Experimental Design Details
Background and motivation
This field correspondence experiment further investigates the discrimination of Ukrainians in elementary school admission primary in the Czech Republic focusing on the effects of the war in Ukraine and the treatment of refugees. Our previous experiment (https://doi.org/10.1257/rct.7362) identified the presence of discrimination against Ukrainians of a large magnitude (a 21 percentage points, s.e. 2.3 p.p., lower response rate to queries from putatively Ukrainian senders, compared to 47 percent response rate to queries from putatively Czech senders.
Over the past several weeks, about 300 000 refugees (officially registered), with almost 17000 children aged 3-6, arrived in the Czech Republic. This creates a demand shock on schools and the need to accommodate Ukrainian children. (There are typically about 100 000 first-graders each year.) Therefore, further understanding the discrimination they may face is of utmost importance. The two aims of this experiment are (i) to update our previous estimates of discrimination faced by Ukrainians, specifically to ascertain whether the discriminatory attitudes changed in the face of the war, and (ii) to ascertain whether refugees are treated differently from the settled Ukrainian minority.
We manipulate three variables of interest: (i) The putative ethnicity of the sender using typical Czech and Ukrainian names. (ii) The literacy level of the sender by varying the grammatical quality of the queries as well as by identifying themselves as refugees. (iii) Within the low literacy arm, we further manipulate the identity of the sender to be either a refugee or a settled immigrant. This will be signaled by the sender stating that they just came from Ukraine and by the sender stating that they have moved from a specific Czech town after one year, respectively.
We use a subset of the sample of schools from the previous RCT. Specifically, we randomly select 200 schools from the Czech ethnicity high-literacy treatment arm, 200 schools from the Czech ethnicity low-literacy treatment arm, 200 schools from the Ukrainian ethnicity high-literacy treatment arm, and 400 schools from the Ukrainian ethnicity low-literacy treatment arm. We keep the assignment of these schools to the treatment arms fixed. However, split the 400 schools in the Ukrainian ethnicity low-literacy treatment arm into two groups, settled immigrants and refugees. This will be signaled, respectively, by the sender stating "I have moved from [a specific Czech town] after one year and by the sender stating they just came from Ukraine.
We note that we have halved our sample size per treatment arm in order to lower the burden this experiment creates on the subjects.
This follow-up experiment thus gives us an insight into several questions:
* Is there a higher demand for schools (due to the influx of refugee children) implying lower response rates in general?
* Did the war affect the discrimination of Ukrainians?
* Does the refugee status alter the pattern of discrimination of Ukrainians?
1. Names and ethnicity signals (Czech, Ukrainian)
Because of legal constraints, neither the Census nor any administrative dataset containing ethnicity and names is available in the Czech Republic. Furthermore, our sender personas are female, whereas the previous correspondence experiments in the Czech context use fictitious personas who are male (Bartos et. al 2016 and Mikula and Montag 2021).
We have therefore selected female names using several sources and steps. We started with surnames in the appendix of Bartos et. al (2016, Czech names) and Mikula and Montag (2021, Czech names). We have selected common-sounding Ukrainian names using Wikipedia and Google search. We have then tested the ethnicity signals associated with these names in an in-class student survey (n = 71, Economics 101 class at Charles University). The students were asked to select the most likely ethnicity for 16 names (eight per ethnicity, randomly ordered). The options of ethnicities to choose from were Czech, Slovak, Ukrainian, Russian.
Based on this first survey, we selected two Czech names (Jiřina Hájková, Věra Svobodová). More than 98 % of students stated that these names are most likely Czech, respectively.
The two names most frequently, 65 % and 77 %, respectively, associated with Ukrainian ethnicity were Anna Shevchenko, Yelyzaveta Tkachenko. We note, that 32 % and 17 % of students, respectively, associated these two names with Russian ethnicity. However, the exact distinction between these two ethnicities is of secondary importance for us. Hence we use these two names as putatively Ukrainian (or mixed Ukrainian and Russian).
We have then selected four Slovak-sounding female names using Google search and Wikipedia. Because there is an overlap between typical Czech and Slovak names, selected Slovak surnames that have typical Slovak diacritics (Ľuptáková), Slovak first names that have Slovak spelling (e.g. Katarína), and Czech first names with Czech diacritics (Jiřina).
After this, we have set up another (out-of-class survey) in which 582 students (Economics class at Masaryk University) were presented with 8 names, four Czech, and four Slovak, randomly ordered, and asked again to assign the most likely ethnicity of these names. The choice was between Czech, Slovak, Hungarian, Ukrainian, and Russian. We have received 294 responses (response rate 50.5 %, 194 respondents self-identified as Czechs, 79 as Slovaks, 19 as other, one declined to self-identify).
Focusing on answers from Czech respondents, over 99 % of students assigned Czech ethnicity to the two putatively Czech names (Jiřina Hájková, Věra Svobodová). The two putatively Slovak names most frequently associated with Slovak ethnicity were Ľudmila Húsková and Katarína Ľuptáková, with 90 % and 87 % of Czech students assigning Slovak ethnicity to them (the second most frequent choice was Ukrainian, with 5 % and 7 % frequency respectively).
April is the enrollment period, during which individual schools organize an official (but non-mandatory) enrollment day in which parents and pupils visit schools and formally enroll. Our goal is to minimize the costs that our queries generate to the school principals. We, therefore, send out a simple two-sentence query stating that the sender (mother) is considering to which school her child should be enrolled and asking whether there will be an open day or an online information meeting for parents. To this query, the principal may simply state "Yes" or "No" and in the former case give a day and time or a link.
3. Literacy signals our queries will signal the parent's socioeconomic status (and/or integration level) via grammatical quality of the queries. In order to obtain low-literacy queries, we have asked several participants in a Czech as foreign language course to draft the queries for us. Grammatically correct queries will be drafted by us. We will send out two versions of the query per literacy signal (identical content but different wording).
4. We will also manipulate the child's gender by simply specifying that the mother wishes to enroll her boy/girl.