Executive Market Segmentation: How Local Density Affects Incentive and Performance

Transcription

1 Executive Market Segmentation: How Local Density Affects Incentive and Performance Hong Zhao, W.P. Carey School of Business Arizona State University First Draft: December 2014 Current Draft: November 2015 *Department of Finance, W.P. Carey School of Business; ** I am grateful to Ilona Babenko, Hank Bessembinder, Michael Hertzel, Laura Lindsey, Patrick McColgan, Luke Stein, Ran Tao, Rong Wang, and seminar participants at Arizona State University, the 2015 China International Conference in Finance, the 2015 FMA Annual Meeting for helpful comments and discussion. All errors are my own.

2 Executive Market Segmentation: How Local Density Affects Incentive and Performance Abstract This paper studies how the density of executive labor markets affects managerial incentives and firm performance. I show that executive markets are locally segmented rather than nationally integrated, and the density of a local market provides executives with noncompensation incentives. In a denser market, executives face stronger performance-based dismissal threats as well as better outside promotion opportunities. These incentives induce executives to bring higher performance to firms in denser markets, especially when executives are young. The results in this paper imply the importance of local market density in managerial incentive alignment. JEL Classification: G30; G34; J42 Keywords: Executive labor market; geographic segmentation; local market density; managerial incentive; firm performance

3 1 Introduction Dismissal threats and promotion opportunities are important source of managerial incentives. Jensen and Murphy (1990) show that dismissal threats account for 10% of a CEO s incentives, and Yermack (2004) finds that additional directorships account for 40% of an outside director s incentives. 1 Moreover, both theoretical and empirical research demonstrates that these non-compensation incentives have positive effects on firm performance. For example, Lazear and Rosen (1981) prove that an executive s effort increases with the size of promotion-based tournament incentive. Kale et al. (2009) and Coles et al. (2013) provide supportive empirical evidence by documenting a positive relationship between firm performance and intra-firm and intra-industry promotion opportunities, respectively. This paper studies market density as one important determinant of both dismissal threats and promotion opportunities. On one hand, executives in a denser area might face stronger dismissal and promotion incentives because of peer competition and outside promotion opportunities. On the other hand, market density might disincentivize executives by providing more backup options in the event of dismissals. Therefore, the goal of this paper is to examine how local labor market density affects managerial incentives and what are the implications on firm performance. For local market density to matter, one necessary condition is geographic segmentation in executive labor markets. 2 If executives tend to move within one large national market instead of many small local markets, then all executives will face the same competition and promotion opportunities. To examine whether the U.S. executive labor markets are geographically segmented, I explore the employment history of board directors and executives covered by the BoardEx database. Using the zip code of firm headquarters, I calculate the moving distance between the old and the new employers every time an individual changes a 1 Applying more relaxed assumptions, Jenter and Lewellen (2014) show that CEO dismissal threats are substantially underestimated in previous literature. 2 The U.S. executive labor markets are commonly viewed as very mobile. Kedia and Rajgopal (2009) write that it is difficult to argue that top executives are geographically immobile (p. 125). Yet, some recent empirical findings challenge this view. For example, Yonker (2014) shows that firms are prone to hire CEOs who grew up in the state of firm headquarters than CEOs from elsewhere. Ang et al. (2013), Bouwman (2013), and Francis et al. (forthcoming) find that local geographic conditions could have substantial impacts on both the level and the structure of executive compensation. 1

4 job. Out of 19, 692 job relocations in my sample, 6, 713 cases have a moving distance of 60 miles or less (i.e. local). To conduct formal tests on segmentation, I compare the realized local hiring percentage with the expected local hiring percentage under the null hypothesis that the executive market is nationally integrated. For each hiring event, the expected local hiring probability is approximated by the number of local firms divided by the number of firms nationwide. I find that the expected local hiring percentage is around 5%, while the realized percentage is 34%. This implies that local hiring bias is 29%, or firms hire local executives seven times more often than expected. A binomial test indicates that this bias is statistically significant. One concern with the above local hiring bias is that it might actually be driven by geographic clustering of industry and firm s tendency of hiring industry insiders. To disentangle industry effect from the bias, I adjusted the expected local hiring probability based on number of firms within the same industry as the hiring firm. With this alternative measure, LHB is still significantly above zero at 25%. Based on the evidence of geographic segmentation, I then study how local labor market density affects executive s implicit incentives through dismissal threats and outside promotion opportunities. 3 Since firms often hire executives from the local pool, firms located in denser markets face more outside candidates and lower replacement cost. Therefore, local density makes firms more likely to dismiss poor-performing executives, and hence creates a performance-induced dismissal threat for executives. Besides dismissal threats, local density could also improve executive incentive alignment through the channel of tournament incentives. To the extent that executives in a denser market have more potential outside positions to be promoted to, both the prize of the tournament and the probability of winning might increase with market density. To provide empirical evidence that density of the local market creates implicit incentives for executives, I first examine the relation between market density and CEO turnoverperformance sensitivity. I measure density as the number of firms within 60 miles of a firm headquarters. Using CEO turnovers during , I find that the sensitivity of CEO turnover to stock performance rises as the density of local executive pool rises. Such effect 3 In this paper, I call managerial incentives from dismissals and tournaments implicit incentives to distinguish them from explicit incentives generated by compensation structures. 2

5 is both statistically and economically significant. An interquartile increase in density raises the turnover-performance sensitivity by 20%. I also find that outside successions are more frequently used by firms in denser markets. These results are consistent with my hypothesis that convenient access to outside candidates in denser labor markets leads to firms dismissing poor-performing incumbent executives more frequently. Next, I investigate whether denser markets also create stronger outside promotion incentives for local executives. Similar to Kale et al. (2009) and Coles et al. (2013), I consider both the tournament prize and the probability of winning. I measure tournament prize as the compensation gap between executive s current compensation and the 90 th percentile of compensation in the local market, and measure winning probability as the realized executive promotion frequency. Empirically, both compensation gap and outside promotion probability are higher in denser labor markets. The effect of local market density are statistically significant and large in magnitude. For executives changing jobs between Execucomp firms, an interquartile increase in market density raises expected tournament prize from $0.86 million to $3.1 million, and raises local outside promotion frequency by about 60%. Although local market density provides executives with implicit incentives through dismissal threats and outside promotion opportunities, it is also possible that density could disincentivize executives by offering more backup options in the event of a dismissal. To test such argument, I construct a sample of executives losing their jobs and examine their subsequent employment outcomes. Applying a similar procedure to Fee and Hadlock (2004), I consider all executives in S&P 500 firms who were under the age of 55 and lost jobs during , and look for their subsequent employment history in a three-year window based on news articles. Regression results indicate that local market density does not help dismissed executives find a new job more easily, obtain a higher-quality position, or experience shorter unemployment duration. One reason for local market density having no effect on post-dismissal employment outcome is that potential local employers could have detailed information on those poor-performing executives and are thus reluctant to hire them. I find empirical evidence supporting this explanation. The probability of firms hiring a local dismissed executive is only half the probability of firm hiring a local non-dismissed executive, 3

6 suggesting that executive reputation spreads in local labor markets. Since local market density generates strong implicit incentives for executives, the last goal of the paper is to examine whether density enhances firm performance through incentive alignment. The empirical challenge here is that market density could have an impact on performance through various channels, so a simple positive correlation between these two variables does not suffice. 4 To distinguish the incentive mechanism from other potential mechanisms, I interact market density with executive s career horizon. The logic, as argued in Gibbons and Murphy (1992), is that executives with a shorter horizon (closer to retirement) should respond less to dismissal and promotion incentives. Using executive s current age as a proxy of horizon, I find that the coefficient on the interaction term between market density and executive horizon is significantly positive in performance regressions. In other words, the positive effect of market density on firm performance is stronger for firms with younger executives. As for economic magnitude, firms in markets at top-quartile density and with executives at bottom-quartile age have a 0.35 (0.01) higher Tobin s Q (ROA) than firms in markets at bottom-quartile density and with executives at top-quartile age. These results are consistent with my argument that executives in a denser market exert more effort in response to stronger implicit incentives and thus improve firm value. I use the number of local firms as the main measure of local labor market density, but I also consider several alternative measures. Since larger firms provide more job positions and more executives than small firms do, I adjust each count of firms in a local market by firm s employment size. Also, if executives often change jobs within rather than across industries, a more appropriate measure of local density would include only local firms which are in the same industry as the target firm. Using these alternative measures, I find that the findings on managerial incentives and firm performance remain unchanged. It is likely the case that the geographic segmentation documented in this paper is driven by firm s and executive s endogenous choices rather than natural barriers. Firms might choose to hire local executives because information asymmetry is less severe in local markets, or it takes less effort to hire those nearby. Executives might choose to relocate locally because the 4 See Marshall (1920), Duranton and Puga (2004), and Rosenthal and Strange (2004) for economic foundations on the effects of geographic clustering on firms. 4

7 physical or emotional moving costs are large. However, to the degree that local density affects incentives as long as firms knows that they are likely to find successors locally and executives know that are likely to obtain their next job locally, the reason that causes segmentation, either endogenous or exogenous, does not matter. The empirical findings suggest that executives in denser markets face stronger incentives and achieve better performance through the channel of local competition and promotions. Yet, density might also be associated with other firm or market characteristics, such as board independence, that have a direct impact on incentives and performance (Knyazeva et al. (2013)). For results on incentives, endogeneity is less of a problem. Even if board independence affects dismissal threats and promotion opportunities, it does not contradict the findings that density enhances incentive alignment. It merely suggests that higher incentives in denser markets might also be created by board independence, in addition to competition and promotions as argued in this paper. Endogeneity could be more of an issue for results on firm performance, where I intend to specify the channel through which density affects performance. To demonstrate the incentive alignment channel, I explore the heterogeneity of the density effect with respect to career horizon. I also control for the board independence channel and find that the main results are not attenuated. This paper contributes to several streams of research in the finance literature. First, by examining whether the U.S. executive labor market is geographically segmented, it contributes to the burgeoning literature on executive market geography. Several recent studies (e.g. Francis et al. (forthcoming), Ang et al. (2013), and Bouwman (2013)) implicitly assume the existence of segmentation in executive markets and find geographic patterns in executive compensation structures. Yet, none of them provides a direct test on the segmentation assumption. To this extent, my paper fills the gap in the literature by documenting a strong bias of firms hiring and executives moving locally. Second, my study relates to the literature on executive incentives. Besides explicit incentives from compensation contracts, another source of incentive alignment comes from implicit incentives, including rank-order tournaments (Lazear and Rosen (1981), Green and Stokey (1983), Kale et al. (2009), Coles et al. (2013)) and performance-induced dismissal threats (Jenter and Lewellen (2014)). My 5

8 results indicate that local market density creates both tournament incentives and dismissal threats, and executives respond to these implicit incentives based on their level of career horizon. Finally, my paper extends the literature studying the effect of geographic agglomeration on firms. Existing work finds that geographic agglomeration affects firms in many aspects, including innovation (Glaeser et al. (1992)), acquisition (Almazan et al. (2010)), dividend policies (John et al. (2011)), and board independence (Knyazeva et al. (2013)). Focusing on executive labor markets, I find that firms located in denser areas are more likely to dismiss poor-performing executives and hire outsiders. Moreover, local density enhances firm value through the channel of executive incentives. My study on executive markets provides an alternative way to interpret the impacts of geographic agglomeration on firm performance. The rest of the paper is organized as follows. Section 2 provides a review of related literature. Section 3 examines geographic segmentation in the U.S. executive labor market. Main results on managerial incentives and firm performance are provided in Sections 4 and 5. Section 4 studies how local labor market density affects dismissal threats and outside promotion opportunities for executives. Section 5 investigates whether market density improves firm performance through the channel of incentive alignment. Section 6 conducts robustness checks and explores alternative explanations. Section 7 concludes. 2 Related Literature 2.1 Executive Market Geography If firms search executive candidates in a nationwide pool and executives are highly mobile, geographic conditions should have no impact on executive compensation, incentive, and etc. Yet, this view of an integrated executive market is not supported by recent empirical studies. Comparing firms in urban and rural areas, Francis et al. (forthcoming) find a positive relation between a firm s headquarters metropolitan size and the total and equity portion of its CEO s pay. They further show that firms in dense areas are willing to offer this compensation premium because agglomeration improves executive local network and human capital accumulation. Ang et al. (2013) find a similar positive relation between local market density and CEO compensation. They refer to sociology literature and explain this positive 6

9 relation as a result of CEO social comparison and social pressure from local peers. Also based on sociology literature, Bouwman (2013) documents that CEO compensation level highly depends on the average level of local peers. She tests several explanations for this phenomenon and concludes that it is most consistent with CEO envy. To the degree that executive compensation has geographic patterns and firm location is largely exogenous to current executives, local geographic conditions could be exploited as instrumental variables in the study on executives. For instance, Coles et al. (2013) use average local compensation as an instrument to CEO compensation and study the effect of industry tournament incentive on firm performance. The study most directly testing on geographic segmentation of executive labor market is Yonker (2014), who finds that firms are five times more likely to hire CEOs who grew up in the same state as firms headquarters (home hiring bias). However, unlike local hiring bias, home hiring bias is not direct evidence on geographic segmentation because executive s hometown could be different from the location of his previous job. 5 Also, Yonker measures geographic proximity at state level and uses a sample consisting of only 1, 162 CEO hirings. These restrictions preclude more detailed and comprehensive conclusions on the overall executive labor market. Using executive job changes covered in BoardEx and calculating zip code distance between executive s old and new firms, my paper addresses these issues. 2.2 Managerial Incentives Managerial incentive has been the core of studies on corporate governance. Hölmstrom (1979) builds the foundation of modern principal-agent model and derives the optimal managerial contract under imperfect information and moral hazard. Following this theoretical work, hundreds of researchers have been investigating the empirical relation between firm performance and executive compensation structures. Important early contributions include Morck et al. (1988) and McConnell and Servaes (1990), who find non-linear and humpshaped relation between firm Tobin s Q and executive ownership. Yet, numerous later 5 Suppose a CEO spent his childhood in California, worked as a senior executive in a New York firm, and then was hired by a Californian firm as CEO. In such case, it is clear that the Californian firm has conducted a nationwide search and the CEO moves across the nation, but the hiring would be classified as a local one if CEO s grew-up area rather than CEO s last job area is used. 7

10 studies using different data and methods yield conflicting results. 6 More recently, Coles et al. (2012) use structural estimation to address the issue of endogeneity and conclude that the hump-shaped relation between firm value and executive ownership is an equilibrium result driven by exogenous firm characteristics. Apart from compensation, internal and external labor market outcomes provide executives with another source of incentives. This paper focuses on executive external tournament incentives and internal dismissal threats. Theoretical foundations of rank-order tournament are developed by Lazear and Rosen (1981) and Green and Stokey (1983). They show that tournaments can replace performance-based contracts as an incentive mechanism and that tournaments can lead to more efficiency under some circumstances. On the empirical side, Kale et al. (2009) measure non-ceo executive s internal tournament incentives using the pay gap between the non-ceo executive and CEO within the firm. They find that tournament incentives have a positive effect on firm performance after controlling compensation incentives and addressing endogeneity. They also show that such positive effect is stronger when executive s perceived promotion probability is higher. Coles et al. (2013) extend Kale et al. (2009) by examining tournament incentives on CEOs. They argue that, although CEOs have no promotion incentives within the firm, they are still in a tournament with CEOs outside the firm. They use the compensation gap within industry as a measure of tournament incentives and find a positive relation between industry tournament incentives and firm performance. Similar to Kale et al. (2009), they find that tournament incentives are stronger when CEOs perceive higher probability of being promoted. Fee and Hadlock (2003) also confirm the existence of promotion incentives by showing that executives who jump to CEO positions at new employers come from firms with superior stock performance. Like promotions, dismissals also generate incentives for executives. Previous studies document a robust negative relation between firm stock performance and executive turnover probability, though the magnitude is modest. 7 Using more relaxed model assumptions such as non-linearity, Jenter and Lewellen (2014) uncover a much larger effect of firm performance on CEO turnover than previous 6 Demsetz and Villalonga (2001) provide a review and discussion on the empirical relation between ownership and performance. 7 See, among others, Coughlan and Schmidt (1985), Warner et al. (1988), Weisbach (1988), Denis et al. (1997), Huson et al. (2001), and Kaplan and Minton (2012). 8

11 studies and thus argue that performance-induced dismissal threat is an essential source of incentives. One feature of labor market incentives, both dismissals and promotions, are that their strength depends on executive career horizon. Promotions become less attractive and dismissals become less threatening when executives are closer to retirement, i.e. shorter career horizon. Gibbons and Murphy (1992) show both theoretically and empirically that horizon affects labor market incentives and firms optimally adjust the level of explicit incentives in compensation contracts based on the strength of career concerns. 2.3 The Effect of Geographic Clustering on Firm Firm location choices and geographic clustering have been discussed by economists since Marshall (1920). Marshall theorizes three primary benefits to firms locating in clusters: knowledge spillovers, labor market pooling, and input providers pooling. Although the empirical evidence on the direct impact of clustering on performance is mixed, economists do find that these three channels exist. For example, using patent citation as a paper trail of knowledge flow, Jaffe et al. (1993) find that knowledge spillovers attenuate with geographic distance since citations are highly spatially concentrated. Glaeser et al. (1992) also finds evidence on knowledge spillovers using data on industry growth. Evidence on labor market pooling and input providers pooling are documented in Costa and Kahn (2000) and Holmes (1999). On the other hand, geographic clustering could also have negative effects on firms. Shaver and Flyer (2000) argue that the strongest firms gain little from clustering, yet suffer when their technologies and employees spillover to competitors. Other costs of agglomeration include transportation congestion, pollution, and crime (Glaeser (1998) and Tabuchi (1998)). Duranton and Puga (2004) and Rosenthal and Strange (2004) provide comprehensive reviews of both theoretical foundations and empirical results of the literature. Besides these economic foundations, geographic agglomeration could also affect firms through reasons well-established in the finance literature. For example, Almazan et al. (2010) find that firms located in clusters have more acquisition opportunities. They also show that these firms maintain more financial slack than their industry peers in order to facilitate potential acquisitions. One reason for firms in clusters having more acquisition opportunities, 9

12 as argued in Uysal et al. (2008), is that geographic proximity leads to information advantage. They find that acquirer returns are significantly higher in local acquisitions than in non-local ones. Market density could also influence firm dividend policy. Based on agency theory, John et al. (2011) show that firms located far away from large metropolitan areas use precommited high dividends as a solution to the potential agency problem due to decreased shareholder monitoring ability. The study most closely related to my paper is Knyazeva et al. (2013). They consider the size of clusters as a proxy of the thickness of outside directors pool. Empirically, they show that firms located in denser markets have higher board independence and better performance. 3 Geographic Segmentation in the U.S. Executive Labor Market This section documents the geographic segmentation in the U.S. executive labor market. To deliver robust results, I consider a set of different measures of expected local hiring percentage under the null hypothesis of a nationwide executive labor market. Further subsample analyses are provided in Appendix B. 3.1 Executive Job Changes Sample I explore individual employment histories covered by the BoardEx database of Management Diagnostics Ltd. BoardEx provides comprehensive biographical information on individuals who have ever been listed as either directors or disclosed top earners in public traded U.S. companies since Currently, the database covers about 71% of firms in Compustat, representing 95% of market capitalization. At each report date, an individual s curriculum vitae is constructed based on the most recent publicly disclosed information. I explore the professional working experience contained in the curriculum vitae. 8 I order each individual s employment history in a chronological order, and record a job change if the his employer in year t is different from the employer in year t+1. Since BoardEx reports job beginning and end dates in either year-month or year based on data availability, I convert all dates into year format. 9 I exclude about 25% job changes where there is a gap 8 Since the employment history of either directors or top earners mainly contains senior executive roles in various companies, I call directors and top earners both executives when I refer to their employment history. 9 I drop employment observations with a missing start year or end year, unless it is an executive s first employment 10

13 between the end year of the old job and the start year of the new job, because executives could have a job and relocate during that gap while just not identified by BoardEx. I further only keep job changes between U.S. public firms because location data on private firms are not readily available. Since large firms benefit more from a nationwide job search and have lower local hiring bias (as supported by the empirical evidence in Appendix B), the bias documented here could be regarded as a lower-bound. To calculate the moving distance between the old and new firms, I merge BoardEx with Compustat by linking the International Security Identification Number (ISIN) from BoardEx with the CUSIP from Compustat. For U.S. firms, the ISIN is constructed by adding US to the front and a single-digit check code to the end of the regular nine-digit CUSIP number. I then merge the zip code of firm s headquarters from Compustat with the latitude and longitude of each zip code from the Census 2000 U.S. Gazetteer. 10 The distance in miles between two zip code areas is calculated using the Vincenty formula. 11 The main sample consists of 19, 692 executive job changes, with 16, 277 unique executives and 3, 743 unique hiring firms. The sample mean (median) total assets of hiring firms are 9.75 (1.63) billion of 2000 U.S. dollars. Compared to the firms in the Compustat universe during the similar time span with mean (median) assets of $5.59 ($0.16) billion, the hiring firms in my sample mainly consists of the large U.S. firms. 3.2 Estimation Strategy Following the literature (e.g. Knyazeva et al. (2013), Bouwman (2013)), I define a firm s local area as the area within a 60-mile radius of the firm s headquarters. I use 100-mile and 250-mile radii as alternative cutoff values. Similar to Yonker (2014), I calculate local hiring record for a missing start year or the last employment record for a missing end year. About one-third observations are dropped. 10 Compustat reports the current headquarters location of firms. Knyazeva et al. (2013) show that the overwhelming majority of firms do not relocate. Even for firms that relocate, most of them remain within sixty miles of their previous location. I also implicitly assume that all executives holding senior positions work at the firm s headquarters. 11 The Vincenty formula is often used in measuring distances in the finance literature (see Pool et al. (forthcoming) for example). It calculates the distance between two points on the surface of a spheroid. The distance in miles between two zip code areas with latitude/longitude (ϕ i, λ i) is calculated as (cosϕ2sin(λ 2 λ 1)) 2 + (cosϕ 1sinϕ 2 sinϕ 1cosϕ 2cos(λ 2 λ 1)) arctan( ) sinϕ 1sinϕ 2 + cosϕ 1cosϕ 2cos(λ 2 λ 1) 11

14 bias (LHB) as the difference between the realized local hiring percentage and the expected local hiring percentage under the null hypothesis that the executive market is nationwide. Formally, LHB = N Ni=1 L N p i N where N is the total number of hiring event in my sample, N L is the number of actual local hiring, and p i is the probability of hiring a local executive for each hiring event i under the null hypothesis. To calculate LHB, I propose several methods to estimate the key element p i. The first and the most straightforward measure is the number of local firms of event firm i divided by the number of firms nationwide. 12 However, this simple ratio measure does not take into account that large firms might provide more executives to local labor market than small firms. Thus, in the second measure I adjust the ratio using firm size. Specifically, each firm count is adjusted by a size weight, which is calculated as the firm s number of employees divided by the average number of employees for all firms in that year. One concern with LHB calculated using either of the two measures of p i is that the bias might actually be driven by a firm s tendency to hire industry expertise rather than locals. 13 For example, consider Google in Silicon Valley. Suppose, at the extreme, that Google only hire executives within the same industry. Since many of the high-technology firms are located in Silicon Valley, one would expect to observe a high realized percentage of local hiring for Google. To address this issue and separate industry effects from local effects, I calculate a third measure of p i as the number of local firms within the same industry as the event firm divided by number of firms of that industry nationwide. Industry is classified based on 2-digit SIC codes. Under this measure, if Google tends to hire industry veterans but not locals, the expected local hiring percentage p i will be high for Google due to the industry clustering effect and LHB should not be different from zero. Finally, the fourth measure of p i adjusts the third measure by firm size as done for the second measure. As for Google in 2010, the 60-mile expected local hiring percentages under p 1 and p 2 12 Throughout the paper, I only consider firms that are covered by Compustat except otherwise noticed. 13 Based on my sample, 48.3% of job changes are within the same 2-digit SIC industry. 12

15 are 0.09 and 0.04, respectively. When industry clustering effects are taken into account, the expected local hiring percentages under p 3 and p 4 increase to 0.16 and To the extent that firms hire executive both within and outside of the industry, LHB estimated using p i from the first two (cross-industry) and last two measures (within-industry) could be regarded as upper and lower bounds of the local hiring bias. 3.3 Results on Local Hiring Bias Table 1 presents the results on local hiring bias based on the 19, 692 executive job changes identified in Section 4.1. In Panel A, I conduct a baseline analysis for the full sample using p 1 in the LHB calculation. The first and second columns list the total number of hiring events N and the number of realized local hirings N L. The third column shows the realized local hiring percentage, which is calculated as column (2) divided by column (1). The fourth and fifth columns show the total number of expected local hiring and the expected local hiring percentage under null hypothesis of a nationwide market. Finally, the last column calculates the local hiring bias as the difference between column (3) and column (5). As reported in Panel A, when a 60-mile is used, 34% of hirings are local. However, if the executive labor market is integrated and nationwide, the expected local hiring percentage should be around 5%. Therefore, the local hiring bias is 29%. In other words, firms hire local executives seven times more often than they would if the market were integrated. In the next two rows, I use 100-mile and 250-mile as alternative cutoffs to define the local area. The magnitude of the local hiring bias remains substantial and is around 29% to 31%. Another thing worth noticing is that although there are 6, 713 hirings within 60 miles, only 410 (1, 951) additional hirings happen between 60 and 100 miles (100 and 250 miles). The number of these additional hirings are actually close to the increase in expected hirings. Hence, it could be that the local hiring bias is mostly driven by the hirings within 60 miles of the firm s headquarters. In Panel B, I replace the unadjusted expected local hiring measure p 1 with size adjusted measure p 2. As shown in the last column, the bias continues to exist and becomes even larger. To address the concern that local hiring bias could actually be driven by firm s tendency 13

16 to hire industry insider, I use the third and fourth measure of p i in Panels C and D. Consistent with the fact that firms within the same industry often cluster together, the expected local hirings are almost double the numbers in Panels A and B. Although the increase in expected local hirings reduces the bias, it is still substantially larger than zero for both unadjusted and size adjusted p i measures and all three distance cutoffs. As argued in Section 4.2, since the cross-industry and within-industry measures of p i provide an upper and lower bounds of LHB, the results in Panels A and B together indicate that the local (60-mile) hiring bias is between 25% to 29%, and firms are three to seven times more likely to hire local executives than expected. In addition to economic magnitude, I also compute statistical significance using a twosided binomial test where a local hiring is considered as a success. Formally, for the binomial test, the number of trials is N, the number of successes is N L, and the probability of success is the average of p i ( N i=1 p i /N). The test results reject the null hypothesis that the executive labor market is integrated for all distance and p i measures in Panels A and B at the 1% level. 4 Local Market Density and Executive Incentive Alignment In Section 3, I document the phenomenon that there exists a substantial geographic segmentation in the U.S. executive labor market. If firms often hire and executives often move locally, then the density of the local labor market could have an impact on executive incentives. In this section, I first provide empirical evidence on how labor market density affects executive s dismissal threats, as measured by turnover-performance sensitivity. Then, I show that executives in denser area have better outside promotion opportunities, in terms of larger compensation gap as well as higher promotion probability. Finally, by studying the subsequent employment outcomes for executives who lose their jobs, I address the concern that the market density might disincentivize executives by offering abundant backup options at the event of dismissals. 4.1 Summary Statistics for Sample Firms The analyses of managerial incentives and firm performance in Sections 4 and 5 are 14

17 based on a sample consisting of firms with available Execucomp, Compustat, CRSP and ISS data from 1996 to I use the Execucomp database to identify CEO turnovers, and for information on executive characteristics including age, compensation, tenure, etc. All firm-level accounting data come from Compustat. The Center for Research in Security Prices (CRSP) provides data on stock returns. I also use the Institutional Shareholder Services (ISS) database (formerly RiskMetrics database) for information on board characteristics and corporate governance. 14 The key explanatory variable is the density of executive labor market in the firm s vicinity. Since the bias of local hiring comes mostly within 60 miles of firm s headquarters (as documented in Table 1), I use 60-mile as the cutoff value to define local area. I use two main measures of local executive market density. Local market density 1 is the total number of firms within 60-mile radius of the sample firm. Local market density 2 adjusts each firm count in the local area with its employment size. Knyazeva et al. (2013) use similar measures to characterize the availability of prospective directors near a firm. In robustness checks, I consider two other measures of density assuming that firms only hire industry insiders. The summary statistics of the main variables are presented in Table 2. The sample contains 28, 603 firm-year observations and 2, 789 unique firms. On average, the local executive pool consists of executives from 371 local firms. This number decreases to 255 after size adjustment is used. To address the skewness of the density measures and to mitigate the effect of extreme values on regression results, I use logarithm in all regression analyses. Panel A also reports other common characteristics of sample firms. Firms on average have total assets of 6.06 and annual sales of 3.31 billions of 2000 dollars. The mean annual stock return, sales growth and return on assets are 19%, 14% and 12% respectively. Executive characteristics are shown in Panel B. A typical CEO is at the age of 56, has been at the helm for 5 years, and owns 3% of firm s stock. When the top management team is considered, the average age drops to 51 and stock ownership drops to 1%. Table A.1 in the Appendix A gives a detailed description of variables used in the paper. 14 Since board and governance data are reported biannually before 2006, I follow the literature (e.g. Gompers et al. (2003) and Bebchuk et al. (2009)) and construct annual time series of governance provision by assuming that it remains unchanged from one report until the next. 15

18 4.2 Performance-Based Dismissal Threats If firms mainly focus on local executive markets rather than the nationwide market, the cost of finding a new executive should be lower for firms located in denser markets. As shown in Parrino (1997), convenient access to strong outside candidates encourages a firm to replace its incumbent executives with outsiders when executive s performance turns out to be low Turnover-Performance Sensitivity To study how local market density affects firm s dismissal policy, I use CEO turnovers covered by the Execucomp database and investigate the turnover-performance sensitivity. Although I focus on CEO turnovers since they are more observable than non-ceo turnovers, the results could be applied to all top managers. Since firms are usually reluctant to announce the true reasons behind CEOs departure and disguise forced turnovers as voluntary (Weisbach (1988), Jenter and Lewellen (2014)), my main measure of CEO turnover includes both forced and voluntary turnovers. The dependent variable in Table 3 columns (1) to (3) is a CEO turnover dummy, which is set to 1 if the CEO is replaced in the subsequent year. I try two methods to minimize the noise on turnover-performance sensitivity due to retirement. First, I include CEO s age in all model specifications as a control variable. Second, as Weisbach (1988) documents that a nontrivial number of departures happens on CEO s sixty-fifth birthday, I exclude all firm-years with CEOs aged between 64 and 66. Following the literature (e.g. Warner et al. (1988), Weisbach (1988)), I use industry-adjusted stock return as the performance measure. To capture other causes of CEO departures, I control for CEO duality, CEO tenure, CEO ownership, board size, board independence, E-index, firm size, and firm age. I use logit models for all regression with industry and year fixed effects, and report marginal effects with robust standard errors clustered at firm level. The first column in Table 3 documents the relationship between firm performance and CEO turnover without the interaction between performance and labor market density. Since a firm is more likely to replace its CEO if its performance becomes worse but might keep the CEO as long as the performance meets some threshold, I use a positive performance 16

19 variable and a negative performance variable to examine whether the turnover-performance sensitivity is asymmetric for firms with performance above and below the industry median. 15 Negative performance is equal to the industry-adjusted return if the industry-adjusted return is negative, and zero otherwise. Positive performance is defined accordingly. The result in column (1) shows that for firms with industry adjusted returns below zero, the performance is negatively related with the probability of a CEO turnover. This negative relation is both statistically and economically significant. An interquartile decline in below-zero performance (0.44) raises the likelihood of turnover by about 6.4%. 16 On the other hand, there is no clear relation between performance and turnover if the performance meets the industry median. The coefficient on positive performance is almost zero. Consistent with previous studies, I also find that industry returns have a negative impact on turnover probability, suggesting that CEOs are dismissed for reasons beyond their controls. Columns (2) to (3) test whether turnover-performance sensitivity increases with local executive market density. In column (2), I use Local market density 1 as the density measure and interact it with both negative and positive performance. As there is no clear relation between performance and CEO turnover for firms with return above industry median, the coefficient on the interaction term between density and positive performance is also not different from zero. On the other hand, the coefficient on the interaction between density and negative performance is significantly negative. For firms with poor performance, the sensitivity of CEO turnover to stock performance rises as the density of local executive market rises. This is consistent with my hypothesis that firms located in denser labor markets have lower search and replacement costs and thus dismiss CEOs with poor performance more frequently. In terms of economic magnitude, the marginal effect of return on CEO turnover is around 0.13 for firms facing labor market density at the bottom quartile (4.25). This effect increases by 20% to 0.16 for firms at the top quartile (6.28). In addition to its effect on turnover-performance sensitivity, density also affects the probability of turnover directly. For firms with poor performance, the zero coefficient on density itself plus the negative 15 Jenter and Lewellen (2014) empirically show that the effects of performance on turnover is non-linear. Also see Hermalin and Weisbach (1998) and Adams and Ferreira (2007). 16 The average CEO turnover ratio for firms with industry adjusted return below zero is 12%. 17

20 coefficient on the interaction term imply that firms in thicker labor markets are more likely to replace CEOs for a given level of performance. Column (3) uses Local market density 2 as an alternative density measure. The statistical and economic significance of the interaction term remains almost unchanged. 17 Although firms often do not announce the true reason of CEO turnovers, I still strive to identify forced turnovers and to check whether the results above are robust. Following the literature (e.g. Parrino (1997)), I classify a turnover as forced if (i) the report says that the CEO was fired, forced out, or departed due to policy differences; or (ii) the departing CEO is under age of 60, did not announce the retirement at least six months in advance, and did not leave for health reasons or acceptance of another position. Using forced turnovers as the dependent variable, I find the results in columns (4) and (5) are similar to the results in columns (2) and (3). Compared to firms in sparse labor markets, firms in denser markets are significantly more likely to fire poor-performing executives. Overall, the results in Table 3 shows that an increase in local labor market density is associated with a significant increase in CEO s turnover-performance sensitivity. This performance-induced dismissal threat could be an important source of incentives for CEOs and presumably all top executives Outside Successions To further support the outside candidates supplying argument, I next investigate how local density affects the probability of firms choosing outside successors. If density encourages firms replacing poor-performing executives by providing more substitutes, outside succession should also be more frequently observed in denser markets. Empirically, I use CEO and non-ceo hirings covered in the Execucomp database during I record a CEO hiring if there is a change in a firm s CEO, and record a non-ceo hiring if an executive appears in his firm s annual proxy for the first time. An executive is classified as an outsider if he has been with the firm for less than one year 17 Similar results are obtained if I split the sample into firms with positive performance and firms with negative performance and estimate the coefficients separately. 18

21 before taking his CEO or non-ceo position. 18 For CEO turnovers, I also generate a variable indicating whether a departure is forced, since successor choice is strongly related to the reason of turnover (Parrino (1997)). Among 2, 288 departing CEOs with available data, 30% are succeeded by outsiders and 20% are ousted. For all 14, 943 non-ceo observations, 25% are outside hirings. Table 4 column (1) shows the relation between local labor market density and outside CEO succession probability using a logit model. The marginal effect of Local market density 1 is positive and significant at the 1% level. An interquartile increase in executive pool density raises the probability of firm hiring an outside CEO by about 5.4%, which is a 18% increase compared to the average outsider ratio. In line with the findings from previous studies, the effect of forced turnover is significantly positive and large in magnitude. Column (2) shows similar results using Local market density 2 as an alternative density measure. In columns (3) and (4), I use the sample of non-ceo hirings. The marginal effect of market density is smaller than the case in CEO hiring, but is still significantly positive. These results reinforce the argument behind Table 3 that firms in denser markets have stronger turnover-performance sensitivity because of more convenient access to outside candidates Outside Promotion Opportunities In addition to internal dismissal threat, external promotion opportunity (rank-order tournament) is another source of implicit incentives. Both theoretical and empirical works show that stronger tournament incentives lead to better performance (e.g. Lazear and Rosen (1981), Green and Stokey (1983), Kale et al. (2009), Coles et al. (2013)). To provide empirical evidence on executives in denser markets facing stronger tournament incentives, I first document that the executive compensation gap is wider in denser markets. Then, I show 18 For CEOs, I search news articles to collect the start date of being CEO and the date of joining the company. For non-ceo executives, I assume the start date of taking the position as the start date of the fiscal year when the executive is first reported in the firm s annual proxy. Observations in the year when a firm first appears in the database are excluded. For non-ceo executives with missing data on the date when they join the company, I code them as insiders. 19 One may concern that the high probability of hiring outsiders for firms in denser markets could reduce the incentive of internal promotion for executives. Yet, for CEOs, since they do not have internal promotion incentive at all, they should not be affected. For non-ceos, although the probability of being promoted as an insider in any given turnover event is lower in denser markets (Table 4), the frequency of turnover is higher (Table 3). 19

22 that the promotion probability is also strongly related to market density Compensation Gap The compensation gap between an executive s old job and his potential new job is usually considered as the prize of a tournament and as the first-order source of incentives. To the extent that the distribution of compensation level is wider in denser market, higher local market density could lead to larger local pay gap. Empirically, I measure the compensation level of a firm as the median total compensation (TDC1) of its top five earners. For each firm, the pay gap is calculate as the difference between its compensation and the compensation of the highest-paid local firm. To address the issue that the highest compensation in a local market and in a particular year might be driven be unusual and transitory events, I use the difference between a firm s compensation and the 90 th percentile of local compensation as a more robust measure of local pay gap. For firms with compensation higher than the 90 th percentile, the gap is coded as zero. Furthermore, since total compensation is mainly comprised of stocks and options which might not be received by the new executive winning the tournament, I also consider salary plus bonus as a more conservative measure of compensation. Logarithm of compensation gap is used in regressions. Table 5 presents the regression results of local compensation gap on local market density. To control for factors that affects a firm s compensation level and thus the pay gap, I add a set of firm and executive characteristics as control variables in the regression. Column (1) uses total compensation and highest-paid local firm in the calculation of pay gap. The coefficient of market density is significantly positive at the 1% level, indicating that compensation gap is wider in denser markets. The coefficients of control variables also show expected signs. For instance, since firm stock performance and size are positively related with its own compensation level, they are negatively related with pay gap. Column (2) addresses extreme values and uses the 90 th percentile of local compensation as the top compensation an executive can obtain if he wins the tournament. The coefficient of market density decreases but is still significantly positive at As for magnitude, an interquartile increase in market density raises logarithm of local compensation gap by For a firm with initial compensation 20