Product Market Competition Shocks, Firm Performance, and Forced CEO Turnover

Product Market Competition Shocks, Firm Performance, and Forced CEO Turnover Abstract We examine the effect of competition shocks induced by major industry-level tariff cuts on forced CEO turnover. Both the likelihood of forced CEO turnover and its sensitivity to performance increase. These effects are stronger for firms exposed to greater predation risk and with products more similar to those of other firms. CEOs are more likely to be forced out in weak governance firms; however, in good governance firms, CEOs are offered higher incentive pay. New outside CEOs receive higher incentive pay and come from firms with lower cost structures and higher asset sales. Performance and productivity improve after forced turnover. Received November 27, 2014; editorial decision July 18, 2017 by Editor David Denis. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online. Since Adam Smith, economists have believed that competition not only is essential for efficient resource allocation but is also a fundamental driver of productivity growth. However, surprisingly little evidence exists showing that competition improves productivity or efficiency. Nickell (1996), for example, finds that empirical support for the notion that competition improves corporate performance is, at best, weak. Nickell examines a panel of U.K. manufacturing firms and finds that more competition, as measured by changes to firm-level rents, leads to more productivity growth. Subsequent work, however, has mainly focused on organizational responses to changes in competition that could be associated with greater productive efficiency. These organizational responses include greater decentralization (Bloom, Sadun, and Van Reenen 2010), technological upgrades (Bustos 2011), and higher incentive pay for top executives (Cuñat and Guadalupe 2009). In this paper, we examine a particular type of organizational response to a change in the competitive environment, namely forced turnover of chief executive officers (CEOs). Specifically, we examine the effect of major industry-level tariff cuts on the likelihood of forced CEO turnover, and the sensitivity of such turnover to a firm’s operating and stock market performance. Major tariff cuts expose domestic firms to competition from lower cost foreign producers, and improving operating efficiency is likely to become crucial for survival. It is unlikely that less efficient CEOs would be able to acquire the skills required for running a tight ship very quickly: thus, forcing these CEOs out and replacing them with those with a track record of running firms efficiently would appear to be a necessary response to foreign competition. Consistent with these expectations, our difference-in-differences (DID) estimations reveal that both forced CEO turnover and the sensitivity of forced turnover to a firm’s operating and stock market performance increase after tariff cuts. These turnover effects predominantly manifest in the first 3 years following the tariff cuts. In our baseline specification, the annual probability of a forced turnover increases more than 43% in the first 3 years after a major tariff cut. The sensitivity of forced turnover to firm’s operating and stock market performance both increase by about 27% after tariff cut. Our main results are reported in the context of linear probability models incorporating firm fixed effects. However, they are robust to probit estimation using industry fixed effects and a variety of additional robustness checks. More intense competition from lower cost foreign producers is likely to alter the product market behavior of domestic firms, which in turn affect the survival prospects of the weakest or the least efficient. We document two product-market-related, competitor-driven strategic effects that come into play more forcefully following tariff cuts and find that the likelihood of forced CEO turnovers increases particularly for firms vulnerable to such effects. First, consistent with the idea that firms with inefficient CEOs are likely to become predation targets (Telser 1966; Benoit 1984; Bolton and Scharfstein 1990), CEO turnover increases more after tariff cuts in industries with higher threat of predation by conservatively financed rivals. Second, firms with more elastic demand curves are likely to lose more market share if their rivals become more efficient and lower costs (Raith 2003). Thus, such firms in turn have a greater incentive to lower costs by firing inefficient CEOs. We find consistent evidence. Inefficient CEOs are likely to be in charge primarily in poorly governed firms – indeed, in our sample, firms with poor governance are much more likely to have lower productivity and higher likelihood of default within their respective industries. When we split our sample on the basis of various measures of governance quality, we find that the effect of tariff cuts on forced CEO turnover is only present for the subsample of poor governance firms. Our results thus suggest that while poor governance allows inefficient CEOs to hold on to their jobs in normal times, this is no longer sustainable when competition from lower cost foreign rivals intensifies. Since exit is costly, boards of such firms—out of reputational concerns, fiduciary responsibility, or heightened shareholder pressure—become proactive when firm survival is threatened. Our finding that more intense competition has the greatest impact in poorly governed firms complements the finding of Giroud and Mueller (2010, 2011), who find that when managers get more entrenched, firm value and performance are adversely affected only in uncompetitive industries. CEO firing decisions are costly processes and it is pertinent to ask why alternative mechanisms of dealing with the threat of competition, such as providing CEOs with more incentive pay, might not be used instead. Indeed, Cuñat and Guadalupe (2009) find that more import penetration is associated with higher pay-performance sensitivity for top executives, and the pay gap widens between the CEO and other top executives. We find similar results for our sample. However, when we split our sample on the basis of our governance measures, we find these effects only for firms with good governance. This is consistent with the view that inefficient CEOs, and in particular those that lack the skills for cutting costs, cannot simply be incentivized to learn the art. Such CEOs are more likely to be in poorly governed firms, and forcing these CEOs out and bringing in more capable ones is the only way to achieve cost efficiency. In contrast, in well governed firms, inefficient CEOs are likely to have already been weeded out. Therefore, the ones at the helm at a given point of time are likely to respond to pay incentives by increasing cost-reducing effort (Raith 2003). We examine outside successions in which the previous employer of the new CEO is a publicly traded firm. Our arguments imply that to cope with competition from lower-cost rivals, boards will bring in CEOs with a track record of running their firms efficiently. We find that relative to the control sample of firms not exposed to tariff cuts, outsider CEOs of firms exposed to tariff cuts tend to come from firms that have lower cost of goods sold; have lower selling, general, and administrative expenses; have higher sales of plant, property, and equipment; and are more likely to have engaged in an asset sale. We also find that such CEOs receive higher pay, more long-term incentive pay, and more performance-sensitive pay, consistent with the notion that the new CEOs are deemed to have skill sets most amenable to incentive provision, like in the good governance firms. Finally, we examine whether forced CEO turnovers are associated with subsequent performance improvements. We perform two types of exercises. First, we match firms that experience forced turnover with other firms in the same 3-digit SIC industry that do not experience CEO turnover. We then compare the change in $$t$$-period average profit margin, return on equity, $$q$$, sales growth and total factor productivity before and after the tariff cut year for the treated and the control groups, where $$t$$ is 1, 2, or 3 years. Treated firms experience significantly higher performance improvement than the control group. Second, for each firm experiencing a forced CEO turnover following a major tariff cut, we randomly pick another firm in the same industry outside the 3-year window around the tariff cut event that also experiences a forced CEO turnover. Each group of firms is then matched to a control group based on industry, size, and the propensity of a forced CEO turnover. We then compare the performance improvements of the two groups relative to their controls (triple difference). We find that performance improvements (relative to controls) are significantly higher following forced CEO turnovers that occur after major tariff cuts than those that occur at other times, suggesting that more intense competition imposes harsher penalties on firms that do not take corrective action. We contribute to the literature in several ways. First, we show that forced CEO turnovers increase in response to more intense competition. Prior studies on the relationship between competition and CEO turnover find limited and mixed empirical evidence. DeFond and Park (1999) find a significantly negative correlation between CEO turnover and the Herfindahl-Hirschman index (HHI) based on publicly listed Compustat firms. However, Ali, Klasa, and Yeung (2009) show that the negative correlation disappears when HHI is instead based on Census data that contains both publicly listed and private firms. An issue with this line of research is that measures such as the HHI could be related to the rate of CEO turnover even when there is no causal relationship between the two. For example, technology shocks or other types of shocks to an industry could lead to both more CEO turnover and exit of weaker firms, leading to more industry concentration. Moreover, cross-industry comparisons of CEO turnover could be comparing steady-state situations when hiring and firing decisions have already taken place. In contrast, our analysis focuses on firms’ CEO retention decisions in response to competitive shocks. These shocks are exogenous to the individual firm or manager, but nonetheless affect firing and hiring decisions, because the benefits from replacing a manager are reshaped by the shocks. Second, we contribute to the literature on the connection between internal and external governance by showing that product market competition can improve internal governance via the forced departure of inefficient CEOs. Third, and finally, we show that CEO dismissals are in fact associated with performance improvements: competition improves efficiency by weeding out inefficient managers. 1. Hypotheses In this section, we develop our hypotheses on how CEO retention decisions of firms are likely to be affected when the industry experiences a significant tariff cut. To do so, we draw on the theoretical literature on competition and incentives (Schmidt 1997; Raith 2003) and CEO dismissals (Hirshleifer and Thakor 1994, 1998; Hermalin and Weisbach 1998, 2003). To the best of our knowledge, no existing theory links competition, incentives, and retention decisions in a unifying framework. 1.1 Tariff cuts and forced CEO turnover Tariffs are typically imposed to protect the market shares of domestic producers from more cost efficient foreign producers. When an industry experiences a significant reduction in that protection due to exogenous lowering of tariff rates (e.g., as part of multilateral trade agreements), domestic producers are suddenly exposed to competition from their lower-cost foreign counterparts. As a result, prices and profit margins in the industry are likely to decrease. To survive, domestic producers need to respond to this competitive threat. There are a number of possible strategies that could increase the survival prospects of a domestic firm – e.g., introducing new products, increasing advertising to differentiate their products from those of their rivals, diversifying into other lines of business (possibly through M&A). However, firms may have to step up their R&D activities before they can launch new products; increasing advertising or entering into new lines of business also requires significant expenditure. For firms experiencing decrease in profitability, raising the required financing from internal as well as external sources could be difficult. As a result, perhaps the most effective strategic response is to become more competitive by running a tighter ship, for example, by cutting costs and selling off loss making divisions. The role of the CEO is likely to be crucial for a firm’s attempt to become more cost-efficient. Some CEOs are empire builders and are unwilling or unable to take the hard decisions that are required to improve efficiency, for example, selling off loss making divisions, laying off workers, or simply leading by example and championing a less profligate corporate culture. Moreover, cost-cutting is not a skill or a trait that a CEO can acquire very quickly. CEOs with management styles that breed inefficiency may not be able to reverse their styles very quickly. Thus, not only does the threat of bankruptcy not discipline them, it may also be difficult to incentivize them to improve efficiency by altering compensation contracts. Therefore, when a firm has not been run efficiently by the incumbent CEO, and improving efficiency is the key to survival, firing that CEO and bringing in someone who has a track record of cutting costs or selling assets is likely to be an effective strategic response. 1.1.1 Tariff cuts and performance sensitivity to forced CEO turnover. A large literature does find that CEOs are more likely to be fired after poor performance. Theory suggests that CEO firings occur because shareholders or the board of directors update their prior beliefs about the CEO’s ability after observing performance. If performance is sufficiently poor, they find it worthwhile to incur the costs associated with firing the incumbent and engaging in a costly search for a new CEO of higher ability (Hirshleifer and Thakor 1994, 1998; Hermalin and Weisbach 1998, 2003). Not enough is known, however, about the extent to which firing decisions in response to poor performance are occurring in this optimal manner, as implied by theory. For example, is the board or are shareholders misattributing poor performance to the CEO when it is due to reasons outside the CEO’s control?1 Is the board trying to protect its reputation at the expense of the CEO’s? When the origin of the adverse shock is not known, it is difficult to conclude that CEO firing decisions in response to poor performance are optimal decisions. An advantage of studying the impact of more intense foreign competition on forced CEO turnover decisions is that we are able to observe (1) whether firms respond in the way suggested by theory; (2) whether the ones most likely to benefit from forced CEO departures respond more; and (3) whether performance does improve for the firms that respond, relative to those that do not. Increase in the likelihood of forced CEO turnover and its sensitivity to performance after tariff cuts are consistent with optimal firing decisions. In the post-tariff cut environment, with the average cost of production in the industry coming down due to competition from lower cost foreign rivals, only higher ability CEOs (in the pool of domestic firms in the industry experiencing tariff cuts) will be able to compete effectively. Put differently, the ability threshold for CEO retention will increase, leading to more forced CEO turnover. Further, the sensitivity of turnover to performance will also increase. If shareholders and the board are learning about CEO ability from firm performance, the likelihood that the CEO retains his job will be lower after tariff cuts for the same underperformance (relative to the average performance of all domestic firms).2 These considerations lead to the following hypothesis: Hypothesis 1. The likelihood of forced CEO turnover, and the sensitivity of forced CEO turnover to firm performance, increases for firms in an industry experiencing a tariff cut.3 1.2 Product market characteristics, competitor reactions, and forced CEO turnover Product market characteristics, and how a firm’s competitors respond to a competitive shock such as a tariff cut, are likely to play an important role for CEO retention decisions. First, since the weaker firms in the industry could be brought to the brink of exit following a major tariff cut, inefficient firms that do not respond immediately by replacing their CEOs are vulnerable to predatory strategies from rival firms. Lower cost rivals, especially the ones that are conservatively financed, may be willing to sacrifice short-term profits by lowering prices to levels that inefficient firms cannot match and therefore must exit the industry. Thus, one would expect that the likelihood of CEO turnover after tariff cuts is higher in industries in which the risk of predation is greater. Second, the firm’s incentive to reduce costs is likely to be stronger if the demand curve for its products is more elastic, that is, when the firm’s products are more similar to those of other firms. This follows because if a firm is relatively more inefficient relative to its competitors, it will lose more market share as the competitors charge lower prices. If competitors respond to tariff cuts by improving efficiency, the cost imposed on the firm of harboring a CEO who is unable to do the same increases. These considerations lead to the following hypotheses: Hypothesis 2. The effect of tariff cuts on forced CEO turnover is stronger (a) in industries in which the most conservatively financed firms and firms with larger cash holdings account for a larger fraction of the industry sales and (b) for firms that have products that are more similar to those of other firms. Part (a) of hypothesis 2 is premised on the notion that predation is most likely when a few large firms have low leverage and are able to sustain temporary reduction of profits or draw on a “war chest.” When weaker firms leave the industry, and the gains accrue to existing firms roughly in proportion to their market share, the largest firms have the most incentive to predate.4 1.3 Exit, incentives, and governance The literature on the effect of increased competition on managerial effort incentives (operating via incentive contracts offered by shareholders to managers) does not necessarily deliver the result that more competition induces more effort. Closest to our setting is Schmidt (1997), who shows that when managers have costs of job separation, the increased threat of liquidation associated with more intense competition reduces the cost to shareholders of providing incentives for cost-reducing effort. Similar to Schmidt (1997), we view the effect of tariff cuts as creating a tournament in which the lowest performers (e.g., the bottom 10%) among the domestic producers must exit. However, there are two key differences. First, we do not regard the problem of inefficiency as a pure “provision of effort” problem as in a standard moral hazard setting. Some CEOs may have very low ability, or effort productivity, when it comes to improving cost efficiency. These CEOs may nonetheless survive without competition shocks because governance is poor. Second, we argue that that exit imposes costs on the shareholders or the board, and when the likelihood of exit for poor performers increases following tariff cuts, inefficient CEOs may have to be fired. Product market competition and corporate governance have often been considered as alternative mechanisms of disciplining management. Giroud and Mueller (2011) find that better governed firms (as measured by the GIM index) earn higher returns than poorly governed firms only in noncompetitive industries, as measured by the HHI. They attribute this result to the possibility that there is greater managerial slack in noncompetitive industries when governance quality is low, but not in more competitive industries, as competition eliminates managerial slack. Consistent with this argument, they find that in noncompetitive industries, poor governance firms make more value destroying investment expenditures and also have lower productivity. Giroud and Mueller (2010) find that firms’ operating performance deteriorates after the passage of state-level business combination laws (which made managers more immune from the discipline of hostile takeover threats) only in noncompetitive industries. Giroud and Mueller (2010, 2011) thus find that governance improvements matter more for firm performance or firm value when the discipline of competition is absent. The “dual” to this result is the proposition that an exogenous increase in the threat of competition matters more when governance is weak. This makes sense in our context because firms with good governance should already be run efficiently as a result of better external governance (e.g., takeover threat) or better internal governance (designing better compensation contracts to incentivize managers or firing inefficient managers). In contrast, poorly governed firms may have managers that entrench themselves via empire building (Morck, Shleifer, and Vishny 1989) and pay little attention to improving productivity or keeping down costs. Thus, when competition breaks out and lower-cost foreign firms gain access to the market, the poorly governed firms are the ones that need to respond to the competitive threat by firing inefficient managers. It is possible that the discipline of competition itself eliminates organizational slack and it is not really necessary to fire the CEO. However, CEOs who have never run a tight ship may not find it easy to acquire the necessary skills quickly, and more importantly, the board or shareholders may not trust such a CEO to successfully implement changes. In other words, while more intense competition might align the incentives of the CEO and shareholders, continuing with the same CEO may be perceived as too risky. Thus, one expects forced turnover of CEOs to increase, especially in poorly governed firms. Who takes the decisions in poorly governed firms, where managers are supposedly entrenched, to fire the CEO? We argue that there are costs of firing CEOs, and as long as the costs outweigh the benefits for the decision maker, CEOs will retain their jobs. For shareholders, these costs are not only the search costs to the firm of finding replacements, but perhaps more importantly, the costs associated with activism and putting pressure on the board. For directors, especially in poorly governed firms, these costs are likely to stem from loyalty to the CEO, who may have appointed them. Similar to Schmidt (1997), we argue that liquidation imposes a different set of costs on both shareholders and directors. The former lose the value of their investment in the firm, and the latter suffer reputational damages, which could involve shareholder lawsuits, losing out on other potential directorial positions, or, if they hold executive positions for their companies, even spillover effects for their companies. The benefits from firing the CEO are the avoidance of these costs. When tariff-cut-induced foreign competition increases the likelihood of liquidation, the benefits may dominate the costs associated with firing the CEO. Thus, the shareholders or the boards become more proactive in poorly governed firms, where inefficient managers are more likely to be found. These considerations lead to the following hypothesis: Hypothesis 3. (a) Firms with poor governance are more likely to be of lower productivity and have higher default probability than firms with good governance. (b) The likelihood of forced CEO turnover, and the sensitivity of that turnover to performance, will increase more in poorly governed firms than in well governed firms after tariff cuts.5 In contrast to poorly governed firms, well governed firms are likely to have CEOs in place who are running the firm efficiently, as implied by the findings of Giroud and Mueller (2011). Thus, CEO firings are unlikely to generate significant benefits for these firms when they face new competition from foreign producers. However, these firms may still need to incentivize their CEOs to respond effectively to the threat from foreign competition. For example, CEOs may need more high-powered incentives to enter new markets or new lines of business which are not in their comfort zone. They may have to be given long-term incentive pay rather than short-term cash compensation to steal market share from weaker firms via predatory tactics (which involve sacrificing profits today for higher future profits). Indeed, Cuñat and Guadalupe (2009) find that more import penetration is associated with higher pay-performance sensitivity for top executives, and with a widening of the pay gap between the CEO and other top executives. We expect that one should see similar effects following tariff cuts, and the effects should be mostly confined to firms with good governance. Hypothesis 4. CEO total pay, long-term pay, pay-performance sensitivity (pay alignment), and the pay gap between CEOs and other non-CEO executives will increase after tariff cuts. These effects should be stronger in firms with good governance than in firms with poor governance. 1.4 Post-turnover performance and tariff cuts We argued above that the main motivation for forcing out inefficient CEOs subsequent to tariff cuts is to improve efficiency. It is possible, however, that boards are firing CEOs to protect their own reputations: if things go wrong later, they would not be blamed for lack of proactivity. In fact, such “scapegoating” is more likely to occur when it is relatively unlikely that performance can be turned around. In contrast, if the CEO is fired to improve efficiency, one should expect the performance improvement to show up in the subsequent performance of the firm. The relevant benchmark here is the performance of peer firms in the same industry that are exposed to the same competition shock, have the same ex ante likelihood of firing the CEO, but do not (e.g., because the CEO is too entrenched). Since performance of most firms in the industry is expected to drop after tariff cuts, the relevant metric is the change in performance compared to that before the tariff cut, for treated firms versus matched control firms (i.e., the “difference-in-differences” of performance). Thus, we expect the following. Hypothesis 5. (a) Firms that fire their CEOs after tariff cuts show more performance improvement relative to prior performance than matched firms in the same industry that do not fire their CEOs. Firing CEOs and improving efficiency should be associated with more significant relative improvement in performance when the competitive threat from lower-cost foreign producers has intensified. Therefore, we should expect that the performance improvements relative to matched industry peers should be stronger for firms that fire their CEOs subsequent to tariff cuts than for firms that fire CEOs in normal times. Hypothesis 5. (b) Improved performance by firms that fire CEOs following tariff cuts (relative to matched firms in the same industry that do not fire CEOs) should be greater when CEOs are fired immediately after tariff cuts than at other times. If managerial entrenchment is an important reason why many firms do not fire their CEOs after tariff cuts, we expect these firms would invite predation from conservatively financed larger firms in the industry. Consequently, firms that do not fire CEOs after tariff cuts, on average, should experience more performance deterioration relative to those that fire CEOs when the possibility of predation is high. In other words, the “triple difference” in performance should be positive. A similar argument applies to firms that face elastic demand curves, as they are likely to lose more market share as their rivals’ lower prices. Hypothesis 5. (c) The effect outlined in Hypothesis 5a should be stronger (1) in industries in which the most conservatively financed firms account for a larger fraction of the industry sales and (2) for firms that have products that are more similar to those of other firms. 2. Sample and Data To measure reductions in import tariffs, we use product-level U.S. import data compiled by Feenstra (1996), Feenstra, Romalis, and Schott (2002), and Schott (2010). These data span the period 1974–2005 and cover 146 manufacturing industries (200–399 SIC range). Following Valta (2012), for each 3-digit (SIC) industry-year, we compute the ad valorem tariff rate as the duties collected by the U.S. Customs Service in the 3-digit industry divided by the free-on-board value of imports to that industry. To identify sizable variation in barriers to trade, we follow Frésard (2010) to characterize tariff reductions in terms of the deviations in the yearly changes in tariffs from their median level. Accordingly, a major tariff cut occurs in a specific industry-year when a negative change in yearly tariff rate is 3 times larger than its median negative change in the industry over our sample period. Moreover, to make sure that large tariff reductions truly reflect nontransitory changes in the competitive environment, we exclude tariff cuts that are followed by equivalently large increases in tariffs within the subsequent 3 years. We also drop all industries for which we do not have tariff data. We obtain CEO turnover data from the Standard and Poor’s (S&P) ExecuComp database, which covers about 1,500 firms each year that are in the S&P 500, S&P mid-cap 400, and S&P small-cap 600 indices. Our sample period covers the years 1993–2005, representing the intersection of our ExecuComp sample and the tariff data. We include all firm-years that have an identifiable CEO (using CEOANN). We obtain stock return data from the Center for Research in Security Prices (CRSP) and firm characteristics from the Compustat Industrial and Segment files. Governance data are from RiskMetrics (formerly called Investor Responsibility Research Center (IRRC)). After merging the tariff data with the CEO data from ExecuComp, we are left with 93 unique 3-digit SIC industries. There are 61 industries experiencing at least one tariff cut during our sample period, with the remaining 32 industries experiencing none. While we mainly focus on the change of import tariff, the change of export tariff could be triggered at the same time as part of the bilateral (or multilateral) agreements between the United States and other countries. Thus, it is not obvious that the demand curve for exporting firms shifts left following the import tariff reduction as the export market can improve simultaneously. Further, exporting firms in industries not subject to tariff cuts could be benefiting from tariff reductions by trading partners of the United States as part of bilateral and multilateral trade agreements, and should be removed from the control group. To address such concerns, we exclude all exporting firms from our sample.6 3. Empirical Methodology and Baseline Results We now explain our empirical methodology and discuss our baseline results. We first discuss the merits of our choice of industry-level tariff cuts as a quasi-natural experiment aimed at avoiding potential issues of endogeneity. 3.1 Tariff cuts as a quasi-natural experiment In trying to understand how competition affects CEO turnover, the main challenge we face concerns finding measures of competition at the industry level that are exogenous to CEO turnover decisions at the individual firm level. Traditional measures of industry concentration such as the HHI are subject to endogeneity: the distribution of sales or output among firms in an industry could well be the outcome of firm strategies rather than be a determinant of such strategies.7 In view of this, several recent papers have used industry-level major tariff cuts as quasi-natural experiments to study how (change in) competition affects corporate policies (e.g., Feenstra 1996; Feenstra, Romalis, and Schott 2002; Frésard 2010; Valta 2012; Frésard and Valta 2012, 2015). These papers contain excellent discussions as to why tariff cuts are a valid quasi-natural experiment in the context of the issues they address. While our dependent variable of interest is different, some of the same arguments apply. In our context, perhaps the most important possibility to rule out is that industry-level tariff cuts are in response to factors that would also have caused more frequent CEO departures in the absence of these cuts. Such a possibility would be plausible if the industries which experience tariff cuts happened to be declining industries. Politicians—perhaps influenced by export lobby groups seeking access to foreign markets as part of reciprocal trade agreements—could have decided to open up to foreign competition industries that would be difficult to salvage. To see if this is the case, and whether our results could be driven by such cases, we do two things. First, we directly test whether the major tariff cuts that occur in our sample period could be predicted by prior industry conditions or trends, such as growth of sales and earnings, market conditions, and so on. We find no supporting evidence.8 Second, we only consider for our empirical tests tariff cuts that were a part of multilateral trade agreements and were across-the-board, and thus not narrowly directed at particular industries. Our results hold for these tariff cuts, which in fact account for a majority of the tariff cuts in our sample period.9 There are good reasons to believe that these multilateral tariff cuts beginning in the early 1990s were the result of broad consensus in the United States in favor of trade expansion (see Destler 2005). There is no reason to believe, however, that lobby groups in declining industries would lobby to reduce tariff protection: in fact, theory and evidence suggests the opposite. Thus, most plausibly, the groups that benefited were exporters, and importers of intermediate goods. We exclude exporters and exclusively focus on firms that produce only for the domestic market in our empirical tests. Figure 1 confirms that tariff cuts affect our sample firms as expected. Figure 1A plots industry tariff rates around tariff cut events at the 3-digit industry level for the affected industries (those with major tariff cuts at the event year) and the control group of unaffected industries (those without major tariff cut from 3 years before to 3 years after the event year). The average tariff cut in the affected industries is about 1.5 percentage points. Figure 1B shows the associated increase in import penetration in the affected industries. As expected, import penetration increases in the industries experiencing tariff cuts rather dramatically, but there is no such effect in the unaffected industries. Figure 1C shows the median return on equity (ROE) of the affected and unaffected industries. ROE falls for the affected firms immediately after the tariff cut, and keeps declining. Finally, Figures 1D–1F show the behavior of aggregate industry investment, sales and cash holdings of the domestic firms, all of which decline around the tariff cuts. However, there are no such trends for the control group. Figure 1 View largeDownload slide Tariff rate and industry performance around tariff rate reduction The figure shows the tariff rate and industry performance in event time for the sample of affected and unaffected industries (based on 3-digit SIC). The sample consists of all industries that are matched to tariff data. Affected industries are those that experience a substantial tariff reduction in the event year. Unaffected industries are those that do not have a substantial tariff reduction in the 3 years before and after event year (including event year). Tariff rates are computed at the 3-digit SIC industry level as duties collected at U.S. Custom divided by the free-on-board customs value of imports. Import penetration is computed at the 3-digit SIC industry level as total imports divided by domestic production plus total imports minus total exports. Aggregate investment, sales and cash holdings are the summations of these variables over all firms in each industry. ROE is return on equity measured by net income divided by shareholder equity. The panels show the median value of each variable in event year for all affected and unaffected industries, respectively. Figure 1 View largeDownload slide Tariff rate and industry performance around tariff rate reduction The figure shows the tariff rate and industry performance in event time for the sample of affected and unaffected industries (based on 3-digit SIC). The sample consists of all industries that are matched to tariff data. Affected industries are those that experience a substantial tariff reduction in the event year. Unaffected industries are those that do not have a substantial tariff reduction in the 3 years before and after event year (including event year). Tariff rates are computed at the 3-digit SIC industry level as duties collected at U.S. Custom divided by the free-on-board customs value of imports. Import penetration is computed at the 3-digit SIC industry level as total imports divided by domestic production plus total imports minus total exports. Aggregate investment, sales and cash holdings are the summations of these variables over all firms in each industry. ROE is return on equity measured by net income divided by shareholder equity. The panels show the median value of each variable in event year for all affected and unaffected industries, respectively. 3.2 Forced CEO turnover Following Parrino, Sias, and Starks (2003), a CEO turnover is classified as forced if the Wall Street Journal reports that the CEO is fired, is pushed from the position, or departs for unspecified policy differences. For the remaining cases, the departure is classified as forced if the departing CEO is under the age of 60 and (1) the Wall Street Journal announcement of the succession does not report the reason for the departure as involving death, poor health, or the acceptance of another position (elsewhere or within the firm) or (2) the announcement reports that the CEO is retiring but does not announce the retirement at least 6 months prior to the succession. The circumstances surrounding the departures of the second group are further investigated by searching the business and trade press using Factiva and LexisNexis for relevant articles to reduce the likelihood that a turnover is incorrectly classified. We also collect information on the departing CEO’s career path and use an additional filter: when a CEO under the age of 60 cannot be found in a new position in any of the databases, we consider the departure as forced. Panel A in Table 1 presents some statistics for CEO turnover rates in our sample. We define turnover in a given fiscal year $$t$$ to occur if the CEO in year $$t $$is no longer the CEO by the following year, $$t+$$1. We exclude all nonstandard turnovers, that is, turnovers due to an acquisition, bankruptcy, or delisting. Panel A of Table 1 presents the level of CEO turnover by year and by type. The turnover rate for standard turnovers is 14.13%. The sum of standard and non-standard turnover rate in our sample (unreported) is 16.69% over the entire sample period, implying average CEO tenure of 6 years. Kaplan and Minton (2012) report a 15.6% turnover rate using S&P 500 firms for a slightly different sample period. The average rate of forced turnovers is 4.64%; that is, 1 in every 22 CEOs is fired in an average year. It has been suggested (Kaplan and Minton 2012; Jenter and Lewellen 2014) that the actual rate of forced turnovers is possibly higher as many forced turnovers are misclassified as voluntary turnovers. Therefore, we also report turnover statistics for an expanded definition in which a departure that results in a new position for the CEO at a smaller firm and with lower pay is also classified as forced. The last two columns in panel A report the statistics corresponding to this definition. This alternative definition results in an average forced turnover rate of 8.51%. Table 1 Summary statistics A. CEO turnover by year     Standard turnover  Forced turnover  Alternative forced turnover  Year  Number  Unconditional probability  Number  Unconditional probability  Number  Unconditional probability  1993  18   5.76  5  1.45  11  3.92  1994  52  11.56  19  4.36  28  6.27  1995  59  12.46  18  3.96  39  8.29  1996  70  13.61  20  4.31  46  8.61  1997  89  16.78  32  6.88  56  9.87  1998  95  17.52  33  6.90  62  10.71  1999  123  20.05  38  7.77  75  10.91  2000  94  17.90  27  5.51  53  9.81  2001  67  11.79  22  4.67  52  8.34  2002  69  13.06  18  3.88  45  8.26  2003  64  12.55  20  4.22  42  8.05  2004  86  15.18  18  3.90  46  7.67  2005  88  15.42  11  2.46  58  9.92  1993–2005  974  14.13  281  4.64  613  8.51  A. CEO turnover by year     Standard turnover  Forced turnover  Alternative forced turnover  Year  Number  Unconditional probability  Number  Unconditional probability  Number  Unconditional probability  1993  18   5.76  5  1.45  11  3.92  1994  52  11.56  19  4.36  28  6.27  1995  59  12.46  18  3.96  39  8.29  1996  70  13.61  20  4.31  46  8.61  1997  89  16.78  32  6.88  56  9.87  1998  95  17.52  33  6.90  62  10.71  1999  123  20.05  38  7.77  75  10.91  2000  94  17.90  27  5.51  53  9.81  2001  67  11.79  22  4.67  52  8.34  2002  69  13.06  18  3.88  45  8.26  2003  64  12.55  20  4.22  42  8.05  2004  86  15.18  18  3.90  46  7.67  2005  88  15.42  11  2.46  58  9.92  1993–2005  974  14.13  281  4.64  613  8.51  B. Descriptive statistics  Variable  Number  Mean  SD  25th percentile  50th percentile  75th percentile  Tariff (%)  6,990  4.887  5.817  1.207  3.253  6.149  ROA  6,990  0.030  0.131  0.126  0.053  0.091  RET  6,803  0.164  0.553  $$-$$0.151  0.088  0.348  Salechg  6,978  0.147  0.906  $$-$$0.005  0.075  0.185  Assets  6,994  7.143  1.597  6.042  7.044  8.141  $$q$$  6,863  2.261  2.166  1.276  1.684  2.486  Age  6,999  56.105  7.729  51  56  61  Volatility  6,263  0.447  0.251  0.277  0.373  0.556  CEO tenure  6,999  7.287  3.357  5  6  9  Institutional ownership  6,999  0.631  0.219  0.493  0.652  0.781  G-index  6,623  9.036  2.715  7  9  11  HHI  6,425  0.195  0.167  0.065  0.141  0.267  Board size  4,560  9.261  2.458  7  9  11  Board independence  4,553  0.664  0.171  0.556  0.7  0.8  B. Descriptive statistics  Variable  Number  Mean  SD  25th percentile  50th percentile  75th percentile  Tariff (%)  6,990  4.887  5.817  1.207  3.253  6.149  ROA  6,990  0.030  0.131  0.126  0.053  0.091  RET  6,803  0.164  0.553  $$-$$0.151  0.088  0.348  Salechg  6,978  0.147  0.906  $$-$$0.005  0.075  0.185  Assets  6,994  7.143  1.597  6.042  7.044  8.141  $$q$$  6,863  2.261  2.166  1.276  1.684  2.486  Age  6,999  56.105  7.729  51  56  61  Volatility  6,263  0.447  0.251  0.277  0.373  0.556  CEO tenure  6,999  7.287  3.357  5  6  9  Institutional ownership  6,999  0.631  0.219  0.493  0.652  0.781  G-index  6,623  9.036  2.715  7  9  11  HHI  6,425  0.195  0.167  0.065  0.141  0.267  Board size  4,560  9.261  2.458  7  9  11  Board independence  4,553  0.664  0.171  0.556  0.7  0.8  The sample consists of all firms in manufacturing industries (200–399 standard industrial classification range) that also have data available on the ExecuComp database between 1993 and 2005. Panel A summarizes CEO turnover for our sample firms. Standard turnover is the sum of forced and voluntary turnover. Under each turnover type, we report the number and the unconditional probability (in percentages). Tariff is the duties collected by the US Customs Service divided by the free-on-board value of imports (in percentages). Forced turnovers are defined in Parrino, Sias, and Starks (2003), with an additional filter: departures of chief executive officers (CEOs) before age 60 and for which we cannot find any record of another position, including directorial appointments, are classified as forced. Alternative forced turnovers are all forced turnovers defined as above plus other turnovers that result in a lower pay at a smaller firm for the departing CEOs. Table A1 defines all other variables. Table 1 Summary statistics A. CEO turnover by year     Standard turnover  Forced turnover  Alternative forced turnover  Year  Number  Unconditional probability  Number  Unconditional probability  Number  Unconditional probability  1993  18   5.76  5  1.45  11  3.92  1994  52  11.56  19  4.36  28  6.27  1995  59  12.46  18  3.96  39  8.29  1996  70  13.61  20  4.31  46  8.61  1997  89  16.78  32  6.88  56  9.87  1998  95  17.52  33  6.90  62  10.71  1999  123  20.05  38  7.77  75  10.91  2000  94  17.90  27  5.51  53  9.81  2001  67  11.79  22  4.67  52  8.34  2002  69  13.06  18  3.88  45  8.26  2003  64  12.55  20  4.22  42  8.05  2004  86  15.18  18  3.90  46  7.67  2005  88  15.42  11  2.46  58  9.92  1993–2005  974  14.13  281  4.64  613  8.51  A. CEO turnover by year     Standard turnover  Forced turnover  Alternative forced turnover  Year  Number  Unconditional probability  Number  Unconditional probability  Number  Unconditional probability  1993  18   5.76  5  1.45  11  3.92  1994  52  11.56  19  4.36  28  6.27  1995  59  12.46  18  3.96  39  8.29  1996  70  13.61  20  4.31  46  8.61  1997  89  16.78  32  6.88  56  9.87  1998  95  17.52  33  6.90  62  10.71  1999  123  20.05  38  7.77  75  10.91  2000  94  17.90  27  5.51  53  9.81  2001  67  11.79  22  4.67  52  8.34  2002  69  13.06  18  3.88  45  8.26  2003  64  12.55  20  4.22  42  8.05  2004  86  15.18  18  3.90  46  7.67  2005  88  15.42  11  2.46  58  9.92  1993–2005  974  14.13  281  4.64  613  8.51  B. Descriptive statistics  Variable  Number  Mean  SD  25th percentile  50th percentile  75th percentile  Tariff (%)  6,990  4.887  5.817  1.207  3.253  6.149  ROA  6,990  0.030  0.131  0.126  0.053  0.091  RET  6,803  0.164  0.553  $$-$$0.151  0.088  0.348  Salechg  6,978  0.147  0.906  $$-$$0.005  0.075  0.185  Assets  6,994  7.143  1.597  6.042  7.044  8.141  $$q$$  6,863  2.261  2.166  1.276  1.684  2.486  Age  6,999  56.105  7.729  51  56  61  Volatility  6,263  0.447  0.251  0.277  0.373  0.556  CEO tenure  6,999  7.287  3.357  5  6  9  Institutional ownership  6,999  0.631  0.219  0.493  0.652  0.781  G-index  6,623  9.036  2.715  7  9  11  HHI  6,425  0.195  0.167  0.065  0.141  0.267  Board size  4,560  9.261  2.458  7  9  11  Board independence  4,553  0.664  0.171  0.556  0.7  0.8  B. Descriptive statistics  Variable  Number  Mean  SD  25th percentile  50th percentile  75th percentile  Tariff (%)  6,990  4.887  5.817  1.207  3.253  6.149  ROA  6,990  0.030  0.131  0.126  0.053  0.091  RET  6,803  0.164  0.553  $$-$$0.151  0.088  0.348  Salechg  6,978  0.147  0.906  $$-$$0.005  0.075  0.185  Assets  6,994  7.143  1.597  6.042  7.044  8.141  $$q$$  6,863  2.261  2.166  1.276  1.684  2.486  Age  6,999  56.105  7.729  51  56  61  Volatility  6,263  0.447  0.251  0.277  0.373  0.556  CEO tenure  6,999  7.287  3.357  5  6  9  Institutional ownership  6,999  0.631  0.219  0.493  0.652  0.781  G-index  6,623  9.036  2.715  7  9  11  HHI  6,425  0.195  0.167  0.065  0.141  0.267  Board size  4,560  9.261  2.458  7  9  11  Board independence  4,553  0.664  0.171  0.556  0.7  0.8  The sample consists of all firms in manufacturing industries (200–399 standard industrial classification range) that also have data available on the ExecuComp database between 1993 and 2005. Panel A summarizes CEO turnover for our sample firms. Standard turnover is the sum of forced and voluntary turnover. Under each turnover type, we report the number and the unconditional probability (in percentages). Tariff is the duties collected by the US Customs Service divided by the free-on-board value of imports (in percentages). Forced turnovers are defined in Parrino, Sias, and Starks (2003), with an additional filter: departures of chief executive officers (CEOs) before age 60 and for which we cannot find any record of another position, including directorial appointments, are classified as forced. Alternative forced turnovers are all forced turnovers defined as above plus other turnovers that result in a lower pay at a smaller firm for the departing CEOs. Table A