Global COE Hi-Stat Discussion Paper Series 011

Research Unit for Statisticaland Empirical Analysis in Social Sciences (Hi-Stat)

The Value of Power in China:
How Do Party Membership and Social Networks
Affect Pay in Different Ownership Sectors?

Hi-Stat Discussion Paper

Shuang LI
Ming LU
Hiroshi SatoNovember 2008

Hi-Stat
Institute of Economic Research
Hitotsubashi University
2-1 Naka, Kunitatchi Tokyo, 186-8601 Japan
http://gcoe.ier.hit-u.ac.jp

The Value of Power in China:
How Do Party Membership and Social Networks Affect Pay in Different
Ownership Sectors? *Shuang LI
School of Economics, Fudan University
Ming LU
School of Economics, Fudan University
andHiroshi SATO
Graduate School of Economics, Hitotsubashi University

Abstract: Party membership and social networks, as two forms of nonmarket power,
have significant effects on personal income. Do the effects vary across different
ownership sectors (suoyouzhi xingzhi)? Using a nationally representative survey ofurban households (China Household Income Project surveys in 1995 and 2002), we
find that (1) party membership can significantly increase personal income, but thiseffect does not significantly differ between different ownership sectors or between theyears 1995 and 2002 and (2) social networks are insignificant in State-Owned
Enterprises (SOEs), while they contribute significantly to personal income in nonSOE sectors.
Keywords: Income, Party membership, Social networks, Ownership, Marketization
JEL Classification: J40, O15, P26, Z13*

Shuang LI and Ming LU: Department of Economics, Employment and Social Security Research Center, andthe China Center for Economic Studies at the School of Economics, Fudan University, Shanghai, 200433, China,
lumingfd@gmail.com. Hiroshi SATO: Graduate School of Economics, Hitotsubashi University, Tokyo, 1868601, Japan, satohrs@econ.hit-u.ac.jp. We are grateful to Zhao Chen for his helpful comments. We gratefully
acknowledge financial support for the CHIP 2002 survey provided mainly by the Ford Foundation, the Swedish
International Development Cooperation Agency (SIDA) and partly by the Japan Society for the Promotion of
Science (JSPS) and Hitotsubashi University. Financial support for the preparation of this paper from the National
Social Science Project (07BJL051, 08BJL008), “985” project of Fudan University, and the Shanghai Leading
Academic Discipline Project (B101), JSPS (Grants-in-Aid for Scientific Research, No. 18203018), Heiwa
Nakajima Foundation, Fukino Project, Hitotsubashi University, and the Global COE Program “Research Unit for
Statistical and Empirical Analysis in Social Sciences”, Hitotsubashi University are greatly acknowledged.

11. Introduction
In recent years, interest among economists in studying the effects of nonmarket power (e.g.,
personality, social networks) on personal income has grown. However, the relationshipbetween nonmarket power and the market system is still unclear. It is possible that nonmarket
forces could either be replaced by the market system during its formation and development, or
lie embedded in the market mechanism with the result of an enhanced return to nonmarket
forces caused by marketization. Market reforms in China, and especially the various degrees
of marketization in different ownership sectors (suoyouzhi xingzhi), provide a rare opportunity
to investigate the relationship between market and nonmarket power. In this paper, there are
two types of nonmarket power. One is membership of the Chinese Communist Party, theChinese variant of political status. The other is social network, which has been extensively
discussed in the literature. In modern China, as a result of the hierarchical political and social
structure and the differences caused by individual political status and social networks, thesenonmarket factors have become important determinants of personal earnings, including
invisible and gray income (e.g., Bian, 1994; Lee, 1998; Knight and Yueh, 2002; Appleton etal., 2005). Nevertheless, little research has explored the relationship between marketizationand the effects of the two nonmarket forces. During the process of gradual economic reform,
the power nurtured by nonmarket factors has not been eliminated by marketization. Instead,those who hold the power could probably realize its value by manipulating the new market
system. China’s marketization can be better understood if we know the returns to nonmarket
forces.
Based on a nationally representative cross-sectional survey of Chinese urbanhouseholds conducted by the China Household Income Project (CHIP) for two reference
years, 1995 and 2002 (hereinafter referred to as the 1995 and 2002 CHIP surveys), we probe
the impacts of Party membership and social networks on urban residents’ incomes in different
ownership sectors. Our results show that Party membership contributes significantly to2

individual earnings, but this effect does not differ by sector, or between 1995 and 2002.
Meanwhile, the benefit of social networks on earned income does depend on the ownershipsector. Social networks are insignificant in state-owned units, where the level of marketizationis relatively low, but brings substantial income premiums in market-oriented parts of the labormarket. If marketization proceeds over time and non-SOE sectors are more subject to market
forces than their SOE counterparts, then our paper provides empirical evidence thatmarketization does not necessarily weaken the influences of nonmarket forces. Instead, therewards for nonmarket power could possibly be realized in monetary terms because the newmarket mechanism contains many nonmarket factors.
These findings can help us forecast the pathway of China’s future marketization. Ifsocial networks are more important in market-oriented sectors during China’s economictransition, then in order to pursue further gains in income, people with a wide social network
will reinvest in social capital to continue to increase their income premium. This might lead toa decline in China’s income mobility, as suggested by the existing literature (Wang, 2005; Yinet al., 2006). Thus, this is a reminder not to be too optimistic about China’s current highincome mobility and that the future effects of intergenerational transmission of social
networks, as well as education, should be considered when investigating income inequality in
China in the long-run. 1 If the establishment of the market system is accompanied by an
expanding social network, the impact of such marketization on China’s future development issomething well worth studying.
The structure of this paper is as follows. In Section 2 we review the relevant literature,and point out its potential problems and our contribution. Section 3 describes the data. Section
4 presents and interprets the empirical results. Section 5 concludes.

1

Previous studies have found that income mobility in postreform China has been significantly higher than inWestern countries such as the US, Germany and Belgium. (Khor and Pencavel, 2005; Ding and Wang, 2005).

3

2. Literature Review
2.1 Communist Party Membership
The role played by Communist Party membership in the labor market has provoked muchcontroversy in the literature. Two studies examine Chinese Communist Party membership as aform of political capital using CHIP surveys. Knight and Yueh (2002), based on another CHIP
survey focusing on urban households for 1999, studied the economic importance of individual
Party membership in urban labor markets. They found that Party membership could enhance
personal income. As a by-product of the research, they categorized the whole sample
according to ownership sector and age cohort, respectively, and then compared thecoefficients on Party membership. They discovered that market-oriented sectors have higher
rewards for Party membership than the nonmarket sector, as does the youngest cohort incomparison with its oldest counterpart. Therefore, they suggest that Party membership would
have an increasing influence on personal earnings because the younger group would be more
subject to market forces and, in addition, the private sector would expand with marketization.
Although interesting, the reason for their finding still needs further investigation. First,
if we want to test whether the partial effects of Party membership differ significantly byownership sector, it might be advisable to establish interactions between Party membershipand ownership sector and age cohort, respectively, based on the overall sample. If theinteraction term is significant, it indicates there is indeed significant difference between
ownership sectors or age cohorts in returns to Party membership. In our paper, we introduce
interaction terms in the econometric model, which are found to be insignificant. Second, theyfound Party membership was more important for younger relative to older groups, but it isstill uncertain whether this stems from the enhanced benefits of Party membership frommarketization. Sato and Eto (2008), using the 2002 CHIP survey, found that younger Party
members tended to have more years of schooling than their older predecessors, thus it might
be possible that the higher income premium in the younger cohort actually reflects the4increasing return to education during marketization, which is confirmed later in our paper.
Appleton et al. (2005), using the 1995 and 2002 CHIP data as well as the CHIP
surveys for 1988 and 1999, found the premium to Party membership in 2002 smaller than thatin 1999. They argued that, because of the changing composition of Party membership, newentrants might have unobserved characteristics that tend to be less productivity-enhancing,which resulted in a declined return to Party membership. However, their differences ofcoefficients on Party membership in these years were statistically insignificant. Again, it isbetter to integrate the data for all years and establish interaction terms for Party membershipand the year dummy. If the interaction term is significantly negative, it demonstrates that the
impact of Party membership on earnings is indeed decreasing significantly over time. In ourpaper, based on the pooled cross-sectional data set for 1995 and 2002, we constructed an
econometric model with the interaction term between Party membership and the year dummy,
only to find that the income premium does not differ between the two years.
Li et al. (2005a) showed that much of the economic return to Party membershipresulted from the effects of omitted ability or family background variables. By employing a
within-twin-pair fixed-effects model to control these omitted variables, they found that theeffect of Party membership in ordinary least-squares estimates all but disappeared. In addition,
considering the strict Party membership selection process, they conclude that the premium toParty membership was mainly attributed to unobserved ability or family background. In fact,
there are some limits to the use of the within-twin-pair fixed-effects model to control
unobserved variables. First, some unobserved qualities, which are cultivated via nurture andmight vary even within a pair of twins, are difficult to control through the first difference
approach. Second, given the possible preference of the parents, the family environment might
not be exactly the same for each twin. Third, if the return to Party membership also depends
on another variable (e.g., ownership sector of the work unit), which differs between the twins,
then the first difference cannot completely control the unobserved factor either.

5Is there a more direct approach to studying the return to Party membership over theperiod of the marketization process? Does Party membership represent individual ability orpolitical capital? Li et al. (2005a) thought it largely indicated omitted ability. Their argument
is that China’s market reform started with a planned economy characterized by depressed
returns to human capital. With the progress of market reform, ability as measured by Party
membership, which is supposed to accord with education, would be rewarded more in marketoriented sectors. In our paper, we add the interaction term between Party membership andownership sector in the income function as well as that of Party membership and year dummy,
using pooled cross-sectional data for 1995 and 2002. If our empirical results showed that botheducation and Party membership had higher rewards in non-SOE sectors or in the more recent
year, then Party membership is probably an indicator of either ability or political capital, andthey would both generate a higher income premium during market reform. However, ourfindings suggest that education is more valuable in market-driven sectors and in 2002, as
expected, while there is no significant difference between returns to Party membership inthese comparison groups. Therefore, it is likely that Party membership reflects both ability
and political capital, and when market reform enhances the return to unobserved ability, it
reduces that of political capital.

2.2 Social Networks
As an informal institution, social networks contribute a great deal to economic development,
as has been verified in the literature. In the labor market, social networks have positive effects
on individual earnings in many countries (e.g., Granovetter, 1995[1974]; Waldinger, 1996;
Bartlett and Miller, 1985; Mortensen and Vishwanath, 1994), and China is no exception (Bian,
1994; Lee, 1998). Furthermore, there is growing interest in the relationship between social
networks and formal institutions. Can social networks be embedded in formal institutions andthus play a more important role, or be weakened by market forces during economic transition?

6

Sociologists have explored the relationship between market transition and social stratification,
focusing on whether the redistributive economy-based stratification mechanisms still
predominate during market transition. 2 “The power transition theory” (Szelenyi, 1978; Nee,
1991, 1996) holds that traditional power might transit to the market during market reform,
hence the redistributive economy-based stratification mechanisms and traditional elites would
decline, and new stratification mechanisms and elites would be nurtured by the market. In
contrast, “The power persistence theory” believes that redistributive economy-based power
still functions during marketization, thus the traditional elites continue to dominate based on
stratification mechanisms. Zhang et al.（2007）studied whether the effects of social capital,
including social networks and public trust, changed with marketization, and found thatgenerally the return to social capital (especially the social network of the household) declinedwith marketization. With the focus on rural poverty, their research used data from rural areas,
providing another piece of evidence about the influence of China’s marketization on the
returns to social capital. However, it might not reflect the whole reality of economic transition.
In our paper, we investigate the returns to social networks in Chinese urban labormarkets and their possible change during marketization, which is the background in our paper.
Based on data from urban China, our research probes differences between the benefits ofsocial networks by ownership sector. Knight and Yueh (2002) found that social networks
contributed to individual earnings in urban labor markets. Additionally, they divided the entire
sample by ownership sector, and discovered that social networks paid more in privately
owned sectors than in the state-owned sector. In our paper, we construct the interaction term
between social network and the ownership sector to test whether there are significant
differences in the returns to social networks by ownership sector.
If we confirm the impact of Party membership and social networks on earnings, we
2

In sociology, redistributive economy refers to the nonmarket trade dominated by national political power, and it
mainly studies the system of redistribution under socialism. On the other hand, market economy is interpreted as
free transactions based on the price mechanism in the market (Szelenyi, 1978). Here we follow the sociologicalliterature since redistributive economy and market system are comparable terms.

7still need to explore their mechanisms, hence it is especially important to study the changing
roles of Party membership and social networks during market reform. In modern China’s
hierarchical political and social structure, political status and social capital normallysymbolize power and can promote higher incomes. During the economic transition, we will
investigate whether returns to power are influenced by market forces. If marketization results
in a normative market system, then the rewards for political and social capital will decline,
and it might be less likely that people with power can pursue higher earnings and thus gain
more power. However, if the conventional political and social structure has entrenched itself
within the newly-born market system, then the market rules might be dominated by people
with power, and political and social capital will be transformed into market power owned byindividuals. In this case, it is reasonable to expect that the returns to political and social
capital will be enhanced rather than depressed by marketization, which might make it difficult
to maintain China’s high income mobility.
Our paper makes the following contributions that build upon previous literature. First,
it is the first formal study of the impacts of marketization, political status and social networks
on personal income by interacting ownership sector with political status and social networks,
respectively. Second, through interaction terms between social capital and ownership sectors,
it explores different effects of social capital in ownership sectors, which have different
degrees of marketization. These findings can help us understand what Party membership andsocial capital really represent, and, more importantly, the nature of China’s market economy
and the future pathway to its economic transition.

3. Data
The data set we use is the China Household Income Project (CHIP) Survey conducted by theInstitute of Economics, Chinese Academy of Social Science and National Bureau of Statistics
in February 2003. The reference year is 2002. The sample size is 6835 urban households and8

20,632 individuals, covering 12 provinces and 70 cities. 3 The provinces are Beijing, Shanxi,
Liaoning, Jiangsu, Anhui, Henan, Hubei, Guangdong, Chongqing, Sichuan, Yuannan andGansu. Since our research concentrates on employed persons, our sample actually includes
information on 9998 individuals in the above 12 provinces in 2002.
Meanwhile, for comparison purposes, we also employ data from 1995. These data arealso from CHIP in 1995. The sample size is 6934 urban households and 21,696 individuals,
and covers 11 provinces and 69 cities. The provinces are Beijing, Shanxi, Liaoning, Jiangsu,
Anhui, Henan, Hubei, Guangdong, Sichuan, Yuannan and Gansu. Again, given the focus on
employed individuals, our sample includes information on 12,290 persons in the 11 provincesin 1995.
Table 1 here
First, we concentrate on information about Party membership. Table 1 is the statisticaldescription of our samples in 2002 and 1995. We summarize the ratios of Party membershipin different ownership sectors as well as income comparison between Party members and nonParty members. The latter is measured by the difference between the mean income of Party
members and non-Party members in all ownership sectors. In 2002, Party members accounted
for 28.58% of the total, an increase of 3.94% over 1995. All ownership sectors experienced
various rises in their percentage of Party membership. However, in both 2002 and 1995, thestate-owned sector had the highest ratio, which is consistent with direct observation. Party
members also earned more than their counterparts on average, and this income gap issignificantly positive. We need to point out that in 1995 the private sector had only one Party
member with an annual income of 3840 and 46 non-Party members with an average annual
income of 6864.30. It is possible that the mean income for non-Party members is higher thanits counterpart because of the small sample size.
Second, we need to find an appropriate operational measure of social capital at theindividual level. Burt (1992) holds that size and density are the two typical measures of an
3

For a detailed description of the CHIP 2002 and 1995 data, see Gustafsson, Li and Sicular (2008).

9

individual’s social network. The former refers to the number of contacts in the network and thelatter their interrelatedness. For example, an individual might have a number of contacts who
know each other. His or her network is denser and smaller than another individual’s network inwhich few contacts know each other. Our measure of social capital is the reported number ofrelatives or friends an individual can ask for help to change his/her job. In urban labor markets,the number of contacts a person can approach for help in job switching is strongly correlatedwith the quality of resources and information he/she can use to increase earning power.
Table 2 presents summary statistics about social capital and related variables based on
2002 data. 4 We see that the mean size of social capital for an employed individual is aroundunity. To understand the significance of social capital, we divide each ownership sector
subsample into two groups, those above the sector mean social capital and those below thatmean. We then calculate the mean income difference between the two groups and test its
significance. We find that the differences are significantly positive in all ownership sectors,
with Others, Self-employed and Foreign and joint venture sectors among the top three.
Table 2 here
In some literature, Party membership is regarded as a type of social capital (e.g.,
Knight and Yueh, 2002), but we disagree for two reasons. First, social capital is based onsocial interaction, while Party membership is a form of political resource under China’s
unique political system. Second, using the 2002 sample, our correlation analysis of social
networks and Party membership revealed a correlation coefficient of only 0.0442. Therefore,
we can exclude the possibility that using Party membership and social networks
simultaneously in the model might cause serious collinearity. It also indicates that Party
membership is really distinct from social capital, so we need to control for them separately in
the income function. 5

4

There is no related information about social networks in the data of 1995.
It should be noted that, because of data limitations, we do not consider differences in social network quality. It
is quite possible that party members are more likely to have contact with more influential people.
510

4. Empirical Investigation
4.1 Differences between Ownership Sectors in Returns to Party MembershipFirst, we follow Knight and Yueh (2002) to study how Party membership influences income.
Using the 2002 data, we partitioned the sample by ownership sector and then compared thecoefficients on Party membership. The income function is as follows.
ln Yi = α 0i + β1 X i + β 2CPi + β 3OCi + β 4 I i + β 5Ci + ui
(1)
In Equation (1), the dependent variable is the log of individual annual income, which wechoose for two reasons. First, total annual income can estimate a person’s earnings more
precisely than their wage. Since the income brought by political status and social network
may not be necessarily transformed into wage growth, it is more appropriate to take total
annual income as our dependent variable. Second, we tried the log of hourly income as analternative dependent variable, but found no notable difference between the two indicators.
Based on previous literature (Knight and Yueh, 2002; Appleton et al., 2005), we employ thefollowing independent variables.

1. Individual characteristics X i . It encompasses a set of variables: gender, education, age
and its square, a marital status dummy (base group is single), other Party member dummy.
Because the turnover rate in China is relatively low and its strong correlation with age, we
do not control for work experience in the model. 6
2. Communist Party membership dummy, CPi , equals 1 if an individual is a Communist
Party member, and 0 otherwise.
3. Job Characteristics include the following. (1) Occupational status dummy OCi .
Occupational status is categorized into 11 segments including self-employed, enterprise
6

Even if we add the work experience variable, it does not influence the results.

11

owner, professional, clerical staff, skilled workers, unskilled workers and so on. based on
the questionnaire. We use the self-employed status as the base group with 10 occupation
dummies. (2) Industry dummy I i . From the questionnaire, we define 15 industry dummies
with the base group of government, Party, and other non-profit agencies.
4. City dummy Ci .

We need to point out that in the questionnaire, there are 12 ownership sectors, but inour paper, we reorganized these categories based on Knight and Yueh (2002) and Appleton etal. (2005) and the Chinese context. Specifically, SOE at central/provincial level, local SOE
and state share-holding company are merged into a single state-owned sector. In addition,
taking into account of the Chinese context, we add the government, Party, and other nonprofit agencies to SOE as a broader state-owned sector. The private sector contains theoriginal private firm (including partnership), rural private enterprise and other share-holding
companies, our self-employed sector includes the original self-employed and rural individual
enterprise 7 , and we combine foreign company and Sino–foreign joint venture into our foreign
and joint venture sector. The remaining two sectors, namely urban collective and otherownership sectors, remain the same.
Table 3 here
Table 3 shows our empirical results. Our t-tests show that Party membership can
generate a positive income premium in the state-owned, urban collective, private and selfemployed sectors. Specifically, the coefficient on Party membership in the state-owned sector
is 0.07, the smallest among the above four. In the private and urban collective sectors it is 0.11
and 0.29, respectively, and it is largest in the self-employed sector, up to 0.43. In theremaining two sectors this coefficient is insignificant, which might be attributed to small
sample size. These results appear to support the conclusion in Knight and Yueh (2002),
7

A small number of urban residents are employed in rural enterprises.

12namely that the returns to Party membership differ by sector. In the market-oriented sectors,
self-employed, private and urban collective, the returns to Party membership seem to behigher than their equivalents in the administrative sectors.
As we mentioned before, if we simply partition the entire sample into the different
ownership sectors, without showing whether the differences of the coefficient on Party
membership among these sectors is significant, the results might not be convincing. Therefore,
we add interaction terms to our model as follows.

ln Yi = α0i + β1 Xi + β2 EDUi + β3CPi + β4Oi + β5CPi *Oi + β6 EDUi *Oi+β7OCi + β8 Ii + β9Ci + ui
(2)
In Equation (2), in addition to the control variables of Equation (1), we add anownership sector dummy Oi (based on the questionnaire, we define six ownership sectors,
namely state-owned, private, self-employed, foreign and joint venture, and other ownershipsectors. We define five sector dummies with the state-owned sector the base group.), and theinteraction term for Party membership and ownership sector, to see what difference, if any,
Party membership makes in determining income in different ownership sectors. Since we
divide the ownership sector into six groups with the state-owned sector the reference group,
we construct five interaction terms, thus we interact Party membership with collective, private,self-employed, foreign and joint venture and other ownership sectors. If the coefficient on aninteraction term is significant, it suggests that the role of Party membership does differ bysector, and not otherwise. We also define the interaction term EDU i * Oi for years ofeducation and ownership sector.
Table 4 here
Table 4 shows the results of Equation (2), and here we focus our discussion on Party
membership CPi and its associated interaction term with ownership sector CPi * Oi . First, we
can confirm the positive premium Party membership generates for earnings. On the13interaction of Party membership and ownership sector, Table 4 demonstrates that all theinteraction terms in question are insignificant, indicating that there is no substantial difference
between the contribution of Party membership among sectors. Next we conduct an F-test forthe joint significance of interaction terms of Party membership and ownership sector. Our
hypothesis is that the five interaction terms are jointly insignificant. The resulting F statistic is0.05 with a P value of 0.9988, which suggests that all the interaction terms are jointly
insignificant and the rewards for Party membership do not vary across ownership sectors.
From the above results we learn that the economic role of Party membership does notdiffer by ownership sector based on the 2002 cross-sectional data set. Here we measure thedegree of marketization by different owner sectors, about which a feasible alternative is tocompare the results in 2002 and 1995 under the assumption that there is a higher degree ofmarketization in the more recent year. If the returns to Party membership rise over time, itsuggests marketization makes Party membership more valuable. Similarly, in the 1995
function we control for individual characteristics X i , ownership sector dummy Oi , 8 industry
sector dummy I i , occupation sector dummy OCi , city dummy Ci , which denote the same asthat in Equations (1) and (2). Again, in order to study how differently Party membership paysin these ownership sectors, we construct the Party membership dummy CPi and its interaction
term with ownership sector CPi * Oi , as well as the interaction term of education andownership sector EDU i * Oi for Equation (3). Table 5 shows pertinent results.
ln Yi = α0i + β1 X i + β2 EDUi + β3CPi + β4Oi + β5CPi * Oi + β6 EDUi * Oi
+ β7OCi + β8 Ii + β9Ci + ui
.
8

(3)

Based on the questionnaire in 1995, we combine the state-owned, at central or provincial level, and local
publicly owned sectors together as a broad state-owned sector, while our collective sector is the integration ofurban collective and township and village enterprise. In our sample, there are only three sample points for thetownship and village enterprise sector. Foreign company and Sino-foreign joint venture are merged, with theothers remaining unchanged.

14

From the 1995 results we found that Party membership enhances earned income, but theinteraction terms for Party membership and ownership sector are all insignificant. Again, ourF-test for the joint significance of the five interaction terms (F = 0.76 and P = 0.5762)
indicates that the value of Party membership does not differ by ownership sector. On the other
hand, the return to education displays significant variance among ownership sectors. To be
more specific, the interactions of education and foreign and joint venture companies, private
sector and urban collective sector are significantly positive, showing that education is more
beneficial in these three sectors than in the state-owned sector. In the foreign and joint venturesector, there is a 4.6% return to each year of schooling while in the private sector it is 6%. If
the reward for education is higher in the market-driven sectors, and Party membership does
not pay more in the nonstate-owned sectors, then the above results can at least serve as side
evidence that Party membership does not completely represent unobserved human capital(ability), otherwise, it should generate higher premiums in market-oriented sectors, as
education does.
Comparing our 1995 results with 2002, we find that although Party membership isgenerally valuable in both years, all the interactions of Party membership and ownershipsectors are insignificant. The coefficient on Party membership is 0.093 in 1995 and 0.086 in
2002. Does that mean that Party membership is more valuable in 1995 than in 2002? To
explore this, we pool the data for the two years and establish Equation (4) 9 , in which we add
the year dummy Yi (1995 is the base year) and interact it with Party membership. In addition,
education is also interacted with the year dummy and ownership sector, respectively,
9

Since the categories of occupation and industry are not exactly the same between the two years, we reorganize
them for the pooled data. Specifically, for 1995, we combine the occupations owner of private or individual
enterprise and owner and manager of private enterprise. For 2002, we combine the occupations owner (manager)of private firm and self-employed as a single sector, which is comparable to the above-mentioned term in 1995.
For the category industry, we combine the two sectors of mineral and geological prospecting and irrigation
administration, which is the counterpart of mining and geological survey and prospecting in 1995. The sectors
electricity, gas and water supply facilities and real estate are brought together as comparable with real estate,public utilities, personal and consulting services in 1995. The other categories remain the same. Occupation isdivided into 10 sections and industry into 14 sections in our pooled data.

15

generating EDU i * Yi and EDU i * Oi accordingly.
ln Yi = α 0i + β1 X i + β 2 EDU i + β 3CPi + β 4Yi + β 5CPi * Yi + β 6 EDU i * Yi + β 7Oi+ β 8CPi * Oi + β 9 EDU i * Oi + β10OCi + β11 I i + β12Ci + ui

(4)

Table 5 here
In Table 5, we see that Party membership raises pay in both years, however, its
interaction with the year dummy is not significant, which means that the reward for Party
membership does not change over time. Furthermore, the insignificance of interactions
between Party membership and ownership sectors suggests there are no differences across thelabor market in the value of Party membership. Furthermore, the hypothesis that theinteractions of Party membership and ownership sectors are jointly insignificant still cannot
be rejected, given the following F-test (F = 0.06, P = 0.9974). By contrast, it is found that the
rewards to education rise over time and the interaction of education and the foreign and joint
venture sector is positive and significant. So no matter whether we use time or the different
types of ownership seen between ownership sectors to measure marketization, we can see thatthe benefit of education goes with marketization, which is consistent with previous studies(Xing ， 2005; Zhang et al., 2005). In contrast, Party membership is not rewarded
differentially by the marketization process, thus we can be more confident that Party
membership cannot be fully interpreted as omitted human capital (ability).

4.2 Differences between Ownership Sectors Concerning Returns to Social Networks

As a nonmarket factor, social networks also play an economic role in the labor market. The
reason social networks at the individual level may raise pay is that they give employedindividuals access to labor market information. To save transactions costs, enterprises tend to
recruit candidates recommended by existing employees who, to preserve their own reputation,refer only people they know well. Therefore, if a job seeker has an abundant social network,
he is likely to have more job opportunities, which will in turn bring higher pay (Ioannides and16Loury, 2004; Zanella, 2004). Because of the absence of social network measurement in the
1995 CHIP survey, we establish the following model based on 2002 data.
ln Yi = α 0i + β1 X i + β 2 EDU i + β 3CPi + β 4Oi + β 5CPi * Oi + β 6 EDU i * Oi + β 7 SN i
+ β 8OCi + β 9 I i + β10Ci + ui

(5)

In Equation (5), SN i denotes a social network at the individual level, which we measureby the reported number of acquaintances from whom someone can seek help when he/shewants a job change. The other variables remain the same as in Equation (2).
Table 6 shows the results of Equation (5). We find that even with the inclusion of social
capital, Party membership, as a type of political status, still has significantly positive effects
on income, whereas the interactions of Party membership and the ownership sector remain
insignificant. Given the weak correlation between Party membership and social capital,
controlling for social capital in the model does not affect the estimates for Party membership.
Next we discuss the role of social capital. Unexpectedly, the coefficient on the social
network itself is not significant. This suggests that a social network does not contribute toincome, but does this apply equally to all parts of the labor market? To investigate whethersocial networks are rewarded differently in all sections of the labor market, we divide theoverall sample into six sections according to ownership sector, and establish the interaction
term SN i * Oi of social network and ownership sector. The ownership sector category stays
unchanged, namely state-owned (base group), urban collective, private sector, self-employed,
foreign and joint venture as well as the other sectors. The model is as follows.
ln Yi = α 0i + β1 X i + β 2 EDU i + β 3CPi + β 4Oi + β 5CPi * Oi + β 6 EDU i * Oi + β 7 SN i
+ β 8 SN i * Oi + β 9OCi + β10 I i + β11Ci + ui

(6)

Table 6 here
For comparison, Table 6 combines the results of Equations (5) and (6), the difference
between which is only whether the equation contains an interaction term for social networkand the ownership sector. It can be seen that despite an insignificant coefficient on social

17capital, all its interactions with ownership sectors are positive and significant. This implies
that because, in Equation (6), the coefficient on social capital denotes the role of the social
network in the base group, then this result suggests that the social network does not assist thebase group. However, the interactions of the social network and ownership sectors are all
positive and significant, indicating that the social network pays differently in the other four
sectors compared to the base group. In other words, the social network can bring monetary
returns to urban collective, private sector, self-employed, foreign and joint venture and others
as well. Specifically, an additional contact can generate an income premium of around 4% in
urban collective, private sector and foreign and joint venture, while in the self-employedsector the reward is up to 7%. However, we cannot rush to the conclusion that social networks
are more valuable in market-oriented sectors simply because they pay more in marketoriented sections of the labor market, because the benefit of social networks may not bedirectly translated into money; in some cases, they might take the form of gray income, whichis extremely difficult to capture in the income function.
In the traditional planned economy, social networks are critical to the allocation of
resources. If an individual has a large number of helpful contacts, it is possible for that person
to dominate the resource distribution. But because of a lack of market mechanisms, the valueof social networks might not present itself in the form of monetary earnings. During themarketization process, the role of social networks may not be offset by the market system,
which is filled with nonmarket power. Instead, the new market system realizes the return tosocial networks in monetary terms.
In previous studies, Zhang et al. (2007) found that in rural China, marketization
weakened the effects of household-level social capital on poverty reduction. A possible
explanation for the difference between rural and urban areas is that, in the former case, social
networks are formed in a conventional and closed society, and the market process generates a
price mechanism distinct from the traditional allocation mechanism, which is conducive to18

expanding the scope of transactions, reducing social interaction in the traditional society as
well as the returns to social networks. However, in urban China, the value of social networks
is not transformed into earned income in the traditional state-owned sector. Beyond that, the
new market system generated by the gradual marketization process is based on the original
social structure, which is consequently embedded into the market system. As a result, this
newly grown market system may raise the value of social networks.
It is worth mentioning that, even if we control for social networks and their interactions
with the ownership sector, the interactions of Party membership and the ownership sector are
still insignificant. The F-test for the joint significance of the interactions associated with Party
membership yield F = 0.07 and P = 0.9961.
In Table 6, the regression on the right-hand side contains the social network and its
associated interactions, which are found to be significant. Because the coefficients on theother variables remain largely unaffected by comparison with previous regressions, this result
is the most comprehensive and covers all the variables with which we are concerned. Basedon these results, we give a brief interpretation of our results for these other variables. (1)Personal characteristics, age and its square are significant and have opposite effects, showing
an inverse U shape for the relationship between age and income. Income rises with age, but
the opposite is true after the turning point of around 56. (2) Education raises pay significantly,
implying the importance of education for earnings. In our estimates, an extra year of
schooling can bring an income premium of 3.7%. From recent empirical studies, the return toeducation in China is still controversial. Based on micro data in 2000, Li and Heckman（2004）found that there was an income premium of up to 11% to advanced education. Li etal. (2005b) used twin data and discovered that the return to education was 8.7% in OLS
estimation, but it fell to 2.7% after the exclusion of fixed effects within twins. It seems that
our estimate is similar to that in Li et al. (2005b), and it also indicates that if we control for
more variables, the bias in the estimate of return to education in OLS is negligible, merely 1%19higher than that in a fixed-effects model using twin data. (3) Regarding marital status, thecoefficient of the married group is positive and significant with the base group of unmarried
individuals, which means marriage can enhance earnings. (4) Ownership sector of a work unit
also affects income. When focusing on coefficients on ownership sectors themselves, itappears that pay does not vary by ownership sector. However, since social networks are more
important in nonstate-owned sectors, income in these sectors is actually higher than that in thestate-owned sector, which is caused by social networks. Meanwhile, in the 1995 data, we seethat education is more beneficial in nonstate-owned sectors, but when it comes to 2002, there
is no remarkable difference in the return to education in all sectors, which might indicate anincreasingly competitive labor market. (5) Occupation groups such as clerical/office staff,
skilled worker, unskilled worker, sales clerk or service worker earn significantly less than thebase group of self-employed, who are paid largely the same as the other groups of owner(manager) of private firm, professional, etc. (6) Only one industry, finance and insurance, has
income significantly higher than the base group of government, Party, and other non-profit
agencies, which earns more or less the same as all other groups.

5. Conclusion

In this paper we have examined the economic roles of political status and social network in
the Chinese urban labor market based on the 1995 and 2002 CHIP surveys. Our main findings
are as follows.
(1) Party membership raises earnings in urban China. The income premium for Party
membership does not vary by ownership sectors. Comparing the results in 2002 and 1995,
we also found that Party members’ income premium did not change over time.
(2) Social networks, as a form of social capital, also exert a positive effect on earnings. The
effects of social networks on earnings between different ownership sectors suggest thatsocial networks do not pay in the traditional state sector while they benefit nonstate-

20

owned sectors significantly.
If we assume that marketization deepens over time and that non-SOE sectors are more
subject to market forces than their SOE counterparts, then our findings imply that whatever
the time or the ownership sector, Party membership does not pay more as marketization
proceeds. We have also shown that Party membership does not represent unobserved ability,
otherwise it would be more valuable in more market-driven sectors. By contrast, wediscovered that social networks assist nonstate-owned sectors more in the Chinese urban labormarket.
The establishment of the market system is a long-term process during which nonmarket
power might penetrate the new market system and in turn influence market reform. In amarket system with abundant nonmarket power, resource distribution will not be simply
subject to the price mechanism, because the social interaction-based social capital will beembedded into the market system. As a result, how will the market system affect the pricemechanism and resource allocation? Does nonmarket power harm social fairness? What does
this mean for the future pathway to long-term economic development and transition to market
institutions? These questions about the impact of nonmarket power on the market system andthe transition process require serious thinking from economists.

21

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24

Table 1: Summary Statistics on Party Membership and Income in 2002 and 1995

OwnershipsectorState-owned
Urban collective
PrivateSelf-employed
Foreignandjoint Venture
Others
Total

35.73
18.66
18.99
6.82

2002
Difference of mean
income between
Party and nonParty members(Yuan）
3672.75***
2267.42***2811.56***
1635.59

14.63
14.06
28.58

Party
membership
ratio (%)

27.81
11.85
2.08
2.94

1995
Difference of mean
income between
Party and nonParty members(Yuan）
2044.36***1372.06***–3024.30
4531.02*3658.43**9.15

351.342476.93***3974.22***

7.46
24.64

977.292086.25***

Party
membership
ratio (%)

Notes: *** denotes significance at the 1 percent level, ** at the 5 percent level, * at the 10 percent level.

25

Table 2: Summary Statistics on Social Capital and Income in 2002

State-owned

Mean size of social
capital (Headcount)
1.27

Collective

1.07

1843.82***

Private1.25

1226.32**

Self-employed

1.05

2043.70**

Ownership sector

Difference in mean income between
the two comparison groups (Yuan)
717.42***Foreign and joint venture

1.63

1903.72*

Others1.10

3206.04***

Total

1.23

1250.95***

Notes: *** denotes statistical significance at the 1 percent level, ** at the 5 percent level, * at the 10 percent level.

26

Table 3: Determinants of Income for Employed Individuals Partitioned into Ownership Sectors (2002)
Panel A: Dependent Variables: Log of annual income
Independent
variables
Male

State-owned
Marginal
Standard error
effect0.129(0.023)***

Urban collective
Marginal
Standard error
effect0.072(0.070)PrivateMarginal
effect0.174

Standard error(0.052)***

Age

0.057

(0.011)***–0.001(0.036)0.029(0.020)Age squared–0.001(0.0001)***–0.000(0.0004)–0.0003(0.0003)

Years of education

0.039(0.005)***–0.001(0.015)0.021(0.011)**Married

0.249(0.056)***0.148(0.186)0.286(0.104)***

Communist Party

0.072(0.026)***0.287(0.092)***0.107(0.067)*

Other Party member
Number ofobservations
Adjusted R2

0.092(0.089)0.365(0.400)–0.003(0.269)

6492

625

1085

0.215

0.190

0.259

Table 3:

Continued

Panel B: Dependent Variables: Log of annual income
Independent
variables
Male

Self-employed
Marginal
Standard error
effect0.221(0.102)**Foreign and joint venture
Marginal
Standard error
effect0.159(0.110)

Other ownership sectors
Marginal
Standard error
effect0.300(0.102)***

Age

0.056

(0.043)0.130(0.052)**0.029(0.035)Age squared–0.0006(0.0005)–0.001(0.001)**–0.0002(0.0004)

Years of education

0.048(0.020)**0.050(0.022)**0.019(0.019)Married

0.169(0.242)–0.480(0.189)**0.170(0.190)Communist Party

0.433(0.204)**0.152(0.167)0.054(0.149)Other Party member
Number ofobservations
Adjusted R2

0.277(0.576)0.081(0.600)Notes:

––––

571

202

999

0.233

0.372

0.069

(1) For brevity, we do not report dummy variables for occupation, ownership sector, and city.
(2) *** denotes statistical significance at the 1% level, ** at 5% level, and * at 10% level.
(3) –––– denotes there is no observation in the sub-sample.

27

Table 4: Determinants of Income after Introducing Interaction Terms (2002)
Dependent Variables: Log of annual income
Independent
Variables

Marginal
effectStandard error

Independent Variables

Marginal
effectStandard error

Male

0.158

(0.020)***Interaction of Party membershipand urban collective–0.008(0.099)

Age

0.045

(0.009)***0.026(0.076)Age squared–0.0004(0.0001)***

0.031(0.156)

Years ofeducation

0.036(0.005)***–0.042(0.186)

Married

0.228(0.045)***–0.005(0.090)Communist Party

0.086(0.027)***–0.018(0.015)

Other Party
member

0.104(0.085)–0.003(0.011)

Urban collective–0.012(0.157)0.005(0.015)

Private0.008(0.124)0.034(0.024)Self-employed–0.193(0.151)–0.013(0.011)–0.215(0.296)–0.059(0.123)Foreign and joint
VentureOther ownershipsectors
Number ofobservations
Notes:

9974

Interaction of Party membershipand private sector
Interaction of Party membershipand self-employed sector
Interaction of Party membershipand foreign and joint venture
Interaction of Party membershipand other ownership sectors
Interaction of education and urban
collective
Interaction of education andprivate sector
Interaction of education and selfemployed sector
Interaction of education andforeign and joint venture
Interaction of education and otherownership sectors
Constant

Adjusted R2(1) For brevity, we do not report dummy variables for occupation, ownership sector, and city.
(2) *** denotes statistical significance at the 1% level, ** at 5% level, and * at 10% level.

28

8.094

(0.208)***0.218

Table 5: Determinants of Income (Cross-sectional Estimates for 1995 & Pooled Cross-sectional Estimates for 1995
and 2002)
Dependent Variables: Log of annual income
1995

1995 & 2002
Marginal
Standard error
effect0.143(0.011)***

Male

Marginal
effect
0.127

Age

0.048

(0.004)***0.047(0.005)***Age squared–0.0005(0.0000)***–0.0005(0.0001)***Years of education

0.015(0.002)***0.012(0.003)***

Married

0.266(0.022)***0.246(0.024)***

Communist Party

0.093(0.013)***0.084(0.018)***0.279(0.112)***

Independent variables

Standard error(0.010)***Year dummy (2002)
Interaction of Party membership and year dummy

0.008(0.024)

Interaction of education and year dummy

0.029(0.004)***

Urban collective–0.280(0.049)***–0.203(0.062)***

Private–0.701(0.248)***–0.056(0.093)Self-employed–0.103(0.142)–0.147(0.100)Foreign and joint venture–0.330(0.169)**–0.246(0.166)

Other ownership sectors
Interaction of Party membership and urban
collective
Interaction of Party membership and private
sectorInteraction of Party membership and selfemployed sector
Interaction of Party membership and foreign andjoint venture
Interaction of Party membership and otherownership sectors
Interaction of education and urban collective–0.622(0.192)***–0.118(0.089)0.009(0.038)–0.007(0.045)–0.705(0.510)0.007(0.059)0.245(0.227)

0.031(0.114)–0.027(0.147)–0.048(0.121)

0.185(0.229)–0.014(0.068)0.008(0.005)*0.001(0.006)

Interaction of education and private sector

0.060(0.023)***0.0001(0.008)Interaction of education and self-employed sector
Interaction of education and foreign and joint
ventureInteraction of education and other ownershipsectors
Constant

0.002(0.016)0.0005(0.010)0.046(0.015)***0.037(0.014)***0.011(0.019)–0.012(0.008)7.395(0.085)***7.453(0.105)***

Number of observations

11599

21573

2

0.429

0.344

Adjusted R
Notes:(1) For brevity, we do not report dummy variables of occupation, ownership sector, and city.
(2) *** denotes statistical significance at the 1% level, ** at 5% level, and * at 10% level.

29

Table 6: Determinants of Income after Introducing Social Network (2002)
Dependent Variables: Log of annual income
Whether interactions of social network andNoownership sector are inclusive
Marginal
Marginal
Independent variables
Standard error
effecteffectMale
0.158(0.020)***
0.157

Yes
Standard error(0.020)***

Age

0.045

(0.009)***0.044(0.009)***Age squared–0.0004(0.0001)***–0.0004(0.0001)***Years of education

0.036(0.005)***0.037(0.005)***

Married

0.229(0.045)***0.227(0.045)***

Communist Party

0.086(0.027)***0.087(0.027)***

Other Party member

0.103(0.085)0.111(0.085)Social network

0.006(0.005)–0.007(0.006)

Urban collective–0.013(0.157)–0.031(0.157)

Private0.006(0.124)–0.016(0.125)

Self-employed–0.195(0.151)–0.259(0.152)Foreign and joint venture–0.215(0.296)–0.236(0.296)

Other ownership sectors
Interaction of Party membership and urban
collective
Interaction of Party membership and private
sectorInteraction of Party membership and selfemployed sector
Interaction of Party membership and foreign
and joint venture
Interaction of Party membership and otherownership sectors
Interaction of social network and urban
collective
Interaction of social network and private
sectorInteraction of social network and selfemployed sector
Interaction of social network and foreign
and joint venture
Interaction of social network and otherownership sectors
Interaction of education and urban collective–0.059(0.123)–0.091(0.123)–0.010(0.099)–0.020(0.099)0.027(0.076)0.028(0.076)0.031(0.156)0.035(0.156)–0.036(0.186)–0.015(0.187)–0.008(0.090)–0.030(0.090)0.038(0.023)*0.035(0.017)**0.069(0.025)***0.037(0.023)*0.056(0.017)***–0.018(0.015)–0.020(0.015)Interaction of education and private sector
Interaction of education and self-employedsectorInteraction of education and foreign andjoint venture
Interaction of education and other ownershipsectors
Constant–0.003(0.011)–0.005(0.011)0.006(0.015)0.004(0.015)0.034(0.024)0.031(0.024)–0.013(0.011)–0.016(0.011)8.089(0.208)***8.126(0.208)***

Number of observations

9974

9974

2

0.218

0.219

Adjusted R
Notes:(1) For brevity, we do not report dummy variables of occupation, ownership sector, and city.
(2) *** denotes statistical significance at the 1% level, ** at 5% level, and * at 10% level.

30