Reference Letters as Incentives

Last registered on August 10, 2026
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Pre-Trial

Trial Information

General Information

Title
Reference Letters as Incentives
RCT ID
AEARCTR-0019327
Initial registration date
August 07, 2026

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
August 10, 2026, 4:54 PM EDT

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

Locations

Country
Pakistan
Region

Primary Investigator

Name
Robert Garlick
Affiliation
Emory University
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Other Primary Investigator(s)

PI Name
Alison Andrew
PI Affiliation
University of Oxford
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PI Name
Erica Field
PI Affiliation
Duke University
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PI Name
Kate Vyborny
PI Affiliation
World Bank
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Additional Trial Information

Status
On going
Start date
2026-01-01
End date
2027-06-30
Keywords
Labor
Additional Keywords
information frictions; limited information; references; skills
JEL code(s)
I25; I26; J24; O15
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Information frictions in the labor market can limit workers’ ability to signal their skills and may disproportionately affect women, who face statistical discrimination and limited access to professional networks. Prior research suggests that verifiable reference letters can improve labor market outcomes, particularly for women. Hence, we study whether the opportunity to earn a reference letter, which makes past work performance visible to employers, affects job selection, effort, performance, and skill investment.

We run a multi-round field experiment embedded in a one time online data-entry job. In Round 1, jobseekers responding to an online advertisement are randomized into one of two arms before deciding whether to sign up: (i) an offer of a verifiable reference letter or (ii) a control condition offering no reference letter. This round measures impacts on selection into the job.

In Round 2, participants who enrolled from the initial (Round 0) control group only are invited to interview for the job, and are re-randomized into two arms : (i) an offer of a verifiable reference letter, or (ii) control condition offering no reference letter, to measure effects on job effort, job performance, and skill investment during the job. If funding allows, we may include (iii) an offer of performance-based bonuses, to benchmark the value of the reference letter to participants.

Outcomes will be analyzed overall and across subgroups, including by gender, to examine heterogeneous treatment effects.
External Link(s)

Registration Citation

Citation
Andrew, Alison et al. 2026. "Reference Letters as Incentives." AEA RCT Registry. August 10. https://doi.org/10.1257/rct.19327-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
The design includes two rounds:

Round 1:
Participants view an online advertisement for the job and, before deciding whether to sign up, are randomized into one of the two arms:
(i) Reference information, offering the opportunity to earn a verifiable reference letter
(ii) Control, offering no reference letter.

Round 2:
Only control-group participants from Round 1 who sign up are re-randomized into one of the two arms:
(i) Reference information, offering the opportunity to earn a verifiable reference letter
(ii) Control, offering no reference letter.
We may include a third treatment arm in round 2, offering performance-based bonuses in order to benchmark the magnitude of the reference effect. At the time of registering this trial, we are not yet sure if we will have sufficient funds to include this arm.

We will also collect information on baseline characteristics as part of the sign-up instrument, including gender, highest education qualification, current education status, current employment status, years of total experience, software-related skills, whether participants uploaded their CV, and whether they provided a reference letter for this job.

We will run analyses both with and without conditioning on covariates and will report whether the results differ. We will use a machine learning method such as random causal forests to assess if treatment effects differ by covariates.
Intervention Start Date
2026-01-01
Intervention End Date
2027-06-30

Primary Outcomes

Primary Outcomes (end points)
Round 1 outcomes:
- Sign-up for the job (binary variable)

Round 2 outcomes:
- Job performance / accuracy
- Job effort
- Skill investment
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The design includes two rounds:

Round 1:
Participants view an online advertisement for the job and, before deciding whether to sign up, are randomized into one of the two arms:
(i) Reference information, offering the opportunity to earn a verifiable reference letter
(ii) Control, offering no reference letter.

Round 2:
Only control-group participants from Round 1 who sign up are re-randomized into one of the two arms:
(i) Reference information, offering the opportunity to earn a verifiable reference letter
(ii) Control, offering no reference letter.
We may include a third treatment arm in round 2, offering performance-based bonuses in order to benchmark the magnitude of the reference effect. At the time of registering this trial, we are not yet sure if we will have sufficient funds to include this arm.

We will also collect information on baseline characteristics as part of the sign-up instrument, including gender, highest education qualification, current education status, current employment status, years of total experience, software-related skills, whether participants uploaded their CV, and whether they provided a reference letter for this job.

We will run analyses both with and without conditioning on covariates and will report whether the results differ. We will use a machine learning method such as random causal forests to assess if treatment effects differ by covariates.
Experimental Design Details
Not available
Randomization Method
Computer
Randomization Unit
Treatment is assigned at the participant level for both rounds. For round 1, treatment is stratified by gender and location of the participant. For round 2, treatment is stratified by gender only. Analysis using panel data on participants will account for within-participant clustering.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
N/A
Sample size: planned number of observations
Round 1: 8984 participants Round 2: 2784 participants The final sample sizes may differ slightly depending on funding.
Sample size (or number of clusters) by treatment arms
Round 1:
- Offer of reference letter: 4492
- Control group: 4492
Round 2:
- Offer of reference letter: 1392
- Control group: 1392
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Duke University
IRB Approval Date
2026-06-15
IRB Approval Number
2025-0221
Analysis Plan