Childbearing Age and Gender Discrimination on Labor Market: A Large-scale Field Experiment

Last registered on December 18, 2021

Pre-Trial

Trial Information

General Information

Title
Childbearing Age and Gender Discrimination on Labor Market: A Large-scale Field Experiment
RCT ID
AEARCTR-0007216
Initial registration date
March 01, 2021

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
March 01, 2021, 10:42 AM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Last updated
December 18, 2021, 10:44 AM EST

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Region

Primary Investigator

Affiliation
ShanghaiTech University

Other Primary Investigator(s)

PI Affiliation
Shenzhen University
PI Affiliation
Shenzhen University WeBank Institute of Fintech and Shenzhen Audencia Business School, Shenzhen University
PI Affiliation
Shenzhen University WeBank Institute of Fintech and Shenzhen Audencia Business School, Shenzhen University

Additional Trial Information

Status
On going
Start date
2020-12-28
End date
2022-06-30
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Despite improving trends in gender equality, gender gaps remain pronounced in modern societies, especially in the labor market. Conventional and restrictive gender norms, which stress women’s family-focused role and men’s career-focused role, further reinforce and reproduce gender inequality. With such gender ideology, employers may think of female employees as less career-minded and potentially incur higher labor costs due to the paid maternity leave. Therefore, female employees would be less favorable than their male counterparts would. Policy interventions, for instance, a rational-designed parental leave policy may help to reduce such gender discrimination in the labor market. In this study, we conduct an experiment using the correspondence testing method on the Chinese labor market. We investigate whether and to what extent gender discrimination exists in the labor market of China. If so, whether the current paid maternity leave plays an unintended role in exacerbating this situation. Understanding these questions is crucial for policymakers to improve the current parental leave policies and reduce the gender gap in the labor market.
External Link(s)

Registration Citation

Citation
Li, Lunzheng et al. 2021. "Childbearing Age and Gender Discrimination on Labor Market: A Large-scale Field Experiment." AEA RCT Registry. December 18. https://doi.org/10.1257/rct.7216
Experimental Details

Interventions

Intervention(s)
We are conducting a large-scale correspondence study to investigate gender discrimination in the Chinese labor market. The intervention is the gender and age of the job applicant, as signaled by the name and relevant information on the resume.
Intervention Start Date
2021-03-15
Intervention End Date
2021-05-16

Primary Outcomes

Primary Outcomes (end points)
The primary outcome of interest is whether a job applicant receives a positive response (callback) from an employer via email, phone call, or text message.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
The secondary outcome is how many times each resume is opened or viewed.
Secondary Outcomes (explanation)
There is a section on the online job application platform called “Who is searching me”, in which candidates can check when, how many times, and to what extent companies have interests in them.

Experimental Design

Experimental Design
We conduct a correspondence study by sending fictitious resumes to apply to online job postings from different firms. We create resumes for female and male candidates, which are similar in all dimensions (education background, previous work experience, professional skills, etc.), except for the names. The name on each resume is purposely designed to signal the job seeker’s gender. We collect related background information and baseline data via surveys and web scraping techniques in the targeted cities and platforms before the intervention starts.
Experimental Design Details
To investigate the effect of different maternity leave policies, we create profiles with three age categories: (socially recognized) pre-fertility age, fertility age, and post-fertility age to capture the potential concerns/discrimination of employers towards fertility-aged women. Specifically, we design job seekers in three age categories: 24, 29, and 34, which are pre-fertility, fertility, and post-fertility ages, respectively, based on the information collected from the baseline survey. We target three types of job vacancy advertisements, which are accounting (generally believed female-dominated occupations), programming (generally believed male-dominated occupations), and HR (generally believed gender-neutral occupations), to explore occupational gender segregation. Therefore, we create 18 candidates (two genders, three age categories, and three types of jobs) and 9 versions of resumes (commensurate experience with ages for each type of occupations).

We apply for jobs posted online in four municipalities (Beijing, Shanghai, Guangzhou, and Shenzhen). Beijing and Shanghai have a shorter duration of the paid maternity leave (128 days), and Guangzhou and Shenzhen have a longer duration (178 days). Within each occupation, we randomly divide firms into two groups (treatment and control groups). We collect firm attributes provided on the online job platform (e.g., company size, type, potential wage, etc.) to make sure that employers in treatment and control groups have balanced characteristics in all dimensions. Within each group of firms, we randomly submit resumes of different genders and age categories to apply for the posted jobs.
Randomization Method
Randomization done in office by a computer.
Randomization Unit
The randomization units are firms that posted job openings online.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
Planned number of clusters: 24 clusters
The cluster unit: 1000 firms
Sample size: planned number of observations
1000 firms * 3 age categories * (8 * 3) clusters = 72000 job applications
Sample size (or number of clusters) by treatment arms
Treatment group: 36000 female job applications
Control group: 36000 male job applications
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
ShanghaiTech University Institutional Review Boards (IRB)
IRB Approval Date
2020-05-21
IRB Approval Number
ShanghaiTech SEM IRB#2020-005

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials