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Bigger Pie, Bigger Slice? The Impact of Higher Education Expansion on Educational Opportunity in China

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Abstract

China’s higher education system has expanded rapidly since 1999. Exploiting variation in the density of university expansion across provinces and high school cohorts and applying a difference-in-differences model, we estimate the impact of higher education expansion on educational access and attainment with a particular focus on students’ family and demographic backgrounds. Results indicate that the expansion of university spots increased both access and graduation rates at 4-year universities, but this improvement was driven by those of higher social status, including males, those with highly educated fathers, han-ethnic and urban students. Females, rural students and those with low-educated fathers also benefited once they were able to graduate from high school. Also, the policy had only a limited effect on the likelihood of graduating from high school. As in other countries, education expansion in China has not led to equal distribution of educational opportunities, and the least socioeconomically advantaged students are missing out.

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  1. Although little empirical evidence exists regarding the role of higher education in economic growth (Bloom et al. 2014; Gyimah-Brempong et al. 2006; Hanushek 2013, 2016), theoretical analysis has shown that higher education has at least three channels to contribute to economic growth: the accumulation of productive capabilities; the generation of new knowledge from innovation; and the promotion of faster adoption of new technologies (Holmes 2013). See Holmes (2013) and Hanushek (2016) for a brief description of the evolution of economic theories/models on the role of human capital and economic development. Researchers also point out that the quality of education and the efficiency of the institutional environment have an important effect on the capability of higher education to boost economic growth (Pissarides 2000, 2001; Hanushek and Wöβmann 2010). Further, despite the conventional concerns of "brain drain" associated with emigration of highly-skilled workers, research suggests that returned migrants and the linkage between emigrants, returned migrants and home-country workers [facilitate more business opportunities, which in turn contribute to economic growth (Saxenian 2006).

  2. Davey et al. (2007) provide details of the gaokao exam and comments on the difficulties of the exam compared to the British GCSE. Yang (2014) provides a more detailed section on the admissions procedures.

  3. Usually universities plan the allocation of admissions quotas with the Ministry of Education and local governments before they release such information in booklets to students (Yang 2014).

  4. Only a small number of applicants who excelled in athletics, arts or academic competitions can be admitted regardless of their gaokao scores (Yang 2014; Wang 2007).

  5. Due to variation in college admissions cut-off scores on the gaokao exam as well as admissions quotas for students from different provinces, students can potentially migrate to regions with lower cut-off scores but higher admissions quotas in order to gain the opportunity to be admitted by the same university. However, regions with lower cut-off scores but higher admissions quotas for local-hukou students tend to be either those in Beijing, Shanghai, and Tianjing (i.e. economically developed cities but with high migration restrictions) or the ethnic-minority concentrated remote areas (such as Xinjiang, Tibet, Yunnan etc.). However, central and regional governments normally see such migration as “illegal” and would cancel gaokao migrants’ eligibility for college admission even if the students have valid gaokao scores (Wang and Chan 2005). Further, national statistics do not show a large proportion of such students overall. In addition, we have excluded observations in the ethnic-minority regions and provinces due to differences in college admissions policies for ethnic minority students. Therefore, it is very unlikely that our sample would be biased by having a large number of gaokao migrants.

  6. Even though there is evidence that returns to higher education do not decrease but rather increase in developing countries after massive college expansion (Carnoy et al. 2013a, b), Ou and Zhao (2016) show that there is a wage decrease for college graduates in China who were employed in high-skilled jobs due to the expansion policy. Such a finding might support our observation in this study that students residing in regions with faster expansion in university spots are more likely to be 4-year-college students and graduates. Therefore, the increasing supply of high-skilled workers (as measured by 4-year-university graduates) could lead to more competition in the labor market of college graduates and subsequently lower initial wages as the study by Ou and Zhao (2016) indicates.

  7. Expected year of completion of high school : (1) reported year of completion of middle school + the length of high school study (if no length of high school study was reported, we assume it is 3 years); (2) reported year of completion of primary school + length of middle school study + length of high school study (if no length of middle school study was reported, we assume it is 3 years; same for missing information on high school study length); (3) year of birth + length of primary school study (if no length of primary school study was reported, we assume it is 6 years) + n (n = 6 if month of birth is between January and August; n = 7 if month of birth is between September and December), where n represents years elapsed between birth and starting primary school.

  8. Though this assumption has limitations, we have run several robustness checks to ensure that the results are valid. We ran regressions based on a sample who never changed their hukou registration status from birth and the results qualitatively resemble our main results. We also ran regressions using individuals’ hukou information at the age of 12. The results (available upon request) are also similar to those using the hukou information at birth.

  9. We have also employed other imputations including combining CFPS 2012 and 2014 follow-up data to fill in the missing information, a single imputation method, using parental educational level instead of father’s education, etc. In short, our major conclusions still hold using any of the imputation methods described.

  10. Compared to probit and logit models, the linear probability model (LPM) produces an unbiased and consistent estimate when variables uncorrelated with the included covariates are omitted from the regression. Meanwhile, it is easier to interpret the estimated results with LPM, especially when there is an interaction term in the estimation equation (Li et al. 2014).

  11. \(({\text{Postreform}}_{i} *\Delta {\text{supply}}_{p} )\) becomes the effect of the excluded background category (after the reform) and will not be explicitly shown. Similarly, \({\text{Postreform}}_{i}\) and \(\Delta {\text{supply}}_{p}\) become the effect of the excluded background category interacting with the Postreform dummy and supply intensity, i.e. \((B_{i} *{\text{Postreform}}_{i} )\) and \((B_{i} *\Delta {\text{supply}}_{p} )\).

  12. For instance, when we examine the policy effects on subsamples by father’s educational level, dummies of father’s educational level will be entered into Equation (1) as B_i. Other dimensions of background variables will be entered in Xi

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Acknowledgements

We are grateful to Massimiliano Bratti, Lingxin Hao, Prashant Loyalka, Sandra McNally and audience at the 2015 AERA conference for their helpful comments. We also thank all anonymous reviewers whose comments have greatly improved this paper. Ou acknowledges funding from the CUHK Direct Grant (#4058038). The opinions expressed here represent opinions of the authors only.

Funding

Funding was provided by CUHK Direct Grant (Grant No. #4058038).

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  1. Department of Educational Administration and Policy, Faculty of Education, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

    Dongshu Ou

  2. Faculty of Education, Southwest University, Chongqing, People’s Republic of China

    Yuna Hou

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Appendix

Appendix

See Fig. 3 and Tables 7, 8, 9 and 10.

Table 7 The expansion of general higher education in China over 1998–2006
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Table 8 Intensity of higher education expansion by Province/region
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Table 9 Test on parallel trend assumption
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Table 10 Robustness checks of sample of stayers (Region of birth = region of current residence)
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Ou, D., Hou, Y. Bigger Pie, Bigger Slice? The Impact of Higher Education Expansion on Educational Opportunity in China. Res High Educ 60, 358–391 (2019). https://doi.org/10.1007/s11162-018-9514-2

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