Influence of Institutional Investors with Common Ownership

This paper shows that higher common ownership of natural competitors is associated with more use of relative performance evaluation (RPE). First, I use compensation data from Execucomp and find that executives receive more rewards for outperforming peer firms if common ownership concentration increases. Second, I manually collect executive compensation contract details from 2014 proxy statements and show that the likelihood of using RPE increases with common ownership. Further, the size of incentive awards tied with RPE increases with common ownership.

These findings suggest that institutional investors with common ownership exert a strong influence on executive compensation in a positive way: less alignment of pay with industry performance. I construct a measure of ownership by common owners and show that institutional investors with common ownership have a stronger influence on compensation than investors without common ownership. Finally, I use the inclusion to S&P 1500 index as an instrumental variable to support a causal connection. These findings signify the potential benefit from governance by large, diversified institutional investors with common ownership.

1 Introduction

There is a growing concern that large, diversified institutional investors own significant shares of natural competitors in product markets. This common ownership weakens incentives to compete because competition, especially in the form of price competition, lowers profits of all firms that the investors with common ownership own.[1] Hence, if the executives of co-owned firms understand these anti-competitive incentives of common owners and are incentivized to maximize the portfolio value of the common owners, the market outcomes will become more monopolistic: higher product prices. There is evidence that common ownership by institutional investors increases product prices in the airline and banking industries (Azar, Schmalz, and Tecu, 2015; Azar, Raina, and Schmalz, 2016). Because most common owners are large, diversified institutional investors who regularly communicate with the executives of portfolio firms, the executives clearly understand the anti-competitive incentives. However, little is known about how executive officers are incentivized to reduce competition.

In this paper, I study the effects of common ownership on executive compensation, specifically on the use of relative performance evaluation (RPE). I use executive compensation as a testing ground for the following three reasons. First, institutional investors are vocal about both the level of compensation and the alignment of pay with performance (Hartzell and Starks, 2003). Second, executive compensation (particularly how compensation changes with own and peer performance) reflects the environment of strategic competition in the product markets (Aggarwal and Samwick, 1999a; Vrettos, 2013). Lastly, agency theory predicts that executive compensation, in particular the degree of RPE, affects competitiveness of industries under imperfectly competitive product markets (Fershtman and Judd, 1987; Fumas, 1992; Aggarwal and Samwick, 1999a; Anton et al., 2016). Specifically, if executive compensation is the mechanism between common ownership and less competition, then common ownership is expected to reduce the use of RPE in order to soften the incentives to outperform peers.

To test this empirical prediction, I use a two-pronged approach to study the effects of common ownership on executive compensation: implicit and explicit approaches. First, I use the Execucomp database and estimate how annual flow compensation changes with own and peer performance — pay-performance elasticities. Then, I test how the ratio of peer pay-performance elasticity to own pay-performance elasticity changes with common ownership. In doing so, I use Modified Herfindahl Hirschman Index Delta (MHHID) that measures the degree of common ownership in each industry, proposed by O’Brien and Salop (2000). However, using the Execucomp database alone is potentially problematic. The implicit test fails to detect RPE with traditionally used industry definitions such as Standard Industrial Classification (SIC) even when firms in the sample explicitly disclose the use of RPE (Gong, Li, and Shin, 2011). Further, estimated pay-performance elasticities may not reflect pay for current or past performance because the majority of performance-based equity awards reported in the Execucomp are designed to be earned upon future performance (De Angelis and Grinstein, 2016). To address these concerns, I complement this implicit approach with an approach that uses a manually collected set of contract details from 2014 proxy disclosures by single-segment firms. Using this data, I study the determinants of the use and size of incentive awards tied with RPE, which is referred to as the explicit test of RPE.[2]

Surprisingly, the results from both the implicit and explicit approaches indicate that RPE is positively associated with common ownership. Hence, executive compensation is unlikely to be the mechanism between common ownership and less competitive outcomes in product markets. The implicit approach shows that the ratio of peer to own pay-performance elasticities is negatively correlated with common ownership. This finding implies that executives receive more rewards for outperforming peers under common ownership, thus having more incentives to compete aggressively. This result is robust to alternative industry definitions such as variants of SIC codes — four-digit SIC, three-digit SIC, and size-split four-digit SIC of Albuquerque (2009) — as well as the text-based fixed industry definitions developed by Hoberg and Phillips (2010) and Hoberg and Phillips (2016). The result is also robust to other measures of compensation, specifically non-equity incentive plan compensation. Using non-equity incentive plan compensation is particularly useful to estimate pay for current or past performance because non-equity incentive plan compensation is disclosed when it is earned upon satisfying performance conditions. The result with non-equity incentive pay confirms the previous finding: more use of RPE in industries with higher common ownership.

The explicit approach corroborates the findings from the implicit approach. The likelihood of using RPE (disclosed in proxy statements) increases with common ownership. This result is robust to alternative industry definitions. One may argue that the use of RPE alone does not necessarily give more incentives to compete because the size of awards tied with RPE could small or because the performance benchmarks with which firms compare their performance could be too broad. These two concerns do not weaken my finding. First, the size of plan-based incentive awards evaluated with performance benchmarking increases with common ownership. Further, firms in industries with higher common ownership are more likely to use industry-specific indices such as Philadelphia Semiconductor Index or FTSE NAREIT US Real Estate Index.

My findings insinuate that institutional investors with common ownership make executive compensation more efficient by reducing pay for luck — more filtering of industry-specific shock that out of executives’ control. However because common ownership, MHHID, is measured at industry-level, drawing inferences on the role of common owners with MHHID potentially loses the firm-level cross-sectional variations of the holdings by institutional investors with common ownership. Hence, I compute individual investors’ contributions to common ownership measure in each industry and list the top 5 institutional investors with the contributions to common ownership — which I refer to as the top 5 common owners in each industry. Using the sum of ownership by the top 5 common owners, I estimate the influence of common owners on the use of RPE at the firm-level. The results confirm that the institutional investors with common ownership are highly influential in RPE use. Specifically, the shares by the top 5 common owners are much more strongly associated with RPE use, compared to total institutional ownership and ownership by top 5 investors without common ownership.

Using the ownership by the top 5 common owners the at the firm-level might be subject to an endogeneity issue. Institutional investors might invest more in firms that use RPE, because it might signal better corporate governance. To address the endogeneity issue, I use the addition to the Standard and Poor (S&P) Composite 1500 as an instrumental variable, similar to Aghion, Van Reenen, and Zingales (2013) who use the addition to the S&P 500 as an instrumental variable for institutional ownership. This instrumental variable is particularly useful in identifying the effects of institutional investors with common ownership because the membership to S&P 1500 primarily increases the ownership by quasi-indexers who are the main source of common ownership. Using this instrumental variable, I find that large institutional investors with common ownership have a significant influence on executive compensation.

I provide an additional evidence to support that institutional investors with common ownership reduce pay for luck. Using Bebchuk, Grinstein, and Peyer (2010)’s CEO luck measure that is based on the timing of stock option grants, I find that the ownership by common owners is negatively associated with the likelihood of granting options at the lowest price of the month. Compared with other types of institutional investors, institutional investors with common ownership are the most significant to deter the lucky grants.

Overall, institutional investors with common ownership reduce pay for luck, which improves the practice of executive compensation. My results imply that the anti-competitive outcomes of common ownership are not likely caused by executive compensation. However, the implications of my work reach even further. At a broader scale, my findings shed a light on the governance by institutional investors with common ownership. Conventional wisdom suggests that investors with diversified portfolios do not have strong incentives to improve governance of individual firms because the stake in each firm is small. This claim is challenged by the presence of large, diversified institutional investors who hold significant shares of portfolio firms. By exploring the influence of common owners on compensation, the paper is one of the first papers to explore corporate governance by institutional investors with common ownership. Specifically, my paper differentiates from the literature on passive investors (e.g. Appel, Gormley, and Keim (2016)) by focusing on institutional investors with common ownership rather than investors with passive investment strategies. By examining the influence of common ownership on executive compensation, this paper expands the current debate on common ownership from anti-competitive product market outcomes to potentially positive governance role by institutional investors with common ownership.

The paper is organized as follows. Section 2 discusses related literature and develops empirical hypotheses. Section 3 presents the findings from implicit approach. Section 4 contains the results from explicit approach. Section 5 discusses the influence of institutional ownership by common owners. Section 6 concludes.

2 Related Literature and Hypothesis Development

My paper is broadly related to two strands of literature: common ownership and executive compensation.

First, my work is related to the literature on the effects of common ownership. The theoretical literature in industrial organization predicts that common ownership of natural competitors in oligopolistic market leads to less competitive outcome (Reynolds and Snapp, 1986; Bresnahan and Salop, 1986; O’Brien and Salop, 2000; Gilo, 2000). This empirical prediction is supported by the evidences in a growing literature that studies the effects of common ownership by institutional investors on product markets. For example, Azar, Schmalz, and Tecu (2015) empirically assess the effects of common ownership on airline ticket prices. Using Modified Herfindahl Hirschman Index Delta, they find that common ownership increases the ticket prices. These anti-competitive effects are further studied in the banking industry (Azar, Raina, and Schmalz, 2016). These findings give rise to active debate on how the anti-competitive incentives of institutional investors lead to actual market outcomes (Elhauge, 2016).

Anton et al. (2016) is the most closely related to my paper. Extending Aggarwal and Samwick (1999a), they theoretically show that common ownership leads to less use of RPE. To test the empirical prediction, they use the same Execucomp database with different empirical specifications. Their evidences suggest that common ownership leads to less use of RPE, which is exactly opposite to the findings in my paper. More specifically, they estimate the dollar change in pay to the dollar change in firm value (pay-performance sensitivities), whereas I estimate the percentage change in compensation with respect to the percentage change in firm value (pay-performance elasticities). The empirical specifications used in my study have some appealing aspects. First, because pay-performance sensitivites decrease in firm size (see e.g. Schaefer (1998)), the failure of controlling for firm size in sensitivities may lead to biased results. Further, as reported in Table 6, stock return is predominantly used to evaluate executive performance in incentive awards tied with RPE. Recognizing that there are no definite answers on the choice of empirical specifications, I complement the implicit approach with the explicit approach. The explicit approach shows that the RPE use (disclosed in proxy statements) increases with common ownership.

Second, the paper is related to the empirical literature on RPE. The study of relative performance evaluation originates from Holmström (1979)’s informativeness principle, which shows that compensation contracts should include any signal that gives additional information about how an agent (CEO) exerts effort (Holmström, 1979, 1982). If a firm’s performance is affected by the CEO’s effort as well as industry-specific factors that simultaneously affect peer firms, then filtering out peer performance results in greater efficiency by producing a more precise signal about the CEO’s effort. Despite such a clear prediction on RPE, the empirical evidence on the use of RPE in executive contracts is mixed (Antle and Smith, 1986; Barro and Barro, 1990; Gibbons and Murphy, 1990; Jensen and Murphy, 1990; Aggarwal and Samwick, 1999b; Janakiraman, Lambert, and Larcker, 1992; Bertrand and Mullainathan, 2001; Albuquerque, 2009). Such mixed evidences give rise to a new literature of RPE by studying how the use of RPE varies with different CEO, firm, and industry characteristics; e.g., CEO’s exposure to labor market (Himmelberg and Hubbard, 2000), CEO’s ability to hedge the risk from compensation contracts (Garvey and Milbourn, 2003), growth opportunities of the firm (Albuquerque, 2014), the volatility in stock return (Aggarwal and Samwick, 1999b), and industry concentration (Aggarwal and Samwick, 1999a). The paper contributes to the literature by showing that common ownership is one of the key determinants of RPE. In addition, my paper contributes to the literature on the explicit use of RPE. In particular, Gong, Li, and Shin (2011), De Angelis and Grinstein (2011), and De Angelis and Grinstein (2016) use 2006 proxy disclosures and study determinants of RPE use. By using 2014 proxy disclosures as a testing ground, my study gives more recent evidences on RPE use as well as examines how different types of institutional investors in terms of portfolio diversification exert influence on RPE use.

If natural competitors in oligopolistic markets are owned by the same set of investors, then optimal executive compensation contract exhibits less RPE. Kraus and Rubin (2010) study the optimal design of executive compensation contracts when two firms are owned by the same investor. In the model, the managers can decide on two types of effort: cannibalistic effort that increases own firm’s profit at the expense of the rival firm’s profit and economy-increasing effort that does not influence the rival firm. In this case, the investor who owns both firms does not give incentives in the form of RPE because it causes the managers of both firms exert more cannibalistic effort and less economy-increasing effort. The intuition is as follows. Granting incentive plans in the form of RPE gives managers more incentives to outperform rival firms. In imperfectly competitive markets, these additional incentives translate into more aggressive competition and decrease the industry-wide profits. Thus, investors with common ownership whose portfolio values depend on the industry-wide profits rather than individual firms’ profits should be unwilling to grant RPE awards. The same mechanism drives the theoretical results of Anton et al. (2016). Hence, if executive compensation is the mechanism between common ownership and less competition, then the use of RPE is expected to decrease with common ownership.

This hypothesis between common ownership and RPE could be tested in the implicit and the explicit approaches as follows. In the implicit approach, the hypothesis implies that the pay-performance elasticity to peer performance is smaller (less negative) in industries with higher common ownership. In addition, a firm’s use of RPE could be measured with the ratio of peer to own pay-performance elasticity — compensation ratio (Holmström and Milgrom, 1987). Hence, this compensation ratio should become less negative under common ownership. In the explicit approach, the hypothesis implies that the likelihood of using RPE decreases with common ownership. Further, the size of incentive awards tied with RPE is expected to decrease with common ownership.

In this section, I present the main results of the implicit approach. I document that common ownership is negatively related to the pay-for-performance elasticity to peer firms’ performance. Further, I show that the use of RPE (defined as the ratio of peer to own pay-performance elasticity) increases with common ownership. The results are robust to different industry definitions: four-digit SIC; three-digit SIC; size-split four-digit SIC à la Albuquerque (2009); and the 10-K based fixed industry classification of Hoberg and Phillips (2010) (FIC). In addition, the results are robust to different measures of compensation: total annual flow compensation based on the grant-date fair values of restricted shares and options (TDC1 in Execucomp) and non-equity incentive plan compensation.

I obtain compensation data from Execucomp, stock return and CPI index from CRSP monthly database, and financial data from Compustat North America.

Executive Compensation

I use ExecuComp database for executive compensation from 1993 to 2014. ExecuComp provides annual panel data of executive compensation in S&P 1500 firms as well as some additional firms.[3] As a baseline compensation measure, I use total annual flow compensation based on the grant-date fair values of equity awards, which is the sum of salary, bonus, nonequity incentive plan compensation, grant-date fair values of stock and option awards, excess earnings on deferred compensation, and other annual compensation. Because institutional investors influence compensation through actions taken by boards, it is suitable to use current flow compensation over which boards exert direct control.[4]

In addition to total flow compensation, I study how non-equity incentive plan compensation changes with own and peer performance. Performance-based equity incentive plan which consists of a huge portion of total compensation often vests upon future performance conditions.[5] Hence, equity compensation may be considered as adjustments to executive incentives, not remuneration for performance (Core and Guay, 1999). In this regard, nonequity incentive plan is useful because executive compensation disclosure rules require the non-equity incentive pay to be disclosed when it is earned. Following the literature, I adjust all compensation measures for inflation in constant 2006 dollars by the value of the CPI index of the fiscal year-end month.

Table 1 presents summary statistics for compensation. An average (median) executive gets a real total annual flow compensation of $2.173 million ($1.029). 70.5% of executives after 2006 receive non-equity incentive plan compensation. For executives who receive nonequity incentive pay, 24.9% (22.4%) of total compensation comes from non-equity incentive plan.

Industry Definitions

In order to measure peer performance as well as common ownership of competitors, well-defined industries are necessary. As the baseline specification, I use four-digit SIC codes from Compustat North America instead of the CRSP SIC codes used in Anton et al. (2016). Previous studies report significant differences in SIC codes assigned by Compustat and CRSP (Guenther and Rosman, 1994; Kahle and Walkling, 1996). By using the SIC codes from Compustat that covers more firms than CRSP, I can assign industries to every firm in Compustat without dropping non-CRSP firms or replacing missing SIC codes of non CRSP firms with Compustat SIC codes.[6] Because the majority of ExecuComp firms operate in multiple segments, I use coarser three-digit SIC classifications to check the robustness. I also use Albuquerque (2009)’s size-split SIC codes which is shown to have more power in detecting RPE. Specifically, I divide each four-digit SIC codes into size quartiles in each fiscal year.[7]

In addition to industry definitions based on SIC codes, I use the text-based fixed industry definition developed by Hoberg and Phillips (2010) and Hoberg and Phillips (2016). Using the product descriptions in annual reports (10-K), Hoberg and Phillips (2010) assign Compustat firms to 25, 50, 100, 200, 300, 400, and 500 industries from 1996 to 2013. This classification of industries could be more useful than SIC codes to identify product market competitors and detect RPE use by the implicit approach, which is shown in Jayaraman, Milbourn, and Seo (2015) I use the finest classifications, FIC-400 and FIC-500, which assign firms to 400 and 500 industries respectively, so industries reflect close product market competitors.

Performance Measure

Following Garvey and Milbourn (2003) and Albuquerque (2009), I use annual stock returns obtained from the CRSP monthly to measure own and peer firms’ performance. I measure own performance as the continuously compounded annual stock return, assuming that dividends are reinvested. To measure peer performance, I use the annual return on value-weighted portfolios of firms in the same industry excluding own firm.[8]

There are advantages to using stock returns instead of other potential performance metrics (e.g. accounting measures such as return on assets or improvement in shareholder wealth used in Jensen and Murphy (1990) and Aggarwal and Samwick (1999a)) Compared with accounting measures such as return on assets, stock returns are less susceptible to earnings management reported in Bergstresser and Philippon (2006). Compared with the change in shareholders’ wealth recently used in Anton et al. (2016), stock returns have three advantages.

Theoretically, which measure is more appropriate depends on whether the marginal product of managerial efforts is invariant to firm size (Hall and Liebman, 1998; Baker and Hall, 2004). When executive officers’ actions affect shareholder value in dollars, then pay-performance sensitivites are appropriate. On the other hand, if the managerial effort affects shareholder return, then pay-performance elasticities are appropriate.[9] With that in mind, stock return is a more appropriate measure of performance driven by strategic decisions on competition because competitiveness in industries affects profitability.

Empirically, the elasticity approach is better at explaining the cross-sectional variations in compensation compared with the sensitivity approach (Murphy, 1999). Further, pay-performance elasticities measured with stock returns are relatively invariant to firm size, whereas pay-performance sensitivities measured with shareholder wealth decrease in firm size (Gibbons and Murphy, 1992; Schaefer, 1998).

Behaviorally, stock return is the most commonly used performance metric in compensation contracts. Table 6 shows that 218 out of 241 RPE firms use stock return as their performance metrics while no firm uses the dollar increase in the market value of the firm as a metric.[10] The importance of aligning executive pay with stock return or total shareholder return is underscored by Institutional Shareholder Services (ISS)’s US Executive Compensation Policies.[11]

Common Ownership Measure

I measure common ownership with Modified Herfindahl Hirschman Index Delta, as proposed by O’Brien and Salop (2000) and used by Azar, Schmalz, and Tecu (2015), Azar, Raina, and Schmalz (2016), and Anton et al. (2016). In O’Brien and Salop (2000), firm j maximizes the sum of shareholders’ portfolio values weighted by their control shares:

where γij is the measure of control rights of firm j by owner i, βik is the cash flow rights of firm k by owner i, and πk is the profit in the Cournot model. The model gives the markup of price over marginal cost as follows:

where η is the elasticity of demand and sj is the firm j’s market share. The increase in markup due to common ownership, N s s γij βik , can be interpreted as the impact j=1 k̸=jjk i

i γijβij of common ownership on product market competition, which is denoted as MHHID.

I closely follow Azar, Schmalz, and Tecu (2015) to construct MHHID. I use Thomson-Reuters 13F database that is based on quarterly reportings by the financial institutions with over $100 million in qualifying assets under management.[12] The database also provides the number of voting shares. I use the proportion of shares to total outstanding shares to proxy the cash flow rights β’s. I use the proportion of voting shares to total outstanding shares to proxy the measure of control rights γ’s.

I construct MHHID to measure the influence of common ownership throughout each fiscal year. At the end of each quarter, I average each institutional investor’s holdings of each firm (as a percentage of shares outstanding) over the previous 12 months, which measures the cash flow right β’s in Equation (1). I also average each institutional investor’s holdings of sole-voting shares (as a percentage of shares outstanding) over the previous 12 months, which proxies the control right γ’s in Equation (1). Using ownership averaged over the previous 12 months enables me to measure the overall influence of institutional investors during that period. Following Azar, Schmalz, and Tecu (2015), I restrict the sample to those with at least 0.5% of shares outstanding.[13] This construction of β and γ excludes the influence of short-term and small-scale investments that are likely to have limited influence on boards.

I construct market share and Herfindahl Hirschman Index (HHI) with sales (in constant 2006 dollars) from Compustat North America Quarterly. Similar to how I handle institutional ownership, I construct a quarterly series on sales over the previous 12 months at the end of each quarter. I then compute market share over the previous 12 months to compute MHHID at the end of each quarter as well as HHI. The resulting data provide HHI and MHHID of each industry over the previous 12 months at the end of each quarter.

Table 1 gives summary statistics for MHHID and HHI. An average (median) industry has MHHID as 1,568 (1,412) and H H I as 3,212 (2,702). Considering common ownership into account greatly increases market power implied by modified Herfindahl Hirschman Index, which underscores the significance of common ownership problem.

Empirical Specification

I use a standard empirical specification to measure pay-performance elasticities used in the literature. I estimate the following panel regression model:

log(Executive Payijt) = kij + αjtOwn Perfjt + βjtPeer Perfjt + βControl Varsijt + εijt, (2)

where Executive Payijt is the compensation to executive i of firm j at time t. The kij is the individual fixed effects for each executive-firm pair. Own Perfjt and Peer Perfjt measure performance of firm j and its peer group respectively. Given the specification, the αjt and βjt measure the percentage change in pay with respect to the percentage change in the shareholder wealth of own and peer firms. So the αjt and βjt in equation (2) measure the pay-performance elasticities. I use control variables to capture variations in compensation that are not related to performance measures.

In order to estimate the effects of common ownership on pay-performance elasticities, I specify pay-performance sensitivities as linear functions of common ownership measure:

αjt =α0 +α1F(MHHIDjt) (3)

βjt =β0 +β1F(MHHIDjt), (4)

where F(MHHIDjt) is the sample cumulative distribution function of common ownership measure in the sample.[14]

Combining equations (2),(3), and (4), the baseline regression model is

log(Executive Payijt) = kij + α0Own Perfjt + α1F(MHHIDjt)Own Perfjt (5) + β0Peer Perfjt + β1F(MHHIDjt)Peer Perfjt + βControl Varsijt + εijt. (6)

Following the literature, I control for industry concentration with HHI (Aggarwal and Samwick, 1999a); firm size with the logarithm of sales (Rosen, 1982); idiosyncratic risk with residual variance from regressing individual stock return on the return on value-weighted industry portfolio over the previous 36 months (Core and Guay, 1999; Aggarwal and Samwick, 1999a); growth options with market-to-book value of assets (Core and Guay, 1999; Albuquerque, 2014); regulation dummy with the indicator variable which equals one for the firms in the gas and electric services industries (Albuquerque, 2009); CEO dummy with the indicator variable which equals one if the executive is the CEO[15]; and executive tenure with the logarithm of tenure defined by the years since the executive joins the firm (Bertrand and Mullainathan, 2001).[16] In addition, I control for common ownership with F(MHHID) to analyze the effect of common ownership on the level of compensation.

I control for year, industry, and individual fixed effects. Year fixed effects are used to control for unobservable time series variations in compensation such as business cycles (Murphy, 1999) and changes in regulation of compensation disclosure (Gipper, 2016). Industry fixed effects capture unobservable variations in compensation due to industry-specific factors such as different executive labor markets (Himmelberg and Hubbard, 2000). I use individual fixed effects as in Albuquerque (2009) to control for unobservable factors of each executive-firm pair. Examples of these unobservable factors include risk aversion, individual-specific value of outside options, and tendency to use specific compensation contracts such as a one-dollar salary. Controlling for individual fixed effects, I exploit the time series variations in common ownership measure within each individual.[17] I cluster all regressions at the firm level because compensation is likely to be correlated within the firm (Albuquerque, 2009; Frydman and Saks, 2010).

Following Holmström and Milgrom (1987), the use of RPE implied by the panel regression model is the ratio of peer to own pay-performance elasticity — the compensation ratio:

βjt = α0 + α1F(MHHIDjt). αjt β0 + β1F(MHHIDjt)

A decrease in compensation ratio — which means a bigger negative weight on peer performance relative to own performance — could be interpreted as a more use of RPE. Thus, if common ownership reduces the use of RPE for less competition, then the compensation ratio βjt/αjt becomes less negative, which gives less rewards to executive officers from outperforming peers. Formally, the influence of common ownership on RPE is tested with the null hypothesis

H0 = ∂(βjt/αjt) ≤ 0, (7) ∂F(MHHIDjt)

against the alternative, Ha : ∂(βjt/αjt) > 0. Rejecting the null hypothesis is consistent ∂F(MHHIDjt) with the hypothesis that executive compensation is the mechanism between common ownership and a decrease in product market competition. I test the hypothesis at the median value of common ownership: F(MHHIDjt) = 0.5.

3.3 Results

In Table 2, I present the main results of the implicit test. Column (1) of Panel A provides the panel regression results without control variables. Pay-performance elasticity to own performance is positive and statistically significant, whereas common ownership does not have a meaningful impact on own pay-performance elasticity. This implies that executives are rewarded for their own firm’s performance regardless of common ownership level. Pay-performance elasticity to peer performance is statistically indistinguishable from zero in the absence of common ownership. The negative coefficient on the interaction terms between peer performance and common ownership indicates that executives are more punished from peer performance under higher common ownership. Panel B provides the result on the hypothesis test. The test statistic, ∂(βjt/αjt) , is negative and statistically significant. ∂F(MHHIDjt)

In sum, the result indicates that RPE implied by compensation increases with common ownership, contradicting the prediction that common ownership reduces RPE to induce less competition.

In column (2), I add control variables to the previous specification. With controls, the interaction between peer performance and common ownership remains negative at qualitatively similar statistical significance and levels. The result from the compensation ratio test in Panel B confirms the previous result: the use of RPE is positively related to common ownership. The coefficients on control variables indicate that executives in firms with higher common ownership, bigger firm size, bigger idiosyncratic risk and better growth options receive more compensation.

The coefficient on common ownership implies that the level of compensation is positively associated with common ownership. This finding is consistent with evidence reported by Anton et al. (2016). This result is counterintuitive in my case. If common ownership increases the use of RPE, then compensation contracts bear less risks by filtering out industry-specific shock from performance evaluation. The decrease in risks will result in less expected compensation. This finding could potentially stem from endogeneity between compensation and common ownership. For example, compensation is higher in industries with more growth opportunities. At the same time, more growth options are associated with higher institutional ownership. If the market-to-book ratio does not properly control for growth options, this may lead to an upward bias on the coefficient on common ownership.

The result on RPE is not only statistically significant but also has quantitatively meaningful. To quantitatively interpret the result, I examine how peer pay-performance elasticity changes as common ownership changes from the lowest to highest in column (2) of Table 2 In industries with lowest degree of common ownership (F(MHHIDjt) = 0), the peer pay-performance sensitivity is a mere −0.007, which is statistically indistinguishable from zero. An increase in the peer performance by one standard deviation (0.324) decreases executive pay by 0.324 · 0.007 = 0.23% under separate ownership. On the other hand, in industries with highest degree of common ownership (F(MHHIDjt) = 1), the peer pay-performance elasticity becomes −0.074. An increase in the peer performance by one standard deviation decreases executive pay by 2.4% under common ownership. Thus, executives under common ownership are more severely punished from underperforming peers.

In columns (3) and (4), I test if the results survive in the CEO and non-CEO executive subsamples, respectively. With the CEO subsample, the result becomes less statistically significant; the interaction term between peer performance and common ownership as well as compensation ratio test loses some statistical significance but retains its sign. Given that the sample size is significantly smaller, the loss of significance is not very surprising. Column (4) shows that the use of RPE increases with common ownership in the non-CEO subsample.

In summary, the evidences show that firms with higher common ownership use more RPE. Executives under common ownership are rewarded more if their firm outperforms its peers. Also, the use of RPE implied by the ratio of peer to own pay-performance elasticity increases with common ownership. The result is inconsistent with the hypothesis that firms under common ownership should use less RPE in order to lessen competition.

3.4 Robustness

The executive compensation literature has shown that the test of RPE using pay-performance sensitivities from Execucomp is sensitive to industry definitions (Albuquerque, 2009). Columns (1) through (4) in Table 3 show that the previous result is robust to different industry definitions: three-digit SIC, three-digit SIC divided by size quartiles of Albuquerque (2009), FIC-400, and FIC-500 developed by Hoberg and Phillips (2010), respectively.[18] In all specifications, the interaction term between peer performance and common ownership measure is negative and statistically significant. Compensation ratio tests presented in Panel B corroborate the finding: the use of RPE is positively correlated with common ownership. The significance levels presented with the alternative industry definitions are stronger than the baseline results with four-digit SIC code. The differences in the statistical significance could be interpreted as a result of the fact that the alternative industry definitions are more representative of product market competitors. As the majority of firms in ExecuComp operate in multiple segments, using three-digit SIC code might reduce the likelihood of assigning firms in incorrect industries. In addition, the size-split SIC codes and the text-based FIC are shown to have better capacities in detecting RPE according to Albuquerque (2009) and Jayaraman, Milbourn, and Seo (2015), which might explain the improvement of statistical significance in the table.

I further test the effects of common ownership on pay-performance elasticities after controlling for other factors such as industry concentration as used in Aggarwal and Samwick (1999a), idiosyncratic risk in Holmström and Milgrom (1987) and Aggarwal and Samwick (1999b), and stock return beta with respect to industry portfolio in Holmström and Milgrom (1987) and Aggarwal and Samwick (1999b).[19] Specifically, I add more interaction terms, assuming that the pay-performance elasticities αjt and βjt in Equation (2) are given by

αjt = α0 + α1F(MHHIDjt) + α2F(HHIjt) + α3Idiosyncratic Riskjt + α4Betajt (8) βjt = β0 + β1F(MHHIDjt) + β2F(HHIjt) + β3Idiosyncratic Riskjt + β4Betajt. (9)

Columns (1) through (5) of Table 4 confirm the evidences that the use of RPE is positively associated with common ownership for four-digit SIC, three-digit SIC, size-split four-digit SIC, FIC-400, and FIC-500 industry definitions, respectively. The coefficients on the interaction term between peer performance and common ownership remain negative, which is corroborated with compensation ratio test. The coefficients on peer performance interacted with control variables imply that the pay-performance elasticity to peer performance is negatively related to idiosyncratic risk and stock return beta. It is notable that industry concentration is no longer statistically significant as in Aggarwal and Samwick (1999a), which imply that the effects of common ownership might be more crucial factor in executive pay than the strategic incentives from competition in industry.[20] The finding is further confirmed with compensation ratio tests presented in Panel B, which are evaluated at the median: F(MHHID) = 0.5,F(HHI) = 0.5,Idiosyncratic Risk = 0.013,Beta = 0.95).

One remaining concern is whether total flow compensation based on grant-date fair values of equity incentive plans is appropriate to estimate the change in compensation in response to performance. As Panel E in Table 6 points out, a majority of equity-based RPE awards rewards future performance such as three-year stock returns since granted.[21] Thus, the value of equity awards in TDC1 might not measure rewards for performance but reflect the changes in executive equity portfolios to keep their incentives (Core and Guay, 1999). To address these concerns, I use non-equity incentive awards that are reported since 2006. Non-equity incentive plan compensation could be useful to detect pay-performance elasticities because it is reported if performance conditions are satisfied.[22] Because 29% of executives do not receive non-equity incentive awards, I estimate a Tobit regression model with one-sided limit at zero.

Table 5 reports the results on non-equity incentive award. The results are broadly consistent with the previous findings. Columns (1) through (3) report the results with four-digit SIC, three-digit SIC, and size-split four-digit SIC codes.[23] All three specifications present evidences for the strong positive correlation between the use of RPE and common ownership: the pay-performance elasticity to peer performance is negatively correlated with common ownership. Compensation ratio tests reported in Panel B confirm the previous findings. The statistical significance of the results with non-equity incentive awards exceeds that with total flow compensation. This difference might be due to the fact that non-equity incentive awards are more representative of pay for performance. In columns (4) through (6), I confirm the findings by running the panel regressions with the logarithm of non-equity incentive plan compensation as the dependent variable with four-digit SIC, three-digit SIC, and size-split four-digit SIC codes. Hence, the results presented in columns (1) through (3) are not driven by whether executives receive non-equity incentive plan compensation but driven by how the level of non-equity incentive changes with performance.

In sum, the results from the implicit approach suggest that the use of RPE is positively related to common ownership. The results are robust with respect to different industry definitions and compensation measures. I also test if the effects of common ownership are present after controlling for other determinants of pay-performance elasticities such as industry concentration, idiosyncratic risk, or stock beta with respect to industry portfolio. The finding suggests that compensation is not the mechanism between common ownership and decreased product market competition. Despite the convincing results, there are concerns about the implicit approach. As Bebchuk and Fried (2004) suggest, incentives from compensation work only if executives recognize the incentives. Thus, the “implied” incentives from realized pay might not be as efficient as contractual details which executives have a full understanding of. Another concern is the endogeneity problem between compensation and performance (Frydman and Jenter, 2010); the correlation between executive pay and performance may appear because performance affects compensation through incentive plan, or because compensation affects performance through executive decision-making. Thus, even if I control for the endogeneity of common ownership measure, the resulting outcome does not necessarily imply that firms under common ownership are more likely to use contracts that give more or less incentives to compete. To address these concerns, I study the effects of common ownership on the incentive awards explicitly disclosed in proxy disclosures.

4 Explicit Approach

In this section, I present my main results with the explicit approach. I document that common ownership is positively associated with the use of RPE and the size of awards tied with RPE. In addition, I test how common ownership is related to performance benchmarks with which firms compare their performance. Specifically, I find that common ownership is positively associated with the use of industry index. These findings corroborate the previous results in the implicit approach.

4.1 Data

RPE from Proxy Disclosures

Since the introduction of Compensation Discussion and Analysis in 2006, firms have been required to provide details on performance-based awards. For the incentive awards evaluated with performance benchmarking, the firms disclose performance benchmarks, metrics, and targets that are used to determine the size and vesting conditions of awards. To test the effects of common ownership on the use of RPE, I collect the executive contract details of single-segment firms in 2014. I try to avoid potential measurement errors in common ownership index, which would arise from including multiple-segment firms by restricting the sample to single-segment firms. I begin with 1,297 single-segment firms that have at least 100 million in total assets in Compustat for fiscal year 2014. Among 1,297 firms, I drop 256 firms that are classified as foreign private issuers.[24] The sample selection process leaves 1,041 firms.

I collect the details of incentive awards tied with RPE from Compensation Discussion and Analysis section of proxy statements. A firm is classified as an RPE firm if it discloses three conditions of RPE: performance metrics, performance benchmark, and size of awards. Examples of performance metrics include stock return (total shareholder return) or accounting measures such as revenue or profit margins. For performance benchmarks, I classify it in three kinds: market index such as S&P 500 Index or Russell 1000 Index, industry index such as S&P Health Care Index or MSCI US REIT Index, and custom peer group consisting of firms selected by the compensation committee. Lastly, RPE firms disclose the size of awards evaluated with RPE.

Because firms disclose plan-based awards for each executive, I collect the executive-level size of incentive awards evaluated with performance benchmarking. Similar to De Angelis and Grinstein (2011), I calculate the proportion of the RPE awards to the total value of plan-based awards by using Grants of Plan-based Awards Table.

Table 6 reports descriptive statistics of executive contract details. There are interesting differences from Gong, Li, and Shin (2011) who report similar descriptive statistics on RPE based on S&P 1500 firms’ proxy disclosures in 2006. While the proportion of RPE firms seems to decrease from 25% in Gong, Li, and Shin (2011) to 23%, 171 firms out of 464 S&P 1500 firms in my sample (37%) use RPE. So RPE usage has become more widespread in relatively bigger firms compared to 2006. Another difference is the performance metrics. Stock return is much more predominantly used in my sample than Gong, Li, and Shin (2011) in which 74% of firms use stock return as a performance metric, justifying my previous choice of stock return as a measure of performance in the implicit test. On average, the executive officers in RPE firms receive 30% of plan-based awards as RPE awards. Thus, if a firm uses RPE, then a significant portion of plan-based awards is evaluated with performance benchmarking.

4.2 Empirical Specification

To examine the determinants of firms’ decision to use RPE, I run the following multinomial logistic regression on the lagged independent variables:[25]

logit(Pr(RPEjt = 1)) = β0 + β1F (Common Ownershipj,t−1) + βControl Varsj,t−1 + εjt, (10)

where the dependent variable RPEjt is an indicator variable that equals one for RPE firms. I control for potential determinants of RPE use: industry concentration with HHI (Aggarwal and Samwick, 1999a); common risk with R-squared from regressing stock returns on the return on industry portfolio over previous 36 months (Gong, Li, and Shin, 2011); growth options with market-to-book value of assets (Core and Guay, 1999; Albuquerque, 2014); firm size with the logarithm of sales (Rosen, 1982); relative performance with stock return relative to peer return and return on assets (ROA) relative to peer firm’s ROA; CEO’s hedging ability with CEO wealth and age (Garvey and Milbourn, 2003); and corporate governance measures such as CEO interlock dummy that equals one if CEO discloses an interlocking position, CEO-chair dummy that equals one if CEO is the chairman of the board, total institutional ownership, the use of compensation consultant, board size, and the proportion of independent directors in the board.

To further estimate the effects of common ownership on the proportion of awards tied with RPE, I use the Tobit model with two-sided limits on zero and one. This model choice is appropriate because only 27% of executive officers receive incentive awards tied with RPE. Thus, I evaluate the Tobit regressions:

RPE Sizeijt = β0 + β1F (Common Ownershipj,t−1) + βControl Varsj,t−1 + εijt, (11)

where RPE Sizeijt is the RPE size of executive i in firm j with control variables included in equation (10). Similar to the panel regressions in the implicit approach, I cluster all errors at the firm-level.

Table 7 reports summary statistics on the firms that have all corresponding independent variables. RPE firms significantly differ from non-RPE firms in may attributes. RPE firms have higher common ownership than non-RPE firms, which support the previous results in the implicit approach. Other than common ownership, RPE firms have lower industry concentration, greater common risk with peer firms, less growth options indicated by lower market-to-book ratio, bigger size, and better corporate governance qualities implied by more use of compensation consultants, bigger and more independent board members.

4.3 Results

Table 8 reports the result from estimating the logistic regression. In column (1), I use four-digit SIC code to define industries. The coefficient on common ownership is positive and statistically significant. This finding supports the previous results from the implicit test: firms in the industries with higher common ownership are more likely to use RPE. For a quantitative analysis, the probability of using RPE increases by 21.87% if a firm moves from the industry with the lowest common ownership to the highest. Given that 23.15% of firms use RPE, such an increase in probability is quantitatively significant. Columns (2) through (5) present the results with three-digit SIC, size-split four-digit SIC, FIC-400, and FIC-500 industry definitions. While the statistical significance in each empirical specifications differs, the results consistently suggest that the probability of using incentive awards tied with RPE is positively associated with common ownership.

Table 9 shows that the size of awards tied with RPE increases with common ownership. Columns (1) through (3) present the estimates from the Tobit model of equation (11) with four-digit SIC, three-digit SIC, and size-split four-digit SIC codes, respectively.[26] The positive coefficients on common ownership imply that the size of RPE awards increases in common ownership regardless of industry definitions. Because common ownership is positively associated with the use of RPE, one may argue that the above results could entirely come from the decision to use RPE, not from the change in the award size within RPE firms. To test this argument, I run the OLS regressions of equation (11) in the subsample with RPE firms. Columns (4) through (6) report the OLS estimates with four-digit SIC, three-digit SIC, and size-split four-digit SIC codes. The positive coefficients on common ownership imply that the size of RPE awards increases with common ownership in the subsample with RPE firms, implying that the previous results with the Tobit model do not come from the decision to use RPE.

The previous findings suggest that both the likelihood of using RPE and the size of awards tied with RPE increase with common ownership. However, inappropriately designed RPE — specifically, using too “broad” performance benchmarks such as S&P 500 — would have a negligible impact on industry competition. To address this concern, I study how 223 RPE firms in my sample choose performance benchmarks from three categories: market index, industry-specific index, and custom index. In doing so, I estimate the multivariate Probit model, which takes into account the possibility that firms may choose multiple benchmarks and that the selection can be correlated.[27]

Table 10 reports the results on the multivariate Probit model. First, the significant correlations among residual errors reported in Panel B justify the use of the multivariate Probit regressions. These negative correlations are due to the fact that most RPE firms use one performance benchmark even though they can choose multiple benchmarks. The coefficient on common ownership reported in column (1) of Panel A show that common ownership is positively associated with using industry index in RPE awards.

The previous result could be interpreted that firms in industries with higher common ownership use more efficient form of RPE. (Holmström and Milgrom, 1987) predict that the contracting efficiency of RPE increases if the noise in own performance measure becomes more correlated to performance benchmarks. In this sense, more use of industry index over market index under common ownership could be interpreted as more efficient choice of benchmarks. In addition, firms under common ownership are more likely to use industry index than custom group of firms. This result could be due to the fact that the use of industry index is less susceptible to opportunistic choice of peers. Agency theory predicts that if a powerful CEO can influence the selection of performance benchmarks, then it could lead to the performance peer selection that justifies higher pay (Dikolli et al., 2016). This prediction is consistent with the empirical evidence presented in Gong, Li, and Shin (2011). Thus, firms in industries with higher common ownership use performance benchmarks that are informative about industry-specific shocks and that are less subject to the opportunistic manipulation.

5 Influence of Institutional Investors with Common Ownership 5.1 The Influence of Common Owners on RPE

The findings from both implicit and explicit approaches imply that the use of RPE awards increases with common ownership. These results reject the hypothesis that firms under common ownership use less RPE in order to reduce competition. To explain these findings, I hypothesize that the presence of large, diversified institutional shareholders improve corporate governance, which consequently leads to greater use of RPE — thus, less pay for luck. Under this hypothesis, the observe effects from common ownership measure can due to the fact that MHHID measures the overall holdings by common owners at the industry-level. If this is true, I expect to have more statistically significant effects by estimating the influence of ownership by common owners defined at the firm-level. In this section, I test this hypothesis by constructing a measure of ownership by the top 5 common owners. Specifically, I use the beginning-of-year membership to S&P Composite 1500 Index as an instrumental variable to establish the causal link between the institutional ownership of common owners and the use of RPE in the Probit model.

There are various reasons why institutional investors with common ownership have a strong role in corporate governance. A huge portion of common ownership is created by large, diversified institutional investors. These investors often have separate corporate governance division with explicitly disclosed governance initiatives. Among the governance initiatives, executive compensation — especially proper alignment of pay and performance — is one of the main concerns for institutional investors. For example, Vanguard explicitly states that “incentives should be structured to reward relative outperformance” rather than to reward “a general rise in stock prices or other market-wide trends”.[28] In addition, the alignment of pay and relative performance is emphasized by ISS to which many institutional investors delegate proxy voting.[29] Such high interest in executive compensation by common owners In addition, institutional investors with common ownership could enhance incentives of directors, because making decisions against these institutional investors would jeopardize future opportunities to serve as directors in other firms. Lastly, common ownership can enhance corporate governance because (i) common ownership intensifies the price drop when investors sell shares due to increased adverse selection problem and (ii) common ownership increases the expected benefit from governance intervention by allowing the investors to keep well-governed firms upon liquidity shock, which is shown by Edmans, Levit, and Reilly (2016).

To test the influence of common owners, I construct the institutional ownership of the top 5 common owners based on four-digit SIC code.[30] In order to identify the top 5 common owners, I construct the MHHIDl for each institutional investor l by computing

N γlj βlk MHHIDl = sjsk γ β .

j=1k̸=j i ij ij

The sum of each institutional investor’s MHHIDl equals the original M H H I D so that MHHIDl could be interpreted as the contribution of the investor l to the common ownership measure. Given {MHHIDl}l for each industry-quarter pair, I construct the list of the top 5 common owners in the order of MHHIDl. For example, Fidelity, Wellington, Blackrock, Price T. Rowe, and Capital Group were the top 5 common ownership in scheduled air transportation industry (SIC: 4512) by the end of 2013. I construct the institutional ownership of common owners as the sum of the ownership of the top 5 common owners.[31] In order to compare the influence of common owners with that of institutional investors without common ownership, I construct the ownership by the top 5 institutional investors with MHHIDl = 0. Table 7 presents the summary statistics on these measures and shows that the top 5 common owners (the top 5 institutional investors without common ownership) own 11.1% (10.6%) of shares outstanding. With the similar level of ownership, if those two types of institutional investors have similar capabilities to influence compensation, then the estimates would be similar.

Column (1) of Table 11 estimates equation (10) with the Probit model as the baseline. The result indicates that total institutional ownership has positive influence on RPE use but it is not statistically significant. Column (2) replaces total institutional ownership with the ownership of top 5 common owners. The result indicates that the holdings of the top 5 common owners are positively associated with the RPE use. Both statistical significance and size of the effect are much bigger than the empirical specification with total institutional ownership. Considering that total institutional ownership measures the overall influence by institutional investors, the result implies that common owners have a stronger influence on pay than other types of institutional investors. Column (3) shows that the ownership by the top 5 institutional investors without common ownership is negatively associated with RPE use. This result is sharply contrasting to the previous result with the top 5 common owners. This difference might be due to the fact that institutional investors without common ownership are generally much smaller than common ownership in assets under management, thus forced to have focused portfolios. The small size might not give enough resources to exert enough influence on compensation, which could lead to less use of RPE.

An important caveat in estimating the influence of common owners at the firm level is endogeneity. The ownership by the common owners might be correlated with uncontrolled confounding factors such as the ownership by non-institutional blockholders. The presence of non-institutional blockholders could lead to a reduction in stock liquidity, which lowers the ownership by common owners. Uncontrolled corporate governance qualities, which would increase the ownership by common owners and the use of RPE at the same time, could also bias the estimates from the logistic regressions.

To address endogeneity concerns, I use the beginning-of-year inclusion of S&P 1500 index as an instrumental variable.[32] The inclusion of S&P 1500 increases the ownership by quasiindexers because indexation forces them to invest in the constituents of S&P 1500. Because quasi-indexers are the main source of common ownership, using the membership to S&P 1500 or other indices satisfies the relevance condition. This claim is further verified by the first-stage estimate presented in column (4) of Table 11. The inclusion of S&P 1500 increases the ownership of common owners by 3%, and this change is statistically significant at the 1% level.[33]

The key assumption for the IV approach is that after controlling for firm size — that is the main factor for a firm’s index membership — the addition to S&P 1500 does not directly affect the use of RPE except through the ownership by the top 5 common owners. Given Standard & Poor’s disclaimer on index assignments, “inclusion of a security within an index is not a recommendation by S&P” for investment purposes, using S&P 1500 membership enables me to reduce biases from uncontrolled factors related to corporate governance qualities or measurement error.[34] Unfortunately, the instrumental variable would be still subject to the endogeneity coming from omitted variables such as the presence of institutional investors without common ownership or non-executive blockholders who have enough shares to influence pay could be problematic.

Fortunately, those factors are likely to bias the coefficient on the ownership by common owners downward. First, although the quasi-indexers would be the most sensitive to the inclusion of S&P 1500, other types of investors would also positively react due to the increased coverage as well as the fund managers’ incentives to benchmark S&P sub-indices. However, column (3) suggests the negative correlation between investors without common ownership and RPE use. Overall, the existence of the investors without common ownership would bias the influence of common owners downward. The opposite logic would indicate that the presence of non-institutional blockholders will cause downward bias on the effects of common owners. Since the firms in S&P 1500 are bigger than excluded firms, non-institutional investors would have more difficulties to form a block at the first place. On the other hand, if the presence of non-executive blockholder would increase the use of RPE to prevent pay for luck.

Considering the effects of omitted variables, the two-stage Probit model is likely to underestimate the influence of common owners. Column (5) presents the results in the second-stage of the instrumental variable Probit model. The coefficient on common owners’ ownership is still positive and statistically significant. Notably, the coefficients become significantly bigger than that reported in column (2). This difference could be due to attenuation bias by measurement errors. Another explanation would be the previous downward bias by different types of blockholders. As non-indexed fund managers could benchmark S&P indices, there is a positve correlation with common owners. Also, the increase in institutional ownership would be associated with lower likelihood of non-institutional blockholding due to decreased free float shares. Altogether, the result in column (2) suffers from the downward bias as well as the result from the two-stage Probit model. Hence, the difference between columns (2) and (5) could be interpreted that the instrumental variable partially mitigates the omitted variable problems.

5.2 The Influence of Common Owners on CEO’s Luck

In this section, I provide additional evidences to support that large institutional investors with common ownership have a strong influence on compensation compared to other types of institutional investors. Specifically, I examine how different types of institutional investors influence CEO’s “lucky” option grants, defined as the opportunistic stock option grants at the lowest price of the month. In doing so, I use the CEO luck database developed by Bebchuk, Grinstein, and Peyer (2010). Motivated with the option backdating scandal, Bebchuk, Grinstein, and Peyer (2010) provide a measure of CEO’s luck from 1996 to 2005, defined as stock option grants at the lowest price of the month. The CEO’s luck defined as above could be deemed as a direct measure of pay for luck whereas the RPE use is an indirect measure to see if compensation depends on industry performance.

Following their analysis, I estimate the following logistic regression model:

logit(Pr(CEO Luckit = 1)) = β0 + β1Institutional Ownershipit + βControl Variablesit + εit

where CEO Luckit equals one if the CEO of firm i receives stock option at the lowest stock price of the month and Institutional Ownershipit is the different measures of institutional ownership that are used in the previous section. Control variables include common ownership; industry concentration; the size of board; the proportion of independent directors in the board; the proportion of independent directors in the compensation committee; CEO-chair dummy that equals one if CEO assumes the role of chairperson in the board; firm size as the logarithm of annual sales; CEO ownership; the logarithm of CEO tenure; new economy dummy defined in Murphy (2003) that equals one if a firm is in the following four-digit SIC industries: 3570, 3571, 3572, 3576, 3577, 3661, 3674, 4812, 4813, 5045, 5961, 7370, 7371, 7372, and 7373; the dummy for the adoption of the Sarbanes-Oxley Act (SOX) which equals one if the fiscal year is greater than 2002; and the director luck dummy which equals one if some independent directors of firm i receives stock option at the lowest price of the month in the fiscal year.[35]

Table 12 report the result on the influence of institutional investors on CEO luck. Columns (1) through (3) show that the ownership by the top 5 common owners is negatively correlated CEO luck while other types of institutional ownership — total institutional ownership and the ownership by the top 5 institutional investors without common ownership — are not significant. Finally, column (4) includes all measures of institutional holdings and show that the strong negative correlation between CEO luck and the ownership of the top 5 common owners survive in the specification. This result is consistent with the previous finding about the influence of common owners on the use of RPE. The large, diversified institutional investors with common ownership exert a much stronger influence on executive compensation than other types of institutional investors, specifically investors without common ownership.

6 Conclusion

In this paper, I analyze the effects of common ownership on executive compensation, especially on the use of RPE. In doing so, I use two approaches. First, I study how payperformance elasticities to own and peer firms’ performance change with common ownership. Then, I examine how common ownership influences contractual details of incentive awards tied with RPE disclosed in proxy statements. Both approaches show that the use of RPE increases with common ownership. In the implicit approach, executives under common ownership receive more rewards when their firm outperforms its rival firms. In the explicit approach, the likelihood of using RPE is positively associated with common ownership. The findings are robust to various concerns on the study of RPE such as the sensitivity to alternative industry definitions and different compensation measures. At face value, the findings indicate that compensation is not the channel between common ownership and anti-competitive outcomes in product market.

At a broader scale, the results insinuate that large, diversified institutional investors with common ownership have a strong influence on executive compensation. To investigate this issue more accurately, I measure individual institutional investors’ contribution to common ownership and list the top 5 common owners in each industry. It turns out that the ownership by the top 5 common owners has a strong influence on the RPE use. I show that the previous finding might not be a mere correlation by using the beginning-of-year inclusion to S&P 1500 as an instrumental variable. I provide additional evidences on the influence of common owners by using the CEO luck database developed by Bebchuk, Grinstein, and Peyer (2010). I find that common owners are negatively associated with “lucky” stock option awards that are granted at the lowest price of the month. Compared with total institutional ownership or the ownership by the top 5 institutional investors without common ownership, the ownership by the top 5 common owners is a much stronger indicator for the use of RPE as well as less CEO luck.

While this paper focuses on the influence on executive compensation, institutional investors with common ownership may provide a strong monitoring in other realms of corporate governance. Most closely related to my paper, it will be important to examine how institutional investors with common ownership influence executive retention decisions, which are shown to be affected by industry performance (Jenter and Kanaan, 2015). In addition, how these common owners influence governance, other than direct communication, would be an important question. I leave the exploration of these additional corporate governance role by common owners to future research.

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[1] For example, Azar (2012) argues the trilemma of achieving shareholder diversification, shareholder value maximization, and product market competition. See also Reynolds and Snapp (1986), Bresnahan and Salop (1986), O’Brien and Salop (2000), and Gilo (2000) who theoretically show these anti-competitive incentives from common ownership.

[2] See Gong, Li, and Shin (2011), De Angelis and Grinstein (2011), and De Angelis and Grinstein (2016) for the analysis of RPE use from 2006 proxy disclosures.

[3] Usually, the additional firms include previous constituents of S&P 1500 that are still trading in stock market.

[4] By using flow compensation, I cannot draw implications on the overall incentives implied by portfolio held by executives.

[5] For example, future performance conditions such as 3-year stock return since the grant date are frequently used as vesting conditions for performance-based restricted share units.

[6] Guenther and Rosman (1994) and Kahle and Walkling (1996) report significant differences in assigning SIC codes by Compustat and CRSP in terms of procedure and data source, which justifies the use of Compustat SIC codes to keep consistency of industries assigned to firms. However, using CRSP SIC codes with or without replacing missing SIC codes with Compustat does not qualitatively change the main results.

[7] Size-split industry definitions with two-digit and three-digit SIC codes result in qualitatively similar results.

[8] Using the return on equal-weighted portfolio yields qualitatively similar results.

[9] Baker and Hall (2004) give some examples of both cases. For example, whether to buy a corporate jet or sell non-core assets can be deemed as the decision that influences shareholder value in dollars. On the other hand, reorganizing firm or developing corporate strategies could be considered as the action that changes shareholder return.

[10] When I consider performance metrics in performance-based awards (PBA), 268 out of 850 firms (31.5%) that explicitly use PBA use stock return as performance metrics. Restricting the sample to financial firms, 78 out of 139 PBA firms in financial sector (56.1%) use stock return to evaluate executive performance, implying that financial institutions use stock return as performance metrics more often than other firms.

[11] See ISS’s US executive compensation policies: https://www.issgovernance.com/file/policy/us- executive-compensation-policies-faq-16-march-2016.pdf (Accessed October 26, 2016)

[12] The 13F database give institution-level holdings. Thus, the estimated common ownership measure is based on the assumption that investment companies aggregate each fund’s voting power at the fund family level, which is reported by Davis and Kim (2007).

[13] Constructing MHHID without this restriction does not qualitatively change the results.

[14] Using sample cumulative distribution function serves as a normalization of common ownership measure to make easy interpretation of regression results. The results are qualitatively unchanged if I use common ownership measure.

[15] Following Albuquerque (2006), I use the date at which an executive became CEO to identify CEOs in each fiscal year. I drop observations with CEO tenure — the difference between the date an executive became CEO and the data date as in the number of years — less than one. Doing so excludes observations without full-year service.

[16] I use the logarithm of tenure to account for potential diminishing gains from experience as measured by tenure. If the date which an executive joins the firm is missing, then the number of prior years that the executive appears in ExecuComp is used as in Carter, Franco, and Giné (2016). Using CEO tenure as in Albuquerque (2009) does not change the results.

[17] Using individual fixed effects has two potential caveats. Given the short panel length of each executive, the estimation could lose some statistical power. Indeed, untabulated results with firm fixed effects that make the panel longer deliver more significant results than those reported in the paper. Second, it is possible that the estimated coefficients purely come from time-series variations rather than cross-sectional variations in common ownership. Untabulated results without individual fixed effects as in Anton et al. (2016) with my specification deliver qualitatively similar results: common ownership increases RPE.

[18] Untabulated results with FIC-100, FIC-200, and FIC-300 give qualitatively similar results to FIC-400 and FIC-500: peer pay-performance elasticity decreases in common ownership at the 5% significance level. Results with FIC-25 and FIC-50 retain the sign on the interaction term between peer performance and common ownership while losing significance level , possibly due to too coarse industry definition.

[19] I construct stock return beta by regressing individual stock return on the return on value-weighted industry portfolio over the previous 36 months.

[20] Note, however, that my results are not readily comparable with Aggarwal and Samwick (1999a) due to the huge differences in the sample as well as empirical specifications.

[21] De Angelis and Grinstein (2016) also give similar evidences from 2006 proxy disclosures. Only 17% of S&P 500 firms use one-year performance horizon.

[22] I don’t include bonus because it is likely to be a discretionary award. Using the sum of non-equity incentive awards and bonus provide qualitatively similar results.

[23] The results with FIC-400 and FIC-500 are omitted for the sake of brevity. Using FIC industry specifi- cations yields qualitatively similar results.

[24] Foreign firms are not required to file proxy statement and disclose executive compensation as detailed as US firms do in the Compensation Discussion and Analysis disclosures.

[25] The use of the lagged independent variables is appropriate because performance-based incentive pay plans are tax deductible only if performance goals are “established in writing by the compensation committee not later than 90 days after the commencement of the period of service” according to section 162(m) of the Internal Revenue Code.

[26] Untabulated results with FIC-400 and FIC-500 give qualitatively similar results.

[27] This model choice differentiates from the multiple-equation multinomial Probit model in which residual errors are assumed to be uncorrelated.

[28] See Vanguard’s executive compensation principles: https://about.vanguard.com/vanguard-proxy- voting/corporate-governance/index.html (Accessed September 29, 2016)

[29] ISS explicitly states that “This relative measure compares the percentile ranks of a company’s CEO pay and TSR performance, relative to an industry-and-size derived comparison group” according to its US executive compensation policies: https://www.issgovernance.com/file/policy/us-executive- compensation-policies-faq-16-march-2016.pdf (Accessed September 29, 2016)

[30] The result remains qualitatively similar with respect to other industry definitions used in the paper.

[31] Using the sum of ownership by all institutions that create common ownership yields qualitatively similar conclusion. I focus on top 5 common owners to focus on the effects of institutional investors that have enough common ownership.

[32] Using the inclusion of S&P 500 as in Aghion, Van Reenen, and Zingales (2013) yields qualitatively similar outcome. However, the membership to S&P 500 gives the weak instrument problem possibly because only 10% of the sample firms are S&P 500 constituents while 44% of the sample firms are in S&P 1500.

[33] Kleibergen-Paap F-stat is 27.25, which indicates that the weak instrument is not a concern.

[34] See S&P’s general disclaimer about index assignment methodology: http://us.spindices.com/ regulatory-affairs-disclaimers/ (Accessed October 26, 2016)

[35] Some of corporate governance variables have different definitions from Bebchuk, Grinstein, and Peyer (2010) for the consistency with the previous section. For example, Bebchuk, Grinstein, and Peyer (2010) use the board independence dummy which equals one if the majority of board members are independent rather than the proportion of independent directors; the same applies to the proportion of independent directors in compensation committee as well. Using the exact variable definitions as Bebchuk, Grinstein, and Peyer (2010) does not alter the result.