why has top executive compensation increased so much in china

Pacific Economic Review, 14: 5 (2009) doi: 10.1111/j.1468-0106.2009.00479.x

pp. 705–716

WHY HAS TOP EXECUTIVE COMPENSATION INCREASED SO MUCH IN CHINA: A EXPLANATION OF PEER-EFFECTS paer_479

705..716

Ruilong Yang Renmin University of China Jidong Yang* Renmin University of China Abstract. Top executive compensation can be affected significantly by peer group pay. This paper investigates the impact of peer effects on the change in top executive compensation based on evidence from China. Empirical results show that if the top three executives’ compensation was lower than the peer group median level in year t - 1, the percentage change in the top three executives compensation in year t would be higher by 0.225%, and that the absolute level of pay would increase by 51 000 yuan. Furthermore, better performance, faster growth and state ownership increase the likelihood of peer effects, while corporate governance variables do not.

1.

introduction

Particularly since the recent global financial crisis, high pay for top executives has attracted wide public attention and stimulated furious debate. Such debates have aroused some important questions, such as what determines top executive compensation, and why top executive compensation has increased so much. Although the research on CEO compensation is increasing (Murphy, 1999), the existing literature does not provide a clear picture regarding top executive payment. Mainstream explanations, which include incentive theory, manager rent theory and market theory, are often controversial and ambiguous1 (Bertrand and Mullainathan, 2001; Bebchuk and Fried, 2003; Murphy and Zabojnik, 2004; Gabaix and Landier, 2008). Furthermore, all the existing literature pays a great deal of attention to how performance and governance affect top executive compensation, while ignoring social and institutional factors that play an important role in the process of setting top executive compensation. This paper investigates why top executive payments have increased so much. In particular, we focus on whether peer effects have an important impact on the change in top executive compensation. Peer effects mean that top executive payment will be affected by peer group payments. On the one hand, a compen*Address for Correspondence: Jidong Yang, School of Economics, Renmin University of China, Zhongguan Cun Street 59,100872 Beijing. E-mail: [email protected]. We would like to thank an anonymous referee for useful comments and acknowledge the financial support of the ‘211 project’ of Renmin University of China. Jidong Yang is also grateful for financial support from the China Scholarship Council 1

See the literature review in Murphy (1999). Gabaix and Landier (2008) also provide a brief review of CEO compensation studies. There is much controversy concerning compensation in the USA. Some scholars argue that manager rents drive high pay (Bertrand and Mullainathan, 2001; Bebchuk and Fried, 2003), and some people argue that high pay is a market equilibrium result (Murphy and Zabojnik, 2004; Gabaix and Landier, 2008).

© 2009 The Authors Journal compilation © 2009 Blackwell Publishing Asia Pty Ltd

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sation committee of a company will design executive compensation by comparing peer group compensation in order to maintain competitive compensation.2 On the other hand, top executives will ask to keep their compensation above the average of their peer group in order to keep up with the Joneses. In the present paper, we identify peer groups by three digit industry category. Our sample includes 994 companies in China stock market from 2001 to 2007. We use the total compensations of top three executives to measure compensation. We focus on percentage change and absolute value change of the top executive compensation. We try to determine how peer effects affect top executive compensation in China. Consistent with peer effects prediction, empirical results show that the top three executives’ compensation will increase by 0.225% in year t, with the absolute level increasing by 51 000 yuan, if their compensation levels are lower than the peer group median level in year t - 1. Furthermore, we try to determine the motive behind peer effects. We find economic variables have a significant effect on the likelihood of peer effects, but governance variables do not. This result tends to support that the motive behind peer effects comes from competitive benchmarking theory (Bizjak et al., 2008). One thing worth noting is that state-owned companies are more likely to use peer effects in driving the increase of compensation. This shows that ownership has an important effect in determining whether or not to use peer effects in practice. We argue that social comparison may be dominant over competitive benchmarking in state-owned companies given the impact of market-orientated reform on compensation and the lack of a clear compensation system. Given the mixed evidence in the existing published literature regarding why top executive pay has increased so much, our paper provides an empirical explanation from the social and institutional perspectives, which have largely been ignored (Schaefer and Hayes, 2009). Following Bizjak et al. (2008), this paper also provides clear evidence that peer effects do drive the increase in top executive compensation for China’s listed companies. Bizjak et al. (2008) argue that peer effects are efficient because of the performance motive, but we point out that ownership plays an important role in determining the use of peer effects. Peer effects might not only be a result of competitive benchmarking, but also be present for other important reasons. Last but not least, our research on peer effects on top executive compensation contributes to the peer effects literature based on experimental and field data (Falk and Ichino, 2006; Mas and Moretti, 2009). The rest of the paper proceeds as follows. Section 2 puts forward our three hypotheses. In Section 3, we describe the data and provide empirical results regarding how peer group compensation affects the change in top executive compensation. We also analyze what factors drive peer effects in China. Section 4 concludes. 2 Bizjak et al. (2008) explores how this competitive benchmarking effects CEO pay based on USA listed company data.


executive pay and peer effects 2.

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hypothesis

Existing empirical works show that it is difficult to explain compensation increases only through performance payments and corporate governance. There is no doubt that specific institutional factors play an important role during the process of compensation design, (O’Reilly and Main, 2005; Ang et al., 2008). For instance, a compensation committee of a board would like to choose competitive benchmarking to design top executive compensation, and competitive benchmarking is mainly based on the median (mean) of the peer group (Faulkender and Yang, 2008). Bizjak et al. (2008) argue that peer effects are very important for US listed companies. In China, how top are executives paid is not transparent in companies’ public reports. However, anecdotal evidence shows that a company will refer to other companies in the same industry to design their executive compensation. Comparing median level peer group compensation is a widely accepted practice by companies. Following Bizjak et al. (2008), we assume that top executive compensation is affected by peer group compensation. Furthermore, top executive compensation increases with the median of the peer group based on peer effects. Therefore, peer effects inevitably drive increases in compensation over time. Finally, we should see an upward ratchet effect in executive compensation over time.3 Hence, our first hypothesis. HYPOTHESIS 1. If peer effects have an impact on top executive compensation and the company prefers to place their executive compensation above the peer group median, then peer effects will cause compensation levels to increase faster. In particular, in order to determine what motivates the peer effects in China, we explore the relationship between the use of peer effects and firm characteristics. We divide firm characteristic variables into two groups: economic motive variables and governance motive variables. Economic variables include company performance, growth and size. Governance variables include the share percent of the dominant shareholder, the proportion of the independent director on the board and the leadership structure. HYPOTHESIS 2. If the use of peer effects is derived from economic motives, then peer effects are efficient; if the use of peer effects is derived from governance motives, then peer effects are inefficient. Finally, there is no doubt that firm behaviours differ in state-owned companies and private companies. Therefore, we have enough reason to wonder that the motive of using peer effects might be related to ownership structure. During the compensation decision process, state-owned companies have different conditions compared to private companies. The biggest difference is that 3 Bizjak et al. (2008) find that CEOs whose pay is below the median pay level of CEOs in firms of similar size and industry receive raises that are twice as large relative to the raises received by CEOs with above median pay.


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a state-owned company has stricter compensation limits due to government intervention (Chen et al., 2005). Therefore, we would like propose Hypothesis 3 to deepen our understanding of what motivates the use of peer effects. HYPOTHESIS 3. Ownership has an important impact on the use of peer effects. In particular, state-owned companies are more likely to use peer effects.

3.

data and estimation

Our data are obtained from the China Stock Market and Accounting Research Database, developed by the Shenzhen GTA Information Technology Company.4 Rather than focusing on CEOs, we limit our attention to top three executives in companies. Our sample comprises 994 companies, including both A shares and B shares in China’s stock market from 2001 to 2007. Because we are focusing on the first-order difference of compensation, we do not include observations for 2001 in the empirical analysis.5 Industry plays a very important role in the process of choosing peer groups for a company (Bizjak et al., 2008). Industry captures the determinant of a company’s choice of peers in China. Some compensation surveys also show that industry comparison is very important in the structuring of executive compensation plans by China’s listed companies. We believe that industry captures the essential information about peer groups. According to the three-digit industry category, we divide 994 companies into 72 different industries.6 This means we have 72 peer groups. We drop those groups that only have 1 company. There are, on average, 17 companies in each group. The median number of members in peer groups is 12. We try to use peer effects to explain why executive compensation has increased so much in China since 2000. We use the following regression equation to test whether peer effects really contribute to higher payments:

COMPENSATION_CHANGE = β0 + β1PEER + β2 X + ϕ + ε.

(1)

COMPENSATION_CHANGE denotes change in compensation. X denotes the set of control variables. j denotes the area, year and industry fixed effect, while e is the error term. The crucial variable is peer effects, PEER. If the use of peer effects contributes to higher payments, the coefficient of peer effects should be positive, consistent with Hypothesis 1. We incorporate a dummy variable, LHPEER, into our model, which is equal to 1 if the top three executives receive compensation lower than the median in the previous year and get bumped above the median in the current year, and is 4

The China Stock Market and Accounting Research Database dataset has been used in previous studies. For instance, see Kato and Long (2006). 5 We eliminate financial company. At the end of 2001, there were 1160 listed companies in China’s stock market in total, including A share and B share. We eliminate those companies for which we cannot find compensation data in the dataset. Finally, we have 994 companies as our sample. 6 There are some companies that change their industry over time. Hence, the same company might be in a different peer group in a different year. © 2009 The Authors Journal compilation © 2009 Blackwell Publishing Asia Pty Ltd

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0 if the executive did not receive a compensation increase that placed them above the median. We refer this dummy as whether peer effects are used in a specific company. We suppose LHPEER to be dependent on X, which is a set of company characteristics. Furthermore,

Prob ( LHPEER = 1) = α + β X + ϕ + ε.

(2)

Then we can use the Logit model to test this relation. The coefficient reflects the relationship between the probability of using peer effects and company characteristics. In particular, we divide company characteristics into economic motive variables, governance motive variables and the dummy of state ownership. In regression analysis, we use two proxies to measure compensation change. One variable is percentage change in compensation, which equals the log difference of the top three executives’ compensation through time. The other variable is absolute change in compensation, which equals the difference in year t and year t - 1 compensation.7 Following Bizjak et al. (2008), to test the effect of peer groups on compensation, we include a dummy variable for the top executive’s payment status relative to the median payment in the industry peer groups. The dummy variable equals 1 when compensation is below the peer group median in the ranking year (t - 1). Correspondingly, the dummy equals 0 when compensation in the prior year is above the peer group median. The coefficient on the peer group dummy measures the effect of payment status relative to the peer group on changes in compensation over the following year. The other variable used to identify the peer effects is a continuous variable. To construct this measure, each year, for each firm, we first calculate the difference between the median lagged compensation for the peer group and the lagged compensation of the top executives. This number is positive when payment is below the peer group median and is negative when payment is above the peer group median. We also introduce control variables. We use log of total assets to measure company size. We use accounting performance to measure company performance, which uses return of net equity. Corporate governance variables include the share percent of the dominant shareholder, the proportion of the independent director on the board, and a dummy of leadership structure and a dummy of B&H share. Dummy of leadership structure is 1 if the same individual serves as both the Chairman and the General Manager, and 0 otherwise. The dummy of B&H is 1 if the company also trades B shares or H shares, and 0 otherwise. Finally, from the identity of the dominant shareholder, we obtain the value of the state-owned company dummy. If the dominant shareholder is the state, the dummy is 1; if the dominant shareholder is others, the dummy is 0. We provide a description of variables in Table 1. Table 1 shows that the mean of top three executives’ compensations is 670 000 yuan (in 2007 RMB). Top three executives’ compensation increases by 92 000 each year on average. In particular, the percentage that the dominant shareholder holds is very high compared with other 7

All compensation information is in real terms (in 2007 RMB thousand yuan). We eliminate inflation by CPI. © 2009 The Authors Journal compilation © 2009 Blackwell Publishing Asia Pty Ltd

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Table 1. Summary of variables Variable

Mean

Total compensation of top 670.90 three executives Percentage change in 0.1631 compensation Absolute change in 92.6393 compensation Peer effects (dummy variable) 0.4736 Peer effects (continuous -133.77 variable) Company performance 0.0369 Company size 14.4337 The share percent of dominant 40.0732 shareholder The proportion of independent 0.3272 director Leadership structure 0.1135 Dummy of B&H share 0.0941 Dummy of state-owned 0.6530 company

Standard deviation 693.01 0.4851

Minimum 11.48 -2.8421

494.5873

-15870.81

0.4993 627.3215

0 -21087.6

0.2241 1.0420 16.7627

-1.9964 5.45 0.39

Maximum 10459.7 3.2021

Number of observations 5833 5833

10003.85

5833

1 859.37

5833 5833

0.9714 20.39 85

5716 5832 5833

0.0685

0

0.66

5773

0.3172 0.2920 0.4760

0 0 0

1 1 1

5779 5833 5833

This table reports the summary of main variables. Our sample is derived from the China Stock Market and Accounting Research Database, including 994 companies from 2001 to 2007. Compensation is calculated as salary plus bonus, and allowances.

stock markets. The mean percentage of dominant shareholders’ share is 40%. This also shows that most of the sample companies are state-owned.

3.1. Does the use of peer groups contribute to higher payments? Table 2 reports the result of equation 1 with OLS regression, with a dummy variable used to identify peer effects. Column (1) ignores other variables. Column (2) introduces area, year and industry fixed effects. Finally, column (3) displays the control variables and the fixed effects. All three models have a similar result: peer effects have a positive relationship with higher payments and are significant at the 1% level. To determine the economic significance of peer effects, we take model (3) as an example. If executive compensation is below the peer group median in the ranking year (t - 1), there will be an 0.225% increase in compensation. Given that the mean of the dependent variable in the total sample is 16%, peer effects explain 1.41% of total change. If executive compensation is 670 000 yuan, peer effects will cause executive compensation increase by 1500 yuan. When we use a dummy variable to identify peer effects, it seems that peer effects do not vary greatly from economic significance. We can also compare different coefficients of peer effects and other variables to see the significance of peer effects. According to column (3), a 1% increase in the return of net equity will drive a 0.22% increase in compensation; and a 1% © 2009 The Authors Journal compilation © 2009 Blackwell Publishing Asia Pty Ltd


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Table 2. Peer effects and compensation change Percentage change in top three executives’ compensation

Peer effects (dummy variable)

(1)

(2)

(3)

0.1682*** (0.0126)

0.1894*** (0.0131)

0.2250*** (0.0139) 0.2272*** (0.0339) 0.0414*** (0.0060) Yes Yes Yes 0.0907 5715

Company performance Company size Area Year Industry R2 Number of observations

No No No 0.0298 5833

Yes Yes Yes 0.0734 5833

This table reports OLS regression results for Equation 1. The coefficient of the dummy of peer effects reflects the relationship between peer effects and executive compensation. Our sample is derived from the China Stock Market and Accounting Research Database, including 994 companies from 2001 to 2007. Compensation is calculated as salary plus bonuses, and allowances. The dependent variable is the percentage compensation change, the independent variable includes peer effects, other control variables and fixed effects. ‘yes’ denotes the controlled fixed effect. R2 is adjusted; parentheses report the robust standard errors, and all regressions cluster at the company level. Stata 9.0 is used in the data analysis. ***denotes 1% significant level.

increase in company size will drive a 0.041% increase in compensation. Therefore, the marginal contribution of peer effects is almost equal to a 1% change in return of net equity. Taking into account the fact that the mean of return of net equity is 0.037, a 1% change is equal to a 27% average net equity return. In fact, it is not easy to fulfil this achievement in practice. Compared with the coefficient of company size, the use of peer effects is equal to a 5% change in company size. We perform further tests by changing the dependent variables and using the continuous variable for peer effects. In this setting, peer effects are equal to the difference between the median lagged compensation for the peer group and the lagged compensation for the top three executives. If peer effects have a positive impact on payment, the coefficient should be positive. We report the results in Table 3. In column (1), we use a continuous variable for peer effects to replace the dummy for peer effects in our previous analysis. The coefficient is positive and significant. The difference in the median lagged compensation for the peer group and the lagged compensation for the top three executives increases by 1000 yuan, while the compensation increases by 0.000197%. In columns (2) and (3), the coefficient of peer effects is positive and significant whether we use a dummy variable or a continuous variable for peer effects. If executive compensation is below the peer group median in the ranking year (t - 1), the top three executives’ compensation increases by 51 000 yuan in year t. If the difference in the median lagged compensation for the peer group and the lagged compensation for the top three executives increases by 1000 yuan, then the top three executives’ compensation increases by 330 yuan in year t. © 2009 The Authors Journal compilation © 2009 Blackwell Publishing Asia Pty Ltd

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Table 3. Peer effects and compensation change- robust test Percentage change in top three executives’ compensation (1) Peer effect (dummy variable) Peer effect(continuous variable) Company performance Company size Area Year Industry R2 Number of observations

Absolute change in top three executives’ compensation (2)

(3)

51.4752*** (14.4084) 0.000197*** (0.000031) 0.2293 (0.0341) 0.0478 (0.0074) Yes Yes Yes 0.1035 5715

169.9215*** (40.1690) 43.47977*** (10.2680) Yes Yes Yes 0.0289 5715

0.3338*** (0.1156) 216.4241*** (41.8935) 102.8023*** (20.7101) Yes Yes Yes 0.1780 5715

This table reports OLS regression results for Equation 1. The coefficient of the dummy of peer effects reflects the relationship between peer effects and executive compensation. Our sample is derived from the China Stock Market and Accounting Research Database, including 994 companies from 2001 to 2007. Compensation is calculated as salary plus bonuses, and allowances. The dependent variable is the percentage compensation change, the independent variable includes peer effects, other control variables and fixed effects. ‘yes’ denotes the controlled fixed effect. R2 is adjusted; parentheses report the robust standard errors, and all regressions cluster at the company level. Stata 9.0 is used in the data analysis.***denotes 1% significant level.

Although our regressions have controlled many factors that might affect executive compensation, it is possible that our findings still suffer from the omitted variable problem. To the extent that any omitted variable is correlated with our measure of relative peer group ranking, our results could arise from spurious correlation between the omitted variables and our peer group measure. We conduct a number of different tests to check the robustness of our results. For all the specifications in Table 4, we estimate the following alternative specifications. We introduce more control variables. The result shows that the coefficient of peer effects does not change much. We test the relationship between lagged compensation change and current compensation change. Compensation change in year t - 1 has a negative impact on current compensation change. This reveals that the change in compensation tends to be mean-reversed. 3.2. Motive of peer effects Although the analysis above shows that peer effects contribute to higher compensation in China’s listed companies, the question remains as to what is the motive behind peer effects. To answer this question, we need to test Hypotheses 2 and 3. In Hypothesis 2, we test what is more important for a company’s decision to use peer effects by separating economic variables and governance variables. The simple intuition is that if using peer effects is more likely an © 2009 The Authors Journal compilation © 2009 Blackwell Publishing Asia Pty Ltd


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Table 4. Robustness test Percentage change in top three executives’ compensation (1)

(2)

0.2237*** (0.0142)

-0.2088*** (0.0230) 0.1789*** (0.0150)

Compensation change in t - 1 Peer effects (dummy variable) Peer effects (continuous variable) Company performance Company size Dummy of state-owned company Company growth8 The share percent of dominant shareholder The proportion of independent director Leadership structure Dummy of B&H share Area Year Industry R2 Number of observations

0.2319*** (0.0349) 0.0388*** (0.0061) 0.0248* (0.0131) 0.0008 (0.0007) -0.0004 (0.0003) 0.0121 (0.1094) 0.0001 (0.0183) 0.0205 (0.0155) Yes Yes Yes 0.0900 5605

0.2312*** (0.0344) 0.0357*** (0.0073) 0.0227 (0.0147) 0.0003 (0.0007) 0.0000 (0.0004) 0.0211 (0.1223) 0.0024 (0.0208) 0.0149 (0.0203) yes yes yes 0.1323 4604

(3) -0.1923*** (0.0232) 0.000178*** (0.000020) 0.2388*** (0.0338) 0.0470*** (0.0076) 0.0051 (0.0152) 0.0004 (0.0007) -0.00007 (0.00040 0.0069 (0.1239) 0.0043 (0.0218) 0.0468** (0.0233) Yes Yes Yes 0.1442 4604

This table reports OLS regression results for Equation 1. The coefficient of the dummy of peer effects reflects the relationship between peer effects and executive compensation. Our sample is derived from the China Stock Market and Accounting Research Database, including 994 companies from 2001 to 2007. Compensation is calculated as salary plus bonuses, and allowances. The dependent variable is the percentage compensation change, the independent variable includes peer effects, other control variables and fixed effects. ‘yes’ denotes the controlled fixed effect. R2 is adjusted; parentheses report the robust standard errors, and all regressions cluster at the company level. Stata 9.0 is used in the data analysis. ***, ** and *denote 1, 5 and 10% significant level, respectively.

economic motive, benchmarking theory tends to be right. In contrast, if the use of peer effects is mainly driven by governance, comparison theory seems correct. In order to contemplate the question about what motivates the peer effects, we need to consider a subsample of the total sample. We can obtain a subsample by limiting executive compensation to be below the peer group median in the ranking year (t - 1). If compensation goes above the peer median in year t, we identify those companies as using peer effects. If compensation is still below the peer group median, we regard those companies as not using peer effects. There are 2797 observations below the peer group median in year t - 1. In year t, 442 of 8

We use the ratio of operating income change to measure company growth. © 2009 The Authors Journal compilation © 2009 Blackwell Publishing Asia Pty Ltd

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Table 5. The motive of use of peer effects The use of peer effects (LHPEER = 1) (1) Company performance Company growth Company size The dummy of state-owned company

1.3097*** (0.3617) 0.0061** (0.0030) 0.3643*** (0.0712) 0.2533* (0.1508)

The share percent of dominant shareholder Leadership structure The proportion of independent director The dummy of B&H share Year Area Industry Pseudo R2 Number of observations

Yes Yes Yes 0.0589 2672

(2)

(3)

0.3453** (0.1511) -0.0003 (0.0043) -0.1584 (0.1911) 0.5526 (0.9225) -0.1122 (0.2446) Yes Yes Yes 0.0515 2696

1.3522*** (0.3703) 0.0059** (0.0030) 0.3586*** (0.0731) 0.2989* (0.1574) -0.0046 (0.0044) -0.1030 (0.1984) 0.4917 (1.0174) -0.1415 (0.2448) Yes Yes Yes 0.0592 2617

This table analyzes the association between the probability of using peer effects and company characteristics with logit regression. We use a subsample by limiting executive compensation to below the peer group median in the ranking year (t - 1). The dependent variable is a dummy variable. The dummy variable is equal to 1 if the top three executives received below median compensation in the previous yea, which is bumped to above the median in the current year, and is 0 if the executive did not receive a compensation increase that places him or her above the median. ‘Yes’ denotes the controlled fixed effect. R2 is adjusted; parentheses report robust standard errors, and all regressions cluster at the company level. Stata 9.0 is used in the data analysis. ***, ** and *denote 1, 5 and 10% significant level, respectively.

these increase their compensation to above the peer group median. We lose some observations in the regression because of missing data for some controlled variables. Table 5 shows the result with a Logit model of Equation 2. Column (1) reports the results without governance variables. We find that there is a positive and significant relation between economic motive and the probability of using peer effects. The companies with better performance, of bigger size and with faster growth are more likely to use peer effects to increase their executive compensation. Column (2) reports the results without economic motive. We find that there is no significant relationship between governance motive and the probability of using peer effects. It seems that governance variables do not drive the peer effects. Column (3) includes all variables mentioned above. Like in Column (2), the governance variable is still not significant. Again, economic motive variables have a significant effect on the use of peer effects. The companies with better performance, bigger size and faster growth are more likely to use peer effects to increase their executive compensation. Given corporate governance could change company behaviour, weak governance will lead executives © 2009 The Authors Journal compilation © 2009 Blackwell Publishing Asia Pty Ltd


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to rely more on comparison in setting their compensation while not considering performance. However, our result does not support this prediction. When we replace the Logit Model with the Probit Model, the result is basically the same. A very important result from Table 6 is that the dummy for state-owned companies is significant in all three regressions. This shows that state-owned companies are more likely to use peer effects to determine their top executive compensation. Like the Hypothesis 3 prediction, state-owned companies lack detailed compensation plans and have stronger compensation regulation. It is a cost-saving way to construct executive compensation by anchoring the median of the same industry. In order to deepen our understanding of peer effects in state-owned companies, we regress equation 1 again, but separate the whole sample according to whether a company is state-owned or not. We then compare the coefficient of peer effects for two subsamples. Our logic is that peer effects will be more substantial for state-owned companies than for non stateowned companies, due to state-owned companies being more likely to use peer effects. The results are provided in Table 6. The empirical results indicate that peer effects have a stronger impact on state-owned companies than on non-state-owned ones, in both the percentage change and the absolute change. The coefficient of peer effects in state-owned

Table 6. Difference between state-owned company and non-state-owned company Percentage change in top three executives’ compensation

Peer effects (dummy variable) Company performance Company size Area Year Industry R2 Number of observations

Absolute change in top three executives’ compensation

(1) Stateowned company

(2) Non-stateowned company

(3) Stateowned company

(4) Non-stateowned company

0.2495*** (0.0176) 0.3036*** (0.0496) 0.0373*** (0.0078) Yes Yes Yes 0.1031 3766

0.2035*** (0.0249) 0.1509*** (0.0462) 0.0443*** (0.0102) yes yes yes 0.0877 1949

62.17*** (19.87) 174.34*** (61.76) 47.56*** (9.0355) Yes Yes Yes 0.0326 3766

36.03*** (19.56) 159.67*** (40.91) 40.54** (19.397) Yes Yes Yes 0.0387 1949

This table reports the OLS regression result for equation 1. We separate sample into state-owned companies and non-state-owned companies. The coefficient of the dummy of peer effects reflects the relationship between peer effects and executive compensation. Our sample are derived from the China Stock Market and Accounting Research Database, and include 994 companies from 2001 to 2007. Compensation is calculated as salary plus bonuses, and allowance. The dependent variable is the percentage compensation change, the independent variable includes peer effects and other control variables and fixed effects. ‘Yes’ denotes the controlled fixed effect. R2 is adjusted; parentheses report robust standard errors, and all regressions cluster at company level. Stata 9.0 is used in the data analysis. *** and **denote 1 and 5% significant level, respectively.


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companies is 0.046% larger than in non-state-owned companies. The absolute increase in value is 26 000 yuan. 4.

conclusion

Although peer effects are a pervasive phenomenon, there are few studies that address how peer effects impact changes in compensation. Our paper provides empirical evidence on the dramatic increase in executive compensation based on peer effects. Indeed, peer effects contribute to higher executive compensation in our study. Empirical results show that top three executives’ compensation in year t will increase by 0.225%, and that the absolute value will increase by 51 000 yuan, if their compensation is lower than the peer group median level in year t - 1, compared with those companies whose compensation is higher than the peer group median level in year t - 1. Better performance, faster growth and state ownership are more likely to be associated with peer effects, whereas corporate governance has no relation with peer effects. There are two important implications of this paper. First, it is necessary to explore executive compensation from social and institutional perspectives. Second, peer effects can be an important factor affecting the increase in compensation. references Ang, J. S., G. L. Nagel and J. Yang (2008) ‘Is There A Social Circle Premium in CEO Compensation?’, Working paper. Available from URL: http://ssrn.com/abstract=1107280. Bebchuk, L. and J. Fried (2003) ‘Executive Compensation As An Agency Problem’, Journal of Economic Perspectives 17, 71–92. Bertrand, M. and S. Mullainathan (2001) ‘Are CEOs Rewarded for Luck? The Ones without Principals Are’, Quarterly Journal of Economics 116, 901–32. Bizjak, J. M., M. L. Lemmon and L. Naveen (2008) ‘Does the Use of Peer Groups Contribute to Higher Pay and Less Efficient Compensation?’, Journal of Financial Economics 90, 152–68. Chen, D., X. Chen and H. Wan (2005) ‘Regulation and Non-Pecuniary Compensation in Chinese Soes’, Economic Research Journal 2, 92–101 (in Chinese). Falk, A. and A. Ichino (2006) ‘Clean Evidence on Peer Effects’, Journal of Labor Economics 24, 39–57. Faulkender, M. W. and J. Yang (2008) ‘Inside the Black Box: The Role and Composition of Compensation Peer Groups’, Working paper, Available from URL: http://ssrn.com/ abstract=972197. Gabaix, X. and A. Landier (2008) ‘Why Has CEO Pay Increased So Much?’, Quarterly Journal of Economic 123, 49–100. Kato, T and C. Long (2006) ‘Executive Compensation, Firm Performance, and Corporate Governance in China: Evidence from Firms Listed in the Shanghai and Shenzhen Stock Exchanges’, Economic Development and Cultural Change 54, 945–83. Mas, A. and E. Moretti (2009) ‘Peers at Work’, American Economic Review 99, 112–45. Murphy, K. J. (1999) ‘Executive Compensation’, in Orley Ashenfelter and David Card (ed.), Handbook of Labor Economics, Vol. 3. North Holland: Elsevier. Murphy, K. and J. Zabojnik (2004) ‘CEO Pay and Appointments: A Market-Based Explanation for Recent Trends’, American Economic Review 94, 192–6. O’Reilly, C. A. and B. G. M. Main (2005) ‘Setting the CEo’s Pay: Economic and Psychological Perspectives’, Stanford GSB Research Paper No. 1912. Schaefer, S. and R. M. Hayes (2009) ‘CEO Pay and the Lake Wobegon Effect’, Journal of Financial Economics 94, 280–90.
