1. Migrant Opportunity and the Educational Attainment of Youth in Rural China Alan de Brauw IFPRI John Giles The World Bank April 10, 2008

2. Growth, Opportunity and School Enrollment Do economic growth and reduction of barriers to labor mobility lead to more human capital investment? Do economic growth and reduction of barriers to labor mobility lead to more human capital investment? –Trade-off: continue in school or look for work –Studied in other developing countries Vietnam: Glewwe and Jacoby (1998, 2004); Edmonds (2004). Vietnam: Glewwe and Jacoby (1998, 2004); Edmonds (2004). India: Kochar (2004). India: Kochar (2004).

3. Our Question and Answer How does increasing “migrant opportunity” affect high school enrollment in rural China? How does increasing “migrant opportunity” affect high school enrollment in rural China? –“Migrant opportunity” or cost of migrating proxied by size of the village labor force employed as migrants. –Why high school? Answer: Less likely. Answer: Less likely.

4. Outline of the Talk Descriptive Evidence:Descriptive Evidence: Role of networks in migration and job searchRole of networks in migration and job search Educational attainment of rural migrants in China’s citiesEducational attainment of rural migrants in China’s cities The Primary Data Source (RCRE surveys)The Primary Data Source (RCRE surveys) Educational attainment of residents in RCRE villagesEducational attainment of residents in RCRE villages Migration behavior from RCRE villagesMigration behavior from RCRE villages Econometric FrameworkEconometric Framework Identification Strategy for the Migrant NetworkIdentification Strategy for the Migrant Network Discussion/Interpretation of ResultsDiscussion/Interpretation of Results Other Possible MechanismsOther Possible Mechanisms

5. Migrant Networks in China As elsewhere, migrants in China use networks to find jobs As elsewhere, migrants in China use networks to find jobs In an urban survey (2001): In an urban survey (2001): 94% of migrants know someone in city before migrating94% of migrants know someone in city before migrating 35% have a close family member, 58% an extended family35% have a close family member, 58% an extended family 56% had arranged a job prior to first migration experience56% had arranged a job prior to first migration experience

6. Human Capital of Rural-Urban Migrants in Urban Areas Education –86 % have middle school educational attainment or less (67 % complete middle school). –14 % have completed high school. Rural Residents Not Engaged in Farming –21 % have completed high school. Migrants Tend to be Young –20 % left village before 18 th birthday –77 % before 40 th birthday

7. Data: Annual RCRE Household and Village Surveys and 2004 Supplemental Survey Primary Data Source: Ministry of Agriculture, Research Center on the Rural Economy (RCRE), Household and Village Surveys from 1986 to 2003. Primary Data Source: Ministry of Agriculture, Research Center on the Rural Economy (RCRE), Household and Village Surveys from 1986 to 2003. We use data from Anhui, Jiangsu, Henan, and ShanxiWe use data from Anhui, Jiangsu, Henan, and Shanxi 52 villages in sample, visited annually since 198652 villages in sample, visited annually since 1986 We use several variables from annual surveys We use several variables from annual surveys Number of migrants from village, other village and household level variablesNumber of migrants from village, other village and household level variables

8. RCRE Supplemental Survey, Summer 2004 Resurveyed 3999 households Resurveyed 3999 households Enumerated educational attainment and work histories Enumerated educational attainment and work histories Enumerated all current and past HH residents (over 16,000 individuals) Enumerated all current and past HH residents (over 16,000 individuals) We have information on all children of household head.We have information on all children of household head. We know educational attainment regardless of residence in village.We know educational attainment regardless of residence in village. Focus for analysis: 3160 individuals who completed middle school between 1986 and 2003. Focus for analysis: 3160 individuals who completed middle school between 1986 and 2003.

9. Cohort Average Educational Attainment

10. Age at Time of First Migration

11. Share of Age Group with Temporary or Long-Term Migrant Employment

12. Outline of Theory Parents are concerned with current and future consumption and the expected future wage of children. Parents are concerned with current and future consumption and the expected future wage of children. Choice over whether to send a child to high school is influenced by: Choice over whether to send a child to high school is influenced by: –Wealth –Credit Markets –Current returns to middle school completion –Expected future benefits from high school graduation –Preferences

13. Theory (Continued) Parents enroll children in high school if expected benefits outweigh costs. Positive effect could be explained by increasing wealth, relaxing credit constraints, or expected returns to education in urban areas. Negative effect if returns to education are low for migrants or potential migrants.

14. Enrollment Demand Migrant opportunity, Migrant opportunity, M vt, measured as the number of village residents employed as migrants outside the county in each year. If networks are important, increases are associated with a declining cost of migrating.If networks are important, increases are associated with a declining cost of migrating. Using a linear probability model, we estimate the effect on high school enrollment: Using a linear probability model, we estimate the effect on high school enrollment:

15. Problem with Simple Estimation M reflects factors that influence both the supply and demand of migrants M reflects factors that influence both the supply and demand of migrants Sources of Bias: Sources of Bias: Positive: if high schools expanded and lowered admissions standards while migrant opportunity also increased.Positive: if high schools expanded and lowered admissions standards while migrant opportunity also increased. Negative: negative shock to the local economy makes migration attractive while making it harder to cover high school tuition.Negative: negative shock to the local economy makes migration attractive while making it harder to cover high school tuition.

16. Identification Strategy Two policy changes Two policy changes National ID card introduced in 1984National ID card introduced in 1984 1988 Reform of residential registration system1988 Reform of residential registration system Residents of different counties received IDs at different times Residents of different counties received IDs at different times Farmers could not simply move to get ID cards Farmers could not simply move to get ID cards

17. Identification Strategy We argue that… We argue that… Differences in timing of ID availability affects network quality.Differences in timing of ID availability affects network quality. “Cost” of migrating falls as legal long-term migrants are capable of providing referrals.“Cost” of migrating falls as legal long-term migrants are capable of providing referrals. Networks time to build up.Networks time to build up. Non-linear function of years since IDs were issued used to identify the migrant network.Non-linear function of years since IDs were issued used to identify the migrant network.

18. Migration vs. Years Since ID Cards Issued

19. Potential Problems with Our Instrument Timing of ID Card distribution is not random Timing of ID Card distribution is not random –Differences in unobservable village characteristics could affect migration network and educational attainment –Demand for migration could have driven ID distribution –Timing of ID distribution could be correlated with trends in educational attainment

20. Implementing our IV Strategy We look for observable differences between villages We look for observable differences between villages

21. Observable Village Characteristics in 1988 Early IDs 1988 IDs Late IDs Share of Productive Assets Controlled by Village 0.400.240.25 Village Average Per Capita Income 627.2*481.5558.1 Per capita income Gini Per capita income Gini 0.230.220.21 Cultivable share of village land Cultivable share of village land 0.690.550.51 Share in mountains Share in mountains 0.140.240.30 Wage earner share in village 0.270.140.16 Average years of schooling, 18-22 8.757.567.50

22. Implementing our IV Strategy We look for observable differences between villages We look for observable differences between villages Look for obvious correlation between trends in educational attainment and timing of ID card distribution Look for obvious correlation between trends in educational attainment and timing of ID card distribution

23. Share of Middle School Graduates Entering High School by Timing of ID Card Receipt

24. Implementing our IV Strategy We look for observable differences between villages We look for observable differences between villages Look for obvious correlation between trends in educational attainment and timing of ID card distribution Look for obvious correlation between trends in educational attainment and timing of ID card distribution In estimation, we include village dummies to control for time-invariant unobservables. In estimation, we include village dummies to control for time-invariant unobservables. Test robustness to inclusion of additional time- varying variables that are likely correlated with village level unobservables. Test robustness to inclusion of additional time- varying variables that are likely correlated with village level unobservables.

25. First Stage Results (# of Migrants from the Village) 1a1b1c5 Functional Form quadraticCubicquarticquartic HH, Village Controls nononoYes F-Statistic on Instruments 22.5720.1217.2715.85 Partial R-Squared 0.0140.0190.0210.019

26. Determinants of HS Enrollment (Table 7) Model Coefficient on # of Migrants Other Controls F-Statistic, Instruments OLS0.001(0.002) 17.27 IV 1 -0.024(0.010)** 16.26 IV 2 -0.024(0.010)** village variables 15.98 IV 3 -0.023(0.010)** village + individual characteristics 15.97 IV 5 -0.019(0.009)** Model 3 + parents’ education + pot. mig. 15.85 IV 6 -0.020(0.009)** Model 5 + additional village variables 13.02

27. The Reduced Form Effect of Issuing IDs F-Statistic on quartic in years since ID was issued is 3.19.

28. Heterogeneity in Migration Effect Across Families? In spite of instrument, perhaps concern that we are “picking up students who might not go to high school anyway.” Who goes to high school? Children from families who have connections, who can afford it, or who have preferences for education. –Father is “professional” –Father has wage employment experience –Father has at least some high school education

29. Results: Migrant Opportunity and Family Characteristics (fix this)

30. If Not School, What Are High School Age Children Doing? Consider activity choice among teenagers of high school age. Look for effects of network on migrant and local employment (general equilibrium effects).

31. Effect of Migrant Opportunity on Activity Choice In School FarmWork Local Wage Labor Migrant Of age to finish middle school -0.023(0.009)-0.005(0.011)-0.001(0.001)0.002(0.001) Age +1 -0.006(0.012)-0.005(0.010)0.010(0.004)0.009(0.002) Age + 2 0.007(0.008)-0.031(0.011)0.012(0.008)0.009(0.003)

32. Share of Young Pursuing Activities Other than Migrant Employment

33. Other Mechanisms A unitary model that does not examine consequences of migration on family composition. Possibility that our result is not driven by low returns to high school, but by effects of parent migration on child educational attainment.

34. Share of Children with Fathers Working as Migrants

35. Years-Since IDs Issued and the Migration of Fathers with 7-12 Year Old Children

37. Years-Since IDs Issued and the Migration of Fathers with 13-15 Year Old Children

39. Conclusion Migrant opportunity has a fairly significant, negative effect on educational attainment in rural China Migrant opportunity has a fairly significant, negative effect on educational attainment in rural China Elasticity at mean size of migrant network: -0.191Elasticity at mean size of migrant network: -0.191 Robust to several extensions of the model Robust to several extensions of the model Inclusion of time-varying variables at village level potentially related to unobservablesInclusion of time-varying variables at village level potentially related to unobservables Inclusion of a range of family characteristicsInclusion of a range of family characteristics

40. Discussion Why? Occupational segregation in cities. –Migrants not employed in jobs that require HS/College education Likely reinforced by general equilibrium effects and higher wages locally subsequent to depletion of the labor force Irreversibility of the high school enrollment decision Implications for long-term inequality within urban areas.