TY - JOUR
T1 - Overeducation, major mismatch, and return to higher education tiers
T2 - evidence from novel data source of a major online recruitment platform in China
AU - Zheng, Yanqiao
AU - Zhang, Xiaoqi
AU - Zhu, Yu
N1 - Funding Information:
Yanqiao Zheng thanks the 2020 ?Qianjiang Talent Plan? of Zhejiang Province of China (grant number: 10407820001) for support. Xiaoqi Zhang thanks Ministry of Education of China Youth Program in Humanities and Social Science (grant number: 20YJC790176) for financial support. We thank Minsheng Weekly for allowing access to the data and participants of the 2019 Conference of Frontiers in Labor Economics in China and the 2018 Conference on China's Reform and Opening Up for comments. All errors are ours.
Funding Information:
Yanqiao Zheng thanks the 2020 "Qianjiang Talent Plan" of Zhejiang Province of China (grant number: 10407820001 ) for support. Xiaoqi Zhang thanks Ministry of Education of China Youth Program in Humanities and Social Science (grant number: 20YJC790176 ) for financial support. We thank Minsheng Weekly for allowing access to the data and participants of the 2019 Conference of Frontiers in Labor Economics in China and the 2018 Conference on China's Reform and Opening Up for comments. All errors are ours.
Publisher Copyright:
© 2021 Elsevier Inc.
PY - 2021/4
Y1 - 2021/4
N2 - We develop a novel approach to study overeducation by extracting pre-match information from online recruitment platforms using word segmentation and dictionary building techniques, which can offer significant advantages over traditional survey-based approaches in objectiveness, timeliness, sample sizes, area coverage and richness of controls. We apply this method to China, which has experienced a 10-fold expansion of its higher education sector over the last two decades. We find that about half of online job-seekers in China are two or more years overeducated, resulting in 5.1% pay penalty. However, the effect of overeducation on pay varies significantly by college quality, city type, and the match of college major with industry. Graduates in STEM (Science, Technology, Engineering and Mathematics) or LEM (Law, Economics and Management) from Key Universities are much less likely to be overeducated in the first place, and actually enjoy a significant pay premium even when they are in the situation.
AB - We develop a novel approach to study overeducation by extracting pre-match information from online recruitment platforms using word segmentation and dictionary building techniques, which can offer significant advantages over traditional survey-based approaches in objectiveness, timeliness, sample sizes, area coverage and richness of controls. We apply this method to China, which has experienced a 10-fold expansion of its higher education sector over the last two decades. We find that about half of online job-seekers in China are two or more years overeducated, resulting in 5.1% pay penalty. However, the effect of overeducation on pay varies significantly by college quality, city type, and the match of college major with industry. Graduates in STEM (Science, Technology, Engineering and Mathematics) or LEM (Law, Economics and Management) from Key Universities are much less likely to be overeducated in the first place, and actually enjoy a significant pay premium even when they are in the situation.
KW - China
KW - Major-industry mismatch
KW - Online recruitment data
KW - Overeducation
UR - http://www.scopus.com/inward/record.url?scp=85099227112&partnerID=8YFLogxK
U2 - 10.1016/j.chieco.2021.101584
DO - 10.1016/j.chieco.2021.101584
M3 - Article
SN - 1043-951X
VL - 66
JO - China Economic Review
JF - China Economic Review
IS - 1
M1 - 101584
ER -