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Data Set Description

When employers’ explicit gender requests were unexpectedly removed from a Chinese job board overnight, pools of successful applicants became more integrated: women’s (men’s) share of call-backs to jobs that had requested men (women) rose by 62 (146) percent. The removal ‘worked’ in this sense because it generated a large increase in gender-mismatched applications, and because those applications were treated surprisingly well by employers, suggesting that employers’ gender requests often represented relatively weak preferences or outdated stereotypes. The job titles that were integrated by the ban, however, were not the most gendered ones, and were disproportionately lower-wage jobs.

The data here is provided by a Xiamen-based job board, XMRC ( Two samples are used, the main analysis sample and the DiD sample.

To construction of the main analysis sample started with the universe of applications that were made on XMRC between January 1, 2018 and October 25, 2019, and the corresponding ads. Only the following job ads were retained:

  1. Job ads that received at least one application both before and after the ad ban (March 1, 2019), and

  2. Applications to those ads that were made between August 3,1 2018 and August 29, 2019.

This results in a sample of 52 complete weeks (26 before and 26 after the ban), in which the first post-ban week begins on Friday March 1, 2019 -- the first day of the ban. This ends with a wide window (almost two years) to make sure that all the job ads that ‘straddled’ the ban were captured. Then only the ads that actually straddled the ban (received at least one application before and after it) were retained. Finally, the analysis sample comprises all applications to those ads that occurred during a one-year window surrounding the ban.

To allow comparison of changes on XMRC around the ban with changes in 2018, also a DiD sample was created. To do so, the main estimation sample was replicated -- which comprises applications that were made between September 2018 and August 2019 -- on two different periods: January -- August 2018 and January-August 2019. The latter period contains the date on which the 2019 ban occurred, and the former contains the date on which it would have occurred in 2018. Unfortunately, these two periods cannot be designed to exactly mimic the main analysis sample because no XMRC data from 2017 are available. While this restricts the length of the pre-ban period in both years to just two months, it allows to compare trends in the main outcomes between 2018 and 2019 on both sides of the ban date. This sample was used to conduct a difference-in-difference analysis of the ban’s effect -- which uses equivalent days or weeks from 2018 as controls for 2019 -- as a robustness check of the main results. Notably, since important events affecting China’s labor market -- especially the Spring Festival -- are determined by the lunar calendar, this new DiD sample requires to line up days and weeks between 2018 and 2019 to that they represent the same days and weeks relative to the start of the Spring Festival in both years.

The primary dataset comprises 3,130,317 applications made by 204,343 workers (resumes) to 116,725 ads, placed by 15,437 firms, resulting in 348,062 call-backs.

Date Created: 2022-09-21

Scope of Data Set


Time Periods: January 2018 - October 2019


Kuhn, P.J. & Shen, K. (2022). What Happens When Employers Can No Longer Discriminate in Job Ads? Research Data Center of IZA (IDSC). Version 1.0. doi:10.15185/w1405.1

Researchers working with the “What Happens When Employers Can No Longer Discriminate in Job Ads?” are obligated to acknowledge the data base and its documentation within their publications, including the DOI, by using this reference.


IZA Discussion Paper(s)


Restricted Access

  1. Kuhn, Peter J.
  2. Shen, Kailing

Textual data


Access to the codes and data is provided to non-for-profit research, replication and teaching purposes. Codes can be accessed via the public documentation link below. The data is available from the Research Data Center (IDSC) of IZA through remote processing via JoSuA.
JoSuA is the IDSC's web based solution which allows remote researchers to compute against sensitive or proprietary data by sending their code to the data when the data may not leave the premises of a data provider/custodian.
Please contact IDSC for any access requests.


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