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              [中国青年经济学者论坛]Gender-Targeted Job Ads in the Recruitment Process: Evidence from China
              发布日期:2019-06-27

              经济学院青年学者论坛


              [题目]Gender-Targeted Job Ads in the Recruitment Process: Evidence from China
              [主讲人]沈凯玲,澳大利亚国立大学经济研究所
              [主持人]陆方文,中国人民大学经济学院
              [时间]2019年6月27日16:00-17:30
              [地点]明德主楼729会议室

              [摘要]We document how explicit employer requests for applicants of a particular gender enter the recruitment process on a Chinese job board. We find that 95 percent of callbacks to gendered jobs are of the requested gender; worker self-selection (“compliance” with employers’ requests) and employer callback decisions from applicant pools (“enforcement”) both contribute to this association, with compliance playing the larger role. Explicit gender requests account for over half of the gender segregation and gender wage gap observed on the board. Ad-level regressions with job title and firm fixed effects suggest that employers’ explicit gender requests have causal effects on the gender mix of applications received, especially when the employer’s likely gender preference is hard to infer from other contents of the ad. Application-level regressions with job title and worker fixed effects show that both men and women experience a callback penalty when applying to a gender-mismatched job; this penalty is significantly greater for women (44 percent) than men (26 percent).

              [主讲人简介] Kailing Shen is an Associate Professor at the Research School of Economics of the Australian National University. She joined ANU in 2015. Before that, she was with Xiamen University in China. Kailing has also been appointed as a research fellow of IZA since 2007. Kailing received her PhD from the University of British Columbia. Her research focuses on empirical analysis of the labor market. So far, her research has covered a wide spectrum of issues, including unemployment insurance, job search and matching, discrimination, gender differentials, income inequality, education, migration, aging as well as within-household behavior. For the last ten years, she has mainly been working with online job board data.

              供稿:张海燕;编辑:杨菲;核稿:陆美贺

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