"Study hard and make progress every day": Updates on returns to education in China

Jie Chen and Francesco Pastore

No 787, GLO Discussion Paper Series from Global Labor Organization (GLO)

Abstract: In this paper, we apply Generalized Propensity Score matching (GPSM) method, which deals with a continuous treatment variable, to estimate the returns to education in China from 2010 to 2017. Results are compared with OLS estimates from the classical Mincerian equation, as well as estimates from two instrumental variable methods (i.e., 2SLS and Lewbel). We use the Chinese General Social Survey data, including a subset newly released in 2020. We find that returns to education in China experienced a slight decrease in 2010-2015, but reverted back in 2017. With the more exible GPSM method, we also find that returns to university education remain higher than returns to secondary or compulsory education. The GPSM estimates are also closer to OLS estimates, compared to both instrumental variable methods.

Keywords: returns to education; endogeneity; continuous treatment; sample selection; GPSM; IV; Lewbel; China (search for similar items in EconPapers)
JEL-codes: I26 J30 (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-cna
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Working Paper: "Study Hard and Make Progress Every Day": Updates on Returns to Education in China (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:glodps:787

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