主题：Gender-Targeted Job Ads in the Recruitment Process: Evidence from China
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.
We document how explicit employer requests for applicants of a particular gender enter the recruitment process on a Chinese job board. Overall, we find that 19 out of 20 callbacks to jobs requesting a particular gender are of the requested gender. Mostly, this is because application pools to those jobs are highly segregated, but men and women who apply to jobs requesting the ‘other’ gender also experience lower callback rates than other applicants. Our findings suggest that explicit gender requests direct where workers send their applications and predict how an application will be treated by the employer, if it is made.