The effect of eldercare on job search: Evidence from a correspondence study

Last registered on October 23, 2025
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Pre-Trial

Trial Information

General Information

Title
The effect of eldercare on job search: Evidence from a correspondence study
RCT ID
AEARCTR-0016940
Initial registration date
October 03, 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
October 06, 2025, 3:17 PM EDT

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

Last updated
October 23, 2025, 1:33 PM EDT

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

Locations

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Primary Investigator

Name
Amanda Gaulke
Affiliation
Kansas State University
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Other Primary Investigator(s)

PI Name
Zack Meyer
PI Affiliation
Kansas State University
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Additional Trial Information

Status
In development
Start date
2025-10-20
End date
2027-03-31
Keywords
Gender, Labor
Additional Keywords
Eldercare, Unemployment Spell, Correspondence Study
JEL code(s)
J14, J23, J16
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
In the United States, about twenty percent of adults provide care to another adult and that prevalence has increased over time (NAC and AARP, 2020). Recent estimates suggest that more people are providing care to another adult than to a child (NAC and AARP, 2020) and the value of unpaid care to other adults with limitations in daily activities totals $600 billion (Reinhard et al., 2023). Given the literature documenting scarring effects from unemployment spells (for example, see Kroft et al., 2013; Eriksson and Rooth, 2014), this raises questions about whether revealing eldercare as the reason for leaving the previous job and as the activity undertaken while unemployed helps or hurts job candidates. We use a correspondence study to quantify whether and to what extent the gap in callback rates between the eldercare providers and the continuously employed group differs from the gap in callback rates between the unexplained (no reason given) group and the continuously employed group.
External Link(s)

Registration Citation

Citation
Gaulke, Amanda and Zack Meyer. 2025. "The effect of eldercare on job search: Evidence from a correspondence study." AEA RCT Registry. October 23. https://doi.org/10.1257/rct.16940-1.1
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Experimental Details

Interventions

Intervention(s)
We randomly assign one of the applicant triplets to have provided eldercare in the past - the reason for leaving the previous job and the time spent unemployed is revealed to be eldercare. We also have the previously used intervention of the unexplained group - the applicant in the triplet that has the same length of gap but no reason for why left the job or what the time unemployed was spent doing.
Intervention Start Date
2025-10-20
Intervention End Date
2027-03-31

Primary Outcomes

Primary Outcomes (end points)
Callback rate
Primary Outcomes (explanation)
A callback can be in the form of an email or a voicemail. Callback is a dummy variable that is set equal to one if an applicant receives an unambiguously positive response (i.e., asking for our applicant to schedule an interview) or a neutral response (i.e., asking for more information). Otherwise, it is set to zero. We study the callback rate within a two-week period.

Secondary Outcomes

Secondary Outcomes (end points)
NA
Secondary Outcomes (explanation)
NA

Experimental Design

Experimental Design
We use a correspondence study to determine whether revealing the eldercare spell results in a smaller gap in
callback rates compared to continuously employed applicants relative to a group that provides no explanation.
This means that we create fictitious applications and submit them to real job postings.

Implementation:
1) collect job ads that meet the study criteria
2) Once we find an eligible job ad, we apply using one of the triplets. We randomize the order and have rules for spacing out applications.
3) After submitting the applications, we measure the callback rate, which is our main outcome of interest.
4) After receiving responses from potential employers, we respond that the applicant is no longer available for the job to minimize costs for firms.
Experimental Design Details
Not available
Randomization Method
In office by a computer
Randomization Unit
Randomization happens at the applicant level
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
We plan to apply using 9,099 applicants or 3,033 jobs (given the triplet)
Sample size: planned number of observations
9,099 triplets
Sample size (or number of clusters) by treatment arms
Each triplet will include an applicant that is continuously employed, one that is part the eldercare treatment group and one part of the explained group. This means 9,099/3=3,033 applications per arm of the experiment
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We picked our sample size based on being able to detect effects within subgroups by sex (we think the eldercare treatment will vary by sex and thus a prior want to build the experiment to be able to directly test for that). Given the means were closer in the pilot for women, we need to use a larger sample size to detect an effect for women and thus do not split the sample 50-50.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Kansas State University
IRB Approval Date
2025-09-22
IRB Approval Number
12359
Analysis Plan

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