The value of CEOs' supply chain experience: Evidence from mergers and acquisitions

Journal Pre-proof The value of CEOs' supply chain experience: Evidence from mergers and acquisitions

Eliezer M. Fich, Tu Nguyen PII:

S0929-1199(18)30326-2

DOI:

https://doi.org/10.1016/j.jcorpfin.2019.101525

Reference:

CORFIN 101525

To appear in:

Journal of Corporate Finance

Received date:

11 May 2018

Revised date:

4 September 2019

Accepted date:

29 September 2019

Please cite this article as: E.M. Fich and T. Nguyen, The value of CEOs' supply chain experience: Evidence from mergers and acquisitions, Journal of Corporate Finance(2018), https://doi.org/10.1016/j.jcorpfin.2019.101525

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© 2018 Published by Elsevier.

Journal Pre-proof

The Value of CEOs’ Supply Chain Experience: Evidence from Mergers and Acquisitions

Eliezer M. Fich* and Tu Nguyen**

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Eliezer Fich is at LeBow College of Business, Drexel University, Philadelphia, PA 19104, USA. Tel. +1-215-8952304, [email protected]. ** Corresponding author. Tu Nguyen is at University of Waterloo, School of Accounting and Finance, 200 University Avenue W, Waterloo, Ontario, Canada N2L 3G1, Tel. +1-519-888-4567x31988, [email protected].

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For helpful comments and suggestions we thank David Becher, Raquel Benbunan, Jay Cai, Naveen Daniel, David Denis, Daniel Dorn, Michael Gombola, Todd Gormley, Jarrad Harford, Jiekun Huang, Mark Huson, Ken Klassen, Mark Leary, Greg Nini, Micah Officer, Hai Ta, Ralph Walkling, David Yermack, and session participants at the FMA 2012 conference, the NFA 2013 conference, and the University of Waterloo. An earlier version of this paper was titled “Acquisitions by CEOs with supply chain expertise.” All errors are our responsibility.

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The Value of CEOs’ Supply Chain Experience: Evidence from Mergers and Acquisitions Abstract

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Acquirer CEOs with experience in the target’s industry supply chain (‘supply chain CEOs’) are associated with wealth effects of first-order importance: they earn 1.5% higher merger announcement returns. Conversely, their targets get a lower share of the merger gains. Acquisitions by supply chain CEOs also exhibit higher synergies, better post-deal accounting performance, and less goodwill written off. These findings withstand checks for endogeneity, anticipation bias, and numerous robustness tests. In takeovers by supply chain CEOs, superior acquirer performance stems from both value creation and rents negotiated away from target shareholders.

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Key words: supply chain, synergies, acquisitions, value creation, bargaining JEL Classification: G30, G34, G32, G34, J24

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Journal Pre-proof Introduction We study whether acquirer Chief Executive Officers (CEOs) with experience in the target’s industry supply chain (hereafter supply chain CEOs) affect merger gains. Our results show that acquirer CEOs with such experience are associated with wealth effects that are economically important for their own shareholders. On average, acquirer firms exhibit a 1.5% increase in the 3-day cumulative abnormal return (CAR) at deal announcement in mergers by supply chain CEOs. This result relies on our empirical analyses of 1,491 completed merger and acquisition (M&A) bids announced during 1997-2014.

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Clearly, the acquirer CEO’s supply chain experience in the target industry is an indirect link between

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the two firms. In light of the academic evidence showing that direct connections between the firms that

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participate in a merger affect their value, the supply chain experience relation might be deemed

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unimportant. Nevertheless, our results indicate that this is not the case. While controlling for different direct links between targets and acquirers,1 we find that supply chain CEOs are associated with large and

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significant gains for acquirer shareholders that, on average, exceed US$270 million during the 3-day

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merger announcement period.

Our conjecture is that the CEO of the acquirer with work experience in key suppliers or customers of

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the target will be more likely to efficiently integrate both companies and improve the value of the merged firm. As a result, supply chain CEOs can generate meaningful synergies through economies of scale,

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economies of scope, and/or improved market power. Knowledge of the target’s industry supply chain may also help the merged firm bridge the information gap with its suppliers and customers, resulting in better terms for the purchase of its inputs and the sale of its output. In addition, familiarity with the target’s industry supply chain can help the merged firm anticipate demand and supply shocks, thereby facilitating better production management. Importantly, experience in the target’s industry supply chain could help the acquirer CEO during premium negotiations with the target managers. 1

Direct ties between targets and acquirers include social connections (Ishii and Xuan, 2014), product market relations (Ahern and Harford, 2014), shared directors (Cai and Sevillir, 2012), prior employment of the acquirer CEO in the target industry (Custódio and Metzger, 2013), geographical proximity (Uysal, Kedia, and Panchapagesan, 2008), and institutional crossholdings (Harford, Jenter, and Li, 2011; Matvos and Ostrovsky, 2008).

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Journal Pre-proof To identify the mechanisms underlying the superior acquirer performance associated with supply chain CEOs, we conduct a number of tests to ascertain whether the gains come from improved synergies (the efficient appraisal hypothesis) or from the bargaining of better terms to capture additional rents from the target (the superior negotiation hypothesis). These hypotheses are not necessarily mutually exclusive and we obtain support for both in the data. Using the division of gains method proposed by Ahern (2012) we find that targets get a relatively lower share of the merger gains (about 1.5%) in deals by supply chain

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CEOs. These results support the superior negotiation hypothesis. Our analyses also show that M&A deals by supply chain CEOs exhibit higher synergies, better post-deal accounting performance, and lower

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amounts of goodwill written off. The findings suggest that some of the gains to acquirer shareholders in

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deals by supply chain CEOs arise from value creation. Our entire evidence validates the views by

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practitioners that CEOs and other senior executives with supply chain experience are more likely to enhance shareholder value (Slone, Mentzer, and Dittmann, 2007).

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To determine the work experience of acquiring CEOs in industries related to the target’s supply

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chain, we implement a two-step procedure. First, for each of the 1,999 acquirer CEOs in our sample, we examine the biographical record available from the BoardEx database, which we complement with

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information from proxy statements, annual reports, and other sources, to produce an individual

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employment history and educational record. We then use the 1997-2012 input-output (IO) matrices published by the U.S. Bureau of Economic Analysis (BEA) to identify a supply chain link between each pair of industries. We define a supply chain reliance relation between two industries whenever one industry sells a significant amount of its output to (or buys a significant amount of its inputs from) another industry. Based on this definition, we classify the acquirer firm’s chief executive as a supply chain CEO if he or she has work experience (as a director or top manager) in a firm operating in an industry that has a product market supply chain reliance relation with the target’s industry. Our empirical specifications also control for potential market place relations between acquirers and targets (noted by Ahern (2012) to significantly affect M&A gains). The evidence from our tests suggests that our results are not driven by acquirer-target connections. 4

Journal Pre-proof Several identification concerns complicate the interpretation of our findings. For example, deals by an acquirer with a supply chain CEO might be fundamentally different from deals performed by a different acquirer. It is also possible that supply chain CEOs might have unobservable characteristics that are different from other CEOs, and these differences lead to different merger performance of the deals with a supply chain CEO. To tackle them, we implement econometric tools to address observed and unobserved heterogeneity at various levels and use Heckman (1979) model to take on selection. Although our

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baseline results prove robust to all of these methods, we recognize that these methods are subject to their own respective caveats. Therefore, we cannot completely rule out the possibility of endogeneity.

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However, the results of the different methods we use would be hard to reconcile with alternative

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explanations, thereby suggesting a causal interpretation of our findings. Importantly, if boards really

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consider supply chain experience as part of their criteria to select and hire CEOs, then, instead of undermining our findings, the possibility of selection actually strengthens our tenet that supply chain

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experience has beneficial firm value consequences.

Before noting the contributions of this paper, it is important to discuss our findings in the context of

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related work. In broad terms, we study whether a bidder’s CEO familiarity with the target’s environment

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affects M&A gains. We track such familiarity with the bidder CEOs’ experience in the target’s supply chain whereas Custódio and Metzger (2013) track it with the bidder CEOs’ previous employment in the

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target’s industry. They find that mergers by these CEOs earn higher acquisition returns only in diversifying transactions. In contrast, we show that supply chain CEOs benefit acquirer shareholders in both diversifying and non-diversifying deals. Yet, in light of the study by Custódio and Metzger (2013), we present several tests that enable us to confidently argue that the supply chain experience of acquirer CEOs is a channel that is different than (and independent from) the effect of CEOs with a prior job in the target’s industry.2 For example, we find that acquirer CEOs who have never held a job in the target’s

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Sections 3.1. and 3.3. herein present these tests. Another important difference between the two papers stems from the fact that Custódio and Metzger show that gains to acquirers arising from their CEO’s prior employment in the target industry come from only from a transfer of rents from target to acquirer shareholders. Conversely, our

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Journal Pre-proof industry but have experience in the target’s supply chain perform better takeovers. These analyses, together with our baseline results, show that the supply chain channel is economically important in its own right as it is associated with an average wealth generation of over US$270 million for the acquirer shareholders upon deal announcement, an increase of over 3.8% in the combined firm’s post-merger accounting returns, and a 12% decline in the amount of goodwill written off. This paper delivers relevant contributions to several strands of the M&A literature. Our results

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showing that deals by supply chain CEOs are more accretive for their shareholders complement the work on the value effects of supply chain links during takeovers. Other studies in this area show that stronger

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product market connections lead to more cross-industry mergers and that mergers spread in waves across

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the network through customer-supplier links (Ahern and Harford, 2014), that the more a target’s industry

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relies on its acquirer’s industry, either as a supplier or as a customer, the less the target gains relative to

(Bhattacharyya and Nain, 2011).

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the acquirer (Ahern, 2012), and that supplier selling prices drop after a downstream consolidation

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Our work also advances the section of the M&A literature that evaluates how certain managerial attributes affect the wealth of shareholders in these transactions. Studies in this area show that CEO and

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director traits affecting acquisitions include: risk tolerance (Graham, Harvey, and Puri, 2013), gender

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(Huang and Kisgen, 2013), target industry employment (Custódio and Metzger, 2013), narcissism (Aktas, De Bodt, and Roll, 2012), age (Jenter and Lewellen, 2015), envy (Goel and Thakor, 2010), and overconfidence (Malmendier and Tate, 2008). The evidence herein also contributes to the evolving literature demonstrating material gains for acquirer shareholders (e.g., Netter, Stegemoller, and Wintoki, 2011; and Golubov, Yawson, and Zhang, 2015), and the segment of the M&A literature considering the importance of certain ties between the acquirer and target firms. Such ties include social connections (Ishii and Xuan, 2014), shared directors

evidence indicates that the gains to acquiring shareholders in deals by supply chain CEOs arise from both superior negotiation and efficient appraisal (surplus generation).

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Journal Pre-proof (Cai and Sevillir, 2012), and institutional crossholdings (Harford, Jenter, and Li, 2011; Matvos and Ostrovsky, 2008). Our empirical results deliver a key implication to theoretical work by Aghion and Tirole (1997) and Harris and Raviv (2005) postulating that CEOs likely possess the greatest informational advantage for M&A. The evidence we present suggests that one potential source of such advantage for acquiring CEOs is having knowledge of the target’s industry supply chain.

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Beyond the M&A literature, our findings provide new evidence to the work considering the firm value effects attributable to the supply chain (e.g., Cohen and Frazzini, 2008; Hertzel, Li, Officer, and

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Rodgers, 2008; and Dass, Kini, Nanda, Onal, and Wang, 2014),3 to studies on managerial experience as a

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trait that enhances performance (e.g., Bertrand and Schoar, 2003; Graham, Li, and Qiu, 2012; Dittmar and

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Duchin, 2015; Denis, Denis, and Walker, 2015; and Malmendier and Nagel, 2015), and to the literature contrasting the productivity of firm-specific and general human capital (e.g., Becker, 1962; and Lazear,

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2009).

2. Data and Descriptive Statistics

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Data on mergers and acquisitions are drawn from the Securities Data Company’s (SDC) M&A database

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from 1997 to 2014. Our sample includes completed M&A transactions with a deal value of at least US$1 million. We exclude spinoffs, recapitalizations, repurchases, self-tenders, exchange offers, transactions for which deal value is not disclosed, leveraged buyouts, privatizations, acquirers seeking an unspecified target, or targets seeking an unspecified buyer, and transactions with less than 50% shares acquired. Acquirers and targets in our sample are publicly traded U.S. companies with accounting, stock market, and board data available from Compustat, the Center for Research in Security Prices (CRSP), and the

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Cohen and Frazzini (2008) find that stock prices are not always fast in incorporating news involving firms that share substantive supply chain links. They argue that future research should examine to what extent different types of information and different delivery paths affect how investors process information. In this regard, we evaluate whether M&A announcements prompt investors to process information related to the acquirer CEOs’ experience (in our case supply chain knowledge of the target’s industry). Our results suggest that investors process such information and impute it in the acquirers’ stock prices.

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Journal Pre-proof BoardEx database, respectively. We also require that both the acquirer and the target have valid NAICS (North American Industry Classification System) codes that belong to one of the 59 industries classified in the U.S. Bureau of Economic Analyses (BEA) summary-level Use Tables for the years 1997-2012.4 These criteria lead to a final sample of 1,491 deals. Our measures of supply chain reliance follow a similar logic as the measures in Ahern (2012) that examine the relative importance between suppliers and customers. 5 Specifically, based on the NAICS in the BEA input-output database, Figure 1 illustrates the supply chain reliance between an industry and the

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target’s industry. The relation could be in a form of upstream reliance if another industry sells at least

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20% of its output to the target’s industry or if the amount of input the target industry buys from that other

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industry accounts for at least 20% of the target industry’s total production. On the other hand, the relation

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could be a downstream reliance whenever another industry’s input provided by the target industry accounts for at least 20% of its total production or when the target industry sells at least 20% of its output

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to that industry.6

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For each pair of industries, we average the 1997-2012 annual input and annual output levels using the

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input-output (IO) matrices produced by BEA to establish their supply chain relations. Specifically, to

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The BEA summary-level Tables for each of the years from 1997 to 2012 were released on January 23, 2014, as part of a comprehensive revision to the industry economic accounts (IEAs). The Use Table classifies 69 intermediate users and 11 final users. (Final users include “personal consumption expenditures,” “private fixed investment,” “change in private inventories,” “exports of goods and services,” “imports of goods and services,” “national defense: consumption expenditures,” “national defense: gross investment ,” “nondefense: consumption expenditures,” “nondefense: gross investment,” “state and local government consumption expenditures,” and “state and local government gross investment.”) The acquirer and target firms in our sample belong to 59 intermediate users, since we exclude 4 government users (i.e., “federal general government,” “federal government enterprises,” “state and local general government,” and “state and local government enterprises”), financial sectors (“federal reserve banks, credit intermediation, and related activities”), and 5 industries that none of the deals in our sample belongs to (i.e., “motor vehicle and parts deals,” “transit and ground passenger transportation,” “warehousing and storage,” “social assistance,” and “legal services”). The 59 industries are reported in Table 1. 5

Ahern (2012) defines the relative importance of a supplier to a customer as

relative importance of a customer to a supplier as

and the

.

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While the 20% cut-off may seem high, we note that “total output” (or “total production”) refers to the total sales to other industries (not total sales to all parties). All results hold when we lower the 20% cut -off (in Table 3, Panel C) and also when we analyze a subset of targets operating in industries that have an important supply -chain relation with at least 5 other industries (in Table 7, Panel B).

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Journal Pre-proof calculate an average input and an average output, we use available data of IO usage for each pair of 59 unique industries in the BEA’s Use Tables. As these concepts are measured at the industry level, they do not necessarily reflect existing trading relations between firms in the industries. Yet, industry-level dependencies are relevant because we are interested in measuring potential (not just existing) trading relations affecting the target firms, which may be a source of gains when the acquirer CEO has experience in the target’s industry supply chain.

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We analyze the biographical information provided for all CEOs in the BoardEx database and their bio-sketch data disclosed by each acquirer firm to the Securities and Exchange Commission (SEC) in

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form 14-DEF. We complement these data with materials from other sources such as corporate annual

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reports, alumni websites, and publications issued by various charitable foundations. This information

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enables us to build an education and work history profile for each of the 1,491 acquirer CEOs in our sample. After matching the NAICS codes in the 59 IO industries with the CEO’s work history, we define

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the acquirer’s chief executive as a supply chain CEO if he or she has work experience in an industry that has a downstream and/or an upstream supply chain reliance relation with the target’s industry. For each

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deal, we exclude the acquirer’s own industry from this process. Using this taxonomy, 100 or just over

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6.7% of the bidding CEOs meet the supply chain classification.

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Table 1 reports the industrial distribution of the 1,491 sample targets and the temporal distribution of the transactions. We code industry affiliation using the 59 IO industry groups used in the BEA Use Tables. The information in Panel A indicates that industries with the highest incidence of supply chain CEOs are comprised of manufacturing sectors (such as machinery, computer and electronic products, and oil and gas extraction) and service sectors (such as ambulatory healthcare services, real estate, and publishing industries). Panel B of Table 1 shows that the number of M&A transactions is higher during 1998-2000 and 2004-2007, which coincide with periods of economic expansion. In contrast, the number of deals decreases during the 2002-2003 and 2008-2011 periods of economic contraction. The temporal distribution of the sample is consistent with the argument in Shleifer and Vishny (2003) that stock market

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Journal Pre-proof health drives merger activity. Panel B also provides the annual distribution of M&A deals involving a supply chain CEO. Table 2 reports descriptive statistics for the sample we study and compares key deal, acquirer, and CEO characteristics across subsamples of transactions with supply chain CEOs. From Panel A, we observe that mergers with these executives earn higher mean and median acquirer returns at deal announcement. On the other hand, the targets of supply chain CEOs receive insignificantly different one-

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day, one-week, or four-week offer premiums. There are no significant differences in relative deal size or the likelihood of cash-only deals, tender offers, or hostile deals in transactions with and without a supply

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chain CEO. Next, we investigate the industry relations between acquirer and target firms. We define a

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merger to be diversifying if the acquirer and target belong to different industries according to the Fama-

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French (1997) 12-industries classification. We use this broad classification to make sure that “diversifying mergers” involve unrelated industries (Custódio and Metzger, 2013).7 While over 35% of deals with

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supply chain CEOs are diversifying, only about 21% of the remaining deals are diversifying. We also report the input-output relations between acquirer and target industries, using two variables that capture

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how much the acquirer’s industry depends on the target’s industry in terms of input (“input dependence

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ratio”) and output (“output dependence ratio”), using IO data. In particular, “input dependence ratio” is calculated by the value of acquirer industry’s input provided by the target industry, divided by the total

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value of the acquirer industry’s output; and “output dependence ratio” is measured as the value of the acquirer industry’s output sold to target industry, scaled by the total value of the acquirer industry’s output. Panel A of Table 2 shows that deals by a supply chain CEO are more likely to involve higher output dependence of the acquirer’s industry on the target’s industry. Panel B shows that acquirers with a supply chain CEO are not different in terms of firm size or in terms of the amount of free cash flow compared to acquirers without a supply chain CEO. Acquiring firms with a supply chain CEO, however, have lower Tobin’s Q ratios and higher leverage. In Panel C,

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We note that in subsequent tests we employ 59 IO industries classification for our fixed effects, however, our results are robust if we control for industry fixed effects using Fama French 12 industries classification.

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Journal Pre-proof we contrast several attributes of supply chain CEOs with those of other CEOs in our sample. On average, supply chain CEOs are older and more likely to be hired from outside the firm. In terms of experience, these CEOs have worked in more companies and industries. These patterns are not surprising since older CEOs are more likely to have a longer corporate trajectory leading to more exposure to several industries. Despite their longer work experience, supply chain CEOs do not appear to have longer tenures in the bidding firms. In addition, these CEOs have not done more M&As, are not more connected or more likely

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to hold an Ivy League degree. Nevertheless, given some notable differences in deal, firm, and CEO characteristics (between supply-chain and non-supply-chain M&A transactions), we control for these

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3. Supply Chain CEOs and Merger Performance

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characteristics in subsequent tests.

This section begins by establishing the baseline finding that M&A deals by supply chain CEOs are met

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with higher acquirer announcement returns. We then examine the potential mechanisms underlying this

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finding. Our analyses try to distinguish between the superior negotiation hypothesis (arguing that the higher returns arise from the negotiation of better terms for acquirers) and the efficient appraisal

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alternative (stating that the higher returns arise due to an effective valuation of the target firm). We note,

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however, that our hypotheses are not necessarily mutually exclusive, as both efficient appraisal and better negotiation can occur in M&A deals by supply chain CEOs.

3.1. Supply Chain CEOs and Acquirer Returns To explore the wealth effects associated with supply chain CEOs, we estimate bidder CARs from day -1 until day +1 centered on the merger announcement date for each transaction. To do so, we use the standard event study procedure to calculate market model abnormal returns using the CRSP value weighted index (Dodd and Warner, 1983).

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Journal Pre-proof Panel A of Table 3 reports five ordinary least squares (OLS) regressions in which the dependent variable is the acquirer 3-day CAR described above. 8 The first three models include Year and (Acquirer Industry X Target Industry) fixed effects, with 59 industries classified using the BEA-IO taxonomy. The inclusion of these fixed effects is important because Ahern (2012) shows that acquirer-target market product connections are important to merger gains. Furthermore, all tests control for deal and acquirer characteristics similar to those in Masulis, Wang, and Xie (2007). In addition, we include variables that

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capture the relations between acquirer and target industries (i.e., diversifying merger dummy, input dependence ratio, and output dependence ratio). We also control for observable CEO-specific

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characteristics related to their general ability. These controls include an Ivy League College graduate

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indicator (as in Chevalier and Ellison, 1999), the number of prior M&A deals executed by the CEO (as in

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Jaffe, Pedersen, and Voetmann, 2013), an indicator for CEOs hired from outside the firm (as in Huson, Malatesta, and Parrino, 2004), the number of current directorships in public firms (as in Fich and

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Shivdasani, 2006), number of industry experience (as in Custódio, Ferreira, and Matos, 2013), and the number of network connections (as in Fracassi and Tate, 2012).

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Model (1) shows a positive relation between the presence of a supply chain CEO and the acquirer’s

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announcement return. That is, investors appear to perceive M&A deals by supply chain CEOs as particularly valuable to the acquirer shareholders. According to model (1), having a CEO with supply

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chain experience increases the acquirer’s abnormal return by about 1.5 percentage point. The size of the supply chain effect is notable when compared to the mean M&A announcement CAR of negative 0.8% for all the bidders in our sample. Indeed, the higher return in deals by supply chain CEOs implies an increase of about US$270 million in terms of market capitalization for the average sample bidder during

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The results continue to hold when we use different event windows to measure acquirer abnormal returns. In another robustness analysis, we perform a two-stage CARs analysis (Shivdasani and Yermack, 1999). In the first stage, we estimate the probability that the acquirer submits an acquisition bid. We use these pro babilities to adjust the market-model CARs by a factor of 1/(1 – p), where p is the estimated probability of submitting a takeover bid. Hence, each probability-adjusted CAR represents an estimate of what the stock-price reaction would have been if the acquisition bid were not anticipated. The results from the 2-stage CAR analysis suggest that our baseline results are not subject to anticipation bias.

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Journal Pre-proof the 3-day announcement period. 9 These results are consistent with both the superior negotiation hypothesis and the efficient appraisal alternative. We note that some of the controls in regression (1) yield results that are in line with the existing literature. For example, as in Golubov, Petmezas, and Travlos (2012), Masulis, Wang, and Xie (2007), and Cai and Sevilir (2012), the acquirer’s size is inversely related to the market’s reaction. Like Malmendier and Tate (2008), we find that the cash payment indicator is positively related to the acquirer announcement return. Similar to the results in Fich, Cai, and Tran (2011), the relative size variable is negatively related to the bidder’s CAR. Consistent with Ahern (2012),

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We also find negative effects of busy CEOs (Fich and Shivdasani, 2006) and

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industry in terms of input.

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we find that the acquirer gets a smaller gains if the acquirer’s industry is more dependent on the target’s

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experience (Custódio, Ferreira, and Matos, 2013).

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positive value of Ivy League CEOs (Chevalier and Ellison, 1999) or of CEOs with more industry

Regressions (2)-(3) in Panel A of Table 3 probe our baseline findings and also help us learn more

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about the effects and importance of the bidder CEO’s knowledge in the target’s supply chain. The

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estimates in regression (2) indicate that M&A deals by supply chain CEOs generate gains for acquirer shareholders in both diversifying and non-diversifying transactions: The coefficient of the dummy

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variable for supply chain CEO is positive and significant, and the sum of ‘supply chain CEO’ and the

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interaction ‘supply chain CEO x diversifying merger’ is also positive and significant (p-value in Wald test = 0.01). These results suggest that CEO supply chain experience is valuable in both diversifying and nondiversifying mergers. There is no significant difference in the effect of supply chain CEOs between the two deal types, as evidenced by the insignificant coefficient of the ‘supply chain CEO x diversifying merger’ interaction. This evidence contrasts with the main finding in Custódio and Metzger (2013) showing that acquirer CEOs who have worked in the target industry earn higher returns only in

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As a benchmark to assess the economic magnitude of this result, we note that during our sample period the smallest firm in the Russell 2000 index has an average market capitalization of US$159 million. So, in essence, the acquirers’ value increase upon deal announcement (related to supply chain CEOs) is about 1.7 times of the average value of a small index firm. See: http://www.russell.com/indexes/americas/tools -resources/reconstitution/marketcapitalization-ranges.page 10 We note that Ahern (2012) focuses on vertical transactions, while our samples include all deal types.

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Journal Pre-proof diversifying mergers. In model (3) we replace a dummy variable for supply chain experience by the length of such experience (with value of zero for non-supply chain CEOs) and find consistent result: longer CEO supply chain experience is associated with higher merger gains to the acquirer. 11 As noted by Gormley and Matsa (2014), because most corporate policies depend on unobservable factors, controlling for common errors (or unobserved heterogeneity) is a major challenge in empirical finance research. One concern affecting identification in our setting that could potentially bias our

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findings is that omitted unobservable CEO characteristics might be correlated with their supply chain experience. For example, supply chain CEOs are more likely to have worked in more firms and in more

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industries, so it is possible that our experience measure is simply tracking general managerial skill (or

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other omitted characteristics) and not tracking the acquiring CEO’s knowledge of the target’s product

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market relations.

Gormley and Matsa (2014) recommend the use of fixed effects as an effective econometric tool for

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addressing unobserved heterogeneity. To address concerns related to CEO heterogeneity, we re-estimate

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our acquirer CAR model in a subsample of transactions involving 21 CEOs that perform 31 M&A deals in which they are classified as supply chain CEOs as well as 73 deals in which they are not. In this

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specification, we can control for unobserved CEO heterogeneity with CEO fixed effects. By analyzing the

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CEOs that make both types of deals (‘supply chain’ deals and ‘non-supply chain’ deals), this approach mitigates the concern that the better performance of supply chain deals is caused by other unobservable characteristics of the CEOs. This test, reported as regression (4) in Panel A of Table 3, indicates that supply chain CEOs are associated with a 2.8% increase in their firm’s CAR upon deal announcement. These findings suggest that our baseline results are not driven by unobservable CEO attributes correlated with their supply chain experience. Nonetheless, we know that an important caveat with the inferences drawn from column (4) in Panel A is that the sample is limited to 104 transactions.

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In an additional analysis, we test given that the acquirer CEO is a supply chain CEO, whether upstream or recent (less than 10 years old) supply chain experience is associated with greater acquirer gains. Our results show that a supply chain CEO’s upstream or recent experience does not further strengthen returns to acquirers.

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Journal Pre-proof Common errors might be a concern at the firm level as well. To address it, we re-estimate our acquirer CAR regression in a subsample involving the same acquirer firms performing deals with and without supply chain CEOs. The subsample analysis uses 21 unique firms that execute 26 takeovers by supply chain CEOs and 99 deals by non-supply chain CEOs. In this test, reported as model (5) of Panel A in Table 6, we control for acquirer firm fixed effects to address unobserved firm heterogeneity. The estimates show that supply chain CEOs are associated with a 2.9% higher acquirer CAR upon deal announcement. This finding suggests that our results are not driven by unobservable acquirer-firm

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characteristics correlated with their CEO supply chain experience.

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3.2. Alternative Supply Chain Experience Cut-Offs and Different Industry Classifications

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The baseline tests in Panel A of Table 3 rely on a definition of supply chain CEOs that employs a 20% cut-off related to the input and output product market transactions of the target industry and other

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industries, using 59 BEA-IO industries. In some of their tests, Cohen and Frazzini (2008) use a 20%

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threshold arguing that it represents a substantive customer-supplier link. We relax the 20% cut-off and, in the first three columns of Panel B of Table 3, re-estimate acquirer CAR tests (similar to model (1) in

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Panel A) with different cut-offs. The number of supply chain CEOs is 123 (or 8.3%) when we use a 15%

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cut-off and according to the first column in Panel B, their associated effect on the acquirer CAR is still positive and significant (0.019, p-value = 0.04). A 25% cut-off lowers the amount of supply chain CEOs to 83 (or just 5.6% of the sample), but as shown in model (2) of Panel B their effect on acquirer returns is still significant (0.018, p-value = 0.04).

12

To further address firm heterogeneity concerns, in robustness tests we include numerous observable control variables to account for the acquirer’s governance (E-index (Bebchuk, Cohen, and Ferrell, 2009), Delaware incorporation (Daines, 2001), institutional ownership (Hartzell and Starks, 2003 and Gormley and Matsa, 2011), CEO-Chairman (-Founder) duality (Masulis, Wang, and Xie, 2007)) and for its information environment (probability of informed trading (PIN) (Aktas, De Bodt, Declerck, and Van Oppens, 2007), analysts coverage and dispersion (Chen, Harford, and Lin, 2015), religiosity level where a firm is headquartered (Hilary and Hui, 2009)). We also control for other features related to the transactions (e.g. financial advisor), for characteristics that simultaneously affect the acquirer and target firms (e.g. geographic proximity), and for potential deal anticipation (e.g. run-up and rumored deals) in untabulated tests. Our results continue to document higher and statistically significant acquirer CARs in M&A deals involving supply chain CEOs.

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Journal Pre-proof One might argue that the measure of CEOs’ experience in target firms’ supply chain is conditioned on target firms operating in industries where they are greatly reliant on an upstream or downstream industry. Specifically, a target firm must have a reliance of at least 20% with another industry before our supply chain acquirer CEO indicator can be coded as one. Thus, targets in these industries might be different from targets in other industries. To address this issue, in model (3) of Panel B (Table 3) we use the top customer/supplier relationship (instead of a cut-off level) to classify a supply chain relationship between

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any pair of industries. Using this taxonomy, 98 (or 6.6%) acquirer CEOs in our sample are coded as supply chain CEOs. Their effect on the acquirer CAR is still positive and significant.

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In the acquirer return regression reported as model (4) of Panel B, we replace the 59 BEA-IO

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industries (which we use for such coding) with an alternative industry classification to mitigate the

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concern that the 59 BEA-IO industries might be too narrow. Specifically, we use a finer BEA-IO industry classification that identifies 371 detailed industries. Because this identification is only provided for 2007,

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we use that year’s information for all other sample years. Using this industry grouping and a 5%

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experience cut-off, we classify 111 CEOs (or 7.4%) as supply-chain CEOs. In model (4) we also use the 371 BEA-IO industries as the basis for the industry fixed effects. This regression also shows a positive

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association between supply chain CEOs and the return accruing to their firms upon the M&A

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announcement. In general, the use of different industry classifications as well as different cut-off levels to identify supply chain experience generate results that are similar to those from our baseline tests. Consequently, we continue to use the 59 BEA-IO industry classification to identify supply chain CEOs and to specify industry fixed effects in our subsequent analyses.

3.3. Supply Chain CEOs vs. CEOs with past work in the target’s industry We are mindful of the work by Custódio and Metzger (2013) showing that M&A deals by bidding CEOs who have worked in the target’s industry earn higher acquisition returns only in diversifying mergers. We note, however, that they use the Fama-French 12 industries to code CEOs with a prior job in the target’s industry whereas we use the 59 BEA-IO industry classification to exclude these executives from our 16

Journal Pre-proof analyses. To address this, the acquirer return test in model (5) of Panel B in Table 3 uses the Fama-French 12 industries to code both supply chain CEOs and the bidder CEOs with prior work in the target’s industry. We employ SIC and NAICS codes to map the 59 IO industries in to Fama-French 12 industries. In model (5), we remove the 491 transactions in which the bidder CEO has held a job in the target’s industry to isolate the effect of supply-chain CEOs. We note that by definition and in Custódio and Metzger’s setting, all CEOs in non-diversifying

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mergers have experience in the target’s industry through their current employment at the acquirer. In contrast, an advantage of our experimental design is that it allows for cross-sectional variation in the

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acquirer CEO’s target-specific supply chain knowledge. In model (5) of Table 3, Panel B, the estimate for

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the supply chain CEO variable is robust to the exclusion of bidder CEOs with a prior job in the target’s

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industry. This result indicates that an acquirer CEO with experience in the target’s supply chain can make value increasing acquisitions even if such experience was not attained through direct employment in the

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target’s industry.

3.4. Synergies and Performance of Merged Firms

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Efficient appraisal is an economic mechanism potentially driving the good performance of acquirers in

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M&A deals executed by supply chain CEOs. Under the efficient appraisal hypothesis, the supply chain experience of the acquiring CEO reduces the likelihood that the executive makes an error when valuing the target. An efficient appraisal implies that the business combination is better poised to generate surplus through an efficient integration of the acquirer firm with the target company. Moreover, if the market’s announcement-period assessment of transactions with these executives (higher M&A announcement CARs) is rational, we should expect substantial improvement in post-deal performance for these mergers. That is, some of the superior gains to acquirer shareholders at deal announcement in mergers executed by supply chain CEOs are related to the generation of surplus. In Table 4, we perform three different analyses to study this hypothesis: a synergy test, a post-deal accounting performance test, and a goodwill write-off test. In all of the regressions reported in Table 4, the key explanatory variable is set to one if the 17

Journal Pre-proof deal is executed by a supply chain CEO and set to zero otherwise. Moreover, all of the tests in that table include Year and (Acquirer Industry X Target Industry) fixed effects. Column (1) of Table 4 reports an OLS model of the total percentage synergistic gain from acquisitions (or merger synergy). This measure is the 3-day CAR for a value-weighted portfolio of the acquirer and the target. As in Bradley, Desai, and Kim (1988), the CAR is centered on the merger announcement date and calculated as the residual from the market model estimated during the one year

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window ending four weeks prior to the merger announcement. The control variables in column (1) of Table 4 yield results that agree with those in the existing

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literature. For example, the inferences related to significant variables such as bidder size and cash

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payment are similar to those in Wang and Xie (2009). According to regression (1), the presence of supply

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chain CEOs is associated with an increase of 2.2 percentage points in synergies. To offer some perspective about the economic magnitude of this estimate, the average synergy for all deals in our

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sample is 2.9%. The evidence of higher synergies in M&A deals by supply chain CEOs appears

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supportive of the efficient appraisal hypothesis. However, we are mindful that our synergy measure depends on asset prices and could be biased by market sentiment.

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Long-run synergies and other longer-term accounting performance metrics are often difficult to

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estimate due to the possibility that distorting factors could occur during the estimation window. With this caveat in mind, in column (2) of Table 4 we perform a test based on an ex-post (long-run) accounting measure of performance. The advantage of this approach is that it provides evidence based on accounting realizations, rather than on future expectations or sentiment embedded in asset prices. Consequently, our operating gain to mergers analysis is unlikely to be biased by either market psychology or investors’ perceptions. Regression (2) uses a subsample of deals by acquirers that make only one M&A deal in a particular year to accurately isolate the post-merger performance. We follow the matching algorithm proposed by Barber and Lyon (1996) based on size and performance to estimate the abnormal postmerger ROA of the merged firm. (Our results are robust to the use of return on equity.) We start with a sample of deals where the merged firms exist for at least 3 years after deal completion. For each acquirer 18

Journal Pre-proof and target in that sample, we identify a matching acquirer and target that satisfy the following criteria: i) the matching acquirer (target) exists for at least 3 years before the deal announcement and 3 years after the deal completion; ii) the matching acquirer (target) does not conduct any SEO or is involved in any M&A (either as a bidder or a target) in the following 5 years after the deal announcement; iii) the matching acquirer’s (target’s) total assets are between 50% and 150% of that of the acquirer (target) in our sample in the year prior to the deal announcement; and iv) the matching acquirer (target) has the

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closest ROA to that of the acquirer (target) in our sample in the year prior to the deal announcement. The combined ROA of the matching acquirer and target is the weighted average of ROA of the matching

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acquirer and the matching target, with the weight being the relative asset value of the acquirer and target

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in our sample in the year before deal announcement. Therefore, the combined ROA of the matching

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acquirer and matching target is an estimate of the combined ROA of the acquirer and target in our sample had they not merged. The abnormal ROA in each of the 5 years after the deal completion is the difference

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between the actual ROA of the merged firm in our sample and the combined ROA of the matching

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acquirer and matching target. The dependent variable in regression (2) is the median abnormal return on assets (ROA) in the 5-year period following deal completion. Note that due to all above requirements in

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sample construction, the number of observations in this regression reduces to 902 deals. The coefficient

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for our main independent variable in model (2) of Table 4, (0.038, p-value = 0.04), indicates significantly better accounting returns following M&A deals by supply chain CEOs. A different way to identify efficient appraisal by supply chain CEOs is by tracking post-merger writeoffs of goodwill. Since goodwill is not amortized, rather than tested for impairment, post-acquisition write-offs of goodwill indicate the accuracy of the target’s valuation. Consistent with this idea, the arguments in Gu and Lev (2011) and Li, Shroff, Venkataraman, and Zhang (2011) suggest that mergers with well-integrated targets and acquirers are less likely to suffer from the post-merger impairment of goodwill. In the last column of Table 4, we study post-acquisition goodwill write-offs by the acquiring firms. Information on the impairment of goodwill is based on the amount of write-offs within five years after the 19

Journal Pre-proof acquisition. Similar to Li et al. (2011), we match each acquisition made by an impairment firm in a given year with all acquisitions made in the same year by firms in the same 2-digit SIC code as the impairment firm to construct a control group. We also require control firms to have non-zero goodwill at the beginning of the impairment year and no goodwill write-offs during our sample period. If a control merger appears many times, we only include one randomly selected deal in the control sample. Our test uses the 958 deals that meet these criteria. Specifically, column (3) reports a Tobit regression in which the dependent variable is the dollar amount of the goodwill write-off of the merged firm relative to its total

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assets.13 We use a Tobit specification instead of OLS since the dependent variable (goodwill write-off) is

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left-censored at zero. Consistent with the evidence in Li et al. (2011), we find larger write-offs of

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goodwill when the merged firms exhibit poor (post-deal) stock return performance. As for our variable of

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interest, according to the parameter estimates, the amount of goodwill written off declines by about 12% following M&A transactions executed by supply chain CEOs. For reference, in our data, the average

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amount of goodwill written off in the year after a completed acquisition is 4.5% of total assets.

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Consequently, our results suggest that acquirer CEO target’s industry supply chain experience is important in preventing big losses in the merged firm. In general, the evidence related to better post-deal

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accounting performance of the merged firms and to lower goodwill write-offs in Table 4 supports the

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efficient appraisal hypothesis and mitigates the concern that the announcement CAR to the bidder is biased by market sentiment.

3.5. Merger Premiums and the Division of M&A Gains Another possible economic channel driving superior acquirer firm performance could be improved bargaining by the supply chain CEOs. Under this superior negotiation hypothesis, we would expect that supply chain CEOs capture a larger share of the gains because they pay lower target premiums. To consider this possibility, the first four OLS models in Table 5 use the 1-day, 3-day, 1-week, and 4-week offer premiums as the respective dependent variables. 13

Our results are robust when we scale goodwill impairment by market capitalization of the merged firm.

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Journal Pre-proof The estimates for several control variables in the premium tests are in agreement with the existing M&A literature. For example, acquisition premiums are inversely related to the acquirer size (Masulis, Wang, and Xie, 2007) and to the relative size of the parties (Cai and Sevilir, 2012). In contrast, premiums increase with the fraction of cash used to pay for the consideration (Aktas, de Bodt and Roll, 2010). More importantly, as predicted by the superior negotiation hypothesis, estimates for the supply chain CEO indicator are negative in models (1)-(4). However, only the coefficient in model (4) is statistically

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significant. Thus, it appears that the target premiums paid by supply chain CEOs are similar to those paid by all other CEOs on average. Therefore, the premium results do not support the superior negotiation

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hypothesis. Yet, it is possible that supply chain CEOs are able to pay lower premiums for targets that are

In such a scenario, supply chain CEOs pay premiums that are statistically

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undervalued target.

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worth more. This could happen, for instance, if supply chain CEOs are capable of identifying an

indistinguishable from the merger premiums paid by all other CEOs while still acquiring their targets at a

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discount. As a result, we cannot conclusively rule out the superior negotiation hypothesis.

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To further test the superior negotiation hypothesis of supply chain CEOs, we study the relative share of the merger surplus that is captured by their targets. Following Ahern (2012), we calculate the surplus

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obtained by the target as the difference in dollar gains between the target and the acquirer divided by the

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sum of the acquirer’s and the target’s market value of equity 50 trading days prior to the announcement date. More specifically, we define division of gains as a measure of the relative gain of the target versus the acquirer for each dollar of total market value. The results in column (5) of Table 5 reveal that in deals executed by supply chain CEOs, the relative gain of the target versus the acquirer is 1.5% lower. Thus, the effect of supply chain CEOs is economically important because in our sample the unconditional mean relative gain for targets is 3.4%. This evidence suggests that supply chain CEOs help their firms keep a larger share of the surplus created at deal announcement. The results from our analyses of merger premium and division of gains imply that some of the acquirer gains come from superior negotiation by supply chain CEOs. The results of the remaining control variables match those in the literature. For example, as in Ahern (2012) and Cai and 21

Journal Pre-proof Sevilir (2012), we obtain a negative estimate for the cash payment variable and a positive estimate for relative size.

4. Endogeneity It is possible that deals by a supply chain CEO are fundamentally different from other deals, and these differences might lead to different market reaction to the merger announcement and post-performance of

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the acquiring firm with a supply chain CEO. For example, a board of directors that values the integration of acquirers with firms in related industries might be more likely to hire a CEO with experience tied to a

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potential target. Therefore, it is difficult to tease out whether the merger gain is due to supply-chain

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knowledge of the CEO or to higher synergies of mergers between related business sectors. Another

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possibility is that CEOs with supply-chain experience might also have more general expertise that is valuable to a merger. In Table 6 we address selection concerns by using Heckman (1979) model. In

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model (1) of Panel A, Table 6, we run a Probit (first stage) regression where the dependent variable

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equals one if a deal in our sample is made by a supply chain CEO and zero otherwise. We control for firm, deal, and CEO characteristics similar to those in our baseline analysis of Table 3, Panel A, model

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(1).

To satisfy the ‘exclusion’ condition in implementing the Heckman model, we use an instrumental

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variable (‘number of vertical links’) to estimate the probability of an acquiring CEO being a supply chain CEO. Specifically, we first identify the pairs of industries (among the 59 BEA industries) that have significant supply chain relation with each other, using the 20% threshold for the amounts an industry sells to (or buys from) another industry (illustrated in Figure 1). We then count the number of significant customers and suppliers for each industry. (Note that “customers”/“suppliers” in this context refer to customer/supplier industries.) Next, we track the employment record of the CEOs in our sample and count the total number of significant suppliers and customers of the industries that the CEOs are associated with. We use that number to instrument for the probability of a CEO being a supply chain CEO. This variable is positively associated with the “supply chain CEO” dummy variable, since the more 22

Journal Pre-proof customers/suppliers (industries) a CEO is exposed to, the more likely that one of the customers/suppliers is indeed the target (industry). This instrument is therefore correlated with the dependent variable of the first-stage regression in the Heckman model. It is less likely that the vertical relations between some industries affect acquisition performance of a firm in a different industry, except through the specific supply chain knowledge about the target, which is our variable of interest. Therefore, the instrumental variable appears to satisfy the exclusion condition. The exclusion condition, however, would be violated

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had the vertical relations among the industries that the CEOs are associated with been correlated with some quality of the CEO that helps her make a better merger deal. Our analyses using CEO fixed effects

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(Table 3) help alleviate such concern.

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Model (1) shows some differences in deal and CEO characteristics of mergers done by a supply chain

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CEO. For example, these deals are more likely to involve acquirers with lower Tobin’s Q or acquirers with lower free cash flow. In addition, supply chain CEOs are more likely to have exposure to more

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industries. As predicted, a CEO is more likely to be classified as a supply chain CEO if she is associated

variable is valid.

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with industries that have more vertical relations with other industries, suggesting that our instrumental

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To address self-selection, in the second stage (regression (2) of Panel A, Table 6), we estimate an

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OLS regression of the acquirer CAR(-1,+1) controlling for the inverse mill ratio derived from the first stage (regression (1)). Other control variables are the same as those used in the first stage regression, except for the instrumental variable. Model (2) shows a positive and statistically significant coefficient of the dummy variable that tracks CEO supply-chain experience. The economic magnitude is very similar to that in the baseline analysis of model (1) of Table 3, Panel A: the market reaction to the acquirer’s stock upon the M&A announcement is about 1.6% higher for mergers by a supply-chain CEO. We next conduct our other analyses of merger performance, target premiums, and division of merger gains, using the Heckman method. To conserve space, we only summarize the estimates for the supply chain indicator from results of the second-stage regressions. Panel B of Table 6 indicates that in mergers with supply chain CEOs, (i) the combined CAR(-1,+1) between the acquirer and target is 2.2% higher, 23

Journal Pre-proof (ii) the abnormal post-acquisition operating performance of the acquirer is 3.2 percentage points higher, (iii) the amount of goodwill written off (estimated from the marginal effect of the coefficient) is about 9.2% lower, (iv) the premiums paid to targets are indistinguishable from those paid to all other targets, except for the lower 3-day premium, and (v) the relative share of the target gains is 1.5% lower. The use of Heckman method in our specific setting is not without potential limitations. For example, in somewhat unobservable ways related to managerial experience, supply chain CEOs might circumvent

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the asymmetry of information related to the target firm; as a result, control for observable characteristics might not entirely address the differences of deals by supply chain CEOs. This issue underscores the

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importance of the analyses reported in models (4) and (5) of Table 3 (Panel A) which address unobserved

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heterogeneity at CEO and firm levels.

5. Supply chain relation between acquirer industry and target industry

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With experience in the target’s supply chain, the acquiring CEO may have a better understanding of the

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target’s operations and business. This knowledge could help the CEO overcome information asymmetry about the value of the deal, preventing the acquirer to overpay for the target. More importantly, these

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CEOs can use their knowledge in the target’s supply chain to improve operating performance of the

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merged firm after the acquisition. We use the (lack of) input/output link between the target industry and the acquirer industry as a proxy for the information asymmetry between the acquirer and the target. We argue that the effect of a supply chain experience is particularly important in deals where the acquirer and the target firm have little or no supply chain relation. Specifically, we define that two industries as not having a supply chain link if both the input dependence and the output dependence ratio between the two industries are zero: the dummy variable ‘no link’ equals one in such cases and zero otherwise. About 59.7% of the deals in our sample involve acquirers and targets that do not have any input/output link with each other. To test this prediction, we repeat the analyses in Tables 3 to 5 using the three main control variables of interest: the supply chain CEO dummy variable, the ‘no link’ dummy variable, and their interactions. 24

Journal Pre-proof Table 7 summarizes the estimates for these three variables in the regressions of acquirer CAR(-1,+1), combined CAR(-1,+1), abnormal ROA, goodwill write-off, target premiums and CAR(-1,+1), and division of gains. The results show that the coefficient of the interaction between ‘no link’ and ‘supply chain CEO’ is positive and statistically significant in the test of acquirer abnormal returns and significantly negative in the tests of target premiums (and abnormal returns) and division of gains. These results suggest that the acquirer CEO’s supply chain experience is more valuable in acquisitions that lack a vertical link between the two parties, consistent with our hypothesis of supply chain CEOs’ ability to

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use their superior knowledge about the target firms’ input and output.

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6. Alternative Specifications

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In Table 8, we re-estimate our acquirer return tests (model (1) in Table 3, Panel A), synergies analyses (models (1)-(3) in Table 4), and premium and division of gains regressions (models (1)-(5) in Table 5). In

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every panel, we alter our basic specification (or use various subsamples) to evaluate the robustness of our

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baseline findings. These robustness tests include all of the control variables and fixed effects we use in previous analyses. However, to save space, we implement a parsimonious reporting approach and only

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tabulate the estimates for the supply chain indicator.

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In Panel A, for example, the re-estimated regressions use the latest available BEA industry classification matrix prior to every deal announcement to identify supply chain CEOs. With this taxonomy, 96 acquirer chief executives (or 6.5% of our sample) are coded as supply chain CEOs. In Panel B, we address the concern that our baseline results could be driven by deals involving targets operating in industries that have highly vertical relations with other industries. Therefore, in Panel B we repeat key analyses in a subsample of 915 deals involving targets in industries that have a supply chain reliance (defined as in Figure 1) with at least 5 other industries. In that subsample, 79 (8.6%) acquirer CEOs are classified as supply chain CEOs. In Panel C, we exclude multi-segment acquirers and multi-segment targets to mitigate the concern that the acquirer and target might be related through some of their non-core segments. In the resulting subsample, which consists of 1,013 transactions, 56 (5.5%) involve a supply 25

Journal Pre-proof chain CEO. In general, the estimates for our supply chain CEO variable in all panels of Table 8 generate inferences and economic effects that are similar to those in the main tests.

7. Conclusions Takeovers are often plagued by asymmetric information between bidders and targets. This issue impairs the ability of acquirer managers to identify target firms that would make good investments. We study

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whether acquirer CEOs with work experience in the target supply chain, which we refer to as supply chain CEOs, bridge the information asymmetry inherent in the takeover process and execute better quality

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deals.

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After withstanding a battery of different controls and robustness tests, our analyses establish a novel

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finding: M&A deals by supply chain CEOs generate significant gains for shareholders of the acquirer firm. On average, acquisitions by these CEOs exhibit a US$270 million increase in their firm’s market

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capitalization upon deal announcement, a 3.8% increase in post-merger accounting returns and a 12%

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decline in the amount of goodwill written off.

To study the potential channels of the value gains to the acquirer shareholders, we propose two

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hypotheses. The superior negotiation hypothesis conjectures that wealth improvements are achieved by

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the negotiation of better merger terms, which enable acquirers to capture additional rents from target shareholders. By contrast, the efficient appraisal alternative argues that supply chain CEOs are less likely to incorrectly value the target, which leads the business combination to produce additional surplus and improved synergies. Our evidence shows that the material gains to acquirer firms related to their CEO’s specific supply chain experience in the target’s industry come from both the generation of surplus (efficient appraisal) and rents appropriated from shareholders of the target firm (superior negotiation). Results from our Heckman tests, which lessen selection concerns, reaffirm the view that specific managerial abilities (in our case, supply chain experience) play a key role in enhancing firm value. Yet, even if corporate boards actually consider supply chain experience as an important qualification to select CEOs, then, rather than being a concern, the possibility of selection reinforces the idea that supply chain 26

Journal Pre-proof experience improves performance. Indeed, our analyses indicate that supply chain CEOs are associated with large and significant gains for their own shareholders. Therefore, based on our evidence, we are left with the conclusion that acquisition skill is determined, at least in part, by the nature of the managers’

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human capital.

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Golubov, A., Petmezas, D., Travlos, N. G., 2012. When it pays to pay your investment banker: new evidence on the role of financial advisors in M&As. Journal of Finance 67, 271-311. Gormley, T. A., Matsa, D. A., 2011. Growing out of trouble: Corporate responses to liability risk. Review of Financial Studies 24(8), 2781-2821. Gormley, T. A., Matsa, D. A., 2014. Common errors: How to (and not to) control for unobserved heterogeneity. Review of Financial Studies 27, 617-661. Graham, J. R., Harvey, C. R., Puri, M., 2013. Managerial attitudes and corporate actions. Journal of Financial Economics 109, 103-121. Graham, J. R., Li, S., Qiu, J., 2012. Managerial attributes and executive compensation. Review of Financial Studies, 25, 144-186. 29

Journal Pre-proof Gu, F., Lev, B., 2011. Overpriced shares, ill-advised acquisitions, and goodwill impairment. The Accounting Review 86, 1995-2022. Harford, J., Jenter, D., Li, K., 2011. Institutional cross-holdings and their effect on acquisition decisions. Journal of Financial Economics 99, 27-39. Harris, M., Raviv, A., 2005. Allocation of decision-making authority. Review of Finance 9, 353-383. Hartzell, J. C., Starks, L. T., 2003. Institutional investors and executive compensation. Journal of Finance 58, 2351–2374. Heckman, J., 1979. Sample selection bias as a specification error. Econometrica 47, 153-161.

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Hertzel, M., Li, Z., Officer, M., Rodgers, K., 2008. Inter-firm linkages and the wealth effects of financial distress along the supply chain. Journal of Financial Economics 87, 374-387.

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Hilary, G., Hui, K., 2009. Does religion matter in corporate decision making in America? Journal of Financial Economics 93, 455-473.

-p

Huang, J., Kisgen, D. J., 2013. Gender and corporate finance: Are male executives overconfident relative to female executives? Journal of Financial Economics 108, 822-839.

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Huson, M., Malatesta, P., Parrino, R., 2004. Managerial succession and firm performance. Journal of

lP

Financial Economics 74, 237-275.

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Jaffe, J., Pedersen, D., Voetmann, T., 2013. Skill difference in corporate acquisitions. Journal of Corporate

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ro

Wang, C., Xie, F., 2009. Corporate governance transfer and synergistic gains from mergers and acquisitions.

Jo

ur

na

lP

re

-p

Review of Financial Studies 22, 829–858.

31

Journal Pre-proof Figure 1: Supply chain reliance

Industry A

Target’s industry

Upstream reliance

Downstream reliance

Industry B buys a significant amount industry

ro

of

Industry A sells a significant amount of its output to the target's industry

-p

Or

Target's industry sells a significant am

Jo

ur

na

lP

re

The target's industry buys a significant amount of its inputs from industry A

Or

32

Journal Pre-proof

Table 1: Target firms’ industrial and temporal distribution This table reports the industrial and temporal distribution of the targets in the sample of 1,491 acquisitions obtained from the Securities Data Company’s (SDC) Mergers and Acquisitions database during 1997-2014. Acquirers and targets are publicly traded U.S. firms with accounting, stock market, and board data available from Compustat, the Center for Research in Security Prices (CRSP), and the BoardEx database, respectively. The industry classification is based on the BEA Use Tables (Summary level) during 1997-2012. Panel A:

S upply chain

Utilities Construction Wholesale trade M anagement of companies and enterprises Educational services Other services, except government Oil and gas extraction M ining, except oil and gas Support activities for mining Wood products Paper products Printing and related support activities Petroleum and coal products Chemical products Plastics and rubber products Nonmetallic mineral products Primary metals Fabricated metal products M achinery Computer and electronic products Electrical equipment, appliances, and components Furniture and related products M iscellaneous manufacturing Food and beverage stores General merchandise stores Air transportation Rail transportation Water transportation Truck transportation Pipeline transportation Panel B: 1997 1998 1999 2000 Supply chain 7 9 3 4 Total 77 106 101 101

6 2 4 1 0 0 7 0 3 0 0 0 4 2 1 0 0 1 9 9 1 0 1 1 1 0 0 0 0 1 2001

Total 38 14 32 40 7 8 65 24 14 4 11 9 6 141 6 6 8 13 65 342 14 2 69 7 3 5 3 5 2 8 2002 2003

9 100

3 76

3 63

Publishing industries, except internet (includes software) M otion picture and sound recording industries Broadcasting and telecommunications Data processing, internet publishing, and other information services Securities, commodity contracts, and investments Insurance carriers and related activities Funds, trusts, and other financial vehicles Real estate Administrative and support services Waste management and remediation services Ambulatory health care services Hospitals Nursing and residential care facilities Amusements, gambling, and recreation industries Accommodation Food services and drinking places Computer systems design and related services Farms Forestry, fishing, and related activities Food and beverage and tobacco products Textile mills and textile product mills Apparel and leather and allied products M otor vehicles, bodies and trailers, and parts Other transportation equipment Other transportation and support activities Other retail Rental and leasing services and lessors of intangible assets M iscellaneous professional, scientific, and technical services Performing arts, spectator sports, museums, and related activities

f o

l a

n r u

Jo

S upply chain 3 0 3 0 0 1 1 11 5 0 10 3 0 1 0 1 0 0 0 0 0 0 2 0 0 0 0 5 0

Industry

o r p

e

r P

Total 91 5 22 13 19 28 4 54 26 4 27 7 3 7 5 4 58 1 1 22 2 9 10 12 3 31 9 40 3

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

Total

1 79

5 80

3 113

14 112

4 77

4 62

6 89

6 59

7 71

5 60

7 65

100 1,491

33

Journal Pre-proof Table 2: Deal, firm, and CEO characteristics This table provides key deal, acquirer, and acquiring CEO characteristics sorted by our supply chain classification. The sample includes 1,491 M&A deals described in Table 1. The acquirer CEO is classified as a supply chain CEO if he or she has experience as a director or top manager in a firm operating in an industry that has a supply chain reliance relation with the target’s indust ry, as described in Figure 1. We average the 1997-2012 annual input-output amount between each pair of industries produced by the U.S. Bureau of Economic Analysis (BEA) to establish their supply chain relations. We check the biography and employment history fo r each acquiring CEO in order to classify them as supply chain CEOs. All variables are defined in the Appendix. The p-values of t-test (mean) and Z-test (median) are reported in parentheses. (2) Deals with supply chain CEOs (N = 100)

(3) Deals without supply chain CEOs (N = 1,391)

Difference (2) - (3)

Median

Mean

Median

Mean

Median

Panel A: Deal characteristics Acquirer CAR(-1,+1) Target CAR(-1,+1) Target 1-day premium Target 1-week premium Target 4-week premium Relative deal size Cash only (0,1) Tender (0,1) Hostile (0,1) Diversifying merger (0,1) Input dependence ratio Output dependence ratio

-0.008 0.269 0.387 0.422 0.479 0.453 0.401 0.235 0.009 0.223 0.022 0.013

-0.004 0.219 0.299 0.328 0.373 0.163 0 0 0 0 0 0

0.005 0.233 0.367 0.402 0.447 0.363 0.350 0.220 0.000 0.350 0.024 0.029

0.000 0.190 0.302 0.326 0.344 0.167 0 0 0 0 0 0

-0.009 0.272 0.388 0.423 0.481 0.460 0.405 0.237 0.009 0.214 0.021 0.011

-0.004 0.221 0.298 0.328 0.377 0.162 0 0 0 0 0 0

(0.04) (0.14) (0.59) (0.60) (0.43) (0.23) (0.27) (0.70) (0.33) (0.01) (0.73) (0.01)

(0.05) (0.27) (0.66) (0.59) (0.52) (0.69) (0.28) (0.71) (0.33) (0.00) (0.59) (0.11)

Panel B: Acquirer characteristics Market capitalization ($ million) Tobin's Q Free cash flow Leverage

18,516 2.324 0.041 0.211

2,907 1.769 0.056 0.179

11,693 1.908 0.026 0.279

3,465 1.570 0.040 0.236

19,001 2.353 0.043 0.206

2,801 1.797 0.056 0.177

(0.18) (0.01) (0.13) (0.00)

(0.29) (0.00) (0.12) (0.00)

58.3 8.1 0.29 1.47 0.73 2.49 1.91 655.7

57.5 5.9 0 1 1 2 2 533

54.5 7.1 0.23 1.69 0.62 1.37 0.99 622.8

55 5.2 0 1 1 1 1 434

(0.00) (0.16) (0.17) (0.36) (0.02) (0.00) (0.00) (0.62)

(0.00) (0.15) (0.14) (0.78) (0.02) (0.00) (0.00) (0.13)

55 5.2 0 1 1 1 1 445

ro

-p

lP

na

ur 54.8 7.2 0.23 1.68 0.62 1.45 1.06 625.1

Jo

Panel C: Acquiring CEO characteristics Age Tenure Ivy League college (0,1) Number of M&A deals Hired from outside the firm (0,1) Number of directorships Number of industries Number of networks

of

Mean

re

(1) All deals (N = 1,491)

Mean

Median

34

Journal Pre-proof Table 3: Acquirer announcement returns This table reports ordinary least squares (OLS) model of the merger announcement returns meeting the 1,491 acquirers in the sample described in Table 1. The dependent variable is the three-day acquirer’s cumulative abnormal returns (CARs) centered on the deal announcement date. In Panel A, supply chain CEOs are defined using BEA -IO 59 industries and a 20% supply-chain cut-off. Panel B uses alternative supply-chain cut-offs and industry classifications. All variables are defined in the Appendix. The p-values are reported in parentheses. * , ** , and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. Panel A: Baseline regressions

Supply chain CEO (0,1) x Diversifying merger (0,1) Length of Supply chain experience (log) Diversifying merger (0,1) Input Dependence Ratio Output Dependence Ratio

re

Size

lP

Tobin's Q Free cash flow

na

Leverage Relative deal size

ur

Cash only (0,1)

Age (log)

Jo

Tender (0,1) Hostile (0,1)

Tenure (log) Ivy League college (0,1) Number of M&A deals by CEOs (log) CEO hired from outside the firm (0,1) Number of directorships (log) Number of industries (log) Number of networks (log)

-0.004 (0.41) -0.079* (0.08) 0.090 (0.21) -0.002* (0.06) 0.001 (0.37) 0.025 (0.21) 0.022* (0.06) -0.013*** (0.00) 0.016*** (0.00) 0.003 (0.45) 0.004 (0.85) -0.012 (0.44) 0.001 (0.60) 0.010** (0.02) 0.007 (0.20) 0.003 (0.45) -0.074*** (0.00) 0.073*** (0.00) 0.000 (0.90)

of

Supply chain CEO (0,1)

ro

Intercept

Dependent Variable: Acquirer CAR(-1,+1) (2) (3) (4) 0.104 0.102 -0.130 (0.10) (0.11) (0.39) 0.015** 0.028** (0.03) (0.03) 0.002 (0.90) 0.009** (0.03) -0.004 -0.004 -0.009 (0.41) (0.40) (0.64) -0.079* -0.079* 0.142 (0.08) (0.07) (0.64) 0.089 0.094 -0.291 (0.22) (0.20) (0.42) -0.002* -0.002* 0.018 (0.06) (0.06) (0.22) 0.001 0.001 0.000 (0.38) (0.38) (0.92) 0.025 0.024 0.325* (0.22) (0.22) (0.09) 0.022* 0.022* -0.022 (0.06) (0.06) (0.84) -0.013*** -0.013*** -0.052* (0.00) (0.00) (0.08) 0.016*** 0.016*** 0.020 (0.00) (0.00) (0.29) 0.003 0.003 0.017 (0.45) (0.46) (0.30) 0.004 0.003 (0.84) (0.87) -0.012 -0.011 (0.44) (0.45) 0.001 0.001 (0.59) (0.59) 0.010** 0.010** (0.02) (0.02) 0.007 0.007 (0.21) (0.21) 0.003 0.003 (0.45) (0.43) -0.074*** -0.074*** (0.00) (0.00) 0.072*** 0.074*** (0.00) (0.00) 0.000 0.000 (0.91) (0.88)

-p

(1) 0.104 (0.10) 0.015** (0.03)

(5) 0.105 (0.81) 0.029* (0.06)

0.004 (0.83) 0.147 (0.31) -0.468* (0.08) 0.005 (0.69) -0.012 (0.16) 0.443** (0.04) 0.108 (0.21) -0.041** (0.02) 0.006 (0.60) 0.011 (0.32)

-0.104 (0.41) 0.017 (0.26) -0.015 (0.43) -0.033 (0.21) -0.036 (0.37) 0.039 (0.62) 0.001 (1.00) 0.040 (0.38)

35

Journal Pre-proof 1,491 Yes Yes No No 0.2781

1,491 Yes Yes No No 0.2776

1,491 Yes Yes No No 0.2776

104 No No Yes No 0.4482

125 No No No Yes 0.6724

na

lP

re

-p

ro

of

(Year) (Acq Ind X Tar Ind) (CEO) (Firm)

ur

Effects Effects Effects Effects

Jo

N Fixed Fixed Fixed Fixed R2

36

Journal Pre-proof Panel B: Alternative supply-chain cut-offs and industry classifications Dependent Variable: Acquirer CAR(-1,+1)

Free cash flow Leverage Relative deal size Cash only (0,1) Tender (0,1) Hostile (0,1) Age (log) Tenure (log) Ivy League college (0,1) Number of M&A deals by CEOs (log) CEO hired from outside the firm (0,1) Number of directorships (log) Number of industries (log) Number of networks (log)

(4) BEA-IO 371 industries, 5% cut-off 0.075 (0.36) 0.016** (0.03) -0.002 (0.72) -0.053 (0.13) 0.093 (0.19) -0.003* (0.07) 0.001 (0.54) 0.021 (0.38) 0.020 (0.19) -0.018*** (0.00) 0.018*** (0.00) 0.002 (0.67) 0.007 (0.81) -0.002 (0.94) 0.002 (0.60) 0.015*** (0.01) 0.010 (0.17) 0.004 (0.35) -0.095*** (0.00) 0.092*** (0.00) -0.001 (0.77)

of

ro

Tobin's Q

-p

Size

(3) BEA-IO 59 industries, top customer or supplier 0.062 (0.44) 0.016* (0.07) -0.002 (0.81) -0.055 (0.11) 0.100 (0.16) -0.003* (0.07) 0.001 (0.57) 0.020 (0.39) 0.019 (0.21) -0.018*** (0.00) 0.017*** (0.00) 0.002 (0.67) 0.006 (0.82) 0.001 (0.95) 0.001 (0.65) 0.015*** (0.00) 0.010 (0.16) 0.005 (0.33) -0.093*** (0.00) 0.092*** (0.00) -0.001 (0.78)

re

Output Dependence Ratio

lP

Input Dependence Ratio

0.066 (0.42) 0.018** (0.04) -0.002 (0.78) -0.053 (0.12) 0.089 (0.22) -0.003* (0.07) 0.001 (0.54) 0.021 (0.38) 0.019 (0.21) -0.018*** (0.00) 0.018*** (0.00) 0.002 (0.66) 0.007 (0.79) 0.001 (0.96) 0.001 (0.68) 0.015*** (0.01) 0.010 (0.16) 0.005 (0.32) -0.095*** (0.00) 0.089*** (0.00) 0.000 (0.82)

na

Diversifying merger (0,1)

0.065 (0.42) 0.019** (0.04) -0.002 (0.80) -0.054 (0.12) 0.091 (0.20) -0.003* (0.07) 0.001 (0.56) 0.021 (0.37) 0.019 (0.22) -0.018*** (0.00) 0.017*** (0.00) 0.002 (0.67) 0.007 (0.81) 0.001 (0.94) 0.001 (0.67) 0.015*** (0.01) 0.010 (0.15) 0.005 (0.33) -0.094*** (0.00) 0.090*** (0.00) -0.001 (0.81)

ur

Supply chain CEO (0,1)

(2) BEA-IO 59 industries, 25% cut-off

Jo

Intercept

(1) BEA-IO 59 industries, 15% cut-off

(5) Fama-French 12 industries, 25% cut-off 0.051 (0.57) 0.021** (0.03) -0.003 (0.72) 0.002 (0.97) 0.065 (0.53) -0.002 (0.38) 0.002 (0.35) 0.033 (0.31) 0.025 (0.16) -0.021*** (0.00) 0.012* (0.07) 0.009 (0.19) 0.011 (0.74) 0.010 (0.63) 0.006 (0.10) 0.014** (0.02) -0.003 (0.74) 0.004 (0.50) -0.130*** (0.00) 0.119*** (0.00) 0.001 (0.78)

N

1,491

1,491

1,491

1,491

1,000

Fixed Effects (Year) Fixed Effects (Acq Ind X Tar Ind) R2

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

0.2261

0.2269

0.2256

0.4462

0.3130

37

Journal Pre-proof Table 4: Merged firm’s performance The dependent variable in the OLS regression in column (1) is the three-day cumulative abnormal stock price return of the merged firm (acquirer and target CARs(-1,+1) weighted by market capitalization). We follow the matching algorithm based on size and performance as in Barber in Lyon (1996) to estimate the dependent variable of the OLS regression in column (2) which is the median abnormal ROA in the 5-year period for each deal in the subsample. Column (3) reports a Tobit regression with the size of a goodwill write-off relative to total assets as the dependent variable. All variables are defined in the Appendix, p-values in parentheses. * , ** , and *** denote statistical significance at the 10%, 5%, and 1% level, respectiv ely.

Supply-chain CEO (0,1) Diversifying merger (0,1) Input Dependence Ratio

ro

Output Dependence Ratio Size

-p

Tobin's Q

re

Free cash flow

lP

Leverage Relative deal size

na

Cash only (0,1) Tender (0,1)

ur

Hostile (0,1)

Ivy League college (0,1)

Jo

Age (log) Tenure (log)

(2) Abnormal ROA -0.025 (0.84) 0.038** (0.04) -0.005 (0.72) -0.593 (0.63) 6.629 (0.70) 0.002 (0.56) 0.004** (0.04) 0.026 (0.48) 0.039 (0.10) -0.005 (0.33) 0.006 (0.48) 0.016* (0.07) -0.007 (0.87) 0.005 (0.88) 0.010* (0.05) 0.021** (0.02) -0.037** (0.01) 0.013* (0.09) -0.087** (0.02) 0.084* (0.05) 0.006* (0.06)

of

Intercept

(1) Combined CAR 0.003 (0.98) 0.022* (0.06) 0.001 (0.94) 0.037 (0.97) 0.666 (0.97) -0.011*** (0.00) 0.000 (0.85) -0.030 (0.39) 0.040** (0.05) 0.006 (0.14) 0.012* (0.07) 0.017** (0.02) 0.121** (0.01) 0.025 (0.32) -0.005 (0.22) 0.021*** (0.00) 0.018* (0.09) 0.011* (0.08) -0.080*** (0.00) 0.058* (0.07) -0.001 (0.84)

Number of M&A deals by CEOs (log) CEO hired from outside the firm (0,1) Number of directorships (log) Number of industries (log) Number of networks (log) Acquirer's one-year post-deal stock return Target 4-week premium Acquirer's goodwill in year t-1

(3) Goodwill Write-off -1.867* (0.05) -0.345** (0.01) 0.142** (0.05) 1.700** (0.02) -1.523 (0.16) 0.033 (0.12) -0.085*** (0.00) -1.529*** (0.00) -0.376* (0.06) 0.311*** (0.00) 0.001 (0.99) 0.019 (0.79) -1.348 (0.52) 0.347 (0.16) -0.057 (0.19) -0.026 (0.69) 0.107 (0.18) 0.185*** (0.00) -0.348 (0.22) 0.361 (0.26) 0.006 (0.82) -0.481*** (0.00) -0.201 (0.13) -0.063 (0.74)

38

Journal Pre-proof 1,491 Yes Yes Yes 0.3838

902 Yes Yes Yes 0.2732

958 Yes Yes Yes 0.3857

Jo

ur

na

lP

re

-p

ro

of

N Fixed Effects (Year) Fixed Effects (Acq Industry X Tar Industry) Fixed Effects (Acq Industry X Tar Industry) R2

39

Journal Pre-proof Table 5: Target premiums and division of gains The dependent variables in regressions (1), (2), and (3) are the SDC target one-day, one-week, and four-week offer premiums, respectively. The dependent variable in model (4) is the three-day target’s CARs centered on the deal announcement date. The dependent variable in regressions (5) is defined as the difference in dollar gains between the target and the acquirer divided by the sum of the acquirer’s and the target’s market capitalization 50 trading days prior to the deal announcement. All variables are defined in the Appendix, p-values in parentheses. * , ** , and *** denote statistical significance at the 10%, 5%, and 1% level, respectiv ely.

Output Dependence Ratio Size Tobin's Q Free cash flow Leverage

Cash only (0,1)

ur

Hostile (0,1)

Jo

Age (log)

Ivy League college (0,1)

1,491 Yes Yes 0.2604

na

Tender (0,1)

Tenure (log)

1,491 Yes Yes 0.2514

lP

Relative deal size

Number of M&A deals by CEOs (log) CEO hired from outside the firm (0,1) Number of directorships (log) Number of industries (log) Number of networks (log) Competing deal (0,1)

N Fixed Effects (Year) Fixed Effects (Acq Ind. X Tar Ind.) R2

(5) Division of gain 0.025 (0.72) -0.015** (0.04) -0.005 (0.32) -0.039 (0.52) -0.053 (0.71) -0.003* (0.07) -0.003 (0.11) -0.037 (0.25) 0.008 (0.54) 0.043*** (0.00) -0.010** (0.02) -0.002 (0.63) 0.052** (0.04) 0.008 (0.63) -0.005* (0.09) -0.001 (0.82) -0.006 (0.32) -0.005 (0.23) 0.039** (0.03) -0.041* (0.05) 0.002 (0.31) -0.014* (0.09)

1,491 Yes Yes 0.2573

1,491 Yes Yes 0.3207

1,491 Yes Yes 0.4444

of

Input Dependence Ratio

(4) Target CAR (-1,+1) 0.339 (0.37) -0.076* (0.06) 0.016 (0.56) -0.269 (0.11) -0.076 (0.82) -0.007 (0.35) 0.008 (0.30) 0.126 (0.30) 0.055 (0.41) -0.052*** (0.00) 0.055** (0.02) 0.038 (0.14) -0.044 (0.75) -0.019 (0.84) 0.008 (0.58) -0.003 (0.91) -0.042 (0.17) -0.008 (0.70) 0.000 (1.00) 0.022 (0.84) 0.001 (0.90) -0.112** (0.01)

ro

Diversifying merger (0,1)

(3) Target 4-week premium 0.269 (0.63) -0.077 (0.23) 0.029 (0.45) -0.243 (0.34) -0.392 (0.40) -0.023** (0.03) 0.010 (0.37) 0.131 (0.47) -0.077 (0.43) -0.042* (0.08) 0.089*** (0.01) 0.018 (0.63) -0.040 (0.81) 0.084 (0.52) 0.021 (0.34) 0.014 (0.68) -0.053 (0.26) -0.033 (0.28) -0.008 (0.95) -0.016 (0.93) -0.001 (0.95) 0.198*** (0.00)

-p

Supply-chain CEO (0,1)

(2) Target 1-week premium 0.446 (0.39) -0.064 (0.28) 0.011 (0.75) -0.237 (0.32) -0.430 (0.33) -0.021** (0.03) 0.010 (0.35) 0.175 (0.30) -0.052 (0.56) -0.041* (0.06) 0.063** (0.05) 0.022 (0.53) 0.067 (0.67) 0.051 (0.67) 0.019 (0.34) -0.012 (0.71) -0.089** (0.04) -0.050* (0.09) 0.088 (0.52) -0.110 (0.49) 0.004 (0.75) 0.170*** (0.00)

re

Intercept

(1) Target 1-day premium 0.393 (0.44) -0.073 (0.21) 0.013 (0.71) -0.241 (0.30) -0.443 (0.30) -0.018* (0.05) 0.012 (0.27) 0.145 (0.38) -0.011 (0.90) -0.039* (0.07) 0.050* (0.10) 0.017 (0.62) 0.102 (0.51) 0.056 (0.64) 0.024 (0.21) -0.012 (0.71) -0.089** (0.03) -0.064** (0.02) 0.069 (0.60) -0.073 (0.64) 0.001 (0.93) 0.177*** (0.00)

40

Journal Pre-proof Table 6: Endogeneity We employ the Heckman (1979) method in this table. The first column of Panel A reports a Probit regression (first stage) of the probability that the M&A deal is executed by a supply chain CEO. The second column in Panel A reports an OLS regression (second stage) of the acquirer CAR(-1,+1) at deal announcement, controlling for the inverse mill ratio derived from the first stage regression . Panel B reports the estimates for the supply chain CEO variable in the second stage regressions of merger performance, target premiums, and division of gains, using the Heckman method. All variables are defined in the Appendix, p-values in parentheses. * , ** , and *** denote statistical significance at the 10%, 5%, and 1% level, respectiv ely. Panel A: Acquirer announcement returns (1) Supply chain CEO (0,1) -11.663 (1.00) 0.292** (0.03)

Intercept Number of vertical links (log)

of

Supply chain CEO (0,1)

Input Dependence Ratio

-p

Output Dependence Ratio

re

Size Tobin's Q

lP

Free cash flow Leverage

na

Relative deal size Cash only (0,1)

ur

Tender (0,1)

Tenure (log)

Jo

Hostile (0,1) Age (log)

Ivy League college (0,1) Number of M&A deals by CEOs (log) CEO hired from outside the firm (0,1) Number of directorships (log) Number of industries (log) Number of networks (log) Inverse mill ratio

-0.345 (0.39) -4.156 (0.27) 3.965 (0.38) 0.016 (0.87) -0.194* (0.09) -2.146* (0.10) 0.692 (0.37) -0.064 (0.82) -0.120 (0.68) 0.166 (0.59) -0.164 (0.98) 0.549 (0.65) 0.112 (0.53) 0.034 (0.90) -0.071 (0.86) 0.044 (0.86) 0.391 (0.73) 3.187** (0.02) -0.124 (0.21)

ro

Diversifying merger (0,1)

(2) CAR(-1,+1) 0.292 (0.21) 0.016** (0.04) -0.008 (0.35) -0.076 (0.17) 0.102 (0.25) -0.005* (0.06) 0.001 (0.41) 0.037 (0.18) 0.013 (0.45) -0.017*** (0.00) 0.019*** (0.00) 0.002 (0.74) 0.098 (0.33) -0.027 (0.39) 0.002 (0.62) 0.014*** (0.01) 0.010 (0.13) 0.005 (0.33) -0.093*** (0.00) 0.090*** (0.00) 0.000 (0.82) -0.018 (0.34)

41

Journal Pre-proof N Fixed Effects (Year) Fixed Effects (Acq Ind X Tar Ind) Pseudo R 2 R2

1,491 Yes Yes 0.6496

1,491 Yes Yes

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na

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-p

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0.2839

42

Journal Pre-proof Panel B: Merged firm’s performance, target premiums, and division of gains Other control variables as in Table 4, model (1) Table 4, model (2) Table 4, model (3) Table 5, model (1) Table 5, model (2) Table 5, model (3) Table 5, model (4) Table 5, model (5)

na

lP

re

-p

ro

of

Inverse mill ratio control Yes Yes Yes Yes Yes Yes Yes Yes

ur

Combined CAR(-1,+1) Abnormal ROA Goodwill write-off One-day premium One-week premium Four-week premium Target CAR(-1,+1) Division of gains

Estimates for Supply Chain CEO (0,1) p-value Coefficient 0.022 (0.05) 0.032 (0.04) -0.270 (0.02) -0.053 (0.39) -0.051 (0.42) -0.066 (0.33) -0.089 (0.06) -0.015 (0.03)

Jo

Dependent variable

43

Journal Pre-proof Table 7: Effects of target-acquirer supply chain link This table analyzes how the supply chain link between target-acquirer industries affects the association between supply-chain CEO and merger performance. “No link” is a dummy variable that is equal to one if the target and acquirer industries do not have any input/output link, i.e. the input dependence ratio and the output dependence ratio between the two industries are zero. The table reports the estimates for the supply chain CEO variable and its interaction with the “no link” variable in the regressions of merger performance, target premiums, and division of gains. p-values are reported in parentheses.

f o

Dependent Variable Acquirer CAR (-1,+1)

Combined CAR (-1,+1)

Abnormal ROA

Goodwill write-off

One-day premium

Supply chain CEO (0,1)

0.014 (0.04)

0.023 (0.09)

0.029 (0.04)

-0.329 (0.02)

-0.001 (0.99)

Supply chain CEO (0,1) X No link (0,1)

0.006 (0.06)

0.002 (0.50)

0.042 (0.21)

-0.090 (0.47)

-0.181 (0.05)

No link (0,1)

0.007 (0.12)

-0.009 (0.90)

0.002 (0.85)

0.019 (0.80)

Table 3 (A), model (1)

Table 4, model (1)

Table 4, model (2)

Table 4, model (3)

Independent Variable

Other control variables as in

n r u

l a

One-week premium

Four-week premium

Target CAR (-1,+1)

Division of gains

-0.002 (0.86)

-0.074 (0.38)

-0.034 (0.56)

0.000 (0.98)

-0.175 (0.08)

-0.130 (0.08)

-0.109 (0.07)

-0.029 (0.05)

-0.004 (0.90)

-0.023 (0.40)

-0.025 (0.44)

0.013 (0.56)

0.002 (0.56)

Table 5, model (1)

Table 5, model (2)

Table 5, model (3)

Table 5, model (4)

Table 5, model (5)

-p

ro

e r P

Jo

44

Journal Pre-proof Table 8: Alternative specifications In this table we re-estimate several regressions similar to those in Tables 3, 4, and 5. In Panel A, we repeat the analyses using the latest BEA industry classification matrix available prior to every M&A announcement to categorize supply ch ain CEOs. In Panel B, we analyze 915 deals where the target industry has either upstream or downstream reliance with at least 5 other industries (reliance between a pair of industries is defined as in Figure 1). In Panel C, we analyze 1,013 deals where the acquirers and targets are singlesegment firms.

of

Estimates for Supply Chain CEO (0,1) p-value Coefficient 0.019 (0.04) 0.018 (0.08) 0.042 (0.06) -0.287 (0.01) -0.075 (0.19) -0.061 (0.30) -0.085 (0.18) -0.062 (0.14) -0.016 (0.05) 0.017 (0.06) 0.028 (0.08) 0.037 (0.06) -0.465 (0.00) -0.051 (0.50) -0.069 (0.38) -0.079 (0.34) -0.142 (0.02) -0.020 (0.05) 0.021 (0.06) 0.027 (0.07) 0.025 (0.05) -0.565 (0.02) -0.100 (0.21) -0.088 (0.29) -0.105 (0.24) -0.107 (0.09) -0.020 (0.09)

lP

re

-p

ro

Acquirer CAR(-1,+1) Combined CAR(-1,+1) Abnormal ROA Goodwill write-off One-day premium One-week premium Four-week premium Target CAR(-1,+1) Division of gains Acquirer CAR(-1,+1) Combined CAR(-1,+1) Abnormal ROA Goodwill write-off One-day premium One-week premium Four-week premium Target CAR(-1,+1) Division of gains Acquirer CAR(-1,+1) Combined CAR(-1,+1) Abnormal ROA Goodwill write-off One-day premium One-week premium Four-week premium Target CAR(-1,+1) Division of gains

3, Panel A, model (1) 4, model (1) 4, model (2) 4, model (3) 5, model (1) 5, model (2) 5, model (3) 5, model (4) 5, model (5) 3, Panel A, model (1) 4, model (1) 4, model (2) 4, model (3) 5, model (1) 5, model (2) 5, model (3) 5, model (4) 5, model (5) 3, Panel A, model (1) 4, model (1) 4, model (2) 4, model (3) 5, model (1) 5, model (2) 5, model (3) 5, model (4) 5, model (5)

na

Panel C: Deals where acquirers and targets are single-segment firms

Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table

ur

Panel B: Deals where target industries have a high vertical reliance with other industries

Dependent variable

Jo

Panel A: Closest BEA matrix

Re-estimated regression

45

Journal Pre-proof

Appendi x: Variable definitions

-p

Age: the age of the acquiring CEO at the time of the acquisition.

ro

of

Abnormal ROA: We follow the matching algorithm proposed by Barber and Lyon (1996) based on size and performance to estimate the abnormal post-merger ROA of the merged firm. We start with a sample of completed deals where the merged firms exist for at least 3 years after deal completion. For each acquirer and target in that sample, we identify a matching acquirer and target that satisfy the following criteria: i) the matching acquirer (target) exists for at least 3 years before the deal announcement and 3 years after the deal completion; ii) the matching acquirer (target) does not conduct any SEO or is involved in any M&A (either as a bidder or a target) in the following 5 years after the deal announcement; iii) the matching acquirer’s (target’s) total assets are between 50% and 150% of that of the acquirer (target) in our sample in the year prior to the deal announcement; and iv) the matching acquirer (target) has the closest ROA to that of the acquirer (target) in our sample in the year prior to the deal announcement. Th e combined ROA of the matching acquirer and target is the weighted average of ROA of the matching acquirer and the matching target, with the weight being the relative asset value of the acquirer and target in our sample in the year before deal announcement . Therefore, the combined ROA of the matching acquirer and matching target is an estimate of the combined ROA of the acquirer and target in our sample had they not merged. The abnormal ROA in each of the 5 years after the deal completion is the difference between the actual ROA of the merged firm in our sample and the combined ROA of the matching acquirer and matching target.

Analyst forecast dispersion: measured similar to that of Garfinkel (2009).

re

CARs (-1,+1): estimated using the standard event study methodology (Dodd and Warner, 1983). Cash only (0,1): equal to one if the merger is financed by 100 percent cash and zero otherwise.

lP

Combined CAR (-1,+1): the three-day cumulative abnormal stock price returns of the combined firm (acquirer and target CARs(-1,+1) weighted by market capitalization). Competing deal (0,1): equal to one if there are more than one bidder firm for a deal.

na

CEO hired from outside the firm (0,1): equal to one if the acquiring CEO was hired from outside the acquirer, and zero otherwise.

ur

CEO worked in target’s industry (0,1): equal to one if the CEO has past employment in the target’s industry, and zero otherwise, as in Custódio and Metzger (2013). Deal value: the total value of consideration paid by the acquirer, excluding fees and expenses .

Jo

Diversifying merger (0,1): equal to one if the bidder and target firm are not in the same Fama-French (1997) 12 industrial group and zero otherwise. Division of gains: the difference in dollar gains between the target and the acquirer divided by the sum of the acquirer’s and the target’s market capitalization 50 trading days prior to the deal announcement, similar to Ahern (2012). Downstream experience: equal to one if the acquiring CEO has experience in an industry that has a downstream reliance with the target’s industry (illustrated in Figure 1) and zero otherwise. For example, the CEO has experience in an industry (industry B) that buys a significant amount of its inputs from the target’s industry; that is, (industry B’s input from target industry / total industry B’s output consumed by other industries) ≥ 20%. Another example is when the target’s industry sells a significant amount of its output to industry B; that is, (target industry’s output to industry B / total target industry’s output consumed by other industries) ≥ 20%. Free cash flow: calculated as operating income before depreciation minus interest expense minus income taxes minus capital expenditures, scaled by total assets. Goodwill write-off: the amount of goodwill impairment by the acquiring firms. Information on the impairment of goodwill is based on the amount of write-offs within five years after the acquisition. Similar to Li, Shroff and Zhang (2011), we match each acquisition made by an impairment firm in a given year with all acquisitions made in the same year by firms in the same 2-digit SIC code as the impairment firm to construct a control group. We also require control

Journal Pre-proof firms to have non-zero goodwill at the beginning of the impairment year and no goodwill write -offs during our sample period. If a control merger appears many times, we only include one randomly selected deal in the control sample. Hostile (0,1): equals one if the deal is characterized by SDC as hostile and zero otherwise. Input dependence ratio: calculated by the value of acquirer industry’s input provided by the target industry, divided by the total value of the acquirer industry’s output, using BEA Use Table (Summary level) during 1997-2012.

Ivy League college (0,1): equal to one if a CEO attended an Ivy League institution as an undergraduate (Brown University, Columbia University, Cornell University, Dartmouth College, Harvard University, Princeton University, University of Pennsylvania, or Yale University), and zero otherwise. Length of supply chain experience (log): the natural logarithm of (1 + the number of years the bidding CEO has supply chain experience). The number of years of supply chain experience is set to zero if a CEO is a non -supply chain CEO.

of

Leverage: calculated as book value of debt divided by the sum of book value of debt and market value of equity.

ro

No link (0,1): equal to one if the target and acquirer industries do not have any input/output link, i.e. the input dependence ratio and the output dependence ratio between the two industries are zero. Number of directorships (log): (the natural logarithm of) the number of board seats in public firms the acquiring CEO holds at the time of the acquisition.

re

-p

Number of industries (log): (the natural logarithm of) the number of different industries where the acquiring CEOs work before the announcements of the acquisitions in our sample, with industries being classified using BEA Use Table (Summary level) during 1997-2012. Number of M&A deals by CEO (log): (the natural logarithm of) the number of M&A deals the CEO has done before the announcements of the acquisitions in our sample.

lP

Number of networks (log): the natural logarithm of the number of social networks the acquiring CEO has at the time of the acquisition, using BoardEx data.

ur

na

Number of vertical links (log): (the natural logarithm of) the total number of significant suppliers and customers of the industries that the CEOs are associated with. We first identify the pairs of industries (using BEA database) that have significant supply chain relations with each other, using the 20% threshold fo r the amounts an industry sells to (or buys from) another industry (as in Figure 1). We then count the number of significant customers and suppliers for each industry. (Note that “customers”/“suppliers” refer to customer/supplier industries.) Next, we track the employment record of the CEOs in our sample and count the total number of significant suppliers and customers of the industries that the CEOs are associated with.

Jo

Old experience (0,1): equal to one if the bidding CEO has supply chain experience that was obtained more than 10 years before the deal announcement, and zero otherwise. One-year post-deal stock return: calculated as the daily compounded stock returns of the acquirer over a period of one year starting three days after the deal announcement. Output dependence ratio: measured as the value of the acquirer industry’s output sold to target industry, scaled by the total value of the acquirer industry’s output, using BEA Use Table (Summary level) during 1997-2012. Recent experience (0,1): equal to one if the bidding CEO has supply chain experience that was obtained less than 10 years before the deal announcement, and zero otherwise. Relative deal size: the ratio of deal value to acquirer’s market capitalization. Return on assets (ROA): calculated as earnings before interest and taxes over total assets. Size: the natural logarithm of the firm’s market capitalization. Target premium (one-day, one-week, and four-week): the premium (one-day, one-week, and four-week) offered to the target firms as reported by SDC. Tender (0,1): equal to one if the deal is a tender offer and zero otherwise.

Journal Pre-proof Tenure: the number of years the CEO has worked in the acquiring firm. Tobin’s Q: defined as the book value of assets minus the book value of equity plus the market value of equity, divided by the book value of assets.

Jo

ur

na

lP

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-p

ro

of

Upstream experience: equal to one if the acquiring CEO has experience in an industry that has an upstream reliance with the target’s industry (illustrated in Figure 1) and zero otherwise. For example, the CEO has experience in an industry (industry A) that sells a significant amount of its output to the target’s industry; that is, (industry A’s output t o target industry / total industry A’s output consumed by other industries) ≥ 20%. Another example is when the target’s industry buys a significant amount of its inputs from industry A; that is, (target industry’s input from industry A / total target industry’s output consumed by other industries) ≥ 20%.

Journal Pre-proof Highlights

Acquirers in deals by CEOs experienced in the target’s supply chain (supply chain CEOs) earn 1.5% higher M&A announcement returns.



Better integrated merger firms and the bargaining of better terms explain the higher returns.



Acquisitions by supply chain CEOs exhibit higher synergies, higher post-deal accounting returns, and less goodwill written off.



Target firms of supply chain CEOs get a lower share of the merger gains.

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