The Effect of Executive-Employee Pay Disparity on Labor Productivity

Transcription

1 The Effect of Executive-Employee Pay Disparity on Labor Productivity Olubunmi Faleye College of Business Admin., Northeastern University Boston, MA, Phone: Ebru Reis Bentley University Waltham, MA, Phone: Anand Venkateswaran College of Business Admin., Northeastern University Boston, MA, Phone: Abstract We study the impact of disparities in compensation between top executives and lower level employees on firm productivity. While tournament theories suggest beneficial effects for such disparities, inequity aversion models predict lower productivity stemming from dissatisfaction among lower level employees. We document three basic results. First, CEO employee pay disparity depends on the balance of power between the CEO and ordinary employees, increasing with CEO power and firm size but declining with labor force unionization and education level. Second, firm productivity is negatively related with pay disparity. Third, the reduction in productivity is greater when the labor force is more unionized while traditional corporate governance variables have no effect on this relation. Our results highlight the effects of the pay inequity that is inherent in promotion-based tournament incentives and are consistent with predictions of inequity aversion models. This version: January 15, 2010 JEL Classification: G34, G35, J33, L14, L35 Keywords: Employee Incentives, Productivity, Executive Compensation, Governance

2 The Effect of Executive-Employee Pay Disparity on Labor Productivity I. Introduction and Background The popular press has consistently raised the issue of overpaid top executives with a specific focus on the large and growing disparity in compensation between top management and rank and file employees. In a recent article, the chief compensation officer at PayScale specifically asks the question of whether CEOs are overpaid compared to rank and file workers. The author, Fred Whittlesey, notes that seemingly overpaid CEOs are paid about 250 to 500 times the average worker s pay. However, he suggests that the issue could well be misinterpreted by the media because of inappropriate metrics used to arrive at these comparisons. 1 In general, however, there seems to be a clear consensus on the importance of CEO compensation relative to the average worker. We address the issue directly and examine three related questions: (i) What are the determinants of top management compensation relative to other employees? (ii) How does the compensation ratio between top management and the average worker affect firm productivity, and (iii) Are there scenarios where these effects are more (or less) pronounced? While the study of top management incentives has received considerable attention in the academic literature, there is scant evidence on the effects of top management compensation relative to lower level employees. A large body of work that examines managerial compensation investigates the effectiveness of top management incentives in enhancing firm value. A fewer number of studies investigate the issue of overpayment of top corporate executives. The motivation for our study stems largely from how incremental compensation percolates through 1 See Fred Whittlesey s (2006) column, The great overpaid CEO debate. 1

3 the ranks of an organization. Consequently, we examine the effects of managerial compensation vis-à-vis employee compensation, on firm productivity. The economics of rank order tournaments (e.g., Lazear and Rosen (1981)) suggests that pay differentials between any two levels in a firm s hierarchy drive employees in the lower level to induce higher effort. Since the best relative performer is promoted in a rank order tournament, higher pay differentials imply a higher compensation on promotion. Thus, the pay differential with the next higher level typically represents the magnitude of the potential prize (on promotion) for the best relative performer. While higher pay-differentials between any two levels in a firm can induce higher effort levels from competitors in a tournament (or employees at the same level), there are other forces that can potentially discourage higher effort levels. Higher tournament incentives through larger pay differentials implicitly result in larger pay inequalities among employees across (and/or within) levels in an organization. Fehr and Schmidt (1999) who introduced the notion of inequity aversion suggest that agents suffer a disutility as the distribution of compensation in their firm departs from an egalitarian distribution. 2 In their model, if the wage spread is too high, an agent may choose not to participate in a tournament or may require higher compensation to account for her disutility from the possibility of losing the tournament. Bolton and Ockenfels (2000) develop this idea and argue that own relative payoff, a measure of how much a person s own pecuniary payoff compares with that of others, motivates people. At the very least, these studies imply that employees are likely to compare their pay with that of others; the weight assigned to others pay may however vary with each employee. The substance of these studies (among others in this 2 They define two sources of dissatisfaction from inequality; envy (when they earn less than the others) and empathy (when they earn more). 2

4 literature) suggests that economic agents take into account not only their own payoff, but also their payoff relative to other agents. Pay inequalities permeate successive levels in a firm and ultimately result in larger CEO-worker pay ratios. Thus, a large management-to-worker pay ratio is likely to result in some degree of dissatisfaction for lower level employees leading to lower productivity. Consider for instance the compensation of a typical top executive (or CEO). In addition to direct compensation in the form of fixed salary and bonus, she also receives equity grants. Further, her compensation includes an indirect component which stems from changes in the value of the firm-specific equity she holds. Proponents of the traditional principal-agent theories support these equity grants as a means to align managerial interests to those of the firm s shareholders. An extension but overlooked part of this argument is whether conflicts of interests percolate through the organization into its rank and file employees. In other words, could large multiples in CEO compensation over the average employee s wages lead to a conflict of interest between the CEO (top management in general) and lower level employees? Is the distribution of wages among all employees in a firm consistent with the minimization of agency problems within the firm as a whole, or does it simply take care of the incentives of top management. Arguably, the decision makers in a firm are at its helm and will affect firm value and productivity more than lower level employees. However, there is considerably heterogeneity in the relative contribution of lower-level employees to a firm s surplus. Anecdotally, the CEO s compensation relative to the average worker varies considerably by industry. This raises the question as to the relation between productivity and wage differentials between the CEO and the average worker. 3

5 In sum, there are two arguments that relate top-management to worker pay ratios and firm productivity. First, rank order tournament theory which suggests that higher pay differentials between any two successive levels in a firm s hierarchy provides greater promotionbased incentives and therefore induces a higher level of effort among the lower tier employees. On the other hand, higher pay differentials between two levels can also lead to discontent among the lower-level employees stemming from an inequity aversion or lack of fairness, potentially resulting in lower productivity. We use a sample of 3,121 firm-years and estimate three ratios based on total, short-term, and long-term compensation: (i) the ratio of the CEO s compensation to compensation per employee, (ii) the ratio of average executive compensation (excluding the CEO) to compensation per employee, and (iii) the ratio of average executive compensation (including the CEO) to compensation per employee. In these measures, compensation per employee is the average wages and salaries paid to all employees in the firm excluding the CEO and other executives reported in ExecuComp. First, we estimate the determinants of the three ratios discussed above. We find that payratio is higher for older CEOs and in larger firms. We also find that pay ratio is negatively related to the percentage of employees who are unionized. This is consistent with the argument that unions have a higher bargaining power for their members vis-à-vis management. The results are by and large similar for all three measures of pay differentials. In further tests for the determinants, we include firm level governance variables such as the board size, classified board, and percentage of outsiders on the board. These variables however, do not appear to influence pay ratio in a significant manner. 4

6 Next, we examine the effect of pay ratios on the factor productivity of firms. We find a statistically significant negative relation between pay ratios and factor productivity. Thus, higher pay ratios appear to reduce the overall productivity of firms. The results are qualitatively similar even after we replace the total compensation ratio with the short-term and long-term compensation ratio with respect to the CEO. This finding is consistent with the inequity aversion argument; rank and file employees are less productive when their pay in relation to the CEO is lower. Finally we investigate if the negative effect of compensation ratios on factor productivity is more (or less) pronounced in certain scenarios that include governance mechanisms or industry characteristics. In these tests, we find that industries with a lower than average union membership among their workers are associated with lower productivity. There appears to be no systematic difference in the effect of pay-ratios on productivity for the governance related variables. We contribute to the literature in two distinct ways. First, we identify several factors that explain CEO-worker pay differential, which has remained an issue of debate for academics, policy makers, and the media. Another contribution of our study is in highlighting the effects of the pay inequity that is inherent in promotion-based tournament incentives. The negative relation between productivity and CEO-worker pay ratios underscores the important role that inequity aversion plays in employee productivity. In the next section, we discuss our sample construction and variables used, followed by a discussion of the determinants of the CEO-worker pay gap in Section III. Results pertaining to the relation between productivity and pay ratios are in Section IV, followed by some robustness checks in Section V, and concluding remarks in the last section. 5

7 II. Data Sources, Sample Selection and Methodology A. Data Sources We obtain CEO and worker compensation data from Standard and Poor s (S&P) ExecuComp, which covers about 1,500 firms per year that are in the S&P 500, S&P Mid-Cap 400, and S&P Small-Cap 600 indices. We define a CEO as the person identified as the Chief Executive Officer of the firm in ExecuComp (CEOANN = CEO), and classify all other executives as Other Executives. Our sample period is from 1993 to 2006 and includes all firmyears that have an identifiable CEO. Our main explanatory variable of interest is the CEO to worker pay compensation ratio, CEO/Staff (Tot) defined as the ratio of the CEO s total compensation to the average employee (or staff) compensation. We obtain information on average employee compensation using total labor expenses (data item 42) and the number of employees (data item 29) in Compustat. Since it is not mandatory for firms to disclose labor expenses, we lose a significant proportion of observations obtained from ExecuComp. Consequently, our final sample consists of 3,121 firmyear observations. In computing the variable CEO/Staff, we first deduct the total direct compensation (date item TDC1) paid to all executives reported in ExecuComp from total labor expenses (data item 29), to obtain an estimate for the total compensation of all employees besides top management. We then divide the resulting amount by the number of employees to obtain the variable Perstaff, the average compensation per employee. The variable CEO/Staff (Tot) is the ratio of a firm s CEO s total compensation (TDC1) to Perstaff. We also compute CEO/Staff (ST) and CEO/Staff (LT) in an analogous manner using the CEO s short-term and long-term compensations, respectively. Similarly, we compute the variable Oth Exec / Staff 6

8 (Tot), which is the ratio of the average total compensation of Non-CEO executives to Perstaff. The variables Oth Exec / Staff (ST) and Oth Exec / Staff (LT) are constructed using the short-term and long-term components of other executives compensations, respectively. Table 1 presents summary statistics for all compensation related variables. The mean (median) value for CEO compensation in our sample is $4.63 ($2.40) million, while the corresponding values for the average worker is $59.87 thousand. Consequently, the mean (median) value of CEO/Staff (Tot) is (52.22). Thus, the average CEO in our sample earns about 95 times the average worker s pay. 3 The mean (median) pay ratio of non-ceo executives to average employee pay is considerably lower, and equals (23.05), since non-ceo executive compensation is significantly lower than CEO compensation. Our main dependent variable is firm productivity. We focus on productivity for several reasons: (i) our objective is to capture the effects of workers incentives as opposed to management incentives, (ii) higher productivity typically translates to higher shareholder value in the long run, and (iii) productivity is an often overlooked aspect of firm performance. We follow the methodology outlined in Faleye, Mehrotra, and Morck (2006) and construct our measure of firm productivity, FacProd, which assumes a Cobb-Douglas production specification for the firm s output. For each firm-year in our sample, FacProd is the residual from a regression of firm output on its principal inputs, labor and capital. Specifically, we assume that each firm's sales are generated by a Cobb-Douglas production function of the form, Y it = AL it β K it α, (1) 3 This figure is lower than Fred Whittlesey s claim that CEOs typically earn around 250 to 500 times the average worker. This, however, could be an artifact of our sample since total labor expenses include items not typically accounted for in popular discussions of CEO ordinary worker pay differentials. 7

9 Where, Y it is net sales for firm i in period t, L it is the number of employees, K it, is net property, plant, and equipment, and A, a, and β are parameters. We employ residuals from our estimation of the logarithmic transformation of (1) as a measure of firm-level total factor productivity, controlling for industry factors by estimating a separate equation for each two-digit SIC industry group. We compute several additional variables to control for differences in firm and industry characteristics. First, we follow the extant literature (e.g., Aggarwal and Samwick (2003) and Kale, Reis, and Venkateswaran (2009)) and define PPS (Pay Performance Sensitivity) as the sum of stock and option sensitivities to a $100 change in shareholders wealth, where; PPS = ((Number of shares held by the manager + delta of options * number of options held by the manager) / total number of shares outstanding) For each executive and CEO in our sample, we compute his individual values for PPS as described above. At the firm level we then define Median Team PPS as the median value PPS among all executives, for each firm-year. The mean value of Median PPS is $0.31 per $100 of shareholders equity. The median firm s executive has no equity holdings in the firm (Median Team PPS = 0). The variable LT Debt/Assets is defined as the ratio of the firm s long-term debt to total assets. We define Firm Size as the natural log of the firm s net sales for the year and Volatility as the variance of the firm s monthly stock returns over the 60-month period prior to the sample year. We define Chair is equal to one when the CEO is also the Chair of the board. 4 We use the percentage of stock ownership at the beginning of the year to obtain the stock-based sensitivity of an executive s equity portfolio. For option holdings, we follow Murphy (1999), and determine an average exercise price for all previously granted options based on their year-end intrinsic value. We treat all option holdings as a single grant with a five-year time to maturity and obtain the risk-free rate from the five-year treasury bills constant maturity series. We compute the average delta of prior option grants using the modified Black-Scholes formula. For complete details of the procedure we use, see Kale, Reis, and Venkateswaran (2009). 8

10 Seventy four percent of all CEOs in our sample also hold the position of Chair. We follow Parrino (1997) and construct the variable Industry Homogeneity to measure the similarity between firms within an industry after isolating market effects. 5 We control for competition among firms in an industry and define Concentration which is the sales-based Herfindahl index for each 2-digit SIC industry. The average age of a CEO (CEO Age) in our sample is 57 years. An important factor that potentially influences labor wages is the level of human capital in a firm/industry. To proxy for this, we measure human capital using the ratio of full-time employees who hold a Bachelors degree or higher to the total number of full-time employees in the industry in any given year. This measure is consistent with studies that use the level of education as a measure of the level of human capital in different nations. 6 The level of education is intended to capture human capital intensity as opposed to labor intensity. We obtain the data used to reflect this variable from the Demographic Profile of the Workforce in the United States section of the Bureau of Census report. Finally, to account for the bargaining power of workers vis-à-vis managers, we compute a measure of unionization of the labor force in an industry. The variable Union is the percentage of unionized members in any 2-digit industry (by year) to the total number of full-time employees in an industry. We obtain these data from the Bureau of Labor Statistics ( The average for the percentage of unionized workforce in our sample is 11%. Mining, construction, and tobacco manufacturing are among the highly unionized industries with Union in excess of 25% while industries such as agriculture and 5 First, we assign firms in the CRSP monthly returns file to their respective two-digit historical SIC industry code (obtained from Compustat data item 324 or DNUM if data 324 is missing) and then regress each firm s prior 60 monthly returns on an equally weighted monthly industry index and the market return. For each firm, we then compute the partial correlation coefficient between the firm s returns and the industry index while holding market returns constant. The variable Industry Homogeneity is the average partial correlation coefficient for all firms within an industry. We use a five-year rolling estimation period for each year in the sample. 6 See, for example, Barro and Lee (1993) and Barro (2001) 9

11 finance have among the lowest proportions of unionization (Union less than 3%). Summary statistics for all variables are presented in Table 3. Panel C of the table provides pair wise correlations between our main dependent variable, Facprod, and the different pay ratios. III. Determinants of the CEO-Staff Pay Ratios We begin our analysis with an investigation of the determinants of the manager-worker pay ratio. Table 4 presents results from regressions of CEO/Staff pay ratios using total, shortterm and long-term compensation on several firm- and industry- based factors. The first three columns in Table 4 are specifications with CEO/Staff (Tot) as the dependent variable, followed by CEO/Staff (ST) as the dependent variable in the next three columns. The dependent variable in the last three columns is CEO/Staff (LT). Within each set, we estimate three specifications; (i) OLS regressions with 2-digit SIC industry fixed effects, (ii) pooled OLS, and (iii) industry adjusted values for the dependent variable and all non-industry independent variables. 7 Standard errors reported in all our analyses are corrected for heteroskedasticity and clustered at the firm level for the pooled OLS regressions and at the industry level for specifications with industry fixed effects or with industry adjusted variables. A. CEO-Staff: Executive and Firm Characteristics First, we find that the estimates on Log (CEO Age) and Chair are positively and significantly related to CEO/Staff (Tot) and CEO/Staff (ST), in the two specifications with industry fixed effects and industry adjusted variables. This is consistent with prior studies which 7 Note that using industry fixed-effects is the equivalent of regressing the industry adjusted dependent variable on industry adjusted values for all independent variables, which effectively demeans all variables at the industry level including industry level variables, such as Concentration and Union. The last specification in each model corrects this problem, by including each non-industry variable, relative to the industry mean and industry variables as they are. 10

12 find that older CEOs and/or CEOs who hold the position of Chair are paid more. We also find a significantly positive relation between firm size and all three measures of the CEO-staff pay ratios, which is consistent with the findings documented by several prior studies (e.g., Murphy (1999)) that CEOs in larger firms are paid more. Thus, the CEO-staff pay ratio in these firms is likely to be higher. We also find a statistically significant and negative relation between Capital/Labor and the CEO-staff pay ratios. Thus, when firms are more capital intensive, the CEO-staff pay ratios are lower, implying that there are lower increases as one moves up in the firm s hierarchy. We find a positive but weak association between leverage (LTD/Assets) and pay ratios. We also find that riskier firms have higher pay ratios, but the results are statistically weak; possibly stemming from higher CEO pay in riskier firms. B. CEO-Staff: Industry Characteristics We employ four industry level explanatory variables; (i) Concentration to proxy for the level of competition among firms within an industry, (ii) Union to account for the bargaining power of the labor force vis-à-vis management, (iii) Education to capture the degree of human capital intensity, and (iv) Industry Homogeneity which measures the commonality across firms within an industry to proxy for the mobility or transfer of employee skills within the industry. We find some evidence that the proportion of unionized workers in an industry is negatively related to the CEO-staff pay ratio. The coefficient estimate on Union is negative in all specifications, but statistically significant at conventional levels only in three out of the nine specifications. We interpret higher union membership to be associated with a higher bargaining power for workers, leading to higher pay for workers, or lower CEO-staff pay ratios. The coefficient estimates on the variable Education are also negative and statistically significant in 11

13 six out of the nine models. This finding suggests that a more educated labor force commands a higher pay, and therefore results in lower CEO-staff pay ratio. We do not find robust evidence to support our conjecture that wages are lower in more competitive industries, which would lead to higher CEO-staff pay ratios. IV. Effect of CEO-Staff Pay Ratios on Firm Productivity We present findings on the relation between CEO-staff pay ratios and firm productivity in Table 5. In all these tests, the dependent variable is FacProd which is our proxy for the firm s productivity. The first and third columns in Table 5 present results with CEO-Staff (Tot) as the main independent variable. In the adjacent columns (two and four) we replace CEO-Staff (Tot), with their short-term and long-term components, CEO-Staff (ST) and CEO-Staff (LT). The first two specifications use an industry fixed-effects model, while the last two are based on industry adjusted variables for all independent variables. 8 All our specifications contain year dummies. The standard errors are adjusted for the presence of arbitrary heteroskedasticity and clustered at the 2-digit SIC industry (see Petersen (2009)). First, we find that the CEO-Staff pay ratios are negative and statistically significant in all four specifications. For instance, the estimate on CEO-Staff (Tot) in the first specification is (t-value = 4.10). Recall that we have two competing hypotheses on the relation between pay ratios and productivity; a positive relation predicted by higher promotion based incentives and a negative relation stemming from lower-level employees aversion to compensation inequities. Our finding a negative relation implies that the latter, i.e. the effect of inequity 8 Since the dependent variable, Facprod is already computed by industry, and requires no industry adjustment. For brevity we do not report results with the pooled OLS models. 12

14 aversion dominates the positive effect of higher promotion based incentives. The negative relation continues to hold in the second specification when total pay ratios are replaced by their short-term and long-term components. However, the magnitude of the coefficient estimate on the short-term pay ratio (-0.604) is considerably higher than the corresponding estimate for the longterm pay ratio (-0.166), which suggests that productivity is more sensitive to inequities in shortterm pay. We find very similar results in the next two specifications, which use industry-adjusted variables. These findings imply that productivity suffers as the disparity in short-term pay between the CEO and rank and file employees increases. Short-term pay mainly comprises of salary and bonus, while long-term pay is generally in the form of equity based compensation. Since the typical compensation for an average employee is largely made up of salary and bonus, inequities stemming from these components are more likely to induce disutility among workers. In contrast, few non-executive employees receive equity grants. Thus, they appear to place a lower weight on disparity in long-term compensation. Our specification also includes the pay-performance sensitivities of the top management team. However, we find no significant relation between Team PPS and FacProd. We include several additional firm- and industry- level control variables in these regressions. We find that productivity is higher in industries with higher similarity among firms (positive coefficient on Industry Homogeneity) and in larger firms. In homogeneous industries, it is easier form employees to transfer their skills across firms within the industry and this induces them to invest more in their skills, since there are more employment opportunities for them. We also find the productivity decreases as the proportion of unionized workers increases (coefficient of Union is negative in all specifications). This is consistent with unions using their bargaining power to 13

15 extract greater leisure or otherwise hold up the employer as argued by Baldwin (1983). We do not find any significant evidence relating the other variables such as leverage, human capital intensity, R&D intensity and industry concentration. Next, we investigate if the effect of the CEO-Staff pay ratios on productivity is either more or less pronounced under certain scenarios and present results in Table 6. For these tests, we interact the CEO-Staff (Tot) variable with a set of industry specific indicator variables and a set of governance related variables. The industry specific indicator variables include UnionD, which equals one if Union is above the median value for Union, and zero otherwise. Similarly, we construct the indicator variables ConcentrationD, and EducationD, based on the median sample values for Concentration and Education, respectively. We then use an OLS specification to regress productivity on total compensation based pay ratios and these interactions. The first column in Table 6 includes the interaction of CEO/Staff (Tot) with UnionD. The next two columns include interactions terms with EducationD and ConcentrationD, respectively. The next three columns are similar regressions but with interaction terms that include three firm specific governance variables: Chair, OutsideD, and BoardSizeD. Here, OutsideD is an indicator variable that equal one if the proportion of outside directors is greater than the median, zero otherwise and BoardSizeD is an indicator variable which equals one if the number of board members is greater than 11 and zero otherwise. The coefficient estimate on the interaction term in the first column in Table 6 is negative (-0.558) and statistically significant. This implies that there is a different in the effect of the pay ratio on productivity for the two sub-samples productivity decreases with unionization is more for highly unionized industries than for productivity decreases associated with less unionized 14

16 industries. Further, since the coefficient on CEO-Staff (Tot) (-0.097) is not statistically significant, it appears that the decline in productivity with unionization occurs only above a threshold level of unionization. For industries with the proportion of unionized workers below the median proportion of 2%, there is no significant relation between unionization and productivity. We find no evidence of any differences in the effect of pay ratios on productivity for any of the remaining five sub-samples, although the coefficient estimate on CEO/Staff (Tot) is statistically significant in four out of these five regressions. For instance, there is no difference on the effect of pay ratios on productivity between firms with larger and smaller boards (interaction term with BoardSizeD is not significant in column 5). The signs on the coefficient estimates on the remaining variables are generally consistent with the results discussed previously in Table 5. V. Robustness and other tests We repeat the analysis in Table 4 for the determinants of the pay ratio by replacing the CEO/Staff (Tot) variable with OthExec/Staff (Tot). These findings are presented in Table 7. The results are by and large similar to our findings with CEO/Staff (Tot) as the dependent variable. Larger firms have higher pay disparities, while each of labor force education level and unionization relates negatively to pay ratios. Also, pay differentials tend to be smaller in capital intensive firms. Next, we re-examine the effects of pay ratios on productivity (presented in Table 5) using disparity in total compensation between other top executives and non-executive employees and 15

17 report these results in Table 8. In the first two columns, we use an industry fixed-effects model, followed by OLS specifications with industry-adjusted independent variables in the next two columns. The dependent variable in the first specification in each set is the ratio of average other executive compensation to average compensation (Oth.Exec/Staff (Tot)), and the dependent variable in the second column of each set is the average total top management team compensation (including the CEO) and average staff compensation (All Exec/Staff (Tot)). The results are in line with all our earlier findings; the negative and statistically significant signs in all four specifications offer further support for the inequity aversion argument. Workers appear to reduce effort and lower productivity when they face large income disparities not just with their CEO, but with other top executives. As before, the equity ownership of top managers, which is reflected in the variable Team PPS, appears to have no significant impact on firm productivity. The effect of the remaining variable is similar to results documented earlier. Finally, we re-estimate whether the effect of other executives and all executives pay ratios are either more or less pronounced under certain industry- and governance- related scenarios. These results are presented in Tables 9 (with other executives) and in Table 10 (with all executives). As with the CEO-Staff pay ratio, the only scenario with a significant difference between the two sub-samples with respect to their effect on firm productivity is the degree of unionization; productivity is not significantly affected by unionization below the median level, but decreases with the level of unionization for industries with higher than average levels of unionization. 16

18 VI. Summary and conclusion The differential in pay between top executives and lower level employees has been the subject of intense debate and commentaries in recent times. While the popular press fixates on the apparent unfairness of CEO compensation that is several times rank and file pay, economic theory suggests that such differential can have positive or negative effects on firm productivity depending on whether tournament incentives or inequity aversion dominates employee actions. Our results suggest that the pay differential between the CEO and ordinary employees is largely dependent on the balance of power between the two parties. The differential is greater when the CEO is more powerful and lower when employees are unionized or highly educated. Furthermore, we find a negative relation between pay differential and firm productivity, with the decline in productivity greater when employees are mostly unionized. Although tournament incentives can be strong and beneficial to the firm, our results illustrate the potential problems that can arise when the competing group is large and the probability of promotion is minuscule for each individual. Ultimately, such a system can encourage feelings of inequity among employees, leading to less effort and poorer productivity. 17

19 References Aggarwal, Rajesh K., and Andrew A. Samwick, 2003, Performance incentives within firms: The effect of managerial responsibility, Journal of Finance 58, Baldwin, Carliss Y., Productivity and Labor Unions: An Application of the Theory of Self- Enforcing Contracts. Journal of Business 56, Barro, Robert J, 2001, Human Capital and Growth, American Economic Review 88, Barro, Robert J, and J W Lee, 1993, International Comparisons of Educational Attainment, Journal of Monetary Economics 32, Faleye, Olubunmi, Vikas Mehrotra, and Randall Morck, 2006, When labor has a voice in corporate governance, Journal of Financial and Quantitative Analysis 41, Lazear, Edward P., and Sherwin Rosen, 1981, Rank-order tournaments as optimum labor contracts, Journal of Political Economy 89, Parrino, Robert, 1997, CEO turnover and outside succession: A cross-sectional analysis, Journal of Financial Economics 46, Petersen, Mitchell, 2009, Estimating standard errors in finance data sets: Comparing approaches, Review of Financial Studies 22,

20 Table 1: Descriptive Statistics (Compensation and Incentives) The table presents summary statistics for all compensation related variables in Panel A. In Panel B summary statistics for incentive measures of CEO, other executives and all executives are presented. All variables are defined in the Appendix. Panel A: Compensation Related Variables N Mean Minimum Lower Quartile Median Upper Quartile Maximum Compensation Related CEO ST Comp. 3,121 1, , , , CEO LT Comp. 3,121 2, , , , CEO Total Comp 3,121 4, , , , , Labor Expense/Employee 3, Panel B: Incentives Median Team PPS 3, CEO/Staff ( ST) 3, CEO/Staff (LT) 3, CEO/Staff (Total) 3, Oth. Exec/Staff ( Total) 3, All exec./staff (Total) 3,

21 Table 2: Industry wise Distribution The table presents distribution of the main dependent and independent variables in the study by 1-digit SIC industry code. All variables are as defined in the Appendix. SIC # % Obs. In Sample % Obs in Compustat FACPROD Log SLE Median Team PPS Relative CEOTOSTAFF % 1.51% % 9.95% % 6.44% % 18.07% % 7.32% % 47.22% % 4.61% % 4.87% Total % %

22 Table 3: Descriptive Statistics (Dependent and Other Independent Variables) Panel A of the table presents summary statistics for the main dependent variables WFACPROD and WLOGSLE. Panel B reports summary statistics for all independent variables. In Panel C we provide the Spearman s correlations among the dependent and the main independent variables. All variables are as defined in the Appendix. Panel A: Dependent Variables N Mean Min Lower Quartile Median Upper Quartile Max FacProd 3, Panel B: Independent Variables LT Debt/Assets 3, R&D/Assets 3, Firm Size 3, Volatility 3, Chair 3, POutside 2, Board Size 2, Concentration 3, Industry Homogeneity 3, Staff Education 3, Union 3, CEO Age 3, Panel C: Table of Correlations FacProd LogSLE Team PPS CEO/Staff (Total) Oth.exec/Staff (Total)t All exec/staff (Total) FacProd LogSLE * Team PPS * * CEO/Staff (Total) * * Oth.exec/Staff (Total) * * * All exec/staff (Total) * * * *

23 Table 4: Determinants of the CEO Staff Compensation Ratios (Total, Short-term (ST), and Long-term) without governance variables The table presents results for the determinants of the CEO-Staff compensation ratios. The first three columns are based on the total compensation of the CEO. The next (last) three columns are based on the ST (LT) compensation ratios. The first specification in each set is estimated using an industry Fixedeffects model, followed by OLS. The third specification is estimated using industry-adjusted values for the dependent and all non-industry independent variables. All variables are as defined in the Appendix. The absolute values of the t-statistics are in parentheses and are computed based on heteroskedasticity robust standard errors. All models contain year dummies. ***, **, * represent statistical significance at the 1%, 5%, and 10% levels respectively. CEO/Staff(Tot) CEO/Staff (ST) CEO/Staff (LT) FE OLS IND_ADJ FE OLS IND_ADJ FE OLS IND_ADJ Log CEO Age ** *** *** *** (2.28) (0.86) (3.04) (2.94) (1.25) (3.24) (0.53) (1.62) (1.64) Chair ** * 4.233** ** (1.61) (2.25) (0.94) (1.94) (2.25) (1.05) (1.45) (2.21) (0.67) LTD/Assets ** ** ** (0.63) (2.30) (0.53) (1.18) (2.14) (0.85) (0.45) (2.39) (0.70) R&D/Assets * ** (1.61) (0.10) (1.66) (0.77) (0.07) (0.87) (1.92) (0.20) (2.03) Size *** *** *** 9.532*** 6.012*** 9.890*** *** *** *** (4.71) (8.95) (4.59) (4.56) (8.08) (4.24) (5.04) (9.18) (4.79) Volatility * * (0.74) (1.64) (1.80) (0.91) (1.63) (1.69) (0.73) (1.62) (1.59) CONCENTRATION * ** (1.71) (0.18) (1.28) (0.08) (0.32) (0.54) (2.40) (0.04) (1.45) Union * ** * (1.78) (1.19) (1.40) (2.66) (1.80) (1.45) (1.25) (0.78) (1.31) Capital/Labor *** ** *** *** ** *** *** ** *** (4.27) (2.38) (3.89) (4.93) (2.45) (3.81) (4.11) (2.28) (3.72) Industry Homogeneity ** ** * * * *** (2.45) (2.63) (1.17) (2.00) (1.72) (0.64) (2.00) (3.03) (1.05) Education * * ** * * ** (0.36) (2.00) (1.81) (2.16) (1.91) (1.78) (0.75) (2.06) (1.64) Constant *** ** *** * *** ** (3.44) (0.40) (2.16) (3.22) (0.53) (1.98) (2.97) (0.81) (2.07) # of Obs / SIC2 3,118 / 41 3,118 3,118 3,118 / 41 3,118 3,118 3,118 3,118 3,118 R-squared

24 Table 5: Effect of CEO Staff Compensation Ratios on Factor Productivity (Total, ST and LT) The table presents results for the effect of the CEO-Staff compensation ratios on factor productivity which is the dependent variable in all specifications. The first two columns use an industry-fixed-effects model while the last two columns are estimated using industry-adjusted values for the dependent and all non-industry independent variables. The first column in each set is based on total and the second is based in ST and LT compensation ratios. All variables are as defined in the Appendix. The absolute values of the t-statistics are in parentheses and are computed based on heteroskedasticity robust standard errors. All models contain year dummies. ***, **, * represent statistical significance at the 1%, 5%, and 10% levels respectively. Regular RHS Industry-Adjusted RHS Tot ST/LT Tot ST/LT FacProd FacProd FacProd FacProd CEO/Staff (Tot) *** (4.10) CEO/Staff (ST) * (1.88) CEO/Staff (LT) *** (3.17) CEO/Staff (Tot)(Adj) *** (2.95) CEO/Staff (ST)(Adj) * (1.90) CEO/Staff (LT)(Adj) ** (2.54) Team PPS (0.34) (0.32) (0.45) (0.45) Industry Homogeneity ** 0.642** (0.91) (0.88) (2.18) (2.18) Union * * * * (1.88) (2.01) (1.83) (1.87) Education (1.27) (1.34) (0.54) (0.61) Size 0.052*** 0.052*** 0.037*** 0.040*** (4.57) (4.58) (3.80) (3.97) Volatlity (0.10) (0.12) (0.68) (0.69) LTD/Assets (0.37) (0.38) (0.39) (0.41) R&D/Assets (0.80) (0.79) (0.20) (0.19) CONCENTRATION 0.857** 0.871** (2.06) (2.03) (1.53) (1.52) Constant *** *** *** *** (4.53) (4.42) (4.23) (4.22) Observations 3,118 3,118 3,118 3,118 R-squared

25 Table 6: Effect of CEO Staff Compensation Ratios with interactions on Factor Productivity The table presents results for the effect of the CEO-Staff compensation ratios and their interactions with industry specification (in the first three columns) and with Governance related variables (last three columns) on factor productivity which is the dependent variable in all specifications. All variables are as defined in the Appendix. The absolute values of the t-statistics are in parentheses and are computed based on heteroskedasticity robust standard errors. All models contain year dummies. ***, **, * represent statistical significance at the 1%, 5%, and 10% levels respectively. Industry Interactions Governance Interactions FacProd FacProd FacProd FacProd FacProd FacProd CEO/Staff (Tot) *** ** ** * (0.87) (3.53) (1.13) (2.26) (2.48) (1.98) Team PPS (0.27) (0.34) (0.33) (0.27) (0.26) (0.25) CEO/Staff*Union D ** (2.46) CEO/Staff*Education D (0.24) CEO/Staff* CONCENTRATION D (0.65) CEO/Staff*Chair (0.56) CEO/Staff*Board Size D (0.42) CEO/Staff*Outside D (0.73) Union ** ** *** *** *** (1.00) (2.04) (2.04) (4.14) (4.24) (4.12) Education *** *** *** (1.42) (1.34) (1.40) (4.06) (4.04) (4.03) Size 0.052*** 0.055*** 0.054*** 0.078*** 0.078*** 0.077*** (5.32) (5.48) (5.13) (5.99) (5.68) (5.74) Volatility (0.04) (0.06) (0.05) (0.37) (0.37) (0.37) LTD/Assets (0.40) (0.31) (0.34) (1.12) (1.09) (1.09) R&D/Assets (0.79) (1.56) (1.59) (1.38) (1.36) (1.41) CONCENTRATION 0.836* 0.807* 0.848** (1.98) (1.84) (2.02) (1.52) (1.56) (1.52) Continued 24

26 Table 6 (Continued) Industry Interactions Governance Interactions FacProd FacProd FacProd FacProd FacProd FacProd Chair (0.49) (0.79) (0.82) Board size *** *** *** (3.15) (3.48) (3.88) Industry Homogeneity (0.23) (0.20) (0.26) Constant *** *** *** (5.25) (4.82) (4.89) (1.53) (1.48) (1.38) Observations 3,118 3,118 3,118 2,024 2,024 2,024 R-squared

27 Table 7: Determinants of the Other Executives Staff and All Executives Staff Compensation Ratios (Total) The table presents results for the determinants of the Non-CEO executives - Staff and all executives Staff total compensation ratios. The first specification in each set is estimated using an industry Fixed-effects model, followed by OLS. The third specification is estimated using industry-adjusted values for the dependent and all non-industry independent variables. All variables are as defined in the Appendix. The absolute values of the t-statistics are in parentheses and are computed based on heteroskedasticity robust standard errors. All models contain year dummies. ***, **, * represent statistical significance at the 1%, 5%, and 10% levels respectively. Oth.exec/Staff (Tot.) All exec./staff (Tot.) FE OLS IND_ADJ FE OLS IND_ADJ Log Age ** ** ** *** (2.24) (0.20) (2.65) (2.67) (0.52) (3.70) Chair * (0.69) (1.33) (0.31) (1.20) (1.77) (0.63) LTD/Assets ** ** (1.03) (2.54) (0.65) (0.86) (2.48) (0.64) R&D/Assets * * * * (1.70) (0.37) (1.80) (1.77) (0.14) (1.88) Size *** 9.521*** *** *** *** *** (6.14) (9.20) (6.01) (5.57) (9.88) (5.28) Volatility ** * (1.02) (2.06) (1.44) (0.81) (1.86) (1.33) CONCENTRATION *** ** (3.33) (0.43) (1.48) (2.53) (0.32) (1.32) Union (1.27) (1.26) (1.68) (1.55) (1.25) (1.58) Capital/Labor *** ** *** *** ** *** (5.27) (2.50) (5.00) (4.80) (2.47) (4.44) Industry Homogeneity *** *** *** *** (3.47) (2.93) (0.56) (3.33) (2.80) (0.36) Education * * * (0.46) (1.78) (1.68) (0.29) (1.86) (1.75) Constant *** * *** * (3.52) (0.24) (1.98) (3.74) (0.09) (1.96) # of Obs. / SIC2 3,118 / 41 3,118 3,118 3,118 / 41 3,118 3,118 R-squared

28 Table 8: Effect of Other executives Staff and All Executives Staff Compensation Ratios on Factor Productivity The table presents results for the effect of the Other executives -Staff and All executives Staff total compensation ratios on factor productivity which is the dependent variable in all specifications. The first two columns use an industry-fixed-effects model while the last two columns are estimated using industry-adjusted values for the dependent and all non-industry independent variables. The first column in each set is based on Other executives and the second on all executives. All variables are as defined in the Appendix. The absolute values of the t-statistics are in parentheses and are computed based on heteroskedasticity robust standard errors. All models contain year dummies. ***, **, * represent statistical significance at the 1%, 5%, and 10% levels respectively. Regular RHS Industry-Adjusted RHS OTH EXEC ALL EXEC OTH EXEC ALL EXEC FacProd FacProd FacProd FacProd Oth.exec/Staff (Tot) *** (3.19) All exec/staff (Tot) *** (3.46) Oth.exec/Staff (Tot)(Adj) ** (2.08) All exec/staff (Tot)(Adj) ** (2.54) Team PPS (0.33) (0.33) (0.45) (0.45) Industry Homogeneity ** 0.650** (0.85) (0.86) (2.22) (2.20) Union * * * * (1.98) (1.97) (1.82) (1.84) Education (1.27) (1.29) (0.50) (0.53) Size 0.054*** 0.053*** 0.039*** 0.039*** (4.71) (4.68) (3.69) (3.84) Volatility (0.15) (0.13) (0.69) (0.68) LTD/Assets (0.36) (0.38) (0.39) (0.40) R&D/Assets (0.83) (0.81) (0.20) (0.18) CONCENTRATION 0.876** 0.870** (2.07) (2.06) (1.54) (1.53) Constant *** *** *** *** (4.43) (4.42) (4.34) (4.27) Observations 3,118 3,118 3,118 3,118 R-squared