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Returns to higher education subjects and tiers in China: evidence from the China Family Panel Studies

Returns to higher education subjects and tiers in China: evidence from the China Family Panel... ABSTRACT Using the China Family Panel Studies, we identify the subjects studied by vocational college and university graduates, with the latter group further divided into ordinary and key universities. While the returns are around 8–10% to attending colleges and ordinary universities, there are higher returns of 12–16% per annum to attending the more prestigious key universities. The recent massive expansion of the higher education sector resulted in reduced returns to all HE types, except for graduates who studied subjects other than LEM (law, economics, and management) or STEM (sciences, technology, engineering and math/medicine) at key universities. We further account for selection on observables into subjects and tiers using the doubly robust Inverse Probability Weighted Regression Adjustment method (IPWRA) approach. While these results are tentative, they suggest that pooled OLS and random-effect models substantially underestimate the effect of attending universities that are more prestigious for graduates of both genders in LEM. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Studies in Higher Education Taylor & Francis

Returns to higher education subjects and tiers in China: evidence from the China Family Panel Studies

Studies in Higher Education , Volume 46 (8): 14 – Dec 11, 2019

Returns to higher education subjects and tiers in China: evidence from the China Family Panel Studies

Studies in Higher Education , Volume 46 (8): 14 – Dec 11, 2019

Abstract

ABSTRACT Using the China Family Panel Studies, we identify the subjects studied by vocational college and university graduates, with the latter group further divided into ordinary and key universities. While the returns are around 8–10% to attending colleges and ordinary universities, there are higher returns of 12–16% per annum to attending the more prestigious key universities. The recent massive expansion of the higher education sector resulted in reduced returns to all HE types, except for graduates who studied subjects other than LEM (law, economics, and management) or STEM (sciences, technology, engineering and math/medicine) at key universities. We further account for selection on observables into subjects and tiers using the doubly robust Inverse Probability Weighted Regression Adjustment method (IPWRA) approach. While these results are tentative, they suggest that pooled OLS and random-effect models substantially underestimate the effect of attending universities that are more prestigious for graduates of both genders in LEM.

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References (45)

Publisher
Taylor & Francis
Copyright
© 2019 Society for Research into Higher Education
ISSN
1470-174X
eISSN
0307-5079
DOI
10.1080/03075079.2019.1698538
Publisher site
See Article on Publisher Site

Abstract

ABSTRACT Using the China Family Panel Studies, we identify the subjects studied by vocational college and university graduates, with the latter group further divided into ordinary and key universities. While the returns are around 8–10% to attending colleges and ordinary universities, there are higher returns of 12–16% per annum to attending the more prestigious key universities. The recent massive expansion of the higher education sector resulted in reduced returns to all HE types, except for graduates who studied subjects other than LEM (law, economics, and management) or STEM (sciences, technology, engineering and math/medicine) at key universities. We further account for selection on observables into subjects and tiers using the doubly robust Inverse Probability Weighted Regression Adjustment method (IPWRA) approach. While these results are tentative, they suggest that pooled OLS and random-effect models substantially underestimate the effect of attending universities that are more prestigious for graduates of both genders in LEM.

Journal

Studies in Higher EducationTaylor & Francis

Published: Dec 11, 2019

Keywords: Returns to university tier and subjects; China; Inverse Probability Weighted Regression Adjustment; Higher Education expansion; I23; I26

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