GCOE Discussion Paper Series
Global COE Program

Human Behavior and Socioeconomic Dynamics
Discussion Paper No.157

In Search of a Better Life:The Occupational Attainmentof Rural and Urban Migrants in China

Ayako Kondo and Dongshu Ou

September 2010

GCOE Secretariat
Graduate School of Economics

OSAKA UNIVERSITY

1-7 Machikaneyama, Toyonaka, Osaka, 560-0043, Japan

In Search of a Better Life:
1The Occupational Attainment of Rural and Urban Migrants in China
Ayako Kondo
ISER, Osaka University (akondo@iser.osaka-u.ac.jp)
Dongshu Ou
Chinese University of Hong Kong (dongshu@cuhk.edu.hk)

Abstract
This paper investigates the occupational attainment and job mobility of permanent ruralto-urban migrants and compares them with migrants who were born with an urban hukou.
Using data from the 2003 China General Social Survey, we examine how much of thegaps in occupational-prestige scores between rural- and urban-born migrants can be
explained by differences in observable characteristics up to the time of migration. We
find that, with controls for these characteristics, the difference in occupational attainment
between rural and urban migrants becomes statistically insignificant or even positive forsome subgroups. In contrast, our analysis of job mobility reveals that rural migrants are
generally more mobile and also more likely to move to better jobs by changing workunits, whereas urban migrants are more likely to be promoted within a work-unit.

JEL-codes: O15, J0, J61, J62
Keywords: internal migration, China, job mobility, occupational prestige, hukou

1. Acknowledgement: Data analyzed in this paper were collected by the research project“China General Social Survey” sponsored by the China Social Science Foundation. This research
project was carried out by Department of Sociology at the Renmin University of China and theSocial Science Division of the Hong Kong Science and Technology University and directed by
Dr. Li Lulu and Dr. Bian Yanjie. The authors appreciate the assistance in providing data by theinstitutes and individuals aforementioned. The views expressed herein are the authors’ own. We
thank Albert Park, Olmo Silva, and the audience at the Centre for Economic Performance
Education Group Meeting for helpful comments. Ayako Kondo gratefully acknowledges
financial support from the Japan Society for the Promotion of Science (#21830056). Dongshu Ou
would like to thank the British Academy for funding support (SG090703).

11. Introduction
Since the 1980s, the rapid growth of labor demand in urban China has induced massive
migration from rural to urban areas, and the inequality between urban residents and rural
migrants has led to a serious social concern. The problem is exacerbated by the household
registration system called “hukou.” In particular, the government grants different social benefits
to families that hold an urban hukou and families that still hold a rural hukou, even if both live inthe same city.2 In addition, a substantial literature has shown that hukou status has enlarged thewage inequality and occupational segregation between urban workers and rural-to-urban migrant
workers who do not hold an urban hukou (Liu, 2005; Lu and Song, 2006; Meng and Zhang,2001).
To facilitate the migration of farmers to urban areas, the government began to grant
permissions for individuals to live and work outside of their places of hukou registration in the
1980s. Removing the migration restriction increased the number of these temporary migrants
sharply: the 2000 Census showed that more than 75 percent3 of the internal migrants in China
were temporary, compared to 25 percent who were permanent migrants who transferred their
hukou registration to the migration destination (Liang and Ma, 2004). Additionally, a sequenceof hukou policy reforms, which took place during the 1980s and 1990s, enabled a greater numberof rural migrants to obtain permanent urban hukou than before.4 According to Deng and2. For example, rural hukou holders who live in urban areas do not have access to
subsidized housing, subsidized medical care, and schooling for their children in the city.
3. See Liang and Ma (2004), Table 4. There were 58,835 temporary migrants (both urban
and rural) out of the 79,053 total migrants in China in 2000.
4. See Chan and Buckingham (2008), Chan and Zhang (1999), and Fan (2002a, 2002b)
for a detailed review of the hukou system and related policy. The dual classification of hukou
registration is based on both the place of regular residence and hukou status (i.e., hukou site and2

Gustaffson (2006), rural-born permanent urban migrants made up 20.3 percent of China’s urban
populations in 2002. Nevertheless, the growing population of permanent rural migrants, as well
as how these migrants assimilate into the urban labor market, have been largely ignored in theliterature.
The purpose of this paper is to examine the occupational attainment and job mobility ofpermanent rural-to-urban migrants who successfully obtained a local urban hukou. We
specifically compared these rural-to-urban migrants with urban-to-urban migrants who were bornwith a hukou from a city other than where they resided at the time of data collection.5 Bothgroups were permanent migrants who obtained a hukou registration in the destination city. To thebest of our knowledge, this is the first study to explore the difference in the occupational
attainment between permanent rural-to-urban migrants and permanent urban-to-urban migrants.
Moreover, the urban-to-urban migrants are a better comparison group for the rural-to-urban
migrants than urban local residents because they are both “outsiders.” Rural-to-urban and urbanto-urban migrants differ in their original hukou type, but they both face high job-search costs anda segregated labor market that favors local urban residents, especially given the reform of stateowned enterprises, in which labor policy was changed to protect local laid-off workers (Cai andWang, 2007; Zhang, 2010). The comparison will shed light on whether an individual’s original
hukou type might affect the rate of assimilation in urban areas.

hukou type; Chan and Buckingham, 2008; Chan and Zhang, 1999). Although most of theexisting literature has used data that only allowed researchers to identify hukou type, the data
used in our paper includes precise information on both where the hukou was registered as well as
the origin of the migrant.
5. We only look at those with a job prior to migration. In other words, individuals who
migrated to urban areas for further education are not included in the analysis. The following
section discusses the analytical framework in more detail.

3

We focus on occupational-prestige scores6 and job mobility. Specifically, we constructoccupational-prestige scores based on the average educational attainment of workers employedin each occupation to capture additional, less tangible job features such as welfare and social
status. Using retrospective employment and migration histories from the 2003 wave of theChinese General Social Survey, we construct panel data of occupational-prestige scores7 andbreak down the variance between rural- and urban-born migrants into components attributable todifferences in observable characteristics and the remaining unexplained portion of the variance.
Furthermore, we explore migrants’ job mobility to understand the evolution of their performanceand opportunities in the urban labor market. We consider within-work-unit and between-workunit job changes for both groups and associate them with the changes in the occupationalprestige scores.
The main finding of the paper is that the gap in occupational-prestige scores between
rural migrants and urban migrants becomes very small and statistically insignificant after wecontrol for the observed characteristics including gender, educational attainment, family
background and the occupational-prestige score of premigration jobs.8 This reveals a similar
6. Some researchers have begun to look at the well-being of rural migrants in China.
Knight and Gunatilaka (2007) found that rural migrants had lower mean happiness despite theirhigher mean income. The psychological literature has shown that permanent rural migrants are
not very vulnerable to poor mental health, which might result from a sense of well being
associated with upward economic mobility and improved opportunities (Li et al., 2007).
Occupational attainment measured by occupational prestige can to some extent capture fringe,
welfare, and other benefits of various jobs (Xu, 2000). Therefore, examining occupational
attainment instead of wages or earnings might also shed light on the well-being of rural migrants.
7. In particular, we create an index of occupational prestige, ranked by the mean ofeducational attainment in an occupation. This is often used in the job-polarization and wageinequality literature to measure job quality. See for example, Autor, Katz, and Kearney (2006;2008). Refer to the current data section and appendix for further details on the scoring method.
8. We also compare the regression results using 2002 wages with the results using 2002
occupational-prestige scores. The results reveal the same conclusion. See table 5.
4integration speed for both rural and urban migrants with the same predetermined characteristics.
Although we do not observe any changes in the gap of occupational-prestige scores within thefirst ten years after migration, the job mobility of rural migrants is higher than that of urban
migrants. We find that rural migrants are more likely to experience job promotions to different
work units, whereas urban migrants are more likely to be promoted within the same work unit.
These growth trajectories might be the cause of higher mobility of rural migrants.
The paper is arranged as follows. The next section summarizes the relevant literature. We
describe our data in section 3 and our empirical strategies in section 4. Section 5 reports the main
findings, and section 6 concludes with policy implications.

2. Institutional Background and Relevant Literature
2.1. A brief overview on hukou and internal migration
The hukou system in China, which was established in the late 1950s, requires every
member of the household to register at the local authority of their permanent residence (Chanand Buckingham, 2008). For several decades the system place strict limitations on migration, butthese restrictions were later relaxed in the late 1970s. The dual classification of hukou
registration is based on both the regular residence and the status (i.e., hukou site and hukou type;
Chan and Buckingham, 2008; Chan and Zhang, 1999). The type can be either agricultural or
nonagricultural. Agricultural hukou holders mostly reside in rural areas; therefore, they are also
known as rural hukou holders. Nonagricultural hukou holders are often called urban hukou
holders because they mostly reside in urban areas. Very limited state socioeconomic benefits
were given to the rural hukou population, and the state utilized the hukou system to impose
significant limits on Chinese citizens’ geographical mobility (Zhao and Liu, 1997). Because5hukou was tied to access to food, education, health services, and other basic necessities in thesystem’s early years, it was very rare or even impossible for people to live in a place outside of
their hukou registration (Fan, 2002a) until the hukou reform in the late 1970s. Rural-to-urban
status transfer (i.e., changing one’s rural hukou status to urban status; known as nong-zhuan-fei
in Chinese) entails state approval with various restrictions (Chan and Buckingham, 2008), andthis process was more difficult to navigate before the late 1970s. As a result, there was very little
labor mobility during this time.
The hukou system was reformed several times over the following decades to help thecountry meet the rapidly increasing labor demand in urban cities due to the economic reforms in
the late 1970s. On the one hand, the rural-to-urban transfer process was simplified and eased. On
the other hand, the introduction of national ID cards and temporary residence permits in the mid1980s induced a new wave of internal migrants (Fan, 2002a) who no longer need to register their
hukou to the area where they work or live. These non-hukou migrants are temporary migrants
without local household registration status.9 In contrast, migrants who transfer their hukou to a
local authority of the migration destination are called hukou migrants or permanent migrants.
The growth of rural-to-urban migrants, both temporary and permanent, has been tremendous: thenumber grew from 8.9 million in 1989 (Zhao, 2005) to 79.7 million in 2005 (Gagnon et al.,2009). Urban-to-urban migrants comprise the second-largest group of internal migrants in China:
they accounted for 4.5 percent of the total population in 2005 (Gagnon et al., 2009), which isonly 1.6 percent less than the rural-to-urban migrants.
2.2. Literature on the occupational attainment of rural migrants

9. This group is also known as the floating population (liu dong ren kou).

6

Most of the existing literature on China’s internal migration has focused on the floating
population, especially temporary rural-to-urban migrants. These studies usually use local urban
residents or workers as a comparison group for temporary rural migrants. Researchers havefocused extensively on the earning differentials between the two groups, including thedeterminants of selection into different occupational sectors. Regardless of data differences(Zhao, 2005), these existing studies establish that wage inequality between the two groups ismainly due to the low educational attainment among the rural population and low returns to
education in the rural labor market (Liu, 2005; Lu and Song 2006). Moreover, the unexplained
wage gap between rural migrants and urban residents might be attributed to the labor market’s
discrimination in favor of urban hukou holders and against rural migrants (Lu and Song, 2006;
Meng and Zhang, 2001).10 For example, Meng and Zhang (2001) find that discrimination within
occupations can explain about 82 percent of the hourly wage differential between urban and rural
migrant workers.
Studies on the determinants of selection into different occupational sectors (Meng andZhang, 2001; Demurger et al., 2009; Gagnon et al., 2009) have shown that both education andexperience play an important role in the occupational segregation between rural migrants andurban residents.11 Nonetheless, De Brauw and Giles (2006) point out that many cities havereserved some occupational categories for registered urban residents and that there is asegregation of rural residents into unskilled service and construction sectors or other relatively
low-skilled jobs that are unwanted by the urban residents.
10. See also Zhao (2005) for a survey of research papers on rural-urban migration issues
in China.
11. For example, Meng and Zhang (2001) find that education increases the probability ofrural migrants of obtaining a white-collar job. A recent study by Demurger et al. (2009) findsthat the sectoral difference is not as important as the differences driven by educational attainment.
7

Gagnon et al. (2009) first point out the substantial differences in comparing the rural
migrants, urban migrants, and urban residents by decomposing the migration effect and hukou
effect. Using Oaxaca-Blinder’s decomposition, the authors find that 40 percent of the observed
wage gap between rural and urban migrants might be due to hukou status. They also compare
urban migrants with urban residents and find a large migration premium. Nevertheless, themigrants, in particular the rural migrants, were discriminated against in sector choice comparedto the urban residents; specifically, formal sectors were less likely to employ rural migrants,
which excluded them from accessing health and educational services. However, in their study,
Gagnon et al. (2009) did not consider the permanent rural-to-urban migrants who obtained anurban hukou. Therefore, our paper complements Gagnon et al.’s (2009) study by comparing thepermanent rural migrants with urban migrants to understand the assimilation of rural migrants
into the urban labor market.
Another paper that is closely related to ours is Deng and Gustafsson’s (2006) study,
which is one of the few studies that examines permanent migrants. Combining several household
surveys, the authors investigate the determinants of permanent migration and the economic
benefits of migration. To study the economic assimilation of permanent rural migrants, they
compare them with local-born urban workers as does most of the other literature. They run
regressions on earnings and control for observed characteristics such as gender, marital status,
age, and ethnicity. For rural migrants, they also control for the years since receiving an urban
hukou. Their results show that the permanent rural migrants who received their urban hukou
before age 25 are well integrated economically in their place of destination. Overall, they
actually receive higher earnings than the local-born urban residents.

8Though the sample size of our data is smaller than Deng and Gustafsson’s (2006), its
retrospective information enables us to examine the job trajectories of the permanent migrants.
Another difference between the current paper and Deng and Gustafsson’s (2006) study is that we
limit our sample to those who migrated in the 1980s or later in order to exclude positively
selected rural migrants who migrated for special policy reasons.12
Few studies show empirical evidence on the job mobility of rural migrants. Though thereform of state-owned enterprises in the late 1980s has increased the mobility of urban workers,
rural migrants have remained more mobile even after controlling for their demographic
characteristics (Knight and Yueh, 2004). On the contrary, Zhang (2010) argues that temporary
migrants have longer job durations than local urban workers and only start to change jobs more
often after they have stayed in their destination city for some time. However, due to data
limitations, Zhang (2010) was not able to distinguish between temporary urban migrants andtemporary rural migrants.
We find that the permanent rural migrants tend to have higher mobility than permanent
urban migrants. In this sense, the current paper complements the study by Knight and Yueh
(2004), which compares temporary rural migrants with local residents and finds a higher
turnover rate for the former group. Moreover, we distinguish between internal job mobility (i.e.,
within-work-unit job changes and promotions) and external job mobility (i.e., between-work-unit
job changes and promotions) and discuss the variations and potential implications for rural andurban migrants.
12. For example, job transfer is a typical reason for state-planned migration before thehukou reform period; in this process, highly educated rural workers are selected for urban
government jobs (Chan and Buckingham, 2008). Consequently, permanent migrants in Deng andGustafsson’s (2006) study are more educated than the permanent migrants in our sample andmight be subject to positive selection bias.

9

3. Data
The data used in this paper come from the CGSS (2003). The 2003 CGSS contains 5,900
urban households in more than 100 counties and districts in 26 provinces, as well as the cities of
Beijing and Chongqing and four independently administered municipal districts.13 Besides
demographic information such as age, sex, education, family background, and household
registration status (i.e., hukou), the CGSS 2003 provides each respondent's employment history
from the first to the current job. The data include details on the nature of employment, including
three-digit occupation codes, management level, professional title, work-unit type, rank of thedepartmental supervisor for the work unit, job-related housing and medical benefits, and so forth.
Our main dependent variable is the occupational-prestige score. It is the z-score of theaverage educational attainment of workers within each occupation normalized by the average
educational attainment of all workers in the sample.14 A more detailed process is described in the
appendix. Although our main analysis is based on the occupational-prestige score, we also
examine job mobility. We exploit rich information on work units and job ranks to capture

13. The CGSS (2003) is conducted jointly by the HKUST’s Survey Research Center andthe the Sociology Department of People’s University of China. The initial survey design uses afour-stage stratified sampling scheme with unequal probabilities. In the first stage, a total of
2,801 county- and district-level units in China are broken into five major strata: (1) the city
districts of Beijing, Tianjin, and Shanghai; (2) the city districts of provincial capital-level cities;(3) the eastern region; (4) the central region; and (5) the western region. From each stratum, apredetermined number of city districts and rural counties are chosen as the primary sampling
units (PSUs). At the end, 125 PSUs are chosen, and in each sampled PSU, 4 districts andtownships are selected. From each of these districts and townships, 2 neighborhood committees
or villager committees are selected, and finally, 10 households are sampled from each sampledcommittee. The final sampling result would have included 5,900 urban households and 4,100
rural households; however, only the urban sample was published. Therefore, our analysis islimited to the 5,900 households that were located in urban PSUs at the time of survey in 2003.
14. As a robustness check, we examine the z-scores of the residuals from regressing theyears of education on a polynomial function of birth year to remove the potential bias of prestige
score due to education variations of different birth cohorts (see the appendix for more detail).

10

upward job moves that are independent of occupational changes. Specifically, we count the
number of job changes, both including and excluding job reassignment within the same work
unit, and moves to better jobs in terms of the characteristics of the work units and job ranks. The
construction is also described in the appendix. In addition, we use personal income in 2002 as
another supplemental variable to capture the gaps between urban and rural migrants that are notreflected by the occupational-prestige score.
We constructed a panel dataset from the retrospective information on hukou statuschanges, migration, education, and job histories. We set the time window as the period from1979 to 2003 to exclude migrations due to special policy reasons such as “up to the mountain,
down to the village.”15 We further limit our sample to rural-to-urban migrants (hereafter, rural
migrants) and urban-to-urban migrants (hereafter, urban migrants). Rural migrants are migrants
from a rural area who reported that they had a rural hukou when they were born.16 Urban
migrants are migrants from an urban area17 who were born with an urban hukou. Lastly, we
restrict our sample to those who have valid information for all variables included in the
regression analysis. Such variables include educational background, parental information, andjobs held in the year prior to the migration. Consequently, this limits our sample to those who
had a job both before and after migration. Details are presented in the appendix (table 1, panel A).
The final sample consists of 433 urban-to-urban migration spells and 208 rural-to-urban
migration spells. Panel A of table 1 illustrates summary statistics of these two groups. Although

15. Please refer to Appendix (page 41) for the meaning of the phrase “up to the mountain,
down to the village”.
16. More precise, rural migrants are migrants who reported a rural-to-urban hukou
transfer (nong-zhuan-fei) and who were holding an urban hukou in the year of 2003.
17. Towns, urban counties, prefecture-level cities (di ji shi), provincial capitals, and themunicipalities directly under the central government (zhixiashi).

11

only one rural migrant experienced rural-to-urban migration twice, a nonnegligible fraction ofurban migrants migrated more than once. The t-test shows that differences between the twogroups are statistically significant in all demographic variables except for the year of migrationand percentage of female. On average, the rural migrants in the sample are less educated than theurban migrants; specifically, about 65 percent of the rural migrants are at or below the middle
school level, whereas about 60 percent of the urban migrants are at the high school level orabove.18 Also, the parents of rural migrants are less educated than those of urban migrants. The
occupations of the rural migrants’ fathers were less prestigious than those of urban migrants.
Furthermore, the rural migrants themselves held less prestigious jobs prior to migration than theurban migrants. These variables indicate that the rural migrants have a lower socioeconomic
background than the urban migrants.
It is worth noting that our sample of rural migrants is, on average, more educated than thegeneral population of China. According to the 1-percent sample survey of the Chinese populationin 2000, the educational distribution of the population aged 50 to 54 (the cohort that corresponds
to the mean and majority of our sample), was as follows: 13.7 percent no schooling, 43.14
percent primary school, 30.38 percent middle school, 9.15 percent senior high school, 2.71
percent associate’s degree; 0.86 percent university, 0.05 percent graduate students. This isprobably because there is a large percentage of rural population in China, who are generally lesseducated. At the same time, our sample is less educated than the data used by Deng andGustafsson (2006).19 Because their sample is much older than ours, it is likely to include
18. This ratio is even higher than that of urban residents who have never migrated, which
is about 50 percent.
19. Deng and Gustafsson’s (2006) sample has an average of eleven years of schooling,
and the percentage of each educational level for rural migrants is 8 percent primary school or
below, 26 percent middle school, 35 percent high school, and 32 percent college or higher. The
12

individuals who had migrated to cities before the hukou relaxation policy. However, older
samples may create a selection bias because early hukou policy was so strict that only highly
skilled and highly educated individuals tended to migrate, usually in response to government
efforts to relocate personnel. We limit our data to individuals who migrated in the 1980s or laterto avoid this potential selection bias.
Panel B of table 1 presents the occupational-prestige scores in each year since migration.
The negative scores (measured by the normalized mean education level) for rural migrants revealthat rural migrants are taking less prestigious jobs; specifically, their occupational-prestige scores
are lower than the general population’s average by around 26 to 33 percent of one standard
deviation. The sample’s urban migrants tend to take jobs with relatively high prestige—about 13
percent to 18 percent of one standard deviation higher than the population average. The tablealso shows little variation over time in the average occupational-prestige scores in the first ten
years after migration for both rural and urban migrants.
Panel C of table 1 presents the number of job changes and upward moves within andacross work units. A sizeable proportion of rural and urban migrants changed jobs and work
units within the first ten years after their migration. In the sample, the rural migrants are moremobile than urban migrants. Moreover, rural migrants are more likely to move to a better job in adifferent work unit than urban migrants. Finally, the average personal income in 2002 was
14,403 Yuan for urban migrants and 10,952 Yuan for rural migrants.

corresponding figures for the urban-born sample are 4 percent, 27 percent, 43 percent, and 26
percent, respectively.

13

Before carrying out the main analysis on the labor-market performance of the rural
migrants and urban migrants, we compare their occupational-prestige scores with those of local
urban residents of the same age and gender.20
Figure 1 illustrates how the difference in the occupational-prestige score from local urban
residents evolves over time for rural and urban migrants.21 We control for age, age-squared, andgender, and thus the graph represents the differences between migrants and local residents withthe same age and gender. We see that the urban migrants are likely to hold jobs with more
occupational prestige than the local urban residents, especially in the first few years after
migration. This may be due to the fact that urban migrants have a higher education than urban
residents who have never migrated.22 Other studies also find that urban migrants have a higher
income level despite the limited choices of work sectors compared to the local residents (Gagnon
et al., 2009). As expected, rural migrants hold jobs with much lower occupational prestige than

20. Ideally, we would like to compare the score between migrants and local residents inthe same geographical area. However, the dataset does not contain such information.
10

10

21. The estimation equation is Yit      k D k itU i    k D k it Ri  θ'X it   it , where k =k 1 after migrationk 1
for migrant i. D10it takes a value
1,2, …9, Dkit takes a value of 1 if year t is kth yearof 1 if ten or more years have passed since migration as of year t. Ui is a dummy for urban
migrants, Ri is a dummy for rural migrants. X is a vector of control variables including age, age
squared, and gender. γk is the gap between rural migrants and native-born urban residents in the
kth year after migration. δk is the gap between urban migrants and native-born urban residents inthe kth year after migration.
22. Among the sample of CGSS, about 20 percent of urban residents who have nevermigrated since the 1978 have primary school or less education, while only 9 percent of the urban
migrants do. The proportion of those who have high school or more education is about 50
percent for urban non-migrants and 60 percent for urban migrants. This difference is not
attributable to the differences in generation because the average age is similar between the twogroups. Furthermore, the composition of educational background of our sample looks similar to
Gagnon et. al (2009)’s data corrected in 2005: Urban migrants: 26 percent college+ ; 36 percent
high school; 32 percent middle school; 6 percent primary school; Urban residents: 22 percent
college+; 31 percent high school; 37 percent middle school; 10 percent primary school.

14the local urban residents. Section 5 investigates how much of this gap can be explained bydifferences in observed human capital such as education and premigration job experience.

4. Empirical Model
To measure the gap between urban and rural migrants, we estimate the following
regression:23
Yit     Ri  θ'X it   it ,(1)

where yit represents the occupational-prestige score for individual i in year t, and Ri is a dummy
variable for rural migrant. Xit is a vector of control variables, to which we add variables gradually.
εit represents the remaining error, which can be correlated within an individual. We cluster ourstandard errors by the individual.
We begin by including only age, age-squared, and sex in Xit to examine the gap between
urban and rural migrants without controlling for any differences in skill level. Then we add themigrant’s own educational background and observe how β changes. Our hypothesis is that
controlling for educational qualification will reduce the gap between the two types of migrants.
To explore the role of family background in explaining the gap in the outcome variable, we add
parents’ years of schooling and father’s occupational-prestige score from the year when the
migrant was 18 years old. Lastly, we add the occupational-prestige score of the last job prior to
migration as a proxy for job experience accumulated before migration.

23. Note that we estimate simple repeated ordinary least squares regressions even though
we have panel data because more than half of the migrants have migrated only once.

15

In order to allow β and θ to vary with years after migration, we estimate equation (1)separately for those in the first to third year after migration, the fourth to sixth year, and theseventh to ninth year.
Furthermore, to explore the change of β year by year, we estimate the following
regression:
9

9

k 2

k 2

Yit     Ri    k D k it    k D k it Ri  θ'X it   it ,(2)and plot the gap in each year subsequent to migration. Dkit is a dummy variable that takes thevalue of 1 if year t is the kth year after migration for migrant i. β+γk is the gap between rural andurban migrants in the kth year after migration for k ≥ 2, and β is the gap for k = 1. As an
additional test, we estimate equations (1) and (2) separately for migrants with different
educational levels.
We further explore the job mobility of rural migrants compared to urban migrants by
estimating an equation similar to equation (1), except that the sample is collapsed into the personlevel instead of the person-year level:Yi     Ri   ' X i   i(3)

The dependent variable Y represents the following four measures of job mobility, all measured
in the first ten years after migration: (a) number of job changes, including job transfer within awork unit; (b) number of work unit changes; (c) number of within-work-unit job promotions; and(d) number of upward moves across work units.
Finally, we examine the gap in personal income in 2002 between urban and rural
migrants. The equation we estimate is the same as equation (3), except that the dependent
variable is personal income in 2002 and the sample is limited to those who had a nonzero income
in 2002.

16

5. Findings
5.1. Occupational Prestige Score
Table 2 presents the estimated coefficients of the dummy for rural migrants in equation(1). Column 1 includes age, age-squared, and sex in the covariates. Column 2 adds education
dummies. Column 3 adds parent’s education and father’s occupational-prestige score. Column 4
adds the occupational-prestige score of the premigration job. Each row corresponds to a separate
regression with a subsample divided by years since migration. First, we see that adding controls
for education substantially narrowed the gap in occupational-prestige scores between rural andurban migrants. For example, in the first three years after migration, the gap is as large as 45
percent of one standard deviation of the scores in the population when holding only age andgender constant, but the gap shrinks to 23 percent when controlling for education. On the other
hand, controlling for parental background does not seem to narrow the gap after we control forage, gender and education differences. Rural migrants still hold less prestigious jobs than urban
migrants, and the difference is statistically significant.
However, with controls for the occupational-prestige score of the last premigration job, thegap in postmigration occupational prestige between rural and urban migrants is no longer
statistically significant. This finding implies that the observed gaps between rural and urban
migrants are likely to be attributed to the differences in their human-capital accumulation prior to
migration rather than some sort of discrimination against rural-born migrants who otherwise
have the same human capital as their urban-born counterparts.
Figure 2 plots the estimated gap between rural and urban migrants in each year after
migration from equation (2). The results confirm the observations from table 2. Furthermore, the17changes in the occupational-attainment gap between these two groups does not seem to changeover time in all specifications. This stability of the gap over time suggests that something that
determines the labor-market outcomes at the time of migration (in this case, the occupationalprestige score) continues to influence the occupational attainment of rural and urban migrants
over their lifecycles. Such determinants also likely include educational attainment and job
experience before migration because the gap between the two groups shrinks substantially after
controlling for these variables.
So, does the gap vary by the educational qualification of the migrants? We run the
regression separately for each educational level. Table 3 (panels A-D) and figures 3A-D show
the gaps in occupational-prestige scores between rural and urban migrants for four subgroups
categorized by educational background. Like table 2 and figure 2, we gradually add explanatory
variables such as parental characteristics and the occupational-prestige score of premigration
jobs. Except for those with post high-school education, rural migrants are taking up jobs with alower prestige score than urban migrants. Yet, after controlling for the occupational-prestige
score of premigration jobs, the gaps are no longer statistically significant, except for the least
educated group.
In contrast, the gap is positive for migrants with more than a high school education. Thismeans that the rural migrants with more than a high school education hold jobs with more
occupational prestige than urban migrants with the same level of education. Furthermore, the gap
has an upward slope, which suggests that the highly educated rural migrants do better the longer
they stay in the urban labor market. Panel D of table 3 shows that the effect is substantial in size:
being a rural migrant is associated with a higher occupational-prestige score (about 30 percent of
one standard deviation higher), holding other observed characteristics constant.

18

It is interesting to note that the level of the gap is smaller for middle school graduates
than that for high school graduates. The evidence is consistent with the argument by De Brauw
and Giles (2006) that the career prospects of high-school graduates in rural areas might not bebetter (or might even be worse) than rural individuals with less education.
We also explore the gap by migration destination. The results are presented in appendix
table 4, panel A. We do not observe statistically significant differences for those who migrated
into small towns; however, the gap between rural migrants and urban migrants remains negative
for those who migrated into counties and other large cities (without controlling for theoccupational-prestige score of the premigration job). The gap gradually reduces along with thelength of stay in large cities (panel C). The gap decreases even further and becomes positive for
migrants who moved to large cities once we control for the premigration occupational-prestige
score, although these trends are not statistically significant.

5.2 Job Mobility
As already indicated by panel C in table 1, rural migrants tend to change jobs more
frequently than urban migrants. To examine whether such differences in job mobility can also be
attributed to the differences in observed characteristics, we estimate equation (3) with thefollowing dependent variables: number of job changes, including job transfer within a work-unit;
number of work-unit changes; number of within-work-unit job promotions; and number of
upward moves between work-units. Because we define an upward change on the basis of workunit type and job rank, the concept can capture some features associated with the job that are notreflected in the occupational-prestige score. Table 4 presents the estimated coefficients of thedummy variable for rural migrants.

19

The first two rows show that rural migrants are more mobile than urban migrants, evenafter controlling for the occupational-prestige score of premigration jobs. This may be because,
as Li (2008) argues, many rural migrants cannot afford to be unemployed for a long time.
Another potential cause of the high mobility of rural migrants is their concentration in highly
competitive, unskilled occupations.
The third row shows negative coefficients of the rural-migrants dummy variable. In
particular, the findings reveal that rural migrants are less likely than urban migrants to bepromoted within work-units, although the estimates are not statistically significant. In contrast,
the fourth row shows that rural migrants are more likely to move to better jobs between workunits.
It is true that a part of this difference is spurious because rural migrants are more likely
to move in general. 24 However, this potential spurious difference cannot explain all of thevariation in the finding. A back-of-envelope calculation of the data (shown in panel C of table 1
suggests that the probability of a between-unit job change being an upward move is 18 percent
for rural migrants but just 10 percent for urban migrants. Table 4 suggests that these differences
are not likely to be attributed to the observed characteristics included in the fourth column. One
possible explanation comes from Li (2008)’s argument that rural migrants lack the full
information about the nature of their jobs and that rural migrants’ first job in an urban labormarket serve as a pilot for them to progress and find a better job (with higher pay) later.

5.3 Income in 2002
24. Knight and Yueh (2006) find that temporary rural migrants are much more mobile
than local urban workers. However, Zhang (2010) claims that temporary rural migrants are lessmobile because they are discriminated against in the labor market. Unfortunately, neither study
examines permanent rural migrants.

20

Finally, we estimate the differences in personal income in 2002 between urban and rural
migrants. The results for personal income (table 5) are consistent with the basic observations
from the occupational-prestige scores (table 2): the gap becomes smaller and statisticallyinsignificant when we add the prestige score of premigration jobs and other explanatory
variables.

6. Concluding Remarks
China’s internal migration has been growing rapidly with the country’s economic
development in recent years. A substantial amount of research has been conducted on the labormarket performance of temporary migrants, especially temporary rural-to-urban migrants.
However, very little empirical work has examined permanent rural migrants, who eventuallytransferred their rural hukou status to an urban status. Moreover, most of the earlier studies onrural migrants have compared them with urban local residents, who are generally in a segmented
labor market due to factors such as local labor-protection policies.
We utilize the retrospective information on employment and migration from the 2003
China General Social Survey to analyze the labor market performance of permanent rural
migrants and permanent urban migrants. We measure the occupational attainment by aneducation-based index that reflects occupational prestige. Furthermore, we explore job mobility
within and between work-units.
Our results suggest that the observed inequality between permanent rural and urban
migrants in occupational attainment can be attributed to differences in characteristics
predetermined by the time of migration. In fact, after controlling for characteristics of one’s
premigration job, the gap between rural and urban migrants is no longer statistically significant,21and in some cases the effect even becomes positive (although not significant). Therefore,
encouraging human-capital investment in rural areas could be an effective policy intervention tolessen the economic disparities between rural-born permanent migrants and their urban-bornpeers (Lu and Song, 2006).
Even though the analysis on the occupational-prestige score reveals a similar integration
speed for rural and urban migrants in the labor market, we find that rural migrants experiencehigher job mobility. Rural migrants are more likely to move upward in a state-owned sector,
obtaining more job autonomy (e.g., increased supervisory role at the job) or receiving more
medical and housing benefits. However, rural migrants tend to have fewer opportunities thanurban migrants for job promotion within work units, although the finding is not statisticallysignificant. Although we cannot pin down where this difference comes from, it is plausible thatrural migrants try to achieve better job outcomes through continued efforts to seek opportunities
outside of their current workplace.
Our findings might indicate that, in addition to the the vast differences in earnings
opportunities between rural and urban areas, rural migrants might be attracted to cities becauseeven relatively less prestigious jobs in cities provides better occupational attainment than manyjobs available in rural areas. This suggests that, by creating more employment opportunities
outside of farming in rural areas, China can possibly slow down the inflow of rural migrants into
cities and mitigate social problems associated with this excessively rapid migration.
Finally, China is quickly granting more permanent hukou residency to rural migrants (Xie,
2010). The hukou relaxation policy intends to settle most members of the floating population in
the cities after the removal of the hurdles created by the hukou system (Zhu, 2007). On the one
hand, a better understanding of permanent rural migrants and their occupational attainment will

22

shed light on how hukou policy might affect the labor-market performance of the floating
population. On the other hand, permanent urban migrants also constitute a large proportion of theinternal migrants in China (Gagnon et al., 2009). Future research on internal migration in China
would do well to provide more understanding of these “less well-known” migrants.

23

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26

Table 1. Summary Statistics

A. Predetermined characteristics

Sample size
1st migration
2nd migration
3rd migration
Mean of explanatory variables
Year of migration
Age at the time of migration% FemaleYears of schooling% primary school or less% middle school% high school% more than high school education
Father’s years of schooling
Mother’s years of schooling
Occupational-prestige score of father’s job in
the year when the respondent was 18
Occupational-prestige score of the job heldin the year prior to migration

Urban
Migrants
433
375
52
6

Rural Migrants
208
207
1
0
t-test stat
0.29
-2.18**
0.23-7.42***

1989.3
32.2
52.9%
11.5
9.0%
30.9%
27.0%
33.0%
7.5
4.5

1989.5
30.7
53.8%
9.6
27.9%
37.0%
22.1%
13.0%
4.8
2.4

-0.10

-1.29

-13.9***

0.12

-0.60

-8.77***-7.1***-6.38***

B. Occupational-prestige score by years since migration
Year sincemigration
1
2
3
4
5
6
7
8
9
10

Urban migrants
mean
sd
0.18
0.69
0.15
0.69
0.15
0.70
0.14
0.68
0.16
0.69
0.17
0.70
0.16
0.71
0.17
0.69
0.16
0.70
0.13
0.70

n
417
399
374
345
327
305
280
258
232
215

mean
-0.31
-0.32
-0.28
-0.27
-0.29
-0.31
-0.30
-0.31
-0.28
-0.33

Rural migrants
sd
0.83
0.82
0.82
0.81
0.81
0.80
0.81
0.83
0.83
0.82

n
189
179
179
171
158
148
137
132
123
118

C. Job mobility in the first 10 year after migration
Number of observations: urban migrants 256, rural migrants 141
Number of job
Number of work unit Number of withinchangeschanges
unit promotion# of changes
urban
rural
urban
rural
urban
rural
0 55.9%
38.3%
84.0%
63.8%
92.6% 95.7%
1 35.6%
42.6%
14.1%
29.1%
5.5%
3.6%
2 6.3%
12.8%
1.6%
5.0%
2.0%
0.7%
3 2.0%
5.0%
0.4%
2.1%
4 0.0%
1.4%
6 0.4%
0.0%

Number of acrossunit upward move
urban
rural
98.1%
91.5%
2.0%
8.5%

27

D. Income in 2002

Income in 2002 (yuan)
Log income

Urban migrants
14403.2
9.24

Rural migrants
10952.0
8.86

total
13224.5
9.11

Table 2. Gaps in the Occupational-Prestige Score between Rural-to-Urban and Urban-to-Urban
Migrants
Coefficients of the rural-migrant dummy variable
(1)
(2)
(3)
(4)
(3) plus the(2) plus
Control variables:
prestige score
Age and sex (1) plus own
parents'of theonly
education
education andSample :premigration
occupation
job-0.449***-0.235***-0.223***-0.0051-3 years after migration[0.070][0.066][0.069][0.054](sample size: 1,737)-0.427***-0.220***-0.197***-0.0094-6 years after migration[0.075][0.069][0.075][0.066](sample size: 1,454)-0.435***-0.217***-0.196**-0.0257-9 years after migration[0.082][0.073][0.079][0.072](sample size: 1,162)
Note: OLS regression with repeated observations from the same individuals. Standard errors are inbrackets and are clustered by individual ID.

28

Table 3. Gaps in the Occupational-Prestige Score between Rural-to-Urban and Urban-to-Urban
Migrants, by Education
A. Primary school or lessCoefficients of the rural-migrant dummy variable(1)
Control variables:
Sample :1-3 years after migration(sample size: 265)
4-6 years after migration(sample size: 220)
7-9 years after migration(sample size: 189)

(2)

(3)

Age, sex, andprimary school
dummy(1) plus parents'
education andoccupation(2) plus the prestige
score of thepremigration job-0.610***[0.139]-0.611***[0.147]-0.568***[0.169]-0.549***[0.168]-0.534***[0.189]-0.493**[0.204]-0.269[0.169]-0.372*[0.203]-0.341[0.233](2)(3)

Age and sex(1) plus parents'
education andoccupation(2) plus the prestige
score of thepremigration job-0.193*[0.110]-0.195[0.126]-0.254**[0.124]-0.105[0.110]-0.116[0.128]-0.222*[0.126]0.071[0.081]
0.037[0.103]-0.053[0.099](2)(3)

Age and sex(1) plus parents'
education andoccupation(2) plus the prestige
score of thepremigration job-0.295**[0.128]-0.277**[0.131]-0.257*[0.150]-0.299**[0.145]-0.225[0.143]-0.244[0.168]-0.122[0.123]-0.101[0.151]-0.157[0.185]

B. Middle school
Coefficients of the rural-migrant dummy variable(1)
Control variables:
Sample :1-3 years after migration(sample size: 571)
4-6 years after migration(sample size: 457)
7-9 years after migration(sample size: 359)

C. High school
Coefficients of the rural-migrant dummy variable(1)
Control variables:
Sample :1-3 years after migration(sample size: 435)
4-6 years after migration(sample size: 361)
7-9 years after migration(sample size: 275)

29

D. Post high school education
Coefficients of the rural-migrant dummy variable(1)Age, sex, andControl variables:
dummies foreducational
Sample :attainment
0.173
1-3 years after migration[0.136](sample size: 466)
0.173
4-6 years after migration[0.126](sample size: 416)
0.251*
7-9 years after migration[0.134](sample size: 339)

(2)

(3)

(1) plus parents'
education andoccupation(2) plus the prestige
score of thepremigration job

0.13[0.140]0.131[0.138]0.231[0.149]0.232*[0.123]0.321***[0.121]0.406***[0.133]

Note: OLS regression with repeated observations of the same individuals. Standard errors are inbrackets and are clustered by individual ID.

Table 4. Gaps in Job Mobility in the First 10 Years after Migration between Rural-to-Urban
and Urban-to-Urban Migrants
Coefficients of the rural-migrant dummy variable; sample size = 397
(1)
(2)
(3)
(4)
Control variables:(2) plus parents' (3) plus the prestige
Age and (1) plus own
education andscore of the
Dependent
sex only education
occupation
premigration job
variables :Number of job changes
0.401*** 0.320***0.380***
0.332***[0.105][0.100][0.097][0.091]
Number of work-unit changes
0.257*** 0.243***0.274***
0.263***[0.076][0.073][0.067][0.065]
Number of within-unit promotions-0.027-0.031-0.033-0.049[0.028][0.028][0.032][0.030]
Number of between-unit upward moves 0.055**
0.052*
0.068***
0.066***[0.027][0.027][0.025][0.025]
Note: OLS regression with repeated observations of the same individuals. Standard errors are inbrackets and are clustered by individual ID.

Table 5. Gaps in income in 2002 between Rural-to-Urban and Urban-to-urban Migrants
Coefficients of the rural-migrant dummy variable
(1)
(2)
(3)
(4)
(2) plus parents' (3) plus the prestige
Control variables:
Age and (1) plus own
education andscore of thesex only education
occupation
premigration job
Sample:All migrants in the sample-0.224** -0.224**-0.173*-0.113(sample size: 350)
[0.088][0.088][0.100][0.101]-0.071-0.071-0.023-0.017
Year of migration ≥1993(sample size: 160)
[0.141][0.141][0.150][0.154]-0.273** -0.273**-0.235-0.14
Year of migration <1993(sample size: 190)
[0.124][0.124][0.142][0.138]
Note: OLS regression with repeated observations of the same individuals. Standard errors are inbrackets and are clustered by individual ID.

30

Table A1. Sample selection(Based on person, not migration spells)
Remaining
DroppedUrban-to-urbanRural-to-urbanUrban-to-UrbanRural-to-urban
Original CGSS 2003
5894
Inconsistent education or job history
5,845
49
Migrants only
1,070
1,117
3658
Have valid job information (i.e., dependent var)
759
668
311
449
Have valid parental information
654
611
105
57
Have premigration job information
374
207
280
404

31

Table A2. Reasons for Migration, by Rural and Urban status, Education, and Year of Migration (%)
Urban migrants by education
By year of migration
Urban
Primary Middle High
1984- 1992- 1997More ~1983
Total
or less school
school1991 1996 2003
Attending school
0.0
0.8
0.9
0.7
0.0
0.6
0.0
2.2
0.7
Joining the Army
2.6
3.8
1.7
3.5
5.8
1.3
2.1
4.3
3.0
Worker-to-cadre transfer (Zhuan Gan)
2.6
3.0
2.6
2.8
5.8
2.6
1.1
2.2
2.8
Other employment-related reasons
43.6
36.4
31.6 42.7 38.4 37.6 49.5
25.8 37.8
Migrated with the family
28.2
28.0
37.6 21.0 27.9 30.6 26.3
26.9 28.3
Political reasons (Zhi nei /Zhi bian, Shang Shan Xia Xiang)
2.6
2.3
0.9
0.0
2.3
0.6
2.1
0.0
1.2
Residential changes, other reasons
20.5
23.5
24.8 28.0 18.6 26.1 18.9
35.5 25.1
Rural migrants by education
By year of migration
Rural
1984- 1992- 1997Primary Middle High
total
More ~1983
1991 1996 2003or less school
schoolAttending school
0.0
1.3
6.5 11.1
5.0
4.2
0.0
4.2
3.4
Joining the Army
1.7
3.9
2.2
0.0
2.5
5.6
0.0
0.0
2.4
Worker-to-cadre transfer (Zhuan Gan)
0.0
3.9
10.9 11.1
0.0
5.6
8.2
6.3
5.3
Other employment-related reasons
36.2
22.1
39.1 48.1 40.0 36.6 36.7
18.8 33.2
Migrated with the family
39.7
31.2
19.6 11.1 25.0 19.7 26.5
45.8 28.4
Purchase (self-purchase of local urban hukou, houses with hukou quota)
5.2
5.2
0.0
0.0
0.0
2.8 10.2
0.0
3.4
Policies related to urban development
13.8
18.2
17.4
3.7
5.0 18.3 10.2
22.9 14.9
Political reasons (Zhi nei /Zhi bian, Shang Shan Xia Xiang)
1.7
7.8
2.2
7.4 17.5
4.2
0.0
0.0
4.8
Residential changes, other reasons
1.7
6.5
2.2
7.4
5.0
2.8
8.2
2.1
4.3

32

Table A3. Distribution of Educational Background, by Rural and Urban status and Year ofMigration
A. Urban Migrants
1979-1983 1984-1991 1992-1996 1997-2003
Primary school or less
Middle school
High school
More

20.9
34.9
20.9
23.3

10.8
31.9
28.7
28.7

3.2
33.7
26.3
36.8

1.1
21.5
31.2
46.2

Total
9.1
30.6
27.2
33.2

All urban migrants
including those
dropped due tomissing variables
15.7
28.5
28.2
27.6

B. Rural Migrants
1979-1983 1984-1991 1992-1996 1997-2003
Primary school or less
Middle school
High school
More

35.0
40.0
17.5
7.5

29.6
33.8
23.9
12.7

22.5
40.8
20.4
16.3

25.0
35.4
25.0
14.6

Total
27.9
37.0
22.1
13.0

All rural migrants
including those
dropped due tomissing variables
32.1
37.1
25.0
5.8

Table A4. Gaps in the Occupational Prestige Score between Rural-to-Urban and Urban-toUrban Migrants, by Migration Destination
Panel A: Small Town
(1)

Control variables:
1-3 years after migration(sample size: 826)
4-6 years after migration(sample size: 703)
7-9 years after migration(sample size: 581)

(2)

Age only
0.211[0.153]0.149[0.207]0.082[0.212](1) plus own
education
0.107[0.207]-0.052[0.255]-0.005[0.254]

(1)

(2)

(3)

(4)

(3) plus the(2) plus parents' prestige score of
education and the premigration
occupation
job-0.047-0.141[0.196][0.198]-0.102-0.228[0.215][0.218]-0.075-0.168[0.207][0.225]

Panel B: County
(3)

(4)

(3) plus the(2) plus parents' prestige score of
education and the premigration
job
occupation

Control variables:

Age only(1) plus own
education

1-3 years after migration-0.524***-0.183-0.541***-0.12(sample size: 321)

[0.151][0.152][0.185][0.112]

4-6 years after migration-0.612***-0.092-0.672***-0.295(sample size: 249)

[0.168][0.176][0.219][0.178]33

7-9 years after migration-0.588***-0.085-0.574**-0.14(sample size: 201)

[0.168][0.146][0.227][0.162]

(1)

(2)

(3)

(4)

Panel C: Other large cities(3) plus the(2) plus parents' prestige score of
education and the premigration
occupation
job

Control variables:

Age only(1) plus own
education

1-3 years after migration-0.459***-0.289***-0.333***0.044(sample size:1229)[0.086][0.080][0.094][0.080]

4-6 years after migration-0.396***-0.247***-0.228**0.095(sample size:1054)[0.091][0.081][0.097][0.091]

7-9 years after migration-0.405***-0.235***-0.231**

0.108(sample size: 831)
[0.099][0.087][0.110][0.109]Note: OLS regression with repeated observations of the same individuals. Standard errors are inbrackets and are clustered by individual ID.

34

Figure 1: Differences in Occupation Prestige Score from Local-born Workers
0.2
0.1
0
-0.1

1

2

3

4

5

6

7

8

9

10+

-0.2
-0.3
-0.4
-0.5
Urban migrants

Rural migrants

Notes:(a)Y-axis represents the gap of occupational-prestige scores between migrants and local urban
residents. X-axis represents the years since migration.
(b) The independent variables included are age, age-squared, and a dummy variable for female.

Figure 2: Changes in the Occupational-Prestige-Score Gaps between
Rural-to-Urban and Urban-to-Urban Migrants over Time
0.1

years after migration

0
1

2

3

4

5

6

7

8

9

-0.1
-0.2
-0.3
-0.4
-0.5
(1) age and sex only(2) (1) + own education(3) (2) + parents' characteristics(4) (3) + premigration job(a)Y-axis is the gap in occupational-prestige scores between rural-to-urban and urban-to-urban
migrants. X-axis represents the length of the migration spell.
(b) Model 1 includes age, age-squared, and a dummy variable for female in the RHS; model 2 builds
on model 1 by adding dummies for the level of education. Model 3 adds father's education, mother's
education, and an occupational-prestige score for father's occupation. Model 4 adds the occupationalprestige score of the respondent's premigration job.
(c) The sample is limited to those with all nonmissing information in the full models.

35

0
-0.1

Figure 3A: Changes in the Occupational-Prestige-Score Gaps between
Rural-to-Urban and Urban-to-Urban Migrants over Time, Primary School and
Below
1

2

3

years after migration
4
5

6

7

8

9

-0.2
-0.3
-0.4
-0.5
-0.6
-0.7
(3) (2) +premigration job age and sex only(2) (1) + parents' characteristics(1) age and sex only

Figure 3B: Changes in the Occupational-Prestige-Score Gaps between
Rural-to-Urban and Urban-to-Urban Migrants over Time, Middle School
0.1

years after migration

0
1

2

3

4

5

6

7

8

9

-0.1
-0.2
-0.3
-0.4
-0.5
(3) (2) +premigration job age and sex only(2) (1) + parents' characteristics(1) age and sex only

Figure 3C: Changes in the Occupational-Prestige-Score Gaps between
Rural-to-Urban and Urban-to-Urban Migrants over Time, High School
0
-0.05

1

2

3

years after migration
4
5

6

7

8

9

-0.1
-0.15
-0.2
-0.25
-0.3
-0.35
-0.4
(3) (2) +premigration job age and sex only(2) (1) + parents' characteristics(1) age and sex only

36

0.45

Figure 3D: Changes in the Occupational-Prestige-Score Gaps between Rural-toUrban and Urban-to-Urban Migrants over Time, More Than a High School
Education
years after migration

0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
1 (3) (2) +premigration2
4
job 3age and sex only

5 (1) + parents'6characteristics7(2)9(1)8age and sex only

37

Data Appendix
Occupational prestige score:
Our measure of occupational prestige is the standardized z-score based on the mean level of educationfor each occupation (measure 1)1.After letting Eduj denote the average years of schooling of all
individuals who work in occupation j, the z-score for the Eduj can be defined as follows:

Zj 

Edu j  Edu
1 N
 ( Edu j  Edu)2
N  1 j 1

We calculate this score for each of the 253 occupations we observed in the 2003 dataset.
We assume that the educational composition of the occupations does not change very much in the
period of 1980-20032. One concern is that the emerging or new occupations might consist of younger
people who are generally better educated than the older generations. To remove the differences in the
average education across cohorts, we obtain the residuals from the regressions of education on aquadratic form of birth year for each correspondent (measure 2). We then compute the z-score by thesame formula we used above but using the residuals. Regression results3 using this method are similar
to what we obtained from the z-scores based on raw education data.
Below is a list of the most popular occupations 1 year after migration by rural migrants and theircorresponding occupational-prestige score by two different measures:
Code

Occupation

Occupationalprestige score(Measure 1)-0.89

Occupationalprestige score(Measure 2)-.88% workers inthis occupation

60

Self-employed

17.26

411

Shop employees andsalespersons

-0.31

993

Simple laboring workers

-0.91

-.86

5.08

510

Planting workers

-1.98

-1.72

4.57

149

Transportationengineering technicians

-.43
7.61

-.71
-0.97

4.06

1

We thank Albert Park for suggesting this measure.
Xu (2000) compared four different scales from sociology studies and argued that there are changes in
occupational-prestige scores for some occupations such as policeman and industrial and commercial
administrators or tax officers. Although the scale we developed for this paper differs from the scales used bysociologists, the relative ranking is comparable. We also used the education level for observed individuals in the
1980s and 1990s to construct the alternative measure (see next page), and the results were very similar to thecurrent scale.
3
These results are not presented in our paper but are available upon request. We also try the following: Rank
occupational prestige based on the percentile distribution of the average education for occupations observed
through 1980 to 2003. Analyses based on this measure are also similar to our preferred measures, except that thecoefficients are on a different scale by definition (i.e., nonstandardized vs. standardized).
2

38

244

Primary school teachers

0.93

0.86

4.06

Below are a few top occupations 1 year after migration by urban migrants and their corresponding
occupational-prestige score by two different measures:
Code

Occupation

Occupationalprestige score(Measure 1)

Occupationalprestige score(Measure 2)% workers inthis occupation

213

Accounting staff

0.64

0.63

7.71

310

Administrative staff

0.5

0.55

7.48

42

Leading cadres ofhealth institutions

411

Shop employees andsalespersons

-0.31

421

Keepers

-0.4

1.09
0.78

5.37
-0.43
5.14
-0.28

4.91

Job Mobility:
The number of job changes and work-unit changes can be derived directly from the survey data.
Below we describe how we define job promotions within the same work unit and across different
units.
By definition, a job promotion is the permanent movement of a staff member from a position in one
job class to a position in another job class of increased responsibility or complexity of duties and in ahigher salary range. To compare the skill requirement, responsibility, and relative pay and job benefitsof within and across different work unit, we will briefly summarize the characteristics of each work
unit and some description on the China’s economy reform prior to 2003.
First, we divide the employment into two groups based on eight work unit types in our data:I.
(1) Party, government, or government agency or office (xingzheng jiguan)(2) State owned institution (shiye danwei4)II.
(3) State-owned enterprise (SOE)(4) Collectively owned institution or enterprise(5) Individually operated enterprise(6) Private institution or enterprise4

Our limitation is that we don’t have the details for the job at state-owned institutions (SOIs). First, there arethree types of SOIs (全额拨款、差额拨款、自收自支事业单位), and some employees are based on temporary
contract. The permanent /open-end contract employees in the first two types of the SOIs enjoy more job security
and benefits.

39

(7) Foreign-investment enterprise(8) Others5
The first group (Group I) is state owned, nonprofit, and has the highest job security and benefits ofany group. The administrative rank only applies to this group and is a very important determinant of
one’s wage level and other benefits.6 Within Group I, job-type 1 (party government, etc.) ranks higher
than job-type 2 (SOE). We define a job promotion if one moves from work unit with a higher rank
and work unit (1) ranks the highest, work unit (2) the second highest. We will define further for otherranks within Group II.
For within work unit change in (1) and (2), the rank of administration will be the second determinant.
In particular, the following categories are ranks from low to high: (a) no rank; (b) below vice-sectionlevel; (c) vice-section-level (Fu keji); (d) section-level (keji); (e) vice-department-level (Fu chuji) (f)department level (Chu ji); (g) vice-bureau-level (fu juji); (h) bureau-level (ju ji); and higher.
The supervision department level is a third determinant of job promotion, though it might beassociated with the administrative rank in some cases. The fourth determinant would be housing andmedical benefits.
Compared to Group I, the work units in Group II have more autonomy and are for-profit. The
administrative rank does not apply to these work units in general. It is worth noting that the stateowned enterprises7 are a partial result of the economic reform of the state-owned sector.
Before the economic reform, there was very little job diversity and mobility under the central
administration. In urban areas, most of the jobs were state owned or in government offices, and they
were called “iron-bowl” jobs, which references the very high job security of these positions. Once
employed, a worker could stay employed until retirement age, except for voluntarily quitting or otherreasons (such as health, etc). There was also a good amount of pension after retirement.
The other sectors, mainly the private sectors, did not emerge until the economic reform in 1980s. It is
well documented that after the market-oriented economic reform, there was a large number of layoffs
of the employees from the state-own enterprises (Mako and Zhang, 2003). Between 1997 and 2001,
the number of SOEs decreased from 262,000 to 174,000. Nonetheless, the wages of the “surviving”
employees has increased rapidly (Parker, 1999). SOE assets have grown significantly, especiallyamong centrally administrated SOEs.8 The Chinese government also used different policies to protect
the SOEs, allowing them to offer above-market wages (Putterman, 1992; Chen, Demurger andFournier, 2005; Zhao, 2002). Furthermore, even though the administrative rank was only available ingovernment jobs, they applied to positions in the SOEs too until the political reform of recent years(Zheng Qi Fen Kai). This assures that most of the jobs at SOEs are superior to employment at othertypes of enterprises with a higher political and economic status both before and after the economic
reform.9
Therefore, the observed employees in the state-owned enterprises (job-type 2) are assumed to enjoy ahigher wage benefit (including bonuses, higher job security, and a lower risk of enterprisebankruptcy).

5

For the “others” category, there were very few cases in our data. We treat them as the lowest rank in terms ofjob preference. So any move from “others” to the other seven types of work units would be treated as upward
mobility.
6
See, for example, “公务员职务级别工资标准对应表”.
7
See, for example, Meng and Perkins (1998) for a definition and more information about SOEs.
8
The average asset size of centrally-administered SOEs has more than doubled since 1997(Mako and Zhang,2003).
9
Zhao (2002) finds that state-sector workers in China earned significantly more than workers in urbancollective and domestic-private enterprises in 1996 when the value of nonwage benefits is included.

40

There are four types of enterprises not owned by the state: collectively owned institutions orenterprises; individually operated enterprises; private institutions or enterprises; and foreign
investment enterprises.
It is important to take the government’s supervisory department into consideration when defining job
promotion for these enterprises. For our analysis, we will define upward mobility as occurring when
the individual transfers from non-SOE to an SOE that is under the supervision of a department either
at a central or a provincial level of government.10
Consequently, job mobility is also defined as being upward (b) district or county (d) street or township(e) neighbourhood or village-committee or (f) other level to a centrally or provincially administered
SOE. A transfer to the central level is considered to be the highest level of upward mobility.11
There is one exception to these classifications. Research shows that the skilled workers in foreign
investment enterprises (FIE) earned more than skilled workers in SOEs in 1990s (Zhao, 2002).
Moreover, FIEs are considerably more profitable (especially for those FIEs under a higher level ofgovernment supervision) than other non-SOEs. Therefore, if a person moved from any work units inGroup 2 to an FIE, and the management level at the previous job is considered to be lower than the
management level at the FIE (the new job), we will define it as upward mobility as well. Heremanagement level is a proxy of skills.
For all the non-SOEs, job security is the highest in collectively owned institutions, where thedecisions of hiring and firing reside among the workers’ union. Additionally, job security is relatively
equal among most of the workers. Therefore, any job changes from other non-SOEs to collectively
owned institutions or enterprises are considered to be upward mobility. One exception to thisclassification is that we consider moves to individually operated enterprises, private institutions orenterprises, or FIEs to be upward mobility if and only if the supervisory level is higher at the newjob.12
Finally, upward mobility is considered to occur for changes from farming or joining the army to any
other type of work.

10

Our limitation here is that we have no information on the details of the work unit, for example, the location.
According to Mako and Zhang (2003), SOE profitability varies by locality as well. We could identify thelocation of the work unit at a provincial level only if the individual has not moved or migrated and thus be able
to assign the current residential province to the work-unit location.
11
Ideally, we would also like to look at the occupational code of the employees. Because certain SOE sectors
are losing, including building materials, chemicals, forestry, food processing, textiles, machinery, urban utilities,
construction, transportation and storage, and commerce. Therefore, for workers moving up one level to thesupervisory department, upward mobility is only defined if the second occupation is not within any of theseindustries. However, in the data, the coding of the industry is only available for the most current job.
12
This is because there is a legal obligation for the department that offers affiliation. The rank of thesupervisory or presiding department for the work unit can reveal some information on the size of the enterprise(i.e., the higher the rank, the larger the size of the enterprise), and the more funding or higher reputation theenterprise might have.

41Years of Schooling:
The following table shows how we convert the different educational levels reported in the CGSS into
the number of years of schooling.
Education Level
Never attended school formally
Primary school13
Middle school
High school
Vocational high school ortechnical school
High-school-level professional
schoolSpecialized college (part-time)
Specialized college (full-time)
University (part-time)
University (full-time)
Graduate school and higher
Traditional apprentice learning(si-shu)14

Original Coding
0
1
2
3
4

Years of schooling
0
6
9
12
12

5

12

6
7
8
9
10
11

14
14
16
16
18
11

Please note that we could not distinguish the differences from high school, vocational high school,
and high-school-level professional school from the total years of schooling. We treat full-time andpart-time college the same in terms of years of schooling, even though the part-time track might takelonger to finish in reality.

13

It is worth noting that primary schools consisted of about five years of schooling in the earlier days.
This is the private schooling in the old days, where only traditional Chinese lectures were taught. It is hard toassign the number of years in school for this category; however, the conventional wisdom is that private
schooling is offered until up to the age of 16. Nonetheless, there were fewer than five observations in thiscategory in our final dataset.

1442Migration Reasons:
The following are a list of reasons of migration surveyed in CGSS and the corresponding explanationof selective terms.
Coding Reasons of migration
1

Attending school (sheng xue);

2

Joining the Army ;3Looking for work (“out-for-work” in CGSS English survey) (da gong);

4

Worker-to-cadre transfer (zhuan gan)5

Labour recruitment (zhao gong)

6

Support the inner or border regions (zhi nei or zhi bian)7“up to the mountains and down to the village” (shang shan xia xiang)

8

Migrated with the family

9

Job transfer

10Self-purchase of valid local urban hukou (e.g., “blue-stamp” (lan ben hukou) or “self-grain”(zi li kou liang hukou) ).

11

Purchasing a house from (or building a house with) a company with a hukou quota

12

Residential changes

13

Transfer along with the work unit (dan wei)

14

Urban development or expansion

15

State policy

16

Expropriation of land

17

Moving to live close to relatives

Explanations on selective terms:
Attending school (shengxue): This generally refers to going to college in an urban area.
Worker-tocadre transfer (zhuan-gan): This refers to transfers from worker to cadre (gong zhuan gan),which began to occur at the beginning of the open-reform policy period. In China, workers and cadres
have differences not only in terms of salary but also social welfare benefits (housing, medical, pension)etc. In general it is difficult for workers from collective ownership to transfer to state-owned sectors.
In a state meeting in December 1979, this means of transfer was targeted for elimination due to aseries of problems insulted from “zhuan gan” (for instance, corruption) . Additionally, as of 1993,
43

China began to use open examination to recruit civil servants, and everyone has to pass a desired levelof the national civil-servant exam in order to work at a government sector or state-owned institution.
Labour recruitment (zhao-gong): This means being employed (in an urban area). The term is different
than da gong. Looking for work (da gong; i.e., going to work in an urban area with qualifications). In
recent years, the term da gong has been used more frequently than zhao-gong. Zhao-gong refers to
the period when the state created employment in urban factories for heavy industrial development.
Supporting the inner and border regions (zhi nei /zhi bian) and “up to the mountains and down to thevillage” (shang shan xia xiang): These are some terms related to the Cultural Revolution. During therevolution, many people became jobless. From 1968, many young people were sent to rural areas to“learn from the farmers.” Most of them were only 16 to20 years old, and were therefore not able tocontinue high school or college. After the Cultural Revolution, they were able to leave the countrysidethrough various means, including “zhao gong,” “joining the army,” and “going to college.”
Migrated with the family (jia shu sui zhuan) and moving to live close to relatives (tou kao qin qi):
This mainly refers to (a) seniors who reside in rural areas and could move into the cities where theirchildren reside and (b) children who reside in rural areas and could move into the cities where theirparents reside.
Self-purchase of valid local urban hukou (e.g., “blue-stamp” (lan ben hukou) or “self-grain” (zi li
kou liang hukou) ) purchasing a house from (or building a house with) a company with a hukou quota:
These fall into the category of “blue stamp” hukou relaxation policy, which attracts investment
through the real estate market and highly educated people.
Residential changes: Moving from one apartment to another in a different residential district within acity. “Such changes in residence are driven in part by housing reforms in urban China, which give
urban residents the opportunity to own their own apartment/house or improve their living space and to
gain access to other amenities” (Liang & Ma, 2004, 476-477).
Terms 13-16 in the table above refer to state policy related to urban development or work-unitchanges due to state economic reform.

44