three essays on employment and compensation ......earnings gap can be attributable to firm-level...
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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
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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
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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.
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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.
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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.
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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
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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
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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
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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.
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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
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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
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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
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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%
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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
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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
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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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33
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