Industry Tournament Incentives

Abstract

We empirically assess industry tournament incentives for CEOs, as measured by the compensation gap between a CEO at one firm and the highest-paid CEO among similar (industry, size) firms. We find that firm performance, firm risk, and the riskiness of firm investment and financial policies are positively associated with the external industry pay gap. The industry tournament effects are stronger when industry, firm, and executive characteristics indicate high CEO mobility and a higher probability of the aspirant executive winning.

Received October 21, 2012; editorial decision March 26, 2017 by Editor Laura Starks.

Prior empirical work has indicated that tournament incentives, specifically the size of the tournament prize, affect the performance of professional golfers (Ehrenberg and Bognanno 1990, Brown 2011), race-car drivers (Becker and Huselid 1992), and football coaches (Fee, Hadlock, and Pierce 2006), and the risk-taking behavior of mutual fund managers (Brown, Harlow, and Starks 1996; Chen, Hughson, and Stoughton 2011). Further, internal tournament incentives appear to provide senior executives in a firm with the incentive to increase firm performance and risk (Kale, Reis, and Venkateswaran 2009; Kini and Williams 2012; Burns, Minnick, and Starks 2017).¹

We apply the primary elements of the logic on tournament incentives for executives inside companies in a novel fashion, specifically to the tournament incentives arising outside the firm in the external labor market for managers. We measure external tournament incentives by the pay differential between one firm’s CEO and the highest-paid CEOs within a group of similar firms that operate in the same product market(s). Our main hypothesis is that high pay and/or other desirable characteristics of the CEO position at other companies in the same or related industries will provide incentives to CEOs at their own companies. If a CEO leads a company that delivers outstanding performance, through high quantity or quality of managerial input or perhaps from taking risk on behalf of shareholders, then that CEO is more likely to be a strong candidate for the industry prize, specifically a more desirable position leading another company. Such a position can be attractive because of high compensation, enhanced span of control, high visibility, and status as CEO of a leading company in the industry. Precisely along these lines, based on 401 survey responses from CEOs of U.S. companies, Graham, Harvey, and Rajgopal (2005) report that over 75% of CEOs assert that upward mobility in the labor market is more important than the compensation scheme at the CEO’s own firm in influencing their own managerial decisions.

In terms of mobility and the tournament prize, there are formal mechanisms, such as the formation of peer groups for benchmarking executive pay, that provide information to top executives on compensation at other firms (e.g., Bizjak, Lemmon, and Nguyen 2011). Of course, peer group comparisons are intended to set pay at the CEO’s own firm rather than induce movement to greener pastures. But executives do move, particularly when they perform well. Fee and Hadlock (2003) find that executives who jump to CEO positions at new employers come from firms that exhibit above average stock price performance. They find that this relation is more pronounced for more senior executives and that no such relation exists for jumps to non-CEO positions. Focusing on CEOs with prior CEO experience in another firm, Gudell (2010) shows that they earn a higher level of total compensation than those with no prior CEO experience, future compensation is positively related to prior performance, and with each move compensation almost doubles.

Notwithstanding the above, there are some grounds for skepticism regarding our central hypothesis. Aggarwal, Knoeber, and Tsoulouhas (2006) argue that internal candidates for a vacant CEO position tend to be favored over external candidates. They find this effect in the data after controlling for candidate qualifications (skill, talent, and experience). Indeed, a large proportion of CEO hires come from inside the firm (68% according to Cremers and Grinstein 2011). This could suggest that when the external labor market is not very active, external tournament incentives are small or absent and have little power to explain firm performance and risk. On the other hand, executives could obtain part of the external tournament prize, at least the portion that appears in the form of an increase in compensation, even without switching firms. The potential external opportunity would put pressure on the current firm’s board to increase the compensation of the sitting CEO, in which case the CEO need not move to extract benefits from the external opportunity. Mechanisms for this extraction include peer benchmarking (Bizjak, Lemmon, and Nguyen 2011) and the board’s counter offer to an actual or anticipated external offer to the CEO. Thus, the external pay gap would provide tournament incentives that would affect firm performance and risk even if the CEO does not depart.

These arguments frame our empirical question. To what extent do industry tournament incentives matter? Focusing on internal tournament incentives, Kale, Reis, and Venkateswaran (2009) examine performance and Kini and Williams (2012) study risk-taking. In a similar manner, we test the hypotheses that firm performance and risk-taking positively depend on the extent to which CEOs face significant industry tournament incentives in the labor market for managers.

We construct two measures of industry tournament incentives, both based on the gap between CEO compensation at the firm of interest and maximal or near-maximal CEO compensation potentially available among firms that are similar based on product market and firm size. Based on a GMM specification with instrumental variables, we find that firm performance, as measured by Tobin’s q, is significantly positively related to both measures of industry tournament incentives. For a one-standard-deviation increase in the CEO external pay gap, |$q$| of the CEO’s own firm increases by an average of 0.34, as compared to the sample mean |$q$| of 2.23.

Likewise, firm risk, as measured by the variance of daily stock returns, is significantly positively related to instrumented industry tournament incentives. For a one-standard-deviation increase in the external pay gap, the variance of the firm’s daily stock returns in the following year increases by 0.004, as compared to the sample mean of 0.030. For evidence on the mechanisms by which CEOs increase risk, we examine investment decisions and financial policy. R&D intensity, firm focus, and leverage all increase in the external industry pay gap, while capital expenditure decreases in the industry pay gap.

Several empirical matters are noteworthy. First, the CEO’s board of directors is unlikely to have much control over the external pay gap, which makes it more likely that our analyses are not contaminated by endogeneity problems and that our finding that industry tournament incentives (ITI) are important for firm performance, risk, and policy is valid. Second, and nonetheless, in addition to including a wide variety of control variables, CEO-firm fixed effects, and lags, we further attempt to address endogeneity concerns by employing instrumental variables and applying GMM in estimation. The two instruments for the CEO external pay gap are (1) the average compensation of geographically close CEOs and (2) aggregate CEO pay in the industry. The latter is most strongly related to the maximal or near maximal end of the pay gap, while the first is relevant for the pay gap primarily through the specific CEO’s compensation. Third, our results hold when we control for the internal pay gap, the standard performance and risk-taking incentives arising from executive compensation (delta and vega), an analogously defined industry size gap, the CEO pay slice, and different industry definitions. Finally, we find that the industry pay gap tends to have a stronger effect on firm performance and risk when industry and executive characteristics indicate high CEO mobility, in which case the probability of the CEO capturing some or all of the tournament prize is higher.

Our analysis contributes to the literature in several respects. First, we apply the logic of promotion-based tournaments to propose the notion of industry tournament incentives for CEOs. Elements of this logic appear in Aggarwal, Knoeber, and Tsoulouhas (2006), Kale, Reis, and Venkateswaran (2009), Kini and Williams (2012), and Burns, Minnick, and Starks (2017), though none pursue the notion. In terms of risk-taking, the idea is explicit in the literature on mutual funds (Brown, Harlow, and Starks 1996; Chen, Hughson, and Stoughton 2011), but the focus therein has been on that particular form of financial intermediation. Thus, our primary contribution is to fashion the idea of industry tournament incentives for corporate CEOs and empirically test the implications for firm performance, risk, and policy. Second, our analysis builds on the large prior literature on managerial incentives. Our design considers together standard compensation-related incentives of the CEO (delta and vega), the recently examined internal promotion-based tournament incentives, and external tournament incentives to provide an empirical assessment of the relative economic importance of the external industry tournament. Third, our results evoke the importance of the external labor market for managerial incentives that affect firm performance and risk. This is particularly relevant for those interested in the ability of shareholders to assemble in concert the full spectrum of incentive and monitoring devices to maximize firm value. Fourth, and finally, our results relate to the public controversy over CEO pay. The industry tournament for CEOs, with the significant pay prize at stake, is a potential mechanism for upward pressure on CEO pay (Jensen, Murphy, and Wruck 2004; Bebchuk and Fried 2001; Bebchuk and Grinstein 2005) and for at least some portion of the volatility of firm stock and accounting returns and of the riskiness of firm policy.

1. Hypotheses and Data

1.1 Hypotheses

In this section we develop our hypotheses. In the two-player tournament setup of Lazear and Rosen (1981), effort is the only choice variable and it positively depends on the size of the prize to the winner. Hvide (2002) introduces risk-taking into that model and shows that both competitors have the incentive to choose infinite risk.² Our approach alters Lazear and Rosen (1981) and Hvide (2002) in two ways. First, we allow one CEO (the incumbent) to start the contest with a lead, perhaps in performance or perceived ability, relative to the CEO who aspires to move up the industry hierarchy. This allows us to place our model in a managerial labor market with some frictions (see below) and also to evaluate the effects of tournament incentives as a function of the probability of winning, as represented by the size of the initial lead. Second, we impose discreteness on the choice of the risk strategy so as to avoid difficulties in concavity of the decision problem and unboundedness of the choice of risk. The tournament prize in the model, the pay gap external to the firm, is the difference between compensation of the highest-paid (leader) CEO and the pay of the aspirant CEO. The aspirant earns the prize if the aspirant unseats the leader. Otherwise the leader retains their position and wins the prize.

Three model implications are most pertinent for our empirical analysis. First, an increase in the size of the prize, the external pay gap, increases the aspirant’s effort and expected performance. Second, in any equilibrium the aspirant prefers and, if feasible, chooses more volatility in the outcome, while the leader prefers and chooses less. Moreover, the preference for higher risk by the aspirant is stronger when the prize is larger. Third, increasing the probability an aspirant can overtake a leader (decreasing the initial lead), all else equal, amplifies the effect of the tournament prize on the aspirant’s incentive to perform well. A previous version of this paper supplied a formal model to convey more precisely the intuition behind our specific hypotheses. For the interested reader, Coles, Li, and Wang (2017) presents the model.

Two traditional approaches to the managerial labor market potentially can serve as the setting for industry tournament effects. One tradition argues that external career concerns and “ex post settling up” in the managerial labor market reduce agency problems between managers and shareholders. Managers with superior performance obtain high-wage, external offers, and perhaps board seats, while managers with poor performance are dismissed and generate low-wage offers or no outside offers. These external labor market effects help discipline managers to work in the interests of shareholders (Fama, 1980).³

A second approach focuses on the matching of managers, who differ in talent, to firms that vary in ways that affect the productivity and value of talent, such as the size or nature of the asset base. When firm and manager characteristics are specialized or difficult to duplicate, executives are sorted or “assigned” to CEO positions based on matching their capabilities and human capital to the asset base (e.g., Rosen 1982; Tervio 2008). Suppose reassignment costs are zero; all parties know, without error, the abilities of the CEOs and the nature and productivity of the asset base to which CEOs are assigned; executive capabilities (e.g., through learning) and the character of the firm’s asset base do not change through time; and the level and contractual form of pay are unaffected by other considerations (such as moral hazard or intra-firm equity concerns). Then firms and executives would be perfectly matched every time, and, depending on bargaining power, CEOs potentially would be paid something like the value of their marginal revenue product.⁴ Executives, who are correctly assigned and paid, would never move. There would be an external pay gap, but it would be irrelevant for executive incentives. In contrast, it is more realistic that executive and firm characteristics are not perfectly known and matching is imperfect. Then firms would learn about the talent of potential CEOs through observing their performance in the job slots they currently occupy. Furthermore, matching can be a dynamic process. Through time executives can learn by doing and firms can require different skills as the firm’s strategy and asset base adjust to market conditions. In any case, if the benefits of rearranging exceed the costs of reassignment, CEOs in the industry tournament would then be reallocated to firms, based on the updated assessment of their ability, whenever the home firm does not respond with enough pay and the benefits of capturing the external pay gap exceed the costs of reassignment.⁵

We hypothesize that these tournament incentive effects appear in data from the managerial labor market. In the time series, the effects of variation in the size of the tournament prize on effort and observable firm performance should apply to the aspirant CEO. Moreover, through time the CEO should employ a risky (less risky) strategy if the CEO is the aspirant (leader). In terms of the cross-section, comparing CEO tournaments that vary only in the size of the prize, a larger pay gap, using the CEO with maximal pay as the reference point, will be associated with higher effort from the aspirant CEO and better expected performance from that firm. Furthermore, the larger the pay gap the stronger will be the preference of the aspirant CEO for risk and, supposing the aspirant CEO has some discretion over the choice of risk, the higher aspirant firm risk will be. In addition, the effects of the tournament prize on firm performance and risk will be stronger when the probability of the aspirant overtaking the incumbent and capturing the prize is larger.

The primary hypotheses are as follows.

   

1.2 Data sources

We obtain CEO compensation data from the Standard and Poor’s (S&P) ExecuComp database, which covers more than 1,500 firms each year including those that are in the S&P 500, S&P mid-cap 400, and S&P small-cap 600 indices. The sample period is 1992-2005.⁶ We obtain stock return data from the Center for Research in Security Prices (CRSP) and firm characteristics from the Compustat Industrial and Segment files. The sample, excluding financial and utility firms, aggregates to 17,702 firm-year observations arising from 2,265 unique firms and 4,136 unique CEOs.

ExecuComp provides data on salary, bonus, stock awards, option grants, and total compensation for named executive officers (NEOs). For total compensation, we use TDC1, which for 2005 and prior eschews using option grant values reported by the companies in favor of using Black-Scholes. Thus, using TDC1 for 1992-2005 means we can use the same valuation approach for options across all firms.⁷

1.3 Measures of the CEO industry tournament prize

To group firms based on the product market, we apply the Fama-French 30 industry classification (FF30).⁸ The discussion above suggests that every CEO in the industry has the incentive to compete for the CEO position in the same industry with the highest pay and perhaps with other desirable attributes, such as increased span of control, perquisites, and visibility. An obvious approach is to define the industry tournament incentive as the compensation gap between the CEO in question and the highest-paid CEO in the same industry. One difficulty is that extreme compensation within an industry in a particular year may be due to an unusual, transitory event, in which case maximal industry CEO compensation is not likely to approximate what the executive would receive if were to win the tournament.⁹ To remove at least some potential outliers, we use the second-highest CEO pay rather than maximal pay.¹⁰ Our first measure of industry tournament incentives, Indgap1, is defined as the compensation gap between the CEO under consideration and the second-highest-paid CEO in the same industry.

Selecting peer groups to form performance percentile benchmarks for CEO pay is a common industry practice. Moreover, peer groups tend to be comprised of firms with similar revenue from the same industry (Bizjak, Lemmon, and Naveen 2008; Faulkender and Yang 2010; Bizjak, Lemmon, and Nguyen 2011). Accordingly, we use net sales to control for firm size. In each year for each industry, we divide firms into two groups based on whether annual sales (net of returns, discounts, rebates, and allowances) is above or below the industry median.¹¹ Based on this sample partition, our second measure of industry tournament incentive, Indgap2, is the compensation gap between the CEO and the second-highest paid CEO in the same size-industry group.¹² For both measures, the notion is that the tournament incentive is larger when the “promotion” prize is bigger and the gap is wider.

Panel A in Table 1 provides descriptive statistics. The industry pay gap measures accord with the substantial level and dispersion of compensation awards to CEOs over the sample period. The median (mean) of our first measure of industry pay gap, Indgap1, using second-highest CEO pay within an industry as the benchmark is |${\$}$|14.3 million (⁠|${\$}$|24.5 million). As expected, the industry pay gap conditional on the same size group, Indgap2, is substantially smaller than Indgap1. Both measures of the industry tournament prize substantially exceed the median (mean) CEO pay of |${\$}$|1.97 million (⁠|${\$}$|4.26 million).¹³

1.4 Dependent variables: Performance, risk, and policy

To measure firm performance (or value) we employ Tobin’s q, defined here as the ratio of the sum of market value of equity and book value of debt to total assets. The sample mean and median for |$q$| are 2.23 and 1.65. As for firm risk, we define Stock return vol as the variance of one year of daily stock returns. Mean and median Stock return vol are 0.030 and 0.025, respectively.

Indicators of the riskiness of firm investment policy include: R&D, defined as R&D expenditures divided by total assets; CAPEX, capital expenditures divided by total assets; # segments, the number of operating segments (Compustat segment database); H-index, the sum of the square of segment sales divided by the square of firm sales (the Herfindahl index in firm sales); and Book leverage, which is book value of interest-bearing debt divided by total assets.

1.5 Internal tournament incentives, delta, vega, and other control variables

Prior studies using U.S. data have documented the empirical effects of within-firm tournament incentives for firm value (Kale, Reis, and Venkateswaran 2009) and risk (Kini and Williams 2012). Burns, Minnick, and Starks (2017) find a positive relation between firm value and the internal pay gap for non-U.S. firms.¹⁴ We follow these studies to calculate Firm gap as the difference between CEO total compensation and median VP total compensation.¹⁵ Vice presidents (VPs) are the group of (up to) four named executive officers who are not CEO. This variable captures the increase in a median VP’s compensation if she wins the internal promotion tournament. As panel A of Table 1 indicates, the median (mean) within-firm pay gap for our sample is |${\$}$|1.30 million (⁠|${\$}$|3.39 million), which is similar to that reported by Kale, Reis, and Venkateswaran (2009) and Kini and Williams (2012). Note that the internal pay gap is dwarfed by the prize in the external tournament.

Also note that there is a mechanical relation between the external and internal tournament incentive measures. Holding all else constant, for each dollar that CEO pay rises, the external (internal) pay gap falls (rises) by a dollar. Nonetheless, the correlations among the internal and two external tournament incentive measures are positive in the cross-section, which suggests that some of the economic forces that determine each are distinct.

We control for other incentive variables previously established to be empirically relevant. For managerial incentives arising from CEO compensation structure, we calculate CEO delta and vega. In computing these measures, we include direct stock ownership, restricted stock, and existing and newly granted stock options, all based on accumulated grants net of dispositions. CEO delta represents the sensitivity of CEO wealth to firm performance. Per Aggarwal and Samwick (2003), we define CEO delta to be the change in executive wealth per |${\$}$|1 change in shareholder wealth. CEO vega is the change in the value of the CEO’s wealth for a 0.01 change in the annualized standard deviation of stock returns.¹⁶

In most specifications, we employ control variables pertaining to CEO attributes (CEO tenure and CEO age), firm attributes (Stock return 1 yr, Sales growth, Book leverage, R&D, Capital investment, and FCF), and industry attributes (Industry stock return vol). See Table A1 (the appendix) for detailed definitions.

2. Instruments and Other Variables Related to the Industry Tournament Prize

We argue that the industry pay gap provides tournament-based incentives for managers to improve firm performance and to increase risk. In performance-on-structure and structure-on-structure regression specifications, however, omitted variables, measurement error, and simultaneity or reverse causation are general concerns (e.g., Coles, Lemmon, and Wang 2011) that are likely to be relevant for our specific empirical questions. One potential line of attack is to supplement a comprehensive set of control variables and fixed effects with instruments. Thus, we estimate a first stage using instrumental variables, control variables, and fixed effects to predict the industry pay gap for each CEO in each year. The second stage regresses measures of firm performance and risk on the predicted industry pay gap, controls (excluding the instruments), and fixed effects.

2.1 Endogeneity concerns

There are several specific endogeneity concerns. First, the industry pay gap potentially is influenced by firm performance and risk. TDC1 (Execucomp) is comprised of salary, annual bonus, other annual pay (including perquisites and benefits), total value of restricted stock granted, total value of stock options granted, long-term incentive payouts (LTIP), and all other pay (such as tax reimbursements and signing bonuses). While salary tends to be set based on performance over the prior year, some of the other components of pay can be affected by contemporaneous performance and risk.

For example, the annual cash bonus schedule often is based explicitly on market or accounting performance in that year (Murphy 1999). Furthermore, long-term incentive cash payouts are based on a performance period longer than one year, so if the executive is in the last year of that period, then performance in that year can affect the LTIP payout. Thus, high performance in that same year is likely to cause an increase in CEO cash compensation. This would lead to a reduction of the gap between maximum pay in the industry and the pay of the CEO and, therefore, would spuriously reduce the estimated relation between performance or value and the industry tournament incentive (ITI).¹⁷

Second, and likewise, to the extent that the payout schedule for an annual bonus scheme or expiring LTIP award is convex in performance, an increase in contemporaneous risk will increase the expected payout in that year, thereby reducing on average the industry pay gap. Some annual bonus schemes and LTIP award schedules are convex for at least some of the performance domain (Murphy 1999). If executives are immediately able to change policy and increase risk in response to ITI, and if the annual bonus and expiring LTIP are significant components of pay, then estimates of the effect of the external pay gap on firm risk can suffer from a spurious negative relation between risk and the industry pay gap.

One approach to reverse causation is to employ lags. In the specifications explaining firm risk, we lag the industry pay gap measure by one year. This addresses the causation problem associated with the annual bonus and expiring LTIPs. Because Tobin’s q is forward looking, insofar as it contains the present value of anticipated future performance, for the models explaining firm performance, we use contemporaneous values of |$q$| and the ITI. Nonetheless, |$q$| also can depend on current performance, so some concern about reverse causation remains.

Third, in terms of omitted variables, it is likely that growth opportunities affect both of the dependent variables, firm performance and risk, and our primary explanatory variable, the external pay gap, through CEO compensation. For example, firms with extraordinary growth opportunities are likely to demand extraordinary talent and skill on the executive team and to pay accordingly. Further, the position of CEO at a firm with high growth opportunities is likely to be demanding and require higher pay. Including a common proxy for growth opportunities, Tobin’s q, does no good because we use it as a key outcome variable. Lagged |$q$| is unsuitable because investment opportunities (and |$q$| and the need for talent) are persistent. Fortunately, other proxies for growth opportunities and the need for talent are available. We use R&D intensity, investment in hard assets, and sales growth, as well as CEO-firm fixed effects, to capture growth opportunities.

2.2 Instruments for the industry pay gap

We utilize two instruments for the external pay gap. First, for each CEO-firm-year we calculate the average compensation of geographically close CEOs. Per Bouwman (2011), we expect the average compensation of geographically close CEOs to be positively associated with the total compensation of aspirant CEOs.¹⁸ To the extent that pay in one location is associated with general conditions in the market for executives, this measure also should be associated with maximal or second-highest CEO pay in the industry. If the former effect dominates, the instrument will be negatively related to the industry pay gap. We follow Bouwman (2011) to construct Geo CEO mean as average total compensation received by all other CEOs of firms headquartered within a 250-km radius of the firm.¹⁹

In terms of the exclusion condition, we do not expect the average level of pay for geographically close CEOs to affect firm performance or risk, except through industry tournament incentives. Submitting this assertion to further inspection, one concern is that production technology or investment opportunities might be correlated with geographic location for some types of firms, such as mining firms that colocate near mineral deposits or high-tech firms spawned from top universities nearby. On the other hand, with modern communication and transportation (distribution) technologies, many firms have broad geographic span and operate and compete on a national or global basis. Thus, there should be at most a weak link between the location of a firm’s headquarters and investment opportunities. We further address the concern through the following means. First, in formulating the instrument, when calculating average compensation of geographically close CEOs, we exclude all CEOs in the same industry (FF30). Second, in the GMM IV models we control for growth opportunities. Third, we employ CEO-firm fixed effects in both regression stages. These and other controls should increase the likelihood that this instrument and the other instrument described below satisfy the exclusion condition. The correlation between Geo CEO mean and Indgap1 (Indgap2) is |$-$|0.28 (⁠|$-$|0.23).

The second instrument we consider for the pay gap is a modification of total CEO pay in the industry. Our measure extends the notion that “fair” wages for employees depend on a firm’s ability to pay (Kahneman, Knetsch, and Thaler 1986), so that total industry CEO compensation reflects the ability of an industry to pay. This is consistent with each CEO being paid more if the industry ability to pay is higher, which accords with the empirical observation that when maximal CEO pay is relatively high in an industry other CEOs in that same industry tend to be highly paid (Dickens and Katz 1986).

To avoid a mechanical relation between industry total compensation and our industry pay gap measures, when we calculate industry aggregate pay we exclude both components of the external pay gap, pay of the aspirant CEO at the firm in question and the “brass ring” (specifically maximal and second-highest pay in the industry or size-based half-industry). The omissions in construction mean the instrument varies in the industry across firms. While the proposed instrument never contains the pay of the CEO in question, common factors shaping the labor market are likely to cause pay of all CEOs in the industry to covary with each other and with omitted CEO pay. In terms of the pay gap, however, it is unclear whether the pay gap would be positively or negatively related to total industry pay, though we expect the former if near-maximal pay is more strongly related to total industry pay than is aspirant pay.²⁰ The correlation between Ind CEO comp and Indgap1 (Indgap2) is 0.52 (0.49). To control for the possibility that average or aggregate CEO talent in the industry drives both total pay to CEOs in the industry and firm performance, in the GMM IV models we control for individual CEO experience and firm/CEO performance. Again, we also include CEO-firm fixed effects in both stages.²¹

The diagnostics below indicate that both instruments are sufficiently correlated with one or both of the components of the endogenous regressor, the external pay gap, after controlling for the exogenous regressors (relevance). While we argue that both instruments affect firm performance and risk mainly through their effect on the industry pay gap, we fully acknowledge the difficulties in identifying proper instruments that satisfy exclusion.²² Readers should apply appropriate caution in interpreting the results.

2.3 Other factors that affect the industry tournament prize

We follow prior research (Graham, Li, and Qiu 2011; Coles, Daniel, and Naveen 2006) in selecting the observable firm and manager characteristics that determine the level of CEO compensation. Variables include firm size, performance, and sales growth, industry volatility, and CEO tenure and age. At the industry level, we control for the volatility of industry performance and the number of CEOs (firms) in the industry comparison group.

2.4 CEO mobility and the industry prize

If executives are mobile and the firm wishes to retain the CEO, the firm will set pay higher to prevent departure and the external pay gap will be lower, all else equal. In contrast, if CEO immobility makes preemptive raises or equilibrating pay adjustments unnecessary, firms can set CEO pay so as to be less than fully responsive to growth in peer firm pay benchmarks, so the external pay gap potentially would be larger

We employ two CEO-level proxies for CEO mobility. First, the probability of turnover for a new CEO should be lower. Second, when a CEO is close to retirement, the likelihood of an immediate promotion to another firm in the same industry is also lower. New CEO equals 1 in the CEO’s first year of service as CEO (0 otherwise). Retire CEO equals 1 if the CEO is older than 65 (0 otherwise).

At the industry level, frictions, such as information costs and the fixed costs of repeatedly but imperfectly solving the assortative matching problem, for example, should reduce executive mobility in the industry (Nickerson, 2013). Restated, lower frictions should imply an active and competitive labor market for CEOs and higher CEO turnover rates, all else equal. To measure industry mobility in the CEO labor market, we calculate the annual unconditional probability of CEO turnover in each industry (Industry Mobility). We define a turnover event if a CEO in one year is not the same person in the next year. In addition, Cremers and Grinstein (2011) argue that firms that require more firm-specific human capital will promote from within, implying that CEOs in such industries are less likely to be replaced by outsiders. This limits the potential outside options of the CEO, which likely diminishes the incentive effect of the industry tournament prize. Following Cremers and Grinstein (2011), we measure the firm-specific talent in each industry by the percentage of insider CEOs in each industry per year (Industry talent). Along the same lines, the likelihood of hiring an outsider as CEO is higher when firms operate in homogeneous industries (Parrino 1997). We follow Parrino (1997) to construct Industry homogeneity, which measures the similarity among firms within an industry after isolating market effects.²³

The number of firms (and corresponding CEO positions) in an industry potentially reflects the degree of competition for those slots. Competition among more candidates for more positions is likely to compress the variation in CEO pay, the gap in pay between the maximum and minimum and, more generally, the gap between the industry maximum and pay at a given firm. On the other hand, more CEOs in the same industry could be associated with larger variation in pay, perhaps due to randomness in performance and thus pay, in which case the typical pay gap need not shrink as the number of firms increases. Whether the former effect dominates the latter, so as to compress the external pay gap, is an empirical question.

2.5 Findings on the determinants of the industry tournament prize

Table 2 presents our findings on the “determinants” of the industry pay gap. All regressions in the table are pooled time-series, cross-sectional regressions with instruments, CEO-firm and year fixed effects, and observable firm, CEO, and industry characteristics. Columns 1 and 2 report results for the baseline specifications. Columns 3 and 4 include additional controls for CEO mobility.

For the baseline results in Columns 1 and 2, the negative coefficients on CEO delta and the internal pay gap suggest that firms factor in the industry tournament when designing the pay structure of their own CEOs. Perhaps a higher external tournament incentive makes internal incentives less necessary. An alternative explanation is that implementing higher CEO delta requires larger stock and option grants and, thus, higher pay and a lower external gap. Also note that the CEO’s pay defines the lower and upper edges of the external and internal pay gaps, respectively, so there is a mechanical negative relation between the two gaps. CEO pay increases in firm size (Murphy 1999), so it is natural to find that Indgap1 decreases in firm size. In contrast, Indgap2 is constructed conditional on firm size. Variation across half-industries drives the association between size and ln(Indgap2). Better firm stock performance is contemporaneously associated with a lower external pay gap. Neither CEO age nor CEO tenure is related to the industry pay gap. The industry pay gap is not related to industry stock return volatility.

The estimated coefficient on the number of CEOs in the industry is significantly negative in all four models, consistent with competition among more candidates compressing variation CEO pay. In Columns 3 and 4, we find little association between the CEO and industry mobility measures and the industry pay gap. The exceptions are a significantly negative coefficient on whether the CEO is new for Indgap2 and a significantly positive coefficient on Industry mobility for Indgap1.²⁴

Turning to our chosen instruments, as expected, the coefficient estimate on ln(Geo CEO mean) is always negative and is significant at least at the 5% level in all four specifications. Likewise, the effect on the industry pay gap of industry “ability-to-pay” (industry total compensation, Ind CEO comp) is positive and significant at the 1% percent level in all models.

3. Industry Tournament Incentive Effects

3.1 Industry tournament incentives and firm performance

Hypothesis 1 suggests a positive relation between firm performance and external tournament incentives. Because of the endogeneity concerns delineated above, on the right-hand side we use the predicted industry pay gap. We use GMM IV because the null hypothesis that there is no heteroskedasticity based on plain vanilla 2SLS IV is rejected.²⁵ As the dependent variables are likely to be time-persistent, here and throughout we report firm-clustered and Newey-West-corrected (up to four lags) standard errors. In order to isolate the effects of external tournament incentives, we control for CEO delta arising from the managerial compensation contracts, NEO tournament incentives internal to the firm, growth opportunities, and other variables. In all regressions, we include year and CEO-firm fixed effects.

Table 3 reports estimates of the relation between industry tournament incentives and firm performance. The dependent variable is contemporaneous Tobin’s q.²⁶ The first three columns present results using Indgap1. For ordinary least-squares (OLS) regressions (Column 1), included here and later as a basis for comparison, the coefficient on ln(Indgap1) is 0.008, which is statistically insignificant. Columns 2 and 3 contain results from the two-stage GMM IV estimation. In the second stage (Column 3), the coefficient on predicted ln(Indgap1) is 0.237 (p |$<$|⁠.01). The Hausman exogeneity test rejects the null hypothesis that the OLS and GMM IV estimates on ln(Indgap1) are the same (p |$<$|⁠.01). The difference in results is consistent with the notion that better firm performance and higher firm value shrink the external pay gap, so that the estimate on ln(Indgap1) is attenuated in the OLS specification through reverse causation. In contrast, the sign and the magnitude of the coefficient using GMM IV are consistent with a positive effect flowing from industry tournament incentives to firm performance. In terms of economic significance, a one-standard-deviation increase in Indgap1, acting through ln(Indgap1) and the estimated GMM IV coefficient, increases Tobin’s q by 0.34, which is economically significant in comparison to the median (mean) Tobin’s q of 1.65 (2.23) in our sample.|$^{\mathrm{\thinspace }}$|²⁷ Based on median (mean) total assets of |${\$}$|964 million (⁠|${\$}$|4.79 billion), this change in |$q$| is equivalent to a change in firm value of |${\$}$|327 million (⁠|${\$}$|1.629 billion).

Columns 4-6 in Table 3 report confirmatory results for similar specifications based on Indgap2. Using GMM IV, the coefficient on ln(Indgap1) in Column 6 is 0.276 (p |$<$| 0.01).|$^{\mathrm{\thinspace }}$|²⁸ Again, the Hausman exogeneity test rejects the hypothesis (p |$<$|⁠.01) that the OLS and IV estimates are the same. The first-stage |$F$|-statistics for both Indgap1 (372.34) and Indgap2 (115.74) indicate that our instruments collectively satisfy the relevance condition, and neither |$J$|-statistic rejects the null.²⁹|$^{\mathrm{,}}$|³⁰

The coefficients on CEO delta (per Columns 3 and 6) tend to be insignificant when |$q$| is the dependent variable, which is consistent with Coles, Lemmon, and Meschke (2012). The coefficients on internal (VP) tournament incentives are positive and statistically significant, which provides support for the findings in Kale, Reis, and Venkateswaran (2009). The signs of the coefficients on the control variables generally are similar to those previously documented (e.g., Coles, Daniel, and Naveen 2006).

Overall, consistent with Hypothesis 1, the GMM IV results suggest that a strong positive relation exists between firm performance and our measures of the CEO industry tournament prize. It appears that the CEO industry pay gap provides executives the incentive to perform better.

3.2 Industry tournament incentives and firm risk

Hypothesis 2 posits that the possibility of being promoted to a “better” firm with higher compensation gives aspirant executives the incentive to increase firm risk. Increasing firm risk, through investment or financial policy, for example, can generate uncertain but potentially extreme performance that increases the likelihood an aspirant CEO is promoted over peer CEOs.³¹Goel and Thakor (2008) provide a model that addresses this connection between tournament incentives and corporate risk-taking. The idea also is familiar insofar as Kini and Williams (2012) find a relation between internal tournament incentives and firm risk and Brown, Harlow, and Starks (1996) and Chen, Hughson, and Stoughton (2011) apply the notion to competition among mutual fund managers.³²

As the dependent variable, we employ Vol, defined in percentage terms as 100 x Stock return vol over calendar year |$t +$| 1. Our primary explanatory variable is the predicted CEO industry pay gap. In determining firm risk CEOs face potential costs of reduced expected utility arising from exposure to risk through performance-contingent compensation. On the other hand, convexity in executive compensation can offset the risk exposure arising from CEO delta to possibly increase the incentive to take risk (Guay 1999; Coles, Daniel, and Naveen 2006). Thus, we control for CEO delta and vega arising from the compensation scheme, as well as for VP tournament incentives (per Kini and Williams 2012), to assess whether industry tournament incentives affect firm risk.

Based on prior literature (Servaes 1994; Bhagat and Welch 1995; Opler et al. 1999; Nam, Ottoo, and Thornton 2003; Coles, Daniel, and Naveen 2006), we also control for firm size, Tobin’s q, book leverage, the growth rate of sales, stock return, and CEO age and tenure. Unobserved variables, such as CEO risk aversion, are likely to affect both the dependent variable (firm risk) and independent variable (the industry tournament incentive). For instance, a risk-tolerant CEO will take more risks and earn more through increased option value, which leads to a lower industry pay gap. These omitted variable problems work against finding a positive link between firm risk and industry pay gap. We seek to mitigate the bias through year and CEO-firm fixed effects and instrumental variable estimation. Table 4 reports the results. The first (last) three columns show the results for Indgap1 (Indgap2).

GMM IV (Column 3) gives a significantly positive coefficient of 0.176 on ln(Indgap1) (p |$<$| 0.01) that is more than 12 times the OLS estimate in Column 1. The Hausman test rejects the null hypothesis that the OLS and GMM IV estimates on Indgap1 are the same.³³ A one-standard-deviation increase in Indgap1 implies a 0.004 increase in stock return volatility, as compared to a median (mean) of 0.025 (0.030).³⁴

Columns 4-6 repeat the analysis of the industry pay gap using Indgap2. The OLS coefficient on Indgap2 is slightly positive and marginally significant (p |$<$|⁠.10), while the GMM IV coefficient is a significantly positive 0.219 (p |$<$|⁠.01), which is more than 30 times the OLS estimate. Again, the Hausman test rejects the hypothesis that the OLS and GMM IV estimates are equal. The first-stage |$F$|-statistics for the two industry pay gap measures exceed 90 and are highly significant (p |$<$|⁠.01) and neither |$J$|-statistic is significant at conventional levels.

The effects of delta and vega on firm risk are positive and mostly significant. The results on vega are consistent with Coles, Daniel, and Naveen (2006). Like in Kini and Williams (2012), the coefficients on the internal pay gap are significantly positive. Coefficients on growth opportunities, as measured by Tobin’s q, are positive and significant.³⁵ The signs of the coefficients on the remaining control variables are generally similar to those documented in prior literature.

In summary, using GMM IV with controls, CEO-firm and year fixed effects, and two different measures of the CEO industry pay gap, industry tournament incentives have a positive effect on firm risk that is economically and statistically significant.³⁶ These results are consistent with Hypothesis 2 and support the contention that a greater industry tournament incentive leads to higher risk-taking by aspirant CEOs.³⁷

3.3 Alternative explanations

First, higher CEO ability would cause better firm performance, higher CEO pay, and a higher internal pay gap, but also a lower external pay gap, in which case we would observe a positive (negative) relation between performance and the internal (external) pay gap. The significantly positive coefficients on the external pay gap in Table 3 are inconsistent with CEO ability as an omitted variable that drives the results. Second, CEO entrenchment is likely to be associated with lower firm performance, higher CEO pay, a lower external pay gap, and also a higher internal pay gap. Thus, we should observe a positive (negative) relation between performance and the external (internal) pay gap. The results in Tables 3 and 4 indicate a positive relation in both cases. Moreover, controlling for the CEO pay slice as CEO entrenchment (Bebchuk, Cremers, and Peyer 2011), the estimated effects of the external tournament incentive on firm performance and risk-taking remain positive and significant.

Third, both pay and other aspects of employment that comprise the industry tournament incentive are likely to be associated with firm size differences. Working for a larger firm likely confers on top executives a broader span of control over economic assets, more perquisites, and higher status. We decompose the predicted external pay gap into the portion that arises solely from differentials in firm size and the remaining that is orthogonal to the size gap. We find that the component of the instrumented external pay gap that is unrelated to the size gap has significant explanatory power for firm performance and firm risk. The above-mentioned results on the CEO pay slice and relative firm size appear in Tables OA1 and OA2, respectively (see the Internet Appendix).

3.4 The probability of winning and the strength of tournament incentives

We argued in Section 1.1 that the likelihood of the aspirant displacing an incumbent affects the industry tournament incentive. Hypothesis 3 asserts that a higher probability of winning increases the incentive effect of a given external pay gap. Lower labor market frictions, higher industry mobility, and executive and firm characteristics that permit job switching are likely to be associated with a higher likelihood, all else equal, of the aspirant being able to displace an incumbent.

3.4.1 CEO characteristics that affect CEO mobility and the external promotion probability

Our proxies for the probability the CEO can capture externally the gains from a favorable update in perceived ability include whether the CEO is new (New CEO) and whether the CEO is near retirement (Retire). In addition, we consider whether the CEO is a founder, the idea being that founders are less likely to abandon the firm they started.³⁸ Following Bebchuk, Cremers, and Peyer (2011), the CEO is designated as a founder (Founder|$=$|1, 0 otherwise) if the CEO’s tenure reported in ExecuComp indicates that the CEO held that position prior to the firm’s first listing on CRSP, which is assumed to be the IPO year. In each year for each measure, we assign CEOs to one of the two subgroups. For each proxy subgroup, we separately estimate the second stage GMM IV models for firm performance (per Column 3 in Table 3) and risk-taking (per Column 3 in Table 4).³⁹

Table 5 shows the results for the three CEO-related proxies for probability of winning the tournament prize. Panel A (B) considers Tobin’s q (Vol) as the dependent variable. The proxies for probability of winning the industry tournament perform fully as predicted. For firm performance, for example, the coefficient on instrumented ln(Indgap1) is positive but insignificant when the CEO has recently assumed office, but positive, twice as large, and significant when the CEO is not new. The Wald chi-square test rejects the equality of the coefficients across the two subgroups. Likewise, the coefficient on the tournament incentives measure is significantly larger when the CEO is not in the impending retirement group and when the CEO is not a founder. Similarly, for firm risk-taking, we find stronger tournament effects for firms with CEOs who are not brand new, CEOs not of retirement age, and CEOs who are not the founder.

Potential interfirm mobility of top executives is an important ingredient in the industry tournament. To frame and provide additional support for our analysis of external tournament incentives, we characterize the attributes of CEOs newly hired from other S&P 1500 firms in the period 1992 to 2005.⁴⁰ Out of 575 outside new CEOs, 249 of them were CEO at a previous employer. The other 326 (56.7%) new CEOs served in at least one senior management position at their prior company.

We argue that part of the prize in the industry tournament is the pay increase an executive receives by moving. Of the 249 new CEOs who were CEO at one or more previous employers (serial CEOs), 184 (or 74%) benefit from an increase in total compensation, with an average dollar increase of |${\$}$|11.8 million. By way of comparison, a CEO hired from inside the firm receives an average increase in pay of |${\$}$|0.90 million.⁴¹ For the remaining 65 outside new serial CEOs who do not receive increased pay, we find that 43 (66%) did not leave the prior company voluntarily. For the other 22 voluntary turnovers in which the new CEOs did not receive a pay increase, 12 (55%) moved to a larger firm. These calculations, combined with evidence from Gudell (2010), suggest that there exists a substantial financial reward for new CEOs with prior CEO experience.⁴²|$^{\mathrm{,}}$|⁴³

3.4.2 Industry characteristics that affect CEO mobility and the external promotion probability

To further assess Hypothesis 3, we now consider whether industry characteristics associated with the probability of capturing externally the gains from a favorable update in perceived ability shape the incentives arising from the industry pay gap. We partition the sample based on the industry measures constructed in Section 3.4. Our first proxy is Industry mobility. Holding pay at the firm constant, the industry tournament should have no effects on incentives if managers cannot move. For example, the industry tournament will be irrelevant if new CEOs come exclusively from inside the firm. We use Industry talent to measure the likelihood that CEO successors are insiders. By a similar logic, we also use Industry homogeneity, because the likelihood of hiring an outsider as CEO is higher in homogeneous industries (Parrino 1997) in which firm-specific human capital is less material and general human capital is most relevant for firm performance. Finally, we consider the number of CEOs, or identically the number of firms, in each industry (Ind #CEO). One possibility, holding constant the probability that any given CEO vacates their position, is that more potential CEO slots implies more vacancies in expectation, so that the executive is in more tournaments and the incentive effects of external pay gap(s) will be stronger. On the other hand, more CEOs also implies more competitors for each slot, so the net effect becomes an empirical question. To summarize, the effect of the industry tournament incentive should be stronger in industries with higher Industry mobility, lower Industry talent, higher Industry homogeneity, and possibly larger Ind #CEO.

To test these predictions, in each year, we split the industries into two subgroups based on whether each of the four proxies is above or below the sample median and then separately estimate the second stage of GMM IV regressions in Table 3 (for performance) and Table 4 (for risk-taking) across the two subgroups. Panel A (B) of Table 6 reports the results with |$ q $| (Vol) as the dependent variable. The evidence supports Hypothesis 3. For example, in industries where Industry mobility is low and CEOs move less frequently, the tournament has little effect on either |$ q $| or Vol, whereas the estimated effects are large and significant when executives are more mobile. Likewise, we find weaker results in industries with more inside CEOs, a lower degree of homogeneity, and fewer CEO positions in the industry.

3.5 Industry tournament incentives and manager decisions

We now assess the potential avenues through which the CEO industry pay gap is associated with firm risk by exploring the relation between industry tournament incentives and the riskiness of investment and financial policy. We hypothesize that aspects of financial and investment policy that increase firm risk, such as R&D intensity, firm focus, and book leverage, will increase in the industry pay gap, while the relation will be negative for investment in hard assets. We use two measures for firm focus, the Herfindahl index for firm sales by industry and the number of operating segments.

Table 7 reports the GMM IV results. In all five specifications the estimated coefficient on Indgap1 has the predicted sign and three estimates are significant at least at 5%. R&D intensity, firm focus, and leverage increase in industry tournament incentives, while capital expenditures and segments decrease.⁴⁴ The results for all five models are similar using Indgap2. In terms of economic significance, a one-standard-deviation increase in Indgap1 implies an increase in R&D intensity of 0.006, compared with average R&D of 0.037, and a decrease in CAPEX by 0.003, compared with the average of 0.064. The same increase in Indgap1 implies a decrease of the number of segments by 1.03 (versus the mean of 4.56) and an increase of the industry Herfindahl index of firm sales by 0.026 (mean H-index is 0.543).

4. Additional Analyses, Robustness Checks, and Discussion

4.1 Time-series variation in the level and effects of industry tournament incentives

To accommodate time-series variation we include year fixed effects and various time-varying control variables in the regression models. Variation through time in labor market conditions for executives, as indicated by trends in executive pay (Gabaix and Landier 2008), external mobility (Ryan and Wang 2012), hiring of CEOs from outside the firm (Murphy and Zábojník 2007; Cremers and Grinstein 2011), and CEO turnover rate (Kaplan and Minton 2012), invites further examination of how the industry pay gap has evolved. As Figure 1A indicates, the averages of both Indgap1 and Indgap2 climb steadily from 1992 to 2000, followed by a large drop in 2001 and 2002. Figure 1A also shows that the average industry pay gap is correlated through time with average CEO compensation.

The industry pay gap varies across firm age and CEO tenure. In Figure 1B, we depict the average industry pay gap among firms with the same age, which is measured by the number of years since the firm has been added to the CRSP database. The plot indicates that the industry pay gap in younger firms is larger than that in older firms and that, once the firm reaches the age of ten years, the industry pay gap starts to shrink. This is consistent with the fact that surviving firms tend to be larger and tend to pay more. Figure 1C tracks the industry pay gap in CEO tenure. The industry gap increases in tenure initially, but on average starts to decline after a CEO has held the position for over nine years. This is consistent with the notion CEOs with long tenure have high ability and the firm increases their pay to retain their talent.

We also assess whether the effects of industry tournament incentives change over time. To gauge effects through time, we re-estimate the GMM-IV models for performance (Column 3 in Table 3) and risk-taking (Column 3 in Table 4) but include predicted ln(Indgap1) interacted with year dummies (using 1992 as the benchmark). In Figure 1D, we depict the coefficient on instrumented ln(Indgap1) in each year from 1992 to 2005, which is calculated as the sum of the coefficient on predicted ln(Indgap1) and the interaction term with the matched year dummy (except 1992). In the same plot, we also include CEO mobility, which is defined as the annual CEO turnover rate. Figure 1D indicates that there is time series variation for the annual coefficients and the effect of industry tournament incentive varies in tandem with CEO mobility. For example, in 1999, both the coefficients and CEO turnover rate spiked. One explanation is that executive mobility was very high at the time, which amplified the incentive effect of the external pay gap.⁴⁵

4.2 Cross-industry variation in the effects of industry tournament incentives

Parrino (1997) documents a large variation in firm homogeneity across industries. Cremers and Grinstein (2011) indicate that the CEO talent pool (measured by the percentage of internal CEOs hires) varies across industries. We now explore cross-industry variation in the incentive effects of the industry pay gap.

To measure the industry tournament incentive in each industry, we re-estimate the second stage of the GMM-IV regression models for firm performance and risk-taking for each Fama-French 30 industries classification, including Utilities and Financials.⁴⁶Table 8 reports the coefficients and economic magnitude for predicted ln(Indgap1) in Columns 1 and 2 (for performance) and Columns 3 and 4 (for risk-taking). Industries that reflect the strongest effects of the external tournament incentive on firm performance include Retail, Transportation, Business Equipment, Health care, and Clothing. Industries with the strongest effects on firm risk-taking include Financials, Food, Retail, and Oil, Petroleum, and Natural Gas. Overall, there seems to be substantial variation in the effect of industry tournament incentives across industries. For both performance and risk, the F-test rejects the hypothesis that the coefficients across industries (FF30) are the same (p |$<$|⁠.01).

4.3 Robustness to industry definition

We perform the analysis of firm performance and risk using Fama-French 17 (FF17) and 48 (FF48) industry classifications. One possibility is that general human capital is a primary factor defining the span of the tournament. This would suggest that FF30 is too narrow and that it is cross-industry differences that drive differences in the effect of the pay spread on firm performance and risk. Then moving to the coarser FF17 will attribute variation previously accommodated by CEO-firm or industry fixed effects to variation in the industry pay gap. On the other hand, if human capital specific to industries more narrowly defined than FF30 determines the span of the tournament, moving instead to FF48 should make the estimated effects stronger as the boundaries of the tournament are narrowed to encompass only the most relevant CEO positions. Table 9 presents the results. For both firm performance and risk-taking, the coefficient on predicted ln(Indgap1) using narrower industries (FF48) is approximately the same as the coefficient using broader industries (FF17). We interpret the results as consistent with the notion that both industry-specific and general human capital are relevant for defining the boundaries of the managerial labor market for external tournament incentives.

4.4 Are the internal and external tournaments symbiotic?

In an internal rank-order tournament among executives for the CEO position, the potential for promotion provides managers with an incentive to provide more productive input. One difficulty, however, with the internal horse-race model is that it need not be time-consistent. Once a well-qualified, effective executive has assumed the CEO position, absent outside options the likelihood that one of the other NEOs will become CEO in the near or medium term is substantially reduced. There would be no immediate reason for the company to dislodge the winner of the horse race, in which case any internal tournament incentives going forward would be significantly diminished. Our conjecture is that it is precisely because of the presence of the external tournament that internal tournament incentives have power. While the firm would want to retain an effective, high-ability CEO, such a CEO is likely to depart when offered a significantly better outside opportunity, in which case the internal horse race is still on, thereby providing significant internal tournament incentives. Accordingly, we surmise that the internal and external tournaments are “supermodular,” insofar as they reinforce the incentive properties of one another.⁴⁷

5. Conclusions

Empiricists have focused on executive wealth-performance sensitivity since the early 1990s (Jensen and Murphy 1990, Bizjak, Brickley, and Coles 1993), on executive risk-taking incentives starting 10 years later (Guay 1999; Rogers 2002; Coles Daniel, and Naveen 2006), and, more recently, on internal tournament incentives for executives (Kale, Reis, and Venkateswaran 2009; Kini and Williams 2012; Burns, Minnick, and Starks 2017). In this paper, we have conceptually and empirically extended the literature on executive incentives to assess industry tournament incentives external to the firm.

Consistent with the predictions of tournament theory (Lazear and Rosen 1981), empirical measures of CEO industry tournament incentives have substantial power to explain firm performance, risk, investment policy, and financial policy. The estimated incentive effects of the CEO external pay gap on |$q$|⁠, firm risk, and policy choices are statistically and economically significant. Moreover, the incentive effects of the industry pay gap are larger when the aspiring executive has a higher probability of winning the tournament and obtaining the prize. We also have found that the magnitude of the effects of the industry pay gap varies across industries and through time. Our results are robust to using 2SLS IV rather than GMM IV, using different industry definitions and time fixed effects, and controlling for the industry size gap, CEO pay slice, and measures of CEO talent and entrenchment. The results are stronger when we use industry fixed effects, rather than CEO-firm fixed effects.

This study has deployed performance-on-structure and structure-on-structure regression analysis. Herein, structure has encompassed various aspects of the organization, including internal and external tournament incentives, CEO delta and vega, financial policy, and investment policy. We have found, among other results, that performance, measured by Tobin’s q, increases in both internal and external tournament incentives. The logical question arises as to why all firms do not increase these incentives and, thereby, increase firm value and |$q$|⁠. Of course, if firms had done so, then we would not have observed the data that give rise to the estimated positive relation in the first place.

At least two explanations for a relation between performance and structure are possible (e.g., Coles, Lemmon, and Wang 2011). One is that the observed positive relation represents equilibrium covariation driven by one or more omitted variables. We have taken considerable measures to eliminate this explanation, including the use of instruments, CEO-firm fixed effects, and numerous control variables. A second potential explanation is that transaction costs, including those arising from other organizational considerations, prevent frictionless adjustment by firms of executive pay. In this case, the empirical estimates reflect an upward-sloping segment of the relation between firm value and the external tournament prize. The argument is particularly plausible for the external pay gap, because a firm has little control over the compensation and perquisites offered at other companies, such as the firm that provides maximal or near-maximal pay. For pay of the CEO at one’s own firm, the firm can be constrained by internal considerations, such as contract design for optimal delta incentives and ideal internal tournament incentives, as well as equity concerns among the executive team. Moreover, the costs of moving or reassignment can imply that a firm need not respond to external opportunities to retain the CEO.

The idea of industry tournament incentives likely extends beyond CEOs to other named executive officers. Just as CEOs appear to be motivated by the possibility of being promoted to another firm in the same industry with higher compensation, so would other executives who possess discretion and influence over firm assets. Indeed, in our sample, 56.7% of new CEOs were not CEO at their prior firm. Though the external industry pay gap is likely to underestimate the size of the tournament prize for non-CEO NEOs (which would be the sum of the external pay gap and the internal pay gap), it is plausible that some of the substantial effects on firm performance and risk we estimate arise from external tournament incentives for those executives.

We are grateful to Warren Bailey, Brad Barber, Joseph Chen, Yael Hochberg, Christopher Hrdlicka, Simi Kedia, Yao Lu, Anil Makhija, Bill Maxwell, Nagpurnanand Prabhala, Ned Prescott, Rahgu Rau, Rik Sen, and Konari Uchida and seminar participants at the 2013 Meeting of the American Finance Association, Arizona State University, Boston College, Chinese University of Hong Kong, Federal Reserve Bank of Richmond, the 2012 FMA-UC Davis Napa Conference, Kansas State University, Kyushu University, Rice University, Rutgers University, Southern Methodist University, the U.S. SEC, University of Alabama, University of Alberta, University of California at Riverside, University of Colorado at Denver, University of Maryland, University of Oklahoma, University of Tennessee, and the University of Western Ontario for helpful comments. We thank Dirk Jenter and Florian Peters for the use of their CEO forced turnover data. We have significantly benefitted from the insight and time of Laura Starks, the editor, and two constructive referees. We thank Jaclyn Einstein and Cindy Gross for careful document preparation. Supplementary data can be found on The Review of Financial Studies web site.

Appendix

References