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Investigating the Impact of the Nine-year Compulsory Education Policy on Female Attainment of College Education in China

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  • Wanqing Wu Shanghai International Studies University

DOI:

https://doi.org/10.58445/rars.52

Keywords:

China, Nine-year Compulsory Education Policy, Gender Inequality, College Education

Abstract

The average value of female pre-tax labor income shares in China decreased substantially from 1990 to 2020 and its value was always under 0.50, which meant females’ pre-tax labor income was less than males’. The education level difference between males and females was also still apparent. After the Nine-year Compulsory Education Law was implemented in 1986, and the Compulsory Education Law of the People’s Republic of China was officially revised in 2006, more and more people were being educated. However, even in 2019, there were still many uneducated women, nearly two and a half times as many as men.

Through this project, we explore how the Nine-year Compulsory Education Policy affects females’ education attainments, especially college education. We describe the trends in female pre-tax labor income share in China from 1999 to 2019. We use linear regression to compare the proportion of females with college education levels before and after the Nine-year Compulsory Education Policy. We compare females’ data with males’ and discuss the possible reasons for inequality. Our results suggest that the Compulsory education policy does have a positive effect on college education levels, but there remains some inequality between males and females in college education.

References

World Inequality Database, http://wid.world/data.

The National People's Congress of the People's Republic of China, www.npc.gov.cn/npc/c2306/200012/b75b3a951ce840b690c75a7507fc0794.shtml.

The Compulsory Education Law, www.npc.gov.cn/npc/c30834/201901/21b0be5b97e54c5088bff17903853a0d.shtml.

The National Bureau of Statistics of China, www.stats.gov.cn/english/Statisticaldata/AnnualData/.

James, Gareth, et al. “Linear Regression”, An Introduction to Statistical Learning: With Applications in R, pp. 61–62.

Scikit-learn: Machine Learning in Python, Pedregosa et al., JMLR 12, pp. 2825-2830, 2011. [7] J. D. Hunter, "Matplotlib: A 2D Graphics Environment", Computing in Science & Engineering, vol. 9, no. 3, pp. 90-95, 2007.

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Posted

2022-11-02 — Updated on 2022-12-22

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