Feb 11, 2008 - such as spatial geography that have the potential to influence firms' choices of ..... to planar distances, and use optimization techniques to identify routes ..... by referring to individual company websites, directories such as the ...
THE PERSISTENT EFFECT OF GEOGRAPHIC DISTANCE IN ACQUISITION TARGET SELECTION
Abhirup Chakrabarti (McGill University) & Will Mitchell (Duke University) February 11, 2008
ABSTRACT The implications of several strands of the spatial geography literature suggest that firms incur substantial costs when they implement geographically distant acquisitions, where implementation costs arise both from searching for potential targets and from undertaking post-acquisition integration. In turn, the prescriptive literature on acquisitions strategy often suggests that firms should seek geographically proximate targets, especially while they gain acquisition experience, in order to limit implementation costs that have the potential to crowd out gains from acquisitions. To date, however, we lack systematic research examining whether expected implementation costs lead acquirers to prefer nearby targets and, if so, how acquisition experience or other factors may reinforce or reduce any such tendency. Indeed, there is no assurance that desirable targets will be located near acquirers, while anecdotal evidence suggests that some firms, at least, undertake distant acquisitions, even early in their acquisition experience. This study examines how the distance between acquiring and target firms influences target selection, exploring conditions under which acquirers exhibit a greater preference for geographically proximate targets and when they seek more distant targets. The study examines 2,070 domestic U.S. acquisitions from 1980 to 2004 by 767 US chemical manufacturing firms founded after 1979. The core conclusion of the study is that distance has a strong and persistent effect on target selection.
1. Introduction This paper examines how spatial geography influences firms’ choices of acquisition targets, based on the premise that geographic distance affects acquisition implementation costs (Green and Cromley 1984; Hauptman and Hirji 1999) and thereby will influence incentives to undertake particular acquisitions. Firms now spend trillions of dollars on acquisitions and undertake more than thirty thousand acquisitions every year according to the SDC acquisitions database, but empirical research has not been able to convincingly establish why many acquisitions fail to create value for acquirers (King, Dalton, Daily, and Covin 2004). Such failure suggests the need for deeper conceptual and empirical examination of factors such as spatial geography that have the potential to influence firms’ choices of particular acquisition targets (Sorenson and Baum 2003) but have received little systematic attention. Systematic variations in the costs and difficulties that surface in the acquisition implementation process frequently arise as a possible answer to the acquisition-performance puzzle, where acquisition implementation refers to activities involved in searching for targets and then carrying out strategic and financial combinations of a target and acquirer (Haspeslagh and Jemison 1989). Jemison and Sitkin (1986a), for example, argue that the implementation process is an important determinant of postacquisition outcomes and suggest that examining acquisition processes can complement the traditional approach of examining financial aspects of acquisitions. In parallel, Trautwein (1990) noted that potential advantages from operational synergies in acquisitions need to be “weighed against the cost of combining and transferring assets” that arises during the acquisition process. However, acquisitions research has not systematically assessed how the potential costs of different elements of acquisition implementation processes will influence acquisition outcomes or, indeed, which acquisitions firms chose to undertake. Scholars and practitioners recognize that implementation can be an unexpectedly costly activity that affects performance outcomes. Implementation costs take several forms. Target search costs include those of waiting to advance a strategy, employing consultants and other external agents, and diverting management attention from other activities (Green and Cromley 1984). Post-acquisition costs include employee resistance (Schweiger and DeNisi 1991; Walsh 1988), slow implementation (Davidson, Rosenstein, and Sundaram 2002), and post-acquisition compatibility problems between business systems 1
(Yunker 1983). As a result, implementation costs could play a significant role in deciding both ultimate acquisition outcomes and initial acquisition choices. Such costs are difficult to assess explicitly, but many studies have discussed the extensive nature of implementation issues (Karim and Mitchell 2000; Pablo 1994; Zollo and Singh 2004) and at least one study has found indirect empirical evidence that firms may face unplanned costs that systematically affect outcomes (Danzon, Epstein, and Nicholson 2004). 1.1 Interest in the Spatial Geography of Acquisitions Examining the spatial geography of acquisitions offers a useful means of assessing how expected implementation costs will influence acquisition strategy. The geographic location and distance of firms vis-à-vis each other is a meaningful indicator of the cost of searching for targets and integrating acquisitions. This is particularly true in related acquisitions, which typically involve redeployment of R&D, manufacturing, marketing, managerial, and financial resources (Capron, Dussuage, and Mitchell 1998). While some interactions may involve costless transmission of resources, many interactions involve knowledge flows and close work among teams. Research indicates that managers may face significant obstacles while implementing such interactions over long distance (Hauptman and Hirji 1999). Several studies also show that distance increases the difficulty of effective communication (Cummings 2007), as well as the cost of seeking and integrating knowledge (Borgatti and Cross 2003; Cummings and Ghosh 2005), all of which are critical for implementation success (Yunker 1983). Sorenson and Baum (2003) have shown that spatial geography influences business strategy and continues to do so even in the face of easier travel and more extensive telecommunications. Consistent with this premise, some firms appear to view the geographic proximity of a target firm to corporate headquarters as an important acquisition criterion. Indeed, some management prescriptions suggest that firms will benefit from following a sequential expansion strategy, in which they begin by acquiring nearby firms before undertaking the difficulties of distant acquisitions, commonly citing Cisco Systems as support for the recommendation (e.g. Holloway, Wheelwright, and Tempest 1998 (revised 2004)). Despite such recommendations and examples, however, it is not clear how systematically firms actually follow spatial strategies in acquisitions. 2
1.2 Theoretical Framework Figure 1 depicts a simple framework that explains the role of spatial geography in acquisitions strategy. The framework has four elements. First, an acquisition decision has both a product and a spatial dimension. Second, acquisition decisions aim to fulfill specific profit-seeking objectives, such as increased operational efficiency or improved competitive position. 1 Third, any benefit can be offset by costs incurred during the acquisition process. Fourth, a feedback mechanism exists, whereby firms rationalize and incorporate lessons learned from past acquisitions into future acquisition decisions. The arrows in the figure indicate which objectives or costs may arise from business relatedness (the product dimension) and from greater geographic distance (the spatial dimension). ********** Figure 1 about here ********** Acquisitions involve two connected decisions, involving product lines and geographic locations. First, firms decide which product lines to expand – a simple categorization would be whether the expansion is in related or unrelated product lines. Unrelated acquisitions are similar to venture capital investments, providing little scope of knowledge transfer or sharing of other resources (e.g., General Electric’s acquisition of the NBC broadcasting company). Related acquisitions (e.g., Novartis’s acquisition of Chiron in the pharmaceutical industry) provide firms with opportunities to gain benefits from economies of scale and scope, from operational synergies, and from improved competitive positions. Such acquisitions allow firms to combine operations, reduce duplicative functions, integrate knowledge, integrate production and distribution systems, and cater to a larger product or geographic market. However, such related acquisitions also require more complicated search activities and greater postacquisition integration, and the potential benefits will be weighed down by costs incurred in the process. Second, a firm must decide where to locate its expanded operations and, therefore, which target to acquire. This introduces a spatial expression that has a cost-benefit implication for the firm. Some acquisitions have explicit spatial expression, such as acquisitions driven by the need to gain access to input materials and markets or to engage in multipoint competition (i.e., to acquire a geographically 1
Other motives include managerial goals such as empire building. This study assumes that most acquisitions reflect profit-seeking motives, and that cases with agency motives do not involve systematic spatial locations.
3
distant target, per se, is not an objective; instead, firms may require distant acquisitions in order to increase access to suppliers and buyers). The extent of spatial dispersion corresponding to these objectives depends on the locations of the firm, its targeted market, and its suppliers. It is difficult to predict the spatial dimension associated with some objectives, such as gaining economies of scale and scope or achieving quick access to technology, but spatial location matters in acquisitions to the extent that distance increases the cost of implementing acquisitions. If distance increases the challenges of implementing acquisitions, including costs incurred in target search and post-acquisition integration, firms will often benefit if they gradually modify their acquisition goals to incorporate such challenges. This is indicated in figure 1 by the arrow from ‘acquisition performance’ to ‘product’ and ‘spatial.’ Research indicates that the locations of suppliers, existing subsidiaries, competitors, and buyers influence target selection decisions (Baum, Li, and Usher 2000; Hannan and Rhoades 1987; Rodriguez-Pose and Zademach 2003). In addition to these factors, search and integration costs will tend to increase with distance. As these costs increase, firms will exhibit a geographic pattern in their acquisitions, preferring geographically proximate targets when they lack information about distant targets (or when they are unable to gather such information), and when they acquire related targets that require a greater degree of assessment and of integration. Our study empirically examines whether firms exhibit such geographic patterns in acquisitions. This represents the feedback mechanism in the above framework, where firms link variation in acquisition performance to implementation costs and then use such information to modify their acquisition goals. We examine whether geographic distance influences target selection, with the aim of determining conditions under which acquirers exhibit a greater preference for geographically proximate targets and when they are willing to seek more distant targets. The core argument is as follows. Distance influences acquisition decision making either if it implies greater search costs, prompting firms to evaluate proximate firms before assessing geographically distant firms, or if acquisition managers perceive greater costs associated with integrating acquisitions that are geographically distant. Perceptions of higher post-acquisition cost or difficulty could be fueled by a lack of adequate information about distant target firms, or by the need for extensive post-acquisition 4
integration and monitoring effort that managers expect to be difficult to implement over long distance. 2. The Acquisition Process: Target Selection and Post-Acquisition Integration Acquisitions are complex processes, frequently including thousands of major steps and non-routine decisions (Wallace 1966). The acquisition process typically begins with a search for potential candidates that are screened during an investigation for strategic and organizational fit, performance, and the amount of resources that an acquisition would need. During this time, acquirers gather extensive knowledge in order to evaluate potential targets (Chakrabarti and Mitchell 2005; Kierulff 1981). Acquirers require financial information, including profitability, size, debt, the current and longer-term strategic value of a target’s assets, and the investment that they will need to provide in order to upgrade and reconfigure the target’s assets (Salter and Weinhold 1981; Weston, Mitchell, and Mulherin 2004). Information about the target firm’s culture also is often critical for integration success, where culture refers to the values and priorities within an organization (Pritchett 1985). Moreover, acquirers need detailed information on the manner in which targets organize their activities, including corporate responsibilities, financial systems, employment policies, job descriptions, performance evaluation systems, benefit plans, profit sharing plans, and purchase and marketing setups (Yunker 1983). Acquirers need to judge how a target’s business skills will fit and complement its own activities (Haspeslagh and Jemison 1991) and what activities the acquirer will need to undertake in order to combine the target and acquirer skills (Capron, Dussauge, and Mitchell 1998; Pablo 1994). Acquirers commonly use both informal and formal methods for gathering information on target firms. First, firms can use information from informal discussions at gatherings such as industry meetings and from managers’ personal and professional contacts. Second, firms can use more formal corporate processes for identifying and investigating individual targets. Such formal approaches may involve brokers, investment banks, and consulting companies that identify, gather information, and carry out due diligence about targets. Several studies suggest that firms use both informal and formal information gathering, with informal subjective factors playing a significant role even as part of more formal objective processes (Daft, Sormunen, and Parks 1988; Green and Cromley 1984; Kierulff 1981; Weston, Mitchell, and Mulherin 2004). 5
Once a candidate is chosen, acquirers begin post-acquisition integration. During this phase acquirers frequently share and cross-utilize R&D, manufacturing, marketing, financial, and managerial resources between themselves and target firms (Capron, Dussauge, and Mitchell 1998). Goals for such resource redeployment include more efficient use of existing resources, as well as of expansion of the scope of activities requiring resources not available within the firm (Karim and Mitchell 2000). Integration involves the synchronized efforts of personnel associated with the finance, human resources, marketing, and production areas (Haspeslagh and Jemison 1991; Johnson 1985; Lajoux 1998; Pritchett 1985; Yunker 1983). The diversity of the task makes integration challenging to plan and implement. Post-acquisition integration can be an unexpectedly costly process that affects performance outcomes. Such costs rise above financial payments for targets, and are often of a human or organizational nature (Chakrabarti 1990; Fried, Tiegs, Naughton, and Ashford 1996). For instance, acquirers may face significant constraints in their attempt to integrate financial systems because of potential problems with communication and over-expectations (Yunker 1983). Similarly, acquirers could face unexpected and direct resistance from employees (Schweiger and DeNisi 1991; Walsh 1988) when they attempt to blend two or more sets of employee relations policies, job descriptions, performance evaluation structures, salary structures, and benefit plans. Subsequently, acquiring firms can take much longer to implement the integration than originally planned. The end result can lead to cancellation of the acquisition (Davidson, Rosenstein, and Sundaram 2002), or to poor performance if conflicting business systems remain incompatible after completion of the acquisition. Such residual incompatibilities in management styles, operating practices, and other structures can lead to insurmountable post-acquisition problems (Altendorf 1986; Buono and Bodwitch 1989; Nahavandi and Malekzadeh 1988; Olie 1994; Sales and Mirvis 1984; Walter 1985). In sum, the acquisition implementation process, including both target search and post-acquisition integration activities, can itself be an important element of acquisition strategy, complementing direct strategic and financial aspects of acquisitions. The remainder of our paper focuses on how geographic distance influences target selection, which is an important step toward investigating the performance implications of implementing geographically distant acquisitions. 6
3. Acquisitions and Spatial Geography This section establishes the need to examine spatial factors in acquisitions strategy. It starts with a brief discussion of the general business location problem before considering the special case of acquisitions. Location decisions often closely relate to growth decisions. While expanding their scale and scope of operations, firms often have to consider where to locate new capacity. Factors such as time and distance from key suppliers and customers can play key roles in such decisions. Acquisitions involve the purchase of firms that have already established business in a particular location. Therefore, the consideration of whether distance increases the challenges of integration gains relative prominence in acquisition-based growth. As a complementary objective, this section argues that spatial geography is a meaningful indicator of process costs of acquisitions. We discuss the current focus of strategic geography, along with potential useful extensions. 3.1 Business Growth and Location Business location is a fundamental issue for all firms. While deciding where to locate procurement, production, and distribution facilities, firms must assess multiple factors, including the cost and time of transportation of raw and finished goods, cost of production, and cost of coordination of activities across a spatially distributed setup. In addition, external factors such as market growth potential, tax laws, legal restrictions, and political risk also play a role in deciding location decisions. Discussion of location decisions arises in a wide range of disciplines, spanning economic geography, operations research, sociology, finance, and economics, while receiving relatively less formal attention within strategic management research. Three themes within business location theory relate location to cost minimization and competition. The first theme considers how firms locate to minimize transportation costs (Francis, McGinnis, and White 1983 offer a typology of such models). Such models assume costs are proportional to planar distances, and use optimization techniques to identify routes that minimize facility-customer or facility-facility travel distances or times. The second theme assesses how firms locate to penetrate new markets or access regions with low costs of production. Market penetration models usually address inter-regional or international growth, 7
while also emphasizing that such expansion is preceded by corporate learning via activities such as export marketing. Some studies report evidence supporting the applicability of such models (Hayter and Watts 1983; Watts 1980). Other models (Vernon 1979) counter the emphasis on growth through market penetration by predicting that firms will locate in regions where production costs are lower and labor productivity is higher (Norman and Pepall 2000). This is particularly likely to happen in industries with mature products and intensive corporate rivalry. The third research theme suggests that firms may choose to expand into certain locations to improve their position in relation to competing firms in other geographic locations (Barnett 1993; Baum and Korn 1996). Empirical studies (Haveman and Nonnemaker 2000) have found that multi-point competition influences both growth and market entry decisions. On one hand, firms may gain from cooperation related advantages with competitors (Bernheim and Whinston 1990; Edwards 1955; Simmel 1950) when they link expansion with multipoint competition. At the other extreme, firms may be able to improve their competitive position vis-à-vis geographically close rivals by locating near customers (Benson and Faminow 1985), with the possibility of preempting further expansion by rivals in the vicinity (Eaton and Lipsey 1979). Research has also examined the relationships between spatial and organization structures. While the specific issue of business location itself has received mixed attention (e.g., Dicken (1971) considered location as a relatively minor issue affecting firm survival), these studies show that organizational and spatial structures interrelate closely. For example, the separation of manufacturing activities, coordinating activities, and strategic decision making typically becomes more pronounced as the firm expands in size and space, leading to a more hierarchical internal structure (Hymer 1972; Simon 1960). Finally, studies have also documented the localization of industries, whereby similar firms locate close to each other to form clusters and accrue benefits of increasing returns to scale at the regional level (Hoover 1948; Krugman 1998; Marshall 1920). For example, firms operating in clusters can gain from spillover of knowledge from proximate firms, access to specialized suppliers, and access to a pooled market of specialized workers. Such economies are external to any single firm, but internal to regions containing clusters of similar firms (Stuart and Sorenson 2003). There is some evidence that such 8
economies attenuate at a distance (Rosenthal and Strange 2003), such that firms that are geographically isolated do not gain from spillovers (see Hoover 1948). Studies have also examined the effect of city size, urban diversity, and within-city organization of economic activities, and also have explored whether the benefits of agglomeration are static or dynamic (see Rosenthal and Strange 2003). In sum, many factors influence business location. The locations of a firm’s suppliers, existing subsidiaries, competitors, and buyers, in conjunction with the type of product or service produced by the firm, are likely to influence where a firm may choose to locate its operations. In turn, firms evolve organizationally as they expand in space. Such evolution creates the need for distinct functions that are not always clear or even necessary in geographically compact firms. In spite of rich prescriptions, firms do not always make optimal location decisions. Some studies describe geographic expansion as riskier than local expansion, with early models depicting a spatially compact risk minimizing manner of expansion that involve first expanding locally before expanding across regions or nations. 3.2 The Role of Spatial Geography in Acquisitions The role of spatial geography has received less attention in the context of acquisitions strategy, with the few studies offering mixed results. Some papers at least implicitly assume that spatial geography has no link to a firm’s acquisition strategy: this view is reflected indirectly in most empirical research on acquisitions. A few studies, however, describe how spatial concerns are intricately linked to a firm’s growth strategy, although much of the development in this literature has been fragmented. This section summarizes this literature. Most models of corporate expansion assume that acquirers have a spatially unbounded search space for target firms. Indeed, the idea that geographic proximity facilitates acquisitions contrasts with the view that industry structure, market opportunities, and target capabilities drive firms’ acquisition strategies. In such views, distance between acquirer and target is not an important consideration because the acquisition premium or discount will reflect the characteristics of target location (Friedman, Fung, Gerlowski, and Silberman 1996). Some studies argue that acquirers commonly focus on the characteristics of geographic locations such as market concentration and future growth opportunities in identifying targets (e.g. Barron, West, and Hannan 1994; Rose 1999), while others suggest that improved 9
communications and transportation infrastructures have reduced the economic significance of distance for acquisitions (Berger and DeYoung 2002). In contrast, a small set of studies in the strategy and economic geography literatures has discussed acquirer-target distance. The general prediction is that the spatial distribution of acquisitions is importantly linked to a firm’s growth strategy. First, firms are expected to consider the spatial dimension more explicitly in some acquisitions than in others. The objectives underlying the acquisition-based expansion – such as horizontal, vertical, or diversified expansion – could influence how importantly firms treat spatial factors and the resulting corporate geography of the firm, as well as how much these factors influence eventual outcomes (Chapman and Walker 1987). For example, the goal of increasing market share in a specific industry may imply that firms need to manufacture the product in an increasingly wider geographic area (Hamilton 1974). Spatial expansion is also the intended consequence when firms enter new geographic markets in existing lines of businesses. While expanding into geographically distant markets, firms may gather important information pertaining to the targeted markets by acquiring targets already operational in such markets (Hayter 1981). The spatial geography of vertical integration is less predictable, however, and depends on the location of upstream and downstream agents. Backward integration could lead firms to resource frontier areas, while forward integration could lead firms to set up operations in market locations (see Chapman and Walker 1987). An important study on the acquisitionbased growth by chains showed that the location of prior acquired subsidiaries and acquirer headquarters played an important role in future target selection (Baum, Li, and Usher 2000), highlighting the link between spatial and organizational expansion. Second, spatial factors can create constraints in a firm’s acquisitions strategy by affecting planning and performance (Chapman and Walker 1987; Leigh and North 1978; Watts 1980). Expansion by internal growth could involve careful geographic planning where firms carefully select the location of the new facility. Chapman and Walker (1987) argue that such planning could be more difficult in the case of acquisition-based growth because target firms have already set up their facilities, and the acquiring firm inherits these locations along with their links to suppliers and customers. To support arguments linking space to expansion constraints, researchers have referred to early 10
work that described how firms possess only limited linkages with other actors across geographic space (Goddard 1971; Taylor 1975; Thorngren 1970; Tornquist 1970), which in turn limits their ability to consider a spatially unbounded set of potential targets. Acquirers require information about targets in order to estimate a target’s current value and assess how the target’s capabilities will integrate with the acquirer’s existing activities. Green and Cromley (1984) find that the likelihood of acquisition in New York City, Chicago, and Los Angeles decreased with target distance from the city centers. These findings lead them to argue that firms with greater ability to gather information – e.g., larger firms that have more resources to spend on information gathering – are more likely to acquire distant targets. Green and Cromley (1984) also describe how factors such as advances in information technology and the absence of locally available targets may influence long distance acquisitions. In sum, pulling together arguments from several strands of prior research suggests that spatial geography will play an important role in acquisitions strategy. The core implication from a fragmented and often indirect body of research is that acquiring firms face constraints that limit their ability to systematically consider a spatially unbounded set of potential target firms. While external factors – such as technology, location characteristics, and legal requirements – may ease or further strengthen these constraints, this issue requires explicit attention. 4. Hypotheses This section develops hypotheses that explore how geography influences a firm’s choice of a particular target from a set of potential targets. We use the term ‘acquisition match’ to indicate such a choice, distinguishing acquisitions that were announced from other potential acquisitions that were not announced. A ‘potential target’ is any firm that fits the expansion objectives of the acquirer. Geographic distance is likely to influence target selection (1) if it implies greater search costs, thereby prompting firms to evaluate proximate firms before evaluating geographically distant firms, or (2) if acquisition managers perceive greater costs associated with integrating acquisitions that are geographically distant. We start with the baseline hypothesis that acquiring firms prefer geographically proximate targets, before discussing conditioning influences that describe when managers link distance to cost of implementation. 11
4.1 Geographic Distance and Target Selection Geographic proximity can facilitate target search and information gathering, increasing the probability that a proximate firm exists in the set of potential targets and is eventually acquired by a particular acquirer. Acquiring managers are unlikely to undertake ongoing environmental scans for information about other firms that might become relevant during the target selection phase of an acquisition (Hambrick 1982); firms are more likely to initiate scanning activities once they need data on such external events (Daft, Sormunen, and Parks 1988). Acquirers are therefore not likely to possess in-depth information about potential targets and are likely to initiate a target search process that would involve evaluating multiple potential candidates for an eventual acquisition. Acquirers, however, are constrained by limited cognitive capacity while searching for external information (Cyert and March 1963). Therefore, cognitive factors such as the need to simplify complex decisions strongly influence the target selection process (Duhaime and Schwenk 1985; Tihanyi, Johnson, Hoskisson, and Hitt 2003). This need to simplify the task of environmental scanning leads to the importance of spatial geography in target search and selection. As we described earlier, acquirers use subjective and objective information gathering processes, both of which are subject to spatial influences. There is substantial evidence from recent studies that subjective influences such as context familiarity, prior knowledge, and perceived competence have important influences in individual investment decisions (Brennan and Cao 1997; Grinblatt and Keloharju 2001; Heath and Tversky 1991; Huberman 2001; Kang and Stulz 1997), and that such influences shape the geography of investment decisions (Coval and Moskowitz 1999, 2001; Sorenson and Stuart 2001). Even objective searches for targets are likely to be spatially bounded. Acquirers and search professionals typically implement searches using criteria based on predetermined expansion objectives. The broader the search criteria, the greater the number of potential targets that acquisition managers need to evaluate. Managers will tend to evaluate geographically proximate targets first in an attempt to simplify the search process (Green and Cromley 1984). One reason is that the time and cost of travel increase with distance, which could constrain the scope of frequent pre or post-acquisition interactions between acquiring and target firm managers. If the spatial extent of areas of which the firm has information linkages is limited, 12
information about a target will decrease with geographic distance, and the cost of accessing such information will increase with distance, posing a constraint in the candidacy of a distant target. Hypothesis 1: The greater the geographic distance between an acquirer and a potential target, the lower the probability of an acquisition match. One question at this point concerns the focal location for measuring distance to a target. In our view, corporate headquarters is the appropriate measurement location. Acquisition strategy typically is a major part of corporate strategy, with corporate personnel taking the lead role in shaping decisions. Green and Cromley (1984) note that corporate personnel typically are responsible for identifying targets and/or working with external agents to gather information. We will treat corporate headquarters as the focal location for assessing proximity, while also considering the role of subsidiary location, as well as the location of previous acquisitions in the analysis. 4.2 Conditioning the Impact of Geographic Distance on Target Selection This section builds upon the previous section to explore conditions when acquisition managers will tend to link distance with greater costs or difficulties of implementation, often eliminating potential targets that are far away. Expectations of higher post-acquisition cost or difficulty could be fueled by uncertainty driven by a lack of adequate information about target firms, or by the need for extensive post-acquisition integration and monitoring effort that is perceived difficult to implement over long distance. Therefore, we examine whether firms with low prior acquisition experience or firms implementing acquisitions of related businesses have a greater preference for geographically proximate targets. Since these factors reflect firm and acquisition level characteristics, they explain why acquiring firms differ in target selection strategy. As we discussed previously, distance influences the quality of information about targets. Acquiring firms need to obtain detailed financial, strategic, and organizational information about targets, as well as assess integration challenges (Pablo 1994). While it is sometimes straightforward to evaluate financial aspects of distant targets, acquisition managers will often have a better understanding of strategic, organizational, and cultural information about target firms that are geographically proximate. Indeed, even financial assessments frequently involve subjective judgments that may benefit from a 13
deeper understanding of proximate targets (Coval and Moskowitz 2001). Firms with greater acquisition experience may be better able to access and assess information on geographically distant targets and hence be less sensitive to distance. With experience, acquirers may be able to improve their ability to manage the acquisition process (Kitching 1967; Paine and Power 1984), due to better information gathering ability (Green and Cromley 1984) and target selection skills (e.g., Bruton, Oviatt, and White 1994). Haleblian and Finkelstein (1999) found that inexperienced acquirers often inappropriately generalized their limited acquisition experience to subsequent dissimilar acquisitions, while more experienced acquirers were able to differentiate between their acquisitions, suggesting that experience played an important role in acquisition success. Experienced acquirers are likely to be better able to assess organizational information for distant targets, and are better able to judge the value of targets’ resources and how a target fits in with the acquirer’s overall strategy. To the extent that lack of acquisition experience constrains the ability of the firm to access and assess information on a bigger set of potential targets spanning a greater geographic expanse, inexperienced firms are likely to exhibit a greater preference for geographically proximate targets. Hypothesis 2: The more acquisitions that an acquirer has undertaken in the past, the less that distance will reduce the probability of an acquisition match. In addition to the acquirer’s ability to identify targets and gather relevant information, characteristics of specific acquisitions influence whether managers expect implementation costs to increase with distance. In particular, greater distance may lead to expectations of greater costs or difficulties for related acquisitions than for unrelated acquisitions. Related acquisitions are likely to involve a greater degree of post-acquisition integration and strategic interaction than unrelated acquisitions. In related acquisitions, firms acquire targets operating in similar business lines in order to capture synergistic benefits and scope economies, in what Karim and Mitchell (2000) call path-dependent resource-deepening expansion. The benefits from such acquisitions commonly require strategic interchange of resources between the acquirer and target firm (Haspeslagh and Jemison 1991). By contrast, in unrelated acquisitions – involving targets in unrelated business lines – targets often receive instructions about performance requirements (such as GE’s demands for 14
performance levels in its NBC broadcasting subsidiary) but they often do not involve extensive systematic interchange of resources between acquirer and target. Such unrelated acquisitions commonly involve only limited integration and allow substantial organizational autonomy (Haspeslagh and Jemison 1991). If unrelated acquisitions involve minimal sharing of resources and hence post-acquisition integration, relatedness in products and services between acquirers and targets should imply a greater level of necessary post-acquisition integration effort (Howell 1970; Pablo 1994; Shrivastava 1986). Integration designs that require high levels of strategic interaction and redeployment of resources between acquirer and target firms can be costly to implement over long distance. Related acquisitions frequently involve redeployment of R&D, manufacturing, marketing, managerial, and financial resources (Capron, Dussuage, and Mitchell 1998). While some interactions involve a costless transmission of information, other interactions involve teams working together closely. Research indicates that managers may face significant obstacles while implementing such interactions over long distance (Hauptman and Hirji 1999). When managers are aware that a greater level of post-acquisition integration is costlier over long distances, they are likely to prefer proximate targets while implementing related acquisitions. Hypothesis 3. The more closely related the businesses of an acquirer and potential target, the more that distance will reduce the probability of an acquisition match. In sum, we argue that geographically proximate targets are more likely to be acquired, especially when acquiring firms have little prior acquisition experience and in cases of related diversification. Discriminating among these factors will help determine how actively firms tend to shape their spatial acquisition strategies. If acquisition experience is the primary factor that shapes the influence of proximity, then firms can overcome the proximity constraint primarily by gradual geographic expansion. By contrast, diversification is a strategic choice with respect to the characteristic of specific potential targets: if this shapes the influence of distance, then firms can exhibit greater flexibility in their geographic expansion. Of course, other factors might influence the ability of the acquiring firm to purchase geographically distant targets; we will address factors such as parent-subsidiary relationships, recent developments in information technology, target size, acquirer age and size, acquirer centrality, and strategic objective in sensitivity analyses. 15
5. Analysis 5.1 Sample The analysis studies U.S. chemical manufacturers. Chemical manufacturing firms use chemical processes to transform organic and inorganic raw materials into finished products. Such firms produce more than 70,000 different chemical substances, including basic chemicals such as carbon dioxide and hydrogen as well as end products such as fertilizers, paints, and soaps. The chemical industry is important in terms of its volume of output, accounting for about 25% of the world-wide production of chemicals, about 2% of US GDP, and about 12 % of all US manufacturing (O'Reilly 2005). The industry is a large employer, providing about a million jobs within the US (BLS 2006). About 55% of its output is used as intermediate inputs by other industries such as healthcare, education, textile, automobile, electrical, electronic, petroleum refining, paper, machinery and instruments, rubber and plastics, agriculture, mining, and construction, while about 25% of its output is used as intermediate products in chemical manufacturing itself (see Lenz and Lafrance 1996). The study examines 2,070 domestic acquisitions announced between 1980 and 2003 by 767 U.S. chemical manufacturing firms that were founded after 1979. Acquirers were identified from the SDC database using the North American Industry Classification System (NAICS) code of their primary industry of operation. Table 1 reports the primary industries of these firms, the number of acquisitions the firms undertook during the 24-year period, and the number of acquisitions announced by all US chemical manufacturing firms, irrespective of their year of founding. The sample includes 36% of all chemical manufacturing acquisitions that occurred during the period. Technology administration (technology.gov) reports that about 9,125 chemical firms were operational in 2006. SDC records show that 2,152 US chemical firms implemented acquisitions during the observation period (i.e., about 24% of the population, assuming a stable population). However, the size distribution of firms is highly skewed in the chemical manufacturing industry. About 46% of firms are very small, employing 9 people or fewer, accounting for only 3.4% of the workforce employed in the chemical manufacturing industry. Only 3.5 % of the population employs more than 250 people, accounting for about 44% of the work force employed in the chemical manufacturing industry (BLS 2006). About 62% of the acquirers in the sample operate in the 16
pharmaceutical and medicine manufacturing area, accounting for 57% of the acquisitions. Table 2 reports the primary industries of the target firms, showing that about 67% of acquisitions were of chemical manufacturing targets. Non-chemical industry targets included firms providing R&D services (8%) and wholesalers (5%). Our focus on domestic acquisitions facilitates the sampling design we outline below and also limits possible influences of political and technological differences that might shape how distance affects cross-border acquisitions. ********** Table 1 and Table 2 about here ********** Acquisitions have long played an important role in the development of the chemical industry. In addition to the general objective of achieving growth and quick access to resources, firms have used acquisitions to achieve economies of scale and operational efficiency in R&D, production, and marketing activities. Danzon, Epstein, and Nicholson (2004) suggest that the need for rapid changes – including greater economies and operational efficiencies – could be driven by industry or firm level shocks. Industry shocks could be driven by excess capacity resulting from demand shocks, which reflect fluctuating conditions in the health and other manufacturing sectors. Such contagion is reflected in the high utilization of chemical manufacturing industry’s output by these sectors. Shocks could also be driven by firm level factors, such as a low expected growth rate in the future. In the pharmaceutical sector, this could be driven by a setback due to the FDA not approving the commercialization of a particular drug. Under such conditions, firms may perceive economies from acquiring and combining operations with other firms, removing duplicative functions and reducing overhead, while catering to a larger geographic market. Danzon, Epstein, and Nicholson (2004) examined determinants and consequences of mergers in the pharmaceutical and biotech industry. They found that large firms merged in response to excess capacity, while small firms merged as a result of financial trouble. Recent reports in the business press and industry analysis (e.g., O'Reilly 2005) describe many chemical acquisitions as a means to consolidate in a saturated environment, where firms used acquisitions as a means to reduce overhead and increase efficiency. 5.2 Focal Variables This section describes the firm- and dyad-level variables that are used to explain the probability that an 17
acquiring firm chooses a particular target from a set of potential targets. Tables 3 and 4 report descriptive statistics for acquiring firms and acquisitions. ********** Tables 3 and 4 about here********** Acquisition. We use a 0-1 dummy variable to indicate whether any dyad of two firms, as we describe below, announced an acquisition in a given year. Acquisition distance is calculated as the log of the miles between coordinates for the zip codes of acquirer and target firm headquarters. The distance dab between two points a and b is given by
d ab = C {arccos[sin (lat a )sin (lat b ) + cos (lat a )cos (lat b ) cos ( long a − long b
)]}
Lat and long refer to the latitude and longitude of locations a and b. C is a constant that converts the result to miles on the surface of the earth. We used C=3,437. There were numerous typological errors and missing data in the zip code and city information within the SDC database, which we corrected manually by referring to individual company websites, directories such as the Medical and Healthcare Market Place Guide, the US Census Bureau website, and various real estate websites. Acquisition experience counts the number of acquisitions the firm has announced from its founding to the year prior to the focal year. Among the 767 firms in the study, 392 implemented only one acquisition, 144 implemented more than 4 acquisitions, and 28 implemented more than 10 acquisitions during the period. We use an interaction between prior acquisition experience and acquisition distance to test hypothesis 2. Related acquisition: A 0-1 variable indicated relatedness. This variable was 1 when the target operated in the same 4-digit NAIC (1,212 cases were related) and 0 when the target operated in another 4 digit NAIC segment (858 cases). Sensitivity tests used a broader definition, which also codes an unrelated acquisition (i.e., a different 4-digit from the primary NAIC of the acquirer) as related if the acquirer has a prior acquisition in this industry. Using this measure, 1,389 of 2,070 acquisitions are defined as related. These measures produced materially equivalent results. An interaction term between relatedness and acquisition distance tests hypothesis 3. 5.3 Control Variables The analysis includes control variables for Acquirer Age, Size, and Diversification. We determined 18
founding dates from company websites and the Factiva publications database. Size is measured as the log of assets, with missing values imputed as a time variant function of the firm’s age, diversification, number of prior acquisitions, and private vs. public status. The SDC database provided information on the number of industry segments in which the acquirer operated, which provides a measure of diversification. Dummy variables indicate whether the acquiring firm is a parent with multiple subsidiaries (Acquirer is multiunit) or a subsidiary itself (Acquirer is subsidiary). These variables control for whether acquiring firms have support from geographically distant business units while making acquisition decisions. Among the 767 acquirers, 510 were single unit firms, 132 were parent firms with subsidiaries, 125 firms were subsidiaries themselves. Acquirer geographic centrality controls the geographic location of an acquirer within a pool of potential targets. The US chemical manufacturing industry is geographically concentrated. Firms locate in regions that have a high concentration of other manufacturing firms (e.g., near the automobile industry in the Great Lakes region; near electronics firms along the West Coast), near petroleum and natural gas manufacturing firms (explaining the high concentration on the Gulf Coast in Texas), and near major industrial ports as chemicals used for production are often imported by sea (BLS 2006). In the current sample, firms from California, New Jersey, New York, Massachusetts, Texas, Pennsylvania, and Florida accounted for over 60% of the sample. The location of the acquiring firm vis-à-vis the population of potential targets may affect how distance explains acquisition probability – acquirers that are more centrally located are more likely to acquire geographically proximate targets than are acquirers that are not centrally located. We included the number of potential targets within 150 miles of the acquirer as a measure of centrality. The larger the number of potential targets within 150 miles, the more central the location of the acquirer. Figure 2 plots the geographic location of acquiring firm headquarters. ********** Figure 2 about here ********** Target is marketing is a 0-1 variable that indicates if the target firm is primarily involved in marketing and distribution activities. Target is larger is a 0-1 dummy variable indicating if the target firm is larger than the acquiring firm. We included the year of acquisition as a control for technological and telecommunications progress that might facilitate the identification and eventual acquisition of 19
geographically distant target firms. We included several additional location level variables. The probability of acquiring a particular target is likely to be higher if the target is more visible than other potential targets. We included a 0-1 variable to indicate if the target was a public firm (Target is public). Among the 2,070 target firms, 807 (39%) were privately held firms, 590 were public listed, 427 were subsidiary units, and 192 were branch units; in addition, 57 acquisitions involved assets of target firms. Target is urban: a 0-1 variable indicating if a target firm’s location was in a designated metropolitan (MSA) or micropolitan statistical areas. Metropolitan and micropolitan statistical areas are identified by the population and level of social and economic integration at the core. Metropolitan statistical areas have at least one urban area inhabited by more than 50,000 people, while micropolitan statistical areas have at least one urban area inhabited by more than 10,000 people. To control for cultural and institutional differences that exist across states, we included dummy control variables that indicate whether or not an acquirer-target dyad operates out of (i) the same state, and (ii) similar sized cities. City size was approximated from existing metropolitan, micropolitan or rural status. Alternative theoretical mechanisms could explain why certain firms systematically acquire distant targets while others systematically acquire targets close by. First, acquiring managers with greater risktaking propensity or those with a greater geographic expanse of social networks could systematically exhibit low sensitivity to distance-related factors while selecting targets; we found no evidence to conclude that such factors systematically influenced target selection decisions over time. Second, target performance might override distance concerns. Target financial performance is not a binding determinant of acquisition, however, particularly if acquirers have intent to dismantle and integrate target resources. As proof of this, poorly performing or bankrupt firms often are acquisition targets in many industries. More importantly, this only poses a problem in this study if mismanagement or poor performance is geographically clustered; it is reasonable to assume that such geographic clustering does not exist in the chemical industry. Finally, the motive of achieving market power to set or lead prices could lead to acquisitions that are geographically clustered. This possibility is somewhat limited for firms in our sample because of the low monopolistic power at the regional level. Prices are determined at the national 20
and, increasingly, global levels. In addition, the census reports that concentration within the chemical manufacturing industry is too low for acquisitions to be a means of increasing market concentration to the extent of affecting chemical prices. 5.4 Methodology This study developed hypotheses concerning the direct effect of geographic distance on acquisition match and two factors – acquisition experience and business relatedness - that may moderate the relationship between acquisition distance and the probability of acquisition match. Testing these hypotheses involves modeling the probability that a firm acquires a particular target firm from among a set of potential targets. This involves creating a dummy variable with value of 1 for the dyad representing acquisitions that occur, and zeros for all potential acquisitions that could have occurred but did not. Using this matrix of 1’s and 0’s, a logit model tests how acquisition distance explains the probability of acquisition (hypothesis 1). Logit interaction models then test the moderating hypotheses (hypotheses 2 and 3). The first step is to define the set of potential acquisitions that could have occurred but did not. Firms must satisfy two important conditions in order to be considered as a potential target. First, a potential target must operate primarily in the same industry as the target that was eventually acquired. This condition identifies which of the target firms match the focal acquirer’s revealed expansion objectives. Second, a potential target must be acquisition-worthy. By acquisition-worthy, we mean targets that provide strategic advantage to the acquiring firm by virtue of their resources, while at the same time being available for takeover. This condition addresses the ‘quality’ difference that exists across firms that were never acquired and firms that were acquired by other chemical manufacturers in a proximate time period. Therefore, one way of identifying acquisition-worthiness is to limit potential targets to only those firms that were eventually acquired by chemical manufacturing firms during a fiveyear window around the focal acquisition. Prior research has used a similar approach (Sorenson and Stuart 2001). However, it is restrictive to assume that all acquirers focus on the same set of targets. Therefore, we also include non-targets in the sample, and run tests for sampling designs using both strong and weak assumptions about what constitutes a potential target. The results differed only slightly between different sampling designs, with the basic conclusions remaining consistent. 21
Once the actual and potential acquirer-target dyads are defined, the estimation can proceed by either analyzing all possible dyads or using endogenous stratification to analyze a subset of the dyads (Cosslett 1981; Manski and Lerman 1977a). Endogenous stratification, in contrast with random and exogenous stratified sampling, involves splitting the observations into sets corresponding to whether the dependent variable is 1 or 0, and then randomly selecting observations from these two sets. Endogenous stratification has been common in binary dependent variable models where there are a much larger number of zeros than ones. This is true in studies predicting wars, vetoes, epidemiological infections, and venture capital investments, as well as in our study on acquisitions (e.g., King and Zeng 2001; Sorenson and Stuart 2001). Assuming an annual average of about 9,125 operational firms during the observation period, the number of possible acquisitions within this set of firms is 9,125(C)2 = (9,125*9124)/2 = 41,628,250. A total of 5,767 acquisitions took place within the sector, while 41,622,483 “possible” acquisitions did not take place. The number of acquisitions that did occur is therefore very small compared to the total possible number of acquisitions. It has been difficult to predict and explain probabilities in such cases, because usual logit regression underestimates the probability of occurrences and data collections methods are often inefficient given the time and resources needed to collect information on the large number of non-occurrences, many of which are irrelevant. When such a discrepancy between 1’s and 0’s exist in the data, sampling by endogenous stratification provides better estimates of the probability of occurrence. In some research contexts, this approach also saves time and resources in data collection. When estimating using the endogenously stratified sample, it is necessary to include a weightbased correction. This is because the fraction of 1’s in the sample is different from the fraction of 1’s in the population. Manski and Lerman (1977a) refer to the procedure as weighted exogenous sampling maximum likelihood estimation (WESML).This involves maximizing the weighted log likelihood n
ln Lw (β | y ) = −∑ wi ln[1 + exp{(1 − 2 yi )xi β }] , where y is the probability, wi = w1 yi + w0 (1 − yi ) , i =1
w1 = τ y , w0 = (1 − τ ) (1 − y ) , τ = the fraction of 1’s in the population = 5,767/41,628,250, and y = 2,070/12,420 the fraction of 1’s in the sample. To interpret the interaction models, we use the following cross derivative of the logit 22
specification when x1 and x2 are the interacting variables where P(x) is the short notation for P(Y=1|X).
∂ 2 P( x) ∂x1∂x2 = β12 [P( x)(1 − P( x))] + (β1 + β12 x2 )(β 2 + β12 x1 )[P( x)(1 − P( x))(1 − 2 P( x))] , To derive standard errors and t-values, we use STATA8’s ‘predictnl’ command. We plot the values of the interaction effects and the corresponding t-values against the predicted probabilities to interpret the interaction effect, because the marginal effect of the interaction term, given by ∂P (x ) ∂x1 x2 , does not represent the interaction effect in index models (Ai and Norton 2003; Wooldridge 2002). These plots are displayed only when their implications contrast with the magnitude, direction, and significance of corresponding coefficients derived from logit regression. We considered sample selection issues in determining a sampling strategy, where the concern is the possibility that a firm’s decision to acquire (rather than expand by other means) is driven by the knowledge of existence of nearby targets. One approach would be to create a sample of potential acquirers, including firms that did and did not announce acquisitions during the study period, and then use a two-step approach that first estimated the likelihood that firms announce acquisitions and then, among the acquiring subset, examined how distance affected target choice (Heckman 1979). This procedure requires a full listing of all chemical manufacturing firms operational after 1979. Our sample accounts for almost all public firms, because most of these firms announced at least one acquisition from 1980 to 2004. Although the COMPUSTAT and OSIRIS databases identified a few additional non-acquiring public firms, we were unable to gather detailed financial information on private non-acquiring firms, which these databases do not list. Instead, we address potential selection issues via the control variables for firm age, size, diversification, and geographic location. Age, size, and diversification influence the mode of firm growth (e.g., Aldrich and Auster 1986; Barnett and Amburgey 1990; Penrose 1959), while acquirer geographic centrality controls for whether a firm’s tendency to grow and acquire is linked to existence of nearby target firms. This conditional mean approach assumes that a firm’s decision to select a particular target is independent of its higher-level decisions to carry out acquisition-based growth once we control for these factors. In sum, we use weighted logit regression, corrected for non-independence of observations drawn from an endogenously stratified sample to test how distance between acquirer-target dyads affected the 23
probability of match. The results report findings derived from an endogenous stratified sample where we match each acquisition that occurred with five other randomly drawn potential acquisitions that could have occurred but did not. It is useful to further discuss our choice of the WESML estimator rather than other analytic approaches. Several studies have examined relationship formation among dyads of potential relationships. These studies create dyads and use these to predict why certain relationships or linkages were formed, while others were not. These dyads are used to distinguish relationships that occur from potential relationships that could have occurred but did not. Some of these studies use logit regression on the full sample of dyads that were created, while other, more recent, studies use a more carefully constructed sampling design along with appropriate corrections to address problems that exist when every dyad is used in the analysis (e.g., Sorenson and Stuart 2001). Recent studies recognize and address two problems associated with using logit regression with such a matched sample design. The first is the problem posed by non-independence of observations when a certain firm appears many times in the database. The second problem is a bias that exists when the number of positive outcomes (here, number of acquisitions) in the sample differs greatly from the number of positive outcomes in the population. Given the index nature of logit regression (Wooldridge 2002), this bias can affect all coefficients. Recent papers have addressed these problems by (1) using an endogenously stratified sampling design, (2) estimating robust standard errors that are corrected for non independence of observations for the same firm, (3) correcting for the ‘rare event’ bias, and (4) correcting a further finite sample bias which appears if only a few ‘relationships’ or ‘linkages’ are observed. King and Zeng (2001) provide a description of this methodology, including how the WESML is operationalized, and how it compares with other estimators. A question that arises is whether McFadden’s choice model or the standard conditional maximum-likelihood estimator (Manski and McFadden 1981; McFadden 1974) is more suited for the present analyses, rather than the WESML (Manski and Lerman 1977b). The conditional logit specification involves (1) setting a finite set of attributes of the different alternatives available, (2) specifying the utility of an alternative as a linear function of these attributes, and (3) estimating 24
coefficients for each attribute. Apart from the popularity of this approach among recent studies (using WESML allows us to be consistent with the approach used in prior research), we have two other reasons for using the WESML approach. First, Hseih, Manski, and McFadden (1985) showed that the conditional MLE is identical to the prior correction method, which involves (1) computing logit estimates and (2) correcting these estimates using prior knowledge about the fraction of 1’s in the population and the fraction of 1’s in the sample (Manski and Lerman 1977b). The WESML approach also uses the fraction of 1’s in the population and in the sample, but used these as weights rather than to correct estimates. Such weighing provides a more robust way of estimating coefficients than the prior correction method, which is more sensitive to misspecification (Xie and Manski 1989). Second, the WESML approach does not require the strict structure that the choice-based approach requires. In the context of the present study, the choice-based conditional logit approach would require (1) generating a finite set of attributes (target size, target performance, target location, target industry, and so on) that would be relevant in every acquisition, and (2) assuming that the ‘value’ of any alternative target to any acquirer is given by a linear function of these attributes. The linear function of attributes would define why targets are ‘valuable’ and why acquirers choose particular targets over others. Given what studies have demonstrated about acquisitions, this appears to a restrictive assumption. For example, while target performance is often cited as an important acquisition criterion, many bankrupt firms are acquired. Similarly, firms may choose targets close by or geographically distant, or in any related or unrelated industry depending on the objectives of the acquisition. The current sampling and estimation strategy does not assume any attribute other than that the target operates in the same industry as the focal acquisition, and that it is operational in a proximate time period. 6. Results
Table 5 starts by displaying five alternative estimators for the main effect of distance and the distancerelatedness interaction that demonstrate the robustness of the results. The prior section discussed our decision to use the WESML approach. The results in table 5 support this decision by showing no firm level unobserved heterogeneity problem while using the WESML approach. 25
********** Table 5 about here**********
Table 6 displays results of the tests of hypotheses 1, 2, and 3. The results in the first column support hypothesis 1, showing that firms tend to prefer geographically proximate targets. The results show an 80% drop in the probability of acquisition between a target that operates in the same zip code and one that is 40 miles away. The corresponding percentage drops are 90.2% and 96.8% for targets located 200 miles away and 2500 miles away, respectively. ********** Table 6 about here**********
An important caveat in interpreting the results is as follows. The dependent variable is an indicator revealing the choice that an acquiring firm made from a set of potential targets. Therefore, we cannot interpret the effects for acquirer or target-specific variables. For example, it is not correct to interpret the coefficient on acquirer size as implying that larger firms were less likely to implement an acquisition. The appropriate test for this prediction would model the probability that a particular firm implements an acquisition versus not acquiring at all. Instead, the acquiring and target firm variables play the vital role of partialling out confounding effects that may also influence choice, giving greater reliability in interpreting the effects of the dyad variables. Columns 2 and 3 of table 6 report interaction effects that correspond to hypotheses 2 and 3. The hypotheses were then tested by plotting and examining appropriate cross derivatives. As described earlier, the marginal effects of the interaction terms calculate incorrect partial derivatives, because they calculate the change in probability per unit change in the multiplicative term, e.g., distance times relatedness (Distance x Acquisition is related). Rather than simply assessing the coefficients of the interaction terms in the regression equation, the correct way to calculate the interaction effect is to use a cross derivative – this gives the change in the relationship between, for instance, relatedness and probability of match, per unit change in distance. We display plots of the cross derivative results when they conflict with the corresponding findings from logit regression. The difference between results based on the coefficients and on the correct cross derivatives is important for the test of hypothesis 2. In particular, the Distance x Prior acquisition experience interaction term (column 2 of table 6) appears to suggest that firms with greater prior acquisition experience were 26
less sensitive to distance. However, this result is not supported by the calculation of the appropriate cross derivatives and their corresponding standard errors. Figure 3 depicts the magnitudes and direction of the cross derivatives, which is consistent with findings in column 2 of table 6. Figure 4 presents the histogram of t-values corresponding to the interaction effect. Thus, the distribution of t-values does not support hypothesis 2; instead, prior acquisition experience does not reduce the preference for geographically proximate targets. As we discuss later, though, several forms of contextual acquisition experience did influence firms’ sensitivity to distance. ********** Figures 3 and 4 about here**********
Column 3 of Table 6 provides strong support for hypothesis 3. As expected, acquirers exhibited a strong preference for geographically proximate targets while implementing related acquisitions. Table 7 further explores the effect of experience, as refinements of hypothesis 2. Table 7 replaces the variable for number of prior acquisitions with three contextual dimensions of experience: prior experience of acquisitions in a distant state (excluding those in the state of the acquirer’s headquarters), cumulative prior acquisition distance, and prior experience in incomplete acquisitions. Table 4 reports low correlations between these variables, permitting joint analysis of their effects. ********** Table 7 about here**********
Columns 2 to 4 of table 7 indicate that contextual experience influenced target selection, even if the number of prior acquisitions did not. Column 2 shows that the location of previous acquisitions influenced focal target selection. Suppose the focal acquisition occurs in a distant state S. Column 2 shows that if the acquiring firm had previously acquired in this state S, it had a significantly higher likelihood of acquiring again in this state (S) even if it targets in this state were geographically more distant compared to other potential targets available to the acquirer. Column 3 then shows that firms that had previously implemented geographically distant acquisitions were less sensitive to distance while implementing the focal acquisition. Finally, column 4 shows that firms that had prior experience in incomplete acquisitions were more sensitive to distance, preferring geographically proximate targets while implementing the focal acquisition. Calculating and plotting cross derivatives produced consistent results with the direction and significance of the interaction effects in table 7; therefore, we report only 27
the tables. Clearly, external contextual experience plays an important role in conditioning spatial preferences, even if simple aggregate acquisition experience has less effect. Tables 8 and 9 investigate several other factors that might influence the effect of distance on acquisition prevalence. Table 8 reports the effect of the presence of parents or subsidiaries, as well as whether sensitivity to acquisitions distance has decreased over time. Parent-subsidiary relationships may shape acquisition strategy. Firms that have relationships with established firms may exhibit a lower preference for geographically proximate targets than stand alone firms. Such relationships might exist in the form of the presence of a parent firm, which is likely to dictate acquisitions strategy, or the existence of a subsidiary firm, which is likely to transmit information about targets that are close to the subsidiary but far from the parent headquarters. Nonetheless, column 1 shows that acquirers with subsidiaries (i.e., acquirers that are parents) did not display a lower preference for geographically proximate targets. By contrast, column 2 shows that acquirers with parent firms (i.e., acquirers that are subsidiaries) were moderately less sensitive to distance. In combination, the results in columns 1 and 2 indicate that knowledge about distant targets is more likely to flow from parent to subsidiary than from subsidiary to parent. This supports the assumption that corporate decisions are made in the headquarters, rather than at the subsidiary level. ********** Table 8 about here**********
In addition, recent developments have generated a greater prevalence of electronic forms of communication and more developed communications infrastructures. Studies have discussed how such advances could lead to more expanded firm boundaries (Afuah 2003), facilitate geographic expansion (Berger and DeYoung 2002) and help managers successfully coordinate functions across dispersed locations (Cohen 2000). Such developments could therefore help acquirers overcome barriers of distance and seek geographically distant targets (Green and Cromley 1984). However, column 3 of table 8 shows that firms’ sensitivity to geographically distant acquisitions has not decreased with time. Instead, these results indicate that the effect of distance is persistent in spite of improvements in communications and transportation infrastructure. Table 9 reports several corollary tests that investigate whether target size, acquirer age and size, 28
acquirer centrality, and strategic objective (marketing versus production) affect the impact of distance on acquisition tendencies. Column 1 examines target size. Many studies have examined how the size of the target influences acquisition outcomes. Early studies discussed the importance of critical mass in acquisitions (Gluck 1979; Kitching 1974; Kumar 1977). These studies explored how target size was an important factor distinguishing which firms eventually were acquired (see Chatterjee 2000), and indicated that larger acquisitions created more value. Empirical studies, though, have found target size sometimes had little influence on performance (Fowler and Schmidt 1989; Newbould, Stray, and Wilson 1976) and might even hurt acquisition performance (Kusewitt 1985). Column 1 shows that firms acquiring larger targets had a greater preference for geographically proximate targets. Columns 2 and 3 of table 9 examine acquirer age and size. Studies suggest that the preference for investing in geographically proximate firms is weaker for larger and better known firms (Dahlquist and Robertsson 2001; Kang and Stulz 1997). Coval and Moskowitz (1999) found a negative relationship between local equity preference and firm size. The search for targets is usually executed by an individual or a group of individuals operating from the headquarters (Green and Cromley 1984), who are likely to use their professional and personal contacts. The geographic extent of the information linkages that these professional and social relationships provide is quite limited (Festinger, Schachter, and Back 1950; Goddard 1971; Taylor 1975; Thorngren 1970; Tornquist 1970). Larger firms are likely to have greater information gathering resources, however, while older firms would have a better understanding of distant markets and competitors, which could enhance the transmission of information about targets (Green and Cromley 1984). Therefore, larger and older firms may be less constrained by spatial geography while implementing acquisitions. However, although column 2 initially appears to indicate that older acquirers had a greater preference for geographically proximate targets, this conclusion is not supported by the cross derivative calculation that figures 5 and 6 depict. Figure 5 shows that the cross derivative calculation yields a similar direction for the interaction, while figure 6 shows that the interaction effect is not significant, i.e., the preference for geographically proximate targets did not change with firm age. Moreover, column 3 shows that acquirer size had no effect on the proximity preference. Column 4 shows that acquirers that were centrally located had a greater preference for proximate 29
targets; this result follows from the greater availability of proximate targets for such acquirers, as compared to other acquirers operating in remote areas. Finally, column 5 shows that when firms acquired marketing targets (as opposed to production targets), they had no greater preference for those that were geographically proximate. ********** Table 9 and Figures 5 and 6 about here**********
In sum, spatial considerations significantly influence which targets are eventually acquired. The core results showed that firms prefer geographically proximate targets, especially while implementing related acquisitions. By contrast, experience had more nuanced influences: although sensitivity to geographic distance did not decline as firms’ number of acquisitions increased, experience with prior location and industry contextual evidence did influence target selection. In addition, sensitivity analyses show that target size, acquirer subsidiary status, and acquirer centrality shape the impact of geographic proximity, while sensitivity to geographic proximity does not change with acquirer parent status, acquirer age and size, or marketing versus production targets. 7. Discussion
An intriguing debate has evolved around whether geographic distance has become a less important constraint on business strategy as the world becomes smaller. Some writers have argued that geography plays little role in firm strategy because advances in technological, telecommunication, and transportation infrastructure allow managers to coordinate activities across long distances (Cohen 2000; Peters 1997). However, several strands of research suggest that geographic distance continues to influence business strategy and performance. International business research suggests that differences in language, culture, technology, institutions, and practices that span geographic distance continue to shape business strategy (Anand and Delios 1997; Delios and Henisz 2003; Feely and Harzing 2004; Hofstede 1980; Hymer 1976; Kogut and Singh 1988; Lakshmanan 1989; Morosini, Shane, and Singh 1998; Sleuwaegen 1998). Other studies have shown that distance affects strategy even within national borders, affecting intra-firm communication (DeSanctis and Monge 1999), financing decisions and return on investment (Coval and Moskowitz 2001), relationship formation, inter-firm interactions, and organizational evolution (Sorenson and Stuart 2001; Stuart and Sorenson 2003), chain expansion (Baum, Li, and Usher 2000), and 30
organizational failure (Kalnins, Swaminathan, and Mitchell 2006). Such studies demonstrate that distance continues to shape business activities, while leaving substantial need to increase our understanding of strategic geography. Our study shows that distance has a persistent effect on acquisition target selection, even within a country. We found that acquiring firms were sensitive to distance while selecting targets, preferring those that were geographically proximate. This effect persisted even as firms aged, grew in size, and gained acquisition experience based on number of prior acquisitions (although the persistence declined with more specific measures of acquisition experience). In addition, this effect persisted over the two decades of the observation period, from 1980 to 2003, as we found no evidence that firms became less sensitive to acquisition distance in recent years. Our finding that distance had a persistent effect on target selection contrasts with the strong results indicating how related acquisitions shaped the preference for proximate targets. This pattern suggests that firms placed greater weight on expected costs relative to specific acquisitions than on their own capabilities that have built with prior experience. A partial explanation for this contrast can be articulated as follows: It is less likely that the top management team will argue ‘since we are experienced, let us acquire a desirable distant target that we would not previously have considered’ and more likely that they will argue ‘since we need to have a great deal of interaction with the target firm, such as providing full management for a related acquisition or dealing with extensive business systems in a large target, it is best if we select one that is geographically closer.’ Overall, these findings parallel micro-level studies on strategic decision making (e.g., Duhaime and Schwenk 1985; Tihanyi, Johnson, Hoskisson, and Hitt 2003). In a study of technological alliances, for example, Tyler and Steensma (1998) found a similar tendency, in which individual managers placed greater emphasis on the opportunities provided by the focal alliance than on their own prior personal experience. The findings reinforce arguments that assert the importance of implementation issues in acquisition strategy (Jemison and Sitkin 1986b). The results suggest that managers typically understand the importance of gathering information and are perceptive about the acquisition process. A reasonable premise is that acquisitions are likely to be difficult to implement at a distance when they require greater 31
pre-acquisition search or post-acquisition integration effort to implement. The finding that acquirers prefer geographically proximate targets while implementing related acquisitions indicates that managers systematically link distance to the cost or difficulties of implementing acquisitions. This study has several limitations that suggest further research. First, we were unable to incorporate international acquisitions in the analysis because it was not feasible to generate meaningful sets of potential targets in multiple countries. A study with a more focused sample could incorporate a larger playing field that includes multiple countries, while attempting to discriminate the effects of space from those of national, political, cultural, and technological boundaries. Second, we based our hypotheses on assumptions that were not explicitly tested in the analysis. Our paper argues that distance influences target search and post-acquisition integration of target firms, but we do not distinguish between these activities, nor do we elaborate which have a greater influence on target selection. Further evaluation of these activities in the context of acquisition decision making has potential implications for the strategic decision making literature, particularly in studying the role of asymmetric information in decision making. Third, in order to track all acquisitions a firm implemented, data limitations constrained our sample to firms founded after 1979. As a result, firms in our sample were no more than about 20 years old. An analysis of acquisitions by older firms (e.g., Dow Chemical) would be insightful. Most generally, this investigation of how geographic distance influences acquisition target selection contributes to the growing body of research examining spatial influences on strategy. Despite recent advances, open questions remain in geographic strategy research. In particular, there have been calls to bridge the gap between strategic management, which largely assumes that firms operate in a spatially homogeneous environment, and economic geography, which does not adequately explain the underlying mechanisms of spatial evolution. Given that location is a fundamental decision for all firms, it is important to continue to examine how firm level goals and resources interact with spatial factors to influence the formulation, implementation, and consequences of business strategy.
32
REFERENCES Afuah, A. 2003. Redefining firm boundaries in the face of the internet: Are firms really shrinking? Academy of Management Review 28(1) 34-53. Ai, C., E. Norton. 2003. Interaction terms in logit and probit models. Economics Letters 80(1) 123-129. Aldrich, H., E.R. Auster. 1986. Even dwarf started small: Liabilities of size and age and their strategic implications. B.M. Staw, L.L. Cummings, eds. Research in Organization Behavior. JAI, Greenwich, CT, 165-198. Altendorf, D.M. 1986. When cultures clash: a case study of the Texaco takeover of Getty Oil and the impact of acculturation of the acquired firm. University of Southern California, LA. Anand, J., A. Delios. 1997. Location specificity and the transferability of downstream assets to foreign subsidiaries. Journal of International Business Studies 28(3) 579-603. Barnett, W.P. 1993. Strategic deterrance among multipoint competitors. Industrial and Corporate Change 2 249278. Barnett, W.P., T.L. Amburgey. 1990. Do larger organizations generate stronger competition? J.V. Singh, ed. Organizational Evolution: New Directions. Sage, Newbury Park: CA, 78-102. Barron, D.N., E. West, M.T. Hannan. 1994. A Time to Grow and a Time to Die - Growth and Mortality of Credit Unions in New-York-City, 1914-1990. American Journal of Sociology 100(2) 381-421. Baum, J.A.C., H.J. Korn. 1996. Competitive dynamics of interfirm rivalry. Academy of Management Journal 39 255-291. Baum, J.A.C., S.X. Li, J.M. Usher. 2000. Making the next move: How experiential and vicarious learning shape the locations of chains' acquisitions. Administrative Science Quarterly 45 766-801. Benson, B.L., M.D. Faminow. 1985. An alternative view of pricing in retail food markets. American Journal of Agricultural Economics 67 296-306. Berger, A.N., R. DeYoung. 2002. Technological progress and the geographic expansion of the banking industry. FRB of Chicago Working Paper 2002-07. Bernheim, B.D., M.D. Whinston. 1990. Multimarket contact and collusive behavior. RAND Journal of Economics(21) 1-26. BLS. 2006. Career Guide to Industries: Chemical Manufacturing. Bureau of Labor Statistics, U.S. Department of Labor, http://www.bls.gov/oco/cg/cgs008.htm. Borgatti, S., R. Cross. 2003. A relational view of information seeking and learning in social networks. Management Science 49 432-445. Brennan, M., H. Cao. 1997. International portfolio investment flows. Journal of Finance 52 1851-1880. Bruton, G.D., B.M. Oviatt, M.A. White. 1994. Performance of acquisitions of distressed firms. Academy of Management Journal 37(4) 972-989. Buono, A.F., J.L. Bodwitch. 1989. The human side of mergers and acquisitions. Managing collisions between people, cultures and organizations. Jossey-Bass, San Francisco. Capron, L., P. Dussuage, W. Mitchell. 1998. Resource redeployment following horizontal mergers and acquisitions in Europe and North America, 1988-1992. Strategic Management Journal 19 631-661. Chakrabarti, A., W. Mitchell. 2005. A corporate level perspective on acquisitions and integration. Advances in Mergers and Acquisitions 5. Chakrabarti, A.K. 1990. Organizational factors in post acquisition performance. IEEE Transactions on Engineering Management 37 259-268. Chapman, K., D. Walker. 1987. Industrial Location. Basil Blackwell. Chatterjee, R.A. 2000. The financial performance of companies acquiring very large takeover targets. Applied Financial Economics 10(2) 185-191. Cohen, N. 2000. Business location decision-making and the cities: bringing companies back. The Brookings Institution Center on Urban and Metropolitan Policy, Washington, D.C. Cosslett, S.R. 1981. Maximum likelihood estimator for choice based samples. Econometrica 49(5) 1289-1316. Coval, J.D., T.J. Moskowitz. 1999. Home bias at home: Local equity preference in domestic portfolios. The Journal of Finance LIV(6) 2045-2073. Coval, J.D., T.J. Moskowitz. 2001. The geography of investment: informed trading and asset prices. Journal of Political Economy 109(4) 811-841. Cummings, J. 2007. Leading groups from a distance: How to mitigate consequences of geographic dispersion. S. Weisband, L. Atwater, eds. Leadership at a distance. Lawrence Erlbaum Associates. Cummings, J., T. Ghosh. 2005. Dealing with distance in team networks: A relational model of communication technology use. Working Paper.
33
Cyert, R.M., J.G. March. 1963. A behavioral theory of the firm. Prentice Hall, Englewood Cliffs, NJ. Daft, R.L., J. Sormunen, D. Parks. 1988. Chief Executive Scanning, Environmental Characteristics, and Company Performance: An Empirical Study. Strategic Management Journal 9(2) 123-139. Dahlquist, M., G. Robertsson. 2001. Direct foreign ovwershi, institutional investors, and firm characteristics. Journal of Financial Economics 71 205-238. Danzon, P.M., A. Epstein, S. Nicholson. 2004. Mergers and acquisitions in the pharmaceutical and biotech industries. NBER Working Paper Series Working Paper 10356. Davidson, W.N., S. Rosenstein, S. Sundaram. 2002. An empirical analysis of cancelled mergers, board composition, and ownership structure. Applied Financial Economics 12 485-491. Delios, A., W. Henisz. 2003. Uncertainty, imitation and locatoin: Japanese multinational corporations 1990-1996. Administrative Science Quarterly 46(3) 443-475. DeSanctis, G., P. Monge. 1999. Communication processes for virtual organizations. Organization Science 10(6) 693-703. Dicken, P. 1971. Some aspects of decision making behavior of business organizations. Economic Geography 47 426-437. Duhaime, I.M., C.R. Schwenk. 1985. Conjectures on cognitive simplification in acquisition and divestment decision making. Academy of Management Review 10(2) 287-295. Eaton, B.C., R.G. Lipsey. 1979. The Theory of Market Pre-emption: The Persistence of Excess Capacity and Monopoly in Growing Spatial Markets. Economica 46 149-158. Edwards, C.D. 1955. Conglomerate bigness as a source of power. G. Stigler, ed. Business Concentration and Price Policy. Princeton University Press, Princeton: NJ, 331-359. Feely, A.J., A.W. Harzing. 2004. The language barrier and its implications for HQ-subsidiary relationships Academy of Management, New Orleans. Festinger, L., S. Schachter, K.W. Back. 1950. Social pressure in informal groups. Harper & Row, NY. Fowler, K.L., D. Schmidt. 1989. Determinants of tender offer post-acquisition financial performance. Strategic Management Journal 10 339-350. Francis, R., L. McGinnis, J. White. 1983. Locational analysis. European Journal of Operations Research 12 220252. Fried, Y., R.B. Tiegs, T.J. Naughton, B.E. Ashford. 1996. Managers' reactions to a corporate acquisition: test of an integrative model. Journal of Organization Behavior 17 401-427. Friedman, J., H. Fung, D.A. Gerlowski, J. Silberman. 1996. A note on "state characteristics and the location of foreign direct investment within the United States". The Review of Economics and Statistics 367-368. Gluck, W.J. 1979. Planning growth through diversification. Managerial Planning 271-276. Goddard, J.B. 1971. Office communication and office location: a review of current research. Regional Studies 5(263-280). Green, M.B., R.G. Cromley. 1984. Merger and acquisition fields for large United States cities 1955-1970. Regional Studies 18 291-301. Grinblatt, M., M. Keloharju. 2001. How distance, language, and culture influence stockholdings and trades. Journal of Finance 56 1053-1073. Haleblian, J., S. Finkelstein. 1999. The influence of Organizational acquisition experience on acquisition performance: A behavioral perspective. Administrative Science Quarterly 44 29-56. Hambrick, D.C. 1982. Environmental scanning and organizational strategy. Strategic Management Journal 3 159174. Hamilton, F.E.I. 1974. A view of spatial behavior, industrial organizations and decision making. F.E.I. Hamilton, ed. Spatial perspectives on industrial organization and decision making. Wiley, London. Hannan, T.H., S.A. Rhoades. 1987. Acquisition targets and motives: the case of the banking industry. Review of Economics and Statistics 67-74. Haspeslagh, P.C., D.B. Jemison. 1989. Acquisitions - myths and reality. Sloan Management Review(Winter) 53-58. Haspeslagh, P.C., D.B. Jemison. 1991. Managing acquisitions: creating value through corporate renewal. The Free Press, New York. Hauptman, O., K.K. Hirji. 1999. Managing integration and coordination in cross-functional teams: an international study of Concurrent Engineering product development. R&D Management 29(2) 179-191. Haveman, H.A., L. Nonnemaker. 2000. Competition in multiple geographic markets: The impact on growth and market entry. Administrative Science Quarterly 45 232-267. Hayter, R. 1981. Patterns of entry and the role of foreign controlled investment in the forest product sector of British Columbia. Netherlands Journal of Economic and Social Geography 72(2) 99-113.
34
Hayter, R., H.D. Watts. 1983. The geography of enterprise: a reappraisal. Progress in Human Geography 7 157-175. Heath, C., A. Tversky. 1991. Preferences and beliefs: ambiguity and competence in choice under uncertainty. Journal of Risk and Uncertainty 4 5-28. Heckman, J.J. 1979. Sample Selection Bias as a Specification Error. Econometrica 47(1) 153-162. Hofstede, G.H. 1980. Culture’s consequences: International differences in work-related values. Sage Publications, Beverly Hills, CA. Holloway, C.A., S.C. Wheelwright, N. Tempest. 1998 (revised 2004). Cisco Systems, Inc.: Acquisitions Integration for Manufacturing. HBS Case OIT26. Hoover, E.M. 1948. The location of economic activity. McGraw-Hill, New York. Howell, R.A. 1970. Plan to integrate your acquisitions. Harvard Business Review(November-December) 66-76. Hsieh, D.A., C.F. Manski, D. McFadden. 1985. Estimation of response probabilities from augmented retrospective observations. Journal of the American Statistical Association 80 651-662. Huberman, G. 2001. Familiarity breeds investment. The Review of Financial Studies 14(3) 659-680. Hymer, S. 1972. The multinational corporation and the law of uneven development. J.N. Bhagwati, ed. Economics and World Order. Collier-Macmillan, London, 113-140. Hymer, S. 1976. The international operations of national firms: A study of direct investment. MIT Press, Cambridge: MA. Jemison, D.B., S.B. Sitkin. 1986a. Acquisitions: The process can be a problem. Harvard Business Review 64(2) 106116. Jemison, D.B., S.B. Sitkin. 1986b. Corporate acquisitions: a process perspective. Academy of Management Review 11(145-163). Johnson, C.H., ed. 1985. Integrating acquired companies: management accounting and reporting issues. John Wiley & Sons. Kalnins, A., A. Swaminathan, W. Mitchell. 2006. Turnover events, vicarious information and the reduced likelihood of outlet-level exit among small multi-unit organizations. Working Paper. Kang, J.K., R. Stulz. 1997. Why is there a home bias: An analysis of foreign portfolio equity ownership in Japan. Journal of Financial Economics 46 3-28. Karim, S., W. Mitchell. 2000. Reconfiguring business resources following acquisitions in the US medical sector. Strategic Management Journal 21(Special issue on the evolution of business capabilities) 1061-1081. Kierulff, H.E. 1981. Finding the best acquisition candidates. Harvard Business Review(January/February) 66-68. King, D., D.R. Dalton, C.M. Daily, J.G. Covin. 2004. Meta-analyses of post-acquisition performance: indications of unidentified moderators. Strategic Management Journal 25(2) 187-200. King, G., L. Zeng. 2001. Logistic regression in rare events data. Political Analysis 9(2) 137-163. Kitching, J. 1967. Why do mergers miscarry? Harvard Business Review 45 84-101. Kitching, J. 1974. Winning and losing with European acquisitions. Harvard Business Review(March/April) 124-136. Kogut, B., H. Singh. 1988. The Effect of National Culture on the Choice of Entry Mode. Journal of International Business Studies 23 411-432. Krugman, P. 1998. Space: the final frontier. Journal of Economic Perspectives 12(2) 161-174. Kumar, P. 1977. Corporate growth through acquisitions. Managerial Planning 27 1-6. Kusewitt, J.B. 1985. An exploratory study of strategic acquisition factors relating to performance. Strategic Management Journal 6(2) 151-169. Lajoux, A.R. 1998. The art of M&A integration. McGraw-Hill. Lakshmanan, T.R. 1989. Infrastructure and economic transformation. A.E. Andersson, D.F. Batten, P. Nijkamp, eds. Advances in Spatial Theory and Dynamics. North Holland, Amsterdam. Leigh, R., D.J. North. 1978. Acquisitions in British industries. F.E.I. Hamilton, ed. Contemporary industrialization. Longman, London, 158-181. Lenz, A.J., J. Lafrance. 1996. Meeting the challenge: US industry faces the 21st century. U.S. Department of Commerce, Office of Technology Policy. Manski, C., S.R. Lerman. 1977a. The estimation of choice probabilities from choiced based samples. Econometrica 45(8) 1977-1988. Manski, C.F., S. Lerman. 1977b. The estimation of choice probabilities from choiced based samples. Econometrica 45(8) 1977-1988. Manski, C.F., D. McFadden. 1981. Alternative estimators and sample designs for discrete choice analysis. C.F. Manski, D. McFadden, eds. Structural analysis of discrete data with econometric applications. MIT Press, Cambridge, MA. Marshall, A. 1920. Principles of economics. MacMillan, London.
35
McFadden, D. 1974. Conditional logit analysis of qualitative choice behavior. P. Zarembka, ed. Frontiers in Econometrics. Academic Press, New York. Morosini, P., S. Shane, H. Singh. 1998. National cultural distance and cross border acquisition performance. Journal of International Business Studies 29(1) 137-158. Nahavandi, A., A.R. Malekzadeh. 1988. Acculturation in mergers and acquisitions. Academy of Management Review 13 79-90. Newbould, G.D., S.J. Stray, K.W. Wilson. 1976. Shareholders' interests and acquisition activity. Accounting and Business Research 201-213. Norman, G., L. Pepall. 2000. Spatial competition and location with mergers and product licensing. Urban Studies 37(3) 451-470. O'Reilly, R. 2005. Industry Surveys: Chemicals. Standard & Poor's, New York. Olie, R. 1994. Shades of culture and institutions in international mergers. Organization Studies 15 381-405. Pablo, A.L. 1994. Determinants of Acquisition Integration Level - a Decision-Making Perspective. Academy of Management Journal 37(4) 803-836. Paine, F.T., D.J. Power. 1984. Merger strategy: an examination of Drucker's five rules for successful acquisitions. Strategic Management Journal 5 99-110. Penrose, E. 1959. The Theory of the Growth of the Firm. John Wiley & Sons, Inc., New York. Peters, T. 1997. The Circle of Innovation: You Can't Shrink Your Way to Greatness. Alfred A. Knopf, Inc., NY. Pritchett, P. 1985. After the merger: managing the shock waves. Dow Jones-Irwin, Homewood: IL. Rodriguez-Pose, A., H.M. Zademach. 2003. Rising metropoli: The geography of mergers and acquisitions in Germany. Urban Studies 40(10) 1895-1923. Rose, P.S. 1999. Diversification in Interstate Banking: The Search for Related Performance Profiles Between Acquiring and Acquired Banking Firms Expanding Across State Lines. Managerial Finance 25 66-85. Rosenthal, S.S., W.C. Strange. 2003. Geography, industrial organization, and agglomeration. Center for Policy Research Working Paper 56. Sales, A.L., P.H. Mirvis. 1984. When cultures collide: issues in acquisitions. J. Kimberly, R. Quinn, eds. New futures: the challenge of managing corporate transitions. Dow Jones-Irwin, Homewood, IL. Salter, M.S., W.A. Weinhold. 1981. Choosing compatible acquisitions. Harvard Business Review(January/February) 117-126. Schweiger, D.M., A.S. DeNisi. 1991. Communication with employees following a merger: a longitudinal field experiment. Academy of Management Journal 34(1) 100-135. Shrivastava, P. 1986. Post-merger integration. Journal of Business Strategy 7 65-76. Simmel, G. 1950. The Sociology of Georg Simmel. Free Press, New York. Simon, H.A. 1960. The new science of management decision. Harper and Row, New York. Sleuwaegen, L. 1998. Cross border mergers and EC competition policy. World Economy 21(8) 1077-1096. Sorenson, O., J.A.C. Baum. 2003. Geography and strategy: The strategic management of space and place. J.A.C. Baum, O. Sorenson, eds. Geography and Strategy. JAI Oxford. Sorenson, O., T. Stuart. 2001. Syndication networks and the spatial distribution of venture capital financing. American Journal of Sociology 106 1546-1588. Stuart, T., O. Sorenson. 2003. The geography of opportunity: Spatial heterogeneity in founding rates and the performance of biotechnology firms. Research Policy 32 229-253. Taylor, M.J. 1975. Organizational growth, spatial interaction and location decision making. Regional Studies 9(313323). Thorngren, B. 1970. How do contact systems affect regional development? Environment and Planning A 2 404-427. Tihanyi, L., R.A. Johnson, R.E. Hoskisson, M.A. Hitt. 2003. Institutional ownership differences and international diversification: The effects of boards of directors and technological opportunity. Academy of Management Journal 46(2) 195-211. Tornquist, G. 1970. Contact systems and regional development. Lund studies in Geography series B 35. Trautwein, F. 1990. Merger motives and merger prescriptions. Strategic Management Journal 11 283-295. Tyler, B., H.K. Steensma. 1998. The effects of executives experiences and perceptions on their assessment of potential technological alliances. Strategic Management Journal 19(10) 939-965. Vernon, R. 1979. The product cycle hypothesis in the new international environment. Oxford Journal of Economics and Statistics 41 255-267. Wallace, F.D. 1966. Some principles of acquisition. W.W. Alberts, J.E. Segall, eds. The corporate merger. University of Chicago Press, Chicago, 165-182. Walsh, J.P. 1988. Top management turnover following acquisitions. Strategic Management Journal 9 173-183.
36
Walter, G.A. 1985. Culture collisions in mergers and acquisitions. P.J. Frost, L.F. Moore, M.R. Louis, C.C. Lundberg, J. Martin, eds. Organizational Culture. Sage, Beverly Hills. Watts, H.D. 1980. The large industrial enterprise. Croom Helm, London. Weston, J.F., M.L. Mitchell, J.H. Mulherin. 2004. Takeovers, restructuring and corporate governance. Pearson Prentice Hall, Upper Saddle River, N.J. Wooldridge, J.M. 2002. Econometric Analysis of Cross Section and Panel Data. The MIT Press, Cambridge, MA. Xie, Y., C.F. Manski. 1989. The logit model and response-based samples. Sociological Methods and Research 17 283-302. Yunker, J.A. 1983. Integrating Acquisitions. Praeger. Zollo, M., H. Singh. 2004. Deliberate learning in corporate acquisitions: post-acquisition strategies and integration capability in US bank mergers. Strategic Management Journal 25(13) 1233-1256.
37
DIMENSIONS Product (relatedness)
Spatial (distance)
ACQUISITION BENEFITS (OBJECTIVES) & COSTS Operational synergy Competitive position Quick access to resources (technology) Benefits, Objectives Economies of scale & scope Proximity to markets Proximity to inputs (raw material, intermediate) Cost of searching for targets Cost of integrating Costs targets (this study)
Figure 1: Theoretical Framework
38
Acquisition performance
Figure 2: Location of US Chemical Acquiring Firms Founded After 1979
Figure 3: Distance X Prior Acquisition Experience Interaction effect
39
Figure 4: Distance X Acquisition Experience Interaction t-values
Figure 5: Distance X Acquirer Age Interaction Effect
40
Figure 6: Selection: Distance X Acquirer Age Interaction t-values
41
Table 1. Number of Firms and Acquisitions across Industry Subgroups, 1980-2003 INDUSTRY Basic chemicals Resin and synthetic rubber Agricultural chemicals Pharmaceutical and medicine Paints, coatings, adhesives Soaps and cleaning compounds Printing, explosives, photographic Total
NAIC 3251 3252 3253 3254 3255 3256 3259
Number of firms founded after 1979 106 52 34 480 25 48 22 767
Number of acquisitions by chemical firms founded after 1979 412 113 107 1180 56 152 50 2,070
Number of acquisitions by all chemical mfg firms 1,398 416 318 2,355 333 558 389 5,767
Table 2. Primary Industries of Targets (for 767 acquirers founded 1980 or later) INDUSTRY Chemical manufacturing Scientific R&D services Non durable goods wholesalers Durable goods wholesalers Computer and electronic manufacturing Medical equipment manufacturing Ambulatory healthcare services Plastics and rubber manufacturing Machinery manufacturing Administrative and support services Others (spanning 80 industries) Total
NAIC 325 541 424 423 334 339 621 326 333 561
42
NUMBER OF TARGETS 1,379 170 111 73 66 55 34 19 23 12 128 2,070
PERCENTAGE OF TARGETS 66.52 8.2 5.35 3.52 3.18 2.65 1.64 0.92 1.11 0.58 6.4 100
Table 3. Acquiring Firm Level Descriptive Statistics 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Acquirer age Prior acquisition exp. Prior incomplete acq. Acquirer size Diversification Acquirer is parent Acquirer is subsidiary Basic chemical Synthetic fiber Agriculture Paints and adhesives Cleaning compounds Inks and explosives Pharmaceuticals Acquirer centrality Acquirer is urban Acquirer is public Number of acquirers Mean Std. dev. Min Max
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
0.15 0.11 -0.07 -0.05 0.08 0.02 0.00 -0.03 -0.04 -0.04 -0.08 -0.01 0.09 0.00 -0.02 0.07 767 6.31 4.46 1 24
0.61 0.02 0.07 0.00 0.00 0.03 -0.02 0.06 0.01 0.00 -0.01 -0.04 0.04 -0.04 0.09 767 0.43 1.63 0 14
-0.03 0.03 -0.01 -0.04 0.04 0.03 0.04 0.02 0.01 0.01 -0.08 -0.04 -0.05 0.11 767 0.07 0.39 0 5
-0.01 -0.02 0.35 0.16 0.08 0.04 0.05 -0.01 0.04 -0.20 -0.02 0.06 -0.36 767 5.68 2.06 -2.30 11.9
0.10 -0.02 0.34 -0.06 -0.01 -0.07 -0.05 0.08 -0.18 -0.06 -0.01 0.01 767 1.82 1.18 1 9
-0.01 -0.03 0.04 0.12 0.01 -0.03 -0.02 -0.03 0.00 -0.04 0.06 767 0.17 0.38 0 1
0.12 0.08 0.11 0.08 -0.04 -0.05 -0.16 -0.05 0.06 -0.55 767 0.16 0.37 0 1
-0.11 -0.09 -0.07 -0.10 -0.07 -0.52 -0.14 0.05 -0.17 767 0.14 0.35 0 1
-0.06 -0.05 -0.07 -0.05 -0.35 -0.07 -0.02 -0.11 767 0.07 0.25 0 1
-0.04 -0.06 -0.04 -0.28 -0.09 -0.02 -0.09 767 0.04 0.21 0 1
-0.05 -0.03 -0.24 -0.08 0.01 -0.08 767 0.03 0.18 0 1
-0.04 -0.33 -0.04 0.00 0.03 767 0.06 0.24 0 1
-0.22 -0.07 -0.02 0.04 767 0.03 0.17 0 1
0.25 -0.01 0.22 767 0.63 0.48 0 1
-0.20 0.04 767 18.3 26.7 0 135
-0.06 767 0.57 0.50 0 1
43
Table 4. Acquisition Level Descriptive Statistics 1 2 3 4 5 6 7 8 9 10 11 12
Acquisition distance Acquisition is related Target is larger Target is a distributor Target is urban Target is public Year of acquisition Target in similar city Prior acquisition in state Prior acquisition distance Prior acquisition in industry Acquisition is cancelled Number of acquisitions Mean Std. dev. Min Max
1
2
3
4
5
6
7
8
9
10
11
-0.33 -0.14 -0.04 0.08 -0.49 0.05 -0.39 0.25 0.09 0.18 -0.39 2070 4.51 2.88 0 8.37
0.14 -0.13 -0.04 0.35 0.06 0.18 -0.18 -0.18 -0.26 0.19 2070 0.59 0.49 0 1
-0.02 -0.04 0.04 0.08 0.06 -0.12 -0.05 -0.13 0.14 2070 0.43 0.50 0 1
-0.03 0.07 -0.04 0.00 0.15 0.18 0.16 0.04 2070 0.25 0.43 0 1
-0.12 0.04 -0.03 0.04 -0.03 0.06 -0.06 2070 0.62 0.49 0 1
-0.06 0.27 -0.10 0.01 -0.10 0.40 2070 0.27 0.45 0 1
0.00 -0.02 -0.15 -0.01 0.03 2070 16.7 4.82 1 24
-0.12 -0.09 -0.12 0.18 2070 0.61 0.49 0 1
0.30 0.40 -0.12 2070 0.09 0.29 0 1
0.32 -0.03 2070 4.45 3.97 0 11.8
-0.13 2070 0.11 0.31 0 1
44
Table 5. Target Selection: Comparing Alternative Estimators
Variable
Acquisition distance Acquisition is related Distance x Acquisition is related Prior acquisition experience Acquirer age Acquirer size Acquirer geographic centrality Target is larger Target is a marketing firm Target is urban Target is public Year of acquisition Constant
1. Logit regression
2. Conditional logit
3. Random effects logit
4. Logit corrected for non independence of observations
-0.27 ** (0.021) 1.10 ** (0.16) -0.22 ** (0.026) .0003 (.0015) -.0023 (.0056) .0019 (0.015) -.004 ** (.0011) 0.14 * (0.062) -0.064 (0.063) 0.076 (0.054) -0.55 ** (0.068) 0.014 * (.0057) -0.17 (0.19) 12,420 1437.80+
-0.29 ** (0.022) 1.10 ** (0.18) -0.23 ** (0.027) -.0005 (.0029) -0.017 (0.067) 0.042 (0.063) -.003 + (.0019) 0.16 * (0.066) -0.088 (0.073) 0.089 (0.056) -0.57 ** (0.071) 0.018 (0.066)
-0.27 ** (0.021) 1.10 ** (0.16) -0.22 ** (0.026) .00031 (.001) -.0023 (.0056) .0019 (0.015) -.0042 ** (.0011) 0.14 * (0.062) -0.064 (0.063) 0.076 (0.054) -0.55 ** (0.068) 0.014 * (.0057) -0.17 (0.19) 12,420 1273.14 ++
-0.27 ** (0.032) 1.10 ** (0.18) -0.22 ** (0.032) .00031 (.00054) -.0023 (.0031) .0019 (0.011) -.0042 ** (.0006) 0.14 * (0.067) -0.064 (0.063) 0.076 (0.051) -0.55 ** (0.08) 0.014 ** (.004) -0.17 (0.2) 12,420 820.32 ++
5. Weighted exogenous sampling maximum likelihood (WESML)^ -0.28 ** (0.022) 0.99 ** (0.164) -0.22 ** (0.027) -0.001 (0.002) 0.001 (0.006) -0.012 (0.016) -0.004 ** (0.001) 0.15 * (0.069) 0.007 (0.068) -0.022 (0.058) -0.17 * (0.067) 0.018 ** (0.006) -7.405 ** (0.205) 12420 806.69 ++
Observations # 12,420 Chi-square (d.f.=12) 1507.24+ + LR Chi-square; ++Wald Chi-square ** p
