Sources of gains in horizontal mergers: Evidence from geographic expansion Douglas Fairhurst Ryan Williams * September 2014 ABSTRACT: We use a novel measure to provide evidence on the debated source of gains in horizontal mergers. Specifically, we create a text-based measure of the geographic expansion resulting from the combination of the bidder and target firms in horizontal mergers. Consistent with improvements in operational efficiencies and not increased market power as the predominant source of gains in horizontal mergers, we find that bidding firm shareholders realize significantly higher abnormal returns in horizontal mergers that result in increased geographic expansion relative to other horizontal mergers. These returns are most pronounced for deals in which opportunities to profit from efficiency gains are high. Further, the gains do not at the expense of target shareholders as these shareholders are no worse off on the announcement of horizontal mergers with high geographic expansion. Collectively, these results suggest that improvements in operational efficiency are the predominant source of gains in horizontal mergers. * Douglas Fairhurst is at the Carson College of Business, Washington State University and can be reached at dj.fairhurst@wsu.edu. Ryan Williams is at the Eller College of Management, University of Arizona and can be reached at rwilliams@email.arizona.edu.
I. Introduction Both antitrust authorities and academic researchers often posit the anticompetitive effects of horizontal mergers, suggesting that gains come at the expense of customers and suppliers through collusive price setting in the resulting oligopolistic industries. 1 In contrast to these findings, a related line of literature provides evidence of efficiency improvements driving gains in horizontal mergers. 2 This debate remains active as it is difficult to empirically disentangle whether gains following mergers come from price collusion or from the improvements in operating efficiency following horizontal mergers. Although horizontal mergers likely result in both market power increases with the potential for collusive behavior as well as operating efficiency gains, the dominant source of gains in horizontal mergers is still subject to debate. In this paper, we use a unique setting to test between the market power hypothesis and the efficiency improvement hypothesis. We consider how the geographical footprint of the merged firm changes after the merger. Namely, does the combined firm expand or contract geographically? Observing ex-post geographical changes allows us to distinguish between the market power and efficiency hypotheses for the following reasons. First, the potential for operating efficiency gains are large in horizontal mergers resulting in geographic expansion. Geographic expansion may lead to efficiency gains in distribution, marketing, and any costs with economies of scale. Further, geographic diversification allows optimization of state tax payments, flexibility in production, and diversification across local capital market conditions. Consistent with these potential sources of gains, geographic expansion is often cited as a source of gains in efficiency for the acquiring firms. For example, Joseph 1 A few examples include of academic papers modeling and documenting increased market power following horizontal mergers include Stigler (1964), Landes and Posner (1981), Kim and Singal (1993), Singal (1996), and Bhattacharyya and Amrita (2011). For the treatment of antitrust implications of horizontal mergers by antitrust authorities, see the Statutory Provisions and Guidelines of the Antitrust Division (http://www.justice.gov/atr/public/divisionmanual/chapter2.pdf). 2 For example, several papers consider announcement returns for the bidder, target, customers, suppliers, and rival firms and find evidence consistent with increased efficiency and not market power being the source of gains in horizontal mergers (Eckbo (1983); Stillman (1983); Fee and Thomas (2004); Shahrur (2005)). 1
D. Rupp, CEO of Olin Corporation, said the following concerning Olin s acquisition of Pioneer Corporation in 2007: Our ability to meaningfully add value through synergies and best practices will benefit our shareholders. The combined companies will have a more diversified geographic footprint, a complementary bleach and HCL product mix and a broader distribution network. Further, mergers with significant geographic expansion limit the ability of firms to collude with rival firms on pricing as this would require coordinating with rival firms in new markets as well. As such, if the gains from horizontal mergers are driven by increased ability to collude, then mergers with significant geographic expansion should be less valuable than other horizontal mergers. Alternatively, if the gains from horizontal mergers are driven by improvements in operational efficiency, then mergers with high geographic expansion should more valuable than other horizontal mergers. We also note that considering the implications of horizontal mergers based on the resulting geographic coverage of firms is consistent with the practices of the Department of Justice in analyzing the impact of horizontal mergers on the competitive landscape. Specifically, tests of the impact of these mergers on product market competition are applied to a group of products together with a geographic region to determine a relevant market. 3 Despite the value of this setting to provide evidence on the source of gains in horizontal mergers, there is surprisingly little empirical evidence regarding the valuation impacts of geographic expansion. One likely explanation for the lack of evidence is the difficulty in identifying a firm s geographic coverage. For instance, Compustat only provides a firm s headquarter location which fails to capture the geography a firm s operations if the firm is dispersed outside of the state that it is headquartered in. To overcome this issue, we follow Garcia and Norli (2012) to calculate a more comprehensive measure of the geographic coverage of a firms operations. Specifically, we use a text 3 See http://www.justice.gov/atr/public/guidelines/hmg-2010.html#4. 2
harvesting software to determine which states a firm mentions in their 10-K. Operating under the assumption that the mention of a state in the firm s 10-K indicates a presence in that state, this methodology provides a more robust measure of a firm s geographic coverage than simply using the firm s headquarter location. We then extend the methodology to develop a measure that captures the geographic expansion of each deal by measuring the overlap of each bidder/target pair in my sample. We classify mergers as having high geographic expansion if there is low overlap in their geographic coverage. We use this classification to test the value implications of geographic expansion in mergers, and, in doing so, provide novel evidence on the source of gains in horizontal mergers. We find evidence of geographic expansion providing value for the bidding firm shareholders. The cumulative abnormal returns to bidding firm shareholders over a three-day window are approximately 2.0% higher in horizontal mergers that are in the top quartile of geographic expansion than for other horizontal mergers, suggesting that horizontal mergers with geographic expansion provide the shareholders of acquiring firms with greater value. This finding is robust to both univariate and multivariate tests, expanding to five-day event windows, and the use of a continuous measure of geographic expansion. These findings suggest that improvement in operating efficiency, not a softening of the competitive environment, is the primary source of gains in horizontal mergers. If the higher shareholder returns in horizontal mergers with high geographic expansion are truly due to improvements in operational efficiency, then the relation should be most pronounced in settings where there are higher opportunities to profit from efficiency gains. Consistent with this argument, we show that the relation between bidder shareholder returns and greater geographic expansion are most pronounced in two key settings. First, one way in which an acquiring firm can realize efficiency gains is to spread expenses of the target firm over a larger base of assets, especially for redundant expenses. As such, the gains to the bidder shareholders should be highest when there 3
are more expenses to be eliminated. I show that horizontal mergers with high geographic expansion are most value enhancing when the target firm s selling, general and administrative expenses are high. Second, the ability to gain from operational efficiencies is greater in more concentrated industries (Perry and Porter (1985)). We calculate the industry concentration as the Herfindahl Index of industry revenue. Consistent with gains from operation efficiency being highest in concentrated industries, we show that the value of geographic expansion in horizontal mergers is highest when the industry in which the firm operates is relatively concentrated. To this point, we argue that our findings suggest that bidder shareholders respond more positively to horizontal mergers with high geographic expansion due expected increases in cash flows resulting from improvements in operational efficiencies. However, the same pattern would hold if deals with higher geographic expansion resulted in a larger wealth transfer from the target firm shareholders to bidder shareholders. If horizontal mergers with high geographic expansion are valuable to bidder shareholders only because they result in lower values for target shareholders, then we are unable to say anything about the gains from the merger. We consider the possibility of a reduction of wealth for target shareholders by evaluating the announcement returns from target shareholders. However, we find no evidence of diminished returns to target shareholders. Announcement returns are higher for target shareholders in horizontal mergers with high geographic expansion. While the difference is only statistically significant in univariate tests, they are not lower regardless of the test used. These findings suggest that the value to bidder firms comes from improvements in efficiency and not a wealth transfer from target shareholders. This paper has three main contributions. First, we contribute to the debate on the value implications of horizontal mergers. While antitrust policy points to the possibility of monopolistic outcomes of horizontal mergers, others often cite the value to shareholders through improvements in 4
efficiency. Our evidence suggests that shareholders place higher value on the potential for operational improvements in horizontal mergers than the potential for increased ability to collude with rival firms. Second, this paper contributes to the discussions of the value of diversification. Contrary to many who argue that diversification leads to a reduction in a firm s value, we find that shareholders react positively to at least one source of diversification, specifically geographic diversification. In this setting, the benefits of either gains increased market power and/or improvements in efficiency outweigh the reductions in the ability for shareholders to diversify. Lastly, this paper makes a methodological contribution to the literature. Garcia and Norli (2012) develop a methodology to provide a more robust measure of a firm s geographic coverage or dispersion. We contribute by extending their methodology and create a measure of geographic overlap that can be calculated for any two public firms. Specifically, we calculate the geographic overlap of bidder and target firms in horizontal mergers. However, this methodology can be applied to any pair for firms in other settings. For example, mapping the geographic overlap of competitor firms may give a more robust measure of the intensity of their competition in the product markets. The remainder of the paper proceeds as follows. Section 2 presents the sample selection, the empirical methodology used to measure geographic expansion, and summary statistics of horizontal mergers included in my sample. Section 3 presents empirical findings of shareholder reactions to merger announcements. Section 4 concludes. II. Data, Sample Selection, and Empirical Methodology 2.1 Sample construction Our sample of horizontal mergers is drawn from deals included in the Securities Data Corporation (SDC) Mergers and Acquisitions database. We include deals announced between January 1, 1993 and December 31, 2009. The sample begins in 1993 as this is the first year that 10-K filings 5
begin to be available electronically, which is required for textual identification of geographic dispersion. We also add several restrictions to the sample to focus on horizontal mergers, following Fee and Thomas (2004). 4 The criteria to be included in the sample are as follows: 1. The bidder did not previously own a majority interest in the target and was seeking to obtain a majority interest through the transaction. 2. The announcement date of the proposed merger or tender offer can be determined via SDC. 3. The bidder and target are both domestic, publicly traded firms with sufficient data from the Center for Research in Security Prices (CRSP) to calculate announcement period abnormal returns. 4. The bidder and target have data available in Compustat. 5. SDC reports the bidder and target share at least one industry of those listed with the same four-digit SIC code. In addition to these criteria, we also require the availability of an electronic version of the both the bidder and target firm s 10-K in the year prior to the merger announcement. This requirement ensures the ability to identify the geographic coverage of the firm prior to the merger. The methodology used to measure the geographic coverage is described in the following section. 2.2 Geographic expansion measure To measure the change in geographic coverage resulting from a bidding firm s acquisition of a target firm, we first must measure each firm s geographic coverage prior to the merger. Yet, the geographic coverage of a firm is difficult to capture for several reasons. First, Compustat provides the state that the firm s headquarters are located in. However, this variable is backfilled, including only the most recent state in which a firm is headquartered. As such, it fails to capture changes in firm location. 5 More importantly, a firm s operations often expand far beyond the location of its headquarters. As 4 We adopt of the criteria of Fee and Thomas (2004) except a removal of deals with firms from financial or regulated industries. In my study, these firms may have particular interest in expanding geographically. However, I intend to run tests to verify the robustness of my results to the exclusion of these deals. 5 Some papers have addressed this issue by using alternative data sources with historic headquarter locations or through hand collection of historic headquarter locations (e.g. Pirinsky and Wang (2006)). 6
such, the headquarter location only captures a portion of the firm s geographic operations. We note that several studies use the headquarter location as a proxy for the location of a firm s operations (e.g., Acharya, Bahai, and Subramanian (2014); Agrawal and Matsa (2013). However, this approach is particularly problematic in this study as a more precise measure of a firm s geographic coverage is necessary to capture the overlap in coverage between a bidder firm and a target firm. To avoid these problems, several papers have used proprietary data that contains a firms segments (e.g. (Gao, Ng, and Wang (2008); Landier, Nair, and Wulf (2009)). Yet, these measures capture geographic dispersion between segments but not necessarily geographic dispersion in operations. For example, Wal-mart may have have one segment, retail sales, but they are very geographically dispersed. Garcia and Norli (2012) take a different approach to identifying the geographic dispersion of firms operations. They identify a firm s geographic dispersion using textual analysis of 10-Ks. Firms systematically report properties and also discuss operations by state. As such, this method captures a more broad measure of geographic dispersion. The first step of our methodology is based off of theirs. 6 To begin, we use data from the Electronic Data Gathering, Analysis, and Retrieval system (EDGAR) of the U.S. Securities and Exchange Commission (SEC). We collect the text of all electronically available 10-K forms during the time period 1993-2009. 7 Using computerized parsing of this text, we then determine whether a firm mentions each state in the United States (U.S.) in each 10-K. If a state is mentioned a minimum of one time, the firm is considered to have operations in that state. The result is a vector of 50 indicator variables for each firm-year denoting geographic presence in each of the states in the U.S. Following Garcia and Norli (2012), geographic dispersion is measured as the sum of the 50 indicator variables. In our sample of 6 A difference between the methodology used by Garcia and Norli (2012) and our methodology is that they used text from four sections of the 10-K while we use the entire 10-K. However, when we calculate distribution of geographic dispersion of firms our sample, it has properties very similar to their measure. 7 Here and throughout the paper 10-K is used generically for all variations of the annual report including the following forms: 10-K, 10-K405, 10-KT, and 10-KSB. 7
horizontal mergers, the mean (median) geographic dispersion is 15.4 (13.0) for bidding firms and 12.3 (10.0) for target firms. The differences in distributions can be seen graphically in Figure 1. The difference in geographic dispersion bidder and target firms is not necessarily suprising given the size differential between these two groups of firms. After measuring the geographic footprint of bidder and target firms, we measure the geographic overlap of the bidder and target firms for each merger announcement. To do so, we use the cosine similarity of each firm s geographic dispersion. Cosine similarity is a measure used to calculate the similarity between two vectors. Cosine similarity is used to measure similarities in text such as product descriptions between bidder and target firms in mergers and acquisitions (Hoberg and Phillips (2010)) and filings of IPO prospectuses (Hanley and Hoberg (2012); Hanley and Hoberg (2010)). We measure the geographic overlap of the bidder and target firm in horizontal merger i using the following formula: G( b, t ) i i 50 s 1 b, s, i t, s, i 50 50 2 2 b, s, i t, s, i s 1 s 1 (1) where b i is the bidding firm in the merger, t i is the target firm in the merger, and ω is the weight of the geographic dispersion for the firm in state s. Specifically, in this case ω is equal to one for each state in which a firm has a presence and zero otherwise. This function is bounded between zero and one. A value of zero means that the bidder and target firm have no states in common while a value of one means the bidder and target have complete overlap in their geographic coverage. For convenience in interpretation, to get to a measure of geographic expansion for the combined firm, we take one minus the geographic overlap measure. This measure of geographic expansion is also bounded between zero and one. However, if the bidding firm and target firm have no states in which they both have a presence, the geographic expansion measure takes a value of one. If the bidding and target firm have 8
an overlapping presence in all states in which they operate, then the geographic expansion variable takes a measure of zero. There is significant variation in the amount of geographic dispersion across deals. The mean (median) level of geographic expansion for deals in our sample is 0.442 (0.439). The min (max) of geographic expansion is 0.074 (0.764). Further, we also create high expansion, an indicator variable that takes the value of one for deals in the top quartile of geographic expansion and zero otherwise. In Figure 2, we provide a graphical illustration of two mergers highlighting what the geographical overlap measure captures. Figure 2.A maps geographic dispersion of two firms, Olin Corporation (bidder) and Pioneer Corporation (target), involved in a horizontal merger. States in which only the bidder has a presence are shaded in light gray. States in which only the target has a presence are shaded in dark gray. Finally, states in which both firms have a presence are shaded in black. These two firms have a presence in 24 states total. However, the target and bidder firm share a presence in only 5 of these 24 states, resulting in 19 unique states between the two firms. Given the difference in geographic coverage, the measure of geographic expansion for this deal is 0.582. This value places the deal is in the top quartile of geographic expansion for all sample deals. As noted in the introduction, the CEO of Olin Corporation cited expansion of the firm s geographic footprint as an expected source of value from the deal. Contrast this first example with the example in Figure 2.B, which shows the geographic overlap of RCN Corporation (bidder) and Neon Communications (target). The two firms have a presence in 22 states, a similar number of total states to the previous example. However, the firms share a presence in 9 states, so the two firms only represent 13 unique states. The measure of geographic expansion for this deal is 0.074, placing this deal in the bottom quartile of geographic expansion for all sample deals. Not surprisingly, RCN s CEO cites strengthening their market power within their current geographic footprint as a motive for the deal. 9
2.3 Sample description Based on the definition used for horizontal mergers and the requirements for the geographic expansion measure, we have a sample of 904 deals. Table 1 presents summary statistics describing the sample. Panel A includes the frequency of deals by year. Deals are spread fairly evenly across the sample time series with a two notable exceptions. First, the availability of electronic 10-Ks through EDGAR was phased in beginning in 1993 with all the 10-Ks of nearly all firms available in 1996. As such, there is a smaller frequency of deals during this phase-in period. Also, there is minor time series variation with a greater frequency of deals in the late 1990s and a slightly lower frequency of deals in other time periods. These fluctuations are consistent with the relation between mergers waves and both technological innovations and access to capital to fund acquisitions (e.g. Harford (2005)). However, the proportion of horizontal acquisitions with high geographic expansion as a proportion of all horizontal acquisitions are spread fairly uniformly across the sample time period. Panel B presents summary statistics by industry using the Fama and French 10 industry classification. There is relatively high geographic expansion in health and manufacturing deals. Alternatively, only 6% of energy deals are in the highest quartile of geographic expansion. This is not necessarily surprising as energy firms are likely more constrained in location by the availability of natural resources while manufacturing deals, for instance, may lend themselves to greater efficiencies through access to more geographic coverage of operations. Panel C presents the summary statistics of deal characteristics such as whether the deal was initiated through a tender offer, whether the deal was hostile in nature, and the type of consideration offered in the transaction. The full sample summary statistics of these characteristics are consistent with previous studies (e.g., Fee and Thomas (2004)). Further, the probability of a deal involving high 10
geographic expansion does not seem to be significantly correlated with the characteristics of the deal as there is close to one-fourth of the sample that has a each deal characteristic that is also in the top quartile of deals with high geographic expansion. 2.4 Calculating announcement period abnormal returns We follow previous studies and calculate the abnormal return for the bidder and target firm using the standard event study methodology. The parameters for the market model are estimated using returns beginning 240 trading days prior to the announcement to 40 trading days prior to the announcement and requiring the presence of at least 100 trading days. The returns used to estimate returns and to calculate abnormal returns are the CRSP value-weighted index returns. We calculate cumulative abnormal returns over both the 3- and 5-day window centered on the announcement of the deal. III. Empirical Results 3.1 Geographic expansion and cumulative abnormal announcement returns As hypothesized in the introduction, the source of value in horizontal mergers differs based the extent to which the deal results in high or low geographic expansion. To test predictions related to this hypothesis, we use the cumulative abnormal return to both bidder and target shareholders over a window centered on the announcement date as described in section 2.4. 3.1.1 Geographic expansion and bidder cumulative abnormal announcement returns We first consider the cumulative abnormal returns (CARs) realized by bidding firm shareholders. If the gains in horizontal mergers are predominantly derived from gains in operating efficiencies, then bidding firm shareholders will respond positively to geographic expansion as these deals allow for greater improvements in efficiencies. Alternatively, if gains are more likely generated from increased market power, then bidder shareholders will respond negatively to horizontal mergers 11
that result in high geographic expansion as this expansion does not improve market power in the markets that the bidding firm operates in. We first test this prediction in a univariate setting in Table 2, Panel A. We find that abnormal returns to bidder firms are 1.88% higher for deals with high geographic expansion. This difference is statistically significant at the one percent level. Further, the average announcement CARs are only significantly negative for deals with low geographic expansion. Finally, announcement CARs are negative for 49.8% of deals with high geographic expansion. By comparison, only 36.6% of announcement CARs are positive for deals with low geographic expansion. These findings are robust to expanding to a five-day announcement window. The higher announcement returns for bidder firms in horizontal mergers supports the notion that value in horizontal mergers is derived from improvements to operational efficiency. However, these results may be driven by firm, industry, deal or time characteristics not accounted for by univariate statistics. In column 1, we re-test these hypotheses in a multivariate setting. We control for the market capitalization of the bidding firm, the relative market capitalization difference between the bidding and target firm, the type of consideration offered in the deal, whether or not the deal was structured as a tender offer, and whether the deal was hostile. Further, we control for both year and industry fixed effects. We find, consistent with the univariate results, significantly higher announcement CARs to bidding firms for horizontal deals with high geographic expansion. The economic magnitude is similar to the univariate results as bidding firm shareholder receive a 1.9% higher cumulative abnormal return surrounding the announcement of the deal. Further, as documented in column 2, the effect is robust to using the continuous measure of geographic expansion. One potential concern with the findings to this point is that there is an omitted industry factor that varies across time and that is correlated with both the extent to which a horizontal merger results in geographic expansion and the abnormal return realized by the bidding firm shareholders. As a 12
potential example of such a factor, there is evidence that mergers occur in waves across both time and industries (Harford (2005)). For such a factor, our year and industry fixed effects will not capture this variation is it neither time- or industry-invariant. To address this concern, models 3 and 4 use fixed effects for each industry and year combination for our indicator and continuous measure of geographic expansion, respectively. These effects absorb any variation that is constant across an industry for a given year. We find that our results are robust to the inclusion of these finer fixed effects suggesting that factors that vary across both industry and time do not drive our result. Finally, to ensure that our results are not sensitive to the window surrounding the announcement date, we re-estimate the findings in columns (1) through (4) but replace the three-day announcement return with a five-day announcement window. The results are reported in models (5) and (6) of Table 3 using industry and year fixed effects and columns (7) and (8) using fixed effects for each industry-year combination. In each case, the positive relation between the geographic expansion in horizontal mergers and the abnormal returns to bidding firm shareholders remains statistically significant. Further, the economic impacts are slight larger than the models using three-day windows. Collectively, the multivariate findings in table 3 provide further support that the predominant source of gains in horizontal mergers is improvements in operating efficiency. 3.1.2 Cross-sectional variation in the relation between geographic expansion and target announcement abnormal returns We next exploit cross-sectional variation in firm and industry characteristics to provide additional evidence that the positive reaction around the announcement of horizontal mergers with high geographic expansion is related to the potential to improve operating efficiencies. First, one sources of improvements in operational efficiencies is the removing redundant costs across both the bidder and target firm. For example, merging firms operating in the same product markets may benefit as the larger firm can better absorb advertising investments that shape demand (e.g. Galbraith (1967)) or administrative costs. As such, target firms with greater Selling, General, and Administrative 13
expenses (SG&A) as a percent of assets may provide target firms with greater opportunities to realize improvements in operational efficiency. If the positive relation between horizontal mergers leading to geographic expansion and the abnormal returns to the bidding firm shareholders, than the previous arguments lead to the prediction that this positive relation is most pronounced when SG&A expenses as a percent of assets are relatively high. We test this prediction in Table 4. Column 1 interacts an indicator variable that takes the value of one if the deal is in the top quartile of horizontal mergers in regards to the extent to which the deal results in geographic expansion with the target firm s SG&A expense as a fraction of its total assets in the year prior to the merger. Consistent with interpretation that positive CARs to bidding firm shareholders is as a result of improvements in operating efficiencies, the effect is more pronounced when the target firm had greater SG&A expenses as a fraction of total assets. As reported in column 2, this cross-sectional variation is robust to using a continuous measure of geographic expansion. Second, the ability to gain from operational efficiencies is greater in more concentrated industries (Perry and Porter (1985)). Intuitively, in industries with less concentration (more competition) firms are incentivized to improve operational efficiencies to remain viable given the competitive landscape. However, weaker competitive forces in more concentrated industries to not provide the same incentive to firms in these industries. As such, we predict that the positive relation between horizontal mergers that result in high geographic expansion and the bidding firm abnormal returns is most pronounced when the firms operate in industries where operational inefficiencies have not already been driven away by industry competition. We test this prediction by interacting an indicator variable that takes the value of one if the deal is in the top quartile of horizontal mergers in regards to the extent to which the deal results in geographic expansion with the Herfindahl index of the industry in which the firms operate in the year prior to the merger announcement. Higher values of the Herfindahl index indicate greater competition 14
in the industry. In other words, our relation between geographic expansion and bidder shareholder announcement returns should be less (more) pronounced for firms in industries with a greater Herfindahl index (less industry competition). In column 3, we find evidence consistent with this prediction. Specifically, the positive relation between geographic expansion and bidder CARs is more pronounced in less competitive industries consistent with the source of gains being derived from improvements in operational efficiencies. This result is robust to the use of the continuous measure of geographic expansion. In columns (5) through (8), we document that these cross-sectional variations are not sensitive to the choice of a three-day window. The pronounced relation between horizontal mergers with high geographic dispersion and bidder CARs when target SG&A expenses are high and when competitive forces are relatively weak is robust to the use five-day announcement windows. In sum, this crosssectional variation provides additional support that the value in horizontal mergers is a product of bidder firms capitalizing on operational inefficiencies. 3.1.3 Geographic expansion and target cumulative abnormal announcement returns While the increased returns to bidding firm shareholder is consistent with expected improvements in operating efficiencies, it is also possible that we have captured deals in which the bidding firm is better able to extract wealth from target shareholders. If the shareholders of target firms are harmed by horizontal mergers, the tests relating to the CARs of bidder firms may be capturing a wealth transfer from target shareholders to bidding firm shareholders and not improvements in operational efficiencies. We test for this alternative explanation by evaluating the abnormal announcement returns to target shareholders. Univariate tests of target abnormal announcement returns are presented in Panel B of Table 2. The average three-day announcement return for target shareholders in deals in the bottom three quartiles of geographic expansion is 22.0%. The corresponding average for target shareholders in deals 15
in the top quartile of deals based on geographic expansion is 23.7%. While the difference is statistically insignificant, returns to target shareholders in deals with high geographic expansion are slightly higher than the returns to target shareholders in other horizontal mergers. Thus, we find no evidence of wealth extraction from target shareholders as we would predict a negative relation between target shareholder CARs and geographic expansion if this were the case. This interpretation continues to hold if we use a five-day return window. To ensure that the CARs to target firm shareholders in horizontal mergers with high geographic expansion, we reevaluate the relation in a multivariate framework. For control variables, we use similar variables we used for regressions with bidder CARs. Specifically, we control for the market capitalization of the target firm prior to the deal announcement, the size of the bidding firm relative to the size of the target firm, the type of consideration used in the merger offer, whether the deal was initiated as a tender offer, and whether the bid was hostile. Across model specifications, we use either an indicator variable for high geographic expansion or the continuous measure of geographic expansion, either a three-day or five-day announcement window, and either the combination of year and industry fixed effects or fixed effects for each industry and year pair. The results of these specifications are presented in Table 5, columns (1) through (8). Regardless of the specification, the target firm s CAR around the announcement of a horizontal merger is positively correlated with an increase in geographic expansion. In each case, the relation is statistically insignificant. The insignificant results may partially explained by the observation that, while the returns to acquiring firm shareholders vary significantly across deals and deal characteristics, the cross-sectional distribution of returns to target shareholders is relatively narrow (Andrade, Mitchell, and Stafford (2001)). However, these non-results suggest that there is not a transfer from target shareholders to bidder shareholders in horizontal mergers that result in high geographic expansion. 16
IV. Conclusion This paper uses a novel measure, namely geographic expansion, to provide evidence on the sources of gains in horizontal mergers. The findings suggest that bidding firms gain from geographic expansion while target firms are no worse off. The evidence in this paper has implications for policy makers when setting antitrust policies. The results imply that the primary source of gains in horizontal mergers is improvements in operational efficiencies and not increased ability to collude with rival firms. 17
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Fig. 2 - Examples of Geographic Overlap A: Geographic overlap for Olin Corp's acquisition of Pioneer Corp B: Geographic overlap for RCN Corp's acquisition of Neon Communications - Presence of bidder only - Presence of target only - Presence of both bidder and target 21
Table 1 Sample Description This sample includes all proposed horizontal mergers and acquisitions announced between 1993 and 2009 that covered in the Securities Data Corporation (SDC) database. % High Expansion is the percent of deals with their measure of geographic expansion in the highest quartile of all sample deals. MVE is the market value of equity obtained from CRSP prior to the announcement of the deal. Panel A presents the summary statistics by year. Panel B presents the deals by industry following the Fama and French 10 industry classification. Panel C presents summary statistics for deals with certain characteristics. The deal characteristics come from SDC. % of Sample % High Expansion Bidder MVE Target MVE Bidder MVE / Target MVE Year Deals Panel A: frequency of deals by year 1993 5 0.55 0.40 3731.15 1813.69 5.40 1994 12 1.33 0.17 14898.93 1220.23 40.37 1995 27 2.99 0.30 5561.67 1443.17 11.54 1996 69 7.63 0.26 9427.74 702.36 39.13 1997 86 9.51 0.23 8681.44 989.97 22.83 1998 97 10.73 0.27 31076.92 2271.43 70.35 1999 86 9.51 0.24 14232.96 1395.52 24.47 2000 64 7.08 0.30 23778.65 1401.23 88.24 2001 35 3.87 0.26 19435.09 1462.53 76.87 2002 44 4.87 0.27 20091.97 512.28 56.63 2003 52 5.75 0.27 15405.53 2956.26 46.20 2004 62 6.86 0.26 32540.59 2848.43 54.99 2005 59 6.53 0.22 24616.54 2437.48 34.67 2006 66 7.30 0.26 12029.48 1127.63 23.42 2007 49 5.42 0.20 23656.60 1085.29 95.11 2008 38 4.20 0.29 33000.72 2609.34 59.36 2009 53 5.86 0.21 23883.82 912.76 45.30 All Deals 904 100 0.25 19704.98 1603.21 48.63 Panel B: frequency of deals by industry Consumer Nondurables 40 4.42 0.23 11830.61 1319.97 32.06 Consumer Durables 14 1.55 0.21 1579.12 426.06 3.90 Manufacturing 100 11.06 0.33 9951.17 1816.43 18.39 Energy 52 5.75 0.06 12817.23 2231.68 15.96 HiTech 334 36.95 0.22 20083.52 910.63 59.23 Telecom 50 5.53 0.32 44771.48 5641.13 68.30 Shops 72 7.96 0.21 7856.47 872.00 19.50 Health 161 17.81 0.38 35511.13 2149.77 91.90 Other 81 8.96 0.20 5270.69 1206.71 6.90 Panel C: deal characteristics Tender offer 194 21.46 0.24 19778.31 720.12 58.52 Hostile 95 10.51 0.24 12210.30 3349.94 8.88 Cash consideration 378 41.81 0.25 26655.49 813.72 77.61 Stock consideration 316 34.96 0.28 16061.56 1742.61 35.33 Mixed consideration 210 23.23 0.22 12676.53 2814.52 16.49 22
Table 2 Announcement Cumulative Abnormal Returns Abnormal return is the abnormal return for a three- or five-day window centered on the merger announcement date and calculated from a market model estimated over the period from 206 to 6 days before the merger announcement using the CRSP value-weighted index. Panel A presents means for the bidding firms. 'Positive, negative' is the number of deals with positive and negative abnormal returns, respectively. t-statistics for abnormal returns are based on tests that standardized prediction errors are equal to zero. Panel B presents the means for target firms. Tests are significant at the 10%, 5%, and 1% levels as denoted by *, **, and ***, respectively. 3 day return window 5 day return window Low Expansion High Expansion Difference (High-Low) Low Expansion High Expansion Panel A: bidder abnormal returns Abnormal Return -2.42% -0.54% 1.88% -2.80% -0.54% 2.25% t-statistic -8.21 *** -1.14 3.26 *** -8.47 *** -1.02 3.49 *** Positive, negative 247, 428 114, 115 236, 439 110, 119 Difference (High-Low) Panel B: target abnormal returns Abnormal Return 22.01% 23.72% 1.71% 22.51% 24.26% 1.75% t-statistic 26.34 *** 16.30 *** 1.03 26.01 *** 16.49 *** 1.02 Positive, negative 599, 76 209, 20 599, 76 209, 20 23
Table 3 Bidder Announcement Cumulative Abnormal Returns This table presents the results of an OLS regression where the dependent variable is the abnormal return for a three- or five-day window centered on the merger announcement date and calculated from a market model estimated over the period from 206 to 6 days before the merger announcement. High Geographic Expansion is an indicator variable for deals in which the geographic expansion measure is in the top quartile of the sample. Geographic Expansion is the continuous measure of one minus the geographic overlap as described in the text. Bidder Market Cap is the market capitalization of the target firm prior to the deal announcement. Relative Size is the market cap of the bidder prior to the deal divided by the market cap of the acquirer. Cash Only (Stock Only) is an indicator variable taking the value of one if the bidder used only cash (stock) consideration in the deal. Tender Offer is an indicator variable taking a value of one if the deal was structured as a tender offer. Hostile is an indicator variable taking a value of one if the deal was not solicited. t-statistics based on standard errors robust to heteroskedasticity are presented in parentheses. Parameters are significant at the 10%, 5%, and 1% levels as denoted by *, **, and ***, respectively. 3 Day Return Window 5 Day Return Window (1) (2) (3) (4) (5) (6) (7) (8) High Geographic Expansion 0.019*** 0.021*** 0.023*** 0.024*** (3.38) (3.55) (3.61) (3.46) Geographic Expansion 0.050** 0.050** 0.060*** 0.055** (2.34) (2.12) (2.63) (2.15) Bidder Market Cap -0.000-0.000-0.000-0.000-0.000-0.000-0.000-0.000 (-0.98) (-1.28) (-1.06) (-1.32) (-0.79) (-1.12) (-0.78) (-1.05) Relative Size 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000** 0.000** (3.28) (3.38) (2.91) (3.02) (2.86) (2.95) (2.29) (2.38) Cash Only 0.033*** 0.034*** 0.030*** 0.031*** 0.038*** 0.038*** 0.035*** 0.035*** (5.18) (5.25) (4.37) (4.40) (5.11) (5.19) (4.30) (4.34) Stock Only -0.020*** -0.020** -0.021** -0.020** -0.015* -0.014-0.018* -0.018* (-2.64) (-2.55) (-2.45) (-2.39) (-1.73) (-1.64) (-1.88) (-1.83) Tender Offer -0.006-0.007-0.009-0.010-0.003-0.004-0.005-0.005 (-1.04) (-1.12) (-1.34) (-1.42) (-0.43) (-0.53) (-0.59) (-0.67) Hostile 0.004 0.004 0.004 0.004 0.001 0.002 0.003 0.002 (0.53) (0.55) (0.50) (0.48) (0.18) (0.20) (0.29) (0.27) Intercept -0.040-0.052 0.019* 0.001-0.049-0.064* 0.001-0.018 (-1.35) (-1.60) (1.79) (0.08) (-1.58) (-1.84) (0.07) (-0.75) N 904 904 904 904 904 904 904 904 Adj. R-sq 0.134 0.130 0.129 0.121 0.115 0.111 0.096 0.089 Industry and Year FEs Yes Yes No No Yes Yes No No Industry x Year FEs No No Yes Yes No No Yes Yes 24
Table 4 Bidder Announcement Cumulative Abnormal Returns and Efficiency Gains This table presents the results of an OLS regression where the dependent variable is the abnormal return for a three- or five-day window centered on the merger announcement date and calculated from a market model estimated over the period from 206 to 6 days before the merger announcement. High Geographic Expansion is an indicator variable for deals in which the geographic expansion measure is in the top quartile of the sample. Geographic Expansion is the continuous measure of one minus the geographic overlap as described in the text. Target SG&A/Assets is selling, general, and administrative expenses scaled by total assets for the target firm in the year prior to the merger. Industry Competition is a Herfindahl index of sales in the same two-digit industry of the acquiring firm in the year prior to the merger. Included controls are those found in Table 3. t-statistics based on standard errors robust to heteroskedasticity are presented in parentheses. Parameters are significant at the 10%, 5%, and 1% levels as denoted by *, **, and ***, respectively. 3 Day Return Window 5 Day Return Window (1) (2) (3) (4) (5) (6) (7) (8) High Geographic Expansion 0.079*** 0.065** * Target SG&A/Assets (3.71) (2.52) Geographic Expansion * Target 0.294*** 0.204** SG&A/Assets (4.01) (2.16) High Geographic Expansion -0.370*** -0.521*** * Industry Competition (-4.23) (-4.61) Geographic Expansion * Industry -0.649** -0.850** Competition (-2.20) (-2.47) High Geographic Expansion -0.004 0.035*** 0.008 0.051*** (-0.61) (4.55) (0.92) (5.62) Geographic Expansion -0.039 0.073** 0.001 0.102*** (-1.40) (2.39) (0.03) (3.05) Target SG&A/Assets -0.008-0.120*** 0.002-0.074 (-0.67) (-3.41) (0.11) (-1.55) Industry Competition 0.083 0.275* 0.169** 0.413** (1.35) (1.82) (2.40) (2.39) Controls Yes Yes Yes Yes Yes Yes Yes Yes N 936 936 938 938 936 936 938 938 Adj. R-sq 0.148 0.147 0.141 0.131 0.110 0.100 0.115 0.097 Industry and Year FEs Yes Yes Yes Yes Yes Yes Yes Yes 25
Table 5 Target Announcement Cumulative Abnormal Returns This table presents the results of an OLS regression where the dependent variable is the abnormal return for a three- or five-day window centered on the merger announcement date and calculated from a market model estimated over the period from 206 to 6 days before the merger announcement. High Geographic Expansion is an indicator variable for deals in which the geographic expansion measure is in the top quartile of the sample. Geographic Expansion is the continuous measure of one minus the geographic overlap as described in the text. Target Market Cap is the market capitalization of the target firm prior to the deal announcement. Relative Size is the market cap of the bidder prior to the deal divided by the market cap of the target. Cash Only (Stock Only) is an indicator variable taking the value of one if the bidder used only cash (stock) consideration in the deal. Tender Offer is an indicator variable taking a value of one if the deal was structured as a tender offer. Hostile is an indicator variable taking a value of one if the deal was not solicited. t-statistics based on standard errors robust to heteroskedasticity are presented in parentheses. Parameters are significant at the 10%, 5%, and 1% levels as denoted by *, **, and ***, respectively. 3 Day Return Window 5 Day Return Window (1) (2) (3) (4) (5) (6) (7) (8) High Geographic Expansion 0.012 0.006 0.011 0.004 (0.76) (0.36) (0.65) (0.22) Geographic Expansion 0.071 0.057 0.053 0.037 (1.34) (0.99) (0.97) (0.61) Target Market Cap -0.000*** -0.000*** -0.000* -0.000* -0.000*** -0.000*** -0.000** -0.000* (-2.72) (-2.62) (-1.91) (-1.83) (-2.78) (-2.70) (-1.99) (-1.93) Relative Size 0.000*** 0.000*** 0.000** 0.000** 0.000*** 0.000*** 0.000*** 0.000*** (3.15) (3.09) (2.49) (2.44) (3.77) (3.72) (3.12) (3.09) Cash Only 0.027 0.027 0.018 0.018 0.024 0.024 0.011 0.011 (1.30) (1.31) (0.79) (0.79) (1.13) (1.13) (0.48) (0.49) Stock Only -0.048** -0.047** -0.062*** -0.061*** -0.045** -0.045** -0.063*** -0.063*** (-2.38) (-2.35) (-2.69) (-2.66) (-2.19) (-2.16) (-2.61) (-2.59) Tender Offer 0.061*** 0.061*** 0.045** 0.045* 0.064*** 0.064*** 0.049** 0.049** (2.87) (2.86) (1.97) (1.96) (2.92) (2.92) (2.06) (2.06) Hostile 0.003 0.003-0.002-0.001 0.005 0.006 0.003 0.004 (0.14) (0.15) (-0.07) (-0.06) (0.26) (0.27) (0.14) (0.15) Intercept 0.039 0.015 0.408*** 0.381*** 0.057 0.040 0.430*** 0.413*** (0.54) (0.21) (3.32) (3.02) (0.78) (0.53) (3.02) (2.84) N 904 904 904 904 904 904 904 904 Adj. R-sq 0.117 0.118 0.143 0.144 0.113 0.113 0.127 0.128 Industry and Year FEs Yes Yes No No Yes Yes No No Industry x Year FEs No No Yes Yes No No Yes Yes 26