dc.contributor.authorSakellariou, Christos
dc.contributor.authorFang, Zheng
dc.identifier.citationSakellariou, C., & Fang, Z. (2015). Returns to schooling for urban and migrant workers in China: a detailed investigation. Applied Economics, 48(8), 684-700.en_US
dc.description.abstractWe use a new data set, the 2009 Rural Urban Migration in China (RUMiC) to estimate returns to schooling in China using instrumental variable (IV) estimation. After identifying a set of instruments, we conduct comprehensive validity and relevance testing of different combinations of instruments as well as robustness analysis of our estimates for rural-to-urban migrants and urban residents in China. We find that our point estimates are in the 6–9% range for urban workers compared to 7–8% for migrant workers. Returns for men (at 8–9%) are slightly higher than for women (at 6–7%). Thus, private returns to education in urban China in 2009 were not as high as other transition and developing countries, but substantial and have increased over time. Comparing OLS and IV estimates, we also find that the attenuation bias due to measurement error is generally large and more important in the migrant sample compared to the urban sample.en_US
dc.format.extent32 p.en_US
dc.relation.ispartofseriesApplied Economicsen_US
dc.rights© 2015 Taylor & Francis. This is the author created version of a work that has been peer reviewed and accepted for publication by Applied Economics, Taylor & Francis. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1080/00036846.2015.1085642].en_US
dc.subjectReturns to Schoolingen_US
dc.subjectInstrumental Variablesen_US
dc.titleReturns to schooling for urban and migrant workers in China: a detailed investigationen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Humanities and Social Sciencesen_US
dc.description.versionAccepted versionen_US

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