Who benefits and how? Gendered effects of higher education expansion on local human capital investment

IF 5.9 1区 经济学 Q1 ECONOMICS
中国经济评论 Pub Date : 2026-05-01 Epub Date: 2026-05-06 DOI:10.1016/j.chieco.2026.102720
Wei Ha , Lu Lin , Sammy Pan , Shu Xu
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引用次数: 0

Abstract

Rapid higher education expansion has significantly altered the distribution of human capital investment across genders in developing countries. This paper exploits geographic and temporal variation in new campus openings during China's large-scale higher education expansion to examine its gendered effects on local human capital investment. Exposure to new campuses increases average schooling by 0.43 years, with most gains accruing to women. The effects spill over to neighboring counties and are stronger in regions with greater initial gender inequality. Mechanism analysis suggests that men benefit primarily through expanded local supply of vocational college seats, while women's gains across all education levels are driven by rising demand for female education. Rural women benefit mainly at the vocational level, whereas urban women gain disproportionately in university access. Our findings highlight the need for gender-specific approaches to promote equitable access to educational opportunities in higher education expansion policy designs.
谁会受益,如何受益?高等教育扩招对地方人力资本投资的性别效应
高等教育的快速扩张极大地改变了发展中国家人力资本投资的性别分布。本文利用中国大规模高等教育扩张期间新校区开放的地理和时间变化,考察其对地方人力资本投资的性别影响。进入新校园,平均受教育年限增加了0.43年,其中获益最多的是女性。这种影响会蔓延到邻近的县,而且在最初性别不平等程度较高的地区更为明显。机制分析表明,男性主要受益于当地职业学院席位供应的扩大,而女性在所有教育水平上的收益是由女性教育需求的上升所驱动的。农村妇女主要在职业层面受益,而城市妇女在进入大学方面获得了不成比例的好处。我们的研究结果强调,在高等教育扩张政策设计中,有必要采用针对性别的方法来促进公平获得教育机会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
中国经济评论
中国经济评论 ECONOMICS-
CiteScore
10.60
自引率
4.40%
发文量
380
期刊介绍: The China Economic Review publishes original works of scholarship which add to the knowledge of the economy of China and to economies as a discipline. We seek, in particular, papers dealing with policy, performance and institutional change. Empirical papers normally use a formal model, a data set, and standard statistical techniques. Submissions are subjected to double-blind peer review.
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