Ethnic Discrimination in Multi-ethnic Societies: Evidence from Russia

Abstract

Field experiments have provided ample evidence of ethnic and racial discrimination in the labour market. Less is known about how discrimination varies in multi-ethnic societies, where the ethnic composition of populations is different across locations. Inter-group contact and institutional arrangements for ethnic minorities can mitigate the sense of group threat and reduce discrimination. To provide empirical evidence of this, we conduct a field experiment of ethnic discrimination in Russia with a sample of over 9,000 job applications. We compare ethnically homogeneous cities and cities with ethnically mixed populations and privileged institutional status of ethnic minorities. We find strong discrimination against visible minorities in the former but much weaker discrimination in the latter. These findings demonstrate how institutions and historical contexts of inter-group relations can affect ethnic prejudice and discrimination.

Full text

Ethnic Discrimination in Multi-ethnic Societies:
Evidence from Russia
Alexey Bessudnov
1,
* and Andrey Shcherbak
2
1
University of Exeter, Clayden Building, Streatham Rise, Exeter EX4 4PE, UK and
2
National Research
University Higher School of Economics, 20 Myasnitskaya ul., Moscow 101000, Russia
*Corresponding author. Email: a.bessudnov@exeter.ac.uk
Submitted February 2019; revised July 2019; accepted August 2019
Abstract
Field experiments have provided ample evidence of ethnic and racial discrimination in the labour
market. Less is known about how discrimination varies in multi-ethnic societies, where the ethnic
composition of populations is different across locations. Inter-group contact and institutional arrange-
ments for ethnic minorities can mitigate the sense of group threat and reduce discrimination. To pro-
vide empirical evidence of this, we conduct a field experiment of ethnic discrimination in Russia with
a sample of over 9,000 job applications. We compare ethnically homogeneous cities and cities with
ethnically mixed populations and privileged institutional status of ethnic minorities. We find strong
discrimination against visible minorities in the former but much weaker discrimination in the latter.
These findings demonstrate how institutions and historical contexts of inter-group relations can affect
ethnic prejudice and discrimination.
Introduction
The field experiment has now become a standard
method for studying racial and ethnic discrimination in
the labour market. In a typical labour market experi-
ment (also known as an audit or correspondence study),
researchers randomly assign a signal of race or ethnicity
to fictitious CVs, apply for jobs and record contacts
from employers. As long as the signal assignment is ran-
dom, the differences in the contact rates across the
groups can be treated as evidence of discrimination.
Such experiments have now been conducted in many
countries [for recent literature reviews, see Rich (2014);
Zschirnt and Ruedin (2016); Bertrand and Duflo
(2017); Baert (2018); and Neumark (2018)]. There is
overwhelming evidence that on average employers con-
tact applicants from majority groups more often than
applicants from minority groups. Racial and ethnic dis-
crimination in the labour market is well documented.
In this article, we present the results of the first ethnic
discrimination experiment conducted in Russia. We
focus on two main questions.
First, attitudes of ethnic majorities towards different
minority groups are not the same, and vary according to
an implicit hierarchy. In Western countries, minority
groups of European origin are usually more widely
accepted than groups of African and Asian origin
(Hagendoorn, 1995). Most field experiments to date
looked at one or only a few minority groups. Even the
larger audit studies rarely had enough statistical power
to provide reliable estimates of the differences in contact
rates across minority groups. In this study, we imple-
ment a design with 10 ethnic groups and a sample size
V
CThe Author(s) 2019. Published by Oxford University Press.
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European Sociological Review, 2020, Vol. 36, No. 1, 104–120
doi: 10.1093/esr/jcz045
Advance Access Publication Date: 8 October 2019
Original Article
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of over 9,000 job applications that allows us to provide
reliable estimates of discrimination for each group and
explore the ethnic hierarchy for multiple groups in
Russia.
Second, we focus on geographical variation in dis-
crimination. Russia is a multi-ethnic federation where,
in some regions, indigenous ethnic groups have a special
institutional status. Are the patterns of ethnic discrimin-
ation and hierarchy the same or different in ethnically
heterogeneous, compared with ethnically Russian,
regions? To answer this question, we conducted our ex-
periment in four Russian cities. Two of them (Moscow
and St. Petersburg) are metropolitan areas, with mostly
ethnically Russian populations. The other two cities
(Kazan and Ufa) are capitals of ethnic autonomies, with
mixed ethnic Russian and indigenous Muslim
populations.
The results show considerable differences in the pat-
terns of ethnic discrimination across these locations. In
Moscow and St. Petersburg, employers discriminate
against visible ethnic minorities of Southern origin but
not against groups of European origin. Discrimination
against ethnic minority men is stronger than that against
ethnic minority women. On the other hand, in Kazan
and Ufa, all ethnic groups are treated by employers in
approximately the same way, with the contact rates for
most groups of Southern origin being only marginally,
and not statistically significantly, lower than for groups
of European origin.
Therefore, the contributions of this article to the
literature on ethnic discrimination are the following.
We show that (i) ethnic discrimination in Russia is most-
ly directed against groups of Southern origin, with not
much discrimination against groups of European origin
(ethnic hierarchy); (ii) men from ethnic minorities face
stronger discrimination compared with women; and (iii)
the pattern and extent of ethnic discrimination differ
across locations with varying ethnic composition of
the populations and the history of ethnic inter-group
relations. These findings contribute to the literature on
the inter-group distance and contact hypothesis showing
how the history of inter-group contact and institutional
arrangements can mitigate the sense of ethnic group
threat.
Ethnic Hierarchies, Discrimination, and
Local Context
Human societies tend to be organized as group-based
hierarchies (Sidanius and Pratto, 2001). Many modern
societies are multi-racial and multi-ethnic and include
large ethnic minorities, often both indigenous and of
immigrant origin. Researchers of inter-group social
distance argue that social status varies across racial and
ethnic groups. In many Western societies North
Europeans have the highest status, followed by South
and Eastern Europeans, Asians, and Africans
(Hagendoorn, 1995). This ethnic hierarchy can be stable
across time (Kleg and Yamamoto, 1998;Ford, 2011)
and is often accepted both by the ethnic majority and by
minorities (Verkuyten, Hagendoorn and Masson, 1996;
Zick et al., 2001), although some ethnic boundaries can
blur over time (Alba, 2005). Survey evidence confirms
that attitudes of natives towards immigrants of different
ethnic origin can vary strongly. Ethnic stereotypes are
group specific. In the United States, respondents rate
White Americans higher than Asians, and Asians higher
than African Americans and Hispanics, on most traits
(Bobo and Massagli, 2001). The British public accepts
immigrants from Australia, but many are more sceptical
about Europeans, and especially immigrants from
Africa, the Caribbean region and South Asia (Ford,
2011).
Correspondence studies have mostly been interpreted
as attempts to measure discrimination in the labour mar-
ket. As most experiments, they often lack external valid-
ity and generalizability (Baldassarri and Abascal, 2017).
By design, these studies are limited to only a few occupa-
tions, skills, locations, racial and ethnic groups, and
channels of recruitment. In most cases, we can only col-
lect data about invitations to interviews rather than ac-
tual job and wage offers. Extrapolating experimental
estimates of discrimination in recruitment to other areas
of the labour market requires us to make many assump-
tions. Theoretically, correspondence studies mostly con-
tributed to separating statistical from taste-based
discrimination and have been detached from the litera-
ture exploring the group threat and contact hypotheses
that often underpin the sociological studies of the atti-
tudes towards immigrants. However, correspondence
tests can also be seen as a tool for measuring group-
specific ethnic prejudice, as revealed in employers’ hiring
decisions. While not coming from nationally representa-
tive samples, experimental studies of ethnic prejudice
have the important advantage of minimizing social
desirability bias. The focus of research then shifts, from
providing unbiased estimates of labour market discrim-
ination, to examining the relative standings of racial and
ethnic groups.
Most correspondence studies only involved one or
two ethnic minority groups and were not designed to
measure group variation in discrimination. When mul-
tiple groups were involved the results often showed that
in Western countries minorities of European origin were
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treated preferentially compared with minorities of non-
European origin (Baldini and Federici, 2011;Booth,
Leigh and Varganova, 2012;Carlsson and Eriksson,
2015;Acolin, Bostic and Painter, 2016;Baert et al.,
2017;Lancee, 2019;Vernby and Dancygier, 2019).
These findings confirm the existence of an ethnic hier-
archy in the labour market and are consistent with the
social distance research, and survey evidence of ethnic
differences in employment and wages (Heath and
Cheung, 2007). Not all minorities are the same, and
some are treated better than others.
Blumer (1958) famously suggested that racial preju-
dice emerges when members of the dominant group per-
ceive a challenge, to their superior social status, from
subordinate out-groups. The group threat hypothesis be-
came one of the pillars of the literature on attitudes to-
wards immigrants (Ceobanu and Escandell, 2010).
Empirically, it is often tested by looking at the associ-
ation between prejudice and real or perceived immigrant
group size, possibly mediated by economic conditions
(Quillian, 1995;Semyonov, Raijman and Gorodzeisky,
2006). Majority members may feel more threatened in
places with a higher proportion of ethnic minority mem-
bers, especially when the economy is poor. The support,
from empirical survey evidence, of the group threat hy-
pothesis has been mixed. When the analysis is conducted
at the regional rather than the country level, some stud-
ies confirm the association between minority group size
and anti-immigrant prejudice in Europe (Markaki and
Longhi, 2013), whereas others fail to find this link
(Hjerm, 2009;Rustenbach, 2010).
Another well-established theoretical approach that is
often discussed in this literature is the contact hypothesis
(Allport, 1954). Under certain conditions, experiencing
positive contact with members of out-groups may re-
duce prejudice (Pettigrew and Tropp, 2006). While the
group threat and contact theories may generate contra-
dictory predictions, they both stress the importance of
contextual factors for inter-group relations. Both theo-
ries imply that the level of discrimination would vary
across locations with different racial and ethnic popula-
tion compositions. More ethnically diverse places may
stimulate inter-group contact that will reduce prejudice.
On the other hand, the influx of ethnically different pop-
ulations may trigger the sense of group threat and pro-
voke negative attitudes towards newcomers.
Correspondence studies showed that in some places
(France, London) housing discrimination against minor-
ities was stronger in locations with more immigrants
(Carlsson and Eriksson, 2015;Acolin, Bostic and
Painter, 2016), whereas in others (Sweden) the effect
was in the opposite direction (Carlsson and Eriksson,
2014).
The population share of ethnic minorities is a rather
crude measure of group threat, and can sometimes be
misleading. In his famous essay, Blumer (1958) notes
that group position is a historical product and is set by
the conditions of initial contact. When looking at the as-
sociation between the ethnic composition of a popula-
tion and the level of prejudice it is important to consider
the historical origins of ethnically diverse locations.
Ethnically mixed populations may emerge as a result of
migration when minority groups, often with a lower sta-
tus in the ethnic hierarchy, move to a territory already
populated by the dominant group, as in the case of the
slave trade in Americas (forced migration) or modern
immigration to Western countries. Most existing studies
of discrimination analysed it in the context of ethnic het-
erogeneity historically produced by immigration.
Another historical cause of ethnically mixed locations is
conquest and colonization, when a dominant group sub-
jugates a territory with an ethnically distinct population.
The European colonization of Asia, Africa, and the
Americas produced many racially and ethnically hetero-
geneous populations across the world. Some ethnically
mixed regions in Europe are also products of earlier col-
onization (Wales and Northern Ireland in the United
Kingdom, the Basque country in Spain). Perceptions of
group threat may be different in places where ethnic het-
erogeneity originated from earlier colonization by a
high-status group and where the indigenous group main-
tains its ethnic identity. We use this observation in our
research design.
The Russian Context
According to the most recent census, in 2010, ethnic
Russians constitute about 80 per cent of Russia’s popu-
lation (Rosstat, 2012). The other 20 per cent, or 26 mil-
lion people, describe themselves as not ethnically
Russian and belong to over 100 ethnic groups. This eth-
nic heterogeneity reflects the history of the Russian state
and is a result both of conquest and colonization (by
ethnic Russians, of territories with indigenous popula-
tions) and of immigration of ethnic minorities to
Russia’s heartlands.
The Grand Duchy of Moscow that originally occu-
pied a relatively small territory in what is known now as
the Central European Russia and was populated pre-
dominantly by Orthodox Slavs, started a rapid territor-
ial expansion in the 15th century. By the 18th century,
when the Russian state became an empire, it acquired
vast territories in the Volga river basin, the Urals,
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Ukraine, and Siberia, populated by indigenous ethnic
groups (Riasanovsky, 2000). By the time of the First
World War and the 1917 revolution, the Russian empire
was a multi-ethnic conglomerate where ethnic Russians
constituted less than a half of the total population
(Mironov, 2017). After the Soviet Union was founded in
1922, the Bolsheviks had a debate about the ‘national-
ities question’ in Soviet Russia. Eventually, they rejected
the orthodox Marxist approach that denied the signifi-
cance of ethnic identities, and adopted the ‘great danger’
concept that argued Russian chauvinism was a greater
danger compared with local ethnic nationalisms. The
political implication of this approach was the adoption
of a policy intended to promote local ethnic identities
and accelerate the social, economic, and cultural devel-
opment of ‘backward’ ethnic groups (Vujacic, 2007), in
what was called in the literature the ‘affirmative action
empire’ (Martin, 2001). The Soviet state introduced eth-
nic quotas in universities and governmental organiza-
tions, promoted ethnic elites, established language
schools, printed books and newspapers in local lan-
guages, and supported intellectuals from ethnic minor-
ities (Slezkine, 1994;Hirsch, 1997,2000). The
‘affirmative action empire’ policy was revoked in the
mid-1930s and many ethnic groups later suffered from
state repression and forced deportations. However,
some of the institutions adopted in this early period
stayed in place and continue to affect Russia’s ethnic
policies until now.
According to the 1936 Constitution, the Soviet
Union was organized as a nested hierarchy of adminis-
trative units (Tishkov, 1997). At the highest level, there
were 11 (later 15) Soviet socialist republics; the Russian
Federation was one of them. Russia further consisted of
autonomous Soviet republics in the territories populated
by the largest ethnic minorities, provinces (oblasts)in
the ethnic Russian heartlands, and territories (krays)in
the colonized territories with ethnically mixed popula-
tions. Krays included ethnic autonomous oblasts, popu-
lated by smaller ethnic groups. With some changes, this
structure, based on the principles of ethnic federalism,
remained in place until the disintegration of the USSR,
and was inherited by modern Russia.
In contemporary Russia, among 85 ‘federation
subjects’, there are 22 ethnic republics and 4 ethnic au-
tonomous districts. Most republics have a ‘titular’ ethnic
group (or in some cases two groups) that is usually
reflected in their names (e.g. Tatarstan for the republic
of Volga Tatars). The population share belonging to
titular ethnic groups varies across the republics.
Chechens are 95 per cent of Chechnya’s population,
whereas in the northern republic of Karelia, the Karels
(a Finno-Ugric people) constitute only 7 per cent of resi-
dents. The language of the titular ethnic group is usually
recognized, in each of the republics, as an official lan-
guage in addition to Russian. The extent to which indi-
genous languages are actually used in everyday life
varies, but most republics have print media and TV and
radio broadcasting in the languages of titular groups.
Titular languages are taught in schools, although exami-
nations have to be taken in Russian. Many republics still
keep the Soviet institutions that were originally designed
to produce native ethnic intelligentsia (such as local
Academies of Sciences, etc.; Gorenburg, 2003;Giuliano,
2011). The system of ethnic quotas in the government
and employment is no longer in place, but the ‘nativiza-
tion’ of local cadres remains at approximately the same
level as in the late Soviet period (Shcherbak and Sych,
2017).
In addition to the conquest of new lands, another
source of Russia’s ethnic heterogeneity has been volun-
tary or forced migration of ethnically non-Russian
groups. Small communities of foreign craftsmen, mer-
chants and soldiers had lived in Moscow since the
Middle Ages, but the first mass migration occurred in
the 18th century, when Catherine the Great invited
colonists from Germany into Russia. About 40,000
came, mostly settling in the Volga river region and mod-
ern Eastern Ukraine (Mironov, 2014). By 1914, over 1
million ethnic Germans moved to the Russian empire
(Osinsky, 1928). WWI and the 1917 revolution marked
the end of the Pale of Settlement (a law that banned
Jews from settling outside the western parts of the em-
pire), and thereafter many Jews moved to the cities in
Central Russia. By the time of the 1926 census, they
constituted 6 per cent of Moscow’s and 5 per cent of
Leningrad’s populations, being the second largest ethnic
group in both cities after ethnic Russians (Perepis,
1928). Rapid industrialization and urbanization in the
Soviet period stimulated internal migration. Soviet col-
onization of the Urals and Siberia involved many ethnic
groups, leading to ethnically heterogeneous populations
in Siberian urban centres.
The collapse of the USSR in 1991 led to further
population flows. Ethnic Russians started to return to
Russia from the former Soviet republics that became
independent states. Following ethnic wars and the de-
terioration of the economic situation in the Caucasus in
the early 1990s, many Armenians, Azerbaijanis, and
Georgians moved to Russia. These migration flows are
hard to quantify, but between the 1989 and 2002 cen-
suses the Armenian population of Russia increased from
0.5 million to 1.1 million people. Russia’s economic re-
covery, that started in the early 2000s, stimulated new
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waves of economic migration, mainly from Ukraine,
Moldova, and Central Asia (Agadjanian, Menjı´var and
Zotova, 2017). Official statistics for the most recent im-
migration flows are poor, but in 2012 there were over 2
million Uzbek and over 1 million Tajik nationals in
Russia, mainly employed in low-skilled occupations in
the Moscow region and in other metropolitan areas.
The number of Ukrainian passport-holders in Russia
was 1.4 million in 2012, and it has significantly
increased after the Russian-Ukrainian military conflict
in 2014 (Bessudnov, 2016).
A number of previous studies used survey data to ex-
plore attitudes towards immigrants in Russia. Anti-
immigrant sentiment is stronger in Russia than in most
other European countries, whereas the explanatory
power of the models that try to predict attitudes towards
immigrants with the indicators of socio-economic pos-
ition and the attitudinal variables is much lower
(Gorodzeisky, Glikman and Maskileyson, 2015;
Bessudnov, 2016), although explanations based on
group threat and economic competition theories cannot
be dismissed (Bahry, 2016). Ethnic Russians are on aver-
age more negative about immigrants than ethnic minor-
ities (Gorodzeisky and Glikman, 2017), and the
opposition towards immigration is often based on racial
prejudice (Gorodzeisky, 2019). There is little evidence
of a strong trend towards increasing or decreasing xeno-
phobia between 1996 and 2012 (Chapman et al., 2018).
In this article, we complement the survey evidence pre-
sented in this literature with experimental results.
The ethnic heterogeneity of Russia’s population
makes it an interesting case for studying ethnic hierar-
chies and discrimination. Russia has large ethnic minor-
ities of both European origins (e.g. Germans, Jews, and
Ukrainians) and non-European origins (e.g. Armenians,
Chechens, Georgians, Tatars, and Uzbeks). There are re-
ligious differences as well; some groups are mostly
Orthodox Christian (Armenians, Georgians, and
Ukrainians), whereas others are Muslim (Azerbaijanis,
Chechens, Tatars, and Uzbeks) or Buddhist (Kalmyks
and Tuvans). Previous research into inter-ethnic social
distance in Russia shows that Slavic minorities of
Eastern European origin are placed higher in the ethnic
hierarchy than minorities of Southern origin
(Hagendoorn et al., 1998;Bessudnov, 2016). Another
differentiating factor is the institutional status of minor-
ities. Ethnic groups whose indigenous settlement area is
within the Russian borders are usually titular, i.e. have
the institutionalized privileged status in ethnic republics
that they perceive as ‘theirs’ (Hagendoorn, Poppe and
Minescu, 2008;Minescu, Hagendoorn and Poppe,
2008;Minescu and Poppe, 2011). Ethnic groups of im-
migrant origin do not have titular rights.
Our research design aims to employ these character-
istics of the Russian case. First, we are interested in
whether ethnic discrimination in the Russian labour
market is group specific and follows an ethnic hierarchy,
in which groups of European origin occupy a higher pos-
ition than non-European groups. Second, we want to in-
vestigate if ethnic discrimination in employment is
context dependent and varies between ethnically
Russian regions and titular ethnic republics.
Research Design
Ethnic Groups
Table 1 shows characteristics of the ethnic groups that
we included in the study. We selected groups of both
European and non-European origin and both titular and
non-titular groups.
We followed the standard practice of signalling eth-
nicity by randomly assigning ethnic names to CVs. We
collected ethnic first names and surnames from a popu-
lar Russian social media website (examples of ethnic
names are provided in the Supplementary Appendix).
To make sure that the names could be recognized as
ethnic by employers, we conducted a survey. In the sur-
vey, we presented a list of names to respondents and
asked them to assign the names to ethnic groups in an
open-ended question, without providing a list of groups.
We recruited a non-probability snowball sample on so-
cial media websites (n¼861). Compared with the gen-
eral population, people in our sample were younger and
more educated, more often female and Moscow and St.
Petersburg were over-represented. Arguably this may
better reflect demographic characteristics of urban HR
employees than a national probability sample.
1
The recognition of ethnic names varied by group (see
Table 2). For four groups (ethnic Russians, Armenians,
Georgians, and Ukrainians) respondents correctly identi-
fied the names in over 80 per cent of the cases. For all
Muslim ethnic groups the identification rates were much
lower. However, most respondents, even when unable
to correctly identify the exact ethnic group for Muslim
names, gave as an answer the name of another Muslim
group. Muslim names have common origins and may in-
deed sound similar. For all ethnic minority groups, ex-
cept Germans, the names were recognized as not
ethnically Russian in over 95 per cent of cases. German
names, arguably the most assimilated group in the list,
were recognized as not ethnically Russian in 85 per cent
of the answers.
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Locations
We conducted the experiment in four cities in Russia.
Two cities, Moscow and St. Petersburg, are large metro-
politan areas in European Russia with mostly ethnically
Russian populations. The other two, Kazan and Ufa,
are capitals of titular ethnic republics in the Volga river
region. Table 3 provides information about the cities’
populations and ethnic composition.
Moscow is Russia’s capital, with a population of over
12 million people. According to the 2010 census, 92 per
cent of the population are ethnically Russian. The census
estimates are unlikely to include many people from the
most recent immigration waves from the Caucasus,
Central Asia, and Ukraine. In 2016, about 500,000 for-
eign workers had a work permit in Moscow and the
Moscow region (Scherbakova, 2017). According to the
census, the largest ethnic minorities in Moscow are
Ukrainians, Tatars, Armenians, Azerbaijanis, and Jews.
The largest groups in the recent immigration wave,
unaccounted for in the census, are Tajiks and Uzbeks.
St. Petersburg, Russia’s capital between 1712 and
1918, is the second largest city in the country, with a
population of over 5 million people. Over 90 per cent
are ethnically Russian; the largest ethnic minorities are
the same as in Moscow.
Table 1. Ethnic groups included in the study
Ethnic group Size in Russia in 2010 (thousand) Region of origin Dominant religion Titular
Ethnic Russians 111,017 European Russia Orthodox Christian
Armenians 1,182 Caucasus Orthodox Christian No
Azerbaijanis 603 Caucasus Muslim No
Chechens 1,431 North Caucasus Muslim In Chechnya
Georgians 158 Caucasus Orthodox Christian No
Germans 394 Western Europe Christian No
Jews 157 Eastern Europe Jewish No
Latvians 19 Eastern Europe Christian No
Lithuanians 31 Eastern Europe Christian No
Tajiks 200 Central Asia Muslim No
Tatars 5,311 Volga region Muslim In Tatarstan
Ukrainians 1,928 Eastern Europe Orthodox Christian No
Uzbeks 290 Central Asia Muslim No
Notes: Population size reported according to the 2010 Russian census. It underestimates the size of ethnic groups in the most recent immigration wave, in particu-
lar, Ukrainians, Tajiks, and Uzbeks.
Table 2. Recognition of ethnic names
Ethnic group Correct (%) Broadly correct (%) Not Russian (%)
Georgian 91 98 100
Armenian 90 96 100
Russian 88 90 12
Ukrainian 82 92 95
Jewish 72 84 99
Tatar 57 90 99
German 42 62 85
Latvian 35 65 100
Lithuanian 22 73 100
Chechen 20 83 99
Uzbek 19 91 100
Azerbaijani 16 90 100
Tajik 12 84 99
Notes: Broadly correct identification includes the following groups. For
Russian and Ukrainian names any Slavic group; for Georgian and Armenian any
group from the Caucasus; for Jewish and German Jews or Germans; for Latvian
and Lithuanian any Baltic group; for Azerbaijani, Chechen, Tatar, Tajik, and
Uzbek names any Muslim group, or generic ‘Caucasus’, or ‘Central Asia’.
Table 3. Characteristics of four locations
City Population
(2017, thousand)
Ethnic composition
(2010), per cent
Moscow 12,381 Russians (92)
Ukrainians (1.3)
Tatars (1.3)
Armenians (1)
St. Petersburg 5,282 Russians (92)
Ukrainians (1.5)
Belarusians (0.9)
Tatars (0.7)
Kazan 1,232 Russians (49)
Tatars (48)
Ufa 1,116 Russians (49)
Tatars (28)
Bashkirs (17)
Notes: Data on the population come from the estimates of the Russian
Statistical Office. Data on the ethnic composition come from the 2010 census.
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Kazan is the capital of the ethnic republic of
Tatarstan. In the 16th century, the Moscow state con-
quered the Khanate of Kazan, populated by groups of
Turkic and Finno-Ugric origin (Romaniello, 2012).
In the late imperial period, ethnic Russians were already
a majority of the city’s population; according to the
1897 census, 74 per cent of the inhabitants spoke
Russian as their mother tongue and 22 per cent spoke
Tatar. In 1920, the city became the capital of the Tatar
Autonomous Socialist Republic, and Tatars—a predom-
inantly Muslim ethnic group—acquired a titular status.
In 2010, 49 per cent of the Kazan population was
ethnically Russian, and 48 per cent Tatar.
Ufa is the capital of the Republic of Bashkortostan
located to the east of Tatarstan, in the region between the
Volga river and the Ural mountains. Bashkirs, the titular
group, were a nomadic Muslim people who acknowl-
edged the authority of the Russian tsar in the 16th cen-
tury. Ufa was founded by Russian settlers in 1574, and
for most of its history had a small ethnic Bashkir popula-
tion. The Bashkir and Tatar languages are mutually intel-
ligible, and the identity boundaries between these two
groups have been fluid (Gorenburg, 1999). In 2010, Ufa
had a 49 per cent ethnic Russian population, 28 per cent
Tatars and 17 per cent Bashkirs.
2
The choice of locations was driven by our research
questions. We have two cities with predominantly eth-
nically Russian populations, located outside ethnic
republics (Moscow and St. Petersburg). Two other cities
(Kazan and Ufa) are capitals of titular ethnic republics,
and in both cities ethnic Russians are about half of the
population. Kazan and Ufa are also large enough (both
have a population of over 1 million people) to simplify
the logistics of the experiment. Kazan and Ufa are most-
ly Russian speaking (for several thousand applications
submitted in these cities we only had one or two cases
when an employer initiated a conversation in a local lan-
guage while contacting applicants on the phone).
Experimental Design
The study was conducted on two most popular Russian
job search websites, with monthly audiences of 3 and 10
million visitors (according to the Yandex.Radar data for
May 2019). The job application process is similar on
both websites. A person looking for a job creates an ac-
count on the website, completing the required fields.
Then the job seeker can browse through vacancies
advertised by firms, and apply online. After an applica-
tion is made, firms gain access to the applicant’s CV,
and decide if they want to contact them. Contact can be
made on the website or by phone.
We created accounts for applicants in four cities and
across four occupations: salesperson (low skilled, high
customer contact); cook (low skilled, low customer con-
tact); sales manager (high skilled, high customer contact);
and computer programmer, specializing in 1C software
3
(high skilled, low customer contact). Each account was
randomly assigned gender and an ethnic name. Creating
accounts was a time-consuming process that could not be
automated. At this stage, we reduced the number of eth-
nic groups to 10, combining several groups pairwise:
Azerbaijanis and Chechens (both Muslim groups from
the Caucasus); Latvians and Lithuanians (Baltic groups);
and Tajiks and Uzbeks (Muslim Central Asian groups).
Our survey shows that, for these groups, employers are
unlikely to identify the names precisely, although most
will be able to attribute them to broader regions.
Thus we have a full factorial design, with two treat-
ments, ethnicity (10 levels) and gender (2 levels), and
two strata, city (4 levels) and occupation (4 levels). This
required the creation of 320 online accounts, 160 on
each website (selected to constitute a fractional factorial
design on each website; Lawson, 2015). For each ethnic
group, we have 32 names (16 male and 16 female). This
is considerably more than in most previously conducted
experiments, reducing idiosyncratic name effects
(Gaddis, 2017). Name was the only signal of ethnicity.
All job applicants were presented as Russian nationals
in the age range 28–35 years, with Russian as their
mother tongue. We assigned to them educational creden-
tials from vocational schools and universities in the city of
job application, and local mobile telephone numbers. For
all applicants we provided previous experience for the last
7 years (two fictitious jobs in the same city). No informa-
tion about marital status or the number of children was
included. Applications were submitted by filling forms on
the websites that included several other mandatory ques-
tions, such as knowledge of foreign languages (we added
English for sales managers and computers programmers,
but not for cooks and sales persons), having a driving li-
cence (‘yes’ for sales managers and programmers, ‘no’ for
cooks and sales persons), skills, responsibilities in both
previous jobs and personal information (e.g. ‘I am an en-
thusiastic and easy going person’). For skills, responsibil-
ities, and personal information, we randomly chose
several bullet points for each candidate from the pre-
prepared occupation-specific lists.
Prior to the main study, but after a pilot study (that
involved sending 1,000 job applications), we conducted
power analysis with the following assumptions: effect
size of 0.2 (corresponding approximately to the differ-
ence between 40 per cent and 30 per cent contact rate);
intraclass correlation of 0.01 where names were treated
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as clusters (this value was determined by the pilot
study); 95 per cent statistical significance level; and
power of 80 per cent. With these assumptions, we
required a sample size of about 8,000 in order to obtain
reliable estimates for 10 ethnic groups, interacted with a
factor with two levels (such as sex or pairwise combina-
tions of cities or occupations).
Data were collected between June 2017 and January
2018. We employed six research assistants who moni-
tored the websites, sent job applications, and recorded
contact made by employers on the websites or on the
phone. When employers contacted applicants on the
phone, research assistants were instructed to politely de-
cline invitations to job interviews.
Results
Contact Rates by Ethnic Group and Location
Overall, we submitted 9,607 job applications. In 36 per
cent of the cases, employers invited applicants for an
interview, either by contacting them on the phone
(21 per cent) or on the website (23 per cent), with some
employers using both communication channels. Table 4
reports contact rates by ethnic group. This is shown
separately for Moscow and St. Petersburg—on the one
hand—and Kazan and Ufa on the other. Figure 1 shows
this information as a dot plot with 95 per cent confi-
dence intervals. We do not have enough statistical
power to report estimates in four cities separately, but
the patterns are similar in Moscow and St. Petersburg,
and in Kazan and Ufa (see Supplementary Appendix for
details). Table 5 presents linear probability models for
being contacted by employers that control for all the
other characteristics of applications (gender, occupation,
city, website, and research assistant) and test for statis-
tical significance of the differences from the reference
group, ethnic Russians.
In Moscow and St. Petersburg, the in-group, ethnic
Russians, have the highest contact rate—41 per cent.
Applicants with Ukrainian, Jewish, and German names
have only slightly, and not statistically significantly,
lower contact rates. On the other hand, all ethnic groups
of non-European Southern origin have significantly
lower contact rates, ranging from 26 per cent
Table 4. Contact rates by ethnic group and location
Ethnic group napplications nresponse Proportion contacted 95%confidence interval Call-back ratio Odds ratio
Moscow and St. Petersburg
Russian 616 254 0.41 [0.35; 0.47] 1 1
Ukrainian 559 220 0.39 [0.34; 0.45] 1.05 0.92
Jewish 604 237 0.39 [0.35; 0.44] 1.05 0.92
German 642 232 0.36 [0.32; 0.40] 1.14 0.81
Latvian and Lithuanian 551 185 0.34 [0.29; 0.38] 1.23 0.72
Tatar 610 170 0.28 [0.23; 0.32] 1.48 0.55
Tajik and Uzbek 570 159 0.28 [0.22; 0.34] 1.48 0.55
Azerbaijani and Chechen 598 165 0.28 [0.23; 0.32] 1.48 0.54
Armenian 610 163 0.27 [0.22; 0.31] 1.54 0.52
Georgian 549 142 0.26 [0.21; 0.30] 1.59 0.50
Kazan and Ufa
Jewish 384 187 0.49 [0.42; 0.55] 0.90 1.24
German 369 167 0.45 [0.38; 0.53] 0.96 1.08
Russian 402 174 0.43 [0.38; 0.49] 1 1
Tatar 343 147 0.43 [0.36; 0.49] 1.01 0.98
Ukrainian 365 155 0.42 [0.38; 0.47] 1.02 0.97
Tajik and Uzbek 373 150 0.40 [0.34; 0.46] 1.08 0.88
Georgian 368 144 0.39 [0.31; 0.47] 1.11 0.84
Armenian 378 148 0.39 [0.34; 0.44] 1.11 0.84
Latvian and Lithuanian 355 138 0.39 [0.33; 0.44] 1.11 0.83
Azerbaijani and Chechen 361 138 0.38 [0.32; 0.44] 1.13 0.81
Notes: Groups ordered by the contact rate within each pair of locations. 95% CI stands for 95% confidence interval, calculated after adjusting standard errors for
cluster-design effects (Green and Vavreck, 2007). Call-back ratio was calculated as the proportion of responses for ethnic Russians divided by the proportion of
responses for an ethnic group. Odds ratios were calculated as the odds of receiving a response, for an ethnic group, divided by the odds of receiving a response for eth-
nic Russians.
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(Georgians) to 28 per cent (Tatars). Applicants with
Latvian and Lithuanian names are in the middle of the
list, with a contact rate of 34 per cent. We observe a
clear ethnic hierarchy in hiring, where all groups of
European origin are given preference compared with
Southern groups of non-European origin, most of whom
are visible minorities.
In Kazan and Ufa, the response rates are higher than
in Moscow and St. Petersburg across all the ethnic
groups. This reflects characteristics of the local labour
markets. In contrast to the results in Moscow and
St. Petersburg, in Kazan and Ufa none of the differences
in the contact rates between ethnic Russians and other
ethnic groups is large or statistically significant. Jewish
and German applicants have the highest contact rates,
closely followed by ethnic Russians and Tatars, who are
contacted by employers with equal frequency. The dif-
ference between ethnic Russians and Tatars, on the one
hand, and other groups of Southern origin, on the other
hand, is only between 2 and 5 percentage points, and
not statistically significant. The overall ethnic hierarchy,
though, is similar to Moscow and St. Petersburg, and
most groups of European origin are contacted more
often than most groups of Southern origin, even if the
differences in contact rates are smaller.
Overall, we find substantial differences in the ethnic
preferences of employers between Moscow and St.
Petersburg, on the one hand, and Kazan and Ufa, on the
other. In the former, there is strong discrimination
against all non-ethnically Russian groups of Southern
origin. In the latter, discrimination is much weaker, to
the extent that—given our sample size—we cannot be
sure that it exists in the population.
Gender Differences in Contact Rates across
Ethnic Groups
Do men and women of ethnic minority origin experience
discrimination to the same extent? To answer this
Figure 1. Contact rates by ethnic group and location.
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question, we fit regression models with interaction effects
between ethnicity and gender. Our sample size is not large
enough to allow for the analysis at the level of individual
ethnic groups (split by location) and we combine all ethnic
groups into two categories: of European origin (Germans,
Jews, Latvians and Lithuanians, ethnic Russians, and
Ukrainians) and of non-European origin (Armenians,
Azerbaijanis and Chechens, Georgians, Tajiks and
Uzbeks, and Tatars). The results are reported in Table 6.
In Moscow and St. Petersburg, discrimination
against men of Southern origin is stronger compared
with discrimination against women, and the difference
is statistically significant. On average, female applicants
from Southern groups are contacted 7 percentage points
less often than female applicants from European groups.
For male applicants the difference is 14 percentage
points. In Kazan and Ufa, we do not find strong
evidence of discrimination, and the interaction effect
between ethnicity and gender is smaller and not statistic-
ally significant.
We conducted a similar analysis for the interaction
between ethnicity and occupation, and did not find
much evidence that ethnic hierarchies vary across occu-
pations in Moscow and St. Petersburg. In Kazan and
Ufa, cooks from Southern groups had about the same
contact rates as European groups, whereas for skilled
occupations (sales manager and computer programmer)
the contact rates for Southern groups were lower than
for European groups. The details of the analysis are
available in the Supplementary Appendix.
Contact on the Phone and on the Websites
In this section, we analyse the communication channels
that employers used for contacting applicants. They
could do this either on the websites (by sending a mes-
sage asking an applicant to contact them) or by making
a call to an applicant’s mobile phone. By sending a
message through the websites employers could avoid ini-
tiating a personal conversation with an applicant on the
phone. Table 7 reports models that look at the probabil-
ity of receiving a phone call as opposed to not receiving
a call, for those applications that got a positive response.
In Moscow and St. Petersburg, all ethnic groups are
less likely to be contacted on the phone, compared with
ethnic Russians. The effect is statistically significant
Table 5. Linear probability models of being contacted by
employers
Dependent variable: contacted
by employer
Moscow/
St. Petersburg (1)
Kazan/
Ufa (2)
Ethnic group
(ref.: ethnic Russians)
Jewish –0.02 0.06
(0.04) (0.04)
Ukrainian –0.02 0.002
(0.03) (0.04)
German –0.05 0.04
(0.03) (0.04)
Latvian/Lithuanian –0.07* –0.04
(0.03) (0.04)
Tatar –0.13*** 0.005
(0.03) (0.04)
Tajik/Uzbek –0.13*** –0.03
(0.03) (0.04)
Azerbaijani/Chechen –0.13*** –0.04
(0.03) (0.04)
Armenian –0.14*** –0.03
(0.03) (0.04)
Georgian –0.15*** –0.03
(0.03) (0.04)
Observations 5,909 3,698
Notes: Linear probability models; standard errors in parentheses. The de-
pendent variable is binary (1 if contacted by employer, 0 if not). All models con-
trol for gender, occupation, city, website, and research assistant’s name
(coefficients not shown). Cluster-robust standard errors applied (clustered by
applicant’s name). Ethnic Russians are the reference group.,
*P<0.05; **P<0.01; ***P<0.001.
Table 6. Interaction between ethnicity and gender
Dependent variable: contacted by employer
Moscow/
St. Petersburg (1)
Kazan/Ufa (2)
Ethnic group
(ref.: European)
Southern –0.07*** –0.02
(0.02) (0.02)
Gender (ref: female)
Male 0.0001 0.01
(0.02) (0.03)
Southern male –0.07* –0.03
(0.03) (0.03)
Observations 5,909 3,698
Notes: Linear probability models; standard errors in parentheses. All the
models control for occupation, city, website, and research assistant’s name
(coefficients not shown). Cluster-robust standard errors applied (clustered by
applicant’s name). Groups of European origin and women are the reference
groups. European origin includes Germans, Jews, Latvians and Lithuanians, eth-
nic Russians, and Ukrainians. Non-European origin includes Armenians,
Azerbaijanis and Chechens, Georgians, Tajiks and Uzbeks, and Tatars.,
*P<0.05; **P<0.01; ***P<0.001.
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for all groups, except Ukrainians. Even Germans and
Jews, two groups that overall are contacted by employ-
ers about as often as ethnic Russians, are considerably
less likely to receive a phone call (by 11 and 12 percent-
age points). For all the Muslim groups, the effect is even
stronger, and the difference from the phone contact rate
with ethnic Russians reaches about 20 percentage
points. In Moscow and St. Petersburg many employers
try to avoid initiating phone conversations with the
members of out-groups, especially Muslim groups of
Southern origin. In contrast, in Kazan and Ufa the differ-
ences in phone contact rates across ethnic groups are
much smaller and none of them is statistically significant
at the 95 per cent level.
On the websites, employers could send an optional
rejection message to unsuccessful applicants. The mes-
sage was automatic, impersonal, and only required
employers to press a rejection button. We interpret
sending a message as a stronger signal of rejection. In
Moscow and St. Petersburg, the probability of being ex-
plicitly rejected is higher for groups of Southern origin
(except Tatars) compared with ethnic Russians. We do
not observe this effect in Kazan and Ufa. The details of
the analysis are available in Supplementary Appendix.
Discussion
In this article, we answer three questions. First, our find-
ings confirm the predictions derived from the literature
on social dominance and ethnic hierarchies
(Hagendoorn, 1995;Sidanius and Pratto, 2001) show-
ing that the ethnic preferences of Russian employers are
structured according to an implicit ethnic hierarchy,
with groups of European origin preferred to groups of
Southern origin. Our second finding speaks to the litera-
ture on intersectionality showing that the strength of dis-
crimination against Southern groups varies by gender,
with men from ethnic minority groups experiencing
stronger prejudice than women. Finally, we contribute
to the study of the group threat and contact hypotheses,
showing that ethnic discrimination can vary across pla-
ces with different ethnic structures of the populations
and the history of inter-group contacts. We will now
discuss these results separately.
In Moscow and St. Petersburg, we find a clear pat-
tern of ethnic discrimination in the job market.
Applicants from the groups of European origin receive
preferential treatment compared with the groups of
Southern origin. As predicted by the theory of ethnic
hierarchies, the in-group, ethnic Russians, has the high-
est contact rate. The contact rates for some other groups
of European origin (Germans, Jews, and Ukrainians) are
similar to those for ethnic Russians, and the differences
between these groups are not statistically significant.
Some of these findings may seem surprising.
Antisemitism, both in the general population and in
state policies, was a feature of Jewish life in the late
Soviet Union, and Jews were discriminated against in
higher education and in a number of white-collar occu-
pations (Pinkus, 1990). The Soviet Union’s collapse in
1991 was followed by large-scale Jewish immigration to
Germany, Israel, and the United States. In the 1990s and
2000s, state discrimination against Jews disappeared,
and antisemitism in Russian society became less pro-
nounced. In a 2015 survey, only 8 per cent of Russians
expressed negative attitudes towards Jews (Levada,
2016;Levinson, 2016). Our results confirm these
findings.
We collected data in 2017, 3 years after the begin-
ning of the Russian–Ukrainian military conflict that
Table 7. Contact on the phone and on the websites
Dependent variable: contacted
on the phone
Moscow/
St. Petersburg (1)
Kazan/
Ufa (2)
Ethnic group
(ref.: ethnic Russians)
Jewish –0.11* –0.07
(0.05) (0.04)
Ukrainian –0.07 –0.01
(0.05) (0.05)
German –0.12* 0.06
(0.06) (0.04)
Latvian/Lithuanian –0.17** –0.03
(0.06) (0.05)
Tatar –0.18** –0.02
(0.06) (0.07)
Tajik/Uzbek –0.22*** –0.10
(0.05) (0.05)
Azerbaijani/Chechen –0.19*** –0.01
(0.05) (0.06)
Armenian –0.16** –0.02
(0.06) (0.04)
Georgian –0.17** –0.02
(0.06) (0.05)
Observations 1,927 1,548
Notes: Linear probability models; standard errors in parentheses. The sample
includes only applications that received a positive response. The dependent vari-
able is 1 if contact was made on the phone and 0 if the phone was not used. The
models control for gender, occupation, city, website, and research assistant’s
name. Cluster-robust standard errors applied (clustered by applicant’s name).
Ethnic Russians are the reference group.,
*P<0.05; **P<0.01; ***P<0.001.
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resulted in the annexation of the Crimea and the estab-
lishment of pro-Russian military regimes in parts of
Eastern Ukraine. The Russian state media closely fol-
lowed the conflict, with a largely anti-Ukrainian stance.
Ukrainian names were well recognized in the survey we
conducted. Yet, we do not find evidence of discrimin-
ation against Ukrainians, who were contacted by
employers about as often as ethnic Russians. Perhaps the
explanation is that many ethnic Russians do not see
Ukrainians as being from a separate nation and, there-
fore, do not perceive them as an out-group. Their views
on the Ukrainian state are more negative than on the
Ukrainian people. In a survey conducted in 2015 in
Russia, 43 per cent said that there was no difference at
all between ethnic Russians and Ukrainians, and another
35 per cent said the differences were minor. Only 3 per
cent reported negative attitudes towards Ukrainians (64
per cent reported positive attitudes; Public Opinion
Foundation, 2015).
For all the groups of Southern origin, the contact
rates in Moscow and St. Petersburg are much lower than
for ethnic Russians. Among the Southern groups, there
is little difference in the contact rates. Partly this can be
explained by the inability of HR employees to differenti-
ate between the names of different Muslim groups (as
shown in our pre-experiment survey). However, two
Christian ethnic groups from the Southern Caucasus,
with members whose names are easily recognized by
Russians (Armenians and Georgians), have contact rates
that are as low as for the Muslim groups. These results
show that religion is not the main factor that structures
Russia’s ethnic hierarchy. The groups of European ori-
gin who are not visible minorities, and are more cultur-
ally Russified (or at least are perceived by ethnic
Russians as Russified) are rarely discriminated against.
In contrast, visible minorities from the South (both
Muslim and Christian) are perceived as out-groups and
are treated more negatively.
How strong is ethnic discrimination in Russia
compared with other countries? In Moscow and
St. Petersburg, the odds ratio for all Southern groups
compared with ethnic Russians ranged between 0.5 and
0.57. In a recent meta-analysis (Zschirnt and Ruedin,
2016), the average odds ratio across 34 correspondence
tests conducted in Western countries was 0.6. In a fam-
ous US study (Bertrand and Mullainathan, 2004), the
odds ratio for African American versus White job appli-
cants was 0.59. Ethnic discrimination in Moscow and
St. Petersburg appears to be close to these estimates.
Note that the signal of ethnicity in our study is relatively
weak: we only randomize applicants’ names and indi-
cate that all applicants are Russian nationals and native
Russian speakers. We do not include photographs in the
applications. This makes our estimates of discrimination
more conservative. In reality, job applicants from ethnic
minorities, who sometimes speak Russian with an accent
and are not Russian citizens, may face stronger discrim-
ination at the stage of recruitment.
With respect to the interaction between ethnicity
and gender, male applicants from discriminated ethnic
groups achieve lower contact rates than female appli-
cants. This is consistent with the results from some other
experimental studies (Arai, Bursell and Nekby, 2016;
Liebkind, Larja and Brylka, 2016;Vernby and
Dancygier, 2019) and contradicts the ‘double disadvan-
tage’ (or intersectionality) hypothesis (Browne
and Misra, 2003).
4
According to a meta-analysis of 37
correspondence tests, white men receive 63 per cent
more callbacks compared with ethnic minority men,
whereas the gap for women is 52 per cent (Quillian and
Nanni, 2018). In the social dominance literature, this is
known as the subordinate male target hypothesis; this
postulates that ethnic discrimination is directed primar-
ily against men from out-groups (Sidanius and Veniegas,
2000;Sidanius and Pratto, 2001). While we do not have
data to test this empirically, it is likely that in Russia eth-
nic minority men are perceived by employers as more
threatening than women. Russia’s post-Soviet history
has seen several ethnic riots that all started after street
altercations between young ethnically Russian men and
migrants from the Caucasus (Foxall, 2014;Arnold,
2018), and popular stereotypes about men from
Southern ethnic groups are often related to impulsive-
ness and aggression (Bodrunova et al., 2017).
Perhaps the most intriguing finding of the study is
the difference in ethnic discrimination between Moscow
and St. Petersburg, on the one hand, and Kazan and Ufa,
on the other hand. In contrast to the findings in Moscow
and St. Petersburg, in Kazan and Ufa we do not find
much variation in the contact rates across all ethnic
groups. In both cities (Kazan and Ufa), ethnic Russians
and Tatars are the two largest ethnic groups, and the
contact rates for them are very similar. The rates are
lower for other groups of Southern origin, but the differ-
ence from ethnic Russians and Tatars is small (odds
ratios vary between 0.81 and 0.93) and not statistically
significant. We believe that this is a unique case, as most
previous experimental studies discovered discrimination
against minority groups.
Why are the results in Kazan and Ufa different from
those in Moscow and St. Petersburg? We only have four
cities in this study, and have to combine them pairwise
to increase statistical power. With, essentially, only two
cases, we are therefore unable to conduct statistical
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analysis and make any generalizations. We can, how-
ever, discuss possible explanations that may be tested in
future studies.
One possible explanation is the ethnicity of employ-
ers. Perhaps ethnically Russian employers discriminate
on the basis of ethnicity and non-Russian employers do
not. As Kazan and Ufa have a higher share of the non-
Russian population this may reduce discrimination.
We think that this is an insufficient explanation for our
findings. Both in Kazan and Ufa, ethnic Russians are
about 50 per cent of the population. We do not have
data on their share among employers, but we were able
to estimate the ethnic composition of the group of HR
employees who responded to job applications (by coding
their first names as ethnically Russian or non-Russian).
In Kazan and Ufa, 28 and 23 per cent of the HR employ-
ees had non-ethnically Russian names, compared with 7
per cent in Moscow and 4 per cent in St. Petersburg.
This suggests that ethnic Russians constitute a majority
of employers in Kazan and Ufa. Besides, surveys
suggest that at the national level the attitudes towards
immigrants from the South, among Tatars, are only
marginally more positive than among ethnic Russians
(Bessudnov, 2016).
Another possible explanation is related to character-
istics of the local labour markets. In Moscow and St.
Petersburg, the labour markets are more competitive
(overall, 33 per cent of the job applications received a
positive response) compared with Kazan and Ufa (43
per cent). Perhaps employers have less space for discrim-
ination when the job market is tight and there are fewer
applicants. This is the argument proposed by Baert et al.
(2015), who show with data from Belgium that discrim-
ination against Turks only exists in occupations with a
larger pool of candidates, and is absent in occupations
where vacancies are more difficult to fill. However, this
is not what we find in our study. In Moscow and St.
Petersburg, cooks had a higher contact rate compared
with other occupations, suggesting a less competitive
job market for cooks, yet the level of discrimination is
similar in all four occupations and is not lower for cooks
(see Supplementary Appendix for details).
As in other countries, in Russia, the internet is only
one of the channels for job search. Several studies that
used data from the 1990s and early 2000s documented
the importance of social networks and personal contacts
in the Russian labour market (Yakubovich, 2005;
Gerber and Mayorova, 2010). Since then job search
mechanisms have evolved. While personal contacts re-
main important, in 2014 76 per cent of Russian firms
and 49 per cent of job seekers (77 per cent in Moscow
and St. Petersburg) used web sites for job search
(Roshchin, Solntsev and Vasilyev, 2017). In this respect,
Russia is not very different from many Western coun-
tries. According to a 2017 survey, 53 per cent of
respondents in Russia used internet job sites, compared
with 37 per cent in the United States and 40 per cent in
Germany (Sakurai and Okubo, 2017). While our experi-
ment was only conducted on the internet, we would
expect similar patterns of ethnic preferences to apply to
other job search mechanisms (Pager, Bonikowski and
Western, 2009) and more generally, other types of social
interaction (housing and rental market, ethnic intermar-
riage, etc.).
One of our arguments, in this article, is that ethnic
discrimination in the labour market is driven not so much
by rational deliberation by employers, or by local labour
market conditions, but rather by underlying ethnic stereo-
types that are often implicit and have roots in the history
of inter-group relations. We believe that the case of
Kazan and Ufa can be better explained by a combination
of two factors—ethnic heterogeneity of the population
and the system of ethnic federalism. This may also help us
resolve a seeming contradiction between predictions
made by the contact and group threat theories.
According to the group threat literature, a large out-
group population is perceived by ethnic majorities as a
threat; therefore, higher ethnic heterogeneity may lead
to ethnic animosity and discrimination. This may well
be the case in Moscow and St. Petersburg, where the
level of ethnic discrimination is high. Both cities recently
experienced mass migration from the Caucasus and
Central Asia, and the ethnic heterogeneity there is largely
the result of migration of ethnic groups that are often
perceived as subordinate in status. In Kazan and Ufa, the
share of non-ethnically Russian population is higher,
but historically this is a result of Russian colonization
rather than migration of non-Russian ethnic minorities.
The population there has been split between ethnic
Russians, Tatars, and Bashkirs for several centuries, with-
out major changes happening in living memory. A long
history of ethnic coexistence may reduce ethnic threat,
both for ethnic Russians and the titular ethnic groups.
The contact theory predicts that more frequent con-
tact between ethnic groups contributes to more positive
inter-group relations, and therefore, ethnic mixing may
reduce discrimination. What is often forgotten is that
according to Allport (1954), inter-group conflict only
ameliorates ethnic conflict under a number of condi-
tions, including equal group status and support by
authorities and institutions. In Moscow and St.
Petersburg, recent immigrants are often occupationally
segregated and work in low-skilled jobs in construction
and services (Lokshin and Chernina, 2013). This reduces
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opportunities for contact with the locals, and when con-
tact occurs it is often in social situations that imply un-
equal status. In Kazan and Ufa, Tatars and Bashkirs are
titular ethnic groups whose special status in the repub-
lics is institutionally recognized. Ethnic Russians living
in these regions do not have a primordial sense of terri-
torial ownership structured along ethnic lines. Ethnic
segregation in the labour market is low, and both ethnic
Russians and Tatars are well represented in white-collar
occupations, although the share of non-manual workers
among ethnic Russians is somewhat larger (Giuliano,
2011). This may create more opportunities for everyday
positive contacts between ethnic groups. Survey evi-
dence suggests that the attitudes towards ethnic minor-
ities of immigrant origin are more positive in Tatarstan
and Bashkortostan compared with Moscow and St.
Petersburg (Bessudnov, 2016).
Most students of ethnic federalism focused their at-
tention on the effects of federalism on separatism (Erk
and Anderson, 2009), whereas inter-ethnic attitudes in
ethnic autonomous regions remain less widely studied
(Alexseev, 2010;Minescu and Poppe, 2011). Our results
are consistent with the findings from China, where
Maurer-Fazio (2012) reported the absence of labour
market discrimination against Mongolians in Inner
Mongolia and against Uyghurs in Xinjiang (although
these results pre-date the recent crackdown on Uyghur
nationalism by the Chinese government). However, our
argument is stronger, as it is not only titular ethnic
groups who are not discriminated against in two of
Russia’s ethnic republics but also other non-indigenous
groups of immigrant origin.
Given a small number of cases, we should be careful
not to over-interpret these findings. Explanations of eth-
nic discrimination and conflict cannot be mechanically
reduced to a few variables (Brubaker and Laitin, 1998).
After all, a federal status and a long history of ethnic mix-
ing did not prevent the ethnic massacre in Yugoslavia
(Oberschall, 2000). Further studies of the effects, on dis-
crimination, of ethnic autonomy and the ethnic compos-
ition of populations, may include a larger sample of
Russia’s regions; as well as cases from Western Europe
(Northern Ireland, Scotland, Wales, Catalonia, the
Basque country), China, India, and ethnic federations in
Africa (such as Ethiopia and Nigeria).
Notes
1 In the experiment, we did not use the same names as
in the survey, but they were selected using the same
methodology.
2 The Bashkir population outside Bashkortostan is
small, and Bashkir names are similar to Tatar ones.
Initially, we included in the experiment a smaller
number of Bashkir CVs, in Ufa only, but the sample
size did not allow us to form any conclusions. We
excluded Bashkir applications from all the reported
analyses.
3 1C is a popular Russian software for accounting and
enterprise management.
4 These results refer to hiring decisions only and can
be different for gender inequalities in career develop-
ment and wages.
Supplementary Data
Supplementary data are available at ESR online.
Data Availability
The data and replication materials for this article are
available at https://github.com/abessudnov/ruAuditPublic.
Acknowledgements
We are grateful to the research assistants who helped us with
data collection: Alisa Alieva, Sergey Konontsev, Vladislav
Kostin, Anastasia Roud, Pavel Savchenko, and Darya
Smirnova. We also thank Jane Elliott; participants in the project
‘Growth, Equal Opportunities, Migration and Markets’; and
participants in seminars and conferences at the following ven-
ues, for their comments and questions: University of Oxford;
University of Amsterdam; University of Exeter; King’s College
London; Laboratory for Comparative Social Research at the
HSE in St. Petersburg; the HSE April 2018 conference; the
Southern Sociological Association 2018 meeting; the American
Sociological Association 2018 meeting; and the European
Consortium for Sociological Research 2018 conference. The
study has been approved by the ethics committees at the
University of Exeter and the National Research University
Higher School of Economics.
Funding
This study has been funded by the British Academy’s
International Partnership and Mobility grant (PM160023) and
additionally supported by the Basic Research Programme at the
National Research University Higher School of Economics
(HSE) and the Russian Academic Excellence Project ‘5–100’.
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Alexey Bessudnov is a Senior Lecturer in quantitative
sociology at the University of Exeter. His research inter-
ests are ethnic minorities and inequalities in education,
in Russia and the UK. His work appeared in the
European Sociological Review, the European Journal of
Public Health, and other journals.
Andrey Shcherbak is a Deputy Head of the Laboratory
for Comparative Social Research at National Research
University Higher School of Economics, Russia. His re-
search interests include ethnicity and nationalism, in
particular, in the post-communist countries, and empir-
ical studies of inter-ethnic relations in historical
perspective.
120 European Sociological Review, 2020, Vol. 36, No. 1
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