Migrant Opportunity and the Migrant Opportunity and the Educational Attainment of Youth in Educational Attainment of Youth in

Rural China Rural China

Alan de BrauwAlan de BrauwIFPRIIFPRI

John GilesJohn GilesThe World BankThe World Bank

April 10, 2008April 10, 2008

Growth, Opportunity and School Growth, Opportunity and School EnrollmentEnrollment

• Do economic growth and reduction of Do economic growth and reduction of barriers to labor mobility lead to more barriers to labor mobility lead to more human capital investment?human capital investment?

– Trade-off: continue in school or look for Trade-off: continue in school or look for workwork

– Studied in other developing countriesStudied in other developing countries

• Vietnam: Glewwe and Jacoby (1998, 2004); Vietnam: Glewwe and Jacoby (1998, 2004); Edmonds (2004).Edmonds (2004).

• India: Kochar (2004).India: Kochar (2004).

Our Question and AnswerOur Question and Answer• How does increasing “migrant opportunity” affect How does increasing “migrant opportunity” affect

high school enrollment in rural China?high school enrollment in rural China?

– ““Migrant opportunity” or cost of migrating proxied by Migrant opportunity” or cost of migrating proxied by size of the village labor force employed as migrants.size of the village labor force employed as migrants.

– Why high school?Why high school?

• AnswerAnswer: Less likely. : Less likely.

Outline of the TalkOutline 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

Migrant Networks in ChinaMigrant Networks in China

• As elsewhere, migrants in China use networks to As elsewhere, migrants in China use networks to find jobsfind jobs

• In an urban survey (2001):In an urban survey (2001):

• 94% of migrants know someone in city before 94% of migrants know someone in city before migratingmigrating

• 35% have a close family member, 58% an extended 35% have a close family member, 58% an extended familyfamily

• 56% had arranged a job prior to first migration 56% had arranged a job prior to first migration experienceexperience

Human Capital of Rural-Urban Migrants Human Capital of Rural-Urban Migrants in Urban Areasin Urban Areas

Education Education

– 86 % have middle school educational attainment or 86 % have middle school educational attainment or less (67 % complete middle school).less (67 % complete middle school).

– 14 % have completed high school.14 % have completed high school.

Rural Residents Not Engaged in FarmingRural Residents Not Engaged in Farming

– 21 % have completed high school.21 % have completed high school.

Migrants Tend to be YoungMigrants Tend to be Young

– 20 % left village before 1820 % left village before 18thth birthday birthday

– 77 % before 4077 % before 40thth birthday birthday

Data: Annual RCRE Household and Village Data: Annual RCRE Household and Village Surveys and 2004 Supplemental SurveySurveys and 2004 Supplemental Survey

• Primary Data Source: Ministry of Agriculture, Primary Data Source: Ministry of Agriculture, Research Center on the Rural Economy Research Center on the Rural Economy (RCRE), Household and Village Surveys from (RCRE), Household and Village Surveys from 1986 to 2003.1986 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 surveysWe use several variables from annual surveys• Number of migrants from village, other village and Number of migrants from village, other village and

household level variableshousehold level variables

RCRE Supplemental Survey, Summer 2004RCRE Supplemental Survey, Summer 2004

• Resurveyed 3999 households Resurveyed 3999 households

• Enumerated educational attainment and work historiesEnumerated educational attainment and work histories

• Enumerated Enumerated all all current and past HH residents (over current and past HH residents (over 16,000 individuals)16,000 individuals)

• We have information on all children of household We have information on all children of household head.head.

• We know educational attainment regardless of We know educational attainment regardless of residence in village.residence in village.

• Focus for analysis: 3160 individuals who completed Focus for analysis: 3160 individuals who completed middle school between 1986 and 2003.middle school between 1986 and 2003.

Cohort Average Educational AttainmentCohort Average Educational Attainment

Age at Time of First MigrationAge at Time of First Migration

10

2030

4050

Age

(Y

ears

)

1987 1989 1991 1993 1995 1997 1999 2001 2003

Year of First Migration

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

0

.1.2

.3

Mig

rant

Sha

re

1987 1989 1991 1993 1995 1997 1999 2001 2003

Year

15 and 16 Year Olds 17 and 18 Year Olds

19 and 20 Year Olds

Outline of TheoryOutline of Theory

• Parents are concerned with current and future Parents are concerned with current and future consumption and the expected future wage of consumption and the expected future wage of children.children.

• Choice over whether to send a child to high school is Choice over whether to send a child to high school is influenced by:influenced by:

– WealthWealth

– Credit MarketsCredit Markets

– Current returns to middle school completionCurrent returns to middle school completion

– Expected future benefits from high school Expected future benefits from high school graduationgraduation

– PreferencesPreferences

Theory (Continued)Theory (Continued)

Parents enroll children in high school if expected Parents enroll children in high school if expected benefits outweigh costs.benefits outweigh costs.

Positive effect could be explained by increasing Positive effect could be explained by increasing wealth, relaxing credit constraints, or expected wealth, relaxing credit constraints, or expected returns to education in urban areas.returns to education in urban areas.

Negative effect if returns to education are low for Negative effect if returns to education are low for migrants or potential migrants.migrants or potential migrants.

Enrollment DemandEnrollment Demand

• Migrant opportunity, Migrant opportunity, Mvt, measured as the number of village residents employed as migrants outside the county in each year.• If networks are important, increases are associated If networks are important, increases are associated

with a declining cost of migrating.with a declining cost of migrating.

• Using a linear probability model, we estimate the Using a linear probability model, we estimate the effect on high school enrollment:effect on high school enrollment:

ihjtitpjhtjtjtit evME yuβXβZ 4310 ''

Problem with Simple EstimationProblem with Simple Estimation

• MM reflects factors that influence both the reflects factors that influence both the supply and demand of migrantssupply and demand of migrants

• Sources of Bias:Sources of Bias:

• Positive: if high schools expanded and Positive: if high schools expanded and lowered admissions standards while migrant lowered admissions standards while migrant opportunity also increased.opportunity also increased.

• Negative: negative shock to the local Negative: negative shock to the local economy makes migration attractive while economy makes migration attractive while making it harder to cover high school tuition.making it harder to cover high school tuition.

Identification StrategyIdentification 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 Residents of different counties received IDs at different timesdifferent times

• Farmers could not simply move to get ID cardsFarmers could not simply move to get ID cards

Identification StrategyIdentification Strategy• We argue that…We argue that…

• Differences in timing of ID availability affects Differences in timing of ID availability affects network quality.network quality.

• ““Cost” of migrating falls as legal long-term Cost” of migrating falls as legal long-term migrants are capable of providing referrals.migrants are capable of providing referrals.

• Networks time to build up.Networks time to build up.

• Non-linear function of years since IDs were Non-linear function of years since IDs were issued used to identify the migrant network.issued used to identify the migrant network.

Migration vs. Years Since ID Cards Migration vs. Years Since ID Cards IssuedIssued

Potential Problems with Our InstrumentPotential Problems with Our Instrument

• Timing of ID Card distribution is not Timing of ID Card distribution is not randomrandom– Differences in unobservable village Differences in unobservable village

characteristics could affect migration network characteristics could affect migration network andand educational attainment educational attainment

– Demand for migration could have driven ID Demand for migration could have driven ID distributiondistribution

– Timing of ID distribution could be correlated Timing of ID distribution could be correlated with trends in educational attainmentwith trends in educational attainment

Implementing our IV StrategyImplementing our IV Strategy

• We look for observable differences between We look for observable differences between villagesvillages

Observable Village Characteristics Observable Village Characteristics in 1988in 1988

Early IDsEarly IDs 1988 IDs1988 IDs Late IDsLate IDs

Share of Productive Assets Share of Productive Assets Controlled by VillageControlled by Village 0.400.40 0.240.24 0.250.25

Village Average Per Capita IncomeVillage Average Per Capita Income 627.2*627.2* 481.5481.5 558.1558.1

Per capita income GiniPer capita income Gini 0.230.23 0.220.22 0.210.21

Cultivable share of village landCultivable share of village land 0.690.69 0.550.55 0.510.51

Share in mountainsShare in mountains 0.140.14 0.240.24 0.300.30

Wage earner share in villageWage earner share in village 0.270.27 0.140.14 0.160.16

Average years of schooling, 18-22Average years of schooling, 18-22 8.758.75 7.567.56 7.507.50

Implementing our IV StrategyImplementing our IV Strategy

• We look for observable differences between We look for observable differences between villagesvillages

• Look for obvious correlation between trends in Look for obvious correlation between trends in educational attainment and timing of ID card educational attainment and timing of ID card distributiondistribution

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

.3

.4.5

.6S

hare

1960 1970 1980 1990

Birth Year Cohort

Received IDs Before 1988 Received IDs in 1988Received IDs After 1988

Implementing our IV StrategyImplementing our IV Strategy

• We look for observable differences between We look for observable differences between villagesvillages

• Look for obvious correlation between trends in Look for obvious correlation between trends in educational attainment and timing of ID card educational attainment and timing of ID card distributiondistribution

• In estimation, we include village dummies to In estimation, we include village dummies to control for time-invariant unobservables.control for time-invariant unobservables.

• Test robustness to inclusion of additional time-Test robustness to inclusion of additional time-varying variables that are likely correlated with varying variables that are likely correlated with village level unobservables.village level unobservables.

First Stage ResultsFirst Stage Results(# of Migrants from the Village)(# of Migrants from the Village)

1a1a 1b1b 1c1c 55

Functional FormFunctional Form quadraticquadratic CubicCubic quarticquartic quarticquartic

HH, Village HH, Village ControlsControls nono nono nono YesYes

F-Statistic on F-Statistic on InstrumentsInstruments 22.5722.57 20.1220.12 17.2717.27 15.8515.85

Partial R-SquaredPartial R-Squared 0.0140.014 0.0190.019 0.0210.021 0.0190.019

Determinants of HS Enrollment Determinants of HS Enrollment (Table 7)(Table 7)

ModelModel Coefficient on # Coefficient on #

of Migrantsof Migrants

Other ControlsOther Controls F-Statistic, F-Statistic, InstrumentsInstruments

OLSOLS 0.0010.001

(0.002)(0.002)17.2717.27

IV 1IV 1 -0.024-0.024

(0.010)**(0.010)**16.2616.26

IV 2IV 2 -0.024-0.024

(0.010)**(0.010)**village variablesvillage variables 15.9815.98

IV 3IV 3 -0.023-0.023

(0.010)**(0.010)**village + individual village + individual

characteristicscharacteristics 15.9715.97

IV 5IV 5 -0.019-0.019

(0.009)**(0.009)**Model 3 + parents’ Model 3 + parents’

education + pot. mig.education + pot. mig. 15.8515.85

IV 6IV 6 -0.020-0.020

(0.009)**(0.009)**Model 5 + additional Model 5 + additional

village variablesvillage variables 13.0213.02

The Reduced Form Effect of Issuing IDsThe Reduced Form Effect of Issuing IDs

F-Statistic on quartic in years since ID was issued is 3.19.

Heterogeneity in Migration Effect Heterogeneity in Migration Effect Across Families?Across Families?

In spite of instrument, perhaps concern that we are In spite of instrument, perhaps concern that we are “picking up students who might not go to high school “picking up students who might not go to high school anyway.”anyway.”

Who goes to high school? Children from families who Who goes to high school? Children from families who have connections, who can afford it, or who have have connections, who can afford it, or who have preferences for education.preferences for education.

– Father is “professional”Father is “professional”

– Father has wage employment experienceFather has wage employment experience

– Father has at least some high school educationFather has at least some high school education

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

1 2 3 4 5

Number of Migrants -0.022 -0.018 -0.015 -0.017 -0.018(0.011) (0.009) (0.009) (0.010) (0.010)

Father is a Professional 0.147 0.258 0.102 0.141 0.162(0.054) (0.084) (0.042) (0.051) (0.049)

-0.016(0.009)

Father had Off-Farm Employment -0.059 -0.079 0.081 -0.047 -0.070(0.040) (0.037) (0.057) (0.038) (0.039)

-0.015(0.006)

Father Enrolled in High School 0.178 0.183 0.171 0.229 0.167(0.037) (0.036) (0.036) (0.054) (0.034)

-0.005(0.005)

Father Completed High School 0.030 0.008 0.030 0.028 0.041(0.040) (0.038) (0.040) (0.036) (0.090)

-0.003(0.008)

Number of Migrants x Father is a Professional

Number of Migrants x Father had Off-Farm Employment

Number of Migrants x Father Completed High School

Number of Migrants x Father Enrolled in High School

If Not School, What Are High If Not School, What Are High School Age Children Doing?School Age Children Doing?

Consider activity choice among teenagers of Consider activity choice among teenagers of high school age.high school age.

Look for effects of network on migrant and local Look for effects of network on migrant and local employment (general equilibrium effects).employment (general equilibrium effects).

Effect of Migrant Opportunity on Activity Effect of Migrant Opportunity on Activity ChoiceChoice

In In SchoolSchool

FarmFarm

WorkWork

Local Local Wage Wage LaborLabor MigrantMigrant

Of age to Of age to finish finish middle middle schoolschool

-0.023-0.023

(0.009)(0.009)

-0.005-0.005

(0.011)(0.011)

-0.001-0.001

(0.001)(0.001)

0.0020.002

(0.001)(0.001)

Age +1Age +1-0.006-0.006

(0.012)(0.012)

-0.005-0.005

(0.010)(0.010)

0.0100.010

(0.004)(0.004)

0.0090.009

(0.002)(0.002)

Age + 2Age + 20.0070.007

(0.008)(0.008)

-0.031-0.031

(0.011)(0.011)

0.0120.012

(0.008)(0.008)

0.0090.009

(0.003)(0.003)

Share of Young Pursuing Activities Other Share of Young Pursuing Activities Other than Migrant Employmentthan Migrant Employment

Other MechanismsOther Mechanisms

A unitary model that does not examine A unitary model that does not examine consequences of migration on family consequences of migration on family composition.composition.

Possibility that our result is not driven by Possibility that our result is not driven by low returns to high school, but by effects of low returns to high school, but by effects of parent migration on child educational parent migration on child educational attainment.attainment.

Share of Children with Fathers Share of Children with Fathers Working as MigrantsWorking as Migrants

.02

.04

.06

.08

.1

Sh

are

1987 1989 1991 1993 1995 1997 1999 2001 2003

Year

7-12 Year Olds 13 to 15 Year Olds

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

-0.3

-0.2

-0.1

0.0

0.1

0.2

Probability of Father M

igrating

0

2

4

6

8

Log

, Num

ber

of O

ut-M

igra

nts

-10 -5 0 5 10 15 Years Since IDs Issued

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

-0.3

-0.2

-0.1

0.0

0.1

0.2

Probability of Father M

igrating

-.3

-.2

-.1

0

.1

.2

Pro

babi

lity

of

Enr

ollm

ent

-10 -5 0 5 10 15 Years Since IDs Issued

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

-0.3

-0.2

-0.1

0.0

0.1

0.2

Probability of Father M

igrating

0

2

4

6

8

Log

, Num

ber

of O

ut-M

igra

nts

-10 -5 0 5 10 15 Years Since IDs Issued

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

-0.3

-0.2

-0.1

0.0

0.1

0.2

Probability of Father M

igrating

-.3

-.2

-.1

0

.1

.2

Pro

babi

lity

of

Enr

ollm

ent

-10 -5 0 5 10 15 Years Since IDs Issued

ConclusionConclusion

• Migrant opportunity has a fairly significant, Migrant opportunity has a fairly significant, negative effect on educational attainment in rural negative effect on educational attainment in rural ChinaChina

• Elasticity at mean size of migrant network: -0.191Elasticity at mean size of migrant network: -0.191

• Robust to several extensions of the modelRobust to several extensions of the model

• Inclusion of time-varying variables at village level Inclusion of time-varying variables at village level potentially related to unobservablespotentially related to unobservables

• Inclusion of a range of family characteristicsInclusion of a range of family characteristics

DiscussionDiscussion

Why? Occupational segregation in cities.Why? Occupational segregation in cities.– Migrants not employed in jobs that require HS/College Migrants not employed in jobs that require HS/College

educationeducation

Likely reinforced by general equilibrium effects Likely reinforced by general equilibrium effects and higher wages locally subsequent to and higher wages locally subsequent to depletion of the labor forcedepletion of the labor force

Irreversibility of the high school enrollment Irreversibility of the high school enrollment decision decision

Implications for long-term inequality within urban Implications for long-term inequality within urban areas.areas.