GCOE Discussion Paper Series
Global COE Program

Human Behavior and Socioeconomic Dynamics
Discussion Paper No. 285

Wage growth through job hopping in China

Kenn Ariga, Fumio Ohtake, Masaru Sasaki and Zheren Wu

November 2012GCOE Secretariat
Graduate School of Economics

OSAKA UNIVERSITY

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

Wage growth through job hopping in
China
Kenn Arigay, Fumio Ohtakez, Masaru Sasakixand Zheren Wu{

November 2012
Abstract
This paper uses a unique survey of the Chinese youth to constructa panel data in which we keep track of geographical and job mobilities.
Our estimation results deliver the following major …ndings.
(1) The sample individuals are highly mobile. Job quits and relocations are frequent and they are closely correlated. We …nd the job
hopping to be highly productive as our estimates indicate each job quit
generates more than .2 log increase in monthly wage.
.(2) The migrant disadvantage in urban labor market is compensatedby their higher job mobility. After four jobs, the expected earnings
di¤erentials essentially disappear. We also …nd that migration and job
mobility are highly selective processes. Our evidence indicates that the
migrants are positively selected.
(3) Job and location mobilities are highly dependent upon family
back ground and personal traits which we interpret as representing unobservable characteristics associated with risk taking, active and optimistic personality, as well as the implied economic incentives to migrate
and keep searching for better jobs.
JEL Classi…cation numbers: J31, J61 and J62

Earlier versions of the paper were presented at Fudan University (China),Thailand
Development Research Institute (Thailand), Kansai Labor Workshop, Trans Paci…c Labor
Seminar 2011 and Conference on Labor Economics at Awajishima (Japan). We thank theparticipants of these seminars and conferences for their valuable comments. We also wish
to thank Masako Kurosawa for her valuable inputs at various stages of the research, and
Liu Yang for her advises on Chinese o¢ cial data . The research reported in the paper is
supported by G-COE (Osaka University) ’Human Behavior and Socioeconomic Dynamics.’yCorresponding author. Mailing address: Institute of Economic Research, Kyoto University Yoshida Honmachi, Sakyoku, Kyoto 6068501 Japan. E-mail: ariga@kier.kyoto-u.ac.jpzInstitute of Social and Economic Research, Osaka University, Ibaraki Japanx
Graduate School of Economics, Osaka University, Osaka Japan{
Department of Economics, Kinki University, Higashi-Osaka, Japan1

1

Introduction" The only way to …nd a better job is to quit the one you
had. Interviews took time away from work, and a new hire was
expected to start right away. ... . The girls talked constantly ofleaving. Workers are required to stay six months, and even then,permission to quit was not always granted. The factory held the…rst two months of every worker’s pay; leaving without approvalmeans losing that money and starting all over somewhere else....
Getting into the factory was easy. The hard part was getting out."( Factory Girls by L. Chan, Picador, 2008)

In a sense, there is nothing new in rural to urban migration in China: weknow why it takes place, what it delivers, at least in the long run, and when
it is likely to subsides. By now a truly voluminous empirical literature on
migrations con…rm some of our conventional wisdom on internal migrationin China. They are motivated primarily by the economic incentives1 : better
work opportunities, better living conditions, better educations for the children, etc. Our own study shown below con…rms the economic incentives as
the most powerful inducement for the people to relocate, and change jobs. A
simple logic suggests that the inducement is more powerful if the expected
gains are larger, which we also con…rm in the subsequent analysis. Whatmakes it unique about internal migration in China is, however, its scale andits signi…cance in an enormous economy in the midst of transformation.
This paper focuses on the joint processes of migration and school to worktransition, using the survey we conducted in 2009 for the Chinese youth aged
mostly between 18-30. Focusing upon the young workers enables us to observe
the crucial process of career formation in the contemporary Chinese labormarket. Our main objective of the analysis is to investigate the interactions
between spacial and labor mobility including rural to urban migration in thecontext of school to work transition. For this purpose, we estimate a dual
decision of workers: they choose location and jobs. We try to unearth multifacetted interactions between location and occupation choices.
Moreover, by focusing on the youth, we show changing characteristics ofinternal migration in more recent years. In 1980s and 1990s, the rural tourban migration was dominated by the adult population. In the most recent
years, however, the migrants become younger and more educated. As wewill see below, the migrant workers in our sample are less attached to theirbirthplace, family occupations, and social network at home town. At thesame time, our survey samples of high school education or less di¤er in many
important ways from those with college education. Even after an explosive
growth of college enrollment in the last decade, the college enrollment rate isstill below 30%, whereas the senior high school enrollment rate is close to 80%.
1
For example, using random samples from 2005 National Census data, Gagnon et al(2008) approximately 61% of migrants in urban areas listed economic reasons as their primary motives for migration.

2

Thus the great majority of the contemporary Chinese youth leave school andstart their working life with high school education. High school graduates are
no longer the elite few even among the cohort of migrant workers. According
to 2009 Survey of migrant workers in China (National Bureau of Statistics
2009), among the young migrant workers from rural areas, 13.5% of them aresenior high school graduates, whereas those with college education comprises
6.4%.
For these reasons, our survey is uniquely suited to estimate the impactof the regulation that potentially hinders the rural to urban migration. At
the same time, our estimations reveal the major determinants of migration
decision which selects those to migrate and others to remain in the rural area.
Our main …ndings are summarized below.
First of all, we …nd the individuals in our survey are highly mobile geographically as well as across jobs. Their mobility pays o¤ handsomely interms of employment status, pay, and job satisfaction. Each job change after(or simultaneously with) voluntary job separations yields roughly .25 increasein log of average monthly wage.
In line with the now voluminous literature on the economic e¤ect of migration and job changes, we need to explicitly take account of the process
through which only some, not all, of the rural residents decide to migrate (tourban areas), and, not all the sample individuals change jobs. An important…nding corrected for such selectivity is that the migrants (to be de…ned later)
are more mobile across jobs and across locations. As a result, the initial wage
gap between those who …nish school in rural area and those in the urban areas
substantially narrows down when we compare their second or third jobs after…nishing the school. After four jobs, or about 5 years of work experience, ourestimations indicate the wage gap between the urban resident and migrants
in our sample all but disappears.
On the other hand, not all the rural born samples migrate to urban areas after school. We will show that decisions on migration and relocation areheavily in‡uenced by family back ground and individual attributes. Moreover,
our estimations of the selection into migrants show that they are positively
selected in terms of unobservable wage residuals. Thus, our results simultaneously con…rm substantial disadvantage of rural migrants and, at the sametime, highly successful career formation among those who migrate and continue to search for better jobs.
In what follows, section 2 reviews selectively the recent literature on thelabor market in China, focusing on those directly relevant to the youth. In
section 3, we o¤er a variety of statistics to provide a bird eye view of thedata and sampled individuals in our survey. Section 4 reports our main
results on wage. In 4.1, we summarize the major econometric issues at handas we try to estimate the causal relation between earnings with migrationand job changes. The substance of the analysis starts the OLS regressions ofwage in 4.2. Our main results are shown in 4.3 and 4.4. In 4.3 we report
the Heckman correction model of wage growth across jobs, using a recursive3

maximum likelihood estimation for four endogenous variables: log wage (wagegrowth), work, relocation and migrate. In 4.4, we take up selectivity issues
arising from unobservability due to rejected wage o¤er, and migration todi¤erent labor markets. Section 5 concludes.

2

A Brief literature review

Given our focus on the interaction between migration and school to worktransition, we need to place our analysis both in the context of spacial andlabor mobilities. Thus our focus of this brief review is on the two issues
centered upon the internal migration in contemporary China.
First we consider who among the rural born population will migrate andwhy. As such, the issue is fundamentally those of selectivity. Job mobility, on
the other hand, is linked to the literature primarily through issues centered
upon the wage gap between urban residents and rural migrants in the urban
labor market.

2.1

Recent changes in internal migration in China

Our survey evidence show that a large share of the school leavers in our sample from rural areas migrate to industrialized provinces mostly on the easternseaboard within the …rst few years after school. Earlier literature on internal migration in China focused primarily upon ’‡oating’migrants: i.e., thoseworking temporarily in urban areas leaving the rest of family members attheir homes in rural areas. According to Li at ILO (Li 2008), the migrant
workers in urban areas exceeded 30 millions by the end of 1980s. By 2006, theestimated size of immigrants rose to 132 million. A popular perception of the
migrant workers is that they are male farmers, uneducated and unskilled, engaged in low paying jobs in construction, service and manufacturing sectors.
2009 Survey of migrant workers in China (National Bureau of Statistics 2009)shows that the most recent data paints somewhat di¤erent pictures. First ofall, their education attainment signi…cantly improved, especially among theyoung cohort (aged between 16 and 29). The gender composition also shows
clear change: among the young cohort, the share of female is now close to50%, compared to less than 30% for those above age 40. The young migrants
also di¤er in other dimensions. For example, the same survey reports that,
whereas overall, 29.5% of the migrant workers were engaged in farming at least
some time during the past year, only 10% did among the younger cohorts.
Industry composition also di¤er signi…cantly between the two groups. In theyounger cohorts, 44% work in manufacturing and less than 10% in construction, whereas for the older cohorts, the shares of the two sectors are roughly
comparable (31.5% in manufacturing and 27.8% in construction). Unlike theolder cohorts, younger migrants moved to urban areas early on: among thecohorts of the age above 30 , the average age of the …rst immigration is 33.7
year, whereas those born in 1980s, the average age of the …rst migration is21.1 years of age.
In short, the latest wave of migrant workers are not only younger, but
4better educated, migrated early (typically immediately after school), and lesslikely to retain farming work back home. It is thus fair to say that their migration decisions seem more permanent and determined to stay longer time in theurban area: the same survey reports that only 15% or less among the young
migrants are determined to return home in the future. Our samples of the
youth with mostly high school education are no longer atypical, selected few.
Their migration decision seem far more permanent than those farm workers
in 1980s and 90s who took up menial jobs in urban areas to supplement theirincome. The same 2009 survey also show that the migrating young cohorts
are better educated than the comparable youth who remain in urban area.
Some of the recent research on migrants con…rm these observations. Using
three coordinated CHIP data sets in 2002, Xing (2010) …nd that permanent
migrants are positively selected from rural population, especially in terms ofeducation. On the other hand, Wu (2010), using a unique survey, …nds thatthe middle level in human capital endowment are most likely to migrate.
As we will show later on, our own …nding does indicate positive selection
bias among the cohorts of the youth with less than college education. One of
our sharper …ndings, however, is that the sorting occurs mainly at the levelof family backgrounds and personal traits, rather than conventional variables
representing human capital.

2.2

Discrimination against migrants in the urban labor market

In spite of these important changes in the composition of migrant workers,
available evidence indicates strongly that the migrants are at disadvantage inurban labor market, in comparison with the urban residents.
By now it is well known that many factors can be potentially responsible for the apparent wedge in earnings between urban residents and migrant
workers in the urban labor market in China.
First of all, the registration system of the country places rather stringent
limitations on migrants to obtain permanent registrations in urban areas.
This alone can account for more than one facets of the gap. To begin with,it e¤ectively prohibits entry to some of jobs in government or state owned
enterprises2 . Moreover, it also excludes migrants from a variety of social
bene…ts available to urban residents, most notably the migrant children are
not allowed to attend local public schools without their registration (hukou)
in the area. Finally, the urban registration (or the lack thereof) can be asource of statistical discrimination. To the extent that the urban residents
and migrant workers di¤er in average quality or skills, the registration can beused as a form of screening device. Notice that these potential factors havedi¤erent consequences.
The hukou system have underwent signi…cant changes in the recent years.
Most notably, with the e¤ective abolishment of the agricultural and non agricultural residencies, the local government decides on whether or not to allow
2

See Table 21 and our discussions in 4.4.3 that con…rm this point.

5permanent residency for the migrants3 . Although the practices vary somewhat from one region to the other, it is fair to say that the di¢ culty ofobtaining permanent residency in urban areas still remains a major factor in
shaping the migrants’works and lives.
Aside from the lack of social securities and access to public schools in theurban area they reside, the migrants are far more likely to work without aformal labor contract. Thus they are more likely to have their wage unpaid,delayed, or retained for the …rst three to six months to prevent the early
turnovers, as our quotation from Chan’s book indicates4 .
Using two comparable household survey data sets for Shanghai in 1995,
Meng and Zhang (2001) …nd evidence of discrimination against rural migrants in terms of both occupational attainment and earnings. They analyze
the extent to which earnings di¤erentials between rural migrants and urban
residents are due to inter- or intra-occupational gaps and …nd that 82 percentof the hourly wage di¤erential is due to unequal payment within occupation.
On the other hand, Demurger, S., M. Gurgand, L. Shi, and Y. Ximing (2008) use micro simulation and decompose the wage di¤erentials into (1)sector allocation, (2) hourly wage, (3) working time, and (4) population structures. They …nd the main source of the gap is (4) and conclude that the gap isdue to pre-market (education opportunities) rather than on-market sources.
Another strand of literature tries to break down disadvantages into migratione¤ect per se and the e¤ect due to discriminations stemming from hukou system. Using Blinder-Oaxaca decomposition, Gagnon et al. (2009) …nd that 40
percent of the observed wage gap between rural and urban migrants might bedue to hukou status.
Assuming at least some portion of the wage gap between urban residents
and migrants is due to the gap in skills or human capital, one interestingquestion is the impact of the in‡ux of migrant workers on the earnings ofurban residents. Meng and Zhang examines the causal relationship between
rural-urban migration and urban native workers’labour market outcomes in
Chinese cities. They …nd that rural migrants in urban China have modest
positive, or zero e¤ects on the average employment and insigni…cant impact
on earnings of urban workers. They conjecture that the reason for the lackof adverse e¤ects is due partially to the labour market segregation between
the migrants and urban natives, and partially due to the complementarity
between the two groups of workers.
Xing (2010) compares the two types of migrants, those with urban registration (the permanent migrants) and the others without. Not surprisingly,
he …nds that the permanent residents are strongly positively selected (out of3
See Chan and Buckingham (2008) for the details. They also argue that as a result of
localization on registration decisions, practices vary across regions. In some small cities,
immigrants now have the possibility of obtaining permanent residency, in exchange forgiving up the land they e¤ectively own at their rural homes.
4
See Meiyang (2007) for wage arrears and various discriminations against immigrant
workers.

6

the rural born population), especially in terms of education. Knowing thatthe college education is one way to obtain urban registration, the distinction
between discrimination and the di¤erence due to education is not as clear cut
as it may sound. Taken together, it seems di¢ cult to draw any de…nitive conclusion on the source of the gap and its relation to the possible discrimination
against the migrant workers.
On the other hand, there exists a broad consensus that at least part of thegaps are narrowed down over time as the migrant workers stay in urban areaand settle down: i.e., migrant workers gradually assimilate to urban life andurban work places. The time needed for assimilation is yet another aspectof the wage gap between the rural migrants and urban resident in the urban
labor market. Deng and Gustafsson (2006) show that the permanent rural
migrants who received their urban hukou before age 25 are well integrated
economically in their place of destination and they actually receive higher
earnings than the local-born urban residents.
In line with the conventional wisdom, our regression results show that theimmigrants earn signi…cantly more than those who decided not to migrate.
On the other hand, our results also con…rm earlier …ndings that comparedto native residents in urban areas, migrant workers do earn less and our best
estimate indicates the presence of 10% gap. However, given the impacts ofbeing migrant on relocations and job quits, our estimated model demonstratethat the initial wage gap between migrants and urban residents narrow downover time.

2.3

Migration and job mobility

Since school to work transition often entails both geographical and job mobility, it is natural to place our analysis in the context of migration impacts
on the wage growth through job changes. To the extent that the migration iscostly by itself but its impact on future wage path is positive, migration decision can be considered as a form of investment. Lehmer and Ludsteck (2008)
use German social security data to analyze the heterogenous returns from
migration in the short run and long run e¤ect on wage growth. In particular,
they …nd the immediate impact is larger for the low skilled, whereas the highskilled workers reap larger long term gains. In the context of contemporary
China, the immediate gains are easy to grasp as the migration from rural tourban regions by itself lifts the over all wage structure they face. On the other
hand, given the large empirical literature, as we have reviewed some above,on the gap between immigrant and residents in urban areas in China, it is not
obvious that the migrants and urban residents face the same labor market.
Thus the e¤ect of migration is accentuated in the case of China because of
the variety of disadvantages placed upon rural migrants in the urban labormarket..
One such disadvantage is information. Many empirical studies on migration decisions …nd a signi…cant positive impact on migration of having family
members or friends and acquaintances in the migration destination. This im7

pact is especially well known among the international immigration of ethnic
Chinese population. Wu (2010) …nds di¤erent self-selection between individuals who have moved as pioneers and migrants from households in which othermembers have already migrated. One way that followers bene…t from thepioneers in the urban labor market is information on jobs.
Knight and Yueh (2004) …nds that the migrants in the urban labor market
is far more mobile than the urban residents. They consider the di¤erence
arising from the di¤erence in the reservation wages between the two types.
Thus, the di¤erent mobility is due to the segregation of the labor markets
which, in turn, is responsible for the gap in the access to the ’good’jobs.
Kondo and Ou (2010), on the other hand, compares two types of permanent migrants and …nds that rural to urban migrants are more mobile acrossjobs and they are more likely to move to better jobs by changing work units,
whereas urban migrants are more likely to be promoted within a work-unit.
Our own results are in line with the ones in Kondo and Ou in that the immigrants are more mobile both in terms of job changes and relocation. Thus,
as we indicated above, immigrants tend to catch up resident workers in theurban labor markets in industrialized provinces.

2.4

Joint decisions on labor and geographical mobilities

Although a large variety of issues surrounding labor markets in China havebeen exposed to rigorous econometric estimations and testing, none, as far as
we know, look into the interactions between location changes such as internalmigration and job mobility. The youth is the crucial period in which people
try di¤erent jobs, di¤erent life styles, and di¤erent locations before they settle
down to a chosen occupation and residence. Focusing upon only one aspect
is often valuable as it simpli…es the analysis, whereas to the extent that theanalysis misses the interactions, it is possible that such analysis su¤ers frommis-speci…cations.
For example, consider the issue of selectivity of migrants in the labormarket. By de…nition, those are the workers who moved out of their family
locations and face a di¤erent labor market than the one in the home place.
Who migrates depends crucially on the comparisons of available jobs in thesemarkets. Mobility depends also upon monetary and psychic cost of relocations. In that case, it is crucially important that we combine information on
geographical and occupational mobilities. Our survey of the Chinese youth isdesigned speci…cally to address these issues, as we see below.

3
3.1

ISYC: The survey results overview and preliminary empirical analysis
Survey Overview

We conducted an Internet based Survey of the Youth in China (hereafter
called as ISYC) aged between 16 and 31 with less than college education.
The survey was conducted between February and March in 2009. The entire

8

set of questionnaire translated into English or original Chinese versions areavailable upon request from the authors. As shown in the top row of Table
3, among the total of 3,336 sampled individuals, 1,972 are males, vocational
school graduates comprise 45% of the total sample, and, about 30% of thesamples are academic high school graduates. Roughly 20% of the sample arehigh school drop outs, and the remaining 5% are the middle school graduates.
Except for the drop outs (proportionately more males than females), thecompositions do not di¤er markedly across gender.
Figure 1 compares the shares of the sampled individuals according to the
31 provinces and specially designated cities in comparison with the overall
population shares aso 2010. Densely populated and industrialized regions are
somewhat over represented in our survey, (especially Guangdong province) incomparison with o¢ cial population data. As the o¢ cial data is based uponresident registration (hukou) system, it is likely to seriously under-report theactual population in residence for those heavily populated area. The problem is even more severe if we restrict our attention to the youth population.
Hence it is unclear to what extent that the survey over-represents those inpopulation centers. In any case, it is clear that our survey does well in termsof representing the wide spectrum of population geographically.
ISYC has two important focuses in the questionnaire. First, we have adetailed set of questions on the last year samples spent at the high school.
In this part of the questionnaire, we asked types of school they attended,commuting methods, selected course and subjects, grades, as well as a host ofquestions on activities during the school. We also have a fairly comprehensive
set of questions on individual attributes, friends and family background. The
second focus of the survey is on relocation and job history. The latter is used
to reconstruct a panel data jobs and relocations as we explain next.

3.2
3.2.1

Creating a panel data on job, wage, and relocation history
Panel construction

One important objective of the survey is to trace job and location changes
as we anticipated large ‡ows across jobs and internal migrations for the sample youth in China. We asked each respondent to answer locations of (1)birthplace, (2) the last school (mostly high school) they attended, and (3)all the residences where they stayed 6 months or longer after the last school
they attended, and (4) the current residence, irrespective of the length they
stayed. For each location, we asked (a) province, (b) administrative unit below province, and (c) whether or not the area is urban, suburb, or rural. We
also asked the calendar year when they started and ended the stay. Our preliminary analysis indicated that the distinction between urban and suburban
is often blurred so we decided to combine these two into urban area.
For job history, we asked to list all the jobs after the last school theyattended which lasted at least 6months, and (if applicable) the current or thelast job, irrespective of the length of the time they worked.
Thus, for location changes, we know the sequence and calendar years of9changes. We have the same information for job history. When we combine
them into a panel, however, we cannot tell the precise sequence of locationand job changes if they occur in the same year. Moreover, as we ask them tolist locations and jobs lasting 6 months or longer, each year can have a record
in which up to three distinct locations or jobs are listed.
Our strategy to deal with this problem is to set up three arti…cial subperiods within each calendar year to accommodate these events. Again, within
location or job history, we know the sequence, but, the timing recorded interms of this sub unit cannot be used to infer the timing between locationand job changes.
3.2.2

Wage information

For each job listed in the survey, we asked (1) types of employment (such as
regular full time, …xed term, etc), (2) types of employer (government, governmental organizations, private …rms, etc), (3) the method used to …nd thejob (i.e., through introduction by a friend, direct application to job advertisement, etc), (4) the reason why you took up the job (multiple choice), (5) the
reason why you quit the job (if applicable), and (6) monthly wage (in Yuan,
or RMB).
For wage information, we asked the respondents to pick one from the 13
wage ranges (the lowest is less than 300 yuan, and the highest is more than
5,000 yuan). We used 250 yuan for the lowest, and 6,000 yuan for the highest,
and for other 11 ranges, we use the center values to convert the answer to themultiple choice into wage rates in yuan. We then converted into log real wage
using the national CPI index. Henceforth, we use this converted log real wage
as out measure of the wage rate. One important caveat on wage is that weonly have single observation of wage for each job, thus unable to keep trackof wage changes, if any, within each job.
Given space limitation, we relegate summary statistics and a brief description of the data to Appendix A.
3.2.3

Migration

Go back to Figure 1 wherein triangle marks indicate the sample shares ofthe survey in terms of the current residence, whereas the shares in terms of
the birth place locations are shown by square marks. It is evident that sizable
migrations have taken place. The biggest winner is Guangdong province with
the net increase in 328, followed by Beijing (138) and Shanghai (117). These
three regions account for 84% of the sum of the province level net increases(795). We select: Beijin, Tianjin , Shanghai, Jiangsu, Zhejiang, and Guangdong as the densely populated centers (PC) of industrialized provinces (anddesignated cities). Aside from small increases in Xingjing and Tibet, only
these 6 regions show net increase in sample residence over the sample size interms of birth place. It should be noted also that all 6 regions are in East
coastal regions. Available data on migrant workers con…rm the concentrationof migrant workers in Eastern coastal regions: according to National Bureau

10

of Statistics of China (2010), among the young cohorts of migrant workers(84.8 millions in total), 72.3% of them currently reside in one of Easternregions.
In what follows, these 6 regions are called PC regions, and the rest are
called Non PC regions. The average wages for each of 31 regions show that
top 6 of them are all in these PC regions. We categorize the residence location
using two indices. First, in each province, we categorize residential locations
into 1: rural areas, 2: urban which include small cities and regional center
cities. Then we classify each province and special cities into PC and Non PC
regions as stated above.
Table 1 shows the distribution of current residence for 4 cohorts of samples
according to their birth places. It is clear that across region mobility is predominantly from rural in non-PC provinces into urban areas in PC provinces.
Among the sample individuals born in rural areas in non PC urban areas,
only 22.8% remain in the location of the same category, whereas 97% of thoseborn in urban areas of PC provinces currently live also in PC provinces.
Given the predominant ‡ows from rural to urban, and from non PC to
PC regions, we call a sample individual immigrant if a person is born in place
other than PC urban areas, but currently resides in PC urban region. Using
these conventions, we …nd that among 3,366 samples in our survey, 2,728 ofthem are born in regions other than PC urban. Among those potential candidates for migrants, roughly 50% (1,327) of them has been migrant, and 1,183(43%) of them are currently migrants, living in PC urban regions. Not surprisingly, migrants are geographically more mobile than non-migrants. They
are more mobile vis a vis those born in regions other than PC urban, andalso against those who are born in PC urban regions. See Table 2 . Beinga migrant is not a permanent status. About 15% of them moved out of PC
urban and reside elsewhere when the survey was taken. Still, by and large,
return migrations is not a very common phenomenon. Again, this observation is supported by earlier …nding that the migrations are more permanent
in nature among the younger cohorts. The survey results also show thatroughly a half of the …rst time migrations occur as they start their last school(most of them are high schools): they attended schools in PC urban regions.
The next peak of immigration is at their third year after graduation and it
is typically the …rst relocation. Again, these observations are consistent withthe earlier …ndings based upon other nation wide statistics or surveys: themigration into industrialized Eastern regions occur early, within a few years
after …nishing schools.

3.3
3.3.1

Job changes and wage growth
Facts

Let us start with some basic tabulations of the data.
Table 3 shows the work experience: 66% currently have jobs, 19% arecurrently jobless but worked in the past, and the remaining 15% or so havenever worked in the past. Table 3 also decomposes the variable according to11

the education attainments. We observe again that vocational school graduates
have the highest averages in terms of current work, and also the share of thosewith regular full time job is the highest. Middle school graduates come second,
then the dropouts, and the academic high school graduates at the bottom.
Among those currently employed, the impact of education attainment di¤er.
At the bottom of Table 3, average monthly wages are shown. Academic andvocational school graduates are more or less comparable in average wages,
and they are followed by the dropouts, and the middle school graduates.
(corrections made). Overall, the average real monthly wage is about 1200
RMB in 2005 prices5 , which is slightly below but quite comparable to theaverage monthly wage of the young immigrant workers, 1,328 RMB reported
in National Bureau of Statistics of China (2010) . As we might have expected,
at least for those with some high school education or better, we do observe
sizable increase in wage over age.
Far more striking than this di¤erence, however, is the impact of the number of jobs held in the past as shown in Table 4. Compared to the …rst jobs,
the …fth job on average earns 88%, 97%, 68%, and 67% more for graduatesof academic high school, vocational high school, high school drop outs, andmiddle school, respectively.
Other variables on job characteristics and job satisfactions suggest strongly
that job hopping pays o¤ handsomely. Table 5 shows the reasons for quitting the job. As the sample individuals experience more jobs, their reasonsfor quitting change. In the …rst and second jobs, the most popular reason is"dead end job", whereas at their third or later jobs, the most popular reason is "found a better job." We also notice that the share of "wage too low"
also increases as they experience more jobs. Similarly, to the question why
you took the job, the most popular reason for the …rst and the second job is"because it is a type of job that I was looking for,", whereas in the third andfourth jobs, the most popular answer is "the job o¤ers opportunity to learn
and master professional skills."
Overall, these tabulations suggest that the individuals in our sample dorather well by changing jobs, in terms of wage they earn, types of jobs theyland, and also in terms of personal satisfaction from the job.
Are frequent job changes and migrations related? Simple tabulations indicate indeed that they are related. For one thing, migrants hold more jobs
after school, controlling for the age. At age 29, migrant have on average
had more than 3 jobs since graduation, compared to about 2 jobs for nonimmigrants. As a …nal piece of suggestive data, Table 6 shows the correlation
between relocations and job changes. The table shows that if a sample continues the same job from one period to the next, the probability of relocation isabout 12%, whereas conditional on job change, relocation probability jumps
up to 28%.
Re‡ecting the job mobility, most of job spells are short: among the com5

If we limit to the current jobs held by the sample individuals, the average monthly wageis 1,740 RMB as of 2009.

12

pleted job spells, 55.7% of them end within one year, and less than 7% ofjobs last more than 3 years. In short, the individuals in our survey are highly
mobile across jobs and across locations. Stepping stone mobility indeed seems
to be at work in China.
3.3.2

Stayers and movers

Having noted that job mobility on average pays o¤ rather handsomely, anatural question to follow is who moves from job to job more frequently?
More importantly, if the wage gains from job mobility is so large, why not
everyone moves?
Below we show that a short answer to the question is simply that those who
did not do well in the initial job tend to move more often, compensating forthe potential wage loss in the initial match by job mobility. The wage growth
due to job mobility indeed compensate for the lower initial wage. Figure 2
shows the wage growth across jobs. Each connected line corresponds to thepath of average log real wage across jobs. For example, the one labeled "twojobs" is for those who have had two jobs since school graduation up to thetime of the survey. In order to control for the di¤erences in age cohorts, welimit the samples to those with age 23 or older. Thus, they have at least
5 years of potential labor market experiences. The following points can becon…rmed from these paths. First of all, those with larger number of jobs
starts with lower average wage. The average slope of wage growth is also
inversely related to the total number of jobs held. In short, those who startup with lower paid jobs are more likely to move and continue to do so. By
the latest jobs, the initial di¤erences all but disappear. For example, the log
di¤erence between the "two jobs" group and the most mobile ("…ve or morejobs") is .229 in the …rst jobs, which is more than o¤set by job mobility as
the real log wage at their latest job exceeds the corresponding average of the"two jobs" group by .05 log points.
Figure 3 is a similar plot for those in PC urban labor markets. The
migrants start with lower wage but they appear to catch up through job
mobility. By their last jobs, the initial di¤erence between those born in PC
urban areas and migrants disappears. In sum, these …gures suggest that thejob mobility compensate for the initial disadvantages, either due to poor job
match, or due to the disadvantage being an immigrant. On the other hand,both spatial and labor mobilities are endogenous choices and we need to see
if and why migrants exihibit higher mobility. In the next section, we makethese points more formally in regression analysis.

4

Wage growth, migration and job changes

In Section 3, we have shown a series of tabulations. They suggest the pivotal role played by job mobility in wage growth. We also noted that the job
mobility is highly selective and the degree of the mobility is systematicallycorrelated with other variables of interests, most notably, geographical mobility. In this section, we report three types of regression results in which we

13

address these issues. In the …rst step, we run OLS and …xed e¤ect regressions. We exploit these results to obtain our measure of the match speci…ccomponent of wage. We use this measure in the second and third sets ofregressions. In the second set of regressions, we estimate Heckman correction
model of wage growth in which we incorporate selectivity of observed wagechanges due to endogeneity of job to job quit decisions. In a similar vein, we
also report the results of recursive maximum likelihood estimation on wage
level that incorporates work, immigration, and relocation decisions. In thelast set of regressions, we address two additional selectivity issues. One is thewithin job wage growth, and the second is immigration decision.

4.1

Econometric issues

To the extent those decisions to take up jobs, to change jobs, and to migratefrom one place to another, ... are all endogenous and possibly entail selectivity
biases, we need to pay due attentions to the following econometric issues inorder to fully address these questions.
First, selectivity bias. Not all the sample individuals choose to work (orto be employed), the decision of which re‡ects a comparison of the net bene…tfrom taking up a job for each worker. A decision to work and a wage o¤er
contain a common unobserved shock which induces selection bias.
The second problem arises because of the endogeneity of migration decision. Our preliminary analysis have shown that those born in rural areas
on average earn higher wage by migrating to the urban areas, especially in
densely populated industrialized provinces. The regression coe¢ cient on migration su¤ers, however, from selectivity bias because migration (to urban
areas) are not randomly assigned outcome, but they are voluntary decisions.
If anything, those who expect to bene…t more from migrations are more likely
to migrate, thus biasing the impact of migration upward. According to ourde…nition, one must be born in areas other than PC urban and choose to reside in PC urban. Thus the selectivity of immigrant refers to those potentiallyable to migrate, i.e., those born in areas other than PC urban.
By the same token, the OLS estimates on the impact of job changes on
wage are also suspect because decision to change a job clearly is also endogenous and we do not observe o¤ers which were not taken.
Moreover, given the signi…cant positive correlation between job to job
quits and relocations, it is likely that (near) simultaneity of these mobility
decisions might as well indicate that job to job quits with or without relocationdi¤er each other. Whether or not to take up a new job in a remote location
probably depends upon the cost of relocation and the net bene…t from the
new wage o¤er.
4.1.1

Regression design

Our approach to the issues summarized above is to model the joint decision
on work, quit, migrate and relocation choices all of which in turn feed into
the wage regression.

14

quit-relocation We assume that relocation causes quit, but not the otherway around. Although this is clearly untrue as some of workers decides to take up a new job which involves relocation, we believe thisis rather inconsequential because what matters is the likely joint e¤ectof quits accompanied by relocation, as opposed to those without relocation. Using the recorded change in residence, we de…ne relocation 1,
a dummy variable which is equal to one in the sub-period where thechange in residence is recorded. As we do not know precise timing of
relocation and quit other than the calendar year of the events, we use
relocation which takes value 1 whenever relocation dummy variable isunity in the previous, current, or in the next sub period. Since our timemeasure that divides one year to three periods is purely arti…cial device
to accommodate more than one job or location changes within one year,
relocation can be used as the variable that can tell if relocation takes
place on or around the time of job changes.
migration We assume migration decision is done independently from quit orrelocation decision. Again, this cannot be literally true. However, migration status is relatively stable as we documented above and relatively
small portion of relocation involves migration while they are currently
working.
Additional speci…cations are the following.
Labor market and migration Given our de…nition of migrant, those bornin PC urban areas cannot be immigrant. Hence we need to estimate
the system for two sets of sub samples. In the …rst subset, we exclude
those born in PC urban regions. Hence we can model the decision
to immigrate. The estimated impact of migration compares the wage
earned by migrants in PC urban regions with those currently in areas
other than PC urban. In the second subset, our samples are limited
to those currently in PC urban areas. Thus the estimated impact of
migrant measures the net disadvantage in earnings of migrants vis a
vis those born in the PC urban areas. By construction, those native
residents in PC urban areas cannot choose to be a migrant, we treat
migrant status as exogenous in this set of samples.
treating quit decisions As we noted, we only have one wage observationfor each job. Under highly restrictive assumption that wage at each job
does not change over time, we only need to consider the endogeneity ofjob to job quit decision in the wage growth regression if all the workers
face the same outside wage o¤er, for each type of workers6 . Otherwise,
we have to assume that wage o¤ers are censored in the sense that some6

Presumption is that those with low wage or unhappy at the current job decide to takeup a new job, which o¤ers the same wage for everyone within the same type, which arecontrolled by regressors in the quit regression.

15

of those continue to stay at the current o¤er rejected wage o¤ers, whichby construction we cannot observe.
4.1.2

A model of on the job search

To …x these ideas, consider a simple model of on the job search. A worker icurrently employed at a …rm k with wage Wtik receives an outside o¤er withprobability t : The Bellman equation is given byJ( ik ;
= Wtik +

i

; ik
t ;
Z

t; Z

t

J 0 >Jikh)

J 0( 0;i

0; t+1 ;

0
t+1 ; Z )J( ik ;

i

; ik
t+1 ;

i
ik
Z
)
dF ( 0 ;t+1;

0wherein is discount factor and F ( 0 ; i ; t+1 ; t+1 ; Z 0 ) is the joint probability
distribution for the state variables. We assume the log of the wage rate(denoted in lower case) is given by a conventional Mincerian model of wage
augmented by job mobility. We posit
wtik = w
eti +
w
eti =

i

ik

+ ik
t

(1)

+ g(expit )(2)wherein i represents innate ability for individual i, ik match speci…c productivity, and g represent general human capital accumulated by previous
work experience7 . Wage o¤ers are given by0

wtik = w
eti +

ik0

+ ik
t

0

If there is no cost of quitting a job and taking up a new one, the myopic
comparison of the current wage and the the wage o¤er is su¢ cient and wehave
qt = 1
if and only if0wtik > wtik
or,

ik0

0

+ ik
t >

ik

+ ik
t

In general, however, we only know that quit occurs when
J 0( 0;i

0; t+1 ;

0ik;
t+1 ; Z ) > J(

i

; ik
t+1 ;

t+1 ; Zik)

Although such a equation does not lend itself to any closed form solution, if we
assume further that the ‡ow utility is given by the wage and the multiplicative
random utility ( it )7
We assume away accumulation of job speci…c human capital in the base case. See,however, 4.4.1 for out analysis of the impact of job speci…c human capital employing aseparately estimated e¤ect of job tenure on log real wage.

16

i

0

; t+1 ;

0
t+1 ; Z )log Utik = wtik +itThen it is sensible to postulate a pair of behavioral equations:qt =t prob(
ikk0

wti =

wti +

i
i
t > d ) > 0)

i
t

+

(3)
(4)

Thus the probability of quitting to a new job is the joint probability that ano¤er arrives and that is acceptable. In order to make some progress on thisspeci…cation, we need to …nd proxies for two crucial sources of unknown laborproductivity, ik and i :We do so by estimating a …xed e¤ect regression for real log wage, andalso by estimating a OLS regression using the same pooled panel of jobs andwages in our survey records. In view of (1), we posit
uiOLS
=
t

ik

+

i

uFt E = eik
t +

i

+ ik
t ;

wherein the second equation decomposes the total residuals into …xed term
and the remaining residuals. Then computeeik = uiOLS
t

i

=

ik

+( i

=

ik

+

bi ) + ik
t

ik
t

eik
t

We consider the construct eik is a noisy signal of the unobservable true
match speci…c productivity, ik8 : We regress this variable over responses onquestions regarding the reasons for taking up and (when applicable) quitting
the job and other job characteristics.
In view of the fact that we only observe accepted outside wage o¤er, themodel (3) and (4) is a censored regression model. Thus we can estimate thesystem using Heckman correction method wherein the wage change is observed only when the worker decides to quit for a new job. Unfortunately,
the model may also su¤er also from endogeneity of two other determinants inwage and quit equations: migration and relocation decisions. Therefore, ourestimation strategy is to posit an augmented Heckman correction model. To
estimate simultaneously the wage growth across job, and three decisions on
8

We are not claiming that we identify the match speci…c productivity in that the observed
log wage clearly contains censored values of match speci…c productivity as is evident from themodel. Ideally a fully dynamic structural model estimation can overcome the identi…cation
problem [For a fully dynamic and structural model incorporating spatial and labor mobility,
see Kennan and Walker (2011)]. Our more modest objective here is to have some proxy forthe impact of the match speci…c productivity as the driver of wage growth and job mobility.

17

job change, migration and relocation, we assume that these decisions are fed
into wage growth and quit equations (1) (2) together with other regressors.
This estimation is done by a joint maximization of likelihoods for these equations wherein we allow correlations in the error terms across equations. In
the main results reported below, we employ cmp.ado routine in STATA. In asimilar manner, we also estimate the log wage equation given in (1) and (2),again employing recursive maximum likelihood. In this second estimation,
Heckman correction is employed against selectivity bias arising from endogenous work decision as well. Note that in view of the model above, the impact
of job change on wage cannot be identi…ed separately from the wage impact
by match speci…c productivity. That is why we need a separately estimation
on log wage together with work, migration, and relocation decisions.

4.2

OLS and …xed e¤ect regressions

Table 7 collects our …rst try9 . The full details of the regressions are relegated to Appendix B, which is available upon request from the author. First
thing we notice is a large and highly signi…cant e¤ect of jno (job number)variable. The estimated coe¢ cients on jno suggest that at least each jobchange increases log wage by .1. Moreover, the impact estimates are larger
in random or …xed e¤ect regressions, than pooled OLS, indicating possible
negative cross section correlations of log real wage with job hoppings. Fixing
individuals, our regressions show that each job change brings about more than
.2 log points increase. As we see in more details below, the large di¤erenceof the estimated coe¢ cient on this variable indicates that sample individuals
are highly heterogenous in terms of job mobility.
As expected, being an immigrant has a signi…cant negative impact whencompared to those born in PC urban labor market, whereas among thoseborn in areas other than PC urban, the same variable has signi…cant positivee¤ect. The impact of cumulative experience on wage is highly signi…cant andpositive in pooled OLS but not in panel regressions, suggesting again thatOLS is likely to su¤er from endogeneity (and selection bias) of work decision.
As we outlined above, we use these regression results to construct ourmeasures of match speci…c productivity: we take the di¤erence between OLS
residuals and individual …xed (random) e¤ects to construct four measures ofbik for each subset of samples:By construction of our measures of eik , we have
wtik

ik
w
btF E + eikt + etw
btF E + eik
t +

ik
t

ik
t

If we use eik directly in the next stages of regressions on wage, we are close
to regressing an identity. To avoid this problem, we regress our measure ofmatch speci…c productivity, eik ; on the following set of variables: job tenure,answers for multiple choice questions on why the respondents took the job,
9

See Appendix Table A7 for the details of the variables used throughout the regressions
results reported below.

18

answers for multiple choice questions on why the respondents eventually quit
the job, year dummy, job number dummy, and a set of variables related toindividual attributes and family background materials10 . We denote the …tted
value of eik by bik :We only note here that the regression results explain about.3 to .4 of the total variances of each measure.

4.3

Main results

In this and next sub sections, we report our major regression results. In
what follows, we only report the results using F E2 for PC urban samples,and F E4 for non PC urban born samples11 . Consult Table R for how di¤erent sets of estimations are employed for our …ve main variables: log(wage);
work, quit, immigrate, and relocation. Regressions for each of these dependent variables are either a part of the system of estimations (e.g., Recursive
Maximum Likelihood Heckman correction, standard Heckman correction), orin single equation (OLS, …xed e¤ect, or probit), Table R is provided for yourreference to see where these results are found. To facilitate comparison among
regression results for each dependent variable, we consolidate the results oneach dependent variable from di¤erent estimations into one Table.
As we have shown above, given that both job mobility and wage levelis driven by the match speci…c productivity, we cannot identify the e¤ect ofjob to job quit in the wage equation. Therefore, we need to estimate two
sets of regressions. In one, we use RML to estimate an expanded Heckman
correction model wherein we estimate wage growth, job to job quit, relocationand migration. In the second set, we estimate a system comprised of log wage,
work, relocation and migration.
As explained above, we assume that migration decision feeds into relocation and quit decisions, but not the other way around. Similarly, we assume
that migration decision feeds into job to job quit decision. Given the relative stability of migrant status, we believe this is a sensible restriction. In
essence, the model is an augmented Heckman correction model in that both
quit and work decisions censor wage o¤ers, and they are in‡uenced by relocation and migration decisions. A brief explanation for each variables used inthese regressions can be found in Appendix Table A7.
4.3.1

Recursive Maximum Likelihood Estimation of Heckman
correction model for wage growth

Since the rejected job o¤ers are not observable, we use Heckman correction
model to account for the censored wage o¤ers not accepted. Our …rst main
results are in Tables 8 and 9 wherein we have an augmented Heckman correction model for the changes in log wage after job changes in which we include
log wage growth, job quit, relocation and migrate as endogenous variables and
10
These regression results as well as full details of regressions reported below are in Appendix B which is available from the corresponding author upon request.
11
See Appendix B for other cases employing alternative proxies for match speci…c productivity, as well as full regressions results. Appendix B is available upon request from theauthors.

19

exploits the recursive structure, using a STATA .ado …le called cmp.ado 12 .
Table 8 summarizes the results for wage growth regressions. First thing that
we notice is the large and signi…cant impact of our measure of the match
speci…c productivity (and the one for the last job), bikt on wage growth. As
expected, it has a very large positive impact whereas the one in the previous
match has a large negative impact. In line with a simple search model, after controlling for the match speci…c productivity, Heckman correction terms(Inverse Mills Ratio at the bottom of the Table) are never signi…cant andquantitatively very small except in the …rst equation for each sub samples
without the match speci…c terms. In Table 8 we also include results of OLS
regressions. In all of these alternative estimation methods and speci…cations,bikt is always highly signi…cant. Thus we conclude from these results that the
improvement in match speci…c productivity is indeed the dominant driving
force inducing job mobility and wage growth. Aside from the dominant impact of match speci…c productivity, other variables are never consistently
signi…cant under alternative speci…cations and they are quantitatively small.
In Table 9, we have the estimated impact of job change on log wage.
Although the estimated impacts in these regressions show that the impacts
do vary widely, their mean estimates are surprisingly close each other andcentered around .27~.28.
Another piece of evidence consistent with our model is shown in Table
9, where we tabulate means of estimated match speci…c productivity at eachjob number. With possible exceptions of …fth or later jobs, means of match
speci…c productivity monotonically increase over job number. If recall our
earlier characterizations of wage growth across jobs in Figure 2, these regression results allow us to interpret the pattern in re‡ection of the improvementof match speci…c productivity as they continue to move jobs. We come back tothis point later on when we analyze wages paid at the current (thus ongoing)
jobs.
4.3.2

Quit decision

Our key probit regression for job to job quits are consolidated in Table 10
wherein we collect job quit probit regressions in RML Heckman correction
model for wage growth and single equation probit under alternative speci…cations. These two types of regression results are qualitatively quite similar,
although the estimated coe¢ cients tend to be more statistically signi…cant in
RML Heckman correction models.
Like in wage growth regressions, our main variable representing match
speci…c productivity is always highly signi…cant and positive for the new job
and negative for the previous job that they quit. Pseudo R2 at the bottom
for single regressions show the dominant explanatory power of these two vari12

The cmp.ado …le is prepared by D.M. Roodman and available at
http://ideas.repec.org/c/boc/bocode/s456882.html
See also: Roodman, D. (2009) "Estimating fully observed recursive mixed-process models
with cmp," Center for Global Development. Washington, DC, working paper 168.

20

ables as the measure jumps from less than .05 to .5 to .6., con…rming again
the decisive impact of the match speci…c productivity. Our main identifying
variable for the quit decision, varatio, the ratio of job vacancies to job seekers is highly signi…cant and positive13 . The quit decision is highly sensitiveon the job availability within the region. Another noteworthy point is thestrong positive impact of relocation, indicating the close correlation between
job and geographical mobility. The large impact of relocation on job quit
decision is, somewhat tautological because, in some cases, young employees
live in company dormitory, especially if they are migrant workers living awayfrom their parents14 . Thus relocation and job to job quit is a single event.
But this is not all. In many cases, relocation precedes job quit, suggestingthat they relocate themselves and start searching for jobs. Another noteworthy point is the impact of migrant variable. The coe¢ cients are unstable but
tends to be negatively signi…cant in PC urban samples (the top half of theTable), whereas the sign is positive and often signi…cant in the case of samples
born in non PC urban regions. At least in PC urban labor market, being amigrant worker by itself tends to reduce labor mobility. Consequently, thefact that migrant workers do exhibit higher job mobility than the resident in
PC urban labor market must be attributed to their higher spatial mobility
which enhances job mobility greatly, as we see below.
Quit and gaps in career We have 2,623 records of job terminations, outof which 2,101 resulted in a next job, and the rest, 522, resulted into either unemployment, non participation, or short time job lasting less than six
months, or any combination thereof, which we do not know. We only knowthat in those 522 cases, there exist lapses in between jobs lasting more thansix months.
We found no evidence that these two types of job terminations made any
substantive di¤erences in subsequent career or wage growth. In particular,when we regress log real wage including a dummy which distinguishes tow
types of terminations from the previous jobs, we found the dummy neverstatistically signi…cant and the estimated coe¢ cient is small in magnitude15 .
This is con…rmed also in Table 8 (pjobloss). The decision to quit a job either
immediately into next job or something else does not seem to exhibit any
substantive di¤erence.
Our tentative conclusion from these two observations is that temporary
career break does not matter very much. Other interpretations are certainly
possible, however. Since our records shows job records only lasting six months
13
Liu Yang allowed us to use her compiled data that she used for her own research (Liuforthcoming).
14
According to 2009 survey of migrant workers conducted by government, 43% of young(19-24) migrant workers live in company dormitory, 15% in construction cite (where theywork) or inside the production facility (of the employer), whereas only 36% live in rented
apartments.
15
Regressions results demonstrating these points are in Appendix B available upon requests from the corresponding author.

21

or longer, there is no way to tell to what extent these interruptions are real
or representing simply short job spells. Given the fact that even among jobs
lasting more than six months, the majority of them do not go beyond one
year, we should expect that many of these apparent interruptions are notreal. But if so, then, most of them do quit jobs to …nd another one within avery short period of interruption or none at all.
4.3.3

Recursive Maximum Likelihood Estimation of Heckman
correction model for log wage

In the second set of RML Heckman correction model, we incorporate work
decision in the estimation.
Our main results of log wage regressions are shown in Table 11. The results are remarkably robust under alternative speci…cations. Our main …ndings can be summarized as follows. First of all, as consistent with resultsin wage growth estimations, match speci…c productivity is highly signi…cantand exerts quantitatively large impact on the log wage. The point estimatesshow that one standard deviation in bik (.13-.19) changes the predicted real
log wage anywhere between .08 to .29. The impact of migrant variable di¤ers
in two subset of samples. In comparison with non-immigrant born in regions
other than PC urban, the impact is consistently positive and the estimatedcoe¢ cient suggest that the immigrants earn 7 to 10% more. In regressions
for samples currently in PC urban labor market, the impact is negative andsigni…cant, indicating that the immigrant earns 10 to 12% less compared tothose born in PC urban. We come back to these points later on when we
asses the results for immigration decision.
Another noteworthy point is the consistently positive impact of …rstjob
which is a dummy variable equal to one if the …rst job is a regular full time
position.
Importance of family and individual background factors Aside fromwage and job mobility, RML estimates probit model for three other endogenous variables, work, relocation, and (to become) immigrant. Before wesummarize the regression results for these variables, we note here the importance of family and individual background as the key factor shaping theenvironment in which these decisions are made. The economic well beingof the family has consistently negative impact on all of these variables. The
impact is negative if the parents’annual income is higher (rich), and also if
the sample individual is the only child. GPA in the last year at high school
has consistently positive impact on all the variables. The sample individuals are more likely to work, immigrate and relocate if the father is dead.
The willingness to take risk (risk ) is also positive and highly signi…cant inall three regressions. All in all, strong economic motivation, good educationback ground, and willingness to take risk are the major inducement to workand mobility.

22

Work decision The results are collected in Table 12 which includes resultsof single equation probit. First point we note is that the Heckman correction
model estimated show consistently the positive selectivity bias ( in work decision) as shown in signi…cantly positive inverse Mills ratio (see the bottomof Table 11 ).
Not surprisingly, migrants are more likely to work. As we noted above,
work decisions also depends highly on the economic needs of the family: samples are more likely to work if they are from not wealthy family, and if theirfathers deceased [not shown in table] . On the other hand, positive impact
of GPA, and negative impact of nostudy [not shown in table] variables are
consistent with the positive selection results.
It is interesting but somewhat di¢ cult to interpret that the impact ofgender di¤erences in two sub samples. In PC urban labor market, male is more
likely to work, whereas in non PC urban born samples, females are more likely
to work. One related point in the regression result is the negative impact offamilybusiness, which indicates that they had to help family business during
the last year in school. This might explain why male in non PC urban samples
are less likely to work.
Relocation decision The results are collected in Table 13. The …rst thing
we notice in the probit regressions for relocation is highly signi…cant and quantitatively large impact of migrant 16 , in comparison with non-immigrants or
PC urban born samples. Moreover, impacts of family and individual background variables are qualitatively similar to the results for migrant probit
regressions: relocations are more likely if they are female, willing to take risk,
comes from not wealthy family, with many siblings. Impacts of variables related to high school experiences are also similar in many variables such as
GPA.
On the other hand, some of the results are indicative that relocations
are also related to personal troubles or failure to get along socially, which
we do not …nd in immigrant probit models. For example, having some work
experience during the high school has positive impact on immigrant, but
negative on relocation. Having experiences of being bullied at school has
positive impact on relocation.

4.4

Selectivity issues

As we have explained with some details, work decision, earnings, job andgeographical mobilities are not only endogenous but we may also encounter
potential selectivity issues. As we have seen above, our wage data is censoredbecause work decision itself is endogenous and we do not observe rejected
wage o¤ers. On top of this, our wage data is one observation only for each
16

Zhang (2010) argue that his estimation of a Cox proportional hazard model indicatesthat migrant workers are less likely to change jobs, which is consistent with our results inTable 14. Our results in Table 17 shows that migrant workers are far more likely to relocate
and then …nd a new job. As Zhang uses a panel of workers living (working) in selected
cities, geographical mobility across cities cannot be analyzed in his data.

23

job, thus we cannot directly observe within job wage changes. Finally, given
uneven developments across di¤erent regions within China, the selection on
where (which region) to work is also an important determinant of the wage
and other employment conditions. Thus we have selectivity issues in this
aspect as well in that we do not observe employment opportunities available
in regions other than the one actually chosen by sample individuals. Thus
decision to migrate induces selectivity bias. In what follows, we investigate
the rami…cations of these selectivity issues in some details.
4.4.1

Within job wage growth

The mirror image of the selectivity due to job quitting is the selectivity ofretained jobs. As we repeatedly noted, we have no information on within
job wage changes. For the sake of argument, let us assume that the wage
reported for each job corresponds to the wage at the end of the tenure. If job
termination is exogenous, our data on the length of tenure and the wage canbe used to estimate the impact of tenure on wage, i.e., the estimate of within
job wage growth. Among the completed job spells, as we have shown above,
each job tends to end sooner if the wage rate is lower. Thus the survivor bias
should favor the wage for jobs that lasts longer, thus generating survivor bias
in the tenure e¤ect estimation. With this caveat in mind, Table 14 shows
our regression results. We use two sub samples. First four columns show
the results using the completed job spells, and the rest of the Table display
the results using the current jobs (i.e., jobs retained as of the survey time).
These latter estimates show the tenure impact on wage is highly signi…cantand ranges between .046 to .052. On the other hand, the estimates forcompleted job spells are small and never statistically signi…cant, which makes
sense given the fact that they quit the job already. These two contrastinge¤ects of tenure is consistent with the presence of survivor bias. Hence weconclude from these regressions that the within job wage growth is not higherthat the estimate using the current (thus ongoing) jobs.
Therefore, we use the point estimate of the impact of tenure on log wage
using the current job spells (the one with the check mark) to construct a
hypothetical log wage as follows:
log w
etik = log wtik + :0222544tenureikt

As tenure is measured in terms of 1/3 of a year, the estimated impact translates to roughly 7% increase per year of the wage17 . As we indicated above, we
believe that this estimate is signi…cantly upward biased re‡ecting the strong
survivor bias as our samples in this regressions are taken from the current(and thus on going ) jobs.
Our hypothesis is that …rst, the wage reported in all the completed jobs
17
We consider this tenure e¤ect way above the most likely magnitude. Our point here isto show that the impact of job to job quit is dominant factor even if we assume a very highwithin job wage growth.

24refer to the wage paid at the end of the job18 .
The estimated mean impact of job to job quit on log real wage is shownat the bottom of Table 9. The patterns are qualitatively similar to the one
we obtained in our main regression results reported above. Naturally, theestimated mean impacts are smaller but still large, ranging between .16 to
.21.
4.4.2

Migration decision

We conducted a similar estimation using treatment model. The idea is thatthose who are born in regions other than PC urban have choice on migration.
In Table 15, we …nd in all regressions the mean predicted value of log wage ishigher for the migrants and after controlling for the endogeneity of migration
decision, and the net impact is between .1 to .15 in log points. The Table also
reports Heckman selection model in which we treat migration as the censoring
device in the sense that the migrants face di¤erent labor markets. We also ran
a mirror image Heckman model for non-migrants The di¤erence in predicted
log wages conditional upon migrant and non-migrant is shown in the table
and the mean di¤erence is somewhat larger than .15. In both treatment andcensoring models, the estimated impacts of migration is positive and thatthey are positively selected.
We conclude from these estimates that migrants are indeed positively
selected in the sense that those who immigrate do earn more on average, evenafter controlling for the selectivity due to endogeneity of immigration decision.
Probit regressions in both treatment and Heckman corrections model are
qualitatively almost identical and the results are summarized as follows. We…nd signi…cant positive impact on becoming a migrant by: female, willing totake risk (risk ), comes from not wealthy family (rich), with many siblings.
These family back ground variables are robust and suggest that those becoming migrant are raised in relatively poor family with many siblings. Although
GPA has positive impact, we …nd those who answered did not study at all
at home (nostudy) has strong positive impact. On the other hand, the self
evaluation of over all high school life (schlife) has strong positive impact,
and the experience of extended absence from school (for reasons other thanillness) carries strong negative e¤ect. These results indicate rather strongly
that migrants are more forthcoming and willing to take risk, sociable, if notvery smart or academically motivated strongly. Note also the impact of thesuccess of the …rst job also has positive impact on becoming a migrant
4.4.3

Migrant workers in urban labor markets

In contrast, the impact on log wage by migrant variable in samples of PC
urban residents carry strongly negative impacts. Although the estimatedcoe¢ cients vary somewhat across di¤erent speci…cations, the results in Table18

We could assume alternatively that the reported wages are at the beginning of the job,or mean of the wage during the job. The substantive results are not sensitive to these
speci…cations as in either way the tenure e¤ect tends to favor the current job over the newjob o¤ers.

25

11 show the impact is statistically signi…cant and around negative .1.
Some of the past studies indicated that the negative impact on wage by
being a migrant worker is due to the fact that the migrants are more willingto take up (low paying) jobs which urban residents are not. Although our
results indeed show that the estimated negative impact is somewhat smaller
once we include our estimated match speci…c productivity, the impact of
migrant variable remains negatively signi…cant. The estimated impact is also
somewhat smaller (but still remain signi…cant) in the absolute magnitude inrecursive maximum likelihood model that incorporates work decision. Thus
we conclude from these results that indeed the migrants are penalized in theirwage in PC urban labor market compared to their original residents.
One …nal question we ask ourselves is, so, what is our bottom line considering the impact of job and location changes? Do the immigrants catch up
or overtake the urban residents in terms of wages they earn in urban labormarkets?
Table 16 shows that our estimation results indicate that they do catchup: the estimated wage gap in the …rst job is .088 log points, but by the…fth or later job, the di¤erence is reversed and the immigrants on average
earns more than PC urban residents by .039 log point19 . As we have seen
above, the driving force of the job mobility is the match speci…c productivity.
The wage growth by job mobility generates improvement in match speci…cproductivity. To see this point, Table 17 summarizes the Blinder-Oaxaca
decomposition of log real wage di¤erence between PC urban born and migrant samples in PC urban labor market20 . Positive contributions in Table 17
indicates that those factors favor native residents. Consistent with the resultsin Table 16, match speci…c productivity components ( ) of the wage favors
migrant workers, whereas the PC urban resident have a large advantage inendowment in individual …xed e¤ect ( ) . The impact of employer types on
the wage di¤erence between the two groups suggest the source of possible discrimination of the migrant workers: residents are signi…cantly more likely tobe employed at government enterprises and they bene…t strongly21 , whereas
the net endowment impacts by foreign and private …rms are negative on residents, thus favoring migrant samples. Most likely, the government related
jobs are available only to the residents with urban registrations, whereas they
do not matter in private or foreign …rms. The total impacts of endowment,coe¢ cients, and interactions are shown at the bottom of the table. Overall,coe¢ cient terms are signi…cantly favorable to the residents.
19
Note, however, that standard deviations in parenthesis of Table 19 suggest that thesedi¤erences are not statistically signi…cant.
20
Lee (2012) employs a similar decomposition method and …nds roughly 10% wage di¤erentials after controlling for the observable characteristics. Lee also …nds that the discrimination impact is larger for male.
21
A similar observation can be found in Gagnon et al (2009), in which they …nd thedisadvantage applies to all the migrants, including those with urban hukou registration.

26

4.4.4Return migrations

Among those born in non PC urban regions, 53% migrated to PC urban
regions at least once. Among them, 21% (195) returned to their home province
at least once. As of the last sample period, 143 or 73% of 195 remain in non
PC urban regions, and the remaining 52 have returned to PC urban regions
again. Table 18 reports OLS regression on log wage and Heckman correction
model for return migrants. The results show that return migrants seem justas successful as migrants are, in comparison with those born in non PC urban
regions and remain in the regions. Selection model in Table 19 con…rms
that return migrants are positively selected among those born in non PC
urban regions, just as the migrant are overall. On the other hand, selection
equation for return migrants di¤er in important ways from those for migrants.
To begin with, the age obviously has a positive impact as returned migrants
must have stayed some time in urban regions before the return. Positive
evaluation of school life and GPA both have signi…cant positive impacts on
migrant, whereas the signs are reversed for returned migrants. On the other
hand, the impact of having a regular full time …rst job has even stronger
positive impact on returned migrant. We also note the impacts of rich andrisk variables are both signi…cant in two cases. It is perhaps fair to say thatat least in our samples, return migration is just as successful on averageas migrants, whereas we also detect some di¤erences in the way some ofindividual attribute variables in‡uence these two decisions. The latter may
well re‡ect non-economic factors inducing them to return.

5

Conclusion

As far as we know, this is the …rst systematic investigations into the transition
from work to school in contemporary China. We emphasized the interactions
between geographical and occupational mobilities among the youth. We foundthat the youth migrating to urban areas after school are positively selected;
they are more likely than the comparable urban resident youth to move geographically. They also expect to have large gains from job to job quits.
The higher mobility pays o¤ in terms of wage and job satisfactions. We …ndthat job and geographical mobilities are highly correlated and they are also
selective.
Setting aside the obvious and inevitable limitations of analyses based upon
one shot surveys, this paper leaves many future research agendas. As wenoted repeatedly, our survey collected single wage rate for each job. Thus
our analysis on within job wage growth is highly speculative. Circumstantial
evidence suggests that our …ndings cannot be applied directly to di¤erent
segments of the Chinese youth, especially the college graduates as they arelargely free to relocate themselves and move their resident registrations.
As we demonstrated in the paper, the labor market itself is in the midstof transformation. It is unclear to what extent and how long this wave ofinternal migration continues. Our characterization of the school to work27

transition in China may well be highly dependent on the current wave of
massive immigration, re‡ecting enormous regional disparities within China.

28

References[1] Ariga, K., Ohtake, F., Kurosawa, M., and Sasaki, M. (2009), ’How dohigh school graduates in Japan compete for regular, full time jobs? An
empirical analysis based upon an internet survey of the youth,’ Japanese
Economic Review, forthcoming[2] Chang, L.T., Factory Girls, Picador, 2008
[3] Chan, K.W. and W. Buckingham (2008), ’ Is China Abolishing Hukou
System?’ The China Quarterly, 195: 582-606.
[4] deBrauw,A., and J. Giles (2008). "Migrant labor markets and the welfare
of rural households in the developing world: Evidence from China,"[5] Demurger, S., M. Gurgand, L. Shi, and Y. Ximing (2009), "Migrants as
second-class workers in urban China? A decomposition analysis," Journalof Comparative Economics 37: 610-628[6] Gagnon, J., T. Xenogiani, and C. Xing (2009), “Are all migrants reallyworse o¤ in urban labour markets: new empirical evidence from China.”
OECD Development Centre Working Paper No. 278.
[7] Knight, J., and L. Yueh (2004), " Job mobility of residents and migrants
in urban China," Journal of Comparative Economics 32: 637-660[8] Kondo, A. and Ou, D. (2010), ’In search of a better life: The occupational attainment of rural and urban migrants in China,’ISER Discussion
Paper793[9] Lee, L. (2012),’Decomposing wage di¤erentials between migrant workers
and urban workers in urban China’s labor markets,’ China Economic
Review 23: 461-70[10] Lehmer, F. and J. Ludsteck (2008), "The returns to job mobility andinter-regional migration," IAB Discussion Paper 200806, Institute fur
Arbeitsmarkt- und Berufsforschung (IAB), Nurnberg [Institute for Employment Research, Nuremberg, Germany].
[11] Li, S. (2008), ’Rural migrant workers in China: Scenario, challenges, andpublic policy,’ILO working paper 89[12] Kennan, J. and J.R. Walker (2011), ’The E¤ect of Expected Income on
Individual Migration Decisions,’Econometrica 71(1) : 211–251[13] Matsumoto, M. and S. Elder (2010), ’Characterizing the school-to-worktransitions of young men and women: Evidence from the ILO School-towork transition survey,’ ILO Employment Sector, Employment Working
Paper 51
[14] Meiyang, W. (2007?) "Wage arrears for and discrimination against migrant workers in China’s urban labor market,’ Institute of Populationand Labor Economics, China Working Paper 67[15] Meng, X., and J. Zhang (2001), "The two tier labor market in urban
China," Journal of Comparative Economics 29: 4845-504[16] Meng,X.,K. Shen and S. Xue(2010), ’Economic Reform, Education Expansion, and Earnings Inequality for Urban Males in China, 1988-2007,’IZA Discussion Paper 4919

29

[17] National Bureau of Statistics of China (2010), Monitoring report for migrant workers in 2009[18] Qu. Zhaopeng, and Zhong Zao (2011), "Evolution of the Chinese RuralUrban Migrant Labor Market from 2002 to 2007," IZA discussion paper
5421[19] Wu, Zheren (2010), ’Self-selection and Earnings of Migrants: Evidence
from Rural China,’Asian Economic Journal 24(1): 23-44[20] Zhang, H. (2010), ’The Hukou system’s constraints on migrant workers’
job mobility in Chinese cities,’China Economic Review 21: 51-64

30

6

Appendix Summary Statistics

6.1

Cross section characteristics

Appendix A additional Tables A1 through A6 collect cross section characteristics of the sample individuals in terms of age, gender, education attainments,
regions of the birth and the current residence, martial status, and labor force
participation. The last table, Table A7, lists variables used in the regression
analyses with a brief description of each variable.
Table A1 shows age composition. The sample individuals are more or lessevenly distributed among age 21 and 27, with roughly 300-350 samples ineach age bracket, but, signi…cantly smaller shares for those with age 20 oryounger and also 28 or older.
Table A2 summarizes the job placement services provided by the school
and the participation to these activities. Naturally, vocational schools o¤er
more job placement services than academic high schools. The participation
rates do not di¤er much between the two types. In either types of schools,
more than 60% of the sample attended all of these events organized by school.
Among graduates of academic high schools, about one third of the sample
said that the school provided none of the placement services we listed (or do
not remember even if they did). As we have noticed in the Japanese survey
in Ariga et al (2011), the graduates of academic high school seem to haveproblem in transition to work. Table A3 shows education attainments andwork experience. 25% of academic high school graduates never worked since
graduation, as opposed to 7% for vocational schools, 13% for middle school
only, and even for high school dropouts, the share is only 15%.

6.2

Families and social status

More than 90% of the respondents have both parents alive and married. Their
joint income distributions are in Table A4. About a quarter of the sample
report that their parents make more than 40 thousand RMB per year (about
a half million yen, or 4,000US$). About 6% of the parents (either one ofthem or both) are ranking government o¢ cials. 13% of them are lower levelgovernment o¢ cials or employees of state owned …rms. Single largest categoryof work is self employment, comprising roughly one third. It is a fair guessthat the majority of them are farmers.
Table A5 shows the parents’ ages at the birth of the sample workers.
The mean age is 26 for mothers, and 28 for fathers. Table A6 shows the
distribution of the number of siblings. Somewhat surprisingly, about 14% ofthem have 3 or more siblings. 30% of them are the only child, and 38% have
only one sibling. Given the fact that 63% of the sample individuals were bornin rural areas, these results are consistent with the common perception thatthe birth control (so called only child ) policy had not been strictly adhered
to in the rural area.
As of the time survey was conducted in early 2009, about 40% of oursamples live alone, with the rest living with one or both parents. Among the31respondents at or above age 25, roughly a half of them live alone, indicating
the typical age at which the youth become independent. About 30% of female
respondents are married or divorced, whereas for male the share is 25%. In
both sexes, 60% of the married samples have children, and about 9% of them
do not live with their children.

32

25
20
15
10

Guangdong
Shandong
Jiangsu
Zhejiang

FujianShanghai

5

Hebei
Hunan

Hubei
Beijing

Guangxi
Jiangxi
Liaoning

Chongqing

Henan

Sichuan

Anhui

Heilongjiang

Tianjin,
Jilin

Shaanxi
Shanxi

Inner Mongolia
Gansu

YunnanGuizhou

Ningxia
HainanXinjiang
Qinghai

Tibet

2

4

Population share in 2010 (%)

Figure 1 Survey and Population Shares
Survey sample share : birthplace
Survey sample share : current residence

45 degree line

68

Average log real wage
6.6 6.87
7.2 7.4

Figure 2 Wage growth across jobs

1

2

3
job number
one job only
3 jobs5 or more jobs

4
2 jobs4 jobs56.8

log of average real wage
7
7.2
7.4

7.6

Figure 3 Wage growth of migrants and PC urban borns

1

2

3
job number
PC urban residents

4
migrant

5

Table 1 Birthplace and current residence locations
Current Pculocation
Pculocation at birth

NonPC rural NonPC urban

PC ruralPC urban

Total

NonPC rural
NonPC urban
PC ruralPC urban

15.5
1.1
0.5
0.0

47.3
72.7
2.5
2.0

1.6
0.0
28.0
0.6

35.6
26.2
69.1
97.3

100
100
100
100

Total

6.2

32.7

7.5

53.6

100

Table 2 Geographical mobility of migrant workers
Total number of Among current residents in pc-urban regions
migrant
Total
locations lived non-migrant
12
3
4
5 or more
Total

1
2
3
4
5 or more
Total

70.74
47.11
51.12
41.65
39.69
54.96

29.26
52.89
48.88
58.35
60.31
45.04

100
100
100
100
100
100

Among those born in non pc urban regions
50.85
49.15
100
24.31
75.69
100
25.05
74.95
100
15.93
84.07
100
17.71
82.29
100
31.22
68.78
100

Table 3 Current Employment Status and wage

Sample Size

164

academic highschool
1,022

never worked

12.8

25.8

7.4

14.6

14.7

worked before, but not
now

19.5

20.1

17.2

22.6

19.2

currently have a job

67.7

54.1

75.5

62.9

66.1

regular full time

59.2

45.6

63.3

50.2

52.2

Average wage (RMB
per month) (current job)

966

1217

1227

1180

1204

middle school

vocational
high school
1,515

HS dropouts

Total

665

3,366

Table 4 Across job wage growtheducation attainment
Job #First job
Second job
Third job
Fourht Job
Fifth or later jobs
Net % gain (%)
Total

middle academic vocational

dropout

total

722
1049
1258
1183
1316

884
1282
1547
1669
1753

925
1358
1429
1612
1556

891
1289
1418
1512
1567

897
1308
1447
1577
1561

963

1145

1179

1133

1148

Figures are average monthly real wage in 2005 RMB pricesTable 5 Job characteristics and reasons for quitting the job
Reasons for quitting the job

1st job

2nd job

Job history
3rd job 4th or later totalcannot get along with colleagues

1.8

2.3

2.3

0.0

2.0

the job not interesting

14.8

10.7

3.7

4.8

12.3

the job different from what I expected

8.5

6.4

5.3

9.7

7.6

too busy at work
dead end job, no future
wage too low
found a better job
others

6.6
30.5
18.2
15.2
4.3

7.3
29.8
13.1
22.9
7.4

15.7
21.6
10.8
33.9
6.6

5.2
11.4
22.8
38.6
7.6

7.7
28.9
16.2
20.0
5.5

Types of employers
central and local governments
Institutions (e.g., schools, hospitals)
state enterpriseforeign firms
domestic private firms
self employment
helping family business
OthersJob history
1st job
2nd job 3rd job 4th or later total
2.4
1.4
0.9
0.8
1.9
4.8
4.1
6.4
1.4
4.7
9.7
8.7
6.8
10.2
9.1
16.2
18.4
14.1
16.0
16.6
47.4
54.8
55.4
61.7
50.7
15.8
11.2
13.7
9.1
14.1
0.9
0.4
0.7
0.0
0.7
2.9
0.9
2.1
0.8
2.2

Job history
1st job
2nd job 3rd job 4th or later total
type of job that I was looking for
31.4
19.3
14.6
17.1
25.9
company well known
7.3
9.0
13.4
6.6
8.4
the pay is good
21.4
10.2
12.7
23.1
17.5
little overtime, many holidays
13.1
13.4
12.2
12.4
13.1
school education useful in the job
4.5
7.1
5.0
1.7
5.2
22.3
41.0
42.2
39.1
30.0
opportunity to learn professional skills
Reasons for taking the job

Table 6 Job changes and relocations
relocation
Total
relocation
Total(1) Job change=0
Frequency
0
25778
1
3509
29287

Percent
88.02
11.98
100

(2)Job change=1
Frequency
0
3326
1
1283
4609

Percent
72.16
27.84
100

Table 7 OLS regressions on log real wage
Regression #1

Samples

relocation
first job
cumexperience
Job number
Constant

3

4

5

PC urban labor market

Regression typemigrant

2
Fixed
Effects

OLS

6

7

8

Samples born in non PC urban regions

RandomEffects

Fixed
Effects

OLS

RandomEffects-0.151-0.143-0.095
0.072
0.086
0.082
0.085
(0.013)*** (0.015)***(0.043)** (0.010)*** (0.011)*** (0.045)* (0.039)**
0.026
0.035
0.012
0.013
0.000
0.015
0.016
0.015
(0.012)** (0.012)*** (0.010)(0.010) (0.010) (0.010) (0.009)* (0.009)*
0.131
0.123
0.082
0.069
0.070
0.116
(0.020)*** (0.020)***(0.054) (0.016)*** (0.015)***(0.046)**
0.016
0.017
-0.009-0.0020.016
0.015-0.001-0.001(0.001)*** (0.001)*** (0.006)(0.004) (0.001)*** (0.001)*** (0.003) (0.002)
0.100
0.105
0.228
0.224
0.102
0.110
0.256
0.249
(0.008)*** (0.008)*** (0.017)*** (0.017)*** (0.007)*** (0.007)*** (0.016)*** (0.015)***
6.096
6.026
6.150
5.624
5.992
5.937
6.212
5.676
(0.130)*** (0.149)*** (0.339)*** (0.329)*** (0.103)*** (0.116)*** (0.215)*** (0.228)***Other regressors included*

Year Dummy
School characteristics andschool life
Individual Attributes
Family Background
Observations
R-squared
Number of samplenum

Yes

Yes

Yes

6,907
0.254

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

6,564
6,564
10,482 9,911
9,911
0.435
0.248
0.285
0.478
810
810
1,171
Robust standard errors in pare *** p<0.01, ** p<0.05, * p<0.1

Match specific productivity ηFE1
main results
Computed as thedifference in resdisuals inYes#3--#1

6,564
0.296

η

RE1

#4--#1

FE2

η
yes

#3-#2

η

RE2

#4-#2

η

FE3

#7-#5

η

RE3

#8-#5

FE4

η
yes

#7-#6

Yes

9,911
1,171
η

RE4

#8-#6

See Appendix Table A7 for varaibles in the regressors. Full regression results are available upon request from the corresponding author
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

Table 8 Heckman correction model for wage growth

Estimation
Methods
Match specific

PC Urban Labor Market
1
2
3
RML
Panel
RML
Heckma Heckmaη(last job)
pjobloss
immigrant
relocation
first job
cumexpConstant

Heckma Heckmaη FE2

productivity
η(new job)

OLS

Non PC Urban born samples
4
5
6
RML
RML
Panel
OLS

η FE4

1.962
2.087
2.024
2.909
(0.362)*** (0.288)***(0.346)*** (0.387)***-1.506-1.655-1.549-1.419(0.368)*** (0.282)***(0.338)*** (0.340)***-0.0190.009 0.018
0.010
0.035 0.038(0.042) (0.048) (0.050)(0.035) (0.041) (0.042)
0.000-0.014-0.021-0.005(0.039) (0.038)(0.110) (0.072)
0.064-0.021-0.059-0.020(0.156) (0.140)(0.484) (0.161)-0.0340.052-0.0240.012(0.037) (0.040)(0.032) (0.033)-0.0170.002-0.001-0.0140.006
0.021(0.004)*** (0.006) (0.006) (0.003)*** (0.005) (0.007)***
0.853
0.731
0.729
0.486
-0.212-0.529(0.420)** (0.561) (0.585) (0.466) (0.480) (0.497)

Other regressors

School type dummy, job characteristics (current and lastin the regressionsjobs), year dummy

Adj. R2
Inverse Mills Ratio 0.545*(0.294)
Observations
9,902

0.23
-0.011(0.033)
5,775

5,775

0.2102-0.273(0.155)
15,1730.001(0.028)8,625

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1
See Appendix Table A7 for varaibles in the regressors. Full regression results are available upon requestfrom the corresponding author

Table 9 Estimated mean impact of job to job quit on log
wage

Equation # in Table 8

PC Urban Labor

Non PC Urban

Marketborn samples

1

wage ignored

Estimated wage growth
conditional on quit>0(standaddeviation )

4

η FE2

Match specific productivity

Tenure effect on

2

5

η FE4

0.087

0.282

0.185

0.281

(0.219)(0.198)(0.175)(0.206)E(η)

first job

0.044

0.015

second job
third job
fourth job
fifth or later

0.131
0.176
0.185
0.163

0.126
0.170
0.209
0.293

Estimated wage growth
conditional on quit>0(standaddeviation )Tenure effect on
wage included

first jobsecond job
third job
fourth job
fifth or later

0.186

0.178

0.203

0.212

(0.171)E(η)(0.196)(0.169)(0.184)

0.003
0.088
0.157
0.211
0.171

-0.008
0.101
0.160
0.229
0.345

*** p<0.01, ** p<0.05, * p<0.1
Robust standard errors in parentheses
See Appendix Table A7 for varaibles in the regressors. Full regression results are available upon requestfrom the corresponding author

Table 10 Job quit decision
PC UrbanEquation # in Table 8
Estimation

η ij
η ijj-1

varatiomigrant
relocation3
first jobrisk
age

Constant

1
Heckman RML

Probit2
Heckma4
Heckma5
HeckmaProbitProbit
n RML
n RML
n RML
14.33318.971
16.24118.741(0.550)***(0.679)***(0.490)***(0.518)***-13.257-16.844-14.958-16.707(0.460)*** (0.621)***(0.511)*** (0.526)***0.137 0.061
0.112
0.059
0.115 0.387
0.342
0.017
(0.108)***(0.054)**(0.02)***(0.047)(0.058)**(0.048)(0.098)(0.105)***-0.138 -0.034-0.201
0.077
0.097 0.114
0.368
0.018
(0.035) (0.044)*** (0.073) (0.066)*** (0.063) (0.034)*** (0.061)* (0.159)**
0.491 0.294
1.337
0.284
0.435 0.261
0.785
0.130
(0.035)*** (0.193)** (0.064)*** (0.131)*** (0.395) (0.034)*** (0.061)*** (0.216)***-0.014 -0.037-0.001-0.0260.027 0.049
0.021-0.025(0.037)(0.066)(0.026)(0.034)(0.062)(0.032)(0.064)(0.061)0.019 0.010
0.007 0.004
0.007 0.015
0.007
0.010
(0.015) (0.006)(0.008) (0.015)(0.015)(0.007) (0.009)** (0.015)
0.021
0.040
0.005
0.075
-0.0730.041-0.0840.071(0.007)*** (0.009)** (0.015)*** (0.015)*** (0.006)*** (0.007) (0.014)*** (0.015)***-2.321 -2.662-2.672 0.549-1.991 -3.908-4.0300.352(0.229)***(0.386)***(0.178)***(0.363)***(0.173)**(0.379)***(0.135)***(0.345)***
ProbitOther regressors in

Family background variables

the regressions
Pseudo Rsq
Observations

Non PC Urban born

0.022
10340

Robust standard errors in parentheses

9,902

0.513
5791

5,775

0.030
16437

0.618
15,173 8643

8,625*** p<0.01, ** p<0.05, * p<0.1

See Appendix Table A7 for varaibles in the regressors. Full regression results are available upon request from the corresponding author.
.

Table 11 Recursive maximum likelihood estimation: Log
real wage
PC Urban labor market

Equation #1

Estimation Method

OLS

System of Equations

ηfirst jobmigrant
male
Acedmic HS
Vocational HS
Middle School
cumexperience

2
3
Recursive
maximumlogwage, work,

4
OLS

relocation

Match specificproductivity

Non PC Urban born

η

5
6
Recursive maximum
likelihood (RML)log wage, work,
immigrant, relocation

FE2

η

FE4

1.301
1.186
(0.072)***(0.070)***
0.098
0.115
0.121
0.100
0.094
0.096
(0.051)* (0.019)*** (0.018)*** (0.043)** (0.016)*** (0.016)***-0.122-0.102-0.039
0.072
0.074
0.103
(0.038)*** (0.013)*** (0.013)*** (0.037)** (0.023)*** (0.023)***
0.023
0.031
0.015
0.075
0.077
0.103
(0.031) (0.012)*** (0.011) (0.026)*** (0.010)*** (0.010)***-0.0840.023
0.031-0.0330.036
0.045(0.050)* (0.018) (0.018)* (0.039) (0.014)** (0.014)***-0.027
0.050
0.034
0.024
0.080
0.067
(0.045) (0.017)*** (0.016)** (0.037) (0.013)*** (0.013)***-0.2310.036
0.054-0.1590.041
0.052(0.070)*** (0.033) (0.032)* (0.061)*** (0.026) (0.025)**-0.0020.014
0.035-0.0020.013
0.031(0.004) (0.001)*** (0.002)*** (0.002) (0.001)*** (0.001)***Other regressors inschool type, job characteristics, job number dummy, year

the regressions

dummy

Constant

7.249
6.318
5.700
6.796
5.404
5.075
(0.256)*** (0.130)*** (0.133)*** (0.191)*** (0.213)*** (0.213)***Inverse Mills Ratio
Observations
6,907
Number of samplenum 851

0.532(0.144)
10,3220.533(0.146)
10,322

10,482
1,238

0.586(0.158)16,4340.581(0.148)16,434

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
See Appendix Table A7 for varaibles in the regressors. Full regression results are available upon requestfrom the corresponding author

Table 12 Work decision
PC Urban labor market
Equation Number
2
3
in Table 11
Estimation MethodsRML logwRML logwProbit
ProbitProbitNon PC Urban Born
5
Probit6

RML logwRML logwProbit

migrant

ProbitProbitProbit

0.128
0.145
0.150
0.149
0.126
0.384
0.389 0.247
0.252
0.230
0.276
(0.080)*** (0.082)*** (0.022)*** (0.023)*** (0.023)*** (0.024)***(0.033)*** (0.033)*** (0.032)*** (0.035)*** (0.036)***age-0.043-0.044-0.050-0.045-0.022-0.021-0.048-0.049-0.056-0.048-0.016-0.014(0.005)*** (0.006)*** (0.005)*** (0.006)*** (0.007)*** (0.007)*** (0.004)*** (0.004)*** (0.004)*** (0.005)*** (0.005)*** (0.005)**
male
0.207
0.186
0.181
0.197
0.206
0.209
-0.096-0.1020.022-0.014-0.026-0.034(0.027) (0.027) (0.027)(0.032)*** (0.032)*** (0.030)*** (0.033)*** (0.034)*** (0.035)*** (0.027)*** (0.028)*** (0.024)
schlife
0.019
0.014
0.021
0.018
0.019
0.018
0.012
0.016
0.024
0.023
0.027
0.025
(0.015) (0.015) (0.016) (0.017) (0.017) (0.017)(0.011) (0.012) (0.013)* (0.013)* (0.013)** (0.013)*
bullied-0.068-0.072-0.099-0.124-0.130-0.1290.063
0.055-0.004-0.006-0.013-0.011(0.024) (0.024) (0.024)(0.027)** (0.027)*** (0.029)*** (0.030)*** (0.031)*** (0.031)*** (0.021)*** (0.021)*** (0.023)
hutoko-0.103-0.098-0.098-0.113-0.093-0.128-0.032-0.034-0.028-0.030-0.028-0.053(0.023) (0.024) (0.024)**(0.026)*** (0.027)*** (0.030)*** (0.030)*** (0.031)*** (0.031)*** (0.021) (0.021)* (0.023)
GPA
0.038
0.112
0.075
0.079
0.075
0.082
0.011
0.059
0.016
0.021
0.020
0.023
(0.014) (0.014) (0.014)*(0.017)** (0.017)*** (0.018)*** (0.018)*** (0.019)*** (0.019)*** (0.012) (0.013)*** (0.013)
parttimejob
0.065
0.074
0.047
0.035
0.025
0.016
0.002
0.008
-0.020-0.013-0.007-0.019(0.031)** (0.032)** (0.035) (0.036) (0.037) (0.037)(0.024) (0.024) (0.027)(0.027) (0.028) (0.028)
familybusiness-0.035-0.047-0.105-0.115-0.111-0.106-0.054-0.059-0.127-0.133-0.127-0.115(0.030) (0.030) (0.033)*** (0.034)*** (0.035)*** (0.035)*** (0.022)** (0.023)*** (0.024)*** (0.025)*** (0.026)*** (0.026)***rich-0.024-0.022-0.007-0.002-0.004-0.034-0.034-0.015-0.015-0.014(0.008) (0.008) (0.008)(0.006)*** (0.006)***(0.006)** (0.007)** (0.007)**(0.007)*** (0.007)***
Constant
1.095
0.770
0.808
0.748
-1.634-1.607
1.384
1.208
0.936
0.750
-1.799-1.988(0.162)*** (0.164)*** (0.155)*** (0.178)*** (0.225)*** (0.227)*** (0.128)*** (0.129)*** (0.123)*** (0.140)*** (0.175)*** (0.178)***School characterisitcs
Other regressors Family background
Family background
Family background
Family backgroundin the regressions
year dummy
year dummy
bithplace D
bithplace D
0.0348 0.0436 0.0994 0.1017
0.03540.0393 0.094
0.0963Pseudo Rsq.
Observations
10,322 10,322 9,753
9,753
9,753
9,753
16,434 16,434 15,365
15,365 15,365 15365

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1
See Appendix Table A7 for varaibles in the regressors. Full regression results are available upon request from the corresponding author

Table 13 Relocation
Non PC urban born

PC urbanCorresponding
Equation# in

2

3

5

6

Estimation

RML

RML

RML

RML

Methods
first jobmigrantage
maleschlifebulliedhutokoGPAparttimejob
familybusiness
rich
risk

Constant

logwage logwage

Probit-0.064-0.065-0.073-0.072(0.029)** (0.029)** (0.029)** (0.029)**
0.301
0.302
0.348
0.298
(0.036)*** (0.036)*** (0.034)*** (0.036)***-0.013-0.012-0.000-0.003(0.006)** (0.006)* (0.006) (0.006)-0.039-0.040-0.072-0.046(0.033) (0.033) (0.030)** (0.033)-0.031-0.031-0.029-0.028(0.017)* (0.017)* (0.016)* (0.017)*
0.114
0.114
0.116
0.110
(0.031)*** (0.030)*** (0.030)*** (0.030)***
0.012
0.012
0.023
0.013
(0.030) (0.030) (0.030) (0.030)
0.068
0.078
0.064
0.074
(0.018)*** (0.018)*** (0.018)*** (0.018)***-0.089-0.089-0.135-0.121(0.036)** (0.036)** (0.035)*** (0.036)***
0.037
0.036
0.069
0.053
(0.033) (0.033) (0.032)** (0.033)-0.022-0.022-0.021(0.008)*** (0.008)***(0.008)***
0.014
0.014
0.014
(0.007)** (0.007)**(0.007)**-0.090-0.155-4.948-4.806(0.816) (0.815) (0.449)*** (0.463)***

Other regressors
in the regressions
Pseudo Rsq
Observations

Probit10,322

Robust standard errors in parentheses

10,3220.0425
10,250Probit-0.079(0.029)***-0.010(0.006)-0.046(0.034)-0.030(0.017)*0.107(0.031)***-0.010(0.031)
0.072(0.018)***-0.101(0.036)***0.042(0.033)-0.019(0.008)**0.016(0.007)**0.387(0.833)

logwage logwage
0.017
0.017
(0.023) (0.023)
0.539
0.531
(0.078)*** (0.078)***-0.020-0.020(0.005)*** (0.005)***-0.167-0.167(0.028)*** (0.028)***-0.017-0.017(0.013) (0.013)
0.118
0.116
(0.025)*** (0.025)***-0.028-0.028(0.024) (0.024)
0.030
0.036
(0.014)** (0.014)**-0.045-0.044(0.029) (0.029)
0.036
0.035
(0.026) (0.026)-0.037-0.037(0.007)*** (0.007)***
0.023
0.023
(0.006)*** (0.006)***-1.150-1.183(0.730) (0.729)

ProbitProbitProbit-0.038(0.023)*0.236(0.027)***-0.004(0.005)-0.152(0.025)***-0.010(0.013)0.102(0.024)***0.004(0.024)0.025(0.014)*-0.058(0.028)**0.053(0.025)**-0.038(0.023)0.216(0.027)***-0.005(0.005)-0.154(0.027)***-0.006(0.013)0.097(0.024)***-0.002(0.024)0.029(0.014)**-0.050(0.028)*0.041(0.026)-0.031(0.006)***0.024(0.006)***-4.212(0.331)***-0.035(0.023)0.430(0.033)***-0.012(0.005)**-0.167(0.028)***-0.009(0.013)0.098(0.025)***-0.045(0.025)*0.028(0.014)**-0.043(0.029)0.046(0.026)*-0.030(0.006)***0.029(0.006)***0.665(0.646)-4.068(0.320)***School characteristics, Individual attributes
family backgroundyear dummy
0.048
0.05630.0346
10,250
10,25016,434 16,434
16,350*** p<0.01, ** p<0.05, * p<0.1

See Appendix Table A7 for varaibles in the regressors. Full regression results are available upon request from the corresponding authorfamily backgroundyear dummy
0.0387
0.0532
16,35016,350Table 14 Impact of Tenure at job on Wage
Samplemigrant
relocation
cumexperiencetenurejno
Job characteristics
Individual and
SchoolUih
Cross Product ofJob
Constant

Observations
R-squared

PC UrbanCompleted job spells

Current (Incomplete) Job Spells
Non PC Urban born√

-0.0950.082
0.085-0.0400.049
0.063-0.011
0.006
0.159
0.168
0.169
0.167
0.173
(0.043)** (0.045)* (0.039)** (0.049) (0.044) (0.045) (0.044) (0.046) (0.038)*** (0.035)*** (0.035)*** (0.035)*** (0.035)***
0.012
0.013
0.016
0.015
-0.050-0.076-0.091-0.080-0.0960.002-0.018-0.023-0.018-0.025(0.010) (0.010) (0.009)* (0.009)* (0.052) (0.050) (0.051)* (0.051) (0.052)* (0.042) (0.040) (0.039) (0.040) (0.039)-0.007-0.001-0.002-0.0000.004-0.007-0.008-0.003
0.002
0.008
0.002
0.002
0.002
0.006
(0.010) (0.009) (0.006) (0.006) (0.004) (0.004)* (0.004)** (0.004) (0.003) (0.003)*** (0.003) (0.003) (0.003) (0.002)**-0.002-0.0020.000-0.000
0.017
0.030
0.031
0.030
0.022
0.010
0.021
0.020
0.020
0.014
(0.009) (0.009) (0.006) (0.006) (0.006)*** (0.006)*** (0.006)*** (0.006)*** (0.005)*** (0.004)** (0.004)*** (0.004)*** (0.004)*** (0.004)***
0.222
0.217
0.256
0.249
0.003
0.078
0.085
0.066
-0.008
0.059
0.061
0.051
(0.041)*** (0.040)*** (0.032)*** (0.031)*** (0.027) (0.025)*** (0.025)*** (0.025)***(0.022) (0.021)*** (0.021)*** (0.021)**
Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No

No

No

No

No

yes

yes

yes

yes

yes

yes

yes

yes

yes

No

No

No

No

No

No

yes

No

yes

No

No

yes

No

yes

6.158
5.631
6.211
5.677
6.730
6.615
6.437
6.499
6.586
6.754
6.585
6.438
6.514
6.538
(0.339)*** (0.328)*** (0.224)*** (0.235)*** (0.219)*** (0.232)*** (0.247)*** (0.231)*** (0.237)*** (0.190)*** (0.165)*** (0.176)*** (0.165)*** (0.165)***
6,564
0.435

Robust standard errors in parentheses

6,564

9,911
0.478

9,911

679
0.135

593
0.324

593
0.358

593
0.301

*** p<0.01, ** p<0.05, * p<0.1

See Appendix Table A7 for varaibles in the regressors. Full regression results are available upon request from the corresponding author

593
0.331

1,008
0.144

954
0.274

954
0.292

954
0.259

954
0.272

Table 15 Probit Regression on migrantCorresponding

5

Equation# in Table 11
Non PC Urban born
Dependent Variable
Estimated impact on
log wagefirst jobage
maleschlifeabsence
GPA
parttimejob
familybusiness
rich
risk

Constant

6

RML

Treatment

logwage

Model

Heckmanmigrant

HeckmanRML
logwagemigrant

migrant

non-migrant migrant

0.094

0.103

(0.016)***0.287(0.026)***
0.148

0.226

-0.186(0.016)***0.290(0.023)***(0.029)***(0.025)***(0.026)***(0.023)***-0.010(0.005)*-0.351(0.025)***0.093(0.013)***-0.286(0.042)***0.013(0.014)0.103(0.028)***-0.033(0.026)-0.049(0.007)***0.015(0.005)***-0.007(0.007)-0.293(0.031)***0.121(0.017)***-0.190(0.052)***0.055(0.018)***0.145(0.035)***-0.006(0.032)-0.062(0.008)***0.036(0.007)***-0.008(0.006)-0.328(0.028)***0.113(0.015)***-0.156(0.047)***0.037(0.016)**0.108(0.031)***-0.056(0.028)**-0.056(0.007)***0.029(0.006)***-0.005(0.006)0.322(0.029)***-0.103(0.015)***0.290(0.049)***-0.022(0.017)-0.153(0.032)***-0.016(0.029)0.054(0.008)***-0.024(0.006)***-0.010(0.005)*-0.350(0.025)***0.095(0.013)***-0.282(0.042)***0.010(0.014)0.104(0.028)***-0.039(0.026)-0.049(0.007)***0.015(0.005)***E(y|imig )E(y|nonimig )=0.1650

0.096

Additional RHS

0.902
0.323
-1.347-2.0460.886(0.177)***(0.310)(0.228)***(0.222)*** (0.177)***
School Characteristics, Indibidual attributes and family

Observations

15,936

9,911

background
13,308
11,96816,434

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1
See Appendix Table A7 for varaibles in the regressors. Full regression results are available upon requestfrom the corresponding author

Table 16 Job Mobility and Predicted Log
Wage*
Job number
First
SecondThird
FourthFifith or later
Total

Mean of Predicted Log Real Wage
samples born inmigrant
Total
PC uran6.793(.237)
6.705(.207)
6.732(.220)
7.034(.209)
6.904(.245)
6.943(.242)
7.113(.210)
6.996(.221)
7.027(.224)
7.165(.241)
7.067(.195)
7.093(.212)
7.168(.288)
7.207(.193)
7.196(.223)
6.988(.264)
6.893(.263)
6.920(.267)*The predictions are based upon the samples and RML estimate shown in equation(3) of
Table 13 . Standard deviations in parenthesis

Table 17 Blinder-Oaxaca Decomposition of Wage Differences
η FE1η
Endowments

μ
η
male
first job

-0.004(0.001)***-0.004(0.002)***-0.100(0.013)***0.060(0.025)**-0.008(0.003)***0.134(0.012)***0.011(0.003)***-0.005(0.002)**-0.066(0.018)***-0.013(0.004)***-0.103(0.026)***0.005(0.002)**0.008(0.004)**0.015(0.006)**0.021(0.009)**

Employer type: gov't

foreign firms
private firms

0.004(0.002)**0.050(0.008)***-0.042(0.008)***-0.031(0.007)***-0.004(0.001)**
0.004(0.002)**0.006(0.002)***0.088(0.009)***-0.051(0.008)***-0.047(0.008)***0.010(0.005)**self employedothersinsig

unemploymentrate

0.370(0.042)***0.039(0.005)***0.015(0.002)***

Constant

0.382(0.215)*0.111(0.013)***0.049(0.010)***0.126(0.013)***

Total

0.001
(0.001)**-0.000(0.001)-0.004(0.005)-0.004(0.002)**0.004(0.002)**0.006(0.002)***0.080(0.008)***-0.048(0.008)***-0.045(0.008)***0.002(0.003)-0.000(0.000)0.014(0.002)***-0.020(0.008)**Regular Fulltime-0.038(0.008)***PC Urban born
Migrants
Difference
Observations
Robust standard errors in parentheses

Interaction

0.027(0.012)**§

cumexperience

gov't. enterprises

0.136(0.012)***-0.064(0.007)***0.000(0.000)0.002(0.001)**

0.002(0.001)**

vocational HS

gov. orgnizations

η FE2

Interaction Endowments Coefficients Interaction Endowments Coefficients

not included in regression

middle schoolacademic HS

Coefficients

7.117(0.012)***6.994(0.007)***0.122(0.014)***
6,907

0.001
(0.006)-0.001(0.004)0.067(0.013)***7.131(0.012)***7.004(0.007)***0.127(0.014)***
6,564*** p<0.01, ** p<0.05, * p<0.1

See Appendix Table A7 for varaibles in the regressors. Full regression results are available upon request from the corresponding author§ The estimates and standard errors are not shown unless siginificant at 5% level-0.026(0.012)**0.061(0.007)***7.131(0.012)***7.004(0.007)***0.127(0.014)***
6,564-0.001(0.005)

Table 18 Comparing Return migrant and migrant samples
OLS
Migrants
Match specificproductivity

η
migrant
returnmigrant
malefirst job
middle schoolacademic HS
vocational HS
cumexperience
Constant
Observations

Heckman Correction model

Returned migrants

η FE2
1.487
(0.069)***
0.068
0.046
(0.015)*** (0.014)***Migrants

Returned migrants

η FE2

η FE2

η FE2

1.507
(0.069)***1.201(0.094)***0.691(0.336)**

0.031
0.046
(0.026) (0.025)*
0.073
0.103
0.068
0.100
0.049
0.080
0.025
0.021
(0.023)*** (0.022)*** (0.023)*** (0.022)*** (0.014)*** (0.014)*** (0.057) (0.056)
0.108
0.105
0.103
0.100
0.093
0.104
-0.121-0.108(0.027)*** (0.026)*** (0.027)*** (0.026)*** (0.023)*** (0.023)*** (0.102) (0.101)-0.158-0.136-0.165-0.139-0.158-0.136-0.165-0.139(0.054)*** (0.053)** (0.054)*** (0.053)*** (0.054)*** (0.053)** (0.054)*** (0.053)***-0.032-0.024-0.032-0.024-0.030-0.0200.025
0.081(0.032) (0.032) (0.032) (0.032) (0.019) (0.019) (0.079) (0.082)
0.026
0.015
0.025
0.015
0.030
0.010
-0.150-0.107(0.031) (0.031) (0.031) (0.031) (0.017)* (0.017) (0.068)** (0.071)-0.0000.021-0.000
0.021
0.015
0.032
0.028
0.036
(0.001) (0.002)*** (0.001) (0.002)*** (0.002)*** (0.002)*** (0.007)*** (0.008)***
6.039
6.051
6.107
6.103
6.039
6.051
6.107
6.103
(0.097)*** (0.092)*** (0.096)*** (0.090)*** (0.097)*** (0.092)*** (0.096)*** (0.090)***
10482
10482
10482
10482
10482
10482
10482
10482

E(y|observed)E(y|unobserved)**Robust standard errors in parentheses

0.16470.16470.15740.1571*** p<0.01, ** p<0.05, * p<0.1

See Appendix Table A7 for varaibles in the regressors. Full regression results are available upon request from the corresponding author

Table 19 Heckman Correction model Probit for Migrant and Returned Migrant
Heckman Correction modl: Selection probit for migrant or returned migrantmigrant

returnmigrantage-0.009-0.008-0.009-0.009-0.009
0.041
0.041
0.041
0.041
0.041
(0.006) (0.006) (0.006) (0.006) (0.006) (0.013)*** (0.013)*** (0.013)*** (0.013)*** (0.013)***male-0.329-0.328-0.329-0.328-0.328-0.119-0.116-0.118-0.120-0.120(0.028)*** (0.028)*** (0.028)*** (0.028)*** (0.028)*** (0.066)* (0.066)* (0.066)* (0.066)* (0.066)*
first job
0.227
0.226
0.227
0.226
0.226
0.642
0.642
0.642
0.642
0.642
(0.025)*** (0.025)*** (0.025)*** (0.025)*** (0.025)*** (0.069)*** (0.069)*** (0.069)*** (0.069)*** (0.069)***schlife
0.110
0.113
0.110
0.112
0.111
-0.127-0.127-0.127-0.127-0.127(0.015)*** (0.015)*** (0.015)*** (0.015)*** (0.015)*** (0.031)*** (0.031)*** (0.031)*** (0.031)*** (0.031)***absence-0.150-0.156-0.149-0.154-0.151-0.134-0.129-0.133-0.132-0.131(0.046)*** (0.047)*** (0.046)*** (0.047)*** (0.047)*** (0.100) (0.100) (0.100) (0.101) (0.101)
GPA
0.039
0.037
0.034
0.042
0.044
-0.120-0.119-0.121-0.114-0.117(0.016)** (0.016)** (0.016)** (0.016)*** (0.016)*** (0.035)*** (0.035)*** (0.035)*** (0.036)*** (0.036)***
parttime job 0.115
0.108
0.116
0.110
0.114
-0.102-0.101-0.101-0.104-0.101(0.031)*** (0.031)*** (0.031)*** (0.031)*** (0.031)*** (0.066) (0.065) (0.066) (0.066) (0.066)
family business -0.052-0.056-0.051-0.055-0.053
0.147
0.150
0.148
0.149
0.149
(0.028)* (0.028)** (0.028)* (0.028)* (0.028)* (0.061)** (0.061)** (0.061)** (0.061)** (0.061)**
risk
0.029
0.029
0.029
0.029
0.029
0.070
0.069
0.070
0.069
0.069
(0.006)*** (0.006)*** (0.006)*** (0.006)*** (0.006)*** (0.014)*** (0.014)*** (0.014)*** (0.014)*** (0.014)***rich-0.058-0.056-0.058-0.057-0.058-0.096-0.096-0.096-0.096-0.096(0.007)*** (0.007)*** (0.007)*** (0.007)*** (0.007)*** (0.015)*** (0.015)*** (0.015)*** (0.015)*** (0.015)***Constant
Observations
Inverse Mills
Ratio-1.323-1.347-1.307-1.357-1.351-4.467-4.467-4.466-4.485-4.482(0.228)*** (0.228)*** (0.228)*** (0.228)*** (0.228)*** (0.454)*** (0.454)*** (0.454)*** (0.456)*** (0.455)***
13308
13308
13308
13308
13308
15072
15072
15072
15072
15072
0.065
(0.024)

Robust standard errors in parentheses

0.021(0.022)0.068(0.024)0.035(0.024)*** p<0.01, ** p<0.05, * p<0.1

0.056(0.024)0.310(0.090)0.322(0.090)0.315(0.090)0.288(0.092)0.289(0.092)

Table A1 Age Composition
age
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
Total

Female
0
2
17
58
106
130
161
134
140
118
144
132
101
93
55
3
1,394

Male
1
4
21
69
132
206
193
206
182
173
184
194
136
158
101
12
1,972

Total
1
6
38
127
238
336
354
340
322
291
328
326
237
251
156
15
3,366

Table A2 Job placement services
at high school
Job placement
Academic Vocational
services
337
245
0
33
16
325
307
1
32
20
205
379
2
20
25
84
285
3
8
19
35
160
4
3
11
36
139
5
4
9
Total
1022
1515
100
100

total
582
23
632
25
584
23
369
15
195
8
175
7
2537
100

Table A3 Education atainements
and work experience
Ever worked?
yes
no
143
21
Middle school1
87.2
12.8
758
264
cademic Highscho
74.17
25.83
1,403
112
cational Highsch
92.61
7.39
568
97
igh school dropou
85.41
14.59
2,872
494
Total
85.32
14.68

164
100
1,022
100
1,515
100
665
100
3,366
100

Table A4 Parents' joint income
Freq.
Less than 10,000RMB
10,000-20,000
20,000-30,000
30,000-40,000
40,000-50,000
50,000-60,000
60,000～80,000
80,000～100,000
100,000～150,000
150,000 or more

Total

Percent

Cum.

639
848
636
384
230
175
124
116
111
80

19.11
25.37
19.02
11.49
6.88
5.23
3.71
3.47
3.32
2.39

3,343

100

19.11
44.48
63.51
74.99
81.87
87.11
90.82
94.29
97.61
100

Table A5 Parents' ages at birth
Variable

Obs

Father's age
Mother's age

Mean
3230
3266

27.96825.914

Std. Dev. Min
4.811
4.453

Table A6 Number of borthers andsisters
number of
siblings

Total

0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
18

Freq.
996
1,266
646
242
104
45
28
10
4
5
6
3
5
2
2
1
1
3,366

Percent Cum.
29.59
37.61
19.19
7.19
3.09
1.34
0.83
0.3
0.12
0.15
0.18
0.09
0.15
0.06
0.06
0.03
0.03
100

29.59
67.2
86.39
93.58
96.67
98.01
98.84
99.14
99.26
99.41
99.58
99.67
99.82
99.88
99.94
99.97
100

Max
16
14

78
78

Appendix Table A7 Definition of variables used in the paper
Variable categoryNamelogavwV
logavw1V
logavwq
VlogavnqVcumexperienceVkinzoku
V
RegFulltimeV
Job and job search
Ftype1- Ftype7
V
Characteristics
route1- route5
Vfirst job
C
jno
V
quit
V
whytakeV
whyleaveV
pjoblossV
Lno
V
pculocation pcuD1-pcuD4
V
pculocation_b pcubD1- pcubDV
residence and relocations pculocation_g pcugD1- pcugDV
migrant
V
relocationV
relocation3
V
edu1 edu2 edu3
Cpublic_imp public_com
Csinroall
C
GPA
C
BFGF
C
schlifeCbulliedC
familybusinessCcourse1 course2
C
School types,
tugaku1 tugaku2
C
charactersitics andleader1 leader2
C
school life
nostudyC
lotstudyCabsenceCabsenceCnofriendC
talkfriendC
sportsC
extra curricularCparttimejob
C
male
C
age
V
Inidividual attributes riskCmarriedCfemalemarriedC
onlychildC
brothers
C
firstbornCrichC
nw_gov1
C
nw_gov2
C
Family background nw_manageCnw_villageC
nw_insCbirthorderC
died_fC
died_mC
divorceC
unemployment
Vlnmeanwage
Vprovince level data andyear3- year14 by3- by14
Vtime dummiestV
varatioV

Variable type

Biref explanationlog of monthly wage converted from answers in 12 wage ranges
logavw in the previous period (measured in t defined below)
censored logavw ( set missing unless the samples changed job in the previous period)
censored logavw ( set missing if the samples changed job in the previous period)
cumulative work experience since school graduation measured in t
cumulative work experience at the current employer measured in t
Dummy for regular full time job
employer type dummies
dummies for the routes used to learn about the current job
dummy=1 if the first job after school was regular full timenumber of jobs lasted 6 months or longer including the current job
dummy =1 if quitted a job in the previous period and start a new job in this periodreason for takin up the current job (multiple choice)reason for quitting the job (multiple choice)dummy==1 if the previous job was terminated by employer
Number of locations lived after school which lasted 6 months or longer, including the current
location type variable (=1,2,3, or 4). See the main text (pcuD1-pcuD4 are dummies)
location type variable for the birthplacelocation type variable for the last school attended
Dummy =1 if born in pculocation 1 to 3 but resides in 4
dummy =1 if Lno (t) > Lno (t-1)
dummy=1 if relocation (t),(t-1), or (t+1)=1
school types (=1 middle school, =2 academic HS, =3 for vocational HS)High school types ( _imp= targeted public HS, _com=other public HS. Default is private HS)
Number of job placement actitivities at HS
Average score during the last year at HS
Dummy=1 if he (she) had a girl(boy) friend at HS
Self evaluation of HS life (=5 highest,=1 lowest)
dummy=1 if ever being bullied at any school attended
helped family business (farm) during the school
curriculum types dummies. _1 for college advancement, _2 for mixed, _3 for others
living arrangements during HS _1 if commuted from home, _2 if stayed at dormitory, _3 othedummy =1 if elected as a class leader (_1) or a member of student council at HS
dummy=1 if did very liitle study outside school
dummy =1 if studied a lot outside school
dummy=1 if been absent or late to school frequently
dummy=1 if been absent for extended periods of time from school (reasons other than sicknes
dummy=1 if very few friends at school
dummy=1 if talked with many friends at school
dummy=1 if spent fair amount of time in sports during HS
dummy=1 if participated in extra curricular activities
dummy=1 if part time jobs during school
measured in years (not t )dummy=1 if the sample agreed to a proverb praising risk taking attitude
marriedfemale and married
only child of the parent
number of siblings
eldest sibling
The family was well to do when the sample individual was age 15
Parent(s) worked for (local) governement (managerial position)
Parent(s) worked for (local) governement
Parent(s) have managerial job(s)Parent(s) worked for viallge office
Parent(s) worked for (local) governemental organizations
birth order among the siblings(biological) Father is dead(biological) Mother is deadParents divorced
Provincial unemployment rate
Provincial log average wage
Year dummy (1996=year1)
Number of periods after school graduation (3 periods=1 year) see the main text.
The ratio of posted vacancies to job applicants in each province