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Multidimensional Measurement and Comparison of China’s Educational Inequality

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Abstract

Educational equity significantly supports the creation of a harmonious society, which has become one of the Sustainable Development Goals of the United Nations for 2030. China’s education level has greatly improved as nine-year compulsory education popularized and enrollment expanded in higher education. However, educational inequality remains a serious issue. This study explores China’s educational inequality from the perspective of regions, urban–rural areas, genders, and age cohorts by constructing and decomposing educational Gini coefficient based on the concept of relative deprivation. The results indicate that China’s average years of schooling continued to improve, and educational inequality showed a downward trend during 2000–2018. Both men and women average years of schooling in cities was higher than that in towns and village even for the youngest age cohort. The Gini coefficient difference across genders, urban–rural areas mainly relates to the oldest age cohort. The Gini coefficient in western region and villages are relatively high, whereas that in the northeast region and cities are relatively low. The gap of the Gini coefficient between cities and towns and between cities and villages is expanding. Inter-group inequality was the main source of overall educational inequality in regions, urban–rural areas, and genders. Therefore, measures should be taken to improve the education level for the underdeveloped areas and reduce inter-group inequality.

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Notes

  1. In this paper, “group” refers to a certain level of a background characteristic (e.g., age groups, regions, genders).

  2. The confidence intervals for China’s education Gini index during 2000–2018 are provided in Table 5.

  3. The share of urban population was 36.22% in 2000 and reached 59.58% in 2018. The data come from China Statistical Yearbook (2001, 2019).

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Funding

This research was funded by the National Social Science Foundation of China (No 1020KZ0118019) and the Characteristic & Preponderant Discipline of Key Construction Universities in Zhejiang Province (Zhejiang Gongshang University—Statistics).

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Authors and Affiliations

  1. College of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, 310018, China

    Gangfei Luo

  2. School of Business, Ningbo University, Ningbo, 315211, China

    Shouzhen Zeng

  3. Lithuanian Centre for Social Sciences, Vilnius, Lithuania

    Tomas Baležentis

Authors
  1. Gangfei Luo
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  2. Shouzhen Zeng
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  3. Tomas Baležentis
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Correspondence to Shouzhen Zeng or Tomas Baležentis.

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Appendix: Bootstrap Confidence Intervals for the Gini Coefficient

Appendix: Bootstrap Confidence Intervals for the Gini Coefficient

We carry out the bootstrap-based analysis to check the significance of the differences in the Gini coefficient across groups. We use the bootstrap technique to study the China’s education Gini coefficient across age cohorts. We construct a sample of size 100,000 by proportionally sampling from the groups of the census population. Then, we construct 2000 bootstrap samples by random sampling with replacement (10,000 draws with replacement are made for each sample). We calculate the average and standard deviation of the Gini coefficients of 2000 bootstrap samples, and obtain the confidence interval (at the 95% confidence level) for Gini coefficient. The implementation of bootstrap follows Osberg and Xu (2000) and Xu (2000). Table

Table 5 Point estimates, standard deviation and confidence intervals of Gini coefficient for 2000–2018
Full size table

5 presents the period-wise results for 2000–2018, whereas Table

Table 6 Point estimates, standard deviation and confidence intervals of Gini coefficient across age cohorts in 2010
Full size table

6 focuses on age-related differences in 2010. The overlap of the confidence intervals indicates the absence of the significant differences between certain two groups of observations (and vice versa).

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Luo, G., Zeng, S. & Baležentis, T. Multidimensional Measurement and Comparison of China’s Educational Inequality. Soc Indic Res 163, 857–874 (2022). https://doi.org/10.1007/s11205-022-02921-w

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