Mergers that Matter: The Value Impact of Economic Links

Transcription

1 Mergers that Matter: The Value Impact of Economic Links Jarrad Harford Foster School of Business University of Washington Robert Schonlau Marriott School of Management Brigham Young University Jared Stanfield Australian School of Business University of New South Wales July 9, 2013 The economic links between firms created by customer, supplier and rival relationships are critical determinants of those firms values. We show that these links predict which firms are more likely to be involved in acquisitions, which pairs of firms are more likely to merge, which targets will attract multiple bidders, and which mergers will have the greatest impact, both on value and in motivating follow on activity. In particular, mergers that affect more economic links are the mergers that matter they have the greatest value impact and cause rivals to react by initiating mergers and changing capital expenditures, R&D and advertising expenses. Keywords: Mergers & Acquisitions, Product Market, Horizontal, Vertical, Supply Chain, Rivals JEL Codes: G34, G30, L22 We thank Kenneth Ahern, Nihat Aktas, Si Li, Todd Mitton, and seminar participants at California State University Fullerton, Exeter University, the University of Utah, the University of New South Wales, the 2013 City University of Hong Kong International Conference on Corporate Finance and Financial Markets, and the Second European Center for Corporate Control Studies Workshop for helpful suggestions.

2 1. Introduction Firms are economically linked to supplier firms for inputs, to customers for sales, and to rivals through competition. Significant events at customer, supplier, and rival firms generate substantial wealth effects at other firms with economic links to these firms (Cai, Song, and Walkling, 2011; Cohen and Frazzini, 2008; Hertzel, Li, Officer, and Rodgers, 2008; Johnson, Kang, Masulis, and Yi, 2011; Song and Walkling, 2000). Acquisitions are major events that affect not just the merger participants, but also firms with links to the participants (Eckbo, 1983; Stillman, 1983; Fee and Thomas, 2004; Shahrur, 2005; Cen, Dasgupta and Sen, 2011). Despite the economic importance of merger activity, and decades of research, we still have only a partial understanding of what causes mergers and what determines their wealth effects. 1 A firm s value is determined by the economic rents it captures through its interactions with customers, suppliers, and rivals, suggesting that we can further our understanding of mergers by directly relating these economic links to acquisition activity. In this paper we focus on the interaction of mergers and firms' economic links and address three questions: How do economic links affect the likelihood of a firm being involved in a merger? How do economic links affect the value impact of the acquisition on merger participants and rival firms? And, how do economic links affect subsequent acquisition and investment activity after a merger within an industry? Acquisitions by their very nature redefine the boundaries of the firm, typically along complementary or overlapping lines of business. For example, merging companies seeking to reduce costs, deal with overcapacity, create synergies, collude, or secure control of input or distribution channels all share the following characteristic: the firms involved have either horizontally or vertically related lines of business. In this paper we explicitly trace these related lines of business by identifying each firm s customer, supplier and rival relationships and hypothesize that these relationships are 1 The research on mergers and acquisitions is vast. Excellent reviews and summaries of the literature include Jensen and Ruback (1983), Andrade, Mitchell and Stafford (2001) and Betton, Eckbo, and Thorburn (2008). 1

3 related to a firm s likelihood of being involved in a merger, the value impact the merger has on related firms, and whether the merger is likely to be followed by other acquisitions within the industry. Direct economic links between firms increase the probability of one of the linked firms acquiring the other firm for several reasons: First, as mentioned above, the motives commonly given for mergers involve related lines of business. Identification of direct economic ties reveals these lines of business and hence likely merger partners. Additionally, there are several specific motivations provided for vertical integration suggested by prior research. Spengler (1950) and Perry (1978b) propose that vertical integration can eliminate existing inefficiencies due to either supplier or customer monopolies. Vernon and Graham (1971), Schmalensee (1973), and Warren Boulton (1974) propose that vertical integration can reduce input substitution. Coase (1937), Williamson (1979), and Klein, Crawford, and Alchian (1978) suggest that costs of contracting with others can motivate vertical mergers. And lastly, material customer supplier relationships are accompanied by significant relationship specific investments by each of the involved firms that can be potentially threatened by outside takeovers of one of the linked firms (Cen, Dasgupta and Sen (2011), Johnson, Karpoff and Yi (2011)). An acquisition along a customersupplier link by one of the participants is one way of protecting and internalizing these valuable firmspecific investments against outside takeovers. A firm s ability to compete is directly affected by its interaction with its customers, suppliers and rivals. Given that acquisitions by their nature shock the existing competitive customer supplier rival environment, mergers that involve firms with more important economic links to other firms within an industry have the potential to alter the existing competitive environment the most. Hence, the overall value impact of an acquisition, as captured not only by announcement returns but also by other related firms subsequent reactions to the acquisition, is related to the number of important economic links affected by the merger. For these reasons, we expect acquisitions that involve higher numbers of 2

4 important economic links within an industry to be associated not only with more volatile announcement returns but also with larger changes in related firms subsequent merger activity, capital expenditures, R&D spending, and advertising as these firms react to the new customer supplier rival environment. We find broad support for the predictions discussed above. Specifically, we find that direct important economic links between two firms strongly increase the likelihood of a merger between the two firms. Using a large sample of firms across 19 years we consider thousands of actual and potential acquisitions and find that firms tend to buy their own suppliers and customers. This specific evidence of vertical integration, while expected, is largely missing from the empirical literature. 2 We also find that firms that share a common customer or supplier firm are more likely to merge, independent of whether the firms compete in the same industry. In robustness tests, we show that these results are not explained by other types of connections between the firms such as social connections between the directors or geographic proximity of the firms. We also find that these acquisitions are generally valueincreasing, providing further evidence that they are driven by economic links rather than by familiarity or other related concepts. Although direct economic links increase the likelihood of a merger between the linked firms, our results also show that the existence of those same links is associated with an overall decrease in merger likelihood given that non linked firms are less likely to merge with firms that have strong economic ties to other firms. 3 This is consistent with the need to protect relationship specific investment. 2 There is a large literature on vertical integration in specific industries in the U.S., but these studies are either highly focused case studies (e.g., Joskow, 1987; Masten, 1984; Klein, 1988; Lerner and Merges, 1998), or focus on internal versus external trade (Antras, 2005). These studies tend to construct their samples entirely of deals that fit their definition of vertical integration rather than consider vertical integration outcomes as one outcome within a larger set of potential deals. Ahern and Harford (2013) do provide evidence of vertical integration using industry level customer supplier data. 3 As an example, consider the acquisition of CAPCO by Eaton in 1996, which is in our sample. Prior to the merger CAPCO s sales to Eaton accounted for 38 percent of CAPCO s revenues, leading analysts to note that the strength of their connection would deter other potential industry rival buyers who would be unlikely to bid for a 3

5 Looking at the value implications, we find that the acquirer s and target s economic links help explain the market reaction to the acquisition. Specifically, higher numbers of important economic links involved in the merger are associated with higher acquirer announcement returns. 4 Finally, as further evidence of the impact of economic links on merger activity, we find that the rate of follow on merger and investment activity is significantly increasing in the number of important economic links involved in mergers in a given period. Mergers that involve more economic links are more likely to generate a reaction from other parties in the network even after controlling for size. Our study contributes generally to the literature on mergers and acquisitions, a small portion of which we cited earlier. As part of our analysis, we directly test how the existence of economic links relates to the likelihood of participating in a merger. As far as we know, we are the first to use a large sample of potential acquisitions to test how vertical, horizontal, indirect, and non shared economic links affect the likelihood of firms engaging in acquisition activity. Our paper adds to the literature that seeks to understand how economic links in the supply chain affect the outcome of corporate events, and hence is directly related to Fee and Thomas (2004), Shahrur (2005), Cen, Dasgupta, and Sen (2011), Johnson, Karpoff and Yi (2011), and Harford and Ahern (2013). Fee and Thomas (2004) and Shahrur (2005) use the effects of horizontal mergers on linked firms to conclude that horizontal mergers are driven by efficiencies rather than market power or collusion. Cen, Dasgupta, and Sen (2011), and Johnson, Karpoff and Yi (2011) both show that firms make relationship specific and costly investments in significant customer firms that can be disrupted via takeovers. In a recent paper, Ahern and Harford company that could lose 38 percent of its business at the stroke of a pen and to comment that The fact that Eaton is their largest customer would keep away any potential white knights. (Kevin Drawbaugh, Eaton Sues CAPCO to Drop Poison Pill in Reuters News14 March 1996, and Eaton Must Bump Bid for CAPCO in Mergers & Acquisitions Report 25 March, 1996). In press releases, Eaton and CAPCO noted that Eaton would gain complete control over the elements of its product line and CAPCO products would gain access to Eaton s global customers. 4 As explained in more detail in Section 3, given data availability the economic links used throughout this paper require that the customer firm account for at least 10% of the sales of the supplier firm s segment. Hence, throughout the paper all references to important or large economic links are based on monetarily significant economic ties between firms of 10% or more of revenues. 4

6 (2013) use industry level data from the Bureau of Economic Analysis to gain insight into the degree to which merger activity follows supply chain connections. They study how those connections allow aggregate merger waves to propagate through the economy after shocks affect more central industries. In an appendix they show that there are small, mostly positive correlations between acquirer announcement returns and acquiring along input output links. Our paper builds on the recent work at the industry level by Ahern and Harford (2013) but is different in its focus and provides several important contributions related to our understanding of how economic links relate to merger activity. Whereas Ahern and Harford (2013) use industry level economic data, our analysis is done at the firm level using actual firm level information on known economic relationships between specific firms rather than projecting industry level supply chain relationships on all firms within the respective industries. While we provide some direct evidence of the incidence and impact of actual customers and suppliers merging, our analysis also provides new insight into the disruptive effect of firm to firm mergers between highly connected firms in the customer supplier network. While related in spirit to Ahern and Harford s study, our focus on identifying the impact of different types of mergers and distinguishing between mergers that have small impact and those that have large impact is different. As detailed above, our paper provides direct evidence on both the positive and negative effects of major trade relationships on merger incidence. Additionally, our study shows that understanding the positional importance of the merging parties in the overall supplier customer network helps identify which mergers matter those that will be more disruptive, as evidenced by greater value impact on other parties and increased responsive actions within the industry. The paper proceeds as follows. In section 2 we review the literature further and develop our hypothesis. Section 3 describes the data and empirical methodology. We present the results of our empirical tests in section 4 and conclude in section 5. 5

7 2. Hypothesis Development As we state in the introduction, the big picture question is what causes mergers and what determines their wealth effect? The more specific research question we ask here is how rival suppliercustomer economic links affect the likelihood and implications of mergers. To address this question we build on prior work that makes use of customer supplier connections described above. Using a similar network approach but at the industry level, Ahern and Harford (2013) show that industry supply chain connections and economic centrality can help explain industry and aggregate merger activity. Our focus is on which firms merge, with whom, and critically, on the value and follow on investment implications of individual mergers. Other recent work by Cen, Dasgupta, and Sen (2011) finds that firms with large, nongovernmental customers benefit from anti takeover laws. They hypothesize that having these large customers increases the cost of an acquisition because acquisitions disrupt long term relationships and firm specific investments that are difficult to contract on. They find that the ROA and abnormal returns of firms with large customers increase with the passage of business combination laws. Hertzel, Li, Officer and Rodgers (2008) show that economic links can transmit the effects of financial distress. Collectively, these papers provide evidence on the potential for economic connections between firms to further our understanding of the impact of corporate events. Here, we propose to make use of the connections as well as a firm s position among the connections to develop predictions about merger activity. To develop our hypothesis we start by characterizing the competitive environment in which firms operate as a network of customer, supplier, and rival economic relationships. Consistent with the papers cited above, each firm s value is determined and affected by its economic links to its specific customers, suppliers, and rivals. Building on this insight, under the Economic Links hypothesis we argue that economic links have a first order effect on which mergers happen and on their impact on the 6

8 merging parties as well as on related firms. The hypothesis implies several specific predictions. In motivating these empirical predictions below, we consider not only how direct economic links between companies affect the likelihood of merging but also how the overall number of important links merging parties have affects subsequent merger activity and investment in their industry. Before discussing the specific predictions, we note that there are many informal ways that firms can be linked (through social networks among boards or direct board interlocks, for example). We argue that even if these links exist, they are driven by the underlying economic links we model. For example, it is the supply chain connections that typically lead to the social connections and board interlocks. Further, as economists, our natural starting point is that economic relations are primary and social connections are secondary. Nonetheless, in robustness tests we control for board connections and geographic proximity. Firms tend to acquire other firms in related lines of business. Direct economic links identify where two firms business models overlap and hence indicate potential future changes in the boundaries of the linked firms. Making an acquisition along a customer supplier link is one way to protect relationship specific investments, better expand the investment, and/or remove redundant operations at the firms that were created to manage interactions between them. Direct economic links include customer supplier links while indirect links include sharing a customer or supplier firm or competing in the same industry. Mergers along economic links have reduced information asymmetry, and potentially increase market power. As summarized in the introduction, there is a large theoretical industrial organization literature motivating vertical mergers. For all these reasons, we predict that the existence of a significant customer supplier link, a shared customer or supplier, or a rival link between two firms is associated with higher likelihood of the given firms being acquired and/or making an acquisition along the link. 7

9 A firm with many large economic links (such as many customers) may be attractive as a target because the acquirer could gain access to those important customers. Alternatively, as argued by Cen, Dasgupta and Sen (2011) and Johnson, Karpoff and Yi (2011), these links often represent substantial relationship specific investments that can be disrupted in a merger. Thus, it is theoretically ambiguous whether more links will make a firm more or less attractive as a target. If the value captured in the links is transferable to the acquirer, the target will be more attractive, but if not, the acquisition would actually destroy value. We are unaware of a way to ex ante determine which links have transferable value and which do not. We start by testing the raw effect of economic links on the probability of being a target or acquirer. This will uncover whether, on average, the value in links is transferable or not. We then examine whether, conditional on a bid, a target with more links attracts more bidders. Our motivation for the second test is to use the existence of the first bid to identify a target where at least one bidder believes the links are transferable. Thus, the second test shows whether transferable links are viewed as valuable. Our second and more novel set of predictions is that mergers involving more links have a bigger potential value impact, both positive and negative. That is, because the merger affects more of the economic network, there is a greater chance for value creation in well motivated and executed mergers and a better chance at value destruction otherwise. This prediction extends beyond the merging parties themselves; rivals of the merging parties will be more affected by more central mergers (those with more links) and so their stock price reaction to the merger will be greater in absolute value as well. Again, the merger could affect them positively, as in market consolidation, or negatively if their competitive position is threatened. Finally, there is a great deal of interest in understanding the clustering of merger activity. Mergers involving many economic links can impact the competitive environment for rivals, customers and suppliers. The greater the impact, the greater will be the need by other firms to react by 8

10 undertaking mergers. Thus, we predict that mergers involving more economic links will generate more follow on merger activity by related firms as well as other evidence of responses at related firms such as changes in capital expenditures, advertising, and R&D. 3. Data To address our research question, we require a database of customer supplier relationships. To create this database we use information from the Compustat segments file from In accordance with accounting regulations, public firms must report financial information about segments that are responsible for 10% or more of the firm s overall sales, profits, or losses. Additionally public firms are required to disclose the existence of important segment customers that provide at least 10% of overall revenue. This is an obvious drawback to our data because we are not able to see all connections (connections < 10% of sales of the segment). As such, we can only measure large connections that are economically important. Given our analysis is using customer/supplier connections as a proxy for relationship specific investment, we would naturally need to enforce a cut off, as a 0.5% customer will generate less focus and relationship specific investment than a 10% customer. The 10% cutoff effectively says that all relationships below 10% are economically unimportant. If this is not the case (i.e. if the cutoff should be lower) we are biased against finding results. Nonetheless, in the multivariate tests we include industry level average customer supplier relationships from the BEA inputoutput matrix to control for these unobserved connections. In most cases firms not only disclose the existence but also the identity of the customer and the revenue dollars attributable to the customer. The customer names are listed in Compustat and refer often in abbreviated or truncated form to public, private, as well as government entities. As noted in Fee and Thomas (2004) the same customer firm is often referred to using slightly different abbreviated references by the same supplier firm across different years. Following Fee and Thomas (2004), Hertzel, 9

11 Li, Officer, and Rodgers (2008), and Cohen and Frazzini (2008) we utilize a code based matching algorithm to identify the customer firms and eliminate references to non businesses. 5 Using the text based matching code we first identify the most likely customer firm matches from lists of historical CRSP and Compustat firm names where the potential matching firms total revenues in that year are at least as large as the dollars reported as flowing from the customer in the Compustat segments file. We then do extensive manual checks augmented by information from Lexis Nexis, Hoovers, SEC filings, and the industry the segment competes in to help determine the identity of the customer firm. Our approach is very similar to the one described in Fee and Thomas (2004). We are conservative in our matching and identify each customer firm as (1) a firm within the CRSP/Compustat universe, (2) a private firm, or (3) as unidentifiable. We include all identifiable firms in the analysis. We then identify each acquirer and target firm from SDC within this set of Compustat and Compustatcustomer firms. Where available, cusips are used to match the SDC firms with those from Compustat. Where not available, the same type of code based and manual matching approach is used to identify the SDC firms as described above for the Compustat customer firms. The set of acquirers and target firms in the sample include all announced and completed US acquisitions in the Thomson Financials SDC M&A Database with announcement dates between January 1, 1991 and December 31, 2009 and coded as a merger, an acquisition of majority interest, or an acquisition of assets. For these deals to remain in the sample we also require that (1) the transaction value be no less than $20 million in 2009 dollars, (2) neither the target nor the acquirer be in the financial sector (SIC ), or classified as a Utility or Other in the Fama French 48 industry classification, (3) Compustat and CRSP have the necessary data for our tests. In some tests, we only 5 We thank Kim Rodgers Cornaggio for providing the text matching code used Hertzel, Li, Officer, and Rodgers (2008). Our matching algorithm built on their approach as described in their paper and was designed to also deal with truncated names and acronyms. 10

12 need data on one party, so the deal can remain in the sample even if CRSP or Compustat data on the other party is missing. Thus the set of firms we include each year in our analysis includes firms we can identify from three sources: Compustat firms, firms listed as customers to Compustat firms, and acquirers and target firms from SDC. Table 1 describes our sample and reports for each year the number of Compustat firms with at least one identifiable customer, the number of identified customers, the number of unique customers, and the number of SDC deals in the sample. The deals listed in this table are only the subset of deals we identified in the network that also had the necessary CRSP and Compustat acquirer variables described in Appendix A. Within the set of Compustat firms with at least one identified customer, the average firm has 2.06 large customers (median of 2) and the 5th percentile (95th percentile) firm has 1 (4). Measures of Economic links Once we have constructed the customer supplier network, we identify which firms are directly connected, as well as which ones are indirectly connected though a common customer, a common supplier, or as rivals. In addition, we also identify which firms have major economic links and how many such connections they have. In network terminology, our measures of a firm s connectedness are measures of network centrality. We first simply count the number of economic links a firm has with other firms. Indegree counts the number of times the firm is mentioned as a large customer while outdegree counts the number of connections a firm lists (how many large customers it supplies to). To facilitate discussion, throughout the paper we will refer to the number of times a firm is listed as a customer (indegree) as Number of Suppliers. Similarly we will refer to the number of customers a firm has (outdegree) as Number of Customers. By controlling for direct and indirect connections between 11

13 acquirer and target firms in addition to the network connections above, we are able to measure the amount of unshared customer supplier relationships the merging parties may have. These measures capture the number and direction of economic connections a firm has in the customer supplier network. Two firms can also be connected through a firm they both have direct connections with. Betweenness is a common network measure that counts the number of shortestdistance paths, known as geodesics (Wasserman & Faust, p.110 1), between firms on which another firm is located. Since the connections in our data have a direction, betweenness centrality requires a firm to be both a large customer as well as have a large customer. Firms with high betweenness are important in the sense that economic activity is more likely to flow through them as it moves through the network. In our case, the economic activity flows between suppliers and customers. 6 Hence, throughout our analysis the main variables of interest in all tests focus on each firm s economic links within the customer supplier network as measured by each firm s Number of Customers, Number of Suppliers, and Betweenness as well as indicator variables for acquisitions that involve a Common Supplier or a Common Customer. Figure 1 provides several example acquisitions that illustrate the differences in these measures. Figure 2 displays the number of deals by year in the sample and the proportion of those deals involving a public target. Our sample is consistent with previous merger studies over this time period (Bena and Li (2012)). Table 2 starts with summary statistics on our measures of economic links. The average Number of Suppliers and Number of Customers for acquirers are 0.45 and 0.27, respectively. Figure 3 displays the proportion of acquirers with a positive Number of Suppliers and/or Number of Customers. As can be seen, the proportion of acquirers with a large customer and/or supplier remains 6 Formally, betweenness is the sum of geodesics ( ) between firms i and k that firm j is on: network. Betweenness j g ik ( j) i,k g ik. This measure is then normalized by the maximum number of paths within a 12

14 fairly steady over time but increases somewhat in merger troughs. Betweenness (being named as a large customer and also naming a large customer), is rarer, with an average of For public targets, which tend to be smaller than acquirers, the average Number of Suppliers and Number of Customers are 0.08 and 0.10, indicating that targets are more likely to have large customers than to be named as a large customer. The proportion of public targets with a large customer and/or supplier is significantly less than public acquirers on a yearly basis. Betweenness is again rarer, at 0.07 for public targets. Looking across deals, we see that the frequency of a direct connection is small, except in the case of horizontal mergers 4. Empirical Results Our control variables are drawn from the existing literature on mergers and acquisitions and we discuss them in the context of each test. For convenience, they are all listed and defined in Appendix A. Because we can only observe connections where one firm purchases at least 10% of the sales of the other, we incorporate an industry pair level measure of connections as a control variable. Specifically, we use the Bureau of Economic Analysis (BEA) 1992 Benchmark Input Output tables. Every five years, the BEA measures how much each industry sells to each other industry, and creates a matrix of these industry level economic links. To capture cross sectional variation in economic links that fall below the 10% level for individual firms, we use the BEA s measure of the economic link between the two firms industries. See Ahern (2011) or Ahern and Harford (2013) for more details on the BEA s input output tables. We choose the 1992 benchmark year because it precedes most of our sample period. Industrylevel economic links are driven by production functions and are relatively stable over time (the correlation between the input output tables of successive benchmark years is typically close to 0.9). 7 7 Throughout the paper all variables that are created at the industry level rely on Fama French 48 industries obtained from Ken French s website, 13

15 Merger Likelihood In this section we investigate how a firm s economic links affect its likelihood of being involved in a merger. To do this we consider a large sample of actual as well as pseudo acquirer target merger pairings. We model these actual and pseudo deals as a function of variables that capture the respective firms economic links. In addition, we include control variables known from the literature to affect merger likelihood, although our results our robust to their exclusion. To create the sample we start with the SDC deals described in Section 3. For this part of the analysis we require customer supplier data over the previous two years, reducing the set of deals to those on or after January 1, This yields 3,937 deals in which we have the requisite information for acquirers (the acquirer sample) and 1,324 deals in which we have the requisite information for targets (the target sample). We then create a matched sample of acquirers to pseudo acquirers from the acquirer sample in order to perform tractable analysis on the likelihood of being an acquirer in an acquisition. We use a similar methodology for target firms. We employ a propensity score matching procedure (Rosenbaum and Rubin, 1983; and Heckman, Ichimura, and Todd, 1997; 1998) that matches a given acquirer with its 5 nearest (firm year) neighbors with replacement. We restrict potential matches to those firm years with propensity scores that have common support between merger participants and non merger participants, that have data in the same year as the actual deal, and that did not participate in a merger or acquisition in the same year or the two years prior to the actual deal. To generate a propensity score, we estimate yearly probit regressions with a binary dependent variable equal to 1 if the firm participates as an acquirer in a deal in a given year and zero if the firm does not. Following the extant literature [e.g., Comment and Schwert (1995), Harford (1999), Edmans, The one exception to this is the industry input/output control variables because the BEA does not provide the data in a format that is easily convertible to Fama French industries. 14

16 Goldstein and Jiang (2012)] we include acquirer sales growth, net working capital, leverage, market tobook, price to earnings, size, cash deviation, abnormal returns of the prior 12 months, mean industry stock return of the prior 12 months, standard deviation of stock returns in the prior 12 months, a regulatory indicator variable, Compustat sales based Herfindahl index, merger wave indicator variable, and economic shock variable as the control variables in these regressions. Using the predicted probabilities (propensity scores) from the estimated probit regressions, we match to each acquirer firm year observation, the corresponding 5 non acquirer firms that have the lowest absolute value difference between propensity scores. Thus, for example, the acquirer matched sample includes 6 observations for each deal with one observation being the actual acquirer target pair and the other 5 observations being the 5 nearest neighbor pseudo acquirers each matched with the actual target from the first observation. Using this approach allows us to analyze the effect of having direct or indirect economic links between the paired firms on the likelihood of a merger between the firms. In creating the matched samples, we allow matching with replacement, which means some firmyears appear in our matched sample several times. Our acquirer matched sample has 19,685 matched firm years, with 12,245 unique firm years. Our target matched sample has 6,620 matched firm years with 2,729 unique firm years. The mean absolute difference between propensity scores is quite small in both samples (<0.001 in both acquirer and target samples), indicating the firm years match well along these control variables. In untabulated results, our analysis is robust to matching using 5 firms with the highest return correlation to the actual merger participant as well as 5 randomly drawn firms with the same SIC code 8. We perform the acquirer and target likelihood analysis on the actual and matched pairs using a fixed effects (conditional) logit regression grouped at the deal level (McFadden, 1973; Kuhnen, 2009; 8 Firms are randomly matched at the 4 digit SIC code level. If less than 5 firms are available in the 4 digit SIC code, we match at the 3 digit SIC code level and so on. 15

17 Dyck, Morse, and Zingales, 2010). We model the likelihood of two specific firms merging as a function of direct, indirect, and non shared economic connections. In these specifications the measures of centrality capture the effects of having economic links that are not shared with the other merger party because we control separately for the existence of direct links between firms. Standard errors are clustered at the deal level and robust to clustering at the firm level. To further our understanding of merger incidence, and provide new evidence on the role of mergers within the supply chain, we look at specific (direct and indirect) connections. As shown in Table 3, we find clear evidence that mergers happen along the supply chain having a large customer or supplier dramatically increases the likelihood of being involved in a merger with your customer or supplier. Economically, we find that a firm is significantly more likely to acquire its customer than its matched peers, with an odds ratio of nearly 4. Similarly, a firm is significantly more likely than its matched peers to be acquired by its supplier. We find similarly large economic effects with upstream mergers as well, with a firm being significantly more likely than its matched peers to acquire its supplier. Similarly, a firm is significantly more likely than its matched peers to be acquired by its customer. Documenting vertical mergers is an intuitive and somewhat expected result. However, because an actual broad sample supplier customer network has not been used before in empirical merger research outside of horizontal merger studies discussed above, there is no large sample evidence documenting the actual likelihood of customers and suppliers merging, and the economically large increases in the likelihood of being an acquirer or a target in these transactions. We also find that mergers are more likely to occur when firms share a common customer or supplier, whether the actual merger is horizontal or cross industry. Economically, a firm is more likely to acquire, or be acquired by, another firm that it shares a significant customer or supplier with. The odds ratios associated with this increase are over 8 and 9, respectively. Again, this is an intuitive result; customers of the same supplier and suppliers of the same customer could gain increased bidding power 16

18 as well as reduce inefficiencies associated with customer supplier relationships. While somewhat expected, we cannot find any previous empirical evidence documenting that sharing a common customer or supplier (regardless of whether you're in the same industry) significantly increases the likelihood of merging. We find that rival firms (firm within the same industry) are significantly more likely to merge than their matched peers. Hoberg and Phillips (2010) report a related result in that firms with similar 10 K product market descriptions are more likely to merge. We control for Hoberg and Phillip s categorization of which firms are rivals in untabulated results. Their data are only available for public firms so we estimate the regressions on a subsample of deals with public acquirers as well as public targets. Our results are qualitatively unaffected by the sample reduction and by the inclusion of the Hoberg Phillips industry measure. In addition to the number of direct and indirect connections, we also look at the number of connections in general. As seen in Table 3, we find that betweenness, which captures firms that are both significant customers and suppliers, consistently predicts being involved in a merger as an acquirer. Coefficient estimates of acquirer and target betweenness in columns 2 and 4 are significantly associated with an increase in the likelihood a firm is an acquirer in a given deal, relative to similar, matched firms. This is an intuitively appealing result; firms at choke points (those with significant customers and suppliers) in the supply chain are more likely to be active in mergers relative to their matched peers 9. These effects are economically significant with a one standard deviation increase in firm betweenness associated with an odds ratio of being an acquirer [target] of exp(0.366*0.603) = 1.25 [1.23] 10. The odds ratio of being a target in a given deal for a one standard deviation increase in betweenness is Betweenness could be proxying for firms with multiple business segments. In untabulated results, we find that the statistical and economic magnitude of betweenness increases when we control for the number of business segments. 10 We calculate the odds ratio by exponentiating the product of the coefficient and the standard deviation. 17

19 Although a firm with both significant up and downstream economic links has a higher likelihood of being involved in a merger, our results show that firms in general that have a high number of large customers or suppliers are associated with an overall decrease in likelihood of merger with firms that do not share the links. Cen, Dasgupta and Sen (2011) and Johnson, Karpoff and Li (2011) argue that critical supply chain connections can be disrupted by a merger, hence decreasing the likelihood of a bid. Our results show that the number of connections a firm has as a large customer (its Number of Suppliers in the table) decreases its likelihood of being acquired relative to its matched peers; the effect is similar for suppliers more large customers decrease their likelihood of being acquired. A one standard deviation increase in a firm s Number of Suppliers (Number of Customers) is associated with an odds ratio of being a target of 0.72 (0.64). Further, having large customers decreases the chance a firm will acquire. The estimated coefficient on Number of Customers implies an odds ratio of being an acquirer of 0.91 for a standard deviation increase in the number of customers. 11 While two firms that are not linked, or have lots of one way linkages to other firms (which could be disrupted by a merger) are less likely to merge, specific firms that have important upstream and downstream linkages (high betweenness) are more likely to be involved in acquisitions. Taken together, these results demonstrate that although mergers may disrupt customer supplier connections, some highly linked firms are valuable enough to offset the risk of disrupting the connections and so attract acquisitions. Overall, the results clearly support the prediction of the Economic Links hypothesis that firm connections through the customer supplier network help to explain who merges with whom, but in complex ways. Lying along the economic paths that connect other firms increases the likelihood of acquiring and being acquired by linked firms, but having many important connections creates 11 Note that we are asking for a given acquirer, what is the likelihood of a particular firm to be targeted. Thus, the target s measures of customer and supplier links are sufficient for identifying variation in the total number of links involved in the potential deal because the number of acquirer links does not vary across potential targets for a given acquirer (and likewise for the acquirer specifications). 18

20 relationship specific investments that decrease the chance of a merger with firms that are not part of those economic relationships. Firms merge up and down the supply chain as well as with other firms connected to them indirectly through supply chain relationships. Merging Party Value Effects We next examine the importance of economic links through their effect on the value impact of mergers. Our hypothesis is that mergers involving more economic links will have greater value impact. In Table 4, we report regressions with the acquirer s announcement return as the dependent variable. The explanatory variables include measures of economic links, measures of direct and indirect supply chain connections, and control variables. Our measure of direct vertical connections identify when one of the merging parties lists the other as a customer. We define an indirect connection to exist when both firms supply to the same customer, both firms are customers of the same supplier, or both firms are in the same industry. As shown in Table 4, we find consistent evidence that mergers involving more economic links create more value. The first row presents the coefficient estimates for the log of the total number of economic links involved in the merger (the sum of the number of customers and suppliers of target and acquirer). Column 1 shows that acquirer announcement returns are increasing in the number of links, with a one standard deviation increase being associated with a 50 basis point increase in CAR. Column 3 shows that the effect is stronger when looking at the absolute value of the acquirer CAR, which allows us to test for impact, whether positive of negative. That is, we hypothesize that deals involving more links should have the potential for greater wealth impact, good or bad. We find this is the case, with a one standard deviation increase in the number of total links being associated with an 90 basis point increase in absolute acquirer CAR. 19

21 The presence of more links in a deal means that there is more potential value creation, but also, if the deal is poorly motivated or executed, more value at risk as well. Under the assumption that managers maximize shareholder wealth, more links should increase the value creation in a deal. However, once we allow for agency conflicts or hubris, deals can be value destroying, and the more valuable economic links involved, the greater is the potential for value destruction, potentially blurring the overall relation between economic links and value creation. As argued in Shahrur (2005), by focusing on the deals where the overall bidder and target returns are positive, we likely eliminate some deals done as a result of agency reasons or deals for other non economic reasons and instead focus on deals done for strategic and efficiency gaining reasons that are more likely to take advantage of the potential synergies associated with the overlapping economic links. For this reason, and consistent with several other papers that also investigate share price reactions as a function of economic links (e.g. see Shahrur (2005), Hertzel, Li, Officer, and Rodgers (2008)) we repeat the analysis from columns 1 and 3 of Table 4 in columns 2 and 4 focusing on just the subset of deals with positive wealth effects. Using Bradley, Desai, and Kim s (1988) approach to calculating the overall CAR to a portfolio of acquirer and target shares weighted by their respective market value of equity, we categorize those deals with positive portfolio announcement returns as wealth creating and those with negative returns as wealth destroying. Here the coefficient on the log of number of ties is similar, but its significance in this smaller sample drops. However, at the same time, the coefficient on target betweenness becomes positive and significant. For wealth creating deals, acquiring a high betweenness target increases the wealth creation, with a one standard deviation increase in betweenness increasing acquirer CARs by 35 basis points. Column 4 tests the effects of economic links on the absolute value of acquirer CARs in the wealth creating sample. We find that the total number of links and target betweenness are associated with an increase in the absolute value of 20

22 acquirer CARs. Economically, a one standard deviation increase in total links (target betweenness) is associated with an 80 (30) basis point increase in the absolute value of acquirer CARs. The measures of direct and indirect links reveal that using either the full or wealth creating sample we find that announcement returns are higher when acquirers buy their customers. There is some evidence the return is lower if the target and acquirer are rivals. Finally, competition (more bidders) reduces the return for the winning bidder. Rival Value Effects In Table 5, we examine how mergers impact the value of rivals to the merging parties. Mergers affecting more economic links could have positive or negative effects on the merging parties rivals. Rivals could benefit if the industry is consolidating so that all firms will see increased pricing power. They could also benefit if the merger is poorly conceived or executed, weakening two firms that previously had been strategically positioned. In contrast, mergers that combine many economic links could improve the strategic position of the resulting firm, leaving its rival in a worse competitive position. In either case, the impact of mergers affecting more economic links should be greater in magnitude, so we use the absolute value of the rivals stock price reaction to the merger announcement as our dependent variable. The results are consistent with our hypothesis. The first row shows that the rival s absolute value change in response to the merger is increasing in the number of links affected. While acquirer or target betweenness does not help explain the rival reaction, the rival s own betweenness does. This is intuitive as a high betweenness firm will be affected more by a merger in its industry network than an otherwise similar but unconnected firm. That is, firms that already are linked between other firms will be more likely to be affected, either directly or indirectly, as the impact of the merger flows through 21

23 those links. We control for direct connections between the rival and the merging parties and find that the merger has a bigger impact on the rival if it is a customer (but not a supplier) of one of the parties. Overall, the results are consistent with the prediction that mergers that affect higher numbers of large economic links are the ones that matter. They have the greatest impact on the value of the merging parties and on the value of their rivals. In the following sections, we examine whether highly linked targets attract more bidders and how rivals change their investment patterns following mergers affecting more links. Competition for Targets A second way of testing whether economic links create value in mergers is to examine the amount of competition for a target. We test whether the competition for a target will increase in its (transferable) economic links. Our results in Table 3 demonstrate that having connections can actually decrease merger propensity. However, our tests in this section focus on targets that receive a bid. The endogeneity of receiving a bid reveals that the connections in this case are transferrable. Conditional on transferrable connections, targets with valuable economic links should attract more competition. To test this, in Table 6 we take the targets from the SDC sample described in Section 3 and model the probability of a target receiving more than one bid as a function of the target s overall centrality and direct economic connections between the acquirer and the target. In the sample, according to SDC, only 2.5% of the targets receive multiple bids. This number understates the overall number of bids for these targets given that many bidders are not revealed to the public (Boone and Mulherin, 2007). However, the SDC data does identify a subset of deals where multiple bids are known to have occurred. Of the 2.5% of the sample that did experience multiple bids, none of the targets had direct connections with the acquirer; no bid that encompassed a previously existing direct economic tie between the acquirer and target was contested. We find that the number of large customers a target has is positively related 22

24 to the probability of the bid being contested; a one standard deviation increase in the target Number of Customers is associated with an odds ratio of suggesting a standard deviation increase in target Number of Customers in this sample is associated with 22% increase in the likelihood of a firm receiving competing bids. These results are consistent with transferrable connections being valuable in acquisitions and provide further support to the importance of economic links. The Impact of High Link Deals on Future Merger Activity and Investment Mergers involving firms with higher numbers of significant economic links lead to increased subsequent activity within the industry because the spill over effect across links would likely lead to more reshuffling of assets and signal potentially larger changes in the supply chain. Thus, we predict that merger activity involving more connected firms leads to more acquisition activity in subsequent months than does merger activity involving firms less connected in the customer supplier network. To test this prediction, we create a panel dataset of Compustat firms from and model each firm s merger, capital expenditures and R&D decisions as a function of the number of economic links involved in acquisitions for that firm s industry over the prior 6 months. These rolling counts of economic links within the industry involved in the mergers are the main variables of interest. If merger activity that involves more connected firms leads to increased merger and investment activity at other firms in the industry, then these two variables will have a significant positive relation to subsequent mergers even after controlling for other factors. For the control variables we use the same variables used in Andrade and Stafford (2004) and Rhodes Kropf, Robinson, and Viswanathan (2005) to control for firm level merger activity. They are cash flow, sales growth, excess return, asset turnover as a measure of capacity utilization, and book leverage. Andrade and Stafford use Tobin s Q measured as M/B of assets. Rather than use this measure of Q we use Rhodes Kropf, Robinson, and Viswanathan (2004) s decomposition of Q into (1) firm specific pricing deviation from short run industry pricing; (2) sector 23

25 wide short run deviations from firms long run pricing; and (3) long run pricing to book. Finally, we include year and industry effects as well as the 6 month moving average of the C&I rate spread (Harford, 2005). Subsequent Merger Activity We estimate a panel logit model where each month a firm is classified as either participating in merger activity (as target or acquirer) or not. We estimate the probability of the firm engaging in merger activity each month as a function of the control variables and rolling economic tie variables described above. Given that the number of economic links involved in acquisitions can change dramatically from month to month across a year we build our panel at the monthly level. We use annual Compustat data and extrapolate the financial information from Compustat in a linear fashion across months between the report dates. We expect the rolling count of economic links involved in acquisitions over the prior 6 months to positively predict the probability of a firm engaging in acquisition activity in month t. To guard against the possibility that the rolling counts are simply proxying for waves or momentum in aggregate industry merger activity we include alternative controls for recent industry merger activity: rolling sum of dollars involved in acquisition activity over the prior 6 months, and rolling count of number of deals involved in acquisition activity over the prior 6 months. We also include a merger wave indicator variable as in the other tables. The results are presented in Table 7. The first two columns show that the number of economic links in an industry involved in mergers in the prior 6 months strongly and positively predicts the likelihood that another firm in that industry will be involved in a merger in a given month. The results show the effect of activity involving more economic links, controlling for the total amount of activity. It is not simply the number of prior mergers that matters, but how connected the merger parties are. Using the results in column 1, a standard deviation increase in the total ties involved in acquisitions over 24

26 the prior 6 months is associated with an odds ratio of 1.28; even after controlling for the number of deals and the existence of a merger wave, a firm is 28% more likely to be engaged in merger activity in a given month if the rolling sum of economic links involved in acquisitions in its industry increases by 1 standard deviation. 12 The results support the prediction that mergers of connected parties have a greater impact on the industry structure, engendering follow on merger activity in response. Impact on Other Spending While mergers are the natural follow on activity to disruptive mergers involving economic links, capital expenditures, R&D and advertising could all react as well. Depending on the motivation for the mergers, the spending response could be positive or negative (e.g. in contractionary merger waves, spending should decrease). Therefore, in addition to follow on merger activity we also examine the absolute value of the change in capital expenditures, R&D and advertising. 13 In columns 3 through 8, we find confirming evidence that mergers involving more economic links shock the competitive environment. The absolute values of spending changes are all positively related to our measures of the number of economic links involved in recent mergers. Overall, the announcement return results, target competition results, and the reaction of others in follow on merger activity and spending changes all support the hypothesis that mergers involving parties with strong economic links in the supplier customer network are those that matter; they have the most potential for value creation, and demand the largest response from others in the network. 12 The standard deviation in the ln(1+economic ties involved in mergers in last 6 months) is Hence the odds ratio for a standard deviation increase is approximately exp(0.163*1.51)= The capital expenditures, R&D, and advertising information comes from Compustat at the annual level. Monthly values are extrapolated between reporting dates. The dependent variable in columns 3 4 of Table 7 is calculated at the monthly level as the abs(monthly percent change in capital expenditure) where the monthly percent change at firm j in month t is calculated as (month t s capital expenditure at firm j minus the average monthly capital expenditure at firm j from the prior year)/(the average monthly capital expenditure at firm j from the prior year). The dependent variables in columns 5 8 involving R&D and advertising are calculated using the same method. 25

27 Robustness In this section we assess the robustness of our results to controlling for additional types of connections as well as stricter definitions of economic connections in untabulated tests. We identify board links, geographic proximity, and joint ventures as potentially correlated links. The results are summarized here. We use RiskMetrics Directors and Legacy Directors databases to identify potential board level connections between firms. This limits our sample to S&P 1500 firms from 1996 onward. After controlling for both board level connections as well as economic links, our inferences are unaffected. The existence of a board link where directors of the bidder or target were directors of the other firm strongly predicts the likelihood of being a deal, consistent with (Rousseau and Stroup, 2011). However, board links have no effect on the acquirer returns to acquisitions. Next, we take the zip code of the acquirer and target firms reported in SDC and calculate the distance between them using the SAS 9.2 zip code file. Again, after controlling for geographic proximity our inferences regarding the importance of economic links and merger outcomes are unaffected Our final alternative economic link is a joint venture. Our results show that domestic joint ventures increase the likelihood of being both an acquirer and a target, but foreign joint ventures have the opposite effect. Most foreign joint ventures have government involvement, which could potentially explain the difference. We find some mixed results that targets with more domestic joint ventures have a larger absolute value impact and foreign joint ventures have a lower absolute value impact. Further, when acquirers have joint ventures with foreign governments, there is a larger absolute value impact on their rivals. Our main inferences remain. Arguably, economic links are more valuable if they are stable. In the next robustness check, we only count economic links between two firms that have been in place for at least 3 or 5 years. Again, our inferences are unchanged. In our final robustness check, we add several governance variables to the 26

28 target and acquirer likelihood regressions to show that the merger likelihood results are robust to the presence of antitakeover provisions. Specifically, in robustness tests we control for the Entrenchment index (Bebchuck, Cohen, and Ferrell (2009), a classified board dummy, and the alternative takeover index from Cremers and Nair (2005) which focuses on classified boards, blank check and poison pill provisions, and restrictions on shareholders calling special meetings. We find that our inferences about the importance of economic links to merger outcomes remain unchanged. 5. Conclusion The economic links a firm has with its suppliers, customers and rivals are critical determinants of its value. Taken together, these links create a network of economic activity among firms. We argue that mergers reshuffle these links giving merging parties access to each other s economic links, moving links between parties inside the new merged firm, and creating strategic challenges to non merging parties affected by the changes in the economic environment. Among other reasons, we predict that firms merge to gain access to economic links, to internalize existing links, and to change the boundaries of the firm. Hence firms with high betweenness have a high likelihood of being targeted in acquisition contests. Further, we predict that mergers that involve parties with high numbers of large economic links have relatively greater impact both on value creation in the merger and on subsequent activity by other related firms. We find broad support for these predictions. The importance of these economic links is evidenced by how they predict which firms merge, which targets generate multiple bidders, which mergers create value and which mergers generate follow on merger and other investment responses by other firms. Our study contributes to the merger literature as well as to the growing literature studying how corporate events affect not only the immediate parties to the event, but also economically linked firms as well. 27

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32 Table 1 Sample Information Year Number of Compustat firms with at least 1 identified customer Number of customers (not unique) Number of unique customers Number of deals ,094 4,019 2, ,276 4,285 2, ,441 4,578 2, ,465 4,645 2, ,714 5,099 2, ,690 5,068 2, ,453 4,489 2, ,291 4,327 2, ,653 3,685 1, ,890 4,134 1, ,844 4,045 1, ,914 4,175 1, ,819 4,032 1, ,807 4,092 2, ,773 4,010 1, ,666 3,853 1, ,633 3,932 2, ,542 3,746 1, ,231 2,894 1,

33 Table 2 Summary Statistics This table presents summary statistics for the variables as defined in the appendix. Mean SD 10% 50% 90% Acquirer Number of Customers and Suppliers Acquirer Number of Suppliers Acquirer Number of Customers Acquirer Betweennes Target Number of Customers and Suppliers Target Number of Suppliers Target Number of Customers Target Betweennes Public Target Number of Customers and Suppliers Public Target Number of Suppliers Public Target Number of Customers Public Target Betweenness Target is Customer Acquirer is Customer Common Customer Common Supplier Rivals Total Ties (Target and Acquirer) Deal Size (2009 Dollars) 1, , ,702.1 Number of Bids Cash Deal Private Deal Relative Size Percentage Cash Sales Growth (Acquirer) Net Working Capital (Acquirer) Leverage (Acquirer) < M/B (Acquirer) P/E (Acquirer) Firm Size (Acquirer) Cash Deviation (Acquirer) Abnormal Returns (Acquirer) < < Mean Return (Acquirer) < Standard Deviation of Returns (Acquirer) Table continued on the next page 28

34 Table 2, continued Economic Shock (Acquirer) Merger Wave (Acquirer) Regulation Indicator (Acquirer) Industry Concentration (Acquirer)

35 Table 3 Likelihood of merger participation as a function of direct and indirect acquirer and target economic ties The table below shows the coefficients from a fixed effects (conditional) logit regression where an indicator variable equal to 1 if a firm participated in a given deal as an acquirer (columns 1 2) or target (columns 3 4), is regressed on various direct and indirect measures of the acquirer and target economic ties within the customer supplier network. The matched sample of 5 pseudo deals per real deal was obtained by matching the 5 nearest neighbors by propensity score using nonnetwork control variables. The independent variables refer to the acquirer s firm characteristics in columns 1 2 and the target s firm characteristics in columns 3 4. P values are shown below the coefficients. Significance at the 1, 5 and 10% levels are indicated with ***, **, and *, respectively. Acquirer Target (1) (2) (3) (4) Number of Suppliers *** (0.717) (<0.001) Number of Customers 0.186*** 1.413*** (0.001) (<0.001) Betweenness 0.366*** 0.824*** (<0.001) (0.001) Target is Customer 1.456* 1.330* *** *** (0.051) (0.054) (<0.001) (<0.001) Acquirer is Customer 2.953*** 2.851*** 2.861*** 3.489*** (<0.001) (<0.001) (<0.001) (<0.001) Common Customer 2.139*** 2.159*** 2.922*** 3.292*** (<0.001) (<0.001) (<0.001) (<0.001) Common Supplier 2.181*** 2.232*** 2.905*** 3.125*** (<0.001) (<0.001) (<0.001) (<0.001) Rivals 3.427*** 3.435*** 3.630*** 3.607*** (<0.001) (<0.001) (<0.001) (<0.001) Sales Growth (0.786) (0.800) (0.779) (0.850) Net Working Capital 1.267*** 1.254*** 0.628** 0.775*** (<0.001) (<0.001) (0.011) (0.002) Leverage 0.360*** 0.339*** ** (<0.001) (<0.001) (0.303) (0.073) M/B * 0.019* (0.114) (0.130) (0.061) (0.028) P/E 0.002** 0.002* (0.047) (0.072) (0.134) (0.172) Firm Size 0.187*** 0.169*** 0.055** 0.089*** (<0.001) (<0.001) (0.050) (0.006) Cash Deviation 0.048*** 0.045*** (0.003) (0.005) (0.557) (0.469) Table continued on the next page 30

36 Table 3, continued Abnormal Returns ** * (0.049) (0.059) (0.818) (0.785) Mean Returns (0.854) (0.867) (0.724) (0.768) Standard Deviation of Returns 7.673** 8.478*** (0.017) (0.009) (0.224) (0.293) Economic Shock (0.607) (0.688) (0.310) (0.395) Merger Wave (0.478) (0.520) (0.853) (0.755) Regulation Indicator (0.205) (0.206) (0.446) (0.379) Industry Concentration 0.021*** 0.021*** 0.020*** 0.027*** (<0.001) (<0.001) (0.004) (0.001) Industry I/O Controls Yes Yes Yes Yes Observations 23,622 23,622 7,944 7,944 Adjusted R squared

37 Table 4 Acquirer announcement returns as a function of economic links involved in acquisitions. This table reports coefficients from regressions of the acquirer CAR( 1,+1) announcement return on various measures of the economic links associated with the acquirer and target firms as well as control variables. CARs are calculated using a market model with parameters estimated from 300 to 60 trading days before the announcement. In columns 1 and 2 the dependent variable if the acquirer CAR( 1,+1) and in columns 3 and 4 the dependent variable if the absolute value of the acquirer CAR( 1,+1). Columns 1 and 3 use the full sample while columns 2 and 4 use only the wealth creating sample. Deals are classified as wealth creating if the combination of acquirer and target announcement returns is positive. The combined CAR return is calculated using Bradley, Desai, and Kim s (1988) approach which calculates the overall CAR to a portfolio of acquirer and target shares weighted by their respective market value of equity. (1) (2) (3) (4) ln(1+total ties) 0.005** *** 0.008*** (0.027) (0.157) (<0.001) (0.001) Acquirer Betweeness (0.286) (0.607) (0.521) (0.265) Target Betweeness *** *** (0.307) (0.001) (0.140) (<0.001) Cash Deal 0.014*** ** (<0.001) (0.154) (0.016) (0.890) Private Target 0.024*** (<0.001) (0.629) Relative Size *** 0.009*** 0.006*** (0.140) (0.008) (<0.001) (<0.001) Firm Size 0.006*** 0.008*** 0.008*** 0.008*** (<0.001) (<0.001) (<0.001) (<0.001) Merger Wave < * < (0.927) (0.088) (0.944) (0.904) Regulation 0.027* 0.096*** *** (0.064) (<0.001) (0.831) (<0.001) Leverage 0.017*** (<0.001) (0.691) (0.119) (0.249) M/B 0.001*** <0.001 <0.001** <0.001 (0.002) (0.460) (0.036) (0.875) Target is Customer 0.060** 0.094*** *** (0.015) (0.005) (0.295) (0.001) Acquirer is Customer (0.758) (0.897) (0.341) (0.837) Table continued on the next page 32

38 Table 4, continued Common Supplier (0.186) (0.748) (0.692) (0.898) Common Customer (0.150) (0.124) (0.286) (0.317) Rivals *** 0.004* (0.125) (0.009) (0.060) (0.113) Constant 0.043*** 0.059*** 0.078*** 0.074*** (0.003) (0.006) (<0.001) (<0.001) Year Controls Yes Yes Yes Yes Industry I/O Controls Yes Yes Yes Yes Observations 3, , Adjusted R squared

39 Table 5 Rival announcement returns as a function of economic links involved in acquisitions. In this table the absolute value of the acquirer s rivals CAR( 1,+1) announcement returns are regressed on various measures of economic links and a series of control variables. The CARs are calculated using a market model with parameters estimated 300 to 60 trading days before the announcement. Rivals are identified using Fama French 48 industries. Errors are clustered by acquirer firm. 34

40 (1) (2) (3) (4) ln(1+total ties) 0.001*** 0.001*** 0.001*** (<0.001) (0.001) (0.001) ln(1+acq Between) (0.265) (0.596) (0.513) ln(1+target Between) (0.420) (0.156) (0.156) ln(1+rival Between) 0.005*** (<0.001) Firm Size 0.008*** 0.008*** 0.008*** 0.008*** (<0.001) (<0.001) (<0.001) (<0.001) Merger Wave 0.002*** 0.002*** 0.002*** 0.002*** (<0.001) (<0.001) (<0.001) (<0.001) Regulation (0.593) (0.460) (0.552) (0.563) Leverage 0.004*** 0.004*** 0.004*** 0.005*** (<0.001) (<0.001) (<0.001) (<0.001) M/B 0.000*** 0.000*** 0.000*** 0.000*** (<0.001) (<0.001) (<0.001) (<0.001) Target is Customer (0.435) (0.248) (0.425) (0.387) Acquirer is Customer (0.337) (0.708) (0.336) (0.333) Common Supplier (0.973) (0.221) (0.912) (0.909) Common Customer (0.360) (0.643) (0.412) (0.398) Rival is Customer 0.018*** 0.019*** 0.018*** 0.017*** (<0.001) (<0.001) (<0.001) (<0.001) Rival is Supplier (0.584) (0.107) (0.562) (0.929) Rivals (0.817) (0.887) (0.796) (0.730) Acquirer Ind Concentration (0.536) (0.447) (0.542) (0.658) Constant 0.065*** 0.066*** 0.065*** 0.065*** (<0.001) (<0.001) (<0.001) (<0.001) Year Controls Yes Yes Yes Yes Industry I/O Controls Yes Yes Yes Yes Observations 1,090,860 1,090,860 1,090,860 1,090,860 Adjusted R squared

41 Table 6 Likelihood of more than one bid as a function of economic links. The dependent variable is set to one if SDC records more than one bidder attempted to acquire the target. Logit coefficients are shown below. (1) Target Number of Suppliers (0.655) Target Number of Customers 1.025*** (0.001) Private Target 2.844*** (<0.001) Merger Wave (0.986) Target Ind Concentration 0.044*** (0.003) ln(target size) 0.238*** (0.001) Common Supplier (0.527) Common Customer (0.156) Constant 4.886*** (<0.001) Year Controls Yes Industry I/O Controls Yes Observations 3,965 Pseudo R squared

42 Table 7 Follow on merger, capital expenditure, advertising, and R&D activity as a function of economic links involved in merger activity. In columns 1 and 2 logit coefficients are reported for specifications where the dependent variable takes on a value of 1 in a given month if the firm engages in merger activity as an acquirer or target. Columns 3 8 report OLS regression coefficients where the dependent variable is the absolute value of the percent change in a given month in capital expenditures (columns 3 and 4), advertising (columns 5 and 6), and R&D (columns 7 and 8). Using monthly data that is extrapolated in a linear fashion between reporting dates, the percent change in capital expenditures (CAPEX) in a given month t for firm j is calculated as (month t s CAPEX at firm j minus the average monthly CAPEX at firm j calculated over the prior calendar year)/(the average monthly CAPEX at firm j calculated over the prior calendar year). The percent change in advertising and R&D are calculated using the same approach. Errors are clustered at the firm level. (1) (2) (3) (4) (5) (6) (7) (8) ln(1+economic ties involved in mergers 0.163*** 0.215*** 0.006** 0.010*** *** ** in last 6 months) (<0.001) (<0.001) (0.036) (<0.001) (0.106) (0.001) (0.125) (0.012) ln(1+number of acquisitions in last *** 0.047*** 0.038*** 0.027*** months) (<0.001) (<0.001) (<0.001) (<0.001) ln(1+dollars spent on acquisitions in last 0.102*** 0.010*** *** 6 months) (<0.001) (<0.001) (0.183) (<0.001) Firm specific error 0.198*** 0.201*** 0.022*** 0.022*** 0.011** 0.011** (<0.001) (<0.001) (<0.001) (<0.001) (0.035) (0.036) (0.125) (0.122) Sector wide error 0.276*** 0.271*** ** 0.007* (<0.001) (<0.001) (0.208) (0.183) (0.350) (0.372) (0.046) (0.050) Long run error 0.278*** 0.273*** 0.008* 0.008* < (<0.001) (<0.001) (0.071) (0.059) (0.953) (0.907) (0.296) (0.312) Cash Flow 0.037*** 0.037*** 0.004*** 0.004*** 0.005** 0.005** 0.001** 0.001** (<0.001) (<0.001) (0.001) (0.001) (0.011) (0.011) (0.013) (0.013) Table continued on the next page 37

43 Table 7, continued Sales Growth 0.019*** 0.020*** 0.018*** 0.018*** 0.016*** 0.016*** 0.010*** 0.010*** (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) Capacity Utilization 0.235*** 0.242*** 0.095*** 0.096*** 0.078*** 0.079*** 0.069*** 0.070*** (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) Leverage 0.456*** 0.431*** < *** 0.075*** 0.061*** 0.061** (<0.001) (<0.001) (0.935) (1.000) (0.009) (0.008) (0.010) (0.010) Firm Size 0.226*** 0.226*** 0.071*** 0.071*** 0.035*** 0.035*** 0.022*** 0.022*** (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) Excess Return 0.181*** 0.184*** 0.164*** 0.164*** 0.069*** 0.069*** 0.064*** 0.064*** (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) Merger Wave ** 0.018** * (0.242) (0.919) (0.045) (0.016) (0.510) (0.305) (0.073) (0.156) Credit Spread *** 0.403*** 0.311*** 0.306*** 0.238*** 0.237*** (0.479) (0.602) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) Industry Concentration <0.001 <0.001 < (0.370) (0.971) (0.810) (0.957) (0.221) (0.188) (0.436) (0.377) Constant 7.775*** 6.792*** 0.091** *** 0.101** (<0.001) (<0.001) (0.050) (0.960) (<0.001) (0.032) (0.931) (0.471) Year Month Controls Yes Yes Yes Yes Yes Yes Yes Yes Industry Controls Yes Yes Yes Yes Yes Yes Yes Yes Observations 857, , , , , , , ,281 Adjusted R squared Pseudo R squared

44 Figure 1 This figure provides examples of acquisitions that involve various types of economic links captured in our analysis. The upper diagrams show acquisitions that involve firms that share a relationship with a common third party firm that acts as either a supplier or customer to both the acquirer and target. The lower diagrams show examples of acquisitions where the acquirer does not share direct economic links with the target firm but where the target firm either has a large number of customers or has a high betweenness centrality measure. Acquisitions Involving Different Types of Economic Links Customer Supplier Supplier (acquirer) Supplier (target) Customer (acquirer) Customer (target) Acquisition with Common Customer Acquisition with Common Supplier Customer Customer Customer Customer & Supplier (target) Non-linked Firm (acquirer) Supplier (target) Customer & Supplier Supplier Acquisition of Firm with High Betweenness Customer Non-linked Firm (acquirer) Acquisition of Firm with High Number of Customers 39

45 Figure 2 This figure displays the number of deals by year in our sample. In addition, it displays the proportion of those deals that involved a public target. Figure 3 This figure displays the proportion of acquirers with at least 1 large customer and/or supplier. 40

46 Appendix A: Control Variables Variable Description acquirer is customer This is an indicator variable that the acquiring firm is listed as the target firm s major customer. abnormal returns Average daily abnormal return at the firm over years t 4 to t 1 betweenness This variable counts the number of shortest distance paths in the supplier customer network between firms on which a given firm is located. Following the network literature it is the sum of geodesics ( g ) between firms i and k that firm j is on: Betweenness j g ik ( j). Given the right skew to this measure is transformed using the natural logarithm of 1 plus this number. capacity utilization The ratio of sales to total book assets cash deal Indicator variable set equal to 1 if the target was acquired using all cash cash deviation The residual from a cash model based on Opler, Pinkowitz, Stulz, and Williamson (2001) Table 4 in year t 1. cash flow The ratio of EBITDA to sales from year t 1 common customer This is an indicator variable that both the acquiring and target firms list the same third firm as a major customer common supplier This is an indicator variable that a third firm lists both the acquiring and target firms as major customers credit spread Spread between commercial and industrial loans and the federal funds rate diversifying deal Indicator variable set equal to 1 if the acquirer and target are in different Fama French 48 Industries economic shock First principal component of the absolute value of: Net income/sales(t 1), sale/assets(t 1), R&D/assets(t 1), CAPX/assets(t 1), employee growth, OIBDP/assets(t 1), sales growth firm size Natural logarithm of total assets from the end of the prior year firm specific error Firm specific pricing deviation from short run industry pricing (model 3 from Rhodes Kropf, Robinson, and Viswanathan, 2005) industry Compustat sales based Herfindahl Index calculated for Fama French concentration 48 industries Industry I/O Industry level variables at the 3 digit SIC level constructed from the controls Bureau of Economic Analysis (BEA) 1992 Benchmark Input Output tables. These are constructed by counting the number of industries a given industry supplies more than 0.25% of their inputs to, the number of industries a given industry purchases more than 0.25% of their inputs from, and the number of shortest distance paths on which a given industry is located in the I/O industry network. leverage Average ratio of book debt to market value of equity over years t 4 to Long run price tobook ik t 1 Valuations implied by current sector multiples deviating from valuation implied by long run multiples (model 3 from Rhodes Kropf, Robinson, and Viswanathan, 2005) mean returns Mean annual industry return from year t 1 Appendix continued on the next page i,k 41

47 Appendix A continued merger wave Indicator variable for a merger wave in a given Fama French 48 industry and year following the approach described in Harford (2005) M/B Average ratio of market value of equity to book value of equity over years t 4 to t 1 net working capital Average non cash working capital divided by total assets at the firm number of customers number of suppliers from years t 4 to t 1 Number of major customers identified by a firm in a given year. Given the right skew in this variable it is transformed using the natural logarithm of 1 plus this number. Number of times a customer is listed as a major customer to other firms in a year. Given the right skew in this variable it is transformed using the natural logarithm of 1 plus this number. private target Indicator variable set equal to 1 if the target was a private firm P/E Average price to earnings ratio of the firm from years t 4 to t 1 regulation indicator Indicator variable for deregulatory events affecting an industry from Viscusi, Harrington, and Vernon (2005) relative size Ratio of deal value as reported by SDC to acquirer total assets rivals Indicator that both acquirer and target firms come from the same industry. sales growth Average annual sales growth at the firm from years t 4 to t 1 sector wide error standard deviation of returns target size target is customer Sector wide short run deviations from firms long run pricing (model 3 from Rhodes Kropf, Robinson, and Viswanathan, 2005) Standard deviation of Fama French 48 industry returns from year t 1 The merger transaction value as reported in SDC. This is an indicator variable that the target firm is listed as the acquiring firm s major customer. 42