Political risk and the factors that affect international bids

Political risk and the factors that affect international bids

Global Finance Journal 28 (2015) 68–83 Contents lists available at ScienceDirect Global Finance Journal journal homepage: www.elsevier.com/locate/gf...

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Global Finance Journal 28 (2015) 68–83

Contents lists available at ScienceDirect

Global Finance Journal journal homepage: www.elsevier.com/locate/gfj

Political risk and the factors that affect international bids Mina Glambosky a,⁎, Kimberly Gleason b, Maryna Murdock c a b c

Brooklyn College, Koppelman School of Business, 2900 Bedford Ave., 514 Whitehead Hall, Brooklyn, NY 11210, United States University of Pittsburgh, Katz Graduate School of Business, Pittsburgh, PA, United States Maryna Murdock, Mike Cottrell College of Business, University of North Georgia, Dahlonega, GA, United States

a r t i c l e

i n f o

Available online 17 November 2015 JEL classification: G34 G15 G38 Keywords: Acquisitions International Political risk

a b s t r a c t This study examines the determinants of bidder returns, target premiums and the likelihood of winning a bid in cross-border acquisitions. We identify how risk associated with the target home country affects these outcomes. Expected improvement in efficiency is the more powerful factor explaining the variation in premiums, while returns to bidders reflect the information asymmetry. Foreign targets are more likely to accept first or lower bids if target countries are culturally close to the U.S. Targets in poor-governance nations are more likely to accept a lower bid, explained by the ability of a good-governance nation acquirer to create benefits for the target. © 2015 Elsevier Inc. All rights reserved.

1. Introduction The subject of cross-border acquisitions has been a focus of financial research for several decades. It is still relevant today, although barriers to international diversification have fallen. The complexity of the mergers and acquisitions process is amplified for cross-border acquisitions by a variety of additional risks and costs, which creates a non-trivial cross-border effect (Moeller & Schlingemann, 2005). Identification and careful assessment of these additional factors are crucial for managerial decision making with the goal of value maximization. In addition, awareness of these additional risks and costs should help analysts in creating more accurate estimates and help investors in making optimal portfolio choices. Researchers have developed theories according to which returns to bidders, premiums paid to targets and the success of a bid can be explained, on one hand, by flows of information and, on the other hand, by expected synergies and improved efficiency. The distinction becomes important in the case of cross-border acquisitions. Which set of factors is more important may affect both the success of a bid offer and the amount of the ⁎ Corresponding author at: Brooklyn College, 2900 Bedford Ave., 514 Whitehead Hall, Brooklyn, NY 11210, United States. Tel.: +1 718 951 3550. E-mail address: [email protected]ny.edu (M. Glambosky)

http://dx.doi.org/10.1016/j.gfj.2015.11.005 1044-0283/© 2015 Elsevier Inc. All rights reserved.

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premium paid for the target. For instance, information flow is an important factor in determining premiums paid for the target, so premiums will be lower for targets in high-risk, low-transparency economies. Conversely, we can consider that synergies and improvements in efficiency are determining factors. Then the association of a high-risk (or low information) country target with a U.S. acquirer will increase future cash flows and such targets may receive higher acquisition premiums to reflect the improved value potential. The focus of this paper is to identify which of these two sets of factors have a more pronounced effect on outcomes of cross-border acquisitions. We conduct a series of tests to identify how risk associated with the target home country affects bidder returns, target premiums and the likelihood of winning a bid. We find that expected improvements in efficiency is the more powerful factor explaining the variation in premiums paid for the target, while returns to bidders reflect the information asymmetry surrounding cross-border acquisitions. This paper contributes to the body of knowledge regarding cross-border acquisitions in three ways. First, we examine how political risk factors affect the premiums paid to targets in cross border acquisition contests. Second, we expand the literature on the previously documented corporate governance spillover effect (Martynova & Renneboog, 2008) by examining governance differentials, which are the function of a broad range of country characteristics, including investor protection, the level of corruption, bureaucratic powers, ethnic tension, among other political risk factors. Third, we analyze the effect of the target's country risk on the outcome of the auction process, and the likelihood of a successful bid. While auction theory in general and its application to auctions of firms is a developed area of research, there has been no attempt to apply the accumulated knowledge to cross-border acquisitions. We find that in cross-border auctions the motivation behind the target's selection of the winning bid varies depending on country risk. There appears to be collusion between the target and the winning bidder when the target is located in a high-risk country, since the probability of a low bid winning increases with the increase in the target country's corruption. The first bid is more likely to succeed when the target country culture has similarities with the U.S. culture, and this result, in application to cross-border acquisitions, is consistent with D'Aveni and Kesner (1993), who demonstrated that inter-related social networks and possible cronyism may influence the target management's decisions and prompt an acceptance of a lower bid (under a realistic assumption that the first bid is likely to be a lower bid under auction framework). 2. Literature and background 2.1. Country risk and target premium Existing literature recognizes that the nature of the business environment in the target firm's home country should be included among explanatory factors in the studies of variations of the outcomes of cross-border activities (Cao & Madura, 2011; Diamonte, Liew, & Stevens, 1996; Francis, Hasan, & Sun, 2008; Glambosky, Gleason, & Madura, 2010; Kiymaz, 2009; Kwok & Reeb, 2000; Mantecon, 2009; Martynova & Renneboog, 2008; Moeller & Schlingemann, 2005). Previous research offers a variety of methods to measure how conducive the economic, political and cultural environment in a particular country is to efficient business management. Variables used in prior research include Enforceability of Contracts (Djankov, Glaeser, Porta, Lopez-deSilanes, and Shleifer (2003)), Anti-Self-Dealing (Djankov, Porta, Lopez-de-Silanes, and Shleifer (2008)), Corporate Governance Regulation Indices comprised of Shareholder Rights Index, Minority Shareholder Protection and Creditor Rights Index (Martynova & Renneboog, 2008) and Index of Economic Freedom compiled by the Heritage Foundation (Francis et al., 2008; Mantecon, 2009). Most studies find significant impact of the differentials of the various measures of country risk and governance features on outcomes of cross-border restructuring efforts. The results, however, are not entirely consistent and vary depending on the sample and the specification of country risk proxies. For example, Martynova and Renneboog (2008) find that returns to both bidders and targets increase with the increase of the (positive) differential between good governance measures of the bidder's and the target's home countries. The authors find support for the beneficial spillover hypothesis, since investors' expectations of the improvement in the target's performance translates into overall increase in wealth. Francis et al. (2008) demonstrate similar outcomes. On the other hand, Kiymaz (2009); Moeller and Schlingemann (2005) and Glambosky et al. (2010) find that bidders gain higher returns when acquiring firms in countries with relatively low risk.

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In addition, previous research found that the target's home country risk may affect other important choices and outcomes in the acquisition, including the mode of an acquisition (Mantecon, 2009), the probability of being acquired (Martynova & Renneboog, 2008), the choice of the method of payment (Cao & Madura, 2011) and others. While there have been studies that examine the impact of political risk factors on bidder abnormal returns, to the best of our knowledge, there have been no attempts to assess the effect of country risk on premiums offered to acquisition targets. Previous research documents that majority of the expected synergistic gains are realized by the target's shareholders in the form of the premiums paid above and beyond the stock price on the day prior to the announcement. Though announcement period returns to the target should reflect expected premiums and expected synergies, to the extent that bidders have superior information, as well as superior ability and incentives to process information about the target than investors, premiums paid to target's shareholders are a better measurement of the potential wealth gain. This contribution is relevant as it reveals the bidder's estimate of the additional value the acquisition is expected to create. Variations in premiums depending on the target country risk should reveal the expected improvements due to better corporate governance and business practices generally accepted in the bidder's country compared to the target's country. 2.2. Abnormal returns to bidders Extensive prior research efforts have been dedicated to the analysis of value effects of cross-border bids. Over the years, depending on the time period and specific samples, various studies found positive (Kang, 1993; Markides & Ittner, 1994; Martynova & Renneboog, 2008; Morck & Yeung, 1991, 1992) or negative (Aw & Chatterjee, 2004; Denis, Denis, & Yost, 2002; Eckbo & Thorburn, 2000; Geringer, Tallman, & Olsen, 2000; Mitchell, Shaver, & Yeung, 1992; Moeller & Schlingemann, 2005) abnormal returns. Several previous studies attempted to tie acquirer's returns to differences in economic development, governance and risk characteristics of the target country (e.g. Francis et al., 2008; Glambosky et al., 2010; Kiymaz, 2009; Manzon, Sharp, & Travlos, 1994; Martynova & Renneboog, 2008; Moeller & Schlingemann, 2005). We incorporate several country risk variables, cultural “distance” between the acquirer and the target, as well as the size and the order of the winning bid and other firm-specific variables in our analysis. We include two definitions of returns to bidder: an immediate change in wealth over a relatively short window around the announcement of an acquisition and long-run abnormal returns. Moeller and Schlingemann (2005) point out that, under market efficiency, long-run returns should not be systematically related to the announcement period returns that incorporate investors' estimates of the value effect of a certain managerial decision. However, previous studies document behavioral-driven deviations of efficient stock pricing, including under-reaction and over-reaction of markets to important publicly known events. To the extent that information asymmetry is more pronounced in the case of cross-border acquisitions, they present a plausible case for such inefficiencies in pricing. It can be argued that for international bids information asymmetry manifests itself both in the bidder's and, more severely, investor's deficiency of verifiable information about the target. This lack of information may lead to inefficiencies in pricing around the time of the announcement. As information gradually reveals itself after the acquisition, we may observe long-run returns that correct the announcement window pricing. Our tests of the effect of cross-border bids on shareholders' value include both abnormal returns around the time of the announcement and longrun abnormal returns. It could be the case that the announcement window abnormal returns are not statistically different from zero for any group of bidders, but long-run returns are positive and significant in the case of successful bids. Such a finding can be interpreted in light of investors' skepticism regarding value creation prospects of international bids. Since investors are aware that the information they have is deficient and possibly unreliable, they cannot distinguish between “good” and “bad” bids upon the time of the bid, and thus assign an average price to all bids. Over time, additional information is provided to the market regarding synergies and efficiency gains (or lack thereof), and markets react accordingly. 2.3. Size and order of the winning bid, auction Our effort to determine the effect of country risk on the cross-border acquisition process includes the analysis of the size and order of the winning bid. This part of the analysis focuses on cases in which more than one

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bidder expresses specific interest in acquiring the target. These cases are often referred to in finance literature as auctions, and a substantial body of research investigates this process. The general auction theory has been developed in economic research (Bulow & Klemperer, 1996; French & McCormick, 1984; Hansen, 1988; McAffee, McMillan, & Whinston, 1989; Riley & Samuelson, 1981) and extended to auctions of firms in cases of mergers and divestitures (Cramton & Schwartz, 1991; Fishman, 1988; Fishman, 1989; Giammarino & Heinkel, 1986; Hansen, 2001; Ye, 2007). Previous authors have noted the confounding deviations in the practice of the bidding process for firms from the standard auction theory. Some examples include targets restricting the number of bidders (Hansen, 2001; Ye, 2007), resisting takeover bids (Giammarino & Heinkel, 1986) and engaging White Knight offers (Calcagno & Falconieri, 2014). Authors appear to be in consensus that these deviations are due to a high degree of information asymmetries between the bidder and the target, given the high cost of obtaining information. Cross-border acquisition research so far has not addressed the behavior of targets in an auction setting. We attempt to close this gap and propose that factors that reflect the target country risk characteristics may have an effect on the selection of the winning bid.

3. Hypotheses 3.1. Acquisition premiums and target country characteristics In a rational world, the goal of the target is to receive the largest bid possible from its eventual acquirer. Theoretically, from the target's perspective, the lower limit of the acceptable premium is the price of the stock on the day the bid is extended, while there is no upper limit to an acceptable bid. The bidder, on the other hand, is limited in its acceptable levels of the bid by its estimate of the increase in value due to the merger, generally explained by the expected synergies and improvement in efficiency. The premium, by definition, is the relative difference in the price the acquirer pays compared to the market value of the target equity on the day of the announcement of the bid. Arguably, independent companies operating in an unfavorable business environment should trade at a discount to a hypothetical firm with identical operating characteristics located in a favorable business environment. To the extent that being owned by a U.S. firm should improve the target firm's operating performance, and thus future cash flows, this improvement translates into an upward correction of the target firm's price. We hypothesize that the acquisition premium, as the measure of the acquirer's expectations of the value gains, should be higher for targets in high-risk countries. H1.1. The acquisition premium to target is higher when the target scores low2 on different measures of country risk. D'Aveni and Kesner (1993) show that inter-related social networks and possible cronyism may influence the target management's decisions and prompt an acceptance of a lower bid. That evidence may be interpreted in a slightly different light in the case of international acquisitions. Though the likelihood of managers literally belonging to the same social networks is lower in case of cross-border acquisitions, target managers may find it easier to relate to managers of similar-culture bidders. Target managers may also perceive that there is a higher likelihood that they would retain their jobs after the acquisition if the acquirer home country culture is similar to that of the target country. These sentiments, though strictly are different from cronyism, are similarly directed at achieving personal objectives and are not aligned with the goal of maximizing shareholders' value. A more shareholder-centered course of action may stem from a prediction that the prospects of lower costs of integration and realization of synergies are more likely in acquisition cases when the cultures of the target and the acquirer are similar. A recent KPMG study, in an attempt to find the key to acquisitions' success points out “… the challenge acquirers face in addressing the ‘softer’ people and cultural aspects of the deal, and in delivering an effective communications plan.”3 These challenges are relatively less daunting for similar-culture acquisitions. 2 3

Low values of risk measures indicate a high-risk environment. KPMG, “Unlocking shareholder value: the keys to success”, Kelly et al., 1999.

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H1. 2. Acquisition premiums to targets are lower (higher) if target country culture is close to (distant from) that of the acquirer. 3.2. Bid size and order as determinants of the bid success in cross-border acquisitions The goal of the target is to maximize the value it will receive from the acquirer. It is in the target's best interest to solicit multiple bids in order to gain higher returns. At the same time, the process of bidding reveals information about the target to the market and may entice subsequent bidders to enter the contest. Multiple bids create an auction atmosphere, which should benefit the target. The first bid acquirer has the choice to bid with a large enough premium to deter additional acquirers or with a lower premium, which allows for more competition. The ensuing auction results in the target receiving a bid that exceeds the bid that would have discouraged competitors originally (Fishman, 1988). The less costly it is for alternative acquirers to gain information, the more likely that the target will be put on “auction” (Easterbrook & Fischel, 1982; Giammarino & Heinkel, 1986; Hirshleifer & Png, 1989). This study focuses on the circumstances where the first or lower bid acquirer successfully acquire the target, results that seem to be counter intuitive to the goal of the target. More specifically, our study considers four outcomes: The first bid acquirer succeeding with a higher bid, the first bid acquirer succeeding with a lower bid, a secondary bid acquirer succeeding with a higher bid, and a secondary bid acquirer succeeding with a lower bid. Scenarios where a first or, for that matter, any bidder in an auction succeeds with a lower bid appears to be counter-intuitive to both the target's agenda and the theory of auctions developed in earlier research. These results may be influenced by the high degree of asymmetric information prevalent in cross-border deals. Conceivably, if the acquirer has better knowledge of the value of the target than its competitors, it may present a large enough initial bid to successfully discourage competition. In contrast, the first bid acquirer winning the target at a lower price may be due to savvy bidding. It is also plausible that if the target is concerned that negative information may become public; it may accept a lower initial offer. We attempt to determine which variables make the first or the low bid acquirer win the contest. The level of asymmetric information may increase for a target under a government that is deficient in regards to ethical behavior, i.e. rule of law, level of corruption, etc. Their governing practices may have a detrimental impact on acquisitions in that country. Limited public information may allow for an informed acquirer to enter the acquisition process and acquire the target at a lower value. In addition, a corrupt government may be in a position to exhort undue influence on the target management to pressure it to accept a bid that is not in the best interest of the target's shareholders. Considering the country risk parameter also allows us to include the possibility that targets in high-risk countries seek “powerful” acquirers that are perceived to be able to “protect” the target from the target country risks. For example, an acquirer through the power to continue or withdraw its investment may establish a relationship with the target country government that would create a more favorable atmosphere for such a target's operations. This may become an important consideration in the target's decision making, to the extent that it may outweigh the pure monetary value of the offer. This consideration may increase the likelihood of an acceptance of a low offer by targets located in high-risk countries. Our hypotheses related to bid order and size include government risk proxy variables and the relatedness of the target and acquirers' cultures on the country level. The vector of variables representing government risk include the level of corruption (CORRUPT), the enforceability of contracts (the Rule of Law, or ROL), the level of bureaucracy (BUROC), the risk of expropriation (EXPROP), the likelihood of government debt repudiation (REPUDIATE) and the level of ethnic tension (ETHNIC). 3.2.1. Government corruption High levels of government corruption could indicate barriers to information flow that could impact the target, post acquisition. In addition, the government corruption may lead to unfair treatment of the acquirer. There is a higher likelihood that a corrupt government may force a target to accept a low bid for reasons not aligned with shareholders' value. Alternatively, a target in a country with high government corruption may seek an acquirer that may protect the target from the abuses by the government. Both dynamics will lead to an increased likelihood of an acceptance of a lower bid by the target. Conversely, acquirers may view a target that resides in a country with higher levels of corruption as vulnerable to acquisition below

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market value, creating more competition for the target and decreasing the likelihood of a successful first or low bid. We thus express the hypothesis in the null form. H2.1. Higher levels of corruption are unrelated to the likelihood that the first or lower bid acquirer will successfully acquire the target. 3.2.2. Rule of Law The extent to which firm legal statutes that protect property rights and business contracts exist and are upheld by the government may have important consequences for cross-border mergers and acquisitions. We consider several possibilities when we develop our hypotheses regarding the effect of the Rule of Law on the target's choice of the winner in an auction. Firm legal statutes relating to business practices, which the government upholds, is an attribute that would maximize cash flows from operations, and, thus, the value of a firm. Furthermore, defined rules of law may remove barriers in the acquisition process making the acquisition process easier and less costly. But the reduced cost to the acquirer will result in more competitive bids for the target, diminishing the possibility of a first or lower bid acquirer successfully acquiring the target. Conversely, if protection and enforceability of legal contracts are relatively low, markets would discount the value of a firm operating under such conditions. Weak rule of law in the target's country may deter acquirers from bidding on the target. Under these circumstances, if any interest in a target is expressed, the bidding process may be less competitive than it would have been for a target from a country with a strong rule of law. Fear of unfair treatment under the country's weak rule of law may discourage potential acquirers, and a decrease in the number of bids that the target receives increases the likelihood that the first or lower bid acquirer will successfully acquire the target. We also consider an alternative scenario where a country with a weak rule of law may present an opportunity to acquire a target at a reduced cost, and thus entice potential acquirers, if they perceive this as an opportunity to acquire the target at a lower value. The entry of additional acquirers would create more severe competition, decreasing the likelihood of a successful first or lower bid acquirer. Again, given that we do not make predictions about the sign of the relationship between rule of law and bid success of the first or lower bidder, we express the hypothesis in the null form. H2.2. Higher levels of rule of law will be unrelated to the likelihood that the first or lower bid acquirer will successfully acquire the target. 3.2.3. Bureaucracy Arguments similar to the influence of government corruption on the probability of a win with a first or a low bid can be applied to the level of government bureaucracy. A high level of government bureaucracy may be an impediment to the successful integration of the target with the acquirer. Higher levels of government bureaucracy may dissuade potential acquirers who are unsure if the expected synergies are large enough to compensate for the added inefficiencies. An acquirer that has the power to cut through the government bureaucracy may be viewed as a valuable partner for the target, in which case an acceptance of a low bid may be in the interest of the target. In contrast, higher levels of government bureaucracy may slow the acquisition process allowing for the entry of additional acquirers, which would decrease the likelihood of a successful first or lower bid acquirer. H2.3. Higher levels of government bureaucracy are unrelated to the likelihood that the first or lower bid acquirer will successfully acquire the target. 3.2.4. Risk of expropriation The target country's level of receptiveness towards direct foreign investment will be positively related to the number of competitive bids that the target receives. Additionally, acquirers may interpret the receptiveness to mean that the acquisition process will be less difficult relative to acquisitions in less receptive countries. Hence the number of potential acquirers should be larger when the government's view of direct foreign investment is more favorable. A more receptive target government increases the competition for the target and reduces the chance for a first or lower bid winning acquirer. When acquirers face a greater possibility of expropriation, they may choose to refrain from bidding, simplifying the acquisition process for first

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or lower bid acquirers. Alternatively, a target in a country with a higher risk of expropriation may attract additional acquirers who perceive an opportunity to acquire the target at a discount. Ultimately, the increased competition for the target lessens the probability of a successful first or lower bid acquirer. The government's view of direct foreign investment is measured by the risk of expropriation, the risk of seizure of property by the government, based on PRS Group ratings. The variable EXPROP is used to represent risk of expropriation in the model. H2.4. Higher levels of government expropriation are unrelated to the likelihood that the first or lower bid acquirer will successfully acquire the target. 3.2.5. Debt repudiation An increased likelihood that the target countries will experience debt repudiation may represent poor business opportunities in those countries. Similarly, the market may perceive instability in the financial sector, stemming from the government's actions. In this case, an acquirer that has the power and expertise to argue its case convincingly adds value to the combined entity and may win an auction with a lower bid. Conversely, a high level of debt repudiation may attract alternative acquirers who perceive this as an opportunity to underpay for the target. An increase in the number of acquirers will reduce the likelihood of successful first or low bid acquirers. Accordingly, an index that measures the likelihood of government debt repudiation is representative of the financial condition of the country. The government's financial condition is measured by PRS Group; the variable REPUDIATE is used in the model variable. H2.5. Higher levels of debt repudiation are unrelated to the likelihood that the first or lower bid acquirer will successfully acquire the target. 3.2.6. National ethnic tension Higher levels of ethnic tensions in the target countries may have a negative impact on the wealth effect of the acquirer, and the ethnic unrest may reduce the number of bids the target receives. Acquirers may be concerned that if ethnic tension results in violence, their employees and assets will be at stake with possibly deadly outcomes. The liability that ethnic tension presents may cause acquirers to hesitate before attempting to acquire a target in an unsettled country; the decreased competition for the target improves the probability of a winning first or lower bid acquirer. In contrast, higher levels of ethnic tension may reduce the likelihood of a winning first or lower bid. Additional acquirers may emerge if they believe that targets in countries with higher levels of ethnic tension are anxious to be acquired and are inclined to accept lower payments. H2.6. Higher levels of ethnic tension are unrelated to the likelihood that the first or lower bid acquirer will successfully acquire the target. 3.2.7. Cultural differences An understanding of the culture of the target can influence the effectiveness of the acquirer post acquisition. If the acquirer and target originate from similar cultural backgrounds, the melding of the two entities may be smoother. If the acquirer and target have fundamental differences in approaching business situations, it could have a negative effect on operations. Targets in broader culture clusters will attract more competitive acquirers whereas isolated, specific cultures discourage acquirers from participating in the acquisition process. The acquirer and target culture is measured using Hofstede's (1997) factors of culture: power distance, uncertainty avoidance, individualism, and masculinity/femininity. H2.7. Similarities in the culture of the target and acquirer countries will increase the likelihood that the first or lower bid acquirer will successfully acquire the target. 4. Data and methodology We collect the sample of international mergers and acquisitions completed after competitive bidding during the time period 1985–2006 from the Securities Data Corporation (SDC) Platinum database. We restrict observations to those deals where the acquirer was listed on the New York Stock Exchange, NASDAQ or American Stock Exchange at the time of the acquisition.

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Country-level characteristics for analysis of the effect of country risk on the outcomes of acquisition bids are constructed using three sources: Transparency International, PRS Group and Hofstede's (1997) factors of culture. Transparency International is the global civil society organization dedicated to fighting government corruption. The organization ranks countries and territories based on perception of how corrupt their public sector is, based on 13 data sources.4 By standardizing these rankings to scores between 0 and 100, Transparency International creates the index that assigns higher values to countries that are perceived as less corrupt and lower values to countries that are perceived as more corrupt. We use this index to assign values to variable CORRUPT that measures the risk of loss due to the perceived level of corruption in the public sector. We use the IRIS dataset, originally constructed for the for the IRIS Center at the University of Maryland, based on data obtained from the International Country Risk Guide (ICRG) by PRS Group, to assign values to variables that measure risk of loss due to weaknesses in the legal system, bureaucratic hurdles, expropriation, repudiation of debt and ethnic tensions. The acquirer and target country culture is measured using Hofstede's (1997) factors of culture: power distance, uncertainty avoidance, individualism, and masculinity/femininity. The hierarchical cluster analysis clusters countries with similar cultural attributes. A dummy variable, CULTURE, is equal to one when the acquirer and target are in the same cultural cluster, as determined through hierarchical cluster analysis. 4.1. Determinants of the premium We use a cross-sectional regression analysis to determine which variables affect the premium received by the target. The premium paid for the target is the dependent variable in each regression model. The premium is measured as the percent difference between the price paid by the acquirer and the market value of the target one day prior to the announcement. We specify the following regression model to explain the variation in the premiums paid for targets in cross-border deals: PREMIUM ¼ α0 þβ1 VALUE þ β2 MVL þ β3 ROE þ β4 DEBT þ β5 CASH þ β6 STRUC þ β7 NUM þ β8 FIRST þ β9 LOW þ β10 CULTURE þ β11 CORRUPT þ β12 ROL þ β13 BUROC þ β14 EXPROP þ β15 REPUDIATE þ β16 ETHNIC þ β17 REL þ εi where PREMIUM = SDC reported premium received by the target ([(Offer price − Closing stock price 1 day prior to announcement) / Closing stock price 1 day prior to announcement] × 100). VALUE = the value of the transaction relative to the value of the assets of the acquirer. Asquith, Bruner, and Mullins (1983) and Jarrell and Poulsen (1989) find that the return to the acquirer improves as the size of the target relative to the size of the acquirer increases. MVL = the natural log of the market value of acquirer in the year prior to the acquisition. Moeller, Schlingemann, and Stulz (2004) document that the performance of large firms is worse than small firms in acquisitions. ROE = the ratio of net income to shareholder's equity. ROE is calculated by dividing after tax earnings by average stockholder's equity. DEBT = the ratio of total debt to total assets of the acquirer. The DEBT ratio represents acquirer leverage; higher levels of acquirer leverage create larger interest payments and a higher probability of financial distress. CASH = a measure of the acquirer's liquid assets. Bruner (1988) found that acquirers with “financial slack,” large holdings of cash or cash equivalents, were more successful. STRUC = the structure of the payment to the target, i.e. whether cash, stocks, or a mix were used. Travlos (1987) finds that the method of payment impacts the return to the acquirer. The studies show that the use of stock has a significantly negative impact on returns.

4

Data can be accessed at http://cpi.transparency.org

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NUM = the number of acquirers participating in the acquisition. FIRST = a dummy equal to one when the first bid acquirer was successful in acquiring the target, zero otherwise. LOW = a dummy equal to one when the lower bid acquirer was successful in acquiring the target, zero otherwise. CULTURE = a dummy equal to one when the culture of the acquirer country and target country are similar, zero otherwise. CORRUPT = managerial perception of corruption; Transparency International score. ROL = risk of loss of value due to weaknesses in legal system or fair enforcement of the law; PRS Group risk score. BUROC = risk of loss of value due to bureaucracy; PRS Group risk score. EXPROP = risk of loss of value due to expropriation; PRS Group risk score. REPUDIATE = risk of loss of value due to the repudiation of payment or refusal to honor contracts; PRS Group risk score. ETHNIC = risk of loss of value due to ethnic tensions; PRS Group score. REL = a dummy equal to 1 if the acquirer and target have the same two digit SIC code. 4.2. Determinants of order and size of the winning bid We apply logistic regression analysis to explain which variables influence the winning bid. The dependent variables in the two regression specifications are BID_ORDER and BID_SIZE. The dependent variable BID_ORDER is equal to 1 when the first bid acquirer successfully acquired the target, and zero otherwise. The dependent variable BID_SIZE is equal to 1 when the lower bid acquirer successfully acquired the target, and zero otherwise. We include a set of variables similar to the variables used to explain the variation in premiums to determine what factors influence the winning bid order and size. BID ORDER=BID SIZE ¼ α0 þβ1 VALUE þ β2 MVL þ β3 ROE þ β4 DEBT þ β5 CASH þ β6 STRUC þ β7 NUM þ β8 FIRST þ β9 LOW þ β10 CULTURE þ β11 CORRUPT þ β12 ROL þ β13 BUROC þ β14 EXPROP þ β15 REPUDIATE þ β16 ETHNIC þ β17 REL þ εi where: BID_ORDER = equal to 1 when the first bid acquirer successfully completes the acquisition, zero otherwise. BID_SIZE = equal to 1 when the lower bid acquirer successfully completes the acquisition, zero otherwise. Where RT is the return over the event period of six, twelve, eighteen months etc., Rit is the return on stock i in month t, and nt is the number of companies that are included in each month (Barber and Lyon, 1997). Two samples are used to explain the long-run return, successful competitive acquirers and unsuccessful competitive acquirers. Both samples are included to determine the impact of the announcement of the bid on shareholders' wealth. 5. Results Table 1 offers a preview of the nature of competitive bidding over time and across industries. Panels A and B list the total number (A) and the average number of bids per target (B) for each year of the sample period. These numbers suggest that competition is a persistent feature in the international market for corporate control. The degree of such competition varies slightly over time, possibly in correlation with the peaks and valleys in the overall mergers and acquisitions activity. Panel C presents the number of “auctions” for targets in different industry groups. Foreign companies involved in manufacturing and mining appear to be the most attractive. Table 2 introduces some basic bidder and deal characteristics. It describes a typical bidder in terms of size and profitability, as well as the relative size of the transaction compared to bidder's assets. Consistent with

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Table 1 Announcements of competitive bids.a,b,c Panel A. by year Number of announcements Year of announcement

Total Sample

Percent of total

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Total announcements

10 32 59 68 37 66 80 85 83 45 64 45 43 52 68 77 33 23 32 45 35 35 1117

0.90% 2.86% 5.28% 6.09% 3.31% 5.91% 7.16% 7.61% 7.43% 4.03% 5.73% 4.03% 3.85% 4.66% 6.09% 6.89% 2.95% 2.06% 2.86% 4.03% 3.13% 3.13% 100.00%

a

This panel provides the distribution of announcements by year. This panel provides the distribution of announcements by Target's Primary SIC code. c This panel provides the distribution of number of acquirers by year. b

Panel B. Average # of bidders per acquisition Number of announcements Year of announcement

Total sample

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Average across years and acquisitions

2.5 3.2 2.8 2.4 2.4 4.7 2.9 3.0 3.1 2.3 2.3 2.2 2.1 2.1 2.1 2.2 2.1 2.2 2.5 2.1 2.0 2.2 2.5

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Table 1 (continued) Panel C. by SIC

SIC code

Number of announcements

Percent of total

Agriculture, forestry, and fishing Mining Construction Manufacturing Transportation, electric, gas, and sanitary services Communications Electric, gas, and sanitary services Wholesale and retail trade Services

100–999 1000–1499 1500–1799 2000–3999 4000–4799 4800–4899 4900–4900 5000–5999 7000–8999 Total targets

8 130 12 311 41 38 28 58 74 700

1.14% 18.57% 1.71% 44.43% 5.86% 5.43% 4.00% 8.29% 10.57% 100%

previous research, acquirers are, on average, large firms with large market capitalization acquiring smaller firms or divisions. Return on equity is in a modest but acceptable range. 5.1. Determinants of premiums in cross-border acquisitions Table 3 reports the results of an OLS regression analysis of target premium. The positive and significant FIRST variable indicates that the successful first bid acquirers pay higher premium to the target. In order to lock-up the target, the first bid acquirer needs to enter a bid large enough to entice the target and discourage potential competitors, consistent with our predictions and models developed in previous research. Further examining the effect of country risk characteristics on the premium paid for the target by adding risk score variables to the analysis, we notice that the CORRUPT variable is positive and significant at 5% level. Acquirers pay a smaller premium for targets that reside in countries with higher levels of corruption. Operating under such conditions is costly, which results in lower future cash flows and thus valuation discounts compared to similar firms operating in a relatively less corrupt world. To minimize potential losses, the target is paid a lower premium. Other measures of risk (ROL, BUROC, EXPROP, and ETHNIC) are also negatively correlated with the premium. These results indicate that targets in countries with high risk yield a discount, which is consistent with our expectations. We also note that the VALUE variable is positive and significant at the 5% level, indicating that the larger the acquisition, the larger the premium the target receives. Thus, our evidence suggests that targets in corrupt countries or high political risk countries are able to extract greater rents from bidders on average, likely by obscuring the true value of assets. This is consistent with H 1.1. The CULTURE variable is negative and significant at the 5% level in all model specifications. This result indicates the target firm's willingness to accept a lower premium from an acquirer located in a country that has

Table 2 Acquirer and deal characteristics. This table provides mean and median descriptive characteristics for acquiring firms in the year prior to their transactions. Assets represent the book value of assets in the year prior to the acquisition. Market value is the value of equity in the year prior to the acquisition. Relative value is the value of the bid relative to value of the assets of the bidder. ROE, return on equity, is the ratio of net income to the book value of total equity. Variable

Mean (median)

Assets ($millions)

18,975.21 (2755.37) 51,924.72 (3300.05) 16.97 (6.47) 10.31 (12.17)

Market value ($millions) Relative value (%) ROE

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Table 3 Cross-sectional regressions on target premium. This table provides the results of multivariate regression where PREMIUM, the SDC reported premium received by the target ([(Offer price − Closing stock price 1 day prior to announcement) / Closing stock price 1 day prior to announcement] × 100) is the dependent variable.VALUE = value of the transaction/assets of the acquirer; MVL = log of market value of acquirer; ROE = return on equity, the ratio of net income to the book value of total equity; DEBT = ratio of debt to total assets; CASH = the value of the acquirer's cash and cash equivalents in the fiscal year prior to the acquisition; STRUC = a dummy equal to 1 when the acquirer uses cash as the method of payment; NUM = the number of acquirers participating in the acquisition; FIRST = a dummy equal to one when the first bid acquirer was successful; LOW = a dummy equal to one when the lower bid acquirer was successful; CULTURE = dummy variable equal to 1 if the firm is in the same cultural cluster based on hierarchical analysis of Hofstede's dimensions of culture; BUR = risk of loss of value due to bureaucracy based on the Country Risk Guide; EXPROP = risk of loss of value due to expropriation based on the Country Risk Guide; REPUDIATE = risk of loss of value due to the repudiation of payment or refusal to honor contracts based on the country risk guide; ROL = risk of loss of value due to weaknesses in legal system or fair enforcement of the law; CORRUPT = managerial perception of corruption based on the Transparency International score; ETHNIC = risk of loss of value due to ethnic tensions based on the country risk guide; REL = if the target and acquirer have the same two digit SIC. Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

CONSTANT

1931.32 (3.153)⁎⁎⁎

3389.29 (4.988)⁎⁎⁎⁎

1634.585 (2.923)⁎⁎⁎

2761.062 (3.644)⁎⁎⁎⁎

2007.573 (3.593)⁎⁎⁎⁎

1135.509 (2.210)⁎⁎

VALUE

43.381 (2.297)⁎⁎ −26.837 (−0.842) 11.016 (0.226) −375.488 (−0.983) 0.003 (0.286) −110.680 (−0.809) −130.971 (−0.952) 361.492 (2.381)⁎⁎ 270.521 (1.663) −466.423 (−2.122)⁎⁎

42.118 (2.305)⁎⁎ −19.825 (−0.648) 5.400 (0.114) −534.374 (1.441) 0.001 (0.158) −130.177 (−0.990) −165.152 (−1.237) 312.141 (2.148)⁎⁎ 269.656 (1.681) −362.663 (−1.727)⁎

41.875 (2.202)⁎⁎ −30.762 (−0.949) 17.580 (0.359) −303.559 (−0.786) 0.003 (0.346) −141.673 (−1.041) −101.369 (−0.734) 342.726 (2.264)⁎⁎ 257.020 (1.547) −512.650 (−2.368)⁎⁎

45.937 (2.422)⁎⁎ −14.169 (−0.451) 0.431 (0.009) −441.158 (−1.162) 0.005 (0.562) −159.714 (1.183) −118.328 (−0.866) 318.866 (2.138)⁎⁎ 209.270 (1.261) −550.399 (−2.626)⁎⁎⁎

791.458 (0.936) 45.267 (2.380)⁎⁎ −15.527 (−0.481) 8.789 (0.179) −426.328 (−1.097) 0.004 (0.434) −148.124 (−1.082) −109.929 (−0.790) 310.329 (2.051)⁎⁎ 264.316 (1.580) −624.969 (−2.900)⁎⁎⁎

44.058 (2.360)⁎⁎ −20.219 (−0.646) 22.188 (0.459) −245.999 (−0.646) 0.001 (0.063) −127.553 (−0.948) −107.080 (−0.787) 284.529 (1.913)⁎ 312.290 (1.898)⁎ −607.210 (−2.921)⁎⁎⁎

45.810 (2.405)⁎⁎ −18.566 (−0.582) 9.771 (0.198) −390.989 (−1.013) 0.004 (0.407) −141.282 (−1.016) −115.523 (−0.833) 310.968 (2.054)⁎⁎ 268.005 (1.603) −598.895 (−2.818)⁎⁎⁎

MVL ROE DEBT CASH STRUC NUM FIRST LOW CULTURE CORRUPT

−174.712 (−2.113)⁎⁎ −395.949 (−4.609)⁎⁎⁎

ROL

−120.334 (−1.805)⁎

BUROC

−155.501 (−2.763)⁎⁎⁎

EXPROP REPUDIATE

39.273 (0.550) −215.153 (−3.117)⁎⁎⁎

ETHNIC REL Adj Rsq F-statistic N ⁎ ⁎⁎ ⁎⁎⁎ ⁎⁎⁎⁎

7.10% 2.808⁎⁎⁎ 259

12.90% 4.493⁎⁎⁎ 259

6.70% 2.687⁎⁎⁎ 259

8.30% 3.126⁎⁎⁎ 259

5.60% 2.390⁎⁎⁎ 259

9.00% 3.336⁎⁎⁎ 259

35.453 (0.261) 5.50% 2.367⁎⁎⁎ 259

Statistically significant at the 10% level. Statistically significant at the 5% level. Statistically significant at the 1% level. Statistically significant at the 0.1% level.

a culture similar to that of the target, which is not consistent with the goal of value maximization. It appears that ignorance of the target's culture is a disadvantage for the acquirer, and results in a higher payment. This is consistent with H1.2.

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Table 4 Logistic regression—bid order. This table provides the results of the multinomial logistic regressions where BID_ORDER = a dummy equal to one when the first bid acquirer was successful, is the dependent variables. VALUE = value of the transaction/assets of the acquirer; MVL = log of market value of acquirer; ROE = return on equity, the ratio of net income to the book value of total equity; DEBT = ratio of debt to total assets; CASH = the value of the acquirer's cash and cash equivalents in the fiscal year prior to the acquisition; STRUC = a dummy equal to 1 when the acquirer uses cash as the method of payment; NUM = the number of acquirers participating in the acquisition; CULTURE = dummy variable equal to 1 if the firm is in the same cultural cluster based on hierarchical analysis of Hofstede's dimensions of culture; BUR = risk of loss of value due to bureaucracy based on the Country Risk Guide; EXPROP = risk of loss of value due to expropriation based on the Country Risk Guide; REPUDIATE = risk of loss of value due to the repudiation of payment or refusal to honor contracts based on the country risk guide; ROL = risk of loss of value due to weaknesses in legal system or fair enforcement of the law; CORRUPT = managerial perception of corruption based on the Transparency International score; ETHNIC = risk of loss of value due to ethnic tensions based on the country risk guide; REL = if the target and acquirer have the same two digit SIC code.

CONSTANT VALUE MVL ROE DEBT CASH STRUC NUM LOW CULTURE CORRUPT

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

−3.952 (11.889) ⁎⁎⁎⁎ −0.024 (0.103) 0.070 (1.241) −0.075 (1.206) −0.681 (0.881) 0.000 (0.011) 0.300 (1.039) −0.073 (0.190) 1.057 (13.481)⁎⁎⁎⁎

−1.569 (1.829) −0.047 (0.248) 0.046 (0.451) −0.068 (0.995) −0.728 (1.013) 0.000 (0.095) 0.381 (1.679) −0.088 (0.328) 0.953 (11.336)⁎⁎⁎⁎ 0.856 (4.013)⁎

−2.939 (8.618)⁎⁎⁎ −0.031 (0.130) 0.067 (1.152) −0.073 (1.127) −0.851 (1.359) 0.000 (0.042) 0.365 (1.546) −0.091 (0.288) 1.006 (12.565)⁎⁎⁎⁎

−2.099 (2.341) −0.042 (0.215) 0.049 (0.660) −0.071 (1.080) −0.719 (0.991) 0.000 (0.065) 0.380 (1.677) −0.075 (0.241) 0.970 (11.854)⁎⁎⁎⁎ 0.805 (3.568)⁎

−2.543 (2.460) −0.042 (0.218) 0.052 (0.698) −0.072 (1.110) −0.728 (1.010) 0.000 (0.059) 0.383 (1.710) −0.069 (0.205) 0.971 (11.884)⁎⁎⁎⁎

−1.653 (2.780)⁎ −0.052 (0.280) 0.047 (0.600) −0.070 (1.072) −0.580 (0.627) 0.000 (0.107) 0.389 (1.740) −0.068 (0.199) 0.991 (12.266)⁎⁎⁎⁎ 0.776 (3.367)⁎

−2.439 (7.445)⁎⁎⁎ −0.047 (0.234) 0.051 (0.698) −0.077 (1.258) −0.693 (0.919) 0.000 (0.065) 0.410 (1.917) −0.062 (0.168) 0.968 (11.760) ⁎⁎⁎⁎

0.538 (1.517) 0.371 (5.665) ⁎⁎

0.647 (2.219)

0.782 (3.371)⁎

−0.117 (0.721)

ROL BUROC

0.175 (2.210) −0.014 (0.017)

EXPROP REPUDIATE

0.031 (0.049) −0.151 (1.438)

ETHNIC REL Likelihood ratio Max-rescaled R2 N ⁎ ⁎⁎ ⁎⁎⁎ ⁎⁎⁎⁎

0.804 (3.667)⁎

45.066⁎⁎⁎ 9.80% 411

410.468⁎⁎ 7.90% 411

408.945⁎⁎⁎ 8.40% 411

411.153⁎⁎ 7.60% 411

411.120⁎⁎ 7.60% 411

409.737⁎⁎ 8.10% 411

0.207 (0.639) 411.289⁎⁎ 8.10% 411

Statistically significant at the 10% level. Statistically significant at the 5% level. Statistically significant at the 1% level. Statistically significant at the 0.1% level.

5.2. Determinants of bid success Tables 4 and 5 report the results of logistic regression that analyzes the factors explaining the variations in the probability of the bidder winning with a first (Table 4) or a lower (Table 5) bid. The most telling results in Table 4 are the positive and significant coefficients for CULTURE and CORRUPT variables.

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Table 5 Logistic regression—bid size. This table provides the results of the multinomial logistic regressions where BID_SIZE = a dummy equal to one when the lower bid acquirer was successful, is the dependent variables. VALUE = value of the transaction/assets of the acquirer; MVL = log of market value of acquirer; ROE = return on equity, the ratio of net income to the book value of total equity; DEBT = ratio of debt to total assets; CASH = the value of the acquirer's cash and cash equivalents in the fiscal year prior to the acquisition; STRUC = a dummy equal to 1 when the acquirer uses cash as the method of payment; NUM = the number of acquirers participating in the acquisition; CULTURE = dummy variable equal to 1 if the firm is in the same cultural cluster based on hierarchical analysis of Hofstede's dimensions of culture; BUR = risk of loss of value due to bureaucracy based on the Country Risk Guide; EXPROP = risk of loss of value due to expropriation based on the Country Risk Guide; REPUDIATE = risk of loss of value due to the repudiation of payment or refusal to honor contracts based on the country risk guide; ROL = risk of loss of value due to weaknesses in legal system or fair enforcement of the law; CORRUPT = managerial perception of corruption based on the Transparency International score; ETHNIC = risk of loss of value due to ethnic tensions based on the country risk guide; REL = if the target and acquirer have the same two digit SIC code.

CONSTANT VALUE MVL ROE DEBT CASH STRUC NUM FIRST CULTURE CORRUPT

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

0.724 (0.441) −0.060 (0.381) −0.107 (2.625) 0.132 (0.535) 0.385 (0.248) 0.000 (0.075) 0.011 (0.001) −0.200 (0.946) 1.055 (13.506)⁎⁎⁎⁎ 0.029 (0.005) −0.308 (4.575)⁎⁎

0.277 (0.056) −0.048 (0.298) −0.091 (1.902) 0.139 (0.528) 0.332 (0.188) 0.000 (0.054) −0.050 (0.028) −0.227 (1.087) 0.956 (11.477)⁎⁎⁎⁎ −0.134 (0.112)

0.017 (0.000) −0.055 (0.356) −0.102 (2.389) 0.148 (0.491) 0.489 (0.395) 0.000 (0.050) −0.046 (0.023) −0.182 (0.824) 1.000 (12.445)⁎⁎⁎⁎ −0.071 (0.031)

−0.127 (0.008) −0.042 (0.261) −0.089 (1.808) 0.135 (0.503) 0.322 (0.176) 0.000 (0.028) −0.054 (0.033) −0.211 (0.963) 0.969 (11.833)⁎⁎⁎⁎ −0.209 (0.283)

−0.430 (0.065) −0.044 (0.269) −0.090 (1.824) 0.136 (0.509) 0.339 (0.195) 0.000 (0.032) −0.050 (0.028) −0.210 (0.939) 0.970 (11.861)⁎⁎⁎⁎ −0.232 (0.344)

−1.042 (0.965) −0.037 (0.234) −0.089 (1.816) 0.125 (0.528) 0.198 (0.065) 0.000 (0.013) −0.049 (0.028) −0.217 (0.989) 0.988 (12.171)⁎⁎⁎⁎ −0.226 (0.341)

−0.749 (0.589) −0.045 (0.264) −0.082 (1.585) 0.110 (0.458) 0.273 (0.133) 0.000 (0.031) −0.006 (0.000) −0.213 (0.904) 0.966 (11.732)⁎⁎⁎⁎ −0.194 (0.254)

−0.150 (1.204)

ROL

−0.164 (1.716)

BUROC

−0.040 (0.126)

EXPROP

−0.010 (0.004)

REPUDIATE ETHNIC

0.137 (1.027)

REL Likelihood ratio Max-rescaled R2 N ⁎ ⁎⁎ ⁎⁎⁎ ⁎⁎⁎⁎

373.093⁎⁎ 7.90% 411

376.399⁎ 6.60% 411

375.826⁎ 6.80% 411

377.429⁎ 6.20% 411

377.549⁎ 6.20% 411

376.513⁎ 6.60% 411

0.236 (0.746) 378.036⁎ 6.40% 411

Statistically significant at the 10% level. Statistically significant at the 5% level. Statistically significant at the 1% level. Statistically significant at the 0.1% level.

The positive coefficient for the CULTURE variable suggests that acquirers from cultural environments similar to the target are more likely to succeed with the first bid. This outcome is consistent with our prediction and H2.7 that, though first bidders are generally at a disadvantage in an auction, the target will prefer the bid offered by an acquirer from a similar culture and marginally more likely to accept that bid.

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Related to H2.1, the positive coefficients for CORRUPT suggest that acquires of targets located in countries characterized by a corrupt government are less likely to succeed with a first bid. This supports the view that acquirers may expect that such a target is available for sale at a discount to the market value, creating more competition for the target and decreasing the likelihood of a successful first bid. Table 4 also indicates that our other political risk variables, rule of law, bureaucracy, risk of expropriation, risk of debt repudiation, and risk of ethnic tension are insignificant in explaining the likelihood of a successful first bid, providing evidence related to our other hypotheses (2.2–2.6). Table 5 reveals that targets in countries with more corrupt governments are more likely than other targets to accept a lower than highest available bid (since the coefficient on CORRUPT is negative and significant). This supports the prediction that when bidding for targets operating in corrupt environments some acquirers may offer benefits besides the pure monetary value of the acquisition bid that would increase the expected future cash flows of the target. Some acquirers may have the ability exhort power over the corrupt government that could reassure the target of the success of the ongoing operations. Table 5 also indicates that, while culture and corruption are relevant determinants of the probability of a low bid winning, our other political risk variables are not. 6. Summary We determine how target country characteristics affect the results of competitive cross-border acquisitions by U.S. firms. In particular, we examine the outcomes of competitive bidding by U.S. firms on foreign targets, specifically conditioned on the size and the order of the bid. We find that characteristics of the target country, specifically its financial and political risk profiles, as well as the differences between the bidders' and acquirers' countries' cultural environment, have an effect on those outcomes. The focus of the analysis is to identify whether the information flow or the improved efficiencies and synergies play a more important role in the functioning of the international market for corporate control. We find that foreign targets are more likely to accept first or lower bids if they are located in countries with a cultural environment that is relatively close to that of the U.S. Targets in corrupt countries are less likely to accept a first bid and more likely to accept a lower bid than are targets in less corrupt countries. The lower likelihood of the acceptance of a first bid supports the prediction that targets in corrupt countries are expected to be valued at a discount to the market value of otherwise similar companies operating under less corrupt governments. An opportunity to acquire a target below market value attracts multiple bidders, which creates an auction atmosphere making an acceptance of a lower bid less likely. The higher likelihood of the acceptance of the lower bid, though counterintuitive in a frictionless market, is quite plausible in an environment where a target's operations are encumbered by the rent-seeking behavior of government officials. Under such conditions, the power of the acquirer from an economically developed country to either continue or to withdraw its investment may sway government officials to allow the target to operate without obstacles that would be imposed on it otherwise in the form of bribe extortions or preferential treatment of competitors. Other facets of our analysis emphasize the importance of the target country's culture and risk characteristics on the various outcomes of cross-border acquisitions. Specifically, similarities in the bidders' and targets' cultures entice the target to accept bids that are, on average, lower, than in cases when the cultures are different. Predictably, acquirers pay lower premiums for targets in corrupt countries, taking advantage of the discounts to the market values due to the additional frictions and inefficiencies created by corrupt governments, and expecting these inefficiencies to be partially corrected through the association with the acquirer from a more efficient environment. Other measures of risk, such as the rule of law, the level of government bureaucracy, the probability of expropriation and the level of ethnic tensions, at their higher levels also tend to yield a discount. 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