Miss-Allocation: Occupational Gender Segregation and Gender Composition Preferences

Last registered on October 22, 2021

Pre-Trial

Trial Information

General Information

Title
Miss-Allocation: Occupational Gender Segregation and Gender Composition Preferences
RCT ID
AEARCTR-0008414
Initial registration date
October 21, 2021
Last updated
October 22, 2021, 10:02 AM EDT

Locations

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Primary Investigator

Affiliation
Stanford University

Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2021-07-01
End date
2021-12-31
Secondary IDs
J16 Economics of Gender
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Despite gender convergence in many areas of the labor market, men and women still work in markedly different occupations. In this paper, I aim to understand whether preferences over the gender composition of one’s occupation can explain continuing occupational gender segregation. To assess the willingness to pay for gender composition of a job, I run a survey in which respondents choose between multiple hypothetical jobs characterized by pay, tasks, and demographics. I then pair the survey results with a structural model to assess the welfare effects of policies that could re-allocate workers across occupations by gender.
External Link(s)

Registration Citation

Citation
Schuh, Rachel. 2021. "Miss-Allocation: Occupational Gender Segregation and Gender Composition Preferences." AEA RCT Registry. October 22. https://doi.org/10.1257/rct.8414-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
The main “intervention” is a survey-based conjoint experiment, where respondents choose between multiple pairs of job offers, with the wages and demographic characteristics randomized. Thus the random intervention is in a sense the wage and demographics that a respondent sees for each job pair.
Intervention Start Date
2021-07-01
Intervention End Date
2021-12-31

Primary Outcomes

Primary Outcomes (end points)
The primary outcome of interest is the profile of average willingness to pay for different gender compositions, estimated separately for men and women.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
I will consider several secondary outcomes of interest, listed below:
• willingness to pay for age and education composition
• heterogeneous preferences within gender, estimated using a latent-class logit model
• heterogeneity in gender willingness to pay by individual covariates, including correlating these
with latent classes as well as estimating single-type logit separately by group, with covariates
including
– true female share of occupation, including both reported female share of firm and cowork-
ers with same job at firm, and female share of reported occupation calculated using CPS
and Census/ACS data
– expectations of jobs with different female shares, including satisfaction with coworker
interactions, task content, schedule, work environment, earnings, promotion, and family
preference
– reported gender attitudes from questions on whether women should stay at home and
men work, whether men or women have it easier in the US these days, and stance on
affirmative action for women in the workplace
– demographics including age, education, income, number of children, race, marital status,
employment status, and industry
• occupation choice given choice between two real occupations with and without gender share
shown (secondary experiment, used here only for robustness)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The main trial is a conjoint experiment varying wages and gender compositions of job offers.
Experimental Design Details
Not available
Randomization Method
Randomization will be done by computer through Qualtrics.
Randomization Unit
Randomization is done at the individual respondent level.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
2000 individuals
Sample size: planned number of observations
2000 individuals
Sample size (or number of clusters) by treatment arms
no treatment arms per se
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
the minimum detectable effect is a 1% willingness to pay, based on simulations
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
Stanford Human Subjects Research and IRB
IRB Approval Date
2020-09-10
IRB Approval Number
57557
Analysis Plan

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