Firm Culture: Can Information Interventions Reduce Gender Gaps in Online Labor Markets?

Last registered on December 18, 2022

Pre-Trial

Trial Information

General Information

Title
Firm Culture: Can Information Interventions Reduce Gender Gaps in Online Labor Markets?
RCT ID
AEARCTR-0008841
Initial registration date
March 15, 2022

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 21, 2022, 1:27 PM EDT

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

Last updated
December 18, 2022, 1:37 AM EST

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

Locations

Region

Primary Investigator

Affiliation
University of California, Berkeley

Other Primary Investigator(s)

PI Affiliation
Barnard College
PI Affiliation
World Bank Research Group
PI Affiliation
Columbia University

Additional Trial Information

Status
In development
Start date
2022-02-01
End date
2023-05-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Africa has both the world’s youngest population and some of the highest rates of youth unemployment globally. While a growing literature links gender and ethnic bias to inefficient economic outcomes across a range of contexts in Africa, there is limited understanding of their contribution to frictions in labor markets. Using new administrative data on 227,194 applicants and over 2 million matches from an online job platform in Nigeria, we study matching and the role of gender and ethnicity in firm hiring. We find significant differences in the matching behavior of applicants by gender. Women tend to be more qualified, by education, for the jobs they apply to than men, but less qualified, by years of experience. Women apply to lower numbers of jobs and more lower level jobs than men, even when qualified for the position. Women are also less likely to be hired for a position than similarly qualified male applicants.

Why do we observe these gender gaps in application and hiring? And can low cost information interventions help reduce gender gaps in applications and hiring in online labor markets? To answer these questions, we implement a randomized control trial (RCT) to examine the effects of low cost information interventions on the observed gender gaps in application and hiring in the online labor market.
External Link(s)

Registration Citation

Citation
Annan, Francis et al. 2022. "Firm Culture: Can Information Interventions Reduce Gender Gaps in Online Labor Markets?." AEA RCT Registry. December 18. https://doi.org/10.1257/rct.8841-1.1
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2022-03-01
Intervention End Date
2022-10-31

Primary Outcomes

Primary Outcomes (end points)
Immediate outcomes:
(1) Job level chosen by a qualified candidate, with qualification based on the years of experience of the candidate, in accordance with the titles ‘Senior’ or not.
(2) Are women more likely to apply to jobs with the female-friendly signal? Does it change the composition of applicants?
(3) Candidate's choice to apply
(4) Relative ranking of women and men with similar characteristics

Long term outcomes:
(5) Job search behavior: Do those receiving the treatment apply to more jobs in next 2 months?
(6) Changes in candidate pool; candidate selected
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Design 1: Effect of Information on Job Application Patterns on Type of Job Applied For.
Treatment: Message on gender gaps in job applications. Applicants will be randomly provided with 2 true statements using evidence from Jobberman's administrative data and then asked to answer if the statements are true or false. (a) Non-female information statement: “More than 50% of jobs are based in Lagos” and (b) Female information statement: “When equally qualified for a job, women are less likely to apply”. Applicants will be randomly provided with the true information about (a) and (b) (i.e., information treatment). Applicants then immediately prompted, upon receiving either information treatment, to choose to apply to one of 3 job levels: (1) Analyst, (2) Senior Analyst or (3) None.


Design 2(i): Effect of Female-Friendly Job Advert on Choice to Apply. We invite applicants to click on job description (from Design 1). For the respondents who choose 'Analyst" or "Senior Analyst" (from Design 1), they are randomly shown 2 different job ads. One describes generic duties of an entry-level part-time temp job with investigators at Barnard, and another which describes these details but includes one more detail that highlights the female-friendly nature of the position using the following prompt: “NOTE: We are committed to improving diversity in the research space and work to ensure that our team is supportive of our employees. We strongly encourage applications from women.” Importantly, the Analyst and Senior Analyst roles have the exact same job description as well.
Treatment: Job description includes a “female-friendly signals”: "We are committed to improving diversity in the research space"; “women encouraged to apply”; remote work, flexible hours" (all features of the job maintained but we randomly vary whether we include these signals or not). Our choice of signals is objectively determined by baseline survey evidence from the Jobberman's platform.

Design 2(ii): Effect of Female-Friendly Job Advert on Choice to Apply. This is similar to Design 2(i) but with listed firms on Jobberman's platform. We recruit 100+ firms willing to participate in the study to allow us to include a female-friendly message signals in their job ad.
Treatment: Among firms who agree, randomly select half to implement now (rest after 6 months). For treatment job ads, advertise to potential applicants via email campaign.


Design 3: Employer assessment of candidate profiles. We invite Hiring Managers to complete an online exercise to shortlist candidates for a given job description, who will then complete a short survey after the shortlisting exercise.
Treatment: Fictional candidate profiles to be rated and ranked (randomize candidate characteristics). We randomize job title (Analyst vs. Senior Analyst) across Hiring Managers.
Experimental Design Details
Randomization Method
Computer software and simple lotteries, while ensuring balance on observable characteristics (Bruhn and McKenzie [2009])
Randomization Unit
At the individual-level
(job applicants on Jobberman, spanning different job sectors)
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
Number of individuals (job applicants): solicit enrollment from active 202,415 candidates on Jobberman platform and depending on the response rate, randomize candidates into the various experimental programs (similar to Allcott et al. [2020, 2021])
Sample size: planned number of observations
Number of individuals (job applicants): solicit enrollment from active 202,415 candidates on Jobberman platform and depending on the response rate, randomize candidates into the various experimental programs (similar to Allcott et al. [2020, 2021])
Sample size (or number of clusters) by treatment arms
50% of individuals (treatment programs) vs 50% of individuals (control programs)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Barnard College - Columbia University
IRB Approval Date
2020-06-03
IRB Approval Number
1920-1110-051

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