understanding, qualitative studies based on primary data will be particularly revealing in arriving at a more reliable picture with robust explanations. Along this direction our study paves the way for future research to untangle the further specifics of the circumstances that surround this new breed of MNE and their international expansion.

ACKNOWLEDGEMENTS We would like to thank two anonymous reviewers and JIBS Departmental Editor Lemma Senbet for their helpful editorial guidance and constructive feedback. We are particularly grateful for outstanding comments and feedback on various versions of the paper received from S?eyda Deligo ¨ nu ¨ l. Seyda’s suggestions, along with those of the reviewers, helped us sharpen our thinking and articulate our arguments in a clearer manner. Finally, we sincerely thank John Fleming for his assistance in editing the final version of the document.
NOTES For a detailed discussion of the risks associated with M&As, see Shimizu et al. (2004). 2 Meyer, Milgrom, and Roberts (1992), Rajan and Zingales (1995), Rajan, Servaes, and Zingales (2000), and Scharfstein and Stein (2000) present models in which divisional managers exert influence to increase the assets under their control. This influence leads, in some cases, to less profitable divisions being subsidized by, and at the expense of, more profitable divisions. 3 Brouthers and Brouthers (2000) provide some support for strategic coherence in regional clusters.
1

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We are fully aware of the limitations of this proxy. As recent corporate scandals in the US unambiguously demonstrate, being subject to a stringent ‘‘corporate governance regime’’ does not necessarily lead to good corporate governance practices. However, despite recent convergence in corporate governance regimes across the world, voluntary subjection to more stringent governance standards rather than their being imposed by home markets can be construed as a signal for improved corporate governance. 5 To best of our knowledge, the information provided by the SDC Platinum database pertaining to transaction value is based on the announcement date. 6 Although Mexico is often classified as a North American country (particularly after being part of NAFTA), we thought that regional designation in Latin America would accurately capture the countries in our sample. 7 There are various other alternative return-generating models used in this context; however, the consensus in the finance literature is that the choice of RGM does not have any significant impact on the event study results. 8 Coutts, Mills, and Roberts (1995) concluded that treating the abnormal returns as being independent can make a substantive difference for the longer event periods. 9 We thank an anonymous referee for the suggestion of using relative size as an alternative. 10 The index is accessible at http://www.fraserinstitute. org/researchandpublications/researchtopics/economic freedom.htm The index is based on scores assigned in five categories: the size of the government; legal structure and property rights; freedom to trade internationally; access to sound money; and regulation of credit, labor and business. 11 We use the maximum likelihood method in our logistic regression estimations. 12 In the multivariate models, the Region 2 dummy proved to be insignificant in all event windows. In the reported model, the Region 2 model was excluded to attain a more parsimonious model. 13 We do not report the univariate analysis tables in the paper, but the results are available upon request from the authors. 14 In our multivariate analysis, level of control enters as an interval variable ranging from 5 to 100%. 15 The analyses of the SCARs for each group, including positive/negative reaction ratios, are not reported here because of space constraints. 16 For instance, see Khanna and Palepu (1997, 1999). 17 Originally we considered the development level of institutional infrastructure and the overall level of

4

economic development as two separate variables. However, because of the high correlation between these two variables in our multivariate model, we focused on the degree of institutional infrastructure development. We use the economic freedom index as a proxy for the development level of institutional infrastructure. As we discuss in the methodology section, the economic freedom index is a scale variable taking values between 1 and 10, where 10 indicates a high level of institutional infrastructure development. 18 For instance, see Yeganeh and Su (2006) for an extensive review of cultural distance measures and a criticism of the Kogut and Singh measure. 19 See Robusto (1957). The haversine formula uses latitudes to measure geographic distance d. Given R?the earth’s radius (mean radius? 6371 km), and Dlat ? lat2 ? lat1 Dlong ? long2 ? long1 a ? sin2 ?Dlat=2? ? cos?lat1 ?:cos?lat2 ?:sin2 ?Dlong=2? p??? p??????????????? c ? 2a tan2? a; ?1 ? a?? then d ? Rc: Details of the index and the scores can be found in an extensive website dedicated to the measurement of globalization: http://globalization.kof.ethz.ch/. 21 If the distance obtained from the latitude and longitude calculation is 3835.914 km or more (the highest being 18,528.5367 km), this shows no geographical proximity: therefore a value of 0 (dummy variable) is assigned to the distance between the acquirer and the target. If the distance is 3835.914 km or less (the least being 9.4935 km), this shows geographical proximity: therefore a value of 1 is assigned. The distance of 3835.914 km was chosen because, according to our data, this is the closest distance between the continental divisions that EMMs expanded (or the expansion announcement was executed) into a particular location. Our latitude and longitude calculations were based on the following figures: the earth’s circumference at the equator is 40,075.16 km and between the North and South Poles is 40,008 km. In addition, we also considered the distances between the continents. Hence distances between continents are regarded as non-proximate, and distances within continents as proximate. The liberty of choosing such a method may easily be justified by Alfred Wegener’s theory of continental drift and/or the drifting of continental shelves, which,
20

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among other things, states that most continents (other than Eurasia) do not share the same tectonic plates that make up the Earth’s surface. This theory is

supported more today than in the past, owing to technological and sophisticated research findings (Kearey & Vine, 1996).

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APPENDIX
PROXIMITY (Geographic and/or Cultural Proximity) In order to measure geographic/cultural proximity, we use two alternative measures. Our first measure is a composite geographic and cultural distance measure. Our geographic distance measure is based on the haversine formula.19 Our cultural proximity measure is based on the KOF index compiled by Keohane and Nye (2000).20 Although the KOF index measures a country’s globalization level, it consists of a sub-index measuring the cultural attributes of each country. Cultural distances are estimated based on the cultural dimension scores of the acquirer and target countries. Cultural and geographic distance measures are then converted into a dummy variable that takes the value 1 if the target country is geographically and/or culturally proximate to the acquirer country, and 0 otherwise.21 Our second measure focuses on cultural distance and is based on Hofstede’s (1980) widely used five cultural dimensions: power distance, individuality, masculinity, uncertainty avoidance, and long-term orientation. Since data for the fifth dimension (long-term orientation) were not available for a large number of countries in our sample, we excluded this measure in our calculation of CDI. The composite cultural distance score is based on the method suggested in Antia, Lin, and Pantzalis (2007). We briefly describe the calculation process for the CDI below. For each transaction i (i?1, y, N, where N?433 in our case) we compute four cultural distance (CD) measures, one for each of Hofstede’s cultural dimensions j ( j takes values from 1 to 4, each expressing a specific dimension: PDI, IDV, MAS or UAI). CDij is the absolute difference between the acquirer country and target country dimension score for the cultural dimension j, and is given by CDij ? Dj;acquirer ? Dj;target
where Dj is the score of one of the cultural dimensions for transaction i. We create a composite CD index (CDI) from four different CD measures.

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The CDI is essentially a composite cultural dimension difference measure. It is computed as follows: CDIi ?
J 1 1X Rankj ?CDij ? N J j ?1

where Rankj(CDij) is the rank function, which assigns a rank for each observation in our sample, from the least different (rank of 1) to the most different (rank of N). CDij is the jth measure of cultural difference for transaction i in our sample, and J represents the number of CD measures. The denominator, J, averages the ranks by the number of different CD variables available for each firm in the sample. Because our sample firms are required to have data on all four CD measures, J is equal to 4. Finally, by dividing by N, we scale the CDI from 0 (least different) to 1 (most different). In other words, a low CDI score (close to 0) implies low cultural distance, and a high CDI score (close to 1) implies high cultural distance.

ABOUT THE AUTHORS ¨ lent Aybar (c.aybar@snhu.edu) is a Professor C Bu of International Finance in the School of Business at Southern New Hampshire University. He earned his PhD from Fisher College of Business at Ohio State University. His teaching and research interests span a range of areas from international corporate finance to risk management. His current research is focused on cross-border mergers and acquisitions, and foreign exchange exposure.
Aysun Ficici (a.ficici@snhu.edu) earned her doctoral degree from Southern New Hampshire University, where she is an Assistant Professor of International Business. Her current research is focused on emerging-market multinationals, global governance, economic regionalism and the EU. She is also a research fellow at the University of Maastricht.

Accepted by Lemma Senbet, Area Editor. This paper has been with the authors for three revisions.

Journal of International Business Studies


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