causes and consequences of return migration: recent evidence from china

Journal of Comparative Economics 30, 376–394 (2002)doi:10.1006/jcec.2002.1781

Causes and Consequences of Return Migration: RecentEvidence from China1

Yaohui Zhao

China Center for Economic Research, Peking University, Beijing 100871, ChinaE-mail: [email protected]

Received February 22, 2002; revised March 15, 2002

Zhao, Yaohui—Causes and Consequences of Return Migration: Recent Evidence fromChina

Return migration is an integral part of the rural to urban labor migration in China. Usingrecent household survey data, this paper analyzes the determinants of return migration andthe economic behavior of return migrants. The main findings are as follows. First, returnmigration is of limited scale and out-migration is still dominant. Second, both push andpull factors affect the return decision. Third, return migrants invest significantly more inproductive farm assets but are no more likely to engage in local nonfarm activities thanare nonmigrants and migrants. These findings suggest that return migrants may play animportant role in the modernization process. J. Comp. Econ., June 2002, 30(2), pp. 376–394. China Center for Economic Research, Peking University, Beijing 100871, China.C© 2002 Association for Comparative Economic Studies. Published by Elsevier Science (USA). All rights reserved.

Journal of Economic Literature Classification Numbers: J61, J24, R23.

1. INTRODUCTION

Rural China has experienced substantial out-migration of labor since the mid-1980’s. Substantial empirical research analyzes the determinants of the migrationdecision, the means of migration, and the migrants’ contribution to householdincome (Hare, 1999; Hare and Zhao, 2000; Knight and Song, 1999; Meng, 2000;Solinger, 1999; Rozelle et al., 1999; Zhao, 1999a, 1999b, etc.). However, researchon the impact of labor migration on the communities from which workers leaveis relatively scarce. In the economics and sociological literature on internal and

1 The research is supported by the Robert McNamara fellowship of the World Bank, the FordFoundation, and the Excellent Young Teachers Program of Ministry of Education, P.R.C. The authorthanks James Kung, Jean Oi, Louis Putterman, Scott Rozelle, and workshop participants at the HongKong University of Science and Technology for helpful comments.

3760147-5967/02 $35.00C© 2002 Association for Comparative Economic StudiesPublished by Elsevier Science (USA)All rights reserved.

RETURN MIGRATION IN CHINA 377

international migration, substantial focus has been placed on the flow of financialresources, i.e., remittances, when assessing the impact of labor migration on thesecommunities. However, relatively little attention is given to the flow of humanresources. As the productive use of financial resources requires the management ofhumans, special attention should be given to return migrants because they representan inflow of both financial and human resources to these communities.

Throughout the world, return migration has been an integral part of labor mi-gration. In the 1990’s, as the outflow of migrants increases, return migration alsobecomes noticeable. About one-third of all migrants are estimated to return totheir native homes (Murphy, 1999). There are two opposing views concern returnmigration in Chinese policy circles. One considers return to be a negative factor be-cause it exacerbates the problem of surplus labor in rural areas. The other considersreturn migrants to be carriers of capital, technology, and entrepreneurship, factorsthat will contribute to the development of their native communities. Reflecting thelatter view, many counties actively attempt to attract back migrant entrepreneurs.2

There is no consensus in the literature concerning the role that return migrantsplay in their native communities and, especially, whether they are more likely tobe consumers or investors. Opinions are related to the perceived circumstancesleading to the migrant’s return. The literature offers several explanations for returnmigration (Stark, 1996). First, a migrant fails to find a good paying job elsewhere.Second, the returns to the human or financial capital accumulated in the destina-tion areas are higher at home. Third, the cost of living is lower at home than in thedestination areas. Migrants returning for the first and third reasons are less likelyto invest in their home community than those returning for the second reason.

In recent years, return migration in China has attracted the attention of severalresearchers. Hare (1999) examines the duration of the migration spell in a countyin Henan province using a household survey conducted in 1995 but she finds veryfew permanent returnees. Murphy (1999) describes the local government policiesdesigned to attract migrants back to two counties in Southern Jiangxi province andexamines the potential contributions that returnees make to economic diversifica-tion. Ma (2001) uses a sample of returnees collected in 1997 and finds a significanteffect of urban employment duration on nonfarm employment upon return.

This paper examines the causes of return migration and the impacts of return mi-grants on their home communities using household survey data collected in 1999.The paper is organized as follows. Section 2 describes the data and characterizesthe differences between migrants, return migrants, and nonmigrants in terms ofpersonal, household, and community aspects. Section 3 employs a binomial logitmodel to analyze the causes of return migration. Sections 4 and 5 examine thepotential impacts of return migration on local economies from two angles. First,

2 For example, Xinfeng and Yudu counties of Jiangxi province offer various forms of practicalassistance to entice successful migrants to return and set up businesses (Murphy, 1999). Wuwei countyof Anhui province invests in infrastructure in order to make the local investment environment moreattractive to returning entrepreneurs (field interview, 1999).

378 YAOHUI ZHAO

we analyze the behavior of return migrants in both consumption and productiveinvestments compared to nonmigrants and migrants. Then, we examine whetherreturn migrants are more likely to engage in nonfarm employment activities.Section 6 summarizes the findings.

2. DATA

The data come from a rural household survey conducted in 6 provinces of Chinain August and September of 1999 by the Ministry of Agriculture. The 6 provincesare Hebei, Shaanxi, Annui, Hunan, Sichuan, and Zhejiang. Two counties in eachprovince, one township in each county, one administrative village in each township,and three natural villages in each village were chosen for the survey. In each villagegroup, 7 to 8 households were chosen randomly for interviews. A total of 824households were interviewed.

In the household interviews, data were collected at both individual and house-hold levels.3 Individual information includes personal characteristics, e.g., age,sex, education, and marital status; actual days of work in 1998 in three types of ac-tivities, farming, local nonfarm work, and migratory work;4 and personal earningsin the last two categories. For people with any migration experience, total monthsof migration and the time of return were recorded.5 At the household level, infor-mation was collected on inputs and outputs of agricultural production and manyother aspects of household economic activities. A separate village survey wasconducted in sample villages to gather information on community characteristics.

Labor migration in rural China is characterized by the migrants’ continuing closelinkage to their home villages, as is evident from their frequent home visits (Hare,1999). To distinguish relatively permanent returns of migrants from temporaryhome visits, we define return migrants as those who had worked as a migrant forany number of months and had returned home by the end of 1998.6 Since the surveywas conducted in August and September of 1999, this definition allows at least8 months of home stay for returnees. Although there is no guarantee that thesereturn migrants would not seek migration work again, the duration is long enoughfor the returnees to participate in local economic activities. Similarly, migrants aredefined as those who worked and lived away from home for any number of monthsin 1998 or 1999 but did not return to stay by the end of 1998. Nonmigrants arethose who had no migration experience at the time of survey. Of all workers with

3 Interviews were conducted in the homes of the households. In most cases, the head of the householdanswered questions. When the household head was absent, another adult member was interviewed. Ahousehold was usually skipped if there was no adult present.

4 Migration is defined as living away from one’s home.5 According to our own field interview experience, family members tend to have a good memory

of their migration experience and that of other family members. This may be due to the fact thatout-migration is a very important event in the lives of rural people.

6 The observations do not include urban-registered workers who return to the villages afterretirement.


TABLE 1Dynamics of First-Time Migration and Return Migration, Sample Survey

Return migrants First-time migrants

Yeara N Current age N Current age

1984 or earlier 6 51.3 15 51.71985 6 51.7 4 42.31986 2 33.5 6 40.01987 5 43.6 12 45.81988 1 60.0 3 37.01989 7 42.7 6 39.81990 1 30.0 14 34.71991 2 47.5 20 34.01992 4 41.8 19 33.11993 5 30.4 16 30.61994 11 35.8 31 31.31995 9 35.1 48 30.11996 16 37.9 33 30.41997 25 33.5 64 24.61998 76 31.2 89 27.81999 — — 59 24.7

Total 176 439

Note. Source: Survey, Ministry of Agriculture (MOA) (1999).a The beginning year of migration is an approximation that has a downward bias that is larger for

earlier years.

valid data points, 78.3% are nonmigrants, 13.4% are migrants, and 8.3% are returnmigrants.7

The first column of Table 1 disaggregates return migrants by year of returnand reports the average age of returnees by year of return. The fact that thereare far more returnees in recent years than in earlier years may be due to severalfactors. First, some of the recent returnees may rejoin the migratory labor forcein the future while earlier returnees who migrate again are not counted.8 Second,migration has been increasing over the years and more migration yields morereturnees. This is confirmed in column two of Table 1 where the number andaverage age of new migrants are reported by the beginning year of migration.9

Along with the increase in return migrants, the number of first-time migrants has

7 We do not claim this distribution to be an accurate description of labor force allocation for allrural households registered in the sample counties or villages. We are unable to gather informationon families that had no adult member present at the time of the survey. This limits our analysis tohouseholds with at least one adult member present in their rural homes.

8 In a Research Center on Rural Economy (RCRE, 2001) study, proportionally more returnees thannonmigrants are found to intend to migrate in the near future.

9 For current migrants, the year of first-time migration is calculated as the current year minus thenumber of years in migration. For returnees, the years spent at home is also subtracted. Because thisassumes no interruption of previous migration activities, the beginning year is an underestimation ofthe true migration years. The earlier the beginning year, the bigger the bias.

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TABLE 2Return Migrants, Nonmigrants, and Migrants, 1999 (%)

ReturnAll Nonmigrants Migrants migrants

No. of workers 2137 1673 289 175% 100 78.3 13.4 8.3

Personal characteristicsMale (%) 52.0 47.8 63.3 73.6Married (%) 83.3 89.5 48.6 80.9Age (years) 39.6 42.0 27.9 35.6Schooling (years) 6.0 5.5 7.7 7.1Illiterate (%) 12.4 14.9 3.8 2.2Primary school (%) 38.9 42.9 18.5 33.7Junior high (%) 41.3 35.1 68.5 54.9Senior high (%) 6.9 6.6 7.6 8.6Technical school or higher (%) 0.6 0.4 1.0 1.1

Migration characteristicsMonths of migration (months) 8.4 0.0 41.7 39.4

Spouse nonmigranta(%) 58.0 66.9 15.0 43.3Married only 87.4 90.2 69.2 67.0

Spouse nonmigrant (%)Household characteristics

Land (mu) 5.2 5.3 5.4 4.2Laborer (person) 3.0 3.0 3.5 2.9Children under 6 0.3 0.3 0.3 0.26–12 year olds 0.3 0.3 0.2 0.3Elderly over 65 0.2 0.2 0.2 0.2

Village characteristicsPer capita income (yuan) 2461.7 2511.2 2223.2 2379.2Proportion of labor in local nonfarm work (%) 24.8 26.3 17.9 21.7

Note. Source: Survey, MOA (1999).a Single workers are assigned a value of zero.

also increased significantly since 1994. In recent years, especially since the mid-1990’s, urban industries have laid off a large number of workers. As a result,many city governments enacted anti-migration policies and a large-scale returnmigration was expected to occur. According to our data, although return migrationhas certainly increased in recent years, so has out migration. Furthermore, the neteffect has been increased migration.10

Of all workers with migration experience, 38.4% are returnees. Of all thosewho first migrated before 1996, only 30.4% had returned by the end of 1998. Thisimplies that more than three-quarters of the returnees had an extended stay in thecities. Regarding the length of migration, an average migrant has spent 41.7 months(Table 2) or 3.5 years working outside the home village, and 20% of these have

10 De Brauw et al. (2002) report that out-migration has not shown any decline in recent years, despiteChina’s economic downturn.


been engaged in migratory work for at least 5 years. These results seem to contra-dict those reported in Zhao (1999a), that the majority (90%) of migrant workersinterviewed in 4 large cities in 1995 by the Ministry of Labor (MOL) had expressedan unwillingness to stay in the cities permanently. The lack of permanent residencestatus for migrants may reconcile these seemingly contradictory findings. Withoutthe legal status to stay, migrants are prepared to leave when forced to, but wouldprefer to stay in cities as long as circumstances permit. Field research in Anhui andSichuan provinces in 1998 (RCRE, 1999) suggests that, when forced to leave onecity, migrants often go to other cities. Returns to home often serve as stopoversbetween two migration trips.

Therefore, the study of return migration can shed light on possible barriers ofpermanent migration. However, since return migration is not the simple mirror im-age of out-migration, the behavior of return migrants is also expected to differ fromthose who have never migrated. This difference is expected to come from threesources. First, return migrants have different labor market experience. Second,return migrants may have more capital. Third, return migrants may have differentexpectations about future career moves. To study the behavioral differences be-tween migrants and nonmigrants, it is important to understand the reasons behindreturn migration.

3. CAUSES OF RETURN MIGRATION

To identify the return migrants in our sample, we compare personal charac-teristics of return migrants with nonmigrants and current migrants in Table 2.Returnees are predominantly married male workers and older than migrants butyounger than nonmigrants. Their educational level lies between that of migrantsand nonmigrants with migrants having the highest education attachment. Judgingfrom personal characteristics, returnees are less competitive in the labor marketthan are migrants, but they are more competitive than nonmigrants. Consideringhousehold characteristics, the land endowment in returnee families was 1.1 to1.2 mu, about 20%, less than for families of migrants and nonmigrants. Laborendowment for returnee households is almost the same as that for nonmigrantfamilies but it is 0.6, or 17%, less than for migrant families. Family composi-tion, measured by number of young children and elderly, does not seem to differsubstantially among the three types of families. However, whether or not one’sspouse is ever a migrant matters significantly. Although 90.2% of the spouses ofnonmigrants have never migrated, this ratio is 69.2% for migrants and 67.0 forreturnees.

Table 2 also shows that there are also differences in village characteristics.Villages to which migrants return have a larger ratio of local nonfarm employmentthan do villages with current migrants, but both ratios are lower than the one invillages with no migrants. Levels of per capita income follow the same pattern,but the difference is quite small.

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To understand the factors determining the decision to return, we use the sampleof migrants and returnees to conduct a binomial multivariate logit analysis. Inorder to make our results comparable to others, we run two regressions. Model Itreats education as a discrete variable whereas Model II uses a continuous variable,namely, years of schooling, for education. The empirical results are presented inTable 3. The following discussion of these results is based on Model I.

According to the estimation, a male worker has a lower likelihood of returningthan a female worker, by 8.5 percentage points, but this effect is not significant. Amarried person is more likely to return than a single worker, by 15.4 percentagepoints, and this effect is significant at the 5% level. Current age is highly significantin explaining return migration. Increasing the age by 10 years above the mean ageof 30.9 years increases one’s probability of returning by 9.8 percentage points.Given that the mean probability of return is 39.8%, this represents nearly a 25%increase in the probability of return. A high tendency for older migrants to returnhas been reported consistently in other studies of return migration (Massey, 1987;Sharda, 1984).

Similar to the findings reported in Massey (1987) for U.S.-bound migrationfrom Mexico, the probability of return declines as the cumulative length of migra-tion increases. One more year of migration above the mean length reduces one’sprobability of return by 2.2 percentage points. In other words, the probability ofsettlement rises as migrants accumulate time in cities. Massey interprets this effectin two ways. First, longer experience in migration gives one longer exposure torisks and thus enables one to better handle the risks of settling. Second, longerexperience in destination area enables one to accumulate social and economic tiesthat enable the eventual settling in the area. These interpretations may apply toChina as well.

Education increases one’s probability of return significantly. The probabilitythat an illiterate person returns is only 4.1%, but the probabilities of return forprimary, junior high, senior high, and technical school graduates are 33.9%, 37.4%,40.2%, and 33.2%, respectively. Using data from Sichuan Province, Zhao (1999a)has shown that more educated workers are less likely to choose migration overlocal nonfarm employment. This result is interpreted to mean that more educatedpeople prefer local nonfarm employment in order to avoid the risks associated withmigration and the cost of being separated from their families. Similar argumentsapply here. Because the more educated should be no less employable than theless educated, the inability to find a job in the host area is unlikely to be themain reason for their return. The decision to return may be attributed instead tothe prolonged difficulties of finding a job that suits their educational attainments.Most migrant jobs tend to be menial and do not reward education. For an oldermigrant with above-average educational attainment and stronger family ties athome, the tolerance threshold in terms of searching for better work opportunitiesin the host area tends to decline much faster than it does for a younger counterpart.11

11 I thank James Kung for raising this point to me.


TABLE 3A Logit Model of Return Migration

Marginal effect Definition ofModel I Model II for Model I (%) marginal effect

Intercept −3.821∗ −2.153∗ — —(1.255) (0.971)

Male −0.369 −0.310 −8.51 Male vs female(0.279) (0.277)

Married 0.748∗∗ 0.747∗∗ 15.41 A married worker whose(0.369) (0.366) spouse has migration

experience vs a singleworker

Age 0.043∗ 0.043∗ 9.79 10 years above mean(0.018) (0.018)

Primary school 2.466∗ — 29.70 Reference: Illiterate(0.914)

Junior high 2.619∗ — 33.22 Reference: Illiterate(0.924)

Senior high 2.738∗ — 36.04 Reference: Illiterate(1.000)

Technical school 2.437∗∗∗ — 29.01 Reference: Illiterate(1.329)

Years of schooling — 0.095∗∗∗ — —(0.052)

Cumulative months −0.008∗ −0.008∗ −2.21 12 months above meanof migration (0.003) (0.003)

Labor −0.228∗∗∗ −0.226∗∗∗ −5.18 One person above mean(0.127) (0.127)

Land 0.059 0.062 1.34 One mu above mean(0.049) (0.048)

Kids under 6 −0.199 −0.189 −4.44 One vs zero(0.267) (0.266)

6–12 year olds −0.090 −0.052 −2.03 One vs zero(0.274) (0.270)

Elderly −0.071 −0.103 −1.60 One vs zero(0.255) (0.253)

Spouse 0.654∗∗∗ 0.638∗∗∗ 16.07 For a married worker:nonmigrant (0.345) (0.338) spouse having migra-

tion experience vsbeing a nonmigrant

Village labor share 3.743∗ 3.644∗ 8.89 10 percentage pointsin nonfarm (1.402) (1.392) over mean

Village per capita income −0.017 −0.015 −0.38 One hundred yuan(0.015) (0.015) over mean

Province dummies Yes

N 432 432N = 1 172 172Log likelihood 451.2 458.4

Note. ∗, ∗∗, or ∗∗∗ indicate that the coefficient is significant at the 1%, 5%, or 10% level, respectively.Dependent variable, return migrant = 1; migrant = 0.

384 YAOHUI ZHAO

Spousal separation appears to be a dominant factor in causing a migrant to re-turn. Evaluated at mean length of migration for married migrants, if one’s spouseis not a migrant, one’s probability of return is increased by 16.1 percentage points,representing an increase of a third above the mean probability of return. However,this effect is only marginally significant. Aside from spouse’s status, the lack ofadult labor seems to be the most important determinant of return migration amongother variables describing household characteristics. If a family has one less la-borer below the mean value, the probability of finding a return migrant in the familywill increase by 5.2 percentage points. Intuitively, as is well documented in the de-velopment literature, agriculture’s seasonality is the primary cause of bottlenecksin farm labor supply during the peak seasons. Depending on the commuting dis-tance, returning for peak season work could be costly. Thus, beyond some distancethreshold, a migrant may choose to return home permanently.

Unlike the effect of labor, land endowment does not have a statistically signifi-cant effect on return migration although the coefficient is positive. The asymmetryin the significance between land and labor may be explained by the fact that thelabor market for farm labor is underdeveloped in rural China (Kung and Lee,2001). Therefore, uninterrupted nonfarm migrant work is constrained whereasland rental transactions have become increasingly active in recent years (Kung,2002). This development allows migrants to stay in cities even during peak sea-sons. Return migration is also negatively related to the number of children andthe number of elderly persons over age 65 but the effects are statistically insignif-icant.

The return decision is influenced by the type of village that a migrant leaves.The extent of available nonfarm economic activities is especially important. If theproportion of laborers employed in nonfarm activities in a village is 10 percentagepoints above the mean of 19.4%, the probability of return is increased by 8.9%.Using the same data, the author has shown that migration occurs where there islimited availability of local nonfarm employment (Zhao, 2001). Perhaps whenthe returnees first leave, there is little off-farm employment opportunities but,when such opportunities have developed in the village, these migrants come back.Testing such a hypothesis would require panel data, which we do not have. Villagelevel per capita income does not have a statistically significant effect on returnmigration.

In summary, return migrants tend to be older, married, better educated, andwith a spouse who has not migrated. Families of return migrants tend to havefewer laborers. Villages of return migrants tend to have more active nonfarmsectors. There are two main reasons for migrants to return. One is the lack ofreward for education and work experience of migrants in host areas because oflabor market segmentation in urban areas. As Meng and Zhang (2001) show,rural migrants are much less likely to obtain white-collar occupations than areurban-registered residents, given individual characteristics including education andexperience. However, there are also psychological costs associated with family


separation. Both factors are linked to the current system of denying migrantspermanent resident status in the host cities. The system prevents migrants fromparticipating fully in the labor market for skilled workers and imposes heavy costson them if they wish to raise families.

4. INVESTMENT BEHAVIOR OF RETURN MIGRANTS

The effects of migration on the economies of home areas are still debated in themigration literature. The core issue is whether remittances sent home by migrantsare earmarked primarily for consumption or used in a productive manner. Whileremittances represent the flow of potential financial capital from destination tohome areas, return migrants represent the inflow of both human capital and financialcapital to home areas. Investment activities require both types of capital. In thissection, we examine the effects of three different types of workers, nonmigrants,migrants, and return migrants, on investment activities in the home communities.Nonmigrants contribute neither remittance capital nor human capital from outsidethe area. Migrants contribute remittance capital but not migration human capital.Return migrants contribute both remittance capital and human capital from outsidethe home area. Hence, the investment behavior is expected to differ among thesethree types of workers.

Table 4 is a simple specification that accounts for the ownership of three typesof assets, namely, consumer durables, housing, and production assets. Consumer

TABLE 4Accounting for Contributions to Asset Ownership from Return Migrants, Nonmigrants,

and Migrants, 1999

Dependent variable, Dependent variable, Dependent variable,log (original value of log (original value log (original value ofconsumer durable) of housing) production machine)

Intercept 7.733∗ 8.951∗ 0.911(0.184) (0.213) (0.558)

No. of returnees 0.393∗ 0.275∗ 1.282∗(0.106) (0.125) (0.319)

No. of nonmigrants 0.307∗ 0.233∗ 0.565∗(0.048) (0.055) (0.144)

No. of migrants 0.224∗ 0.248∗ −0.043(0.065) (0.075) (0.196)

Dependents 0.234∗ 0.086∗∗∗ 0.573∗(0.040) (0.047) (0.123)

Province dummies Yes Yes Yes

Obs 798 788 807Adj-R2 0.272 0.080 0.090

Note. ∗, ∗∗, or ∗∗∗ indicate that the coefficient is significant at the 1%, 5%, or 10% level, respectively.

386 YAOHUI ZHAO

durables are consumer goods whereas housing can be regarded as both consumptionand investment. Production assets are investment goods. The number of the threetypes of workers in a household is entered as regressors. The number of dependentsis included to control for differences in the demand side, especially for consump-tion assets. Because asset ownership takes the logarithm form, the coefficientsare interpreted as percentage effects. Regional differences are controlled for byprovincial dummy variables.

As Table 4 indicates, the marginal contributions of return migrants in all formsof assets are consistently higher than those of the two other types of workers.12

This is true for both nonproductive and productive assets, but the difference inproductive assets is more profound than it is for consumer durables or housing.One more return migrant in a family increases the value of consumer durables by39.3%, compared to 30.7% for nonmigrants and 22.4% for migrants. The differ-ence in housing value is the smallest with 27.5% for return migrants, 23.3% fornonmigrants, and 24.8% for migrants. Considering the value of productive assets,one more return migrant increases the value by 128.2% while one more nonmigrantincreases the value by 56.5%. Having one more migrant in the household does nothave a significant effect on production assets. Since the asset values are originalvalues, some assets may have existed prior to migration. However, studies havefound that migrant laborers generally come from middle-income families (Du,2000; Roberts, 2000). Therefore, a significant part of the assets must be investedsavings from migratory work.

Field research confirms that, when migrants return to their home villages, theybring home savings accumulated during migratory work. Although some fail toaccumulate any money, most succeed in bringing home a significant amount ofmoney. The results from Table 4 suggest that return migrants are an importantsource of investment activities in rural areas. Return migrants may not have morewealth than migrants, but they are physically present to undertake the investmentin the home village.

Productive machines can be further disaggregated into farm and nonfarm ma-chines. Table 5 shows the distribution of machines in physical units. We classifythreshers, water pumps, plowing machines, seeder/planter, and harvesting ma-chines as farm machines. Grain processor, clothing machines, wood processors,feed processors, automobiles, and other machines are classified as nonfarm ma-chines. Tractors can be used as both farm and nonfarm machines, so we dividetheir value between farm and nonfarm purposes according to the labor hours usedin each type of activity by the family. On average, a household has 1590 yuanworth of agricultural farm machines and 1864 yuan worth of nonfarmt machines.

Table 6 presents regressions in which the original values of farm and nonfarmmachines are explained by the number of workers in each of the three categories, as

12 F-tests for the equality of coefficients indicate that marginal contributions of all three types ofworkers are significantly different from each other except in the model for housing values.


TABLE 5Household Ownership of Productive Machines, 1999

Machine type Frequency

Thresher 268Water pump 213Tractor 122Plowing machine 113Seeder or planter 40Harvesting machine 30Grain processor 29Clothing processor 28Wood processor 18Automobile 17Feed processor 7Other machines 112

Note. Source: Survey, MOA, 1999.

TABLE 6Accounting for Contributions to Productive Investment from Return Migrants, Nonmigrants,

and Migrants, 1999

All machines Farm machines Nonfarm machines

I II I II I II

Intercept 0.433 0.395 0.165 0.128 −0.120 −0.136(0.797) (0.799) (0.761) (0.764) (0.769) (0.772)

No. returnees 0.885∗ — 0.899∗ — 0.093 —(0.298) (0.286) (0.289)

Returnee 1992 and before — 1.270∗∗ — 1.238∗∗ — 0.236(0.651) (0.620) (0.627)

Returnee after 1992 — 0.822∗ — 0.845∗ — 0.070(0.313) (0.299) (0.302)

No. nonmigrants 0.536∗ 0.539∗ 0.359∗ 0.362∗ 0.433∗ 0.434∗(0.144) (0.144) (0.140) (0.140) (0.141) (0.141)

No. migrants −0.028 −0.030 0.195 0.193 −0.421∗∗ −0.422∗∗(0.202) (0.202) (0.196) (0.196) (0.198) (0.198)

Dependents 0.605∗ 0.610∗ 0.517∗ 0.521∗ 0.186 0.188(0.123) (0.123) (0.116) (0.116) (0.117) (0.117)

Village labor share −3.309∗ −3.325∗ −3.306∗ −3.319∗ −1.291 −1.297in nonfarm work (1.051) (1.052) (0.988) (0.989) (0.998) (0.999)

Village per capita income 0.037∗ 0.038∗ 0.043∗ 0.044∗ 0.021 0.021(0.013) (0.013) (0.013) (0.013) (0.013) (0.013)

Province dummies Yes Yes Yes Yes Yes YesN 807 807 807 807 787 787R2 0.09 0.09 0.13 0.13 0.05 0.04

Note. ∗, ∗∗, or ∗∗∗ indicate that the coefficient is significant at the 1%, 5%, or 10% level, respectively.Dependent variable, Log (original value of productive assets).

388 YAOHUI ZHAO

well as variables representing village types, i.e., nonfarm share of labor and incomelevels. Returnees are further disaggregated into early returnees and later returnees.After some experimentation, 1992 appears to be the year that distinguishes theinvestment behavior of early and later returnees.

All specifications in Tables 6 indicate that the extent of nonfarm employment ina village is the most powerful explanation of household ownership of all produc-tive or nonfarm machines. Taking a more detailed look at the equations for farmand nonfarm machines, we find that this negative effect comes mainly from re-duced ownership of farm machines. The effect on nonfarm machines is statisticallyinsignificant. Hence people are more likely to invest in farm machinery in areaswhere farming remains a more important part of the rural economy. In the equationfor farm machines, the coefficient for returnees is more than double that for non-migrants, so that an additional returnee in a family increases investment in farmmachines by twice as much as an additional nonmigrant. When we disaggregatethis group into early and later returnees, we find that early returnees have a greatertendency to invest in farm machines than later returnees. An additional workerwho returned in 1992 or earlier increases the family’s value of farm machine by150%, while an additional returnee after 1992 increases the value by only 92%.One possible explanation for this result is that earlier returnees are more attachedto agriculture than recent returnees.

The equations for investment in nonfarm machines do not indicate any significantcontribution from returnees. The marginal contribution of migrants is even negativeand significant indicating that an additional migrant in the family reduces thehousehold’s investment in nonfarm machines. Household investment in nonfarmmachinery seems to come mainly from those who have never engaged in migrationas these coefficients are positive and significant.

To summarize, returnees purchase no fewer consumer durables and build no less-valuable housing than migrants or than those who have never migrated. However,they do invest more heavily in production machines. Upon more detailed exam-ination, this investment is made primarily in farm machines while nonmigrantsinvest most heavily in nonfarm machines.

5. EMPLOYMENT BEHAVIOR OF RETURN MIGRANTS

Enabling more productive investments in rural areas is only one potential ben-efit that return migrants bring to their home villages. We are also interested inexamining whether return migrants contribute to the development of nonfarm ac-tivities in the home areas. As Johnson (2000) asserts, China will likely face a largedecline in farm employment within the next three decades. The proportion of therural workforce engaged in farming is projected to decline from 41% in 1997 toabout 10% in 2030. This represents an absolute decline in farm employment of180 million. One potential source of off-farm employment is in local areas withincommuting distance.


Our sample villages already have significant nonfarm economic activities inthat 54.79% of all household earnings come from farming, 30.18% from lo-cal nonfarm activities, and 15.02% from migration.13 To understand the typesof economic activities undertaken by return migrants in 1998, we exclude thosewho returned in that year because these people are likely to be in a transitionphase. Eighty-nine returnees remain in our subsample. A simple tabulation ofthe proportion of these returnees in farm versus nonfarm activities compared tononmigrants indicates that 51.7% of return migrants are in nonfarm activities,compared with only 40.4% of the nonmigrants. However, as Table 2 indicates,return migrants are different in personal as well as household characteristics fromnonmigrants. For example, they are better educated and younger. To measurethe pure impact of the experience of migration, we need to control for thesecharacteristics. Table 7 presents a logistic specification of employment choicefor the subsample of returnees who came home prior to 1998 and for nonmi-grants.

The results in Model I indicate that sex, age, and education all have significanteffects on the likelihood of off-farm employment. The probability that a maleworker is in off-farm work is 38.5% higher than that for a female worker. Thelikelihood that a male worker participates in nonfarm work is three times that of afemale worker as the female probability of nonfarm work is 18.7%, while that of amale is 57.2%. Being 10 years older than the mean age decreases one’s probabilityof off-farm employment by 16.6%. Because the average probability of nonfarmemployment is 40.3%, the age effect is rather large.

Education, especially senior high school education or above, plays an importantrole in determining nonfarm employment. Evaluating other variables at their meanvalues, the probabilities of nonfarm activities for illiterate, primary school, juniorhigh school, senior high school, and technical school graduates or higher are 30.5%,32.3%, 36.6%, 58.7%, and 79.4%, respectively. Both the magnitudes and statisticalsignificance of the educational effects are much higher in the choice between farmand nonfarm employment than between migration and nonmigration. The findingthat education is important in securing off-farm employment is consistent withprevious surveys (Kung and Lee, 2001; Hare, 1999; Zhao, 1999a). The advantagein securing off-farm employment also provides additional incentive for the moreeducated migrants to return to their home villages.

Household characteristics also influence the likelihood of nonfarm work. If afamily has one additional mu of land above the average, the probability for nonfarmwork is reduced by 1.8%. Increasing the number of laborers by one above the meanraises the probability by 3.5 percentage points. Compared with families having nomigrant worker, the probability of a household with one migrant worker engagingin nonfarm work will be reduced by 12.2%. Stated differently, a laborer from a

13 This group excludes 19 households that had negative incomes for either one of the three categoriesof income.

390 YAOHUI ZHAO

TABLE 7Logit Models of Nonfarm Activities

Marginaleffects for Definition

Variable Model I of marginalModel I Model II Model III mean (%) effect

Intercept 0.093 −0.003 0.143 — — —(0.544) (0.619) (0.546)

Male 1.760∗ 1.802∗ 1.763∗ 0.52 38.50 Male vs female(0.150) (0.153) (0.150)

Married 0.127 0.146 0.123 0.90 2.91 A married worker(0.266) (0.270) (0.266) vs a single worker

Age −0.072∗ −0.075∗ −0.073∗ 41.66 −16.57 10 years above mean(0.008) (0.008) (0.008)

Primary school 0.234 0.218 0.219 0.43 5.02 Reference: Illiterate(0.252) (0.252) (0.252)

Junior high 0.408 0.388 0.402 0.37 9.00 Reference: Illiterate(0.271) (0.272) (0.271)

Senior high 1.364∗ 1.406∗ 1.375∗ 0.07 32.46 Reference: Illiterate(0.347) (0.351) (0.347)

Technical school 2.323∗∗ 2.041 2.114∗∗∗ 0.004 51.73 Reference: Illiterate(1.265) (1.301) (1.230)

Land −0.078∗ −0.074∗ −0.077∗ 5.39 −1.80 1 mu above mean(0.020) (0.022) (0.020)

Labor 0.150∗∗ 0.163∗∗ 0.147∗∗ 2.92 3.47 1 person above mean(0.076) (0.079) (0.077)

Migrants −0.544∗ −0.570∗ −0.547∗ 0.29 −12.15 1 vs 0(0.150) (0.155) (0.150)

Kids under 6 −0.132 −0.146 −0.127 0.25 −3.03 1 vs 0(0.148) (0.153) (0.148)

6–12 year olds −0.003 −0.030 −0.009 0.29 −0.06 1 vs 0(0.135) (0.141) (0.135)

Elderly −0.284∗∗∗ −0.265∗∗∗ −0.279∗∗∗ 0.21 −6.43 1 vs 0(0.150) (0.153) (0.151)

Village labor share 5.628∗ 5.914∗ 5.646∗ 0.26 13.73 10 percentage pointsin nonfarm (0.558) (0.923) (0.560) over mean

Migration experience 0.000 −0.000 0.000 2.27 0.01 1 mo above mean(months) (0.005) (0.005) (0.005)

Returnee −0.182 −0.201 0.06 −4.22 1 vs 0(0.349) (0.354)

Returnee in 1992 — — 0.486 0.02 —or earlier (0.501)

Returnee after — — −0.531 0.04 —1992 (0.401)

Returnee ∗ age — — — — —Province dummies Yes No Yes — —Village dummies No Yes No — —N 1574 1574 1574N = 1 635 635 635Log likelihood 1422.35 1399.22 1418.86

Note. ∗, ∗∗, or ∗∗∗ indicate that the coefficient is significant at the 1%, 5%, or 10% level, respectively.Dependent variable, nonfarm worker = 1, full-time farmer = 0.


nonmigrant family is 42.9% more likely to engage in local nonfarm activity thana laborer from a migrant family. Thus, local nonfarm employment may be viewedas a substitute for labor migration.

The probability of nonfarm employment is strongly determined by the avail-ability of nonfarm opportunities in local areas. Using the proportion of laborersin local nonfarm activities in the entire village as a proxy for the availability ofoff farm employment, we find that increasing the proportion by 10 percentagepoints raises the probability of off-farm employment by 13.7 percentage points.

Finally, after controlling for these personal, household, and community char-acteristics, we find that whether or not a person is a return migrant has no effecton the likelihood of nonfarm employment. In other words, return migrants do notseem to have a higher probability of participation in nonfarm activities. To furtherconfirm this finding, we substitute village dummy variables for province dummyvariables in Model II, but the result remains. Because the number of returneesin a household is an aggregate measure that includes returnees of all times, wedisaggregate the returnees into early and later returnees in Model III to allow fordifferent employment behavior between the two groups. The results show that thesign for early returnees, i.e., those who returned in 1992 or earlier, is positivewhile the sign for later returnees, i.e., those who returned after 1992 is negative.However, neither is statistically significant.

The fact that return migrants are not more likely to engage in nonfarm activi-ties does not mean that the migration experience has no bearing on the choice ofemployment upon return. Some unobservable factor that induced a worker intomigration and eventually caused the return may mitigate against local nonfarmemployment. Such a negative effect might offset the potential positive effect ofmigration experience on local off-farm work. Other explanations are plausible; forexample, by working continuously off-farm in local areas, nonmigrants may accu-mulate specific human capital in local nonfarm labor market. This specific humancapital may serve as a barrier to entry into off-farm employment for returnees.

The survey identifies self-employed versus employed workers among thoseworking in nonfarm sectors. In the regression of the probability of self-employment,the returnee coefficient again fails to show any statistical significance. Unfortu-nately, our data do not allow us to distinguish entrepreneurial farmers from ordinaryfarmers. Combined with the evidence presented in Section 3 that returnees are morelikely to buy farm machines, returnees may farm differently from nonmigrants.

6. SUMMARY AND CONCLUSION

Return migration is an integral part of any out-migration process. In recentyears, many large Chinese cities have enacted anti-migration policies that aimed atexcluding migrants from many sectors of urban employment due to the economicslowdown and the massive layoff of urban-registered workers. As some of themigrant workers were unable to find alternative employment opportunities, they

392 YAOHUI ZHAO

were forced to return to their home villages. This raises a concern that returnmigration may interrupt the transfer of labor out of agriculture and this processis central to modernization in China. Using a household survey carried out in6 provinces in the fall of 1999, we find that return migration has indeed taken placebut on a limited scale. Of all migrants in the past decade, only 38.4% returned homeby the end of 1998 and the cumulative length of stay away from home exceeds3 years. The data indicate that most of the displaced migrants have not returnedhome permanently upon leaving their jobs. Either they find other employment inthe same city or they leave for other host areas, possibly after a short return tripback home. Furthermore, coinciding with the return flow of migrants is a largeroutflow of first-time migrants, leading to a net outflow of labor from the villages.This phenomenon suggests that the structural problems in the urban labor markethave not prevented the transfer of labor into urban areas.

This paper analyzes the reasons for return migration and the possible impact ofreturn migration on home areas. Analysis of the causes of return shows that educa-tion increases one’s likelihood of returning, as does age, marital status, and spousalseparation. These indicate that the return decision is based on a combination ofboth pull and push forces. The push force stems from under-rewarding migrants’human capital in cities, due probably to the migrant’s lack of access to skilled jobs.The pull force comes from family separation and the expected higher likelihoodof obtaining decent nonfarm jobs by the more educated in their home villages.The policy of denying permanent urban residency to the migrants contributes toboth elements. If migrants are given better access to employment, schooling forchildren, and housing for families, we would expect to see less return migrationand more permanent settlements.

Since the majority of return migrants are in their primary productive age, havingan average age of 35.6 years, return migrants are expected to be primarily produc-ers rather than consumers. Therefore, they are capable of making contributionsto the economy of their villages, especially by investing at home. An analysisof household ownership of assets reveals that return migrant families own moreconsumer durables, housing stock, and productive machinery than both migrantand nonmigrant families. However, the difference in productive machinery is moreprofound than it is for the other two assets. A more detailed analysis reveals thatreturn migrants are more likely to invest in farm machines and earlier returneesinvest even more than do recent returnees.

An analysis of employment activities indicates that slightly less than half ofthe return migrants went back to full-time farming, which employs over half ofthe nonmigrants. The difference disappears after controlling for education, age,and sex. Binomial models of employment choice show that return migrants areno less likely to work as full-time farmers than those who have never migrated.This does not mean that the migration experience adds little to future earningsopportunities in the rural areas. The fact that return migrants invest more in farmmachinery may imply that they are specializing in certain farm operations, most


notably harvesting, plowing, and threshing.14 In addition, the increased availabilityof farm machinery improves labor productivity in agriculture, thereby facilitatingthe transfer of labor outside of agriculture in future years.

China is facing the daunting task of the transfer of its rural labor force out ofagriculture. In the next three decades, agricultural laborers will be reduced by3% annually (Johnson, 2000). Central to the issue of this transformation process iswhether migrants will be allowed to bring with them their families and settle perma-nently in cities. A positive answer would imply a lower cost of transition. Althoughbig cities are typically the preferred destination, the capacity with which large ur-ban agglomerations such as Beijing, Tianjin, and Shanghai are able to absorb therural surplus workers may be limited. Hence, the political barriers erected by thesemunicipalities to prevent rural migrants are likely to be enormous. This may ex-plain why the Chinese government is eager to promote the development of, andsettlement in, small towns and cities within reasonable commuting distance fromvillages as an alternative to the traditional urbanization process. From this develop-ment perspective, return migrants, with their above-average human and financialcapital, are expected to play a critical role in China’s modernization process.

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causes and consequences of return migration: recent evidence from china

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