Can Governments Effectively Regulate Levels and Growth ...

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Can Governments Effectively Regulate Levels and Growth Rates of CEO’s

Compensation?

Some Evidence from the Chinese 2009 Regulation

Ling Mei Cong

School of Accounting

Curtin University

[email protected]

Zoltan P. Matolcsy

School of Accounting

University of Technology, Sydney

[email protected]

Fifth Early Draft; 5 March 2014

Abstract

Currently, there is an ongoing debate in the literature on whether CEO compensation should

be regulated. Empirical evidence based on western countries questions the effectiveness of

the regulations, whilst little research has been done in emerging economies. This paper

provides evidence in this area by examining the effectiveness of the 2009 Regulation issued

by the Chinese government to cap CEOs’ compensation. Specifically, we investigate if the

2009 Regulation changed the level and growth rate of CEO compensation in Chinese SOEs.

We also test if the pay-performance relation changed after the 2009 Regulation. Our

statistical results show that the levels of CEO cash compensation and CEO-worker pay ratio

in Chinese SOEs did not decrease. The growth rates of the CEO cash compensation and

CEO-worker pay ratio were not affected by the 2009 Regulation. Meanwhile, there is no

evidence that the 2009 Regulation changed the pay-performance relation in Chinese SOEs.

Findings from our study can help regulators to reconsider how to further improve the

effectiveness of these regulations and/or whether regulating executive compensation may

only have political rather than economic benefits.

Key words: regulation, CEO compensation, Chinese SOE

mailto:[email protected]

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1. Introduction

Over the last decades, policy makers and commentators all around the world have expressed

concerns about the level and growth rate of executive compensation, which has been

reinforced by the global financial crisis (GFC) during the late 2000s. These concerns have

lead, all around the world, including the US, UK, EU, Australia and China, the introduction

of legislation and/or regulations to curb increases in executive compensations. These

legislations/regulations either explicitly reduce different parts of executive compensations,

such as the base salaries (e.g. Spain, France) or the size of the bonus components of executive

compensations (e.g. EU)1. Further, some of these regulations (e.g. the US, UK, Australia)

enhance shareholders right to explicitly vote on executives’ and directors’ compensation

proposals at the annual general meetings (hereafter ‘say on pay’ or SOP)2.

Similar to Western governments, the Chinese government has released several regulations to

limit executive pay of State Owned Enterprises (hereafter SOEs) in response to societal

concerns. The most important of these regulations is The Guideline for Regulating

Compensation of Executives in Charge of Central Enterprises (hereafter 2009 Regulation)

issued by six central government departments in 2009 (Xinhua Net 2009). The 2009

Regulation requires that the basic salary of executives in SOEs must not exceed five times the

average pay of the workers and the upper limit for bonus is three times their basic salary

(Xinhua Net 2009). Following the central government, various local governments in China

also issued similar regulations in the same spirit of the 2009 Regulation to regulate local

SOEs.

As the results of the above regulatory changes, there is now emerging academic literature,

which provides evidence on the effectiveness of these regulations. The primary focus of this

research is based on the SOP regulations. For example, Armstrong, Gow and Larcker (2013)

provides some US evidence on shareholders’ voting on future CEOs’ equity compensations,

Ferri and Maber (2013) provide some of the UK evidence, whilst Monem and Ng (2013)

provides the Australian evidence on the same issue. However, we are not aware of any

1 In early 2012, the Spain government announced basic annual salaries at state-owned firms would be limited to €105,000 (The Guardian, 2012). Recently, the French government announced plans to limit the salaries of French state firms to €450,000 per year (France 24, 2012).

In February 2013, the E.U. agreed to cap bankers’ bonuses at twice their salaries (Time 2013). 2 In 2002, the U.K. was the first country mandated an annual non-binding shareholder vote on executive pay (Ferri and Maber 2013). In

2010, the U.S. SEC issued Dodd–Frank Act requiring firms subject to the federal proxy rules to provide shareholders with an advisory vote

on executive compensation (Armstrong et al. 2013). In 2011, Australia introduced ‘two-strikes’ rule, which states if 25% of shareholders

vote against a firm’s remuneration report at two consecutive annual general meetings, the entire board may have to stand for re-election within three months (Monem and Ng 2013).

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studies, which would explicitly provide evidence on regulators’ ability to reduce the levels

and the growth rates of executive compensation by nominating fixed targets.

Accordingly, the objective of our study is threefold. First, we investigate whether the 2009

Regulation reduces the absolute levels of CEO compensation and the levels of CEO

compensation relative to average workers’ pay in Chinese SOEs. Second, we examine if the

2009 Regulation curbs the absolute and relative growth rates of CEO compensation in

Chinese SOEs. Third, we test if the 2009 Regulation enhances the pay-performance relations

in Chinese SOEs.

The motivation of our paper is twofold. First, China offers a unique setting to examine the

regulations on CEOs’ compensation for a number of reasons. First, unlike Western countries

which experienced low economic growth after the global financial crisis, China achieved a

steady and high economic growth since 20093. This offers a unique opportunity to examine if

the restriction on CEOs’ compensations by a government in an economy that leads the

world’s economic growth rate as opposed to economies where the economic growth rate has

been low. Hence the observed changes in executive compensation in low growth economies

may be driven by poor macroeconomic performances rather than restrained by the firms.

Second, unlike many western regulations which focus on SOP, the Chinese pay regulation

focuses on predetermined target levels and the ratios of CEO compensation to average

employee pay, hence we can provide a different insight into the effectiveness of government

regulations on CEOs’ compensation. Third, we do not have an identification problem as the

2009 Chinese government regulation is imposed on SOEs only, whilst the executive

compensation of non-SOEs is not regulated. This unique setting enables us to have an

experimental design where we have a ‘natural’ control for macroeconomic and institutional

factors such as regulatory changes, macroeconomic conditions, and/or changes in the labour

market.

The second motivation for this paper is to build on a strand of literature that examines the

Chinese executive compensation. Extant studies on executive pay in China focus on the

determinants of executive compensation (e.g. Kato and Long 2006; Firth et al. 2007; Conyon

3 According to the World Bank, China's GDP growth rates were 9.2%, 10.4% and 9.3% for 2009, 2010 and 2011 respectively, while the average GDP growth rate of the western countries (the US, UK, EU) was below 3% during the same period of time.

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and He 2011; Wang and Xiao 2011; Lam et al 2013; Hu et al. 2013), pay-performance

sensitivity (e.g. Firth et al. 2010; Conyon and He 2012), non-pecuniary compensation (e.g.

Matolcsy et al. 2006; Chen et al. 2010; Adithipyangkul et al. 2011; Gul et al. 2011), and

executive compensation contracts (e.g. Li et al. 2013). Whilst ‘say on pay’ has been an

emerging ‘hot topic’ in western literature (e.g. Conyon and Sadler 2010; Ferri and Maber

2013; Monem and Ng 2013), little research has paid attention to the regulation on pay issues

in China. We are only aware of a study by Hu and Monem (2012), who find that a higher

worker-CEO pay ratio is associated with unintended consequences such as lower profitability

and lower Tobin’s Q. However, Hu and Monem (2012) use the 2005-2009 data, hence does

not directly examine if the 2009 Regulation is effective or its economic consequences. Our

study differs to Hu and Monem (2012) by using data before and after 2009 to directly

investigate the effects and economic consequences of the 2009 government regulation.

Therefore, this research fills the void in the literature and generates most comprehensive

evidence on the effect of regulation on CEOs’ compensation in the Chinese setting.

The evidence of our study is based on 3,182 Chinese SOE firms for the period 2006 - 2011.

Two sets of analysis were conducted using the pre-2009 SOEs as the non-SOEs as the control

samples respectively. The SOEs and non-SOEs are matched within year, industry and firm

size. Our results show the 2009 Regulation does not reduce the levels of total cash

compensation or the CEO-worker pay ratio of SOEs. The 2009 Regulation does not decrease

the growth rates of CEO total compensation or the CEO-worker pay ratio, either. Fixed

effects regressions suggest the 2009 Regulation fails to affect pay and performance relation in

the SOEs. Overall, our findings offer little support for the effectiveness of government

regulation of CEOs’ compensation in the Chinese setting.

Our research makes several significant contributions to the literature. First, it is one of the

first to provide direct, large sample evidence on whether compensation limitations imposed

by the Chinese government really work or not. Due to the socialist nature of the Chinese

government, the Chinese authorities face the pressure to ease the enlarging income gap.

Findings from our research supply important empirical evidence to check if social media’s

doubts about the effectiveness of the regulations hold or not. Second, it builds on the ‘say on

pay’ literature using the unique setting of China. Our evidence is consistent with that strand

literature on SOP from all around the world, which questions the effectiveness of

governments’ regulations to curb executive pay by enhancing shareholders’ voting rights.

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Our evidence suggests that setting fix targets for executive pay levels and/or growth rate may

also be ineffective. Third, our study has important implications for policy makers and

regulators with respect to the pay/performance relation between executive pay and firm

performance as well. Results from this research show that the capping regulation does not

improve the pay-performance link. Findings from our study, thus, can help regulators to

reconsider how to further improve the effectiveness of these regulations and/or whether

regulating executive compensation may only have political rather than economic benefits.

The rest of our paper proceeds as follows. Section 2 provides a literature review and draws

hypotheses. Section 3 presents the research design of the paper. This is followed by results

reporting in Section 4 and robustness checks in Section 5. Finally, Section 6 concludes the

study.

2. Literature Review and Hypothesis Development

2.1 Literature review of regulation and executive compensation

Currently there are conflicting theoretical perspectives on the effect of, and the need for

regulating executive compensation. The capture theory, represented by Bebchuk et al. (2002),

advocates the government regulation on executive pay. Arguments based on capture theory

suggest that as managers become more entrenched they extract rent from companies by

paying themselves excessively (Bebchuk et al. 2002). Bebchuk and Spamann’s (2010) view

is that the government has a legitimate interest in the compensation structures of firms such

as financial firms due to its responsibility to safeguard the economy. Thus, the pay-setting

process should not be left to unconstrained choices of informed players inside firms and

government intervention can be an important tool for regulation. Scholars holding this

perspective generally believe that the strengthened monitoring on the CEOs by imposing

CEO pay capping or shareholder voting can increase management’s responsibility, strengthen

pay-performance link and reduce the executive pay level (e.g. Bebchuk et al. 2007; Davis

2007; Clarkson et al. 2009).

In contrast, arguments based on agency theory questions the need for regulation on executive

pay and suggest that government regulation may lead to sub-optimal pay practice (Gordon

2009; Bainbridge 2011). Proponents of the agency theory (Jensen and Murphy 1990; Murphy

and Zábojník 2008; Murphy 2011) maintain market forces are able to determine efficient

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contracts for executives. Thus, firms should leave the market to adjust the CEO compensation

and the consequence of pay regulations could be both unintended and costly (Murphy 2011).

Murphy (2011) explains that a large part of the regulatory failure is that the regulation is

often mis-intended.

The emerging empirical academic evidence is generally consistent with the arguments of

agency theory. For example, there is evidence that political intervention such as SOP laws

have a negative effect on the valuations of some firms (Larcker et al. 2011; Larcker et al.

2013), and that pension funds’ actions on executive compensation may be driven by political

agendas, potentially destroying shareholder value (Bainbridge 2011; Larcker and Tayan,

2012). Larcker et al. (2011) note an alternative explanation when the regulations are less

restrictive than expected and their laxness surprises the market, they could lead to unintended

consequences such as ineffectiveness of the regulations or firm value decrease.

Recent evidence based on the ‘say on pay’ legislation also casts doubts on the effect of the

government regulation. For instance, Conyon and Sadler (2010) find little evidence that the

UK ‘say on pay’ materially alters the subsequent level of CEO compensation. Armstrong et

al. (2013) find shareholder votes for equity pay plans have little substantive impact on firms’

incentive compensation policies. Meanwhile, Ertimur et al. (2013) conclude that though SOP

votes have an effect on compensation practices in firms with high SOP voting dissent, they

do not have a detectable impact on their quality. Cuñat et al. (2013) and Iliev and Vitanova

(2013) find no evidence that the US ‘say on pay’ affect the level or composition of the

executive compensation4 .

Little research has been conducted in the emerging market context. We only find two papers

in the literature5. Based on South Korean firms, Garner and Kim (2010) find that the capping

system only reduces cap-salary differences in firms with an efficient external monitoring

system (such as foreign shareholders). To our knowledge, Hu and Monem (2012) is the only

paper on Chinese firms. They find that a lower CEO-worker pay ratio in Chinese SOEs leads

to unintended consequences such as lower Tobin’s q, lower accounting profitability, more

frequent CEO promotion and more increases in average worker’s pay in poor performance

4 However, other studies find political interventions do take effect. For example, Bebchuk and Spamann (2010) show that compensation

structure coupled with capital structure induces U.S. bank executives to take excessive risk, and regulation on pay is necessary to remedy the

problems. Using a large cross-country sample of observations from 39 countries, Correa and Lel (2013) document that compared to control

group of firms, ‘say on pay’ laws are associated with a lower level of CEO compensation.

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firms. Their paper, however, does not provide direct evidence if government regulation

affects the compensation level or growth rate.

2.2 The 2009 Regulation and levels and growth rates of CEOs’ compensation

On September 16th

, 2009, six administrative departments of China’s central government6

issued The Guideline for Regulating Compensation of Executives in Charge of Central

Enterprises (2009 Regulation). The 2009 Regulation was cited as the Chinese version of

regulation on executive pay and thus became a milestone in ‘say on pay’ history in China

(Xinhua Net, 2009). This regulation applies to Chinese central SOEs7 in each industry and

supersedes prior industry specific regulations such as those released by Ministry of Finance

to limit the top executive compensation in state owned finance firms8 (SCMP 2009).

The issuance of the 2009 Regulation is in response to growing resentment at the excessive

pay to CEOs in Chinese SOEs and the enlarging CEO-worker pay gap (ifeng, 2009). Its

objective is to achieve an executive compensation system with a reasonable structure, an

appropriate level and standardized monitoring for Chinese SOEs (Xinhua Net, 2009). The

guideline raised five principles to be upheld in designing the compensation contract. First, the

principle of combining market adjustment and government monitoring; second, the principle

of unifying stimulation and restriction; third, the principle of considering both short-term and

long-term incentives; fourth, the principle of coordinating executive pay rise with worker pay

growth; fifth, the principle of complementing the compensation system improvement with

regulating the supplementary insurance and occupational consumption (Xinhua Net, 2009).

The 2009 Regulation prescribes that the CEO compensation should comprise three parts:

basic salary, bonus and long-term incentive pay. The document states that as the long-term

incentive schemes are still in a trial period in China, the guideline focuses on the first two

types of compensation. But SOEs are encouraged to explore the incentive schemes cautiously

(Xinhua Net, 2009). The 2009 Regulation requires that the basic salary of executives of SOEs

should not exceed five times the average pay of employees in the previous year and the bonus

6 The six central government departments are: Ministry of Human Resource and Social Security, Organisation Department, Ministry of

Supervision, Ministry of Finance, State-owned Assets Supervision and Administration Commission of the State Council (SASAC) and

China National Audit Office. 7 Central SOEs are SOEs controlled by the central government represented by the SASAC.

8 It also supersededs the 2002 regulation issued by the State Commission on Asset Control requiring that the top executives’ annual

compensation of central SOEs should not exceed 12 times the average employees’ pay.

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pay should not exceed three times the basic salary. The 2009 Regulation is applicable to 2009

executive compensation in Chinese central SOEs (ifeng, 2009). The regulation is then

replicated at the lower provincial level and applies to local SOEs9 (Xinhua Net, 2009).

Since its implementation, doubts have been cast on the effect of the 2009 Regulation, as

anecdotal evidence seems to show that CEO compensation levels in SOEs have not dropped

(Xinhua Net 2012). Further anecdotal evidence also seems to show that the specific capping

targets of CEOs compensation has not been ‘tight’ enough (ifeng 2009). Given the socialist

nature of the Chinese government and the above anecdotal evidence, we agree with Murphy’s

(2011) argument, that regulations of CEOs compensation are driven by politicians and their

political agenda, rather than concern with creating shareholder value. Accordingly, our

hypotheses are:

H1: The 2009 Regulation does not change the level of CEO compensation in Chinese SOEs.

H2: The 2009 Regulation does not change the growth rate of CEO compensation in Chinese

SOEs

2.3. 2009 Regulation and the pay-performance relation

Compared to the effect of regulation on compensation contract, fewer studies have directly

examined the effect of regulation on pay-performance relation. For instance, Ferri and Maber

(2013) report that UK firms react to negative ‘say on pay’ voting outcomes by removing

controversial CEO pay practices criticised as rewarding for failure and increasing the

sensitivity of pay to poor performance. Monem and Ng (2013) investigate the effect of 2011

‘say on pay’ legislation in Australia and show that the pay-performance link did not change

in 2011, but improved significantly in 2012. A prior study by Clarkson et al. (2009) also find

that increased shareholder oversight (through ‘no’ votes on the remuneration report) in

Australia strengthens the pay-performance link and makes the pay setting process more

accountable. Meanwhile, the international study by Correa and Lel (2013) concludes that ‘say

on pay’ laws lead to a higher pay for performance sensitivity.

9 Various provinces issued similar rules to constrain local SOEs’ CEO compensation. For instance, six local government departments in

Jiangxi Province issued a guideline that the basic salary of executives of local SOEs should not exceed three times the average pay of

employees in the previous year and the bonus pay should not exceed three times the basic salary (Jiangxi Government, 2013). Xiangfan City

in Hunan Province issued a guideline stating the the basic salary of executives of local SOEs should not exceed four times the average pay

of employees in the previous year and the bonus pay should not exceed three times the basic salary (Xiangfan City, 2009). Some local

governments (e.g. Suzhou City, Yancheng City) use a formula to calculate the bonus. Generally speaking, all local SASACs require the bonus should not be more than three times the basic salary no matter using a fixed target or a formula.

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Prior studies on Chinese pay-performance sensitivity (e.g. Firth et al. 2010; Conyon and He

2012) generally find a positive relationship between CEO compensation and firm

performance. However, whether the relation will be influenced by the government capping

regulation remains an open question. Proponents of capture theory may argue that the

government regulation strengthens the external governance environment and the CEOs may

be more accountable. Hence under the regulation pressure, the compensation contract is more

closely related to their performance and consequently increases the pay-performance link. On

the other hand advocates of agency theory may claim that 2009 Regulation capping the CEO-

worker pay ratio, provide less incentives for CEOs to achieve better performance, hence the

pay-performance sensitivity may even decrease. However, as prior studies also show

executives of Chinese SOEs receive pecuniary and non-pecuniary compensations (Chen et al.

2010), hence the CEOs may still be motivated to maintain the performance. Consequently,

we expect that the compensation capping regulation does not change the pay-performance

relation in Chinese SOEs. Accordingly, we predict:

H3: The 2009 Regulation does not change the pay-performance relation in Chinese SOEs.

3. Research design

3.1 Sample and data

Table 1 illustrates our sample selection process. This paper collects 2006 - 2011 data of all

Chinese SOEs with an A-share listing on Shanghai or Shenzhen Stock Exchanges10

.

Following Kato and Long (2006) and Firth et al. (2010), a listed firm is identified as an SOE

if its ultimate controlling shareholder is the government or government entity. Consequently,

5,127 observations are identified as SOEs between 2006 and 2011. New listing firms (387

observations) were excluded from the sample since these firms do not have continuous

compensation information during 2006 – 2011. Firms with nil compensation were also

removed from the sample (258 observations), as these are usually due to data errors or

unusual circumstances. In line with Ferri and Maber (2013), to ensure consistency in the

sample composition between pre (2006 - 2008) and post (2009 - 2011) periods, the analysis is

restricted to firms with at least one year in the pre-period and one year in the post-period.

Firms with missing data were excluded from the sample. 884 observations were thus

eliminated. We also eliminated the top and bottom 1% of the observations from the sample.

10

Chinese firms can issue A-Shares or B-Shares on the two domestic stock exchanges. As B-shares have not been actively traded in the

recent years (Chen et al. 2010), firms only issuing B-Shares have been excluded from our sample.

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The final data set is an unbalanced panel of 3,182 SOE observations over the period of 2006

– 2011.

The CEO compensation data, governance and other financial data are collected from the

China Stock Market and Accounting Research (CSMAR) database. To form a control sample

to compare with the SOEs, data for non-SOEs are also collected from the CSMAR.

Following a similar sample selection process, the final dataset of non-SOEs include 2,893

firm year observations. The matched pairs of SOEs and non-SOEs are drawn from the two

respective samples.

3.2 Regression models

3.2.1 The model to test H1

To test the effect of 2009 Regulation on CEO compensation level, two sets of regressions are

conducted. The following four regression models are hence estimated.

Ln(Comp)i = α0 + α1Post09dumi + α2CentralSOEi + ∑ Governance characteristicsi +

∑ CEO characteristicsi + ∑

Economic controlsi +βiInd_dumi +

βiReg_dumi + βiYear_dumi + εi (1)

CWPi = α0 + α1Post09dumi + α2CentralSOEi + ∑ Governance characteristicsi +

∑ CEO characteristicsi + ∑

Economic controlsi +βiInd_dumi +

βiReg_dumi + βiYear_dumi + εi (2)

Ln(Comp)I = α0 + α1SOEi + ∑ Governance characteristicsi + ∑

CEO

characteristicsi + ∑ Economic controlsi +βiInd_dumi + βiReg_dumi +

βiYear_dumi + εi (3)

CWPi = α0 + α1SOEi + ∑ Governance characteristicsi + ∑

CEO characteristicsi +

∑ Economic controlsi +βiInd_dumi + βiReg_dumi + βiYear_dumi + εi (4)

The first set of regressions (Models 1 and 2) is based on the SOE full sample. Specifically, it

tests if the post-2009 cash compensation and CWP ratios in SOEs have changed relative to

the pre-2009 period. The second set of regressions (Models 3 and 4) uses matched non-SOEs

as the control sample to see if the post-2009 compensation and CWP ratio in SOEs are

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significantly different to the non-SOE counterparts. A three-way matching method11

is used

to match SOEs and non-SOEs. We match the SOEs and non-SOEs firstly on industry,

secondly on financial year and thirdly on sales revenue. Consequently, 1,156 matched pairs

are generated from the original samples.

In the above four models, we use two proxies to measure the absolute and relative levels of

compensation respectively, namely, CEO cash compensation (denoted Comp) and CEO-

worker pay ratio (denoted CWP). Consistent with prior studies (Firth et al. 2010; Conyon

and He 2011; Wang and Xiao 2011), given the equity-based compensation is still rare in

Chinese companies, CEO cash compensation (includes salary, bonus and other cash

payments) is used to measure CEO compensation12

. The CEO-worker pay ratio is calculated

as the CEO total cash compensation divided by the average employee cash pay, whilst the

average employee cash pay is total cash paid to employees divided by number of employees.

This is consistent with Chen et al. (2010) and Firth et al. (2010).

In the first set of analysis, a dummy variable (denoted Post09dum) is created to indicate the

pre and post periods. The Post09dum equals to one [1] when the data is from 2009-2011 and

zero [0] when the data is from 2006-2008. A dummy variable is also created to indicate

central and local SOEs in the first set of analysis. A central SOE is defined as a firm

controlled by the SASAC representing central government and a local SOE is an SOE firm

controlled by local government and government agencies. CentralSOE is coded one [1] if a

firm is a central SOE and zero [0] otherwise. In the second set of regressions, a dummy

variable (denoted SOE) is included to distinguish SOEs from non-SOEs. SOE is coded one [1]

if its ultimate controller is the government or government entity and zero [0] otherwise.

Prior literature (e.g. Armstrong et al. 2010) finds that corporate governance characteristics

affect the level of CEO compensation. For example, Core et al. (1999) report CEO pay is

higher when the board is larger and more outside members are appointed by the CEO.

Independent directors tend to provide more effective monitoring over the compensation

contract (Fama and Jensen 1983; Coles et al. 2008). Meanwhile, Cyert et al. (2002) report

that CEO pay is 20 - 40 percent higher when the CEO is the chairman of the board.

11 An alternative to the matched-pair design is to use matched treatment and control observations based on propensity scores of the treatment

variable. Propensity score-based matching has been done in recent compensation studies (e.g., Armstrong et al., 2010; Armstrong et al.

2012). However, we do not employ this design because there is no established literature on the determinants of the SOE and non-SOE firms. 12 In the main analysis, the nominal compensation is used. The robustness checks use CPI-deflated 2006 RMB to re-test the hypotheses. All results remain statistically the same.

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Consistent with prior literature, this paper includes three variables to control for corporate

governance characteristics: board size (denoted Bdsize), board independence (denoted

Inddirs%) and duality (denoted Duality). Bdsize represents the number of directors on the

board. Inddirs% is the fraction of directors that are independent. Duality is an indicator

variable equal to one [1] if the CEO is also the chair of the board, zero [0] otherwise.

CEO characteristics are frequently found to impact CEO compensation. Hill and Phan (1991)

report a positive association between CEO tenure and CEO compensation. Armstrong et al.

(2012) shows CEO tenure affects the compensation level. CEO turnover often affects the

compensation level as well. When a new CEO is appointed, there is usually a change in the

compensation contract. Hence CEO tenure (denoted CEO_tenure) and CEO turnover

(denoted CEO_turnover) are controlled for in the regression. CEO_tenure is measured as the

number of years of service of the current CEO. CEO_turnover is an indicator variable equal

to one [1] if a CEO changes during the year, zero [0] otherwise.

A number of economic characteristics, including firm characteristics and firm performance

are found to affect the level of CEO compensation. Prior literature generally finds a positive

association between firm size and CEO compensation (e.g. Core and Guay 1999; Garbaix and

Landier 2008). We use the natural logarithm of sales to control for firm size (denoted

LnSales). Financial risks were found to influence the CEO compensation level as well (Core

et al. 1999). Therefore, leverage (denoted Lev), computed as year-end total liabilities divided

by total assets, is included in the regression. Consistent with prior literature (Matolcsy et al.

2006; Firth et al. 2010), a dummy variable Foreign is included to indicate if a firm issues B-

Share, H-Share or other foreign shares. A firm will be coded one [1] if it issues foreign shares

and zero [0] otherwise. Meanwhile, the percentage of shares controlled by the ultimate

shareholder (denoted ConShPer) is also included in the model to control for the ownership

structure. To control for investment opportunities, book-to-market value (denoted BMV) is

included in the model.

Firm performance has been well documented to influence compensation levels (Lambert and

Larcker 1987; Sloan 1993; Core et al. 2008; Armstrong et al. 2012). Accounting returns and

stock returns are common proxies for performance. In line with prior literature (Core et al.

1999, 2008), we use the return of assets (denoted ROA) and annual stock returns (denoted

RET) to measure performance. To control for risk factors, the standard deviations of

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accounting and stock performance (Std3ROA and Std3RET) are also included in the model.

Given cash compensation is usually based on the previous year’s performance (Perry and

Zenner, 2001; Core et al. 2008), all performance measures included in the regressions are the

lagged numbers.

To control for the industry specific fixed effects, 13 dummy variables for industries are

included13

. Due to different levels of economic development, firms from different regions

vary in compensation levels (Firth et al. 2007). Consistent with prior literature (Chen et al.

2010), to control for the regional fixed effects, 3 dummy variables are created: East, Central

and West. A firm is coded one [1] if it is from the region and zero [0] otherwise. Six year

dummies are also constructed to control for year fixed effects where the variable is equal to

one [1] if a firm is from a year and zero [0] otherwise.


To test the effect of the 2009 Regulation on the growth rates of CEO cash compensation and

CWP ratios, the following two sets of regression models are estimated:

ΔCompi = α0 + α1Post09dumi + α2CentralSOEi + ∑ ΔGovernance characteristicsi +

characteristicsi + ∑ ΔEconomic controlsi + βiInd_dumi + βiReg_dumi +


ΔCWP = α0 + α1Post09dumi + α2CentralSOEi + ∑ ΔGovernance characteristicsi +

characteristicsi + ∑ ΔEconomic controlsi + βiInd_dumi + βiReg_dumi +


ΔCompi = α0 + α1SOEi + ∑ ΔGovernance characteristicsi + characteristicsi +

∑ ΔEconomic controlsi + βiInd_dumi + βiReg_dumi + βiYear_dumi + εi (7)

ΔCWP = α0 + α1SOEi + ∑ ΔGovernance characteristicsi + characteristicsi +

∑ ΔEconomic controlsi + βiInd_dumi + βiReg_dumi + βiYear_dumi + εi (8)

Models 5 and 6 use the SOE full sample, and Models 7 and 8 are based on the matched

sample. As shown in the regressions, cash compensation growth rate (denoted ΔComp) and

change in the CWP ratio (denoted ΔCWP) are used as dependent variables. ΔComp and

13

The CSRC classifies Chinese firms to 13 industries: A: Agriculture and fishery, B: Mining, C: Manufacturing; D: Electricity, water and

other energy manufacturing and supply; E: Construction; F: Transportation and logistics; G: Information technology; H: Wholesales and

retails; I: Finance and insurance; J: Real estate; K: Service; L: Communication; M: Others. Following this classification, 13 indicator variables are created where the variable is equal to one [1] if a firm is from an industry and zero [0] otherwise.

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ΔCWP are measured as an annual percentage of change in CEO cash compensation and CEO-

worker pay ratio respectively. The independent variables are the dummy variables of

Post2009dum and SOE. Control variables are measured as the changes in continuous

variables of governance characteristics and economic factors.


To further test the association between CEO compensation and subsequent firm performance,

the following OLS regressions are estimated based on the SOE full sample:

ROAt+1,i = + Ln(Comp)t,i + Ln(Comp)t,i*Post09dumi + Post09dumi + LnSalest,i

+ Levt,i + Std3ROAt,i + iInd_dumi + iReg_dumi + iYear_dumi + εi (9)

RETt+1,i = + Ln(Comp)t,i + Ln(Comp)t,i *Post09dumi + Post09dumi + LnMkvt,i +

BMVt-1,i + Std3RETt,i + iInd_dumi + iReg_dumi + iYear_dumi + εi (10)

ROAt+2,i = + CWPt,i + CWPt,i*Post09dumi + Post09dumi + LnSalest,i+ Levt,i +

Std3ROAt,i + iInd_dumi + iReg_dumi + iYear_dumi + εi (11)

RETt+2,i = + CWPt,i + CWPt,i*Post09dumi + Post09dumi + LnMkvt,i + BMVt,i

+ Std3RETt,I + iInd_dumi + iReg_dumi + iYear_dumi + εi (12)

Using matched non-SOEs as the control sample, the second set of regression models testing

H3 are as follows:

ROAt+1,i = + Ln(Comp)t,i + Ln(Comp)t,i*SOEi + SOEi + LnSalest,i + Levt,i +

Std3ROAt,i + iInd_dumi + iReg_dumi + iYear_dumi + εi (13)

RETt+1,i = + Ln(Comp)t,i + Ln(Comp)t,i *SOEi + SOEi + LnMkvt,i + BMVt-1,i +

Std3RETt,i + iInd_dumi + iReg_dumi + iYear_dumi + εi (14)

ROAt+2,i = + CWPt,i + CWPt,i*SOEi + SOE + LnSalest,i+ Levt,i + Std3ROAt,i

+ iInd_dumi + iReg_dumi + iYear_dumi + εi (15)

RETt+2,i = + CWPt,i + CWPt,i*SOEi + SOEi + LnMkvt,i + BMVt,i +

Std3RETt,I + iInd_dumi + iReg_dumi + iYear_dumi + εi (16)

We examine both operating and stock performance in line with Core et al. (1999). The

accounting measure is ROA and the market-based measure is RET. Both measures are

calculated for the subsequent one and two years after compensation is awarded.

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To test if the pay-performance relation changes after 2009, an interaction of compensation

and Post09dum is created (denoted Ln(Comp)*Post09dum and CWP*Post09dum). The

original interacting variables of Ln(Comp), CWP and Post09dum are also included in the

respective regressions. Consistent with the ideology in Core et al. (1999), it is expected that if

the 2009 Regulation increases the pay-performance link in the Chinese SOEs, the signs on

the Ln(Comp)*Post09dum (or CWP*Post09dum) interaction term will be positive and

significant. If the 2009 Regulation fails, the coefficients on interaction terms will be

insignificant.

Similarly, when matched non-SOEs are used as the control sample, an interaction of

compensation and SOE is constructed (denoted Ln(Comp) *SOE and CWP*SOE). It is

expected that if the pay-performance link in the Chinese SOEs is different to non-SOEs, the

signs on the interaction of CEO cash compensation (or CEO-worker pay ratio) and SOE is

positive and significant. If the 2009 Regulation fails, the coefficients on interaction terms will

be insignificant.

Consistent with prior studies (e.g. Core et al. 1999), control variables include size (LnSales or

LnMkv), leverage (Lev), book-to-market value (BMV), and risk (Std3ROA or Std3RET) as

well as industry, region and year indicators.

4. Results reporting

4.1 Main results

Table 2 reports the descriptive statistics of CEO compensation in Chinese SOEs for the full

sample and subsamples partitioned by year, industry and region. Over 2006 to 2011, the

average (median) cash compensation of Chinese SOEs is 496,070 RMB (352,300 RMB)14

,

which is much lower than the U.S. companies in the similar period (Murphy 2011). The

average CEO-worker pay ratio is 7.747. This is higher than that reported by Firth et al. (2010)

(3.50 for 2000-2005) and Hu and Monem (2012) (6.59 for 2005-2009)15

. The average cash

compensation growth rate over 2006 – 2011 is 25.16% for the SOE full sample. On average

the CWP increased by 13.00% from 2006 -2011.

14

Using an average exchange rate of 1US$ = 6.9 RMB during 2006 -2011, the mean and median CEO compensation in Chinese SOEs is

about 73,480US$ and 52,170US$. 15

Frydman and Saks (2007) report that in the U.S. the ratio of CEO pay relative to average worker pay rose from 30 in 1970s to

approximately 120 by 2000.

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Table 2, Panel A reports the mean and median values of CEO compensation split by year.

Overall, the data in Panel A suggest a rising trend of total cash compensation. The regulatory

changes seem to not have curbed the overall CEO compensation level. In contrast, the CEO-

worker pay ratios dropped in 2008 and 2009, but then went up in 2010 and 2011. The growth

rate of total cash compensation or CWP seems to not to have a specific trend. The highest

growth rate of total cash compensation (CWP ratio) appeared in 2006 and the lowest

appeared in 2008.

Table 2, Panel B shows industry distribution of mean and median values of CEO

compensation in Chinese SOEs. Not surprisingly, on average CEOs in the finance industry

receive the highest pay of over 2 million RMB. The second highest paid industry is real estate

(930,937 RMB), which could be due to the real estate boom in China in recent years.

Meanwhile, CEOs in the agriculture and utilities industries are paid the lowest (315,859 and

406,757 RMB respectively). As expected, on average the CEO-worker pay disparity for the

finance industry is the highest (10.559) among all industries, which is followed by the

services industry (10.344). Of all sectors, the utilities industry has the lowest average CEO-

worker disparity (5.232). The IT industry has a slightly higher ratio (5.352). Importantly,

none of the industry has exceeded the CEO-worker pay ratio limit set by the 2009 Regulation.

This could mean that the 20-times cap is actually a rather generous limit. Regarding the cash

compensation growth rate, CEOs in the agriculture and communication industries had the

highest average pay increase during 2006 to 2011, whilst the pay rises for finance and utilities

sectors are the lowest. In terms of changes in CWP ratio, agriculture and communication are

again the two with the quickest increase in the CWP gap, whilst transportation and mining

industries are the two slowest.

Panel C of Table 2 demonstrates CEO compensation in Chinese SOEs partitioning by the

level of government supervision. About 20% of the SOEs are supervised by the central

government and the rest by local governments. On average, CEOs in these central SOEs are

paid a higher level of cash compensation than local SOEs. The CWP ratios in central SOEs

are also higher than their peer local SOEs. Meanwhile, the cash compensation growth rates

and CWP growth rates are at a similar level.

Table 3 presents the comparison of descriptive statistics for the SOE full sample between pre

and post-regulation periods. The average total cash compensation and CEO-worker pay ratio

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have both increased after 2009. The t-statistics show the difference between mean values is

only significant for total cash compensation (p<0.01). The Wilcoxon-Mann-Whitney tests,

however, indicate that the median differences in total cash compensation and CEO-worker

pay ratio are both statistically significant. The growth rates of cash compensation and CWP

ratios dropped after 2009, but neither of the difference is significant. SOE firms tend to have

a significantly smaller board after 2009 compared to the pre-2009 period (p<0.05). The

average independent director percentage in the post-period is significantly higher than the

pre-period, whilst CEO turnover is significantly higher as well (p<0.01). Meanwhile, firms

tend to have significantly larger sales, higher leverage, and lower book-to-market ratio in the

post-2009 period than the pre-period (p<0.01). Table 3 also shows the average ROA levels

were not significantly changed after the 2009 Regulation. The average and median stock

returns are, however, significantly lower in the post-2009 period (p<0.01).

Table 4 reports the contrast of post-2009 descriptive statistics of SOEs and matched non-

SOEs. The average total cash compensation for SOEs (546,831RMB) is moderately lower

than non-SOEs (595,330RMB) (p<0.10). The Wilcoxon-test result shows the medians of

CEO cash compensation are not significantly different. The CEO-worker pay ratios for non-

SOEs are significantly higher than SOEs in both the pre- and post-2009 periods (p<0.01).

The growth rates of cash compensation and CWP of SOEs are lower than non-SOEs though

neither difference is significant. Regarding CEO and firm characteristics, on average, SOEs

tend to have a bigger board and a lower percentage of CEO duality arrangement. SOEs also

have a bigger size, a higher leverage level, a higher percentage of firms issuing foreign shares,

a more dominant controlling shareholder and a higher book-to-market ratio. Meanwhile, the

accounting returns and stock returns of SOEs are significantly lower than non-SOEs.

4.2 Effect of 2009 Regulation on levels of compensation

Table 5 reports the multivariate results testing H1 based on two benchmarks, the pre-2009

SOEs (Panel A) and non-SOEs (Panel B). In panel A, the F-statistics of both two models are

highly significant. The adjusted R-square values are moderate. The coefficient on Post09dum

is positive and significant in Model 1 (p<0.01), whilst insignificant in Model 2 though

negative. This indicates that the absolute level of CEO cash compensation in Chinese SOEs

increased rather than decreased after the 2009 Regulation, whilst the CWP ratio does not

change significantly after the 2009 Regulation. This finding is consistent with prior western

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studies such as Perry and Zenner (2001) and Conyon and Sadler (2010). The results, therefore,

support the H1.

Regarding control variables, the coefficients on the CentralSOE is positive and highly

significant in the Model 1 (p<0.01), which shows that central SOEs have significantly higher

CEO total cash compensation than local SOEs. In line with expectation, firms with CEOs

chairing the board, longer CEO tenure, lower CEO turnover and larger size tend to have

higher CEO cash compensation and CWP ratios. Higher leverage firms pay significantly

lower CEO total compensation. Firms issuing foreign shares tend to have higher total CEO

compensation and CWP ratio. This is likely because these firms use foreign firms as

benchmarks for their CEO compensation. There is a negative relationship between shares

controlled by the ultimate shareholder and the total cash compensation (CWP ratio). During

2006-2011, firms with a lower book-to-market ratio pay CEOs more cash compensation. The

performance measures are significant in some models. Specifically, ROA is positive and

significant in both Models 1 and 2, which means the total CEO cash compensation and CWP

ratio are both driven by them. Meanwhile, stock returns and Std3ROA are positively

associated with CEO total cash compensation.

Table 5, Panel B shows regression results testing H1 using non-SOEs as the control sample.

The F-statistics of Models 3 and 4 are highly significant (p<0.01). The R-Square values of the

two models show reasonable explanatory power. The coefficient on the SOE is negative and

significant in the Model 2 only (p<0.01), meaning that the CWP ratio of SOEs is significantly

lower than non-SOEs. This seems to show that the regulated SOEs have significantly lower

CWP ratios relative to non-SOEs. In terms of control variables, the directional signs of these

variables are all consistent with those in Panel A and the majority of these control variables

remain significant in Panel B.

Overall, results show that the 2009 Regulation did not decrease the cash compensation level

or the CWP in Chinese SOEs compared to the pre-2009 period. However, relative to the non-

regulated firms (non-SOEs), the post-2009 CWP ratios of regulated firms (SOEs) are

significantly lower.

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4.3 Effect of 2009 Regulation on growth rates of compensation

Results testing H2 are reported in Table 6. Again Panels A and B use two alternative control

samples to examine the effect of 2009 Regulation on the growth rates of cash compensation

and CWP ratios.

Table 6, Panel A presents regression results based on the SOE full sample., The F-value of

the Models 1 and 2 are significant (p<0.01). The adjusted R-Squares of both models are

rather low (below 1%). None of coefficient on Post09dum is significant, this means the

growth rates of cash compensation and CWP ratio in Chinese SOEs have not changed after

2009. Most of the control variables are insignificant with some exceptions. Specifically, in

Model 1 ΔLn(sales) is positive and significant, whilst ΔBMV is negative and significant.

ΔConShPer and ΔBMV are negative and significant in Model 2.

Results based on the matched sample are shown in Table 6, Panel B. Again, both models are

significant, though the adjusted R-Squares are low. The coefficients on SOE are insignificant

in both models. This means after the 2009 Regulation, the growth rates of CEO cash

compensation or CWP ratio in Chinese SOEs are not significantly different to those in non-

SOEs. Regarding control variables, ΔLn(sales) and ΔRET are significant in Model 3 and

ΔBdsize is significant in Model 4.

In summary, consistent with expectation, the two sets of tests both show that the 2009

Regulation does not change the growth rates of cash compensation or CWP ratios. Hence, H2

is supported.

4.4 The impact of 2009 Regulation on pay-performance relation

Table 7 reports the effect of 2009 Regulation on the pay-performance relation in Chinese

SOEs using the SOE full sample. Panels A and B show the relation between pay and

subsequent one and two year performance respectively.

Table 7 shows the F-statistics are highly significant in all eight models. The adjusted R-

Square values vary across the models with stock performance models tend to have much

higher explanatory power (from 0.241 to 0.374) than operating performance models (from

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0.064 to 0.080). Consistent with expectation, none of coefficient in the eight models is

significant. This shows the pay and the operating (stock) performance link does not increase

after the 2009 Regulation. Therefore, H3 is supported.

For the subsequent one-year operating performance, the coefficient on total cash

compensation is positive and significant (p<0.01). This means consistent with findings of

Firth et al (2010), there is a positive link between total cash compensation and operating

performance in China. However, the coefficient on the CWP is not significant though positive.

Regarding the subsequent one-year stock performance, neither the coefficient on the

Ln(Comp) nor CWP is significant. Meanwhile, the coefficients on Post09dum are negative

and highly significant in both Models 3 and 4, which means the post-2009 stock performance

in Chinese SOEs is worse than pre-2009. This is consistent with the descriptive statistics.

Panel B reports the pay and subsequent two-year performance link. Only the coefficient on

total cash compensation is highly significant in Model 5, which suggest a positive pay-

operating performance link. Post09dum are again significant in Models 7 and 8, which

indicate the stock returns dropped significantly after 2009.

Table 8 examines the pay-performance relation difference between SOEs and matched SOEs

for the post-2009 period. The coefficients on the interactions are insignificant in all eight

models. This indicates that the pay-performance link in the SOEs is not different to the

matched non-SOEs after 2009. The 2009 Regulation seems to not to have an effect on the

pay-performance relation. H3 is, therefore, accepted.

5. Robustness checks

A number of sensitivity tests were conducted to ensure the robustness of the main results.

First of all, for the second set of regressions, the pre-2009 non-SOE data are also included in

the regressions to ensure the post-2009 differences between SOEs and non-SOEs are due to

the 2009 Regulation, not because of pre-2009 differences. An interaction term of Post09dum

*SOE is created to re-run regressions testing H1 and H2. Results are reported in Table 9. As

shown in Table 9, the only significant interaction term is in Model 2. This means the CWP

ratios in the SOEs did decrease after 2009 relative to the non-SOEs, which confirms the

findings from the main analysis in Table 5.

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Secondly, results in Tables 5 and 9 show that whilst we find a significant difference in CWP

ratios between SOEs and non-SOEs, the CEO cash compensation were not significantly

different between the two groups. Questions might be raised if the CWP difference is due to

SOEs increased their employees’ wages faster than their counterparts. Table 10, Panel A

presents the comparison of the average employee wages and their growth rates between SOEs

and non-SOEs. The SOEs have significantly higher average employee wages than the

matched non-SOEs in both pre and post-2009 periods. Prior to 2009, the growth rate of

average employee wages is significantly lower than non-SOEs. However, the growth rate of

the average employee wages in the post-2009 period is not significantly different to the

matched peers. This suggests that the difference in CWP ratios was possibly due to the

increase in the employee wages in the SOEs. In Table 10, Panel B, we re-run the CWP

regressions based on the matched sample by including the growth rate of employee wages as

a control variable. Results show that when the regressions are based on the post-2009

matched sample, the coefficient on the SOE remains significant. However, when the

regressions are based on the pooled pre and post-2009 matched sample, the coefficient on the

interaction term becomes insignificant. It seems that the CWP difference between the SOEs

and non-SOEs is because of the changes in the employee wages. Therefore, results suggest

that the 2009 Regulation in fact did not change the levels of CEO compensation.

Thirdly, to ensure the inflation does not affect the main results (Perry and Zenner 2001; Rose

and Wolfram 2002), CEO cash compensation is replaced with CPI-deflated 2006 RMB16

. The

ΔComp numbers are smaller than original numbers, but all results remain statistically the

same. Fourthly, in Equations (9) to (16) the operational performance is remeasured using

ROE, and the stock performance is replaced by one year (or two years) buy-and-hold returns.

The main results for stock performance models are slightly less significant and the operating

performance models stay qualitatively similar. In addition, the operating and stock

performance proxies are remeasured as the industry-adjusted performance. Again the main

results stay unchanged. Finally, the results still hold when the finance firms were excluded

from the sample. The results are also robust to restricting the analysis to firms with available

data in at least two (rather than one) years in both pre- and post-periods.

16

According to China Statistical Year Book, the CPI indices over 2006 – 2011 are 100, 103.3, 104.3, 97.8, 101.8, and 103.8 respectively

using 2006 as the base year. Due to the limit of space, the following sensitivity test results are not tabulated but are available upon request.

of 35

Overall, the sensitivity test results confirm the main findings of this study that the 2009

Regulation did not decrease the cash compensation or CWP ratios in the SOEs. The growth

rates of CEO cash compensation and CWP in the SOEs did not drop after 2009 and the pay-

performance relation is not affected by the 2009 Regulation. Although the SOEs have

relatively lower CWP ratios compared to the matched non-SOEs, sensitivity analysis suggests

this is achieved by improving the employee wages rather than reducing the CEO

compensation.

6. Conclusions

An emerging line of literature examines if regulations on CEO compensation really work.

Existing empirical evidence (e.g. Conyon and Sadler 2010; Cai and Walkling 2011; Ferri and

Maber 2013) has been mainly focused on western countries. To extend this line of literature

to developing countries, we examine the effectiveness of the 2009 Regulation issued by the

Chinese government. With rare research has been conducted in this area, this study makes

original contributions to the literature.

Results from this study show the 2009 Regulation did not reduce the level of CEO cash

compensation or the CEO-worker pay ratio in Chinese SOEs. The growth rate of CEO cash

compensation or CWO ratio in the SOEs was not affected by the 2009 Regulation either. The

pay-performance link in the SOEs did not change after the 2009 Regulation. Findings from

this research, therefore, suggest the government capping intervention did not work in China.

This supports the agency perspective, showing the government intervention does not have an

effect in constraining the levels or growth rates of CEO compensation. Our findings build on

the existing ‘say on pay’ literature, by providing evidence that whilst increased shareholder

voting rights have little influence on the CEO compensation practice, setting fix targets for

executive pay levels and/or growth rate may also be ineffective. Findings from our study,

thus, can help regulators to reconsider how to further improve the effectiveness of these

regulations and/or whether regulating executive compensation may only have political rather

than economic benefits. Overall, as one of the first studies to examine if the Chinese

regulation on pay works, this study adds important new evidence into the emerging ‘say on

pay’ literature.

of 35

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Table 1: Sample selection process of the Chinese SOEs

Initial SOE

sample

Post-2006

New Listing

Nil cash

compensation

Data missing Outliers Final SOE

sample

2006 790 (-) (63) (187) (87) 453

2007 830 (40) (42) (165) (85) 498

2008 841 (51) (26) (130) (77) 557

2009 863 (73) (43) (123) (51) 573

2010 888 (98) (40) (146) (59) 545

2011 915 (125) (44) (133) (57) 556

Total 5,127 (387) (258) (884) (416) 3,182

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Table 2: Mean and median (in parentheses) values of CEO compensation in Chinese SOEs

This table presents the descriptive statistics for CEO compensation in Chinese SOEs partitioning by year, industry and

region. Comp is total CEO cash compensation. CWP is the ratio of CEO cash compensation to the average worker’s cash

compensation. ΔComp is the annual growth rate of CEO cash compensation. ΔCWP is the annual growth rate of CEO cash

compensation to the average worker’s cash compensation.

N Comp CWP ΔComp ΔCWP

Total 3,182 496,070

(352,300)

7.747

(5.316)

25.16%

(10.36%)

13.00%

(-0.61%)

Panel A: by year

2006 453 288,000

(232,260)

7.110

(5.046)

28.76%

(11.63%)

18.90%

(2.53%)

2007 498 416,708

(298,050)

7.951

(5.247)

37.77%

(21.00%)

18.75%

(3.08%)

2008 557 475,482

(351,800)

7.573

(5.251)

15.53%

(3.82%)

3.43%

(-7.62%)

2009 573 483,186

(355,900)

7.345

(5.223)

19.86%

(6.79%)

16.23%

(4.27%)

2010 545 587,471

(426,900)

8.002

(5.520)

27.44%

(13.25%)

14.10%

(0.40%)

2011 556 680,990

(498,950)

8.424

(5.469)

23.81%

(9.20%)

8.24%

(-3.71%)

Panel B: by industry

Agriculture 48 315,859

(238,384)

9.580

(4.670)

40.40%

(11.22%)

34.49%

(12.40%)

Communication 38 534,529

(363,700)

8.055

(6.477)

30.99%

(9.29%)

24.57%

(4.92%)

Construction 135 497,438

(407,400)

6.675

(4.305)

23.87%

(14.31%)

11.57%

(1.80%)

Finance 32 2,106,559

(1,290,700)

10.559

(7.350)

14.69%

(0.45%)

17.50%

(-11.17%)

IT 46 498,925

(463,500)

5.352

(5.105)

24.84%

(14.59%)

13.89%

(8.90%)

Manufacturing 1,776 425,373

(302,750)

7.753

(5.471)

25.56%

(10.16%)

12.63%

(-0.80%)

Mining 142 515,082

(485,200)

8.055

(6.477)

21.34%

(11.94%)

8.66%

(-7.02%)

Others 66 583,057

(499,450)

10.133

(7.227)

25.08%

(4.71%)

15.66%

(-0.10%)

Real estate 169 930,937

(533,916)

8.797

(4.032)

30.62%

(13.23%)

20.84%

(3.48%)

Services 88 458,227

(366,400)

10.344

(6.859)

26.16%

(9.06%)

13.80%

(-3.84%)

Transportation 203 502,038

(455,000)

6.330

(4.987)

21.78%

(9.47%)

6.35%

(-1.69%)

Utilities 179 406,757

(344,300)

5.232

(4.083)

20.35%

(7.00%)

9.31%

(-3.18%)

Wholesale and

retail 260

561,715

(444,600)

8.088

(5.649)

24.92%

(13.68%)

14.03%

(4.53%)

Panel C: by level of government supervision

Central 655 562,983

(445,400)

7.941

(5.409)

24.02%

(10.39%)

12.08%

(0.30%)

Local 2,527 478,726

(339,470)

7.697

(5.294)

25.46%

(10.34%)

13.24%

(-0.87%)

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Table 3: Descriptive statistics for the SOE full sample

***, ** and * are significant at 1%, 5% and 10% respectively

This table compares descriptive statistics of SOEs between pre-2009 and post-2009 periods. Comp is total CEO cash compensation. CWP is the ratio of CEO cash compensation to the average

worker’s cash compensation. ΔComp is the annual growth rate of CEO cash compensation. ΔCWP is the annual growth rate of CEO cash compensation to the average worker’s cash

compensation. Bdsize represents the number of directors on the board. Inddirs% is the fraction of directors that are independent. Duality is an indicator variable equal to 1 if the CEO is also the

chair of the board, 0 otherwise. CEO_tenure is the number of years of service of the current CEO. CEO_turnover is an indicator variable equal to 1 if a CEO changes during the year, 0

otherwise. Ln(Sales) represents the natural logarithm of sales. Lev equals average total liabilities divided by average total assets. Foreign is an indicator variable equal to 1 if a firm issues B-

Share, H-Share or any other foreign shares, 0 otherwise. ConShPer is the percentage of shares that is controlled by the ultimate shareholder. BMV (book to market value) is measured as total

common equity divided by market value. ROA is the ratio of earnings before interest and taxes to total assets. RET is the annual stock return. Std3ROA and Std3RET represent the three year

standard deviation of ROA and RET respectively.

Full sample (2006-2011)

(N=3,182)

Pre-2009 (2006-2008)

(N= 1,508)

Post-2009 (2009-2011)

(N=1,674)

Post vs Pre-2009

t-test Wilcoxon

Mean Std Dev Q1 Median Q3 Mean Std Dev Median Mean Std Dev Median (Mean)

(Median)

Comp 496,070 599,779 209,625 352,30

0

583,850 399,753 529,492 298,050 582,836 644,634 428,050 8.698*** -13.872***

CWP 7.747 8.762 3.226 5.316 9.213 7.559 8.714 5.193 7.917 8.805 5.406 1.153 2.456**

ΔComp 0.252 0.609 -0.059 0.104 0.347 0.268 0.618 0.114 0.236 0.601 0.099 -1.485 -1.638

ΔCWP 0.130 0.610 -0.196 -0.061 0.270 0.131 0.631 -0.012 0.128 0.590 -0.002 -0.118 -1.179

Bdsize 9.650 2.031 9.000 9.000 11.000 9.730 2.060 9.000 9.580 2.003 9.000 -2.081** -2.602***

Inddirs% 0.358 0.047 0.333 0.333 0.364 0.353 0.043 0.333 0.362 0.051 0.333 5.128*** 4.061***

Duality 0.080 0.270 0.000 0.000 0.000 0.080 0.279 0.000 0.070 0.262 0.000 -1.127 -1.127

CEO_tenure 3.305 1.877 1.917 3.000 4.333 3.379 1.488 3.083 3.165 1.980 3.083 -0.167 -0.121

CEO_turnover 0.160 0.370 0.000 0.000 0.000 0.110 0.318 0.000 0.210 0.405 0.000 7.162*** 7.106

Ln(sales) 9.338 0.649 8.938 9.271 9.682 9.233 0.600 9.195 9.432 0.677 9.363 8.735*** 8.974***

Lev 0.537 0.221 0.390 0.545 0.673 0.522 0.207 0.531 0.551 0.230 0.562 3.620*** 3.961***

Foreign 0.120 0.323 0.000 0.000 0.000 0.120 0.320 0.000 0.120 0.326 0.000 0.403 0.403

ConShPer 0.400 0.146 0.290 0.399 0.505 0.395 0.143 0.393 0.404 0.149 0.403 1.569 1.361

BMV 0.694 0.265 0.488 0.700 0.909 0.769 0.249 0.790 0.630 0.262 0.609 -14.947*** -14.580***

ROA 0.033 0.068 0.012 0.033 0.058 0.033 0.070 0.031 0.034 0.066 0.033 0.594 0.961

RET 0.562 1.148 -0.349 0.286 1.209 0.722 1.351 0.589 0.418 0.899 0.071 -7.430*** -1.801*

Std3ROA 0.033 0.098 0.007 0.015 0.035 0.033 0.116 0.014 0.034 0.078 0.016 0.102 3.083***

Std3RET 1.119 0.627 0.708 1.011 1.402 1.086 0.683 0.967 1.149 0.570 1.042 2.843*** 4.679***

of 35

Table 4: Comparison of SOEs and non-SOEs using the matched sample (post-2009)

***, ** and * are significant at 1%, 5% and 10% respectively

This table compares descriptive statistics for SOEs and matched non-SOEs for the post-2009 period. Comp is total CEO cash

compensation. CWP is the ratio of CEO cash compensation to the average worker’s cash compensation. ΔComp is the annual

growth rate of CEO cash compensation. ΔCWP is the annual growth rate of CEO cash compensation to the average worker’s

cash compensation. Bdsize represents the number of directors on the board. Inddirs% is the fraction of directors that are

independent. Duality is an indicator variable equal to 1 if the CEO is also the chair of the board, 0 otherwise. CEO_tenure is

the number of years of service of the current CEO. CEO_turnover is an indicator variable equal to 1 if a CEO changes

during the year, 0 otherwise. Ln(Sales) represents the natural logarithm of sales. Lev equals average total liabilities divided

by average total assets. Foreign is an indicator variable equal to 1 if a firm issues B-Share, H-Share or any other foreign

shares, 0 otherwise. ConShPer is the percentage of shares that is controlled by the ultimate shareholder. BMV (book to

market value) is measured as total common equity divided by market value. ROA is the ratio of earnings before interest and

taxes to total assets. RET is the annual stock return. Std3ROA and Std3RET represent the three year standard deviation of

ROA and RET respectively.

Mean

(N=1,156)

Median

(N=1,156) Analysis of differences

SOE

Non-

SOE SOE

Non-

SOE

Mean (t-test) Median (Wilcoxon)

Diff t Diff z

Comp 546,831 595,330 383,250 406,000 48,501 -1.852* -22,750 -1.139

CWP 7.541 10.540 5.275 7.297 -2.999 -7.585*** -2.022 -9.121***

ΔComp 0.242 0.291 0.104 0.075 -0.049 -1.488 0.029 0.898

ΔCWP 0.148 0.180 0.006 -0.013 -0.032 -1.130 0.019 1.058

Bdsize 9.380 8.840 9.000 9.000 0.540 7.226*** 0.000 7.289***

Inddirs% 0.363 0.363 0.333 0.333 0.000 -0.169 0.000 0.469

Duality 0.008 0.230 0.000 0.000 -0.222 -10.419*** 0.000 10.185***

CEO_tenure 3.176 3.337 3.853 4.000 -0.161 -1.925* -0.147 -1.220

CEO_turnover 0.210 0.180 0.000 0.000 0.030 1.417 0.000 1.417

Ln(sales) 9.237 9.182 9.220 9.146 0.055 2.448** 0.074 2.913***

Lev 0.531 0.509 0.537 0.506 0.022 2.354** 0.031 3.197***

Foreign 0.007 0.004 0.000 0.000 0.003 2.832*** 0.000 2.828***

ConShPer 0.388 0.321 0.392 0.299 0.067 10.720*** 0.093 10.842***

BMV 0.583 0.555 0.552 0.538 0.028 2.668*** 0.014 2.132**

ROA 0.035 0.041 0.031 0.039 -0.006 -2.172*** -0.008 -4.275***

RET 0.432 0.487 0.114 0.161 -0.055 -1.459** -0.047 -1.322

Std3ROA 0.033 0.033 0.016 0.018 0.000 -0.029 -0.002 -1.853*

Std3RET 1.147 1.139 1.060 1.040 0.008 0.375 0.020 0.490

of 35

Table 5: Regression of effect of 2009 Regulation on compensation levels and CWP ratio

Panel A: Use the SOE full sample Panel B: Use the matched sample

Ln(Comp) CWP Ln(Comp) CWP

(1) (2) (3) (4)

Intercept 8.825*** -16.345*** Intercept 7.760*** -32.416***

Post09dum 0.340*** -0.014 SOE 0.025 -0.124***

Central 0.041*** 0.010

Bdsize 0.061*** 0.014 Bdsize 0.051*** 0.053**

Inddirs% 0.026* 0.002 Inddirs% 0.023 0.005

Duality 0.051*** 0.088*** Duality 0.091*** 0.112***

CEO_tenure 0.068*** 0.058*** CEO_tenure 0.029 0.040*

CEO_turnover -0.088*** -0.044** CEO_turnover -0.091*** -0.036

Ln(sales) 0.322*** 0.226*** Ln(sales) 0.356*** 0.244***

Lev -0.081*** -0.021 Lev -0.045** -0.028

Foreign 0.060*** 0.047** Foreign 0.046*** 0.059***

ConShPer -0.088*** -0.185*** ConShPer -0.049*** -0.078***

BMV -0.040** -0.008 BMV -0.069*** -0.022

ROA 0.165*** 0.066*** ROA 0.150*** 0.079***

RET 0.035* 0.032 RET 0.057 0.013

Std3ROA 0.035** 0.003 Std3ROA 0.004* 0.011

Std3RET 0.001 -0.014 Std3RET -0.010 0.035

Ind_dum Yes Yes Ind_dum Yes Yes

Reg_dum Yes Yes Reg_dum Yes Yes

Year_dum Yes Yes Year_dum Yes Yes

Adj R-Square 0.410 0.107 Adj R-Square 0.346 0.126

F-value 82.732*** 15.087*** F-value 51.948*** 14.840***

N 3,182 3,182 N 2,312 2,312 ***, ** and * are significant at 1%, 5% and 10% respectively

Note: ΔRET is removed from the regression due to multicollinearity problem.

This table presents if the 2009 Regulation affects levels and CWP ratios of CEO compensation in Chinese SOEs. Ln(Comp)

is the natural logarithm of total CEO cash compensation. CWP is the ratio of CEO cash compensation to the average

worker’s cash compensation. ΔComp is the annual growth rate of CEO cash compensation. ΔCWP is the annual growth rate

of CEO cash compensation to the average worker’s cash compensation. Post09dum is an indicator variable equal to 1 for

observations in the period 2009 –2011 (post 2009 regulation) and 0 for observations in the period 2006-2008 (pre-2009

regulation). Inddirs% is the fraction of directors that are independent. Duality is an indicator variable equal to 1 if the CEO is

also the chair of the board, 0 otherwise. CEO_tenure is the number of years of service of the current CEO. CEO_turnover is

an indicator variable equal to 1 if a CEO changes during the year, 0 otherwise. Ln(Sales) represents the natural logarithm of

sales. Lev equals average total liabilities divided by average total assets (in millions of dollars). Foreign is an indicator

variable equal to 1 if a firm issues B-Share, H-Share or any other foreign shares, 0 otherwise. ConShPer is the percentage of

shares that is controlled by the ultimate shareholder. BMV (book to market value) is measured as total common equity

divided by market value. ROA is the ratio of earnings before interest and taxes to total assets. RET is the annual stock return.

Std3ROA and Std3RET represent the three year standard deviation of ROA and RET respectively. Ind_dum, Reg_dum, and

Year_dum are industry, region and year dummies respectively.

of 35

Table 6: Regression of effect of 2009 Regulation on the growth rates of cash compensation and CWP

Panel A: Use the SOE full sample Panel B: Use the matched sample

ΔComp ΔCWP ΔComp ΔCWP

(1) (2) (3) (4)

Intercept 0.217*** 0.145*** Intercept 0.140 10.275

Post09dum -0.006 -0.047 SOE 0.039 -0.139

Central -0.001 0.005 ΔBdsize -0.019 -0.037*

ΔBdsize 0.007 0.001 ΔInddirs% -0.030 0.008

ΔInddirs% 0.001 -0.002 ΔCEO_tenure 0.012 0.013

ΔCEO_tenure 0.013 0.011 ΔLn(sales) 0.123*** -0.012

ΔLn(sales) 0.064*** -0.004 ΔLev -0.032 -0.075

ΔLev -0.024 0.002 ΔConShPer 0.015 -0.031

ΔConShPer -0.028 -0.038** ΔBMV -0.029 0.045

ΔBMV -0.076** -0.072** ΔROA 0.019 -0.034

ΔROA 0.016 0.032 ΔRET -0.046** 0.003

ΔRET -0.018 -0.007 ΔStd3ROA 0.003 0.017

ΔStd3ROA -0.008 -0.009 ΔStd3RET -0.002 0.010

ΔStd3RET 0.035 0.021 Ind_dum Yes Yes

Ind_dum Yes Yes Reg_dum Yes Yes

Reg_dum Yes Yes Year_dum Yes Yes

Year_dum Yes Yes


F-value 3.029*** 2.119*** F-value 1.955*** 6.063***

N 3,109 3,109 N 2,272 2,272 ***, ** and * are significant at 1%, 5% and 10% respectively

Note: ΔRET is removed from the regression due to multicollinearity problem.

This table presents if the 2009 Regulation affects growth rates of CEO compensation and CWP ratios in Chinese SOEs.

ΔComp is the annual growth rate of CEO cash compensation. ΔCWP is the annual growth rate of CEO cash compensation to

the average worker’s cash compensation. Post09dum is an indicator variable equal to 1 for observations in the period 2009 –

2011 (post 2009 regulation) and 0 for observations in the period 2006-2008 (pre-2009 regulation). ΔBdsize, ΔInddirs%,

ΔLev, ΔConShPer, ΔBMV, ΔLn(sales), ΔROA, ΔRET, ΔStd3ROA, and ΔStd3RET are the annual changes in the respective

variables. Ind_dum, Reg_dum, and Year_dum are industry, region and year dummies respectively.

of 35

Table 7: Pooled cross-sectional regressions of 2009 Regulation on pay-performance relation using the SOE full sample

Panel A: Performance t+1 Panel B: Performance t+2

ROA t+1 RET t+1 ROA t+2 RET t+2

(1) (2) (3) (4) (5) (6) (7) (8)

Intercept -0.264 ** -0.118 1.292 1.618*** -0.242* -0.104* 0.352 0.331***

Ln(Comp) 0.172*** 0.024 0.150*** -0.001

Ln(Comp)*Post09dum 0.019 0.012 0.027 0.014

Post09dum 0.040* -0.156*** 0.010 -0.585***

CWP 0.028 0.005 0.029 0.015

CWP * Post09dum 0.012 0.004 0.005 -0.002

Post09dum 0.062*** -0.155*** 0.030 -0.585***

Ln(Sales) 0.102*** 0.166*** 0.106*** 0.153***

Lev -0.155*** -0.179*** -0.182*** -0.199***

Std3ROA 0.084*** 0.087*** 0.050** 0.052***

Ln(Mkv) -0.279*** -0.268*** 0.130*** 0.130***

BMV 0.383*** 0.384*** -0.378*** -0.377***

Std3RET -0.019 -0.018*** 0.064*** -0.064***

Ind_dum Yes Yes Yes Yes Yes Yes Yes Yes

Reg_dum Yes Yes Yes Yes Yes Yes Yes Yes

Year_dum Yes Yes Yes Yes Yes Yes Yes Yes

Adj R-Square 0.080 0.066 0.374 0.373 0.075 0.064 0.241 0.241

F-value 21.340*** 15.967*** 127.561*** 127.180*** 16.234*** 13.718**** 60.450*** 60.463***

N 3,182 3,182 3,182 3,182 2,626 2,626 2,626 2,626 ***, ** and * are significant at 1%, 5% and 10% respectively.

This table presents if the pay-performance link in Chinese SOEs changes after 2009 Regulation using the SOE full sample. Panels A and B report subsequent one-year and two-year performance

respectively. ROA is the ratio of earnings before interest and taxes to total assets. RET is the annual stock return. Ln(Comp) is the natural logarithm of total CEO cash compensation. CWP is the

ratio of CEO cash compensation to the average worker’s cash compensation. Post09dum is an indicator variable equal to 1 for observations in the period 2009 –2011 (post 2009 regulation) and

0 for observations in the period 2006-2008 (pre-2009 regulation). Ln(Sales) represents the natural logarithm of sales. Ln(Mkv) is the natural logarithm of market value. BMV (book to market

value) is measured as total common equity divided by market value. Std3ROA and Std3RET represent the three year standard deviation of ROA and RET respectively. Ind_dum, Reg_dum and

Year_dum are industry and region dummies respectively.

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Table 8: Pooled cross-sectional regressions of 2009 Regulation on pay-performance relation of Chinese SOEs using the matched sample

***, ** and * are significant at 1%, 5% and 10% respectively.

This table examines the pay-performance link for the post-2009 period using the matched sample. Panels A and B report subsequent one-year and two-year performance respectively. ROA is the

ratio of earnings before interest and taxes to total assets. RET is the annual stock return. Ln(Comp) is the natural logarithm of total CEO cash compensation. CWP is the ratio of CEO cash

compensation to the average worker’s cash compensation. Post09dum is an indicator variable equal to 1 for observations in the period 2009 –2011 and 0 for observations in the period 2006-

2008. Ln(Sales) represents the natural logarithm of sales. Ln(Mkv) is the natural logarithm of market value. BMV (book to market value) is measured as total common equity divided by market

value. Std3ROA and Std3RET represent the three year standard deviation of ROA and RET respectively. Ind_dum, Reg_dum and Year_dum are industry and region dummies respectively.

Panel A: Performance t+1 Panel B: Performance t+2

ROA t+1 RET t+1 ROA t+2 RET t+2

(1) (2) (3) (4) (5) (6) (7) (8)

Intercept 0.315 0.628*** 0.322** 0.611*** -0.082 -0.084* -0.051 -0.268***

Ln(Comp) 0.069** 0.055** 0.006* -0.057*

Ln(Comp)*SOE 0.375 0.007 0.665 0.003

SOE -0.401 -0.024*** -0.712* 0.027***

CWP 0.024 0.056** 0.032 -0.028

CWP * SOE 0.024 -0.020 0.010 0.006

SOE -0.037 0.003*** -0.049** 0.023

Ln(Sales) -0.113*** -0.090*** 0.080*** 0.086***

Lev 0.111*** 0.103*** -0.081*** -0.085***

Std3ROA 0.128*** 0.126*** 0.058** 0.059**

Ln(Mkv) -0.135*** -0.126*** -0.026 -0.042*

BMV 0.022 0.021 0.077*** 0.079***

Std3RET -0.051*** -0.054*** 0.028 0.031

Ind_dum Yes Yes Yes Yes Yes Yes Yes Yes

Reg_dum Yes Yes Yes Yes Yes Yes Yes Yes

Year_dum Yes Yes Yes Yes Yes Yes Yes Yes

Adj R-Square 0.043 0.038 0.285 0.285 0.018 0.016 0.347 0.346

F-value 7.970*** 7.151*** 62.219*** 62.223*** 3.091*** 2.854*** 60.051*** 59.598***

N 2,312 2,312 2,312 2,312 1,558 1,558 1,558 1,558

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Table 9: Cross-sectional regressions using the pooled (both pre-and post 2009) matched sample

Panel A Panel B

Ln(Comp) CWP ΔComp ΔCWP

(1) (2) (3) (4)

Intercept 7.670*** -26.438*** Intercept 0.321** 0.258***

Post09dum 0.231*** 0.033 Post09dum -0.107*** -0.059

Post09dum*SOE -0.012 -0.045** Post09dum*SOE 0.096 0.032

SOE 0.034* -0.083*** SOE -0.083 -0.059**

Bdsize 0.058*** 0.057*** ΔBdsize -0.001 -0.016

Inddirs% 0.027** 0.008 ΔInddirs% 0.004 -0.016

Duality 0.100*** 0.121***

CEO_tenure 0.011*** 0.023 ΔCEO_tenure 0.001 0.002

CEO_turnover -0.085*** -0.057***

Ln(sales) 0.330*** 0.220** ΔLn(sales) 0.071*** 0.017

Lev 0.050*** 0.021 ΔLev -0.001 -0.004

Foreign 0.052*** 0.045***

ConShPer -0.065*** -0.092*** ΔConShPer -0.015 -0.016

BMV -0.121*** -0.043** ΔBMV 0.001 -0.013

ROA 0.185*** 0.076*** ΔROA 0.002 -0.002

RET -0.146*** -0.038* ΔRET 0.004 0.008

Std3ROA -0.003 -0.008 ΔStd3ROA 0.012 0.010

Std3RET 0.027 0.055*** ΔStd3RET -0.035* -0.018

Ind_dum Yes Yes Ind_dum Yes Yes

Reg_dum Yes Yes Reg_dum Yes Yes

Year_dum Yes Yes Year_dum Yes Yes


F-value 101.906*** 23.501*** F-value 3.828*** 1.407*

N 4,262 4,262 N 4,159 4,159 ***, ** and * are significant at 1%, 5% and 10% respectively.

This table presents the robustness checks for the effect of 2009 Regulation on cash compensation and CWP ratios and their

growth rates using the pooled matched sample of both pre-2009 and post-2009 data. Ln(Comp) is the natural logarithm of

total CEO cash compensation. CWP is the ratio of CEO cash compensation to the average worker’s cash compensation.

ΔComp is the annual growth rate of CEO cash compensation. ΔCWP is the annual growth rate of CEO cash compensation to

the average worker’s cash compensation. Post09dum is an indicator variable equal to 1 for observations in the period 2009 –

2011 (post 2009 regulation) and 0 for observations in the period 2006-2008 (pre-2009 regulation). Inddirs% is the fraction of

directors that are independent. Duality is an indicator variable equal to 1 if the CEO is also the chair of the board, 0

otherwise. CEO_tenure is the number of years of service of the current CEO. CEO_turnover is an indicator variable equal to

1 if a CEO changes during the year, 0 otherwise. Ln(Sales) represents the natural logarithm of sales. Lev equals average total

liabilities divided by average total assets (in millions of dollars). Foreign is an indicator variable equal to 1 if a firm issues

B-Share, H-Share or any other foreign shares, 0 otherwise. ConShPer is the percentage of shares that is controlled by the

ultimate shareholder. BMV (book to market value) is measured as total common equity divided by market value. ROA is the

ratio of earnings before interest and taxes to total assets. RET is the annual stock return. Std3ROA and Std3RET represent the

three year standard deviation of ROA and RET respectively. Ind_dum, Reg_dum, and Year_dum are industry, region and year

dummies respectively. ΔBdsize, ΔInddirs%, ΔLev, ΔConShPer, ΔBMV, ΔLn(sales), ΔROA, ΔRET, ΔStd3ROA, and ΔStd3RET

are the annual changes in the respective variables.

of 35

Table 10: Analysis of the moderating effect of employee wages on CWP ratios

***, ** and * are significant at 1%, 5% and 10% respectively.

This table presents if employee wages affect CWP ratios. EmpWg is the average employee wages paid by a firm for a certain

year. ΔEmpWage is the annual changes in the average employee wages. Ln(Comp) is the natural logarithm of total CEO

cash compensation. CWP is the ratio of CEO cash compensation to the average worker’s cash compensation. ΔComp is the

annual growth rate of CEO cash compensation. ΔCWP is the annual growth rate of CEO cash compensation to the average

worker’s cash compensation. Post09dum is an indicator variable equal to 1 for observations in the period 2009 –2011 (post

2009 regulation) and 0 for observations in the period 2006-2008 (pre-2009 regulation). Inddirs% is the fraction of directors

that are independent. Duality is an indicator variable equal to 1 if the CEO is also the chair of the board, 0 otherwise.

CEO_tenure is the number of years of service of the current CEO. CEO_turnover is an indicator variable equal to 1 if a CEO

changes during the year, 0 otherwise. Ln(Sales) represents the natural logarithm of sales. Lev equals average total liabilities

divided by average total assets (in millions of dollars). Foreign is an indicator variable equal to 1 if a firm issues B-Share, H-

Share or any other foreign shares, 0 otherwise. ConShPer is the percentage of shares that is controlled by the ultimate

shareholder. BMV (book to market value) is measured as total common equity divided by market value. ROA is the ratio of

earnings before interest and taxes to total assets. RET is the annual stock return. Std3ROA and Std3RET represent the three

year standard deviation of ROA and RET respectively. Ind_dum, Reg_dum, and Year_dum are industry, region and year

dummies respectively.

Panel A: Comparison of employee wages in SOEs and matched non-SOEs

Mean

Median

Analysis of differences

SOE

Non-

SOE SOE

Non-

SOE

Mean (t-test) Median (Wilcoxon)

Diff t Diff z

Pre-2009 (N=975)

EmpWg 67,304 49,459 48,166 35,847 17,845 6.598*** 12,319 9.639***

ΔEmpWage 0.211 0.322 0.148 0.187 -0.110 -3.569*** -0.039 -2.450***

Post-2009 (N=1,156)

EmpWg 87,682 70,560 65,930 49,645 17,122 5.543*** 16,285 10.658***

ΔEmpWage 0.147 0.185 0.115 0.128 -0.038 -1.738 -0.013 -1.524

Panel B: CWP regressions controlling for employee wages

CWP CWP

(1) (2)

Intercept 23.491*** Intercept -26.174***

SOE -0.050** Post09dum 0.029

Post09dum*SOE -0.041

SOE -0.090***

ΔEmpWg -0.078*** ΔEmpWg -0.074***

Bdsize 0.062*** Bdsize 0.058***

Inddirs% 0.007 Inddirs% 0.008

Duality 0.122*** Duality 0.122***

CEO_tenure 0.040 CEO_tenure 0.030

CEO_turnover -0.060*** CEO_turnover -0.052***

Ln(sales) 0.292*** Ln(sales) 0.219***

Lev 0.020 Lev 0.019

Foreign 0.076*** Foreign 0.044***

ConShPer -0.060*** ConShPer -0.090***

BMV -0.042* BMV -0.041**

ROA 0.136*** ROA 0.075***

RET -0.062*** RET -0.036

Std3ROA 0.024 Std3ROA -0.006

Std3RET 0.028 Std3RET 0.057***

Ind_dum Yes Ind_dum Yes

Reg_dum Yes Reg_dum Yes

Year_dum Yes Year_dum Yes

Adj R-Square 0.240 Adj R-Square 0.126

F-value 32.807*** F-value 23.722***

N 2,312 N 4,262

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