Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter April 29, 2022

Better School, Better Score? Evidence From a Chinese Earthquake-Stricken County

  • Xuan Leng ORCID logo and Xinyan Liu ORCID logo EMAIL logo

Abstract

Little is known about the long-term impact of postdisaster government support on students’ educational outcomes in selective high schools. Using the instrumental variable approach and administrative education data on an earthquake-affected county, we found that entering a selective high school in a postdisaster government-supported county reduces the college entrance examination scores of students and, thus, their success in attending college. Suggestive evidence showed that the redistribution of educational resources across schools could be a reason for this negative impact of selective schools. We conclude that raising awareness of this educational misallocation problem can be of great importance in improving the average level of human capital in rural areas.


Corresponding author: Xinyan Liu, Business School, The Chinese University of Hong Kong, Cheng Yu Tung Building, 12 Chak Cheung Street, Shatin, N.T., Hong Kong, China, E-mail:

Acknowledgments

We would like to thank Chiu Yu Ko, Xiaoyu Xia, Jian Zou, Yongzhi Sun, Ruichao Si, Yankun Kang for helpful comments and suggestions. We also thank seminar participants at the 12th International Symposium on Human Capital and Labor Markets, the 16th China Labor Economics Forum, the 4th China Labor Economics Frontier Forum.

  1. Author contribution: All errors are our own. All authors are equally contributed in this paper.

  2. Conflict of interest: All authors declare that they have no conflict of interest.

  3. Ethical approval: This article does not contain any studies with human participants performed by any of the authors.

Appendix A

Table A1:

Sample matching detail.

Matched Unmatched middle Unmatched high
sample school sample school sample
(1) (2) (3)
2015–2018 Matching 4350 1261 4977
2016–2019 Matching 4531 1012 4624
2017–2020 Matching 3855 1536 4235
Total observation 12736 3798 13836
  1. In Panel A, 2015–2018 Matching refers to matchings between 2015 middle school graduates and 2018 high school graduates. 2016–2019 Matching refers to matchings between 2016 middle school graduates and 2019 high school graduates. 2017–2020 Matching refers to matchings between 2017 middle school graduates and 2020 high school graduates. Notice that in the baseline regression where CEE score served as the outcome variable, we have 12,108 observations because we dropped the sample with missing CEE score and samples in vocational school.

Table A2:

Sample attrition.

Attrition or not
(1) (2)
Gender 0.001
[0.001]
Age 0.000
[0.001]
Year fixed effect Yes Yes
Middle school score Yes Yes
Middle school score(class-level) Yes Yes
Observations 12,108 12,108
R-squared 0.004 0.004
  1. Each column represents a separate regression in which the dependent variable is a dummy variable indicating a missing value for the respective survey item, and the independent variable is student’ gender dummy and student age. Robust standard error is presented in the bracket.

Figure A1: 
Map of transition rate into selective-high school in each neighborhood.
Figure A1:

Map of transition rate into selective-high school in each neighborhood.

Table A3:

Validity of instrument variable.

(1) (2) (3)
Dependent variable Middle school score Gender Age
Instrument −7.68*** 0.004 0.042***
[0.066] [0.003] [0.005]
Middle school score No Yes Yes
Middle school score(class-level) Yes Yes Yes
Year fixed effect Yes Yes Yes
Observations 12,108 12,108 12,108
R-squared 0.035 0.001 0.027
  1. This table presents the validity of distance instrument where middle school score. Column (1) shows that middle school score is imbalance, and Columns(2)–(3) presents the results by controlling for middle school score. Robust standard error is presented in the bracket.

Table A4:

The effect of attending selective high school on drop out probability.

Dependent variable Drop out probability
Reduce form 0.000 0.000 0.000
[0.002] [0.002] [0.002]
First stage −0.071*** −0.071*** −0.070***
[0.002] [ 0.002] [0.002]
Second stage −0.007 −0.007 −0.007
[0.005] [0.004] [0.005]
Year fixed effect Yes Yes Yes
Gender No Yes Yes
Age No No Yes
F-statistics 808.532 809.974 790.851
Middle school score Yes Yes Yes
Middle school score(class-level) Yes Yes Yes
Observations 12,108 12,108 12,108
  1. Robust standard error is presented in the bracket. The dependent variable is drop out probability, which is measured by whether the student has CEE score or not. Students whose CEE score is missing or 0 are defined as drop-out student, and vice versa. The accurate coefficients of reduce-form is 0.0004747 for Column (1), 0.0004697 for Column (2) and 0.0004646 for Column (3). ***indicates significance at 1%, **at 5% and *at 10%.

Figure A2: 
The difference of peer quality at selective and non-selective school.
Figure A2:

The difference of peer quality at selective and non-selective school.

Table A5:

Research on the impact of elite schools.

Author Data Methodology Result
Park et al. (2015) Student from rural counties in Gansu province from 1997 to 2001 RDD Attending a magnet high school increases CEE scores by 0.387 standard deviations
Wang and Sun (2015) Student from rural county in eastern China in 2009 and 2010 Fuzzy RDD Attending a magnet high school increases CEE scores by 0.139–0.187 standard deviations
Fabregas (2017) Individual data in Mexico city from 2011 and 2012 RDD Students who barely scored above admission cut-off are 0.2 standard deviations likely to be worse off
Anderson et al. (2016) Middle school students in Beijing in 2005 RDD Elite exam high schools decrease students’ test score by 0.326 standard deviation
Dee and Lan (2015) Chinese urban students from 2006 to 2008 RDD Entering elite school reduces students’ college entrance examination score by 0.104 standard deviation
Denning et al. (2021) Students information from 1995–2008 in Texas OLS Improving students’ rank reduces the probability for retention by roughly 4 percentage points
Yu (2020) 2013 and 2014 waves of the China education Panel survey OLS One standard deviation increase in a student’s ordinal rank leads to 0.14 increase in math
Murphy and Weinhardt (2020) Students in England 2003/2004 to 2007/8 OLS One standard deviation increase in rank is associated with increases in later test scores by 0.085 standard deviations
  1. This presents an overview of the effect of selective high school on students’ academic performance. First four paper employs RDD strategy that are closely related to the present paper, and the latter three paper focus on using OLS estimation.

Appendix B: Aggregate effect of educational misallocation

A back-of-envelope calculation of the aggregate effect of rural human capital can be listed in three steps as listed below. All data are obtained from the Poverty Monitoring Report of Rural China in 2017.

  • Step 1: The number of middle and high school students in our studied county in 2017 was 33,393.

  • Step 2: In 2017, the total number of middle and high school students from poverty-stricken counties in China was 11, 630, 000.

  • Step 3: According to our estimation in Table 2, choosing the appropriate high school can increase the probability of attending colleges by 6.8%. That is, for 33,393 students in 2017, approximately 2,270 students may attend colleges if they choose the appropriate schools.

  • Step 4: In this way, we can say that approximately 790,588 students in the poor national counties are able to reach the college admission line if they choose high schools in a sensible way.[14]

References

Abdulkadiroğlu, A., J. Angrist, and P. Pathak. 2014. “The Elite Illusion: Achievement Effects at Boston and New York Exam Schools.” Econometrica 82 (1): 137–96.10.3386/w17264Search in Google Scholar

Anderson, K., X. Gong, K. Hong, and X. Zhang. 2016. “Do Selective High Schools Improve Student Achievement? Effects of Exam Schools in China.” China Economic Review 40: 121–34. https://doi.org/10.1016/j.chieco.2016.06.002.Search in Google Scholar

Ardington, C., A. Menendez, and T. Mutevedzi. 2015. “Early Childbearing, Human Capital Attainment, and Mortality Risk: Evidence from a Longitudinal Demographic Surveillance Area in Rural KwaZulu-Natal, South Africa.” Economic Development and Cultural Change 63 (2): 281–317. https://doi.org/10.1086/678983.Search in Google Scholar

Barkley, D., and M. Henry. 2004. “Does Human Capital Affect Rural Economic Growth? Evidence from the South.” In Research Report 03-2004-02. Clemson, SC: Regional Economic Development laboratory, Clemson University.Search in Google Scholar

Brown, P. H. 2006. “Parental Education and Investment in Children’s Human Capital in Rural China.” Economic Development and Cultural Change 54 (4): 759–89. https://doi.org/10.1086/503582.Search in Google Scholar

Bullock, C. 2007. The Relationship Between School Building Conditions and Student Achievement at the Middle School Level in the Commonwealth Of Virginia. PhD diss, Virginia Tech.Search in Google Scholar

Calonico, S., M. D. Cattaneo, and R. Titiunik. 2014. “Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs.” Econometrica 82 (6): 2295–326. https://doi.org/10.3982/ecta11757.Search in Google Scholar

Caruso, G., and S. Miller. 2015. “Long Run Effects and Intergenerational Transmission of Natural Disasters: A Case Study on the 1970 Ancash Earthquake.” Journal of Development Economics 117: 134–50. https://doi.org/10.1016/j.jdeveco.2015.07.012.Search in Google Scholar

Dale, S., and A. B. Krueger. “Estimating the Return to College Selectivity over the Career Using Administrative Earnings Data.” NBER Working Paper 17159, 2011.10.3386/w17159Search in Google Scholar

Dee, T., and X. Lan. 2015. “The Achievement and Course-Taking Effects of Magnet Schools: Regression-Discontinuity Evidence from Urban China.” Economics of Education Review 47: 128–42. https://doi.org/10.1016/j.econedurev.2015.05.007.Search in Google Scholar

Denning, J. T., R. Murphy, and F. Weinhardt. 2021. “Class Rank and Long-Run Outcomes.” Review of Economics and Statistics, 1–45. https://doi.org/10.1162/rest_a_01125.Search in Google Scholar

Dobbie, W., and R. G. FryerJr. 2014. “The Impact of Attending a School with High-Achieving Peers: Evidence from the New York City Exam Schools.” American Economic Journal: Applied Economics 6 (3): 58–75. https://doi.org/10.1257/app.6.3.58.Search in Google Scholar

Duflo, E., P. Dupas, and M. Kremer. 2011. “Peer Effects, Teacher Incentives, and the Impact of Tracking: Evidence from a Randomized Evaluation in Kenya.” American Economic Review 101 (5): 1739–74. https://doi.org/10.1257/aer.101.5.1739.Search in Google Scholar

Elsner, B., and I. E. Isphording. 2017. “A Big Fish in a Small Pond: Ability Rank and Human Capital Investment.” Journal of Labor Economics 35 (3): 787–828. https://doi.org/10.1086/690714.Search in Google Scholar

Fabregas, R. 2017. “A Better School but a Worse Position? The Effects of Marginal Middle School Admissions in Mexico City.” Harvard University Working paper, Harvard University.Search in Google Scholar

Florence, M. D., M. Asbridge, and P. J. Veugelers. 2008. “Diet Quality and Academic Performance.” Journal of School Health 78 (4): 209–15. https://doi.org/10.1111/j.1746-1561.2008.00288.x.Search in Google Scholar

Gao, M., and Y. Yang. 2006. “Which Is the Main Reason for Income Inequality in Rural China: Physical Assets or Human Capital?” Economic Research Journal 41 (12): 71–80 (in Chinese).Search in Google Scholar

Glewwe, P., A. Park, and M. Zhao. 2016. “A Better Vision for Development: Eyeglasses and Academic Performance in Rural Primary Schools in China.” Journal of Development Economics 122: 170–82. https://doi.org/10.1016/j.jdeveco.2016.05.007.Search in Google Scholar

Gong, J., Y. Lu, and H. Song. 2018. “The Effect of Teacher Gender on Students’ Academic and Noncognitive Outcomes.” Journal of Labor Economics 36 (3): 743–78. https://doi.org/10.1086/696203.Search in Google Scholar

Heckman, J. J., J. E. Humphries, and G. Veramendi. 2018. “Returns to Education: The Causal Effects of Education on Earnings, Health, and Smoking.” Journal of Political Economy 126 (S1): S197–246. https://doi.org/10.1086/698760.Search in Google Scholar

Hoekstra, M. 2009. “The Effect of Attending the Flagship State University on Earnings: A Discontinuity-Based Approach.” Review of Economics and Statistics 91 (4): 717–24. https://doi.org/10.1162/rest.91.4.717.Search in Google Scholar

Hou, F., and F. Zhang. 2007. “Empirical Study on Investment and Spillover of Rural Human Capital and Difference between Urban and Rural Areas in China.” Journal of Finance and Economics 8: 118–31 (in Chinese).Search in Google Scholar

Jia, R., and H. Li. 2021. “Just Above the Exam Cutoff Score: Elite College Admission and Wages in China.” Journal of Public Economics 196: 104371.10.3386/w28450Search in Google Scholar

Lefgren, L., and M. I. Frank. 2006. “The Relationship Between Women’s Education and Marriage Outcomes.” Journal of Labor Economics 24 (4): 787–830. https://doi.org/10.1086/506486.Search in Google Scholar

Li, H., L. Meng, X. Shi, and B. Wu. 2012. “Does Attending Elite Colleges Pay in China?” Journal of Comparative Economics 40 (1): 78–88. https://doi.org/10.1016/j.jce.2011.10.001.Search in Google Scholar

Liu, X., and Y. Xu. 2021. “Unexpected Opportunity for Girls: Earthquake, Disaster Relief and Female Education in China’s Poor Counties.” China Economic Review 70: 101701. https://doi.org/10.1016/j.chieco.2021.101701.Search in Google Scholar

Murphy, R., and F. Weinhardt. 2020. “Top of the Class: The Importance of Ordinal Rank.” Review of Economic Studies 87 (6): 2777–826.10.3386/w24958Search in Google Scholar

Park, A., X. Shi, C. T. Hsieh, and X. An. 2015. “Magnet High Schools and Academic Performance in China: A Regression Discontinuity Design.” Journal of Comparative Economics 43 (4): 825–43. https://doi.org/10.1016/j.jce.2015.10.013.Search in Google Scholar

Sekhri, S., and P. Matters. 2020. “Wage Premium and Value Addition in Elite Colleges.” American Economic Journal: Applied Economics 12 (3): 207–25. https://doi.org/10.1257/app.20140105.Search in Google Scholar

Shi, P. Q. G., and R. Guo. 2008. Comprehensive Analysis and Assessment of Wenchuan Earthquake Disaster. Beijing: National Disaster Reduction Commission of China. (in Chinese).Search in Google Scholar

Teachman, J. D. 1987. “Family Background, Educational Resources, and Educational Attainment.” American Sociological Review 52 (4): 548–57. https://doi.org/10.2307/2095300.Search in Google Scholar

Wang, J., and Z. Sun. 2015. “Does Key Senior High Schools Improve Students’ Academic Performance? A Regression Discontinuity Study in County F.” Peking University Educational Review 4: 82–109. (In Chinese).Search in Google Scholar

Wang, L. 2012. “Economic Transition and College Premium in Urban China.” China Economic Review 23 (2): 238–52. https://doi.org/10.1016/j.chieco.2011.11.001.Search in Google Scholar

Wang, L. 2013. “How Does Education Affect the Earnings Distribution in Urban China?” Oxford Bulletin of Economics & Statistics 75 (3): 435–54.10.1111/j.1468-0084.2012.00697.xSearch in Google Scholar

Yu, H. 2020. “Am I the Big Fish? The Effect of Ordinal Rank on Student Academic Performance in Middle School.” Journal of Economic Behavior & Organization 176: 18–41. https://doi.org/10.1016/j.jebo.2020.05.006.Search in Google Scholar

Zhu, J., S. Liu, and Y. Li. 2021. “Removing the ‘Hats of Poverty’: Effects of Ending the National Poverty County Program on Fiscal Expenditures.” China Economic Review 69: 101673. https://doi.org/10.1016/j.chieco.2021.101673.Search in Google Scholar

Received: 2021-04-26
Revised: 2022-04-13
Accepted: 2022-04-14
Published Online: 2022-04-29

© 2022 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 28.3.2024 from https://www.degruyter.com/document/doi/10.1515/bejeap-2021-0140/pdf
Scroll to top button