{"title":"中国不对称的雇主学习和基于性别的统计歧视","authors":"","doi":"10.1016/j.chieco.2024.102258","DOIUrl":null,"url":null,"abstract":"<div><p>We test if employers in China learn asymmetrically about worker's productivity and the implication on statistical discrimination against women. Using data from the 2018 survey of China Family Panel Studies (CFPS), we find evidence of asymmetric employer learning for non-college graduate workers. Furthermore, employers statistically discriminate against female workers without college education at time of hiring. This statistical discrimination against women does not decrease over time because asymmetric employer learning is found to occur mostly for men. In contrast, no evidence of employer learning or statistical discrimination against women is found for college graduate workers.</p></div>","PeriodicalId":48285,"journal":{"name":"中国经济评论","volume":null,"pages":null},"PeriodicalIF":5.2000,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Asymmetric employer learning and gender-based statistical discrimination in China\",\"authors\":\"\",\"doi\":\"10.1016/j.chieco.2024.102258\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>We test if employers in China learn asymmetrically about worker's productivity and the implication on statistical discrimination against women. Using data from the 2018 survey of China Family Panel Studies (CFPS), we find evidence of asymmetric employer learning for non-college graduate workers. Furthermore, employers statistically discriminate against female workers without college education at time of hiring. This statistical discrimination against women does not decrease over time because asymmetric employer learning is found to occur mostly for men. In contrast, no evidence of employer learning or statistical discrimination against women is found for college graduate workers.</p></div>\",\"PeriodicalId\":48285,\"journal\":{\"name\":\"中国经济评论\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.2000,\"publicationDate\":\"2024-08-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"中国经济评论\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1043951X24001470\",\"RegionNum\":1,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"中国经济评论","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1043951X24001470","RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
Asymmetric employer learning and gender-based statistical discrimination in China
We test if employers in China learn asymmetrically about worker's productivity and the implication on statistical discrimination against women. Using data from the 2018 survey of China Family Panel Studies (CFPS), we find evidence of asymmetric employer learning for non-college graduate workers. Furthermore, employers statistically discriminate against female workers without college education at time of hiring. This statistical discrimination against women does not decrease over time because asymmetric employer learning is found to occur mostly for men. In contrast, no evidence of employer learning or statistical discrimination against women is found for college graduate workers.
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.