THREE ESSAYS ON EMPLOYMENT AND COMPENSATION IN CHINA

by

Lin Xiu

A thesis submitted in conformity with the requirements for the degree of PhD in Industrial Relations

Centre for Industrial Relations and Human Resources University of Toronto

© Copyright by Lin Xiu 2010

  

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Three Essays on Employment and Compensation in China

Lin Xiu

PhD in Industrial Relations

Centre for Industrial Relations and Human Resouces University of Toronto

2010

Abstract

The three essays in this dissertation address two prominent labour market and human

resource management issues in contemporary China: gender pay differentials; and pay-

performance relationship in managerial compensation. Using three unique data sets, this

dissertation examines three areas: the managerial gender pay gap in top corporate jobs; the

effect of state ownership and managerial power on CEO compensation; and the gender pay

compensation differentials in base pay, performance pay and total pay.

The first chapter uses a unique data set from a survey of firms and managers in China

to examine the managerial gender earnings gap in China. The results show that female

managers receive much lower pay than male managers. A larger portion of the gender

earnings gap can be attributable to firm-level characteristics than individual characteristics.

Female managers tend to have fewer firm-level characteristics that are associated with higher

pay, and when they do, they tend to receive a smaller pay premium for those characteristics.

The second chapter uses a data set constructed for the study based on corporate

annual reports. Results indicate that CEO compensation is positively related to the financial

performance of the firms in both state controlled and non-state controlled firms. The

compensation level, after controlling for various pay-determining factors, is higher in non-

state controlled firms and for CEOs with greater managerial power. The strength of the pay-

performance link is stronger in non-state owned firms compared to state owned firms (as

indicated by the interaction between performance and state ownership). When state

controlled firms and non-state controlled firms are analyzed separately, the pay-performance

link is significantly weaker for CEOs with greater managerial power in non-state controlled

  

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firms, and weaker but not significantly so in state controlled firms (as indicated by the

interaction terms between firm performance and managerial power variables). Whether

CEOs are recruited from outside of the firm or from inside of the firm does not have an effect

on either the CEO compensation level or the strength of the pay-performance link.

The third chapter examines whether and how the gender pay gap varies across

different pay schemes: base pay, performance pay and total pay. The results show that

women receive about three-quarters of male pay for each of the dimensions of base pay,

performance pay and total pay, before adjusting for the effect of different pay determining

factors. Decomposition analysis of the different components of pay (base pay, performance

pay and “other” pay) indicate that males earn about 30% more than females in total pay with

the gender gap in performance pay (35%) and in “other” forms of pay (28%) both being

greater than the gap in base pay (25.5%). The unexplained or potential discriminatory

component, however, is smaller for performance pay and “other” forms of pay compared to

base pay, suggesting that there is not more discriminatory discretion in the awarding of

performance pay and the “other” forms of pay compared to base pay.

  

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Acknowledgement

This thesis would not have been possible without my thesis committees’ consistent

support and advice throughout the process. I owe my deepest gratitude to my supervisor,

Professor Morley Gunderson, for guiding and coaching me on how to think and work as a

researcher. I also would like to thank Professor Mike Campolieti for his statistical guidance,

and thank Anil Verma and Rafael Gomez for their interest in my work.

I am grateful to the administrative staff at the Centre for Industrial Relations and

Human Resources led by Deborah Campbell. I wish to thank Monica Hypher, Bruce Pearce,

and Vicki Skelton for the continuous support that you provided to my research needs.

I wish to thank my colleagues and fellow students for their support, as well as the

comments on my research during the PhD seminars.

I wish to thank my parents, Zhongshan Xiu and Xiuju Man, for the consistent

encouragement, endless support, and greatest sacrifices that they have made so that I could

accomplish my work. I wish to thank my husband, Yufei Ren, who has supported me all the

time. Finally, I dedicate this thesis to my son, Alexander Ren, who has been my primary

motivation to succeed.

  

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Table of Contents 

Chapter 1: Managerial Gender Pay Gap in Top Corporate Jobs in China

1. Introduction ......................................................................................................................... 1 

2. Literature Review ................................................................................................................. 3 

2.1 Gender Pay Differentials in China .................................................................................. 3 

2.2 Literature on CEO Compensation in China .................................................................... 6 

3. Data and Descriptive Statistics ............................................................................................ 8 

4. Regression and Decomposition Results ............................................................................ 10 

4.1 OLS Regression with Pooled Sample ........................................................................... 11 

4.2 OLS Regression with Males and Females Separately .................................................. 12 

4.3 Decomposition of the Male-Female Managerial Pay Gap ............................................ 15 

4.4 Sub-Decomposition of the Characteristics and Returns Components .......................... 18 

5. Concluding Discussion ...................................................................................................... 21 

Table 1 .................................................................................................................................... 23 

Table 2 .................................................................................................................................... 24 

Table 3 .................................................................................................................................... 25 

Table 4 .................................................................................................................................... 26 

Table 5 .................................................................................................................................... 27 

References............................................................................................................................... 28

Chapter 2: Pay-For-Performance in Executive Compensation in China: The Impact of the State Ownership and Managerial Power

1. Introduction ........................................................................................................................ 31 

2. China’s SOE Reforms and the Managerial Labour Market ................................................ 32 

3. Literature and Expected Relationships ............................................................................... 35 

Pay-Performance Relationship ............................................................................................ 35 

State-ownership and Pay-Performance Relationship .......................................................... 36 

Managerial Power and the Pay-Performance Relationship ................................................. 37 

External Hiring and CEO Compensation ............................................................................ 39 

4. Data, Variables, and Summary Statistics ............................................................................ 40 

  

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4.1 Data ............................................................................................................................... 40 

4.2 Variables and Summary Statistics................................................................................. 41 

5. Methodology and Results ................................................................................................... 43 

5.1 OLS Regression on Pooled Sample .............................................................................. 44 

5.2 Separate Sample on SOEs and Non-SOEs .................................................................... 46 

5.2 Separate Sample on SOEs and Non-SOEs with Interaction Terms on Managerial Power and Outside Hiring .............................................................................................................. 47 

6. Conclusion and Discussion ................................................................................................. 47 

Table 1 .................................................................................................................................... 49 

Table 2 .................................................................................................................................... 50 

References............................................................................................................................... 51

Chapter 3: Male-Female Compensation Differentials in China: Base Pay, Performance Pay and Total Pay

1. Introduction ........................................................................................................................ 54 

2. Wage System Reform and Gender Pay Differentials in China ........................................... 55 

3. Literature and the Receipt of Performance Pay .................................................................. 61 

4. Data, Variables and Summary Statistics ............................................................................. 64 

4.1 Data ............................................................................................................................... 64 

4.2 Variables ....................................................................................................................... 65 

4.3 Summary Statistics and Unadjusted Earnings Ratios ................................................... 66 

5. Estimating Equations and Empirical Results ..................................................................... 67 

5.1 Pay Gap Equations with a Gender Dummy Variable ................................................... 67 

5.2 Separate Male-Female Pay Equations (Total Pay) ....................................................... 69 

5.2.1 Model 1: Basic Regression with Personal and Human Capital Variables ............ 70 

5.2.2 Model 2: Adding Ownership Type ........................................................................ 72 

5.2.3 Model 3: Adding Occupation and Rank Within the Organization ........................ 72 

5.3 Marginal Effects of Being Female on Performance Pay: Tobit Estimates ................... 73 

5.4 Decomposition Results ................................................................................................. 75 

5.4.1 Linear Decompositions for OLS Regressions(Base Pay and Total Pay) ............... 75 

5.4.2 Non-linear Decompositions for Tobit Regressions ................................................ 76 

  

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5.4.3 Results .................................................................................................................... 77 

5.5 Sub-decomposing To Illustrate Relative Contribution of Different Variables ............ 78 

6. Summary and Discussion .................................................................................................. 80 

Table 1 .................................................................................................................................... 85 

Table 2 .................................................................................................................................... 86 

Table 3 .................................................................................................................................... 88 

Table 4 .................................................................................................................................... 90 

Table 5 .................................................................................................................................... 91 

Table 6 .................................................................................................................................... 92 

Table 7 .................................................................................................................................... 93 

Table 8 .................................................................................................................................... 94 

References............................................................................................................................... 95 

Appendix ................................................................................................................................ 98 

  

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Chapter 1

Managerial Gender Pay Gap in Top Corporate Jobs in China

1. Introduction

China has a long tradition of Confucianism, which emphasizes the subordinate roles

of women in the society, as illustrated by the famous saying “nuzi wucai bianshi de”, which

translated literally means “lack of talent is a virtue of women”. Such beliefs diminished

during the planned economy (1949-1978) when the Chinese central government

implemented a system of national wage scales based on the socialist egalitarianism principle

whereby wage dispersion due to human capital characteristics was suppressed. Nevertheless,

the portion of females in top organization jobs still remains low and gender earnings

differentials still exist. This paper analyzes gender earnings differentials among Chief

Executive Officers (CEOs), executives and top managers (hereafter all generally referred to

as managers) in China.

The motivation for, and main contributions of this study, are threefold. First, the

question of whether and how females are treated financially differently from their male

counterparts in contemporary Chinese organizations is of great interest to the three parties in

the employment relationship: government policy makers, employers, and employees. If pay

differentials do exist and are largely due to different treatments that men and women receive

at the workplace, then policy makers may need to address the issue through equal pay and

equal employment opportunity initiatives. In contrast, if the gender pay differential is largely

due to the lower education or training received by female managers, then policies regarding

education and manager development and training would be more relevant than equal pay

  

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issues so as to address differences in the acquisition of “human capital”. From the

organization’s perspective, if gender pay differentials are largely due to institutional barriers

that disadvantage women, or other discriminatory factors at the workplace, then firm

performance will suffer because organizations are not maximizing the likelihood that pay and

hiring is based on the productivity of managers. From the employees’ side, it is interesting to

know how these highest paid women are paid after they cross the “glass ceiling” and enter

the managerial ranks.

Second, in this study, the unobserved differences between men and women are

minimized as we focus on a specific occupation group (managers) where men and women

are more likely to share some common unobserved characteristics such as career ambition.

This is crucial for identifying the factors that lead to gender pay differentials since the

unexplained part of male-female wage differentials could reflect labour market

discrimination, but could also be due to differences between men and women that are

generally unobservable, such as differences in career commitment or job motivation

(Bertrand and Hallock, 2001).

Third, the study uses data from a survey of firms and managers in 2006 from Liuzhou,

Guangxi, China. An important advantage of using this survey data is that it contains

information on both the managers and the firms in which they work. The inclusion of firm

characteristics into the analysis will help to identify the factors that underlie the managerial

gender pay differential. For example, Drolet (2002) using the Canadian Workplace and

Employee Survey found that when workplace and industry measures were included, the

“explained” component of the gender pay differential increased substantially. Also,

  

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compared to earlier studies on managerial pay, we have a relatively larger portion of women

in our sample (21.6%), which facilitates more accurate estimates of gender pay differences.

This paper is structured as follows. Section 2 will draw on two major literatures:

studies on gender pay differentials in China and CEO compensation studies in China, and

will briefly discuss the relevant findings from these two literatures. Section 3 will introduce

the data and methodology. Results will be presented in Section 4. Section 5 provides a

general discussion and highlights some policy implications.

2. Literature Review

2.1 Gender Pay Differentials in China

There is a growing literature on gender earnings differentials in China since the

middle 1990s. These studies tend to find that male pay exceeds female pay by about 15% to

25% before adjusting for differences in their pay determining characteristics. This substantial

unadjusted earnings gap has been increasing in the past two decades (e.g. Appleton et al.,

2005; Bishop, Luo and Wang, 2005; Maurer-Fazio & Hughes, 2002; Ng, 2007; Shu & Bian,

2003; Zhang et al., 2008).

In terms of how the gender earnings gap (referring to “1 minus female-male pay

ratio”) varies across the wage distribution, the research generally finds the gap to be greater

at the lower end of the wage distribution than at the upper end (Bishop et al., 2005; Chi & Li,

2008; Zhang et al., 2008; Milimet & Wang, 2006;). In more recent years, however, the

gender gap of highly paid workers is widening greatly (Zhang et al., 2008). The previously

cited figures refer to the gross gender earnings gap, before adjusting for the effect of other

factors that may explain at least part of the gap.

  

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Some of the studies have decomposed the overall pay gap into an “explained”

component attributed to differences in their endowments of wage-determining characteristics

such as education and labor market experience, and an “unexplained” portion (often labeled

as discrimination) due to differences in the pay that male and female workers receive for the

same wage-determining characteristics.

Most (but not all) studies found the unexplained or discriminatory component in

China to be greater than the explained component attributable to differences in endowments

of pay determining characteristics. This was the case in Bishop et al. (2005), Gustafsson and

Li (2000), Liu et al. (2000) and Wang and Cai (2008), but not in Hughes and Maurer-Fazio

(2002). For instance, Bishop et al. (2005) found the “unexplained” portion of the pay gap to

be 71% in 1988 and 61% in 1995. Gustafsson and Li (2000) found the “unexplained” portion

to be 52.5% in 1988 and 63.2% in 1995. Hughes and Maurer-Fazio (2002) found the

"unexplained" portion of the total gender wage gap to be around 40% in 1992.

Among the growing literature on the gender pay differentials in China, only a few

studies have examined how the share of the “unexplained” portion varies along the wage

distribution. Bishop et al. (2005) showed that in 1995, the unexplained share is 78.4%, 67.2%,

57.4%, 55.1% and 59.9% in 1995 and 97.8%, 57.2%, 52.2%, 58.6% in 1998, respectively at

the 0.10, 0.25, 0.50, 0.75 and 0.90 quantiles. These numbers indicate that the unexplained or

“discrimination” portion is largest at the bottom part of the distribution although it was

slightly decreasing from 1988 to 1995. Chi and Li (2008) also found that both the gap and

the subcomponents are largest in the lower tail of the distribution and indicate that there is a

“sticky floor” effect. Millimet and Wang (2006) found that discrimination explains one-third

to one-half of the total predicted pay gap in the lower tail of the distribution, and little of the

  

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gap in the upper tail with CHIP (1995) data. A similar pattern was documented in Zhang et

al. (2008).

In terms of the returns to marriage, Hughes and Maurer-Fazio (2002) found that both

the unadjusted gender pay gap and the “unexplained” portion are larger for married women

compared to their unmarried counterparts, highlighting that the “marriage penalty” for

females also prevails in China. In particular, they find that although married women

generally earn more than single women, the gross gender pay gap is higher for married

women than for unmarried women. Moreover, after controlling for other factors, married

women earn more (about 3% to 5%) in state-owned enterprises and collective enterprises,

and earn less (about 12%) in joint venture companies than single women. Women in more

competitive sectors (more subject to market forces) experience a marriage penalty while men

experience a marriage premium. The unexplained portion is larger for married workers than

for single workers. They also found the gender pay gap to be smaller for college educated

women and that the discriminatory component decreases with education attainment.

With respect to industrial segregation, Wang and Cai (2008) grouped the industries

into four sectors from highest paid to the lowest paid group based on the average wages of

different industries in the China Statistical Yearbook 2002. Then relying on the China Urban

Labour Survey (CULS) conducted in 2001, they examined the industry segregation and the

inter-sectoral and within-sectoral gender earning differentials. They found that females were

concentrated in the lower paid industries while 24.3% of men and 15.6% of women worked

in the highest paid group. The female-male hourly wage ratio was 0.77, with 94% of the gap

attributable to the within-sectoral gender pay differential and 6% to sectoral distribution

  

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differences. Of the inter-sectoral gender pay gap, about 61% can be attributed to

discrimination.

With respect to occupation segregation, Hughes and Maurer-Fazio (2002) did not find

any evidence of women in China being segregated into low paying occupations and hence

argued that occupational segregation by gender is not an important factor in urban China.

They did find, however, that the gap varied within occupations with the gap being greatest in

higher paid occupations, such as ranked administrators, engineering staff and technical

workers.

In summary, the various studies yield the following generalizations: there is evidence

of an overall gender pay gap; the discriminatory or unexplained component is larger than the

explained component; the gap is lower among the more educated, in higher paying jobs, and

it is greater for married persons.

2.2 Literature on CEO Compensation in China

In contrast to the large number of CEO compensation studies in Western countries,

only a handful of studies have been published about China in this area and most of the papers

have been undertaken in the past decade. Ten articles have been identified, one published in

1995, and the other nine published between 2000 and 2008. Six of the articles used data from

listed publicly traded firms, while four used survey data, focusing mainly on State-Owned

Enterprises (SOEs).

CEO compensation in these papers usually refers to cash compensation including

base salary, bonuses, and commissions. Share ownership by executives is very low in China

(Xu, 2004). This is so in large part because after some debate the government decided not to

allow listed firms to offer stock options to executives (Firth, Fung and Rui, 2007).

  

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Beginning in 1998, listed companies are required to disclose top management compensation,

so the studies using data from listed firms are becoming more common after that time.

These studies have shown consistent evidence of a positive correlation between firm

performance and the compensation of top managers. For example, Buck, Liu and Skovoroda

(2008) showed that executive pay and firm performance mutually affect each other through

reward and motivation. Firth et al. (2007) found a positive pay-performance relationship in

China when performance is measured as return on assets, although the relationship is not

significant when performance is measured by stock returns for the period 1998-2000. Kato

and Long (2006) extended the data range to 2002, and obtained a higher and significantly

positive pay-for-performance relationship. Groves et al. (1995) and Mengistae and Xu (2004)

showed that top management pay in state-owned-enterprises (SOEs) depends on firm

performance. The consistency of the results across various studies in China differs from more

mixed evidence on the pay-for-performance relationship found in the U.S. (e.g. Devers et al.,

2007; Conyon and Murphy, 2000; Core et al., 1999).

In China, various other factors have impacted on CEO compensation. For example,

Firth et al. (2007) showed that compensation is higher in foreign owned companies and

lower in state-owned companies, and firms with joint CEO/Chairman positions are less likely

to use performance-based pay. Ding, Akhtar and Ge (2006) analyzed firm-level data from

three major cities (Shanghai, Nanjing and Guangzhou), and showed that ownership, firm size,

firm age, location and industrial sector, have significant impacts on managerial compensation.

  

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3. Data and Descriptive Statistics

This paper used survey data from firms and top managers from Liuzhou, Guangxi,

China. Questionnaires were delivered and reclaimed anonymously by the Federation of

Industry and Commerce of Liuzhou, Guangxi. The questionnaire contains information on

firms and the top managers, including gender, age, political status (Communist Party

member or not), education, marriage status, source of employment earnings, job tenure and

receipt of business training. The enterprise questionnaire contains information on industry,

registered capital (as a proxy for firm size), years the firm has been in business, and number

of employees.

1050 questionnaires were distributed, 1017 returned the questionnaires, among which

582 answered both of the key questions on gender and pay. The effective response rate is

55.4%. Regression imputation procedures were employed to deal with the missing values for

a number of variables: “capital” (36 missing values), “years the firm has been in business”

(23 missing), marital status (1 missing), age (6 missing), job tenure (12 missing), and number

of employees (51 missing). The descriptive statistics before and after imputation are close.

For example, the average job tenure for males is 9.33 years for 450 observations (before

imputation), and 9.34 years for 456 observations (after imputation). After missing value

imputation, the sample size increased from 530 to 582.

There were 126 females, accounting for 21.6% of the total sample. This relatively

larger percentage of females than earlier studies on CEO or top organization executives (e.g.

2.4% in Bertrand and Hallock (2001) with US data, 4% in Kato and Long (2006) with

Chinese listed firm data from the year of 1998-2002) is likely due to the fact that most firms

in this data source are small and medium size firms while the above studies employed data

from listed firms, usually larger in size.

  

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As illustrated in table 1, average female managers earned 162,400 Yuan in total

compensation, compared to 246,400 Yuan for male managers, implying a female-male pay

ratio of 0.675 or a pay gap of 32.5%. Total compensation consisted of base wages, bonuses,

stock options, commissions and profit sharing. Unlike data from publically listed firms

where stock options and profit sharing are not allowed, our survey data contains information

on these components.

Table 1 shows that women in top managerial positions work for smaller firms.

Female top managers’ firms were 10% smaller when firm size was measured as registered

capital, and 66% smaller in terms of number of employees. The average number of

employees per firm for male and female top managers was 90 and 31, respectively. When

the fraction of women by deciles of registered capital is computed, women constituted about

26% of top management employment in the bottom three deciles and only 16% in the top

decile. Earlier (non Chinese) studies on executive compensation show that CEOs tend to be

paid more in larger firms (e.g. Murphy, 1999). Our subsequent analysis will indicate how

much of the gender gap can be attributed to the under-representation of women in larger

firms.

Women in the sample on average were about 4 years younger than the men (40.0

versus 44.1 years old), and had 2 fewer years of seniority in their company (7.1 versus 9.3

years).

As the respondents were CEOs/Chairs or other top managers in the company, we

created a variable called “president/Chair”, indicating whether the respondent was the very

top manager of the company: 68% of men and 55% of women reported they were CEOs or

Chairs.

  

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Due to the small sample size and the fact that most of the industries are concentrated

in a few sectors, we were only able to categorize the industry into two broad categories—

service and non-service—with 29% of men and 40% of women working in the service

industry. For the same reason, the education categories1 were combined and respondents

were grouped into three categories: less than high school, high school, college/university or

higher. Women and men had roughly the same education level, although 57% of male CEOs

and 44% of female CEOs had taken business training. 93% of males and 82% of females are

married and 33% of males and 24% of females are CCP (Chinese Communist Party)

members, which is an indicator of how close they are with the local government.

The general pattern that emerges from the descriptive portrayal is that male managers

disproportionately have characteristics that tend to be associated with higher wages. More

specifically, male managers tend to be employed in larger firms in non-service sector jobs,

they have longer tenure with the firm, are more likely to be married, to have taken business

training, and to be a CEO/Chair rather than a manager. In the next section, the independent

impact of these different factors is analyzed, as is their relative importance in “explaining”

the male-female wage gap.

4. Regression and Decomposition Results

In this section, we investigate how various characteristics of CEOs and the

enterprises in which they work might account for the gender pay gap. We first examine the

effect of various variables on the gender pay gap by looking at how the gender coefficient

1 The survey contains information on 8 education categories: less than elementary school, elementary school, junior middle school, high school, two years college, university, graduate (master level), and graduate (PhD level).

  

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changes as more wage-determining factors are controlled for. We then compare the OLS

estimates for males and females and use decomposition methods to more precisely examine

how much of the gender gap is explained by the various pay-determining characteristics and

how much remains “unexplained” in the sense of pay differences for the same observable

characteristics.

4.1 OLS Regression with Pooled Sample

The dependent variable is the log of pay, as shown in the data section, consisting of

base wages, bonuses, commissions, stock options, and profit sharing. Two sets of

independent variables are utilized —firm characteristics and individual characteristics. Firm

characteristics include registered capital, years the firm has been in business, industry,

executive rank in the firm, and whether managers received profit sharing. Individual

characteristics include age, job tenure, marital status, party membership, education, and

business training.

Table 2 shows the results of the pay regressions. The unconditional gender gap is

about 32.5% (column 1). Age and job tenure hardly explain any of the gap (column 2), and

neither do marital status and CCP membership (column 3). The education variables are

statistically significant, but do not contribute to explaining the gender gap as the gender

coefficient does not decrease when education variables are added into the model (column 4).

The gender pay differential reduces to 26.6% when business training variables are controlled

for (column 5). In total, individual characteristics explained 18.2% of the total pay gap.

Next, we examine the effect of firm characteristics on the gender pay gap. When firm

size, as measured by the logarithmic of firm capital, is controlled for, the gender pay

differential fell to 22.0% (column 6). Adding further industry and company history variables

  

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does not contribute to reducing the remaining gender pay gap (column 7). Adding the

executives’ rank (column 8) reduces the gender gap by 2.9 percentage points compared to

column 7. Finally, column 9 examines the effect of the compensation payment method,

specifically whether company profit was part of the executives’ compensation. When it was

controlled for, the gender pay gap is reduced by another 3.9 percentage points. Comparing

column 9 and column 5, illustrates that firm characteristics explain 10.7 percentage points or

32.9% of the total gender pay gap.

Since firm characteristics might involve mechanisms through which the gender pay

differential between male and female managers is manifested, it may not be desirable to

control for such firm characteristics. As such, we focus on the simple model (column 5) that

only controls for personal and human capital characteristics, as well as the expanded model

(column 9) that also controls for firm characteristics (both highlighted in bold in table 2).

Of the overall pay gap, 15.9% of the pay advantage of male managers continues to

persist, unexplained by any of the above factors. The results above indicate that if female

managers had the same human capital characteristics as males, their pay would increase from

67.5% of male’s pay to 75.6% of males’ pay. Further, if they were managing the same

enterprises as males, they would be earning 84.1% of what male managers earn.

4.2 OLS Regression with Males and Females Separately

Table 3 presents the regression results separately for males and females based on the

expanded model that includes both individual and firm-level characteristics. The separate

regressions allow each of the coefficients to differ for males and females, and they provide

the information for the Oaxaca decomposition outlined subsequently.

  

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Age by itself is not significantly related to managerial pay for either males or

females.2 Job tenure has a strong positive effect for both men and women, being substantially

larger for women. Calculating the marginal effects (evaluated at the mean) implies that an

additional year of job tenure is associated with pay increases of 4.8% for men and 7.8% for

women. The inflection points where pay reaches maximum are 21.3 years for men and 16.8

years for women. Pay increases with job tenure up to these points and then decreases

afterwards.

Married men earned more than single men although the effect is not statistically

significant at conventional levels (t=1.59) while married women did not earn differently than

single women (t=-0.95) after controlling for other individual characteristics and firm

variables. This finding is consistent with Bishop et al. (2005) who found that the return to

marriage was significant for males but not significant for females. A marriage premium for

men is common in the literature and likely reflects the fact that men are more likely to gain

family support for their work after married, while women tend to assume more family

responsibilities after married.

Another striking difference in coefficients is with respect to the CCP membership

variable. Male CCP members earned significantly less than non-CCP members while for

females the effect of party membership is statistically insignificant. This negative or non-

significant effect is somewhat surprising. It may occur because managers who may be good

at politics may not be good as managers in business. Similar findings were shown in Bishop

et al. (2005) which found that returns to CCP are lowest for higher earning workers.

2 Age was originally entered in quadratic form as age and age squared. For both the male and female regressions, the quadratic term was statistically insignificant and quantitatively very small so that the inflection points were beyond the age range in the data. Since the effect of age is essentially linear over the range of the data, we eliminated the quadratic age term.

  

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The return to education is higher for women than for men in general, another

common result found in the literature. In particular, compared to persons whose education

level was less than high school, those who completed high school had pay that was higher by

32.5% in the case of men and by 47.1% in the case of women (albeit for women the effect is

not statistically significant). College and university education does not make a difference for

male earnings while it is associated with a significant and large 79.5% increase in pay for

females. As distinct from most studies on pay determination, our data set allows us to

include business training as a regressor. Business training is associated with substantially

higher pay for both men (25.7%) and women (34.9%) although the effect is not statistically

significant at conventional levels for women.

Firm size has a significant positive large effect on the pay of both and male and

female managers. Specifically, a 10% increase in the registered value of the capital of the

firm is associated with higher pay of 2.2% for males and 1.6% for females. This highlights

that the concentration of females in smaller firms is important in accounting for the gender

gap between male and female managers. As well, firm size is also important in explaining

the in-group variation in pay for both male and female managers.

Whether firms are in the service industry does not have a significant effect on

managerial pay for either males or females. The same applies to the age of the firm.

Interestingly, holding a president/chair positions is associated with a substantial and

statistically significant 46.3% higher pay for females but a smaller and statistically

insignificant 13.5% higher pay for males. Presumably, females who arrive at that top

position are elites in the executive ranks. To better discern whether profit sharing is

important as part of the reported pay, we include a profit-sharing dummy variable in the

  

15  

regression. Those who had profit sharing as part of their pay earned much more than those

did not—a 50% more for males and 72.3% more for females.

The R-square’s indicate that the proportion of the variation in pay explained by the

variables included in the regressions was 14.4% for males and 29.8% for females.

4.3 Decomposition of the Male-Female Managerial Pay Gap

Following Oaxaca (1973) and Neumark (1988), we use two different specifications to

decompose the gender pay differential into two components: one component attributable to

gender difference in their endowments of observable wage-determining characteristics (mean

values of their explanatory variables); and the other is attributable to differences in the pay

they receive for the same wage determining characteristics (differences in the regression

coefficients). The latter component is often labeled as the component due to discrimination

since it reflects differences in pay for the same wage-determining characteristics.

The Oaxaca (1973) decomposition is:

(1)

The alternative proposed by Neumark (1988) is:

(2)

In both cases, Ys is a measure of compensation, the Xs are the pay determining

characteristics or independent variables in the pay equations, the ßs are the estimated

coefficients or monetary returns to the pay determining characteristics, the subscripts m, f

and p denote males, females and pooled sample respectively, and denotes mean pay and

the mean of the characteristics.

The first term on the right hand side of equation (1) represents the portion of the gap

that is “explained” by differences in the endowments of wage determining characteristics

  

16  

between males and females where those endowments are evaluated according to male pay

structure. The second term on the right hand side for equations (1) represents the

“unexplained” portion or differences in the returns that male and females receive for the

same worker and workplace characteristics.

For the Neumark (1988) specification of equation (2) the first term on the right hand

side also represents the portion of the gap that is “explained” by differences in the

endowments of wage determining characteristics, but here the endowments are evaluated

according to the pay structure from a pooled male-female regression, on the grounds that

both male and female pay would change if equal pay were achieved. The last two terms for

equation (2) represent the pay gap attributed to differences in how males and females are

paid relative to the pooled norm and evaluated for the same characteristics. According to

Jann (2008), an issue with the Neumark (1988) approach is that it may inappropriately

transfer some of the unexplained part of the differential into the explained component due to

the residual group difference spilling over into the slope parameters of the pooled model.

Following the solution proposed by Jann (2008), a group indicator is included in this pooled

model as an additional covariate3.

Table 4 gives the results from the two decomposition methods. Several findings

emerge. First, when firm characteristics are excluded (simple model), the two decomposition

methods yield similar results, indicating that gender differences in the coefficients or returns

tend to dominate gender differences in characteristics or endowments of wage-determining

characteristics. The proportion of the male-female managerial pay gap attributed to

differences in returns for the same characteristics (i.e., the often-labeled discriminatory

3 The group indicator in this case is gender (female=1, male=0), i.e. including gender as an explanatory variable in the pooled male-female regression. 

  

17  

component) are very close ranging from 78.8% based on the male weights to 81.8% based on

the pooled weights. Because differences in characteristics matter so little in this simple

specification that controls only for differences in individual characteristics, the adjusted ratio

of female/male pay does not increase much when adjusting only for differences in individual

characteristics. Specifically it increases from an unadjusted ratio of 0.67 to an adjusted

gender pay ratio around 0.74. This result indicates that if men and women held the same

individual characteristics, whether they were paid according to male pay structure, or the

general (pooled) pay structure, women would be paid slightly less than 3/4 of men’s pay.

Second, when firm characteristics are included in the expanded model, the proportion

of the gap explained by differences in the coefficients or returns for the same characteristics

(the discriminatory component) dropped substantially, but they remained similar whether

based on the male weights (51.1%) or the pooled weights (48.9%). Since we believe the

male weights or the pooled weights to be the appropriate non-discriminatory norm and they

yield almost identical results we conclude that about half of the overall gap is attributed to

differences in returns for the same characteristics, and half due to differences in wage

determining characteristics based on the extended model. Because the portion attributed to

returns dropped considerably and the portion attributed to characteristics increased

considerably when the firm-level characteristics were added, the adjusted ratios increased

substantially to around 0.84. In essence, if females had the same individual and firm-level

characteristics as males, the ratio of their pay would be substantially higher, about 84% of

male pay. Conversely, the remaining gap, attributable to differences in the returns they

receive for the same characteristics, would be considerably reduced.

  

18  

This highlights the importance of firm-level factors in influencing the pay of females

relative to males. In essence, females tend to be disproportionately employed in firms that

pay lower wages. The next section outlines the relative importance of the different factors

that influence the pay gap, including firm-level characteristics.

4.4 Sub-Decomposition of the Characteristics and Returns Components

The previous analysis suggested that about half of the gender gap in managerial

compensation can be explained by differences in wage determining characteristics of males

and females and the other half can be explained by differences in the returns males and

females receive for the same endowments of wage determining characteristics, based on the

extended model to include firm-level characteristics. It is informative to sub-decompose

those overall components into their subcomponents to determine, for example, the relative

importance of male-female differences in firm-level tenure in the characteristics component,

and the relative importance of male-female differences in the returns to firm-level tenure in

the returns component.

While this can be done for the characteristics component, the returns component

cannot be sub-decomposed because it is not invariant to the choice of reference group when

dummy variables are used. In particular, although the sum of the contributions of the single

indicator variables (i.e., the total contribution of the categorical variables) is invariant to the

choice of reference group, the detailed coefficients effects attributed to dummy variables are

not invariant to the choice of the omitted group (Oaxaca and Ransom, 1999). Changing the

reference category not only alters the results for the singly dummy variables but also changes

the sum of the coefficients effect of the categorical variables.

  

19  

Several solutions have been proposed to deal with this identification problem, such as

Nielsen (2000), Gardeazabal and Ugidos (2005) and Yun (2005). Nielsen’s method is not

suitable for this particular analysis as it cannot distinguish the constant term from dummy

variables and becomes cumbersome if there are several sets of dummy variables. The

Gardeazabal and Ugidos (2005) and Yun (2005) method follow the same procedure to

restrict the coefficients for the single categories to sum to zero; that is, to express effects as

deviations from the grand mean. In the Gardeazabal and Ugidos method, the dummy

variables are transformed by implementing restricted least squares estimation before model

estimation (Gardeazabal and Ugidos, 2005). More conveniently, in Yun’s method, standard

dummy coding is used for model estimation, and then one can transform the coefficients

vectors so that deviations from the grand mean are expressed and the coefficient for the base

category is added (Yun, 2005). When these methods are applied to such transformed

estimates, the results of the decomposition are unaffected by the choice of the reference

category. In the following analysis, we use the Yun (2005) procedure to apply the

transformation of dummy variables sets and report the contribution of a categorical predictor

to the returns or coefficient part of the decomposition.

We focus the analysis based on the extended model to include both individual and

firm-level characteristics, and we utilize the male weights (which gave similar results as the

pooled weights) because the male pay structure best represents the non-discriminatory norm

(Hughes and Maurer-Fazio, 2002). The results are shown in table 5. The first panel describes

the breakdown of the proportions explained by each set of explanatory variables or wage-

determining characteristics. The characteristics of firms account for more (35.4%) of the

gender pay differential than do the individual characteristics (13.5%). Further, the

  

20  

concentration of female executives in smaller firms tends to be the driving force behind the

explained component of the gender pay gap. About 27.7% of the gender pay gap is explained

by firm size, as measured by registered capital. Other variables or characteristics that

contribute substantially to the gender pay gap are job tenure, which explains 13.3% of the

gender pay differential, marital status, which explains about 12.4%, and business training,

which accounts for 10.1%.

The second panel illustrates the effects of the coefficients or different returns that

male and female managers receive for the same wage-determining characteristics. About half

(51.0%) of the total wage differential is due to differences in such returns, often labeled as

discrimination. In particular, different returns to workplace characteristics account for almost

all the effects. The negative sign of “individual characteristics” and “group membership”

indicate that women were not discriminated against by being female or for their individual

characteristics as a whole. However, further examination shows that although women were

treated favorably compared to men with the same job tenure and education, women did

received lower returns on all other individual characteristics. For example, a large portion

(25.3%) of the gender pay differential comes from the different treatment of men and women

with the same CCP membership status. Also, the “marriage penalty” explains 62.9% of total

gender pay differential. The large negative percentage for “college, university or higher”

dummy variable highlights that the returns to higher education is greater for females than for

males, but this effect does not show up until the college level. The effects of different returns

or discrimination on firm characteristics are more obvious. The fact that female leaders

managing firms of the same size as males received less pay than male leaders explains 73.1%

of the gender pay differential. In addition, female profit sharing receivers receiving less pay

  

21  

than male profit receivers is another important source of the pay discrimination, attributing to

27.2% of the gender pay differential. The negative signs of the other variables (service

industry, firm history and being president) indicate that females are not discriminated due to

these firm characteristics. In essence, if females were working in the same industry, in firms

of the same age, and at the same rank as males, they would not receive lower pay.

5. Concluding Discussion

The total compensation (base wages, bonuses, stock options, and profit sharing) of

male managers exceeded that of female managers by 32.5% before any adjustments are made

for differences in characteristics that can affect pay. Regression analysis reveals that this

gross male-female managerial gap in compensation does not change much when individual

characteristics are controlled for in the regressions. In contrast, controlling for the effect of

firm characteristics does substantially reduce the gender pay gap. This is the case especially

for firm size and to a lesser extent for executive rank and whether profit sharing was part of

compensation, although not for industry and company history variables. When both

individual and firm characteristics are included in the regressions, about half of the overall

gap is attributed to differences in returns for the same characteristics (i.e., “discrimination”)

and half due to differences in wage determining characteristics. In essence, if female

managers had the same individual and firm-level characteristics as male managers, the ratio

of their pay would be substantially higher, around 84% of male pay.

When the characteristics (explanatory variables) and the returns (coefficients)

components are sub-decomposed to illustrate the relative contribution of the different factors,

the firm-level characteristics account for more (35.4%) of the gender pay differential than the

  

22  

individual characteristics (13.5%). Further, the concentration of female executives in smaller

firms tends to be the driving force (about 27.7% of the gap) behind the explained component

of the gender pay gap. When the approximately half of the gap that is due to the coefficients

or different returns that male and female managers receive for the same wage-determining

characteristics is sub-decomposed, different returns to workplace characteristics are

important in explaining the gap, and this is especially the case for the lower returns that

female managers receive for increases in firm size.

In essence, about half of the overall gender gap in managerial compensation reflects

differences in their endowments of pay-determining characteristics and half reflects

differences in pay for the same characteristics. For each of these components, differences in

firm-level variables are more important than are differences in individual characteristics.

That is, female managers tend to have fewer firm-level characteristics that are associated

with higher pay, and when they do, they tend to receive a smaller pay premium for those

characteristics. This is especially the case for the firm size variable where female managers

are less likely to be employed in the higher paying large firms, and when they are, they

receive a smaller firm-size premium.

  

23  

Table 1: Summary Statistics

Total Male Female Diff. Sig. t-Value

Variable Mean S. D. Mean S. D. Mean S. D.

Ln(Pay) (Pay Unit: 10,000 Yuan, 2006) 1.93 1.32 2.00 0.06 1.68 0.12 2.46

Registered Capital (Unit: 10,000 Yuan) 274.33 1018.34 280.53 1015.99 251.89 1030.59 0.28

Number of employees/100 0.77 2.46 0.90 2.75 0.31 0.62 2.42

Age of manager (years) 43.29 8.68 44.13 8.81 40.26 7.47 4.51

Job tenure (years) 8.84 6.66 9.34 6.99 7.01 4.92 3.52

President (1, president; 0, non-president) 0.65 0.48 0.68 0.47 0.55 0.50 2.77

Service Sector (1, service; 0, non service) 0.32 0.47 0.29 0.46 0.40 0.49 -2.42

Education

Less than High School 0.20 0.40 0.21 0.02 0.19 0.04 0.44

High School 0.23 0.42 0.23 0.42 0.24 0.43 -0.29

College, University or higher 0.57 0.50 0.57 0.50 0.57 0.50 -0.11 Business Training (1, received; 0, not received) 0.54 0.50 0.57 0.50 0.44 0.50 2.56

Married (1, married; 0, single) 0.90 0.30 0.93 0.26 0.82 0.39 3.75

CCP Member (1, member; 0, non-member) 0.31 0.46 0.33 0.47 0.24 0.43 1.91

Firm Age (years since established) 8.32 7.01 8.50 7.37 7.66 5.48 1.19

Profit Sharing (1, received; 0, non-received) 0.14 0.35 0.15 0.36 0.10 0.31 1.43

Obs. 582 456 126

  

24  

Table 2: Regression of ln(pay) on Firm Characteristics and CEO Individual Characteristics

VARIABLES Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9

Mean (Lnpay) 1.93 1.93 1.93 1.93 1.93 1.93 1.93 1.93 1.93

Female -0.325** -0.316** -0.311** -0.304** -0.266** -0.220* -0.227* -0.198 -0.159

(0.132) (0.133) (0.134) (0.133) (0.132) (0.128) (0.129) (0.129) (0.129)

Age of Manager -0.009 -0.009 -0.005 -0.007 -0.012* -0.011* -0.012* -0.011*

(0.007) (0.007) (0.007) (0.007) (0.007) (0.007) (0.007) (0.007)

Job Tenure 0.102*** 0.101*** 0.105*** 0.104*** 0.107*** 0.110*** 0.105*** 0.096***

(0.024) (0.024) (0.024) (0.023) (0.023) (0.024) (0.024) (0.024)

Job Tenure2 -0.003*** -0.003*** -0.003*** -0.003*** -0.003*** -0.003*** -0.003*** -0.003***

(0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

Married 0.107 0.087 0.051 0.069 0.068 0.095 0.134

(0.188) (0.186) (0.184) (0.179) (0.180) (0.180) (0.178)

CCP Member -0.083 -0.138 -0.202* -0.219* -0.225* -0.218* -0.219*

(0.119) (0.121) (0.121) (0.118) (0.119) (0.118) (0.117)

High School 0.447*** 0.377** 0.318** 0.313** 0.313** 0.338**

(0.163) (0.162) (0.158) (0.159) (0.158) (0.157)

College & Above 0.444*** 0.293** 0.174 0.168 0.177 0.259*

(0.144) (0.147) (0.145) (0.145) (0.145) (0.145)

Business Training 0.448*** 0.272** 0.271** 0.261** 0.292***

(0.113) (0.114) (0.114) (0.114) (0.113)

Ln(Capital)

0.197*** 0.201*** 0.204*** 0.208***

(0.034) (0.035) (0.035) (0.034)

Service Sector 0.096 0.100 0.072

(0.113) (0.113) (0.112)

Firm Age -0.001 -0.000 0.000

(0.008) (0.008) (0.008)

President 0.218** 0.205*

(0.109) (0.108)

Profit Sharing 0.546***

(0.151)

Constant

2.003*** 1.852*** 1.785*** 1.275*** 1.257*** 0.802** 0.744** 0.591 0.420

(0.061) (0.302) (0.325) (0.363) (0.358) (0.357) (0.364) (0.371) (0.370)

Observations 582 582 582 582 582 582 582 582 582

R-squared 0.010 0.042 0.043 0.061 0.086 0.137 0.138 0.144 0.164 Note: Standard errors in parentheses *** p

  

25  

Table 3: Regression of ln(pay) for Male and Female Separately, Expanded Model 9

VARIABLES Male Female

Age of Manager -0.011 -0.012

(0.007) (0.016)

Job Tenure 0.085*** 0.134**

(0.027) (0.067)

Job Tenure2 -0.002*** -0.004

(0.001) (0.003)

Married 0.366 -0.278

(0.230) (0.292)

CCP Member -0.270** 0.043

(0.130) (0.290)

High School 0.325* 0.471

(0.179) (0.329)

College, University or Higher 0.140 0.795**

(0.167) (0.307)

Business Training 0.257** 0.349

(0.128) (0.244)

Ln(Capital) 0.221*** 0.158**

(0.040) (0.070)

Service Sector 0.130 -0.246

(0.131) (0.223)

Firm Age -0.004 0.007

(0.009) (0.023)

President 0.136 0.463**

(0.126) (0.223)

Profit Sharing 0.500*** 0.723**

(0.167) (0.363)

Constant 0.391 0.166

(0.424) (0.739)

Observations 456 126

R-squared 0.144 0.298

Note: Standard errors in parentheses

*** p

  

26  

Table 4: Decomposition of Gender Pay Differentials

Individual Characteristics Model (Model 5) Expanded Model (Model 9)

Total Differential "Explained" "Unexplained" Adjusted Ratio "Explained" "Unexplained" Adjusted Ratio

Male Pay Structure 0.325 0.069 0.256 0.744 0.159 0.166 0.834

(Oaxaca 1973) 100% 21.2% 78.8% 48.9% 51.1%

Pooled Pay Structure 0.325 0.059 0.266 0.734 0.166 0.159 0.841

(Neumark 1988) 100% 18.2% 81.8% 51.1% 48.9%

  

27  

Table 5: Relative Contribution of Various Factors to the Gender Pay Gap (Expanded Model, Male Pay Structure, Yun (2005) Sub-decomposition)

Pay gap % of the pay gap

Total 0.325 100.0

Explained 0.159 48.9

Individual Characteristics 0.044 13.5

Age of Manager -0.044 -13.4

Job Tenure 0.043 13.3

Married 0.040 12.4

CCP Member -0.024 -7.4

Education -0.005 -1.5

less than high school -0.003 -0.9

high school -0.002 -0.6

college, university or higher 0.000 0.0

Business Training 0.033 10.1

Firm Characteristics 0.115 35.4

Industry (service) -0.015 -4.5

Firm History (years) -0.003 -1.0

President 0.018 5.6

Profit Sharing 0.025 7.7

Firm Size 0.090 27.7

Unexplained 0.166 51.0

Constant/Group Membership -0.009 -2.8

Individual Characteristics -0.019 -6.0

Age of Manager 0.048 14.8

Job Tenure -0.217 -66.9

Married 0.205 62.9

CCP Member 0.082 25.3

Education -0.142 -43.7

less than high school 0.051 15.6

high school 0.029 8.8

college, university or higher -0.221 -68.1

Business Training 0.005 1.6

Firm Characteristics 0.194 59.8

Industry (service) -0.036 -11.0

Firm History (years) -0.080 -24.7

President -0.016 -4.8

Profit Sharing 0.089 27.2

Firm Size 0.237 73.1

  

28  

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Chapter 2

Pay-For-Performance in Executive Compensation in China:

The Impact of State Ownership and Managerial Power

1. Introduction Executive compensation has attracted considerable attention throughout the world

over the past several decades as a result of the dramatic increase in the absolute level of

compensation as well as the ratio of executive pay to the average workers’ wage (Murphy,

1999). However, most of the empirical research on managerial compensation has dealt with

American firms with only a few studies drawn from Canadian and European companies.

Similar research on managerial compensation in Asia in general, and in China in particular, is

scarce. The rapid economic development of China in the past several decades, however, has

resulted in a rapidly growing managerial labour market, which provides an opportunity to

compare the levels and structure of Chinese managerial compensation with the patterns

found elsewhere. As the largest transitional economies in the world, China is also generally

characterized as a mixed economy with more than half of the state-enterprises having gone

through the privatization process.

This study contributes to the existing literature in several aspects. First, it utilizes a

newly-constructed unique data. Among the few studies that have examined CEO

compensation in China, most have used the China Stock Market Accounting Research

(CSMAR) data which contains limited information on CEO’s individual characteristics. This

study uses a dataset that we code directly from the annual reports of listed firms based on

content analysis which allows us to examine the influence of various factors on the pay-for-

performance relationship in CEO compensation, such as whether the CEO is promoted

  

32  

within the firm or recruited from outside of the firm, whether the CEO also assumes the

board Chair, board member, or is not on the board, and the education level of the firm’s

workforce. Second, the study contributes to expanding the small literature on the effect of

state ownership on managerial pay and the relationship between compensation and corporate

performance (Buck, Liu and Skovoroda, 2008; Kato and Long, 2006; Zhu, 2007), by

examining how the pay-for-performance relationship differs in state controlled and non-state

controlled firms. Third, to our knowledge this is the first study to examine the effect of

managerial power on the pay-for-performance relationship with Chinese data. Recent studies

in the U.S. literature have shown that managerial power plays a significant role in CEO

compensation and can better explain the recent trend in CEO pay than agency theory (e.g.

Bebchuk et al., 2003). Using the source of CEOs and their status on boards of directors as

measures of managerial power, we examine whether and how managerial power influences

the level of CEO compensation and the pay-performance relationship, and whether these

effects differ in state controlled and non-state controlled firms.

The paper is organized as follows. Section 2 briefly discusses the Chinese reform of

state controlled enterprises (SOE’s) and the development of the managerial labour market in

the past three decades. Section 3 outlines the research questions and corresponding

hypotheses based on theoretical expectations and earlier empirical research. Section 4 briefly

describes the data and empirical methodology. The results are presented in section 5, and

section 6 concludes with a general discussion and policy implications.

2. China’s SOE Reforms and the Managerial Labour Market

Before the 1980s, all companies in China were owned by the state and these state-

owned enterprises were the lowest link in the chain of command of the central planning

  

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machinery (Mengistae and Xu, 2004). This was a setting where the directors of firms were

less of a business executive than a civil servant responsible for the implementation of a set of

“plan targets” routinely passed down by a national or regional planning hierarchy. At that

stage, firms were required to remit all of their profits into the state budget. Top managers in

the companies were paid according to their corresponding ranks in the government hierarchy.

The reforms began with the state relinquishing part of its control over incomes

generated by enterprises through a variety of profit retention schemes introduced between

1980 and 1984. From 1984 on, state-owned enterprises were no longer obliged to remit all of

their profits to the state. Although they continued to be required to make payments into the

state budget, this would be in the form of a pre-specified quota of profits. At the same time

they were allowed to retain a fixed proportion of the same quota (from 60% to 100% of

profits above the quota) for the purpose of financing their own investment and bonus

schemes. A profit retention policy was introduced in various experimental forms in selected

regions and then was enforced in the entire country by the end of 1984.

Another reform process came at the end of 1984. The government abolished profit

remittance into the state budget and replaced it with a profit tax at a maximum rate of 55%.

The firm could use the after-profit tax as before for investment, product development, bonus

schemes, and employee welfare benefits. Enterprise directors were given more power in

making business decision and internal personnel decisions. In an apparent attempt to balance

the assumption of personal risk by the director for enterprise performance, the director’s

reward was allowed to exceed the pay of the average worker by as much as 10 times (Byrd,

1992).

Although in this stage the appointment, evaluation, and dismissal of the directors of

  

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the Chinese SOEs were still made by central or regional government bureaucracies and often

reflected political priorities of the controlling government, managerial efforts were being

rewarded and managerial resources were being assigned in accordance with criteria

established by the market forces. According to Groves et al. (1995), the managerial labour

market began to form at this stage as evidenced in two ways: first, managerial turnover rates

were comparable to those in developed market economies and appeared to be sensitive to

enterprise performance; second, managerial earnings significantly increased with enterprise

profits and enterprise sales.

The second stage of reform began in 1992, when an “annual salary system” was

introduced to SOEs, and the state also began to sell part of SOE shares in some former SOEs

in order to establish “socialist market economic system”. As a result, SOEs were allowed to

establish their own internal wage structure within the overall budget guidelines set by

government.

In the past two decades, companies with state-owned shares had more and more

independent authority in decision-making. In many SOEs, especially those listed on the stock

market, top managerial positions were no longer appointed by state authorities but open to

competition (Lin et al., 2004), which further fostered a competitive managerial labour market.

Executive compensation structures and pay-performance sensitivity in SOEs are still

different from that in non-SOEs, however, because SOEs are still concerned with political

and social issues, while private enterprises pursue profit maximization more readily (Lin et

al., 2004).

  

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3. Literature and Expected Relationships

Pay-Performance Relationship

According to agency theory (Jensen and Meckling, 1976), to motivate the risk-averse

self-interested managers to adopt actions that are in the best interest of shareholders, the

principle or shareholders should design a compensation scheme that links the agent’s

compensation to the observed corporate performance. Numerous earlier studies have

attempted to discern whether, and to what degree such a link between pay and performance

exists in practice (e.g. Gray and Cannella, 1995; Murphy, 1999). Most research has found

that there is a positive relationship between managerial compensation and firm performance

although the link appears weak (e.g. Murphy, 1999; Lambert et al., 1993). A few studies,

however, find that there is no relationship between these two variables (e.g. Gray and

Cannella, 1995) or even a negative relationship (e.g. Gomez-Mejia and Wiseman, 1997). The

reason for the mixed evidence might be that pay-performance relationship is moderated by

other factors such as unionization (Gomez and Tzioumis, 2005) and the degree of risks that

organizations face (Bloom and Milkovich, 1998). Several previous studies on the pay-

performance linkage in China found a positive and significant link of financial performance

measures to executive compensation (Groves et al., 1995; Kato and Long, 2006; Mengistae

and Xu, 2004; Zhu, 2007). We hypothesis that before considering moderating effects, higher

CEO pay is associated with higher corporate performance in Chinese enterprises after

controlling for various pay-determining factors.

H1: CEO compensation is positively related to the corporate performance variables

after controlling for other pay-determining factors.

  

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State-ownership and Pay-Performance Relationship

Previous research has suggested that the pay-performance relationship is influenced

by firm ownership. Political and regulatory constraints such as unionization (DeAngelo and

DeAngelo, 1991; Gomez and Tzioumis, 2005), political pressure (Joskow, Rose, and

Wolfram, 1996), and disclosure of pay (Murphy, 1986; Dial and Murphy, 1995) are found to

have substantial effects on the upper tail of managerial compensation, leading to both lower

overall levels of managerial pay and reduced pay-performance sensitivity. State ownership,

as one form of regulatory constraint, also has substantial effects on both the executive pay

level and the pay-performance relationship. This is particularly the case in transitional

economies. For instance, Jones and Kato (1997), using Bulgaria data (1989-1992), found that

the pay-performance relationship is significant in private companies but not in state-owned

companies. The country they draw their inference from is a place where the privatization rate

in that period was lower than in China for the period under our analysis. Kato and Long

(2006) using data of Chinese listed firms found that the strength of the link between

executive compensation and firm performance varies across firms with different ownership

structures—the link is weaker for firms with a higher percentage of government ownership.

Similar findings are shown in Liu and Otsuka (2004).

During the 15th National Congress of the Chinese Communist Party in September

1997, the government of China announced plans to sell, merge, or close the vast majority of

SOEs in the call for increased “public ownership” (privatization). Due to the fact that even in

SOEs, the government required the most of these firms to reform their administration process

and regard profitability as the primary goal, we hypothesize that the pay-performance

relationship exist in both state controlled and non-state controlled firms, but the strength of

the relationship is higher in non-state-owned firms.

  

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H2: The pay-performance relationship is stronger in non-state-owned companies

compared to state-owned companies controlling for other factors.

Managerial Power and the Pay-Performance Relationship

Several recent studies have shown that managerial power plays a significant role in

executive compensation in the United States. Some argued that managerial power theory can

better explain the recent trend in CEO pay than the optimal contract model derived from

agency theory (e.g. Bebchuk et al., 2003). Managerial power refers to four dimensions of top

managers’ power: structural power, ownership power, expert power, and prestige power.

Structural power is defined as the power that executives gain from the formal organizational

structure. Managers in higher levels of the organizational hierarchy have the power to control

the behaviour of their subordinates. The second source of top managers’ power, ownership

power, depends on their relationship with owners or, to an extreme, on whether they are

among owners of the company. Managers with ownership power are more likely to succeed

in influencing decisions of the board since they have a long and close relationship with board

members. Expert power comes from the ability of managers to deal with the environmental

uncertainty of an organization. When an organization is faced with difficulties from inside or

outside, a manager who is an expert in dealing with these problems has extensive power.

Personal prestige, the fourth source of managerial power, is defined as a managers’

reputation in the institutional environment and among stakeholders. Prestige can be

interpreted in many different ways, such as managers’ standing in the “managerial elite” or

their influential power and network in the society including the relations with government

and other institutions. Prestige provides managerial power since it also contributes to

diminishing the uncertainty of the institutional environment faced by firms (Finkelstein,

  

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1992).

Top managers with high managerial power have more opportunity to influence both

the level and structure of executive compensation in a way that weakens or distorts the pay-

performance relationship (Bebchuk et al., 2003) since they have more power to control the

decisions of board of directors on the compensation package. Executives might also employ

compensation consultants to justify their request for higher compensation (Dorff, 2005).

Some studies have shown that managerial power plays a role in the executive compensation

decision-making process (e.g. Dorff, 2005; Lambert et al., 1993; Newman and Mozes, 1999).

Newman and Mozes (1999), for example, found that the level of CEO pay is significantly

higher, and the pay-performance relation significantly lower, when the compensation

committee contains at least one “insider”, a manager whose salary is influenced by the

committee decision. Similarly, Anderson and Bizjak (2000) showed that CEOs who sit on

their own compensation committees receive higher levels of pay and tend to have higher

stock ownership.

In this study, we use CEO’s status on the board as a measure of managerial power,

and hypothesize that CEOs who also assume the position of the board Chair have substantial

influence on the decision of the board as whole, followed by those CEOs who are board

members but not the chair. Those who are neither board chairs nor board members have the

least managerial power. Based on the managerial power theory, this study tests the following

two hypotheses:

H3: CEO compensation levels are positively related to CEO’s managerial power.

H4: The strength of the pay-performance relationship is negatively related to CEO’s

managerial power.

  

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External Hiring and CEO Compensation

Murphy and Zabojnik (2004) highlighted that, over the past three decades labour

markets have become more important in determining the level of executive pay. They

propose a model in which firms choose between filling a top-managerial position with an

internal or external candidate. The choice is made based on a trade-off between “matching”

and “firm-specific skills”. If the firm hires the CEO from outside, it foregoes valuable firm-

specific skills which can only be acquired through internal promotions, but it is able to hire

from a larger pool of candidates, which enhances the possibility of better matching of

managers and firms. This model predicts that an increase in the importance of general

managerial abilities relative to firm-specific mana