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Sibling Rivalry: Evidence from China’s Compulsory Schooling Reform

  • Guanfu Fang EMAIL logo and Yu Chen
Published/Copyright: December 14, 2020
The B.E. Journal of Economic Analysis & Policy
From the journal The B.E. Journal of Economic Analysis & Policy Volume 21 Issue 2

Abstract

This study investigates the causal effect of older siblings’ schooling on their younger siblings’ schooling and labor market outcomes by exploiting the temporal and geographical variations in the implementation of compulsory schooling laws in China. Reform exposure is quantified as the number of years that an individual is eligible for compulsory education. We find that older siblings’ exposure to compulsory schooling reform had negative impacts on their younger siblings’ academic achievement and labor market performance. We provide some suggestive evidence for the mechanism of resource reallocation within households. These findings suggest that we may be overestimating the social benefits of compulsory schooling reforms by ignoring the resources constraints within households and the spillover effects on siblings.

Keywords: compulsory schooling reform; educational achievement; labor market performance; sibling spillover
JEL Classification: I21; I28; J12; J24
Corresponding author: Guanfu Fang, Shanghai University of International Business and Economics, No. 1900, Wenxiang Road, Songjiang District, 201620, Shanghai, China, E-mail:
Appendix: Additional Analysis
Figure A1: Implementation time of compulsory schooling laws.Darker colors of the heat map are associated with more late policy implementation.
Figure A1:

Implementation time of compulsory schooling laws.

Darker colors of the heat map are associated with more late policy implementation.

Figure A2: Mean years of education by cohort.The data are from the CFPS. The sample is restricted to the respondents with siblings.
Figure A2:

Mean years of education by cohort.

The data are from the CFPS. The sample is restricted to the respondents with siblings.

Figure A3: Parallel trend analyses for main outcomes.The figure shows the coefficients on timing dummies and their 95% confidence intervals from the regressions of outcome variables on a sequence of dummies for time periods up to 4 years before and 14 years after the reform. All regressions control for own reform exposure, fixed effects for birth year, province, survey wave, gender, ethnicity, number of siblings, older sibling’s birth year, and older siblings’ gender, birth spacing between siblings, and the interactions of older siblings’ year-of-birth dummies and provincial characteristics in 1985. Standard errors are clustered by province. On the x-axis, time 0 is when the reform was implemented in the province and the older sibling was 15 years old, i.e., the first reform cohort, and the other numbers indicate the difference between age 15 and the age of older sibling when the reform took effect. The reference group is the cohorts born more than 4 years before the first reform cohort.
Figure A3:

Parallel trend analyses for main outcomes.

The figure shows the coefficients on timing dummies and their 95% confidence intervals from the regressions of outcome variables on a sequence of dummies for time periods up to 4 years before and 14 years after the reform. All regressions control for own reform exposure, fixed effects for birth year, province, survey wave, gender, ethnicity, number of siblings, older sibling’s birth year, and older siblings’ gender, birth spacing between siblings, and the interactions of older siblings’ year-of-birth dummies and provincial characteristics in 1985. Standard errors are clustered by province. On the x-axis, time 0 is when the reform was implemented in the province and the older sibling was 15 years old, i.e., the first reform cohort, and the other numbers indicate the difference between age 15 and the age of older sibling when the reform took effect. The reference group is the cohorts born more than 4 years before the first reform cohort.

Figure A4: Placebo tests using pseudo policy time.The figure gives the histogram graphs for the placebo treatment effect. We draw 1,000 placebo treatment years for each province (sampled from the full support of the estimation sample’s potential years, 1986–1994). Then, using the treatment status assigned by these placebo years, we estimate the placebo treatment effect on the main dependent variables for each draw. The control variables include own reform exposure and fixed effects for province, survey wave, gender, Han ethnicity, number of siblings, older siblings’ gender, birth spacing between siblings, and the interactions of older siblings’ year-of-birth dummies and provincial characteristics in 1985. The thick line indicates the estimation results using the true treatment time.
Figure A4:

Placebo tests using pseudo policy time.

The figure gives the histogram graphs for the placebo treatment effect. We draw 1,000 placebo treatment years for each province (sampled from the full support of the estimation sample’s potential years, 1986–1994). Then, using the treatment status assigned by these placebo years, we estimate the placebo treatment effect on the main dependent variables for each draw. The control variables include own reform exposure and fixed effects for province, survey wave, gender, Han ethnicity, number of siblings, older siblings’ gender, birth spacing between siblings, and the interactions of older siblings’ year-of-birth dummies and provincial characteristics in 1985. The thick line indicates the estimation results using the true treatment time.

Figure A5: Placebo tests using neighborhood siblings.The figure gives the histogram graphs for the placebo treatment effect. We draw 1,000 placebo siblings for each individual (sampled from siblings within the same province). Then, using the treatment status assigned by these placebo siblings, we estimate the placebo treatment effect on the main dependent variables for each draw. The control variables include own reform exposure and fixed effects for province, survey wave, gender, Han ethnicity, number of siblings, older siblings’ gender, birth spacing between siblings, and the interactions of older siblings’ year-of-birth dummies and provincial characteristics in 1985. The thick line indicates the estimation results using the true treatment time.
Figure A5:

Placebo tests using neighborhood siblings.

The figure gives the histogram graphs for the placebo treatment effect. We draw 1,000 placebo siblings for each individual (sampled from siblings within the same province). Then, using the treatment status assigned by these placebo siblings, we estimate the placebo treatment effect on the main dependent variables for each draw. The control variables include own reform exposure and fixed effects for province, survey wave, gender, Han ethnicity, number of siblings, older siblings’ gender, birth spacing between siblings, and the interactions of older siblings’ year-of-birth dummies and provincial characteristics in 1985. The thick line indicates the estimation results using the true treatment time.

Table A1:

China’s educational system.

Education levelEntry ageSpecific stageNote
Primary education4Kindergarten
5
6
7Primary education
8
9
10
11
12
Secondary education13Junior high school
14
15
16Senior middle schoolInclude vocational senior high school and regular high school.
17
18
Tertiary education19University, Junior College StudiesJunior college studies may last for 2–5 years, and university is almost 4 years
20
21
22
Postgraduate education23+Master and PhD educationGenerally, Master Degree may last for 2–3 years, and PhD will study for 3–5 years

  1. The table shows age and school year levels through the main phases of education in China.

Table A2:

Correlation between reform timing and socioeconomic variables in 1985.

VariablesCorrelation coefficientp value
Population−0.2630.160
Population, age less than 6−0.2730.145
Population, male−0.2600.165
Railway mileage−0.1450.445
Highway mileage−0.0170.928
Number of books published−0.1970.298
Number of health institutions−0.1250.512
Number of primary schools−0.1430.452
Number of middle school−0.2510.182
Number of high school−0.1420.454
Fraction of middle school graduates−0.5520.002
Ratio of agricultural output value0.5500.002
Consumer expenditure per capita (log)0.2850.127
Fixed investment per capita (log)−0.1820.335
GDP per capita (log)−0.3310.074
GDP growth rate0.3220.083
Government expenditure per capita (log)0.1610.393
Government expenditure on education, science, culture, and public health per capita (log)0.1520.438
Government investment per capital (log)−0.2720.154

  1. The table presents the correlation coefficients between reform timing and socioeconomic variables in 1985 and their significance levels. The sample size is 30. Hainan province is.

Table A3:

Sensitivity analysis.

(1)(2)(3)(4)(5)(6)
Differential trendsOne-child policyLength of schoolingGovernment expenseMigrationFunctional form
Years of education−0.250**−0.157**−0.176**−0.167**−0.200**−0.406**
(0.103)(0.071)(0.065)(0.076)(0.086)(0.187)
Primary education−0.008−0.003−0.003−0.002−0.004−0.005
(0.007)(0.005)(0.004)(0.005)(0.006)(0.013)
Middle school−0.021***−0.016**−0.021**−0.019**−0.019**−0.020
(0.008)(0.008)(0.007)(0.008)(0.008)(0.024)
High school−0.027*−0.018**−0.018**−0.020**−0.020***−0.065***
(0.013)(0.007)(0.007)(0.008)(0.006)(0.013)
Tertiary education−0.018*−0.013**−0.013**−0.013**−0.017**−0.037**
(0.010)(0.005)(0.005)(0.006)(0.006)(0.016)
Math test score−0.094−0.069−0.126−0.109−0.112−0.281
(0.189)(0.095)(0.087)(0.112)(0.109)(0.240)
Word test score−0.474**−0.258*−0.292*−0.239−0.295−0.695
(0.198)(0.148)(0.144)(0.152)(0.175)(0.500)
Short-term memory−0.045−0.029−0.040−0.015−0.003−0.066
(0.043)(0.030)(0.029)(0.025)(0.031)(0.089)
Long-term memory−0.034−0.003−0.0120.0120.022−0.091
(0.032)(0.027)(0.024)(0.025)(0.028)(0.080)
Log income−0.030−0.029−0.029−0.030−0.034*−0.076*
(0.026)(0.020)(0.019)(0.020)(0.019)(0.042)
Employment0.000−0.000−0.001−0.0010.003−0.025*
(0.008)(0.003)(0.003)(0.005)(0.004)(0.014)
Agricultural work0.021***0.014***0.015***0.011*0.015***−0.001
(0.007)(0.004)(0.004)(0.005)(0.005)(0.013)
Administration position−0.006**−0.005***−0.005***−0.004**−0.003*−0.010
(0.002)(0.001)(0.001)(0.002)(0.002)(0.007)
ISEI score−0.873**−0.670***−0.727***−0.635***−0.579***−1.283*
(0.390)(0.190)(0.189)(0.195)(0.173)(0.730)
Prestige score−0.379*−0.330***−0.351***−0.375***−0.319**−0.939**
(0.209)(0.107)(0.103)(0.113)(0.140)(0.381)
Province FEYESYESYESYESYESYES
Own cohort FEYESYESYESYESYESYES
Sibling cohort FEYESYESYESYESYESYES
Other controlYESYESYESYESYESYES

  1. Each cell reports the coefficient of older sibling reform exposure from an OLS regression where the explanatory variable is the row variable. Column 1 controls for the interactions of older sibling birth year dummies and provincial characteristics in 1985, including the GDP growth rate, the fraction of junior high school graduates, the log of government expenditure per capita, and the log of fixed investment per capital. Column 2 controls for the average monetary penalty rate for an unauthorized birth in the year of birth. Column 3 controls for the average years of primary and middle schooling of the cohort born in the same county. Column 4 controls for the log of the provincial government expenditure per capita on education, science, culture, and public health when the respondents were 6–15 years old and the log of the government expenditure per capita on education, science, culture, and public health when the older siblings were 6–15 years old. Column 5 restricts the sample to those who had not left their hometowns before the age of 12. Column 6 employs an alternative measurement of reform exposure, which is a step function of the duration of reform exposure during ages 6 to 15. Other control variables include fixed effects for survey wave, urban area, gender, Han ethnicity, number of siblings, older siblings’ gender, birth spacing between siblings, and the interactions of older siblings’ year-of-birth dummies and provincial characteristics in 1985. Standard errors are clustered by province. ***significant at 1% level, **at 5%, *at 10%.

Table A4:

Fertility decision.

Variables(1)(2)
Number of younger siblingsBirth spacing
Older sibling exposure−0.004−0.027
(0.018)(0.037)
Observations5,2665,266
R-squared0.3660.757
Province FEYESYES
Own cohort FEYESYES
Sibling cohort FEYESYES
Other controlYESYES

  1. Other control variables include fixed effects for survey wave, urban area, Han ethnicity, older siblings’ gender, and the interactions of older siblings’ year-of-birth dummies and provincial characteristics in 1985. Standard errors are clustered by province. ***significant at 1% level, **at 5%, *at 10%.

Table A5:

Adjusted statistical inference

(1)(2)(3)(4)
Estimates on older sibling reform exposureUnadjusted p valueBootstrap p valueFDR-adjusted p value
Years of education−0.163**0.0330.0750.045
(0.072)
Primary education−0.0030.6050.7080.387
(0.005)
Middle school−0.017**0.0270.0830.045
(0.007)
High school−0.018**0.0170.060.033
(0.007)
Tertiary education−0.014**0.0150.0650.033
(0.005)
Math test score−0.0750.4570.4630.297
(0.099)
Word test score−0.259*0.0850.1170.071
(0.144)
Short-term memory−0.0330.2750.3650.177
(0.030)
Long-term memory−0.0060.8290.9540.387
(0.026)
Log income−0.0270.1860.1440.15
(0.020)
Employment−0.0010.8020.8890.387
(0.003)
Agricultural work0.014***0.0040.010.016
(0.004)
Administration position−0.005***0.0020.0990.016
(0.001)
ISEI score−0.677***0.0010.0020.016
(0.188)
Prestige score−0.339***0.0030.0240.016
(0.102)

  1. Column 1 report our baseline estimates of older sibling reform exposure effects. Control variables include individual reform exposure, fixed effects for reform exposure, birth year, province, survey wave, gender, ethnicity, number of siblings, older sibling’s birth year, older sibling’s gender, birth spacing between siblings, and the interactions of older siblings’ year-of-birth dummies and provincial characteristics in 1985. Columns 2 to 4 report conventional, wild cluster bootstrap, and FDR-adjusted p values, respectively. ***significant at 1% level, **at 5%, *at 10%.

Table A6:

Heterogeneous impacts by gender.

(1)(2)
MaleFemale
Years of education−0.191*−0.203**
(0.104)(0.093)
Primary education−0.002−0.007
(0.009)(0.006)
Middle school−0.023***−0.017
(0.008)(0.012)
High school−0.030**−0.013
(0.012)(0.010)
Tertiary education−0.011−0.019**
(0.008)(0.009)
Math test score−0.073−0.187
(0.146)(0.130)
Word test score−0.254−0.352
(0.208)(0.258)
Short-term memory−0.026−0.028
(0.034)(0.041)
Long-term memory0.0070.014
(0.038)(0.035)
Log income−0.049**0.008
(0.022)(0.029)
Employment−0.0070.006
(0.007)(0.005)
Agricultural work0.012**0.017*
(0.005)(0.009)
Administration position−0.002−0.008***
(0.003)(0.001)
ISEI score−0.224−1.251***
(0.317)(0.443)
Prestige score−0.167−0.545*
(0.318)(0.272)
Province FEYESYES
Own cohort FEYESYES
Sibling cohort FEYESYES
Other controlYESYES

  1. Each cell reports the coefficient of sibling reform exposure from an OLS regression where the explanatory variable is the row variable. Columns 1 and 2 report estimates for males and females separately. Other control variables include fixed effects for survey wave, urban area, gender, Han ethnicity, number of siblings, older siblings’ gender, birth spacing between siblings, and the interactions of older siblings’ year-of-birth dummies and provincial characteristics in 1985. The numbers of observations for total years of education and certain educational credentials in Columns 1 and 2 are 2,618 and 2,579, respectively. The numbers of observations for word and math test scores in Columns 1 and 2 are 3,669 and 3,898, respectively. The numbers of observations for short-term memory in Columns 1 and 2 are 2990 and 3,187, respectively. The numbers of observations for long-term memory in Columns 1 and 2 are 2949 and 3135, respectively. The numbers of observations for log income in Columns 1 and 2 are 5,167 and 3,900, respectively. The numbers of observations for employment and agricultural jobs in Columns 1 and 2 are 7,965 and 8008, respectively. The numbers of observations for administrative position in Columns 1 and 2 are 5,989 and 6049, respectively. The numbers of observations for occupation ISEI and prestige scores in Columns 1 and 2 are 5,571 and 4,898, respectively. Standard errors are clustered by province. ***significant at 1% level, **at 5%, *at 10%.

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Received: 2020-07-05
Accepted: 2020-12-01
Published Online: 2020-12-14

© 2020 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Personality Traits and Household Consumption Choices
  4. Public Health Insurance and Impacts on Crime Incidences and Mental Health
  5. How Education Empowers Women in Developing Countries
  6. Do Large Corporate Tax Cuts Boost Wages? Evidence from Ohio
  7. Endogenous Peer Group Effects on Adolescents’ Crime Reporting Intentions
  8. Sibling Rivalry: Evidence from China’s Compulsory Schooling Reform
  9. Good Co(o)p or Bad Co(o)p? Redistribution Concerns and Competition in Credit Markets with Imperfect Information
  10. Information and Communication Technology Adoption and the Demand for Female Labor: The Case of Indian Industry
  11. Local Labor Markets and Child Learning Outcomes in India
  12. The Intended and Unintended Effects of Opioid Policies on Prescription Opioids and Crime
  13. Why do women become teachers while men don’t?
  14. Letter
  15. Are There Peer Effects from English Learners in Elementary Schools? Evidence from an IV Approach
https://doi.org/10.1515/bejeap-2020-0210
Keywords for this article
compulsory schooling reform; educational achievement; labor market performance; sibling spillover

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Personality Traits and Household Consumption Choices
  4. Public Health Insurance and Impacts on Crime Incidences and Mental Health
  5. How Education Empowers Women in Developing Countries
  6. Do Large Corporate Tax Cuts Boost Wages? Evidence from Ohio
  7. Endogenous Peer Group Effects on Adolescents’ Crime Reporting Intentions
  8. Sibling Rivalry: Evidence from China’s Compulsory Schooling Reform
  9. Good Co(o)p or Bad Co(o)p? Redistribution Concerns and Competition in Credit Markets with Imperfect Information
  10. Information and Communication Technology Adoption and the Demand for Female Labor: The Case of Indian Industry
  11. Local Labor Markets and Child Learning Outcomes in India
  12. The Intended and Unintended Effects of Opioid Policies on Prescription Opioids and Crime
  13. Why do women become teachers while men don’t?
  14. Letter
  15. Are There Peer Effects from English Learners in Elementary Schools? Evidence from an IV Approach
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