Valuing Fertility Benefits in Job Choice

Last registered on May 21, 2025
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Pre-Trial

Trial Information

General Information

Title
Valuing Fertility Benefits in Job Choice
RCT ID
AEARCTR-0016033
Initial registration date
May 16, 2025

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
May 21, 2025, 3:28 PM EDT

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

Locations

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Primary Investigator

Name
Evan Starr
Affiliation
University of Maryland
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Other Primary Investigator(s)

PI Name
Emily Nix
PI Affiliation
University of Southern California
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PI Name
Jason Sockin
PI Affiliation
Cornell University
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Additional Trial Information

Status
In development
Start date
2025-06-01
End date
2026-12-31
Keywords
Labor
Additional Keywords
Fertility Benefits, Willingness to Pay
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This project seeks to estimate the extent to which job seekers value fertility benefits offered by employers (e.g., coverge for out-of-state abortions, IVF, egg-freezing).
External Link(s)

Registration Citation

Citation
Nix, Emily , Jason Sockin and Evan Starr. 2025. "Valuing Fertility Benefits in Job Choice." AEA RCT Registry. May 21. https://doi.org/10.1257/rct.16033-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
The project will include a conjoint survey experiment that asks respondents to choose between two jobs with randomized components. These randomized components include compensation as well as a variety of fertility benefits.
Intervention Start Date
2025-09-01
Intervention End Date
2025-10-31

Primary Outcomes

Primary Outcomes (end points)
1. Job choice indicator (binary): Selected job A or B in each task
2. Implied willingness to accept lower pay for each benefit component
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
1. Self-reported importance of fertility benefits
2. Heterogeneity in preferences by demographic characteristics, such as gender, income, education, relationship status, fertility experience.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The experimental design will follow a conjoint survey experiment design, where respondents are asked to choose between a series of two job offers. Those job offers will contained a variety of randomized benefits, including compensation, as well as fertility benefit levels.
Experimental Design Details
Not available
Randomization Method
The randomization will be done in an office by a computer.
Randomization Unit
The unit of randomization will be the job offer level.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
8000
Sample size: planned number of observations
We plan to survey US workers aged 20-50 with an oversample of women of childbearing age. We are targeting 8000 completed surveys. With four distinct decisions, this leads to 32,000 respondent-task observations.
Sample size (or number of clusters) by treatment arms
We plan to survey 8000 respondents, with four distinct decisions, leading to 32,000 respondent-task observations.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We arrived at the estimate 8,000 based on some preliminary power calculations. We’d like our survey to be nationally representative, with an oversample for child-bearing age women, but we want to retain sufficient power for even our smallest subgroup comparisons of interest where we think effect sizes are likely smaller. For example, our preliminary results with the Indeed and Glassdoor data suggest potential sorting based on gender and political lines. Suppose our final sample is 30% male (due to the female oversample), and that political ideology is split 50/50. In order to detect a compensating differential of 5% among men with different political ideologies with 70% power, we need 1200 men in each political group. Aggregating these percentages up gives us 8,000 total responses
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
University of Maryland College Park (UMCP) IRB
IRB Approval Date
2025-05-14
IRB Approval Number
2323989-1
Analysis Plan

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