The Unintended Consequences of Education Barriers for Migrant Children on the Academic Performance of Local Students: Evidence from Random Class Assignment in China
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Xuekai Zhang
Abstract
This paper examines whether education barriers for migrant children have negative effects on local students as well. By exploiting the random assignment of students to classes within each grade of a school, we find migrant peers who are prevented by policies from attending local senior high schools have negative effects on the academic performance of local students in China. Further heterogeneity analysis reveals that the negative effects primarily exist in the subjects of Chinese and English, and are more pronounced to urban students and students with middle and high scores. Mechanism analysis suggests that the negative effects may come from the lowered learning time investment and the worsened learning environment. The findings of this paper suggest that education barriers targeting migrant children can also have adverse effects on local students. Therefore, relevant reforms are needed to promote the integration of migrant and local students.
Funding source: Humanity and Social Science Youth Foundation of Ministry of Education of China
Award Identifier / Grant number: 24YJC790238
Funding source: National Social Science Youth Foundation of China
Award Identifier / Grant number: 25CJY148
Funding source: Research Innovation Project of Nanjing University of Finance and Economics
Award Identifier / Grant number: XKYC3202401
Acknowledgments
We would like to thank the editor and the anonymous reviewers for their valuable comments. We are grateful to Xueqian Zhao for her suggestions on the writing. All remaining errors are ours.
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Research funding: We gratefully acknowledges the financial support from the Humanity and Social Science Youth Foundation of Ministry of Education of China (24YJC790238), the National Social Science Youth Foundation of China (25CJY148) and the Research Innovation Project of Nanjing University of Finance and Economics (XKYC3202401).
Disadvantaged migrant students versus non-disadvantaged students.
| (1) | |
|---|---|
| School-grade fixed effects | |
| Average raw test score | 0.454 |
| (1.571) | |
| Raw test score of Chinese | −0.529 |
| (1.109) | |
| Raw test score of math | 1.074 |
| (2.126) | |
| Raw test score of English | 0.482 |
| (1.860) | |
| Gender | 0.034 |
| (0.042) | |
| Only child | −0.046 |
| (0.058) | |
| Health status | −0.014 |
| (0.027) | |
| Hukou status | 0.062 |
| (0.053) | |
| Age | 0.008 |
| (0.058) | |
| Father’s education | −0.093*** |
| (0.031) | |
| Mother’s education | −0.078*** |
| (0.028) | |
| Family economic status | −0.040* |
| (0.023) |
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In Table A1, each cell reports the result from a separate regression of the relevant variable on the dummy variable indicating whether she/he cannot attend local senior high schools with school-grade fixed effects. Standard errors are clustered at the class level. *p < 0.1; **p < 0.05; ***p < 0.01.
Descriptive statistics of schools with higher versus lower concentrations of migrants.
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | |
|---|---|---|---|---|---|---|---|---|---|
| Migrant students | Disadvantaged migrant students | Non-disadvantaged migrant students | |||||||
| Lower | Higher | p-Value | Lower | Higher | p-Value | Lower | Higher | p-Value | |
| Government funding | 1,343 | 1,550 | 0.584 | 1,045 | 1,831 | 0.032 | 1,538 | 1,367 | 0.652 |
| School rank | 3.875 | 3.529 | 0.304 | 3.750 | 3.647 | 0.762 | 3.812 | 3.588 | 0.507 |
| School location | 2.438 | 2.294 | 0.772 | 2.500 | 2.235 | 0.592 | 2.312 | 2.412 | 0.841 |
| Standardized average test score | 68.16 | 66.91 | 0.232 | 68.63 | 66.46 | 0.034 | 68.19 | 66.88 | 0.208 |
| Standardized test score of Chinese | 68.36 | 67.41 | 0.382 | 68.97 | 66.83 | 0.044 | 68.49 | 67.29 | 0.269 |
| Standardized test score of Math | 68.19 | 67.09 | 0.257 | 68.64 | 66.66 | 0.035 | 68.16 | 67.11 | 0.278 |
| Standardized test score of English | 68.32 | 66.89 | 0.178 | 68.87 | 66.38 | 0.016 | 68.27 | 66.94 | 0.210 |
| Average raw test score | 79.13 | 76.20 | 0.649 | 76.55 | 78.62 | 0.747 | 80.51 | 74.90 | 0.381 |
| Raw test score of Chinese | 81.73 | 79.03 | 0.580 | 79.82 | 80.83 | 0.837 | 82.18 | 78.61 | 0.463 |
| Raw test score of Math | 78.31 | 74.72 | 0.638 | 73.99 | 78.79 | 0.529 | 79.99 | 73.15 | 0.368 |
| Raw test score of English | 77.56 | 75.48 | 0.777 | 76.18 | 76.78 | 0.934 | 79.55 | 73.61 | 0.418 |
| Gender | 0.520 | 0.521 | 0.965 | 0.515 | 0.525 | 0.651 | 0.522 | 0.519 | 0.865 |
| Only child | 0.534 | 0.487 | 0.613 | 0.453 | 0.564 | 0.224 | 0.564 | 0.459 | 0.247 |
| Health status | 0.120 | 0.150 | 0.126 | 0.127 | 0.144 | 0.411 | 0.119 | 0.151 | 0.105 |
| Hukou status | 0.336 | 0.461 | 0.173 | 0.432 | 0.371 | 0.508 | 0.333 | 0.464 | 0.150 |
| Age | 14.09 | 13.79 | 0.208 | 13.98 | 13.89 | 0.716 | 14.12 | 13.76 | 0.129 |
| Father’s education | 0.237 | 0.210 | 0.704 | 0.185 | 0.259 | 0.29 | 0.241 | 0.206 | 0.626 |
| Mother’s education | 0.214 | 0.177 | 0.566 | 0.158 | 0.229 | 0.271 | 0.220 | 0.171 | 0.446 |
| Family economic status | 0.069 | 0.079 | 0.574 | 0.061 | 0.086 | 0.123 | 0.069 | 0.079 | 0.527 |
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The p-Value refers to the statistical significance of the mean difference in the corresponding variable between groups. Disadvantaged migrant children refer to migrant children who cannot attend local senior high schools. Non-disadvantaged migrant children refer to migrant children who can attend local senior high schools. The coefficients in the table represent the means of the school-level variables. ‘Higher’ indicates that the proportion of migrant children in schools is above the median proportion. Government funding refers to the annual per capita financial allocation. School rank refer to the five-tier ranking of schools within the county, with 1 representing the lowest rank and 5 representing the highest. School location refers to the five-tier classification of the area in which a school is situated within the county, with 1 indicating a location in the city center and 5 indicating a location in the rural area. Standardized test score refer to the test scores standardized at at the school–grade level, with a mean of 70 and a standard deviation of 10.
Comparison of classes with or without random assignment.
| (1) | |
|---|---|
| School-grade fixed effects | |
| Proportion of disadvantaged migrant children | 0.032 |
| (0.034) | |
| Proportion of non-disadvantaged migrant children | 0.016 |
| (0.061) | |
| Average test score-class level | −3.650 |
| (8.343) | |
| Raw test score of Chinese-class level | −3.038 |
| (5.314) | |
| Raw test score of Math-class level | −3.144 |
| (10.395) | |
| Raw test score of English-class level | −4.505 |
| (10.720) | |
| Gender-class level | 0.003 |
| (0.038) | |
| Only child-class level | −0.060 |
| (0.100) | |
| Health status-class level | −0.015 |
| (0.031) | |
| Hukou status-class level | 0.023 |
| (0.103) | |
| Age-class level | −0.184 |
| (0.130) | |
| Father’s education-class level | −0.063 |
| (0.088) | |
| Mother’s education-class level | −0.064 |
| (0.091) | |
| Family economic status-class level | 0.027 |
| (0.021) |
-
In Table A3, each cell reports the result from a separate regression of the relevant variable on the dummy variable indicating whether the class is not randomly assigned with school-grade fixed effects. Dependent variables are calculated by class average. Standard errors are clustered at the school level. *p < 0.1; **p < 0.05; ***p < 0.01.
Comparison of schools with or without random assignment.
| (1) | |
|---|---|
| County fixed effects | |
| Proportion of disadvantaged migrant children | −0.020 |
| (0.013) | |
| Proportion of non-disadvantaged migrant children | −0.167** |
| (0.078) | |
| Government funding | 38.903 |
| (356.381) | |
| School location | 0.484 |
| (0.545) | |
| School rank | 0.011 |
| (0.397) | |
| Average test score-school level | 0.726 |
| (5.378) | |
| Raw test score of Chinese-school level | 0.529 |
| (4.074) | |
| Raw test score of Math-school level | 2.906 |
| (6.574) | |
| Raw test score of English-school level | −1.108 |
| (6.032) | |
| Gender-school level | −0.006 |
| (0.030) | |
| Only child-school level | 0.054 |
| (0.066) | |
| Health status-school level | 0.003 |
| (0.016) | |
| Hukou status-school level | 0.069 |
| (0.070) | |
| Age-school level | 0.239 |
| (0.173) | |
| Father’s education-school level | −0.045 |
| (0.051) | |
| Mother’s education-school level | −0.027 |
| (0.048) | |
| Family economic status-school level | 0.009 |
| (0.014) |
-
In Table A4, each cell reports the result from a separate regression of the relevant variable on the dummy variable indicating whether the school is excluded with county fixed effects. Dependent variables are calculated by school average. Government funding refers to the annual per capita financial allocation. School rank refers to the five-tier ranking of schools within the county, with 1 representing the lowest rank and 5 representing the highest. School location refers to the five-tier classification of the area in which a school is situated within the county, with 1 indicating a location in the city center and 5 indicating a location in the rural area. Standard errors are clustered at the county level. *p < 0.1; **p < 0.05; ***p < 0.01.
Class-level balancing tests.
| (1) | (2) | |
|---|---|---|
| No fixed effects | School-grade fixed effects | |
| Gender-class level | −0.006 | 0.192 |
| (0.108) | (0.180) | |
| Only child-class level | 0.481 | 0.342 |
| (0.401) | (0.495) | |
| Health status-class level | 0.138 | 0.353 |
| (0.112) | (0.353) | |
| Hukou status-class level | −0.027 | −0.210 |
| (0.445) | (0.293) | |
| Age-class level | −4.526*** | 2.298 |
| (1.214) | (2.494) | |
| Father’s education-class level | 0.201 | −0.090 |
| (0.320) | (0.307) | |
| Mother’s education-class level | 0.207 | 0.128 |
| (0.315) | (0.427) | |
| Family economic status-class level | 0.156 | −0.121 |
| (0.094) | (0.472) |
-
Columns (1) and (2) report results without and with school-grade fixed effects estimates from separate regressions of the relevant class-level variables on the proportion of migrant children who cannot attend local senior high schools in the class. Dependent variables are calculated by class average. Standard errors are clustered at the class level. *p < 0.1; **p < 0.05; ***p < 0.01.
Testing for random assignment.
| (1) | |
|---|---|
| Student disadvantage migrant status | |
| Class-level leave-me-out proportion of disadvantage migrant | −0.036 |
| (0.097) | |
| Grade-level leave-me-out proportion of disadvantage migrant | −39.924*** |
| (5.572) | |
| School-grade fixed effects | YES |
| Observations | 3,572 |
| R 2 | 0.756 |
-
Standard errors are clustered at the class level. *p < 0.1; **p < 0.05; ***p < 0.01.
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