A Study on Ukraine War Veterans

Last registered on December 17, 2025
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Pre-Trial

Trial Information

General Information

Title
A Study on Ukraine War Veterans
RCT ID
AEARCTR-0015942
Initial registration date
May 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
May 06, 2025, 5:13 AM EDT

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

Last updated
December 17, 2025, 10:36 PM EST

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

Locations

Country
Russian Federation
Region

Primary Investigator

Name
Sergey Alexeev
Affiliation
Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status
Completed
Start date
2024-09-01
End date
2025-11-30
Keywords
Crime, Violence, & Conflict, Labor, Post-conflict
Additional Keywords
Labor Market Discrimination, Ukraine War Veterans, Correspondence Experiment, Veteran Reintegration, Employer Bias, Russia-Ukraine Conflict
JEL code(s)
J71, J64, J24, I38, H56
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study investigates labor market discrimination against Ukraine war veterans in Russia using a Bayesian sequential correspondence audit. The experiment compares callback rates for fictitious résumés that signal voluntary participation in the “special military operation” in Ukraine with otherwise similar résumés that signal only routine mandatory conscription. The primary objective is to estimate whether employers are less likely to invite applicants to interview when they highlight voluntary, financially incentivized war service rather than standard conscription, holding other qualifications constant. The study focuses on entry- and mid-level vacancies in major Russian cities and selected smaller locales, using large online job platforms as the sampling frame.

The trial uses a two-arm matched-pair design and analyzes data with a Bayesian sequential procedure that updates posterior beliefs about the callback difference after each batch of applications. The main estimand is the difference in callback probabilities between conscript and veteran résumés. Secondary analyses explore heterogeneity by job type (white- versus blue-collar positions) and location (major urban centers versus smaller cities), subject to sample size limitations. The broader goal is to provide behavioural evidence on how voluntary Ukraine war service is valued—or potentially penalized—in civilian labor markets under authoritarian conditions where direct measurement of attitudes toward the war is constrained.
External Link(s)

Registration Citation

Citation
Alexeev, Sergey. 2025. "A Study on Ukraine War Veterans." AEA RCT Registry. December 17. https://doi.org/10.1257/rct.15942-1.2
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
This study uses a correspondence audit to test for discriminatory hiring responses to different types of prior military service. For each eligible vacancy, two fictitious résumés are submitted:
(i) a “treatment” résumé that signals voluntary participation in the “special military operation” in Ukraine (for example, “Served during Special Military Operation, 2022–2023”), and
(ii) a “control” résumé that signals completion of routine mandatory conscription in peacetime (for example, “Completed mandatory military service, 2018–2019”).
All other observed characteristics (education, work experience, skills) are held constant within each pair. The intervention consists of experimentally varying the signal of voluntary Ukraine war service versus ordinary conscription in order to measure the resulting difference in callback rates from employers.
Intervention (Hidden)
The audit targets entry- and mid-level vacancies posted on large online job platforms (e.g. HeadHunter, SuperJob) in Russia. Sectors include retail, logistics, information technology, administration and manufacturing. Cities include Moscow, St. Petersburg and a set of smaller regional centers. Within each vacancy, the two résumés are submitted in randomized order and use randomly assigned, common Russian male names, unique email addresses and virtual phone numbers. A callback is defined as any positive employer response (e.g. invitation to interview, request for additional information) received within 30 days. The trial uses a Bayesian sequential design: data are analyzed in batches of 20 vacancy pairs using Beta(1,1) priors and binomial likelihoods. The initial plan is to continue sampling until the posterior probability that the callback rate is higher for conscripts than for volunteers exceeds 0.975, or the maximum of 300 vacancy pairs is reached. A secondary decision rule was specified to assess whether the effect is small (absolute difference below 0.05) with high posterior probability. Sensitivity analyses use alternative priors and a linear probability model including job type and region as covariates.
Intervention Start Date
2024-09-01
Intervention End Date
2024-12-31

Primary Outcomes

Primary Outcomes (end points)
The primary outcome is the callback indicator for each résumé, defined as 1 if the employer sends a positive response (e.g. interview invitation, request for more information) within 30 days of application, and 0 otherwise.
Primary Outcomes (explanation)
The main estimand is the difference in callback probabilities between the two résumé types, Δ=𝑝𝐶−𝑝𝑉, where 𝑝𝐶 is the probability of a callback for résumés signalling completion of conscription, and 𝑝𝑉 is the probability of a callback for résumés signalling voluntary participation in the Ukraine war. A negative Δ indicates a hiring penalty for applicants who highlight voluntary war service. The analysis will model callbacks as Bernoulli outcomes with beta priors in a Bayesian framework, and will report posterior means and 95% credible intervals for Δ. For comparison, conventional linear probability models with treatment indicators and basic controls (job type, region) will also be estimated, as is standard in correspondence audits.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary analyses will explore whether callback differences vary by job type (white-collar positions such as IT and administration versus blue-collar positions such as logistics and manufacturing) and by location (major urban centers versus smaller cities). For each subgroup, the same difference in callback probabilities Δ will be estimated using the Bayesian framework. These analyses are explicitly exploratory: the planned sample size and sequential stopping rule are chosen to detect an overall effect, not to provide high-powered tests of heterogeneity.
Secondary Outcomes (explanation)
Secondary analyses will explore whether callback differences vary by job type (white-collar positions such as IT and administration versus blue-collar positions such as logistics and manufacturing) and by location (major urban centers versus smaller cities). For each subgroup, the same difference in callback probabilities Δ will be estimated using the Bayesian framework. These analyses are explicitly exploratory: the planned sample size and sequential stopping rule are chosen to detect an overall effect, not to provide high-powered tests of heterogeneity.

Experimental Design

Experimental Design
Data collection proceeds in batches of 20 vacancy pairs. For each arm (volunteer, conscript), callbacks are modelled as binomial outcomes with independent Beta(1,1) priors on the callback probabilities. After each batch, the posterior distribution of
Δ=𝑝𝐶−𝑝𝑉 is approximated by Monte Carlo sampling (10,000 draws). The primary decision rule is to stop data collection if the posterior probability that Δ>0 exceeds 0.975, or if the study reaches a maximum of 300 vacancy pairs. A secondary rule is to assess whether the effect is negligible, defined as ∣Δ∣<0.05with posterior probability at least 0.90. Sensitivity analyses will use Beta(2,2) priors and a linear probability model with job type and region as covariates. All deviations from this design and the realised sample size will be documented in the eventual paper.
Experimental Design Details
Data collection proceeds in batches of 20 vacancy pairs. For each arm (volunteer, conscript), callbacks are modelled as binomial outcomes with independent Beta(1,1) priors on the callback probabilities. After each batch, the posterior distribution of
Δ=𝑝𝐶−𝑝𝑉 is approximated by Monte Carlo sampling (10,000 draws). The primary decision rule is to stop data collection if the posterior probability that Δ>0 exceeds 0.975, or if the study reaches a maximum of 300 vacancy pairs. A secondary rule is to assess whether the effect is negligible, defined as ∣Δ∣<0.05with posterior probability at least 0.90. Sensitivity analyses will use Beta(2,2) priors and a linear probability model with job type and region as covariates. All deviations from this design and the realised sample size will be documented in the eventual paper.
Randomization Method
Randomization is performed in office by a computer, assigning applicant details (e.g., names, emails, phone numbers) and pairing treatment/control résumés to job postings.
Randomization Unit
The unit of randomization is the job posting, with each posting receiving one pair of résumés (one treatment, one control).
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
No
Explanation: The treatment (veteran vs. conscript résumé) is assigned at the individual résumé level within each pair, and pairs are independently assigned to job postings. There is no clustering by groups (e.g., firms or regions), as each job posting is treated as an independent unit.

Sample size: planned number of observations
300 job postings (each receiving one pair of résumés).
Sample size (or number of clusters) by treatment arms
600 résumés (300 pairs, with each pair consisting of one treatment and one control résumé).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
150 job postings for the treatment arm (veteran résumés) and 150 job postings for the control arm (conscript résumés), as each of the 300 job postings receives one pair of résumés (one treatment, one control).
Supporting Documents and Materials

Documents

Document Name
Project Description
Document Type
proposal
Document Description
Project Description "State Heroes, Social Pariahs: A Study on Ukraine War Veterans"
File
Project Description

MD5: fc18f4423d3a67d0fe4584174b1a2a73

SHA1: e05fb105cad2cf5b7ad4be62aa97166d8c8c5ffe

Uploaded At: May 08, 2025

Document Name
Human Ethics Application
Document Type
irb_protocol
Document Description
Human Ethics Application
Project identifier: 2025/HE000654
Project title: State Heroes, Social Pariahs: A Study on Ukraine War Veterans
Chief Investigator: Dr Sergey Alexeev
CI Affiliation: NHMRC Clinical Trials Centre / FMH Centres and Institutes / Faculty of Medicine and Health
Initial application assessment pathway: Lower risk research
File
Human Ethics Application

MD5: 20893442f4f17b7dc6df49ba0abcb8d8

SHA1: ce24c01705b89a6ee8af80a5fe92c48250169f4b

Uploaded At: May 08, 2025

IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

Post-Trial

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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

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Reports & Other Materials