| Field | Before | After |
|---|---|---|
| Field Trial Title | Before State Heroes, Social Pariahs: A Study on Ukraine War Veterans | After A Study on Ukraine War Veterans |
| Field Trial Status | Before on_going | After completed |
| Field Abstract | Before This study investigates labor market discrimination against Ukraine war veterans in Russia, using a Bayesian sequential correspondence audit conducted from September to December 2024. Comparing callback rates for résumés signaling voluntary Ukraine conflict service versus routine conscription, we find a significant hiring penalty for veterans, with a callback rate 60% that of conscripts with ∆ = 0.183 and 95% CI [0.051, 0.312]. This penalty, likely driven by perceptions of veterans as mercenaries or resistance to the Kremlin’s war narrative, reveals a fractured social contract in an authoritarian context where dissent is suppressed. The labor market thus serves as a critical lens for eliciting latent public sentiment, offering insights into the economic and social costs of Russia’s war-driven militarization. Findings extend the discrimination literature, highlighting veteran status as a distinct axis of bias. | After 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. |
| Field Last Published | Before May 08, 2025 04:12 PM | After December 17, 2025 10:36 PM |
| Field Intervention (Public) | Before This study investigates discriminatory hiring practices against Ukraine war veterans in Russia through a correspondence audit. Two types of fictitious résumés are submitted to job postings: one signaling voluntary service in the Ukraine conflict (treatment group, labeled "Served during Special Military Operation, 2022–2023") and one signaling routine mandatory conscription (control group, labeled "Completed mandatory military service, 2018–2019"). The intervention tests employer responses to these military service signals to quantify any hiring penalty associated with voluntary, financially incentivized participation in the conflict, distinct from general military service. | After 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. |
| Field Primary Outcomes (End Points) | Before The key outcome variable is the callback rate for résumés submitted, defined as the proportion of applications receiving a positive employer response (e.g., interview invitation or request for additional information) within 30 days. | After 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. |
| Field Primary Outcomes (Explanation) | Before The callback rate is a binary outcome (1 for a callback, 0 otherwise) calculated for each résumé. The primary comparison is the difference in callback rates between the treatment group (résumés signaling Ukraine conflict service) and the control group (résumés signaling mandatory conscription), denoted as Δ=pC−pU\Delta = p_C - p_U\Delta = p_C - p_U, where pCp_Cp_C is the conscript callback rate and pUp_Up_U is the veteran callback rate. This difference quantifies the hiring penalty associated with voluntary Ukraine service. No constructed outcomes (e.g., indices) are used; the analysis focuses directly on raw callback rates and their difference, as is standard in correspondence audits (Bertrand and Mullainathan, 2004). | After 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. |
| Field Experimental Design (Public) | Before The study employs a two-arm matched-pair correspondence audit design with a Bayesian sequential approach. Fictitious résumés, identical except for military service signals, are submitted to job postings: the treatment group signals voluntary service in the Ukraine conflict ("Served during Special Military Operation, 2022–2023"), while the control group signals routine mandatory conscription ("Completed mandatory military service, 2018–2019"). The design compares callback rates to quantify the labor market penalty associated with voluntary, financially incentivized Ukraine service, isolating it from general military experience. Data are analyzed in batches, stopping when sufficient evidence of discrimination is detected. | After 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. |
| Field Intervention (Hidden) | Before The correspondence audit targets entry- and mid-level job postings on platforms like HeadHunter (hh.ru) and SuperJob (superjob.ru) across sectors (retail, logistics, IT, administration, manufacturing) in cities including Moscow, St. Petersburg, and smaller locales. Each pair of résumés is identical in qualifications (e.g., education, 3–5 years’ experience, skills) but differs in the military service signal. Applicant details (e.g., common Russian male names, unique emails, virtual phone numbers) are randomized to ensure comparability. A callback is defined as any positive employer response (e.g., interview invitation, request for additional information) within 30 days, tracked via dedicated voicemail and email accounts. The Bayesian sequential approach analyzes data in batches of 20 paired applications, stopping when P(Δ>0∣Data)>0.975P(\Delta > 0 \mid \text{Data}) > 0.975P(\Delta > 0 \mid \text{Data}) > 0.975 or after 300 pairs, ensuring efficient evidence collection. | After 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. |
| Field Secondary Outcomes (End Points) | Before Exploratory analyses examine callback rate differentials by job type (white-collar vs. blue-collar) and geographic region (urban centers like Moscow and St. Petersburg vs. smaller cities). | After 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. |
| Field Secondary Outcomes (Explanation) | Before Secondary outcomes are constructed as subgroup-specific callback rate differences (Δ\Delta\Delta ) for white-collar jobs (e.g., IT, administration) versus blue-collar jobs (e.g., logistics, manufacturing) and for urban versus smaller city job postings. These are calculated similarly to the primary outcome, using the same beta-binomial Bayesian framework, but stratified by job type and region. Due to the sample size (60 pairs), these analyses are exploratory and not powered for definitive inference, serving to identify potential heterogeneity in discrimination patterns for future research. | After 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. |
| Field Public locations | Before No | After Yes |
| Field | Before | After |
|---|---|---|
| Field Document Name | Before | After Human Ethics Application |
| Field File | Before |
After
2025_HE000654_v0_01 - Application Form.docx
MD5:
20893442f4f17b7dc6df49ba0abcb8d8
SHA1:
ce24c01705b89a6ee8af80a5fe92c48250169f4b
|
| Field Description | Before | After 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 |
| Field Public | Before | After Yes |
| Field | Before | After |
|---|---|---|
| Field Document Name | Before | After Project Description |
| Field File | Before |
After
2025_HE000654_v0_01 - Project Description Protocol,.docx
MD5:
fc18f4423d3a67d0fe4584174b1a2a73
SHA1:
e05fb105cad2cf5b7ad4be62aa97166d8c8c5ffe
|
| Field Description | Before | After Project Description "State Heroes, Social Pariahs: A Study on Ukraine War Veterans" |
| Field Public | Before | After Yes |