Ukrainian refugees and labor market discrimination

Last registered on November 04, 2022


Trial Information

General Information

Ukrainian refugees and labor market discrimination
Initial registration date
March 25, 2022

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
March 28, 2022, 7:11 AM EDT

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

Last updated
November 04, 2022, 7:47 AM EDT

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



Primary Investigator

Masaryk University

Other Primary Investigator(s)

PI Affiliation
Masaryk University
PI Affiliation
Cardiff University
PI Affiliation
Masaryk University

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
We quantify the extent to which Ukrainian refugees (with either Ukrainian and Russian ethnicity) caused by the 2022 Russian invasion of Ukraine, face discrimination in access to the Czech labor market. Understanding the role of discrimination in access to the Czech labor market during a sudden and unprecedented inflow of refugees is especially important, due to Czechia’s lack of previous comparable immigration experience and due to public budget constraints. To deal with this new situation, the Czech government has adopted the so-called “Lex Ukraine” based on which Ukrainian refugees can start to work as soon as they enter Czechia, without additional bureaucratic burden for the prospective employer, as if they were permanent residents. To measure the differential treatment of refugees, we conduct a correspondence test. Using email addresses from a popular online repository of Czech firms, we send standard unsolicited job inquiries from female childless workers, for basic unskilled jobs (i.e., ISCO 8 and 9) in which language, other skills, and experience are irrelevant. In these inquiries, we randomly assign names to signal ethnicity and we disclose applicants’ status (i.e., citizen, refugee, or permanent resident) in various ways. In the analyses, we explore the static dimension of discrimination, that is, average attitudes during the period covered by the experiment, as well as the dynamic dimension of discrimination, that is, how attitudes vary during the experiment. To dig into the mechanisms, we leverage on two strategies. First, we compare Ukrainian refugees (with either Ukrainian and Russian ethnicity) to Ukrainian and Russian permanent residents. Second, we conduct a multi-wave survey of Czech firms that are inquired about their knowledge of the law, their intention to hire refugees, and their expectations about the downsides of hiring refugees.

While other countries have adopted a similar (set of) law(s) and thus a similar experiment could in theory be conducted, the Czech context is particularly suitable because of at least two reasons. First, this is a country with a Slavic language (i.e., Czech) which is the language family of Ukrainian and Russian. Thus, these languages are similar and language barriers are expected to be economically irrelevant, especially for low-skilled jobs. To put it into context, based on various language proximity indexes, Czech and Ukrainian/Russian are more similar than Italian and Spanish or Swedish and Norwegian; the difference between Czech and Ukrainian/Russian is just a bit larger than that between Czech and Slovak, which can be used almost interchangeably. Second, due to the Russian invasion of Czechoslovakia in '68, there is a historical and lingering resentment toward Russians. Thus, we expect ethnic misidentification to be more likely to emerge than in other countries; as a consequence, Ukrainian-Russian refugees are more likely to be treated as if they were Russians. More details on this are discussed in the remainder of the document.
External Link(s)

Registration Citation

Coufalova, Lucie et al. 2022. "Ukrainian refugees and labor market discrimination." AEA RCT Registry. November 04.
Experimental Details


To measure the differential treatment of refugees, we conduct a matched pair email correspondence test, in which each employer receives an email indicating the applicant is Czech and an email indicating the applicant is non-Czech, that is, either a refugee (i.e., with either Ukrainian or Russian ethnicity) or a permanent resident (i.e., with either Ukrainian or Russian citizenship). These two emails are sent in a randomized order (e.g. sometimes the first email is from the Czech applicant, other times it is from the non-Czech applicant), a few days apart.
Every answer from employers is going to be followed by a courteous email, where the fictitious applicant thanks for the response and adds that she has found another job already. This is done to minimize the employer's inconvenience.

Given the protraction of the conflict and the civil unrest caused by sanctions consequences (e.g. increase in the gas price), we are prolonging the experiment, while expecting that this new scenario could worsen the attitudes toward refugees.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
We record employers’ responses to our email inquiries. The primary outcome is a binary variable for receiving a positive response to the query. We create various versions of this binary outcome.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
We measure the time to respond, in days.
We measure the courteousness of the responses.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We signal the refugee status using some version of the following phrase: “I just came from Ukraine”; for refugees, we add one closing sentence similar to “Apologies for possible grammar mistakes, I am translating this message with Google translate, from Ukrainian (or Russian) to Czech.” We signal the permanent resident status with a phrase such as “I am Ukrainian (or Russian) and I am a permanent resident;” for them, there is no closing sentence citing Google translate. To signal the applicant does not have any child we insert a version of the following sentence in the message “My schedule is flexible since I do not have any kids.”

The main signal for whether the applicant is Czech, Ukrainian, Ukrainian-Russian/Russian is represented by combinations of names and surnames. Czech applicants are randomly assigned a combination of typical names and surnames, while non-Czech applicants are assigned a combination of typical names and surnames based on being either Ukrainian or Ukrainian-Russian/Russian. An employer has a 100 percent probability to receive a message from a Czech applicant. The assignment of the non-Czech identity and status is random, so we expect that an employer has approximately a 25 percent probability of receiving a message from a Ukrainian refugee, a Ukrainian-Russian refugee, a Ukrainian permanent resident, and a Russian permanent resident. Please note that these probabilities are approximated; the final distribution may not reflect this distribution due to random sampling. We use five combinations of name and surname per ethnic group; the file "Combination of name and surname by ethnicity" in the section "Docs & Materials" reports them in full, with the consulted sources.

Rather than applying for advertised job vacancies, we “fish for discrimination,” that is, we send short written messages to represent unsolicited job applications for ISCO 8 and 9 jobs; these are jobs that refugees usually apply to. This is a usual practice in Czechia, for basic jobs, based on opinions from Czech human resources experts. These are jobs for which previous experience is not required, a CV is not necessary, only basic education is required, there is no contact with customers, and the employee is not expected to manage coworkers. Based on these characteristics and based on the proximity of Czech to both Ukrainian and Russian languages, one could safely assume that employers’ perceived productivity differences between different fictitious candidates are economically risible. We aware of only one field experiment to study the role of refugees' language on their chances to find a low-skilled job, and this study does not find evidence that language has a statistically significant effect on chances to increases chances of being hired (Ek et al., 2021).

To collect our sample of auditable employers, we proceed in two steps. First, we define the jobs we are going to apply for. Based on statistics from the Ministry of Labor, we select those ISCO 8 and 9 jobs in which female third-country nationals (i.e., neither Czech nor EU citizens) are more frequently employed (i.e., jobs that together represent 75% of the third-country nationals in ISCO 8 and 9). The selection of these jobs strengthens the message's realism and further reduces the probability that employers perceive the lack of knowledge of the Czech language as being a productivity-reducing characteristic. Second, from the national statistical office registry of Czech firms, we select a random sample of firms that typically employ workers in the jobs defined in the first step. Firms' probability of being selected is weighted by the regional population out of the national population. This procedure allows us to make sure that we contact employers from every region and proportionally to the population of that region. Firms' contact emails are obtained from Orbis. From Orbis, we additionally collect detailed information on each contacted employer (e.g., yearly revenue, size).

Additionally, at least with a monthly frequency, we conduct a multi-wave survey of Czech firms. The list of these firms and their emails is provided by a Czech survey consultant company; relying on this repository of firms, insures a high survey response rate. This survey inquires firms about three general topics: (i) knowledge of the “Lex Ukraine” (e.g., the perceived difference compared to the usual third-country work permit), (ii) intention to hire refugees (e.g., general intention to hire, number of refugees the employer expects to hire), (iii) perceived downsides of hiring refugees (e.g., measured by the expected importance of lack of vacancies, language barriers, health issues, expected refugees’ length of stay). The collected information is aggregated at the region-wave level; additional or alternative dimensions could be considered (e.g., ISCO level 2 job depending on the number of observations per job).
Experimental Design Details
From March 23rd to March 27th, we are conducting a pilot with the same characteristics as the main experiment.
The data from these few days are meant to indicate whether and how we should adjust the experimental design.
In case no particular issue rises, these data are going to be added to the data obtained from the main experiment.
Randomization Method
Randomization is done in the office by a computer.
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
We expect to contact 4800 employers

[these numbers and the numbers in the following points include the observations collected during the pilot]
Sample size: planned number of observations
Each employer receives two emails, so we have 4800 x 2 = 9600
Sample size (or number of clusters) by treatment arms
Each employer receives two emails, one from a Czech applicant (control group) and one from either of the four treatment arms: a Ukrainian refugee, a Ukrainian refugee with Russian ethnicity, a Ukrainian permanent resident, and a Russian permanent resident.
4800 Czech fictitious applicants
1200 Ukrainian refugee fictitious applicants
1200 Ukrainian refugee with Russian ethnicity fictitious applicants
1200 Ukrainian permanent resident fictitious applicants
1200 Russian permanent resident fictitious applicants
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials


Document Name
AEA RCT - Combinations of names and surnames by ethnicity
Document Type
Document Description
AEA RCT - Combinations of names and surnames by ethnicity

MD5: 7c188c1c258d21ee14b5836e0a3bb125

SHA1: d492d1c55559f42e835c7a1b2eb3f8a089abe080

Uploaded At: March 25, 2022

Document Name
AEA RCT - IRB School Research Ethics Committees
Document Type
Document Description
AEA RCT - IRB School Research Ethics Committees

MD5: 4fe0228276c3e6614daf316e396f6d8e

SHA1: f4c4f49eb9391246173372269e1556e6a440b3c9

Uploaded At: April 08, 2022


Institutional Review Boards (IRBs)

IRB Name
School Research Ethics Committees
IRB Approval Date
IRB Approval Number
SERC reference: 704
Analysis Plan

Analysis Plan Documents

AEA RCT - Analysis plan

MD5: c1d84184a9ceb2f59c212638c118ee50

SHA1: 8f0abfbdae55eb22cb229c3a59564c4e4d8739b6

Uploaded At: March 25, 2022


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