The Echo of Stalinism: Ethnic Diversity and Social Capital in

The Echo of Stalinism: Ethnic Diversity and Social Capital in

The Echo of Stalinism: Ethnic Diversity and Social Capital in
Contemporary Russia∗
Anastasia Aladysheva
Department of International Economics, The Graduate Institute of International and
Development Studies (IHEID)†
March 2013
Abstract
This paper disputes conventional wisdom that ethnic diversity has a negative impact on
development. More specifically, it explores the impact of regional ethnic structure on social
capital as measured by trust, and links ethnic diversity to history as a way to control for the
endogeneity problem which is often ignored in existing empirical literature. Using individual
level data from the Russian Life in Transition Survey (2010) I study the impact of ethnicity
on three different types of trust: general, interpersonal and institutional. The endogeneity
of ethnic diversity is addressed by using a two-stage least squares procedure. The first stage
results show that regional ethnic structure, measured by the standard Herfindahl index, was
strongly influenced by the deportations conducted in the Soviet Union under Joseph Stalin
between 1928 and 1953. The paper finds strong evidence of the impact of deportations
on historical and contemporary regional ethnic structure. In particular, the deportations
decreased ethnic fragmentation in the regions of destination by about nine percent. The first
stage results are robust to using other measures of ethnic diversity, such as ethnolinguistic
polarization and ethnic fragmentation, computed at different levels of aggregation. Secondly,
and contrary to the findings from the existing literature, ethnic diversity per se is insignificant
in building social capital in most cases.
JEL codes: Z13 (Economic Sociology); P20 (Socialist Systems).
∗
I thank my advisor Jean-Louis Arcand for his guidance throughout the work on this paper, and Matthias Rieger
for useful ideas. Additional thanks are due to participants at the seminars at the Graduate Institute for helpful
discussions. All errors are my own.
†
Address: Pavillion Rigot (R10), 11a Avenue de la Paix, 1202 Geneva, Switzerland. E-mail: anastasia.aladysheva@graduateinstitute.ch
1
Introduction
In an era of globalization and population mobility, the role of ethnicity has become prominent.
There is a widespread view in the recent economic and political science literatures that ethnic
diversity has a negative impact on development. Ethnic diversity was found to impede economic
growth (Easterly and Levine 1997), lead to high corruption (Mauro 1995), and hamper the
provision of public goods and policies (Alesina and La Ferrara 2005; Miguel and Gugerty 2005).
In the political science literature many scholars argue that ethnically diverse societies are more
prone to intense domestic conflicts (Ellingsen 2000; Horowitz 1985; Reynal-Querol 2002). For
instance, Montalvo and Reynal-Querol (2005a,b) claim that political instability, caused by ethnic
wars, may result in low investment, and thus indirectly, in poor economic performance. However,
understanding the mechanisms of ethnic interactions and their economic effects remains an
interesting and controversial issue.
The economic role of ethnic diversity is a double edged sword. On one hand, it may result in
a lack of cooperation between ethnic groups - due to linguistic, cultural or religious differences
- which, in worst case, may lead to violent conflicts, and as a result be harmful for economic
development. But on the other hand, it may act as a ’melting pot’, bringing a variety in
terms of abilities, experiences and cultures which may lead to innovation and creativity. This
paper explores the mechanisms through which ethnic diversity and economic development are
interlinked. First, I argue that it is more appropriate to estimate its effect on social capital,
which is defines as ”features of social organization such as networks, norms and social trust”
(Putnam 1993) and which is found to be an important determinant of economic prosperity
(Fukuyama 1995; Gambetta 1990; Keefer and Knack 1997; Putnam 1993; Thomas 1996). The
main argument behind this choice is that peoples perceptions of other ethnicities and ethnic
self-identification determines or is determined by interaction, cooperation and trust which are
the fundamentals of social capital as a concept. These fundamentals are the informal institutions
which determine the way the societies are functioning, and as a result, impact their prosperity.
In other words, social capital serves as an intermediate step between ethnic interactions and
economic performance.
Second and more importantly, I question the conventional wisdom that ethnic diversity
2
really matters in the formation of social capital in general, and that the causal link really exists.
Whether it is peoples perceptions towards other ethnic groups which shape the norms in the
society, or it is social environment itself which determines ethnic structure, is not clear. This
causes endogeneity problem in empirical analysis. This paper proposes an identification strategy
which treats ethnic diversity as endogenous to social capital.
Much of the literature that explores the impact of ethnicity on economic development has a
common limitation in a way that it fails to account for the endogeneity of ethnic diversity. One
of the most widely cited paper in economics literature that studies the impact of ethnic diversity
on economic outcomes is by Easterly and Levine (1997). The authors argue that roughly 25% of
the difference in growth experiences between African and Asian economies can be attributed to
the greater ethnic diversity in Africa. Easterly and Levine (ibid.) claim that ethnic diversity may
have a direct link with economic growth that is independent of the policy and infrastructure
indicators. These results were questioned by Arcand et al. (2000b) on the grounds that the
African sub-sample is often quite limited, and the relationship is unstable. Second, it is more
appropriate to use an index of ethnic polarization as a measure of ethnic diversity rather than
an index of ethnic fragmentation. Finally, Arcand et al. (ibid.) argue that ”the various tests of
the effect of ethnicity on the quality of policy are far from being conclusive”.
Other economists investigate the indirect link between ethnic diversity and economic outcomes. For example, Mauro (1995) finds strong evidence that ethnic diversity leads to high
corruption, as bureaucrats may favor members of their same group. Mauro (ibid.) argues that
”the extent to which countries are fractionalized along ethnic lines is exogenous and unrelated
to economic variables other than through its effects on institutional efficiency”. Alesina et al.
(1999), Alesina and La Ferrara (2005) and Miguel and Gugerty (2005) find strong evidence that
ethnic diversity leads to lower provision of public goods and policies. Their main argument is
well generalized and formulated by Habyarimana et al. (2007) who state that due to linguistic,
cultural or religious similarities, co-ethnics are more closely linked on social networks and thus
plausibly better able to support cooperation through the threat of social sanction.
Studying the effect on social capital, Alesina and La Ferrara (2002) find that ethnic fragmentation in the United States leads to lower levels of interpersonal and institutional trust.
However, the index of racial fragmentation which is used by Alesina and La Ferrara (ibid.) may
3
capture racial income inequality which exists in the United States and which may be the main
source of distrust (Alesina et al. 2012).
Finally, researchers investigate political salience channel through which ethnic diversity could
be important. In a natural experiment on Zambia-Malawi border, Posner (2004) finds that what
matters is not the nature of cleavage, as the ethnic groups studied are the same, but the size of
the groups it defines and whether or not they will be useful vehicles for political competition.
This finding is supported in the cross-country analysis in Africa by Eifert et al. (2010).
Ethnic struggles for power and natural resources may, as history shows, result in civil conflicts. As a consequence, civil conflicts may, in turn, change country’s ethnic structure by changes
in ethnic self-identification, migrations or ethnic cleansing, which cause endogeneity problem in
cross-country empirical analysis. Montalvo and Reynal-Querol (2005b) argue that it is religious
polarization rather than ethnic fragmentation that matters for conflicts and, in turn, economic
growth. They find a quantitatively large negative coefficient associated with their index of polarization in a standard growth regression. However, their empirical analysis does not spell
out the mechanisms through which polarization is detrimental, and they completely ignore the
endogeneity issues. Desmet et al. (2012), by using a method of ethnolinguistic trees, argue that
”deep cleavages, originating thousands of years ago, lead to measures of diversity that are better
predictors of civil conflict and redistribution than those that account for more recent and superficial divisions. The opposite pattern holds when it comes to the impact of linguistic diversity
on growth and public goods provision, where finer distinctions between languages matter”.
To sum up short literature review, the controversy remains: why do some countries seem to
be immune from ethnic conflicts and stay economically prosperous despite the fact that they are
more ethnically diverse than the others? What are the main mechanisms behind the causal effect
of ethnic diversity on economic and political outcomes? Finally, if ethnic diversity matters, is
that the human nature to distinguish between ”us” and ”others” which leads to low cooperation
and ethnic distrust, or are these peoples perceptions on ethnicity which are formed endogenously
and which could be changed?
This paper therefore contributes to the existing literature and empirically examines the
impact of contemporary ethnic diversity on social capital in Russia. Russia is a good setting
for this type of work for several reasons. First, according to the 2002 census, there are more
4
than 180 different ethnic groups living in Russia today.1 Many regions, as the census data
shows, are ethnically very heterogeneous, so ethnic interactions are more probable than in less
heterogeneous areas. So studying multi-ethnic regional structure can shed light on a broader
picture of ethnic interactions and their effects.
Second, in the Soviet era, the culture of social capital was shaped by Young Pioneer organisations and the Komsomol (Young Communist League), whereas during the political transition
these organisations disappeared. After the collapse of the Soviet Union, during the ’dashing
nineties’, the rules of life changed dramatically and the role of social capital underwent changes.2
Observing the revival of social capital in a post-communist country is therefore an interesting,
and not very widely studied question in the economics literature.
Finally, and most importantly, this paper solves the endogeneity problem by proposing a
unique identification strategy. Country’s ethnic structure in many cases is shaped by historical
events:3 for instance, the slave trade in post-colonial African countries, ethnic massacres in
the former Yugoslavia, or ethnic deportations in the Soviet Union. In what follows, using the
historical example of the Soviet Union, this paper establishes a link between deportations and
ethnic structure at the regional level, and uses it as an instrument in a structural equation which
studies the effect of ethnic structure on social capital.
Forced migrations took place many times through-out Russian history. However, during the
Soviet era they reached a massive scale. Historians recognise that the deportations were particularly intense under the rule of Joseph Stalin (1928-1953). More than 6 million people within
the country were affected by the Soviet deportations, of which 70 percent were ethnic (Polian
1
For 70 years there existed a common social term - ”Soviet people”, and a identification with different ethnic
groups reappeared only after the collapse of the Soviet Union in 1991.
2
Many political scientists have addressed the issue of social capital and democratization in post-communist
countries (Gibson 2001; Marsh 2000). The Russian literature related to the role of social capital during the
transition (Yurakov 2004) has a rich conceptual framework, but lacks a formal empirical analysis.
3
One of the recent papers which studies the roots of ethnic diversity is written by Ahlerup and Olsson (2012).
The authors provide a rigorous literature review and a description of existing theories on the determinants of
contemporary ethnic structure in the countries. Ahlerup and Olsson (ibid.) describe the constructivist theory,
which relates ethnic diversity to the rise of agriculture and the emergence of state and the concept of nation,
and evolutionary view, which states that the duration of human settlements since prehistoric times has a strong
positive association with current levels of ethnolinguistic diversity and which they test using the data on human
genome by each ethnic group. Besides the constructivist and evolutionary theories, Michalopoulos (2008) argues
that geographical characteristics such as land quality and elevation (but, in general, latitude, longitude and
humidity) determine ethnic diversity. Finally, Horowitz (1985), Diamond (1997) and Leeson (2005) study the
impact of Western colonialism on ethnic diversity and Fletcher and Iyigun (2009) study the impact of conflicts.
In this context, this paper adds to the latter set of literature, along with Western colonialism and the conflicts,
and studies the impact of Soviet deportations on ethnic diversity.
5
2004). In what follows, I instrument ethnic diversity with the deportation variable constructed
using the official Soviet documents first systematized and published by Pobol and Polian (2005).
First stage results demonstrate a strong effect of deportations on regional ethnic structure. On
average, inward migrations decreased regional ethnic fragmentation (measured by the standard
Herfindahl index) by 9 percent. This result stay robust when I use a flow variable, which captures the number of Germans deported from the Southern and Volga regions, as an instrumental
variable for ethnic fragmentation.
As for the structural relationship, I follow Putnam’s definition of social capital, as the ”features of social organisation such as networks, norms, and social trust”, and focus on - trust
(Putnam 1993). Using recent Russian Life in Transition Survey (2010) I construct measures of
general, interpersonal and institutional trust. My hypothesis, contrary to the prevailing wisdom,
is that ethnic diversity per se is not related to social capital outcomes. When estimating the
effects of ethnicity on trust using forced migrations as an instrumental variable, I show that
ethnicity is insignificant in determining different types of trust in most cases. The first and the
second stage results are robust to using other measures of ethnic diversity, such as ethnolinguistic
polarization and ethnic fragmentation, computed at different levels of aggregation.
In addition, I explore the extent to which trust is influenced by individual socio-economic
characteristics, as well as by the regional income heterogeneity and crime rate. As found in
earlier studies, household income and age are both strongly positively related to trust. On the
contrary, income heterogeneity and crime rate are found to be insignificant in formation of social
and institutional trust.
The remainder of this paper is organized as follows. Section 1 provides a short excursus into
the concept of social capital and various measures of trust used in this paper. Sections 2 and 3
present the data and the empirical model. The results are summarized in Section 4. Section 5
provides some robustness checks and extension. Concluding remarks are offered in Section 6.
1
The Concept of Social Capital and Measures of Trust
The concept of social capital and its importance for institutional and economic development was
recognized by economists since at least Putnam (ibid.), Fukuyama (1995) and Thomas (1996). As
6
Putnam argues, social capital ”facilitates coordination and cooperation for the mutual benefit”
of the members of society, and thus leads to economic development. While perceptions of the
economic benefits brought by social capital are confirmed in many studies, what is less clear is
what shapes social capital, and especially whether ethnicity matters?
In a recent speech, Putnam (2007) describes ’contact’ and ’conflict’ theories, which are based
on two opposing views concerning the impact of diversity and immigration on social capital.
Contact theory suggests that if we have more contact with people of other ethnic and racial
backgrounds, we will all begin to trust one another more. Conversely, conflict theory suggests
that the more we are brought into physical proximity with people of another race or ethnic
background, the more we ’stick’ to our own and the less we trust the ’other’. Based on evidence
from the United States, Putnam (ibid.) found support for the conflict theory, which implies
that ethnic diversity brings social isolation. However, in the cross-country study by Gesthuizen
et al. (2009), the conflict theory was refuted. The authors found that economic inequality and
a national history involving continuous democracy plays a more significant role in explaining
cross-national differences in social capital in Europe.
Gesthuizen et al. (ibid.), however, do not control for reverse causality problem. If, according
to Putnam (2007), conflict theory is indeed true, then less trusting people would choose to move
to a less ethnically diverse region. On the other hand, if contact theory is more plausible, then
we will observe the opposite form of behaviour. Moreover, many cross-sectional studies suffer
from omitted variable bias stemming from cultural factors that are difficult to account for.
Another challenge comes from how to properly measure social capital? Since social capital is
an intangible and abstract concept, to measure it appropriately is a very difficult task (Gilligan
et al. 2011). This paper focuses primarily on trust, as it is a major component of social capital,
and has been widely used in the existing economics literature. Putnam et al. (1993), Gambetta
(1990) and Keefer and Knack (1997) recognised the importance of trust for institutional and
economic performance.
Measures of trust may be highly subjective, especially when constructed using the standard
survey question ”Generally speaking, would you say that most people can be trusted, or that
you can’t be too careful in dealing with people?”. They may represent the general trusting
personality of the individual, or sometimes the subject of trust is not clearly defined. Therefore,
7
in order to be more specific, I study three different categories of trust: general, interpersonal and
institutional. The key differences between the types of trust studied here are as follows. General
trust represents attitudes towards other people in general, and this measure does not specify
any particular subject it is addressed to. Interpersonal trust, on the other hand, represents the
attitudes towards specific people within a social circle or outside of it. It may comprise family,
friends, people of another ethnic group or religion, etc. Institutional trust refers to the trust
which people have towards institutions, its organisations or the people who administer those
systems. For example, it may be addressed towards the political, legal or economic institutions,
the police, the army, etc. In this distinction I follow Alesina and La Ferrara (2002).
A growing body of research on trust emphasizes the distinction between general versus
interpersonal trust. Uslaner and Conley (2003) argue that agents exhibiting ’particularistic’
trust have weaker connections with people outside of their social circle, and are more likely
to see the world in terms of ’we’ and ’they’ than individuals with high levels of general trust.
Some studies focus specifically on family ties, and show that strong family connections can be
detrimental for trust outside their family circle. For example, La Porta et al. (1997) found
evidence that family ties are bad for the development of large firms. Ermisch and Gambetta
(2010) show that people with strong family ties have a lower level of trust in strangers than
people with weak family ties, which can be explained by the level of outward exposure: lack of
motivation and social experience. On the contrary, Bahry et al. (2005) found that high ingroup
or ’particularistic’ trust is no barrier to faith in people of another ethnic group.
Another distinction can be made between interpersonal and institutional types of trust.
Uslaner (2002), Newton (1999) and Newton (2001) argue that political and social trust are not
related. Studying the impact of ethnic diversity, Alesina and La Ferrara (2002) show that racial
fragmentation affects how much people trust other individuals but it does not influence levels
of trust in a variety of different institutions. Above all, in their study, the general level of
trust is only slightly, sometimes even negatively, correlated with the levels of trust in different
institutions. The results drawn from economic studies are somewhat puzzling and, in general,
not consistent with the results found in the early sociology and psychology literatures, which
claimed that the basis of trust is in the family, and that so-called ’relational trust’ does extend
from social trust to trust in government organizations (Job 2005).
8
In general, the evidence from the existing literature on trust is controversial. This may
be partially due to the use of improper measures of trust. Trust or any other element of social
capital, such as generosity, are desirable social traits and so respondents may answer in the more
socially desirable way even if it does not correspond to their true attitudes. For this reason,
many researchers start using behavioural measures from the experimental activities which allow
us to observe subjects’ behaviour in a controlled laboratory setting (Cardenas and Carpenter
2008; Gilligan et al. 2011; Henrich et al. 2004; Karlan 2005). Since conducting behavioural
activities is beyond the scope of the current study, this paper focuses on the standard approach,
and attempts to furnish more evidence regarding the linkage between different types of trust
based on standard measures. However, for the future research it would be advantageous to use
behavioural measures from the experimental activities which could be conducted in Russia.
2
Data
2.1
Trust and individual socio-economic characteristics
All individual data come from the Life in Transition Survey conducted by the European Bank
for Reconstruction and Development and the World Bank in 2010. This survey differs from
the previous one, conducted in 2006, by containing more questions on different categories of
trust and covering more individuals. Overall, the new survey covers 1,580 individuals in 36
administrative regions in Russia.
First, I follow the standard approach adopted in the recent literature (Alesina and La Ferrara
2002; Tabellini 2005) and as a proxy for general trust use a measure of self-reported trust based
on individual responses to the following survey question: ”Generally speaking, would you say
that most people can be trusted, or that you can’t be too careful in dealing with people?”. The
general trust variable is an integer that takes values going from 1 - complete distrust to 5 complete trust.
Second, the survey allows one to divide trust into interpersonal and institutional. Individual
trust includes an extent of trust in the family, neighbourhood, people you meet for the first
time, friends and acquaintances, people of another religion, and people of another nationality.
Institutional trust can be divided into political trust (trust in the Presidency, the cabinet of
9
ministers, regional and local governments, the Duma, political parties), trust in legal institutions
(courts), military (armed forces, the police), economic institutions (banks and the financial
system, foreign investors, non governmental organisations (NGOs), trade unions) and religious
institutions. All the measures of interpersonal and institutional trust are categorical variables
that use the same scale as the general trust variable.
Table 1 shows the means of all trust variables and their pairwise correlations with the general
trust measure. (Table 8 in the Appendix presents the full table of correlations.)
Table 1: Pairwise correlations of different types of trust with general trust
[1]
Means
Trust in
General trust
family
neighbourhood
people you meet for the first time
friends and acquaintances
people of another religion
people of another nationality
the Presidency
the cabinet of ministers
the regional government
the local government
the Duma
courts
political parties
the armed forces
the police
banks and the financial system
foreign investors
NGOs
trade unions
religious institutions
3.21
4.87
3.73
2.54
4.10
2.93
2.96
3.42
3.11
2.88
2.81
2.79
2.72
2.51
3.23
2.66
2.81
2.59
2.66
2.92
2.94
[2]
Correlations with
general trust
1
0.03
0.27*
0.39*
0.19*
0.31*
0.28*
0.32*
0.29*
0.25*
0.29*
0.29*
0.26*
0.24*
0.20*
0.27*
0.20*
0.20*
0.21*
0.21*
0.16*
Column [2] of Table 1 shows that almost all measures are positively and significantly correlated with the general trust, except for trust in family. Almost all respondents, 91 percent,
answered that they completely trust their family members. This is not the case for the other
trust measures, the means of which are on average lower than the mean of trust in family. This
confirms that family ties are very strong in Russia.
Detailed descriptive statistics of other socio-economic characteristics are presented in the
10
Appendix in Table 9. Individual covariates include age, gender, level of education (in years) and
marital status, which are found to be important determinants of trusting behaviour (Alesina
and La Ferrara 2002). The majority of the respondents are women. The average age is 47 years
old. Around 52 percent of the individuals are married.
Labour related variables, such as employment status and household income, are also taken
into account. Employment is coded as a dummy variable based on the response to the following
survey question: ”Did you work for income during the last 12 months?”, and is equal to 1 if a
person answered ”yes”, and 0 otherwise. Around 59 percent of the individuals worked for income
during the last 12 months. The survey does not provide the monetary value of household income,
but is coded as a ten-category scale variable, from 0 to 10, where a higher score indicates that
the household belongs to the richer class of people in the country. Mean value of household
income is almost 4 points.
Finally, religion affiliation is included as a covariate to test whether different religious beliefs
may influence attitudes towards social interactions (ibid.). The majority of the respondents (80
percent) belong to Christian Orthodox.
2.2
Ethnolinguistic diversity
To construct a measure of ethnolinguistic diversity I follow a standard approach and use the
ethnolinguistic fragmentation index (ELF), which is computed as one minus the Herfindahl index
of ethnolinguistic group shares. The index reflects the probability that two randomly selected
individuals from a region belong to different groups.
ELFj = 1 −
N
X
2
πij
,
(1)
i=1
where πij is the share of ethnolinguistic group i (i = 1, ..., N ) in region j.
There are 182 ethnic4 groups listed. The most ethnically diverse regions correspond to the
Ural regions, northern territories of Eastern Siberia, the Caucasus and northern territories of
European part of Russia. This can be explained by the fact that these territories were histor4
Through-out the paper I assume that ethnic divisions from the censuses 2002, 1959 and 1926 also include the
linguistic component. Therefore, I assume that the terms ethnic and ethnolinguistic are identical for Russia.
11
ically inhabited by many indigenous and relatively small ethnic groups.5 However, the diverse
ethnic composition of these regions was also influenced by the Soviet deportations. The ethnic fragmentation index is the highest for Bashkortostan (0.72), Mariy-El (0.58) and Tatarstan
(0.56). Some historians note that by 1959 these regions became multi-ethnic due to the very
large inward migration during the 20th century (Beznosova 2003).6 Western parts of the country
are largely populated by the Russian ethnic group, which makes them relatively homogeneous.
2.3
Deportations
Until the end of the 1980s the topic of deportations was forbidden in the Soviet Union and only
recently, in the 1990s, have the related archive documents became available. Deportations also
took place in the Russian Empire, but their scale reached a peak under J. Stalin as a part of
the policy of repression from the 1920s until the 1950s.
I use the term ’deportations’ to refer to forced resettlements to labour scarce and remote
areas based on ethnic or social grounds. This paper does not take into account ’standard’ imprisonment, in which people, mostly on an individual basis, were sent to far off destinations and
were used as the force labour (’The Gulag Archipelago’). Instead I focus on forced resettlements,
in which large groups of people, sharing either the same ethnicity or the same social class, were
moved to special villages.7
A publication by Pobol and Polian (2005) is the most complete recent collection of various
types of official documents that are related to deportations conducted during the Stalinist period. The documents comprise orders given by party and state authorities, as well as reports
concerning the progress of resettlements, written by the leaders of the state and regional NKVD
departments8 . Pobol and Polian (ibid.) enumerate 53 deportation campaigns during the 19281953 period, of which 38 campaigns (73 percent) were ethnic. In terms of the number of people
affected, 5.8m were deported within the country, of which 3.4m (57.5 percent) on ethnic grounds.
5
46 out of 182 ethnicities living in Russia are considered as indigenous.
As such, ethnic groups such as Bulgarians, Chinese, Koreans, and Bashkirs, which had never appeared before
in these regions, made their appearance. The population of Polish, Latvians, Lithuanians, Estonians, Armenians,
Georgians increased by 10 or even 100 times (Beznosova 2003).
7
Both imprisonments and deportations were under the jurisdiction of the same authority, GULAG NKVD
(Chief Administration of Corrective Labour Camps and Colonies of the People’s Commissariat for Internal Affairs
of the USSR).
8
People’s Commissariat for Internal Affairs of the USSR
6
12
According to the Soviet census of 1926, the number of deportees amounted to almost 6 percent
of the total population of 100.6m.
Historians recognise that the primary reasons for ethnic deportation campaigns were ’security
issues’ and preparations for the Second World War. The campaigns affected many ethnic groups,
mostly minorities, from the Western borders of the country, which, from the point of view of
the country leader, could desert or change sides during the war.
In addition to the ethnic groups, the deportations affected individual peasants, the ’kulaks’
social class, who were criticised by the authorities for supposedly worsening an agricultural crisis
in 1927 after the introduction of the New Economic Policy (NEP). In order to overcome the
crisis the government took the decision of confiscating grain from wealthy individual farmers.
The scale of this policy was so large, and the accompanying measures were so drastic that the
term ’dekulakisation’ became appellative and was associated with the violent dispossessions.
Peasants who were subject to ’dekulakisation’ were resettled and their farms were given over to
public use, thus jump-starting the new Soviet policy of collectivisation.
Due to the large scale of the Soviet deportation campaigns, and the use of the settlers as a
cheap labour force, the economic consequences of deportations were notable. The special settlers
were employed in many industries: forestry, mining, fishing, metallurgy, fuel, construction (Pobol
and Polian 2005; Shashkov 1995). The impact of deportations on regional ethnic diversity has
never been widely studied empirically, mostly due to the lack of reliable data. In this context,
Pobol and Polian (2005) is highly influential work that was among the first to provide rough
estimates of the number of deportees either by region of destination, or by region of departure.
The flow estimates do not exist for many of the campaigns. It is, however, possible to construct a dummy variable for the regions of destination. In what follows I code a dummy variable
inward9 , which is equal to 1 if a region was a destination for a deportation, and 0 otherwise.
Table 2 presents the list of the regions included in the sample, and the corresponding values of
inward dummy. Figure 1 presents the distribution of inward instrumental variable. Majority of
the destination regions are located in the labour scarce and remote areas in Siberia, North-West,
Ural and the Far East. The regions for which inward dummy is equal to 0 are located in the
9
I also construct outward dummy, which equals to 1 for the regions of departure, but, empirically, it is a
weaker instrument.
13
European part of Russia, Volga and the South. These regions, though not exactly corresponding
to the areas of departure, are the regions of the ’black earth’, where the ’dekulakization’ took
place.
Table 2: Regions of destination of deportations
Geographic area
Central
North-West
Volga
Arhangelskaia
Murmanskaia
Bashkortostan
Nizhegorodskaia
Samarskaia
Permskaia
South
Rostovskaia
Far East
Habarovskiy krai
Primorskiy krai
Altaiskiy Krai
Irkutskaia
Kemerovskaia
Krasnoiarskiy krai
Novosibirskaia
Omskaia
Chelyabinskaia
Sverdlovskaia
Tumenskaia
Siberian
Ural
2.4
Regions of destination
inward = 1
Jaroslavskaia
Other regions
inward = 0
Moscow
Ivanovskaia
Kurskaia
Voronezhskaia
Tverskaia
St. Petersburg
Ulianovskaia
Chuvashiya
Mariy-El
Saratovskaia
Orenburgskaia
Penzenskaia
Tatarstan
Udmurtiya
Stavropolskiy krai
Krasnodarskiy krai
Volgogradskaia
Other regional characteristics
Among other explanatory variables I also include regional socio-economic characteristics which
could explain variation in trust levels, such as inequality (Gini coefficient) and the crime rate
(Alesina and La Ferrara 2002). High crime rates may be associated with lower insecurity and,
14
Figure 1: Destinations of Soviet deportations (1928-1953)
as a consequence, lower trust levels. High income inequality is considered to be a source of
distrust in a community if we assume that people are more likely to trust individuals who are
more ’similar’ (Alesina and La Ferrara 2002). The data on crime rate and income heterogeneity
come from the Russian Federal State Statistics Service as of 2010. Detailed summary statistics
of all regional covariates are included in the Appendix in Table 10.
3
Empirical model
For region j, the first stage equation to be estimated is:
ELFj = α0 + Inwardj α1 + xij α2 + yj α3 + ij ,
(2)
where the exogenous covariates from the second stage are a vector of individual characteristics
xij and a vector of regional characteristics yj . Inwardj is an instrumental variable associated
with the deportations. ij is an unobserved error term.
The second stage is given by:
15
T rustij = β0 + ELFj β1 + xij β2 + yj β3 + εij ,
(3)
where T rustij is either general, interpersonal or institutional. εij is an unobserved error term.
The exclusion restriction implied by the instrumental variable regression (2) is that, conditional on the covariates included in the regression the deportations more than 60 years ago have
no direct effect on social and institutional trust today.
4
Estimation results
Table 3 Panel A reports the first-stage regression (2) results of the ELF index on the instrumental
variables. Columns [1] to [8] differ by including different exogenous covariates from the second
stage regression (3). In most cases, the coefficient on the excluded instrumental has a negative
sign and is statistically significant. In particular, due to the inward deportations the regions
of destination are on average 9 percent less ethnically diverse than the other regions. Though
the information on the ethnicity of the deportees was not always available, it is likely that the
negative sign is explained by the deportees being from one of the big ethnic groups, for instance
Russians, in the regions of destination.
Table 3 Panel B presents the results of the estimation of equation (3) by instrumental variables. In column [1] I include the ELF index as a single covariate. The coefficient is interpreted
as follows: a one unit increase in the index leads to a 0.72 unit increase in the level of general
trust. Most importantly, the coefficient associated with the ELF index is positive and statistically insignificant. Table 12 in the Appendix presents the OLS estimations. Column [1] of
Table 12 corresponds to column [1] of Table 3. The coefficient associated with the index in
the OLS estimation is four times smaller than in the 2SLS results, suggesting that there is a
downward bias in the OLS results. If the perception of not trusting people of another ethnicity
holds, than we would expect that less trusting people will choose to live in more ethnically
homogeneous communities. As such, the correlation between the omitted variable (in this case,
the negative perception) and the ELF index will be negative, causing a downward bias in the
OLS estimate of the impact of ethnicity.
Column [1] of the Table 3 includes age as another explanatory variable. The coefficient
16
Table 3: IV Estimations. Dependent variable: general trust
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
Panel A: First stage for Ethnolinguistic Fragmentation
inward
R2
-0.091**
(0.037)
-0.091**
(0.037)
-0.091**
(0.037)
-0.091**
(0.037)
-0.090**
(0.037)
-0.088**
(0.037)
-0.086**
(0.042)
-0.117*
(0.065)
0.122
0.122
0.123
0.124
0.125
0.117
0.119
0.132
Panel B: Second stage for General Trust
ELF
0.718
(1.610)
age
0.555
(1.597)
0.006**
(0.002)
female
0.556
(1.598)
0.006**
(0.002)
0.012
(0.069)
married
0.643
(1.624)
0.006**
(0.003)
0.617
(1.629)
0.005*
(0.003)
0.137
(1.655)
0.008***
(0.003)
-0.076
(1.815)
0.008***
(0.003)
-0.782
(2.044)
0.008**
(0.003)
0.172*
(0.092)
0.176*
(0.091)
-0.082
(0.073)
0.160*
(0.088)
0.153*
(0.087)
0.139*
(0.081)
0.127***
(0.034)
0.124***
(0.034)
1.057
(1.402)
0.126***
(0.033)
work
income
Gini
crime
N
-0.0001
(0.0001)
1,578
1,578
1,578
1,558
1,558
1,510
1,510
1,510
Notes: equations are estimated by the 2SLS. Robust standard errors, clustered by region (oblast), are in
parentheses. Significance levels are as follows: *** p<0.01, ** p<0.05, * p<0.1. Other marital status dummies
(divorced, separated and widowed) are included in the regressions in columns [4] to [8], but are not reported
in the Table.
associated with age is positive and statistically highly significant, suggesting that older people
tend to be more trusting. Column [3] includes a gender dummy. It indicates that gender is
not a significant determinant of social capital, which is in contrary to what other studies find.
Marital status dummies are added in column [4]. Being married increases individual general
trust. However, this estimate should be taken with caution, as marriage may be endogenous to
trust. Alesina and La Ferrara (2002) hypothesise that more trusting people are more likely to
get married.
Columns [5] and [6] include labour related covariates: working status and household income.
Of the two, household income matters more for social capital. However, the two covariates may
also be endogenous to trust if more trusting people are more likely to get a job, or may be
involved in more risky activities, which generate a higher level of income.
17
Finally, in the last two columns, I control for regional characteristics, such as the inequality
(Gini coefficient) and the crime rate. Both variables are insignificant in determining trust.
It is worth noting, once ethnicity is instrumented, ethnic diversity is not an important
determinant of general trust. This finding lies in contrast to the existing literature, which states
that ethnolinguistic diversity has a negative effect on social capital.
However, self-reported general trust is not a precise indicator of trust, because it may be
attributed to different people inside or outside the respondent’s social circle. Moving to interpersonal trust, Table 4 reports the results of the estimations, where the dependent variable is trust
in family members, neighbours, people you meet for the first time, friends and acquaintances, or
people of another religion or nationality. In all the estimations, the ELF index is an insignificant
determinant in shaping interpersonal trust.
Table 4: IV estimations. Interpersonal trust
ELF
age
income
N
[1]
general
trust
[2]
in the
family
[3]
in the
neighbourhood
[4]
in the people
you meet
for the first time
[5]
in the friends
and
acquaintances
[6]
in the people
of another
religion
[7]
in the people
of another
nationality
0.037
(1.676)
0.008***
(0.003)
0.127***
(0.032)
0.606
(0.420)
-0.001
(0.001)
0.004
(0.010)
-1.149
(1.293)
0.011***
(0.002)
0.037
(0.025)
-0.834
(1.845)
0.010***
(0.003)
0.090***
(0.027)
0.028
(1.389)
0.000
(0.002)
0.039*
(0.022)
-1.103
(1.669)
0.004
(0.003)
0.043
(0.035)
-1.239
(1.883)
0.006**
(0.003)
0.061*
(0.034)
1,510
1,493
1,502
1,502
1,505
1,479
1,479
Notes: equations are estimated by the 2SLS. Robust standard errors, clustered by region (oblast), are in
parentheses. Significance levels are as follows: *** p<0.01, ** p<0.05, * p<0.1. Marital status and religious
affiliation dummies are also included in all the regressions, but are not reported in the table. The results of
the first stage are presented in Table 3.
Tables 5 and 6 present the results from the regressions on institutional trust. Table 5 presents
results where the dependent variable is a political trust measure. As with interpersonal trust,
the effects of ethnic diversity on social capital are statistically insignificant in all cases. The
same holds for trust in legal, economic or religious institutions (see Table 6).
18
Table 5: IV estimations. Political trust
ELF
age
income
N
[1]
general
trust
[2]
in the
Presidency
[3]
in the cabinet
of ministers
[4]
in regional
government
[5]
in local
government
[6]
in
Duma
[7]
in political
parties
0.037
(1.676)
0.008***
(0.003)
0.127***
(0.032)
-0.647
(1.854)
0.009***
(0.003)
0.143***
(0.033)
-0.757
(1.544)
0.010***
(0.003)
0.129***
(0.029)
-0.930
(1.469)
0.005**
(0.002)
0.140***
(0.025)
-0.617
(1.802)
0.007***
(0.002)
0.144***
(0.027)
-1.573
(1.310)
0.008***
(0.002)
0.137***
(0.021)
-0.063
(1.162)
0.006***
(0.001)
0.119***
(0.029)
1,510
1,504
1,507
1,503
1,503
1,504
1,502
Notes: equations are estimated by the 2SLS. Robust standard errors, clustered by region (oblast), are in
parentheses. Significance levels are as follows: *** p<0.01, ** p<0.05, * p<0.1. Marital status and religious
affiliation dummies are also included in all the regressions, but are not reported in the table. The results of
the first stage are presented in Table 3.
Table 6: IV estimations. Trust in legal, economic and religious institutions
ELF
age
income
N
[1]
general
trust
[2]
in
courts
[3]
in the
armed
forces
[4]
in the
police
[5]
in the banks
and the
financial
system
[6]
in
foreign
investors
[7]
in
NGOs
[8]
in
trade
unions
[9]
in
religious
institutions
0.037
(1.676)
0.008***
(0.003)
0.127***
(0.032)
-2.558
(1.658)
0.004*
(0.002)
0.121***
(0.027)
-1.358
(1.481)
0.012***
(0.003)
0.094***
(0.027)
-2.114
(2.020)
0.008***
(0.003)
0.148***
(0.021)
-0.204
(1.501)
-0.003
(0.002)
0.188***
(0.029)
-1.047
(1.418)
-0.001
(0.002)
0.156***
(0.027)
-1.823
(1.545)
0.003*
(0.001)
0.136***
(0.023)
1.811
(2.077)
0.006***
(0.002)
0.092***
(0.025)
0.269
(1.343)
0.008***
(0.002)
0.094***
(0.023)
1,510
1,504
1,502
1,504
1,498
1,494
1,498
1,482
1,495
Notes: equations are estimated by the 2SLS. Robust standard errors, clustered by region (oblast), are in
parentheses. Significance levels are as follows: *** p<0.01, ** p<0.05, * p<0.1. Marital status and religious
affiliation dummies are also included in all the regressions, but are not reported in the table. The results of
the first stage are presented in Table 3.
5
Robustness and Extension
5.1
Ethnolinguistic trees
Desmet et al. (2012) propose a new method to measure ethnolinguistic diversity by using linguistic trees, which describe the genealogical relationship between the world’s languages. They
argue that different levels of linguistic cleavages may have heterogeneous effects on political and
economic outcomes.
I follow their method of constructing ethnolinguistic trees and use three different levels of
aggregation. To construct ethnolinguistic trees I use data on the population of ethnic groups
19
for all the regions in the sample from the 2002 census. At each level of aggregation I construct
the ethnolinguistic index.
For the lowest level of aggregation, ELF , I use all 182 ethnicities present in the census, and
this is, essentially, the index considered in the previous estimations. In order to construct the
second and third levels of aggregation I use the Atlas Narodov Mira (1964) and combine ethnicities into ethnolinguistic groups, ELF (2), and ethnolinguistic families, ELF (1). An example of
the ethnolinguistic tree for the Uralic family of ethnicities is given in Figure 2. Based on the
figure, the Uralic family represents the deepest ethnolinguistic cleavage, while, for instance, the
Hungarians - the most recent ethnolinguistic cleavage.
Khanty
Ugric
Hungarians
Mansi
Nenets
Uralic
Samoyed
Nganasan
Lapp
Selkup
Veps
Komi
Mordvinian
Finns
Estonian
Udmurts
Fins
Mari
Karelians
Figure 2: Ethnolinguistic tree for the Uralic family
Table 7 Panel A columns [2] and [3] reports the first stage results. Deportations have a
20
strong effect on ethnolinguistic diversity of all levels of aggregation.
Table 7 Panel B columns [2] and [3] reports the results from regressing general trust on the
ELF(2) and ELF(1) indexes. The results show that the coefficients associated with the indexes
are of the same magnitude as those associated with the ELF. Contrary to Desmet et al. (2012),
the estimation results do not show that deep ethnolinguistic cleavages matter more than recent
ones.
5.2
Polarization
Esteban and Ray (1994), Arcand et al. (2000a,b) and Montalvo and Reynal-Querol (2000, 2005b)
argue that ethnic polarization rather than ethnic fragmentation determines ethnic conflicts, and,
in turn, is harmful for economic development.
I test this hypothesis by estimating the effect of ethnolinguistic polarization on social capital.
I use the measure of ethnolinguistic polarization proposed by Montalvo and Reynal-Querol
(2000):10
P OLj = 1 −
N X
1/2 − πi
i=1
1/2
πi
(4)
The polarization index captures how far the distribution of the ethnic groups is from the
bipolar distribution, and in contrast to ethnic fragmentation index, reaches it’s maximum when
there are two ethnic groups of equal size. In the sample out of 36 regions, Tatarstan is the most
polarized region, with the index equal to 0.91, and Kurskaya oblast is the least polarized region,
with the index equal to 0.15.
The estimated equations (2) and (3) thus become:
P OLj = γ0 + Inwardj γ1 + xij γ2 + yj γ3 + ξij ,
(5)
T rustij = φ0 + P OLj φ1 + xij φ2 + yj φ3 + νij ,
(6)
and
10
Esteban and Ray (1994) suggest another measure of polarization, which includes the income per capita for
each ethnic group, and other constants. This index is not used here due to data limitations.
21
Figure 3: Regions of departure
correspondingly.
Table 7 column [4] reports result from the regression of general trust on the POL index. The
results do not suggest that polarization better explains social capital than diversity measures.
The estimated coefficient on POL index remains statistically insignificant.
5.3
Outflow instrument
As another robustness check, I construct another instrument which reflects the deportation
outflows. This measure is constructed based on the report by Pobol and Polian (2005) on the
number of German deportees by region of departure.11 Figure 3 presents the distribution of
outf low instrumental variable.
Including both instruments in the first stage allows me to test the ensuing over-identifying
restrictions. So the estimated equations (2) and (3) thus become:
11
Data on deportations of Germans as of 25 December 1941 (pp.284-286; Pobol and Polian 2005)
22
ELFj = ψ0 + Inwardj ψ1 + Ln(German deportees)j ψ2 + xij ψ3 + yj ψ4 + σij ,
(7)
T rustij = ϕ0 + ELFj ϕ1 + xij ϕ2 + yj ϕ3 + ωij ,
(8)
and
correspondingly.
Column [5] of table 7 shows that both instruments are statistically significant. The high
p-value associated with the Hansen test (0.75) allows me not to reject the null hypothesis of
exogeneity. Moreover, the results confirm the importance of deportations as determinants of
regional ethnic structure.
5.4
Early ethnolinguistic diversity
Finally, I look at the early ethnolinguistic diversity, ELF 1959, by using the data from the
1959 census. Column [6] of table 7 presents the results from the estimations. The coefficient
associated with the inward dummy becomes statistically insignificant. If one assumes that the
data from 1959 census are reliable, then one would expect a stronger effect. However, historians
have argued that the data from the 1959 census should be taken with caution (Zhiromskaya
et al. 1996).
6
Concluding remarks
This paper has explored the role of ethnic diversity in socio-economic development. I have
argued that ethnicity is endogeneous to social capital outcomes. I instrument it by deportations
variable constructed using data on the regions of destination from Soviet historical sources.
The first stage results show a negative and statistically highly significant effect of deportations on ethnolinguistic diversity, measured by the standard Herfindahl index. In particular, the
Soviet deportations decreased regional ethnic fragmentation by 9 percent. The results are robust
to using other measures of ethnic diversity, such as ethnolinguistic polarization and ethnolinguistic fragmentation computed at different levels of aggregation. The results are also robust
23
Table 7: IV estimations. Robustness checks
[1]
[2]
[3]
[4]
[5]
[6]
-0.132**
(0.052)
-0.009*
(0.005)
0.240
-0.076
(0.053)
-0.015***
(0.005)
0.262
Panel A: First stage
inward
-0.086**
(0.035)
-0.104***
(0.034)
-0.096**
(0.039)
-0.147***
(0.046)
0.167
0.229
0.197
0.235
ln(German deportees)
R2
Panel B: Second stage for General Trust
ELF, 2002
0.037
(1.676)
ELF(2), 2002
-0.419
(1.504)
0.031
(1.397)
ELF(1), 2002
0.034
(1.512)
POL, 2002
0.022
(0.983)
ELF, 1959
age
income
Hansen test
p-value
N
0.008***
(0.003)
0.127***
(0.032)
0.008***
(0.003)
0.127***
(0.032)
0.008***
(0.003)
0.127***
(0.033)
0.008***
(0.003)
0.127***
(0.032)
0.008***
(0.003)
0.127***
(0.031)
-0.648
(1.498)
0.008***
(0.003)
0.127***
(0.031)
1,510
1,510
1,510
1,510
0.73
1,510
0.92
1,510
Equations are estimated by 2SLS. Robust standard errors clustered by region are in parentheses. Significance
levels are as follows: *** p<0.01, ** p<0.05, * p<0.1
to using different instrumental variable, such as the number of German deportees by region of
departure.
The findings from the estimations of the structural equation suggest that ethnicity is not a
major determinant of general, interpersonal, or institutional trust in most cases. These results
are opposite to the prevailing wisdom, which claims that ethnic diversity is a cause of conflicts,
low social capital, poor institutions and economic performance. Moreover, individual characteristics, such as age and household income, matter more than regional income inequality and
crime rate.
However, there are several challenges associated with these results. First, the measure of
trust, obtained from the standard survey questions, could be highly subjective, and not reflecting
the real attitudes of the individual. Taking this into consideration and conducting experimental
activities, which permit obtaining behavioural measures of social capital, could be useful. Sec-
24
ond, measures of ethnic diversity used in this paper do not capture ethnic income inequality,
which has been recently shown to be strongly inversely related to per capita income (Alesina
et al. 2012). These are the extensions for future research.
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29
Appendix Figures and Tables
(a) Ethnolinguistic fragmentation, 2002
(b) Ethnolinguistic fragmentation, 1959
(c) Ethnolinguistic polarization, 2002
(d) Ethnolinguistic polarization, 1959
Notes: 1. ELF index, calculated for all regions: 0 - complete homogeneity, 1 - complete heterogeneity
2. POL index, calculated for all regions: 0 - no polarization, 1 - absolute polarization
3. Data source: author’s calculations based on the 1959 and 2002 censuses.
Figure 4: Ethnic structure in Russia
30
31
1
0.36*
0.27*
0.26*
0.26*
0.23*
0.21*
0.19*
0.21*
0.15*
0.19*
0.13*
0.17*
0.16*
0.09*
0.03*
0.11*
0.11*
0.16*
1
0.25*
0.46*
0.43*
0.24*
0.25*
0.24*
0.24*
0.25*
0.23*
0.20*
0.21*
0.25*
0.20*
0.16*
0.23*
0.17*
0.20*
1
0.31*
0.31*
0.12*
0.07*
0.07*
0.11*
0.09*
0.09*
0.07*
0.05*
0.10*
0.12*
0.07*
0.10*
0.16*
0.08*
1
0.80*
0.21*
0.22*
0.20*
0.22*
0.21*
0.17*
0.15*
0.18*
0.19*
0.17*
0.15*
0.18*
0.17*
0.18*
1
0.23*
0.22*
0.21*
0.22*
0.22*
0.15*
0.16*
0.19*
0.19*
0.18*
0.18*
0.18*
0.13*
0.15*
1
0.75*
0.64*
0.55*
0.60*
0.45*
0.45*
0.42*
0.46*
0.30*
0.29*
0.28*
0.32*
0.29*
1
0.71*
0.59*
0.65*
0.46*
0.51*
0.44*
0.45*
0.31*
0.31*
0.31*
0.35*
0.31*
1
0.78*
0.68*
0.52*
0.50*
0.43*
0.48*
0.33*
0.29*
0.29*
0.35*
0.31*
1
0.66*
0.49*
0.48*
0.42*
0.47*
0.32*
0.28*
0.31*
0.37*
0.32*
1
0.55*
0.60*
0.43*
0.49*
0.36*
0.37*
0.37*
0.40*
0.30*
1
0.52*
0.46*
0.60*
0.44*
0.33*
0.31*
0.34*
0.28*
1
0.40*
0.44*
0.41*
0.41*
0.38*
0.40*
0.33*
1
0.54*
0.34*
0.26*
0.27*
0.32*
0.31*
1
0.44*
0.34*
0.33*
0.32*
0.30*
1
0.56*
0.40*
0.31*
0.24*
1
0.67*
0.33*
0.34*
1
0.42*
0.42*
1
0.43*
1
trustb trustc trustd truste trustf trust1 trust2 trust3 trust4 trust5 trust6 trust7 trust8 trust9 trust10 trust11 trust12 trust13 trust14
Notes: Corresponding variable names are as follows. General trust (trust). Personalised trust: in the family (trusta), neighbourhood (trustb), people you meet
for the 1st time (trustc), friends and acquaintances (trustd ), people of another religion (truste), people of another nationality (trustf ). Institutional trust:
in the Presidency (trust1 ), cabinet of ministers (trust2 ), regional government (trust3 ), local government (trust4 ), Duma (trust5 ), courts (trust6 ), political
parties (trust7 ), the armed forces (trust8 ), the police (trust9 ), banks and the financial system (trust10 ), foreign investors (trust11 ), NGO’s (trust12 ), trade
unions (trust13 ), religious institutions (trust14 ). One star (*) corresponds to the 1 percent significance level.
trust
trusta
trustb
trustc
trustd
truste
trustf
trust1
trust2
trust3
trust4
trust5
trust6
trust7
trust8
trust9
trust10
trust11
trust12
trust13
trust14
Means Correlations
trust trusta
3.21
1
4.87
0.03 1
3.73
0.27* 0.19*
2.54
0.39* -0.05
4.10
0.19* 0.16*
2.93
0.31* 0.02
2.96
0.28* 0.04
3.42
0.32* 0.04
3.11
0.29* 0.06*
2.88
0.25* 0
2.81
0.29* 0.03
2.79
0.29* -0.02
2.72
0.26* 0
2.51
0.24* -0.01
3.23
0.20* 0.04
2.66
0.27* 0.02
2.81
0.20* -0.01
2.59
0.20* -0.04
2.66
0.21* -0.02
2.92
0.21* 0
2.94
0.16* 0.04
Table 8: Pairwise correlations of different types of trust
Table 9: Descriptive statistics. Individual level data
Obs
Mean
Median
Std. Dev.
Min
Max
Trust
General trust
1,580
3.21
3
1.11
1
5
Personalised trust
family (trusta)
neighbourhood (trustb)
people you meet for the 1st time (trustc)
friends and acquaintances (trustd)
people of another religion (truste)
people of another nationality (trustf )
1,560
1,570
1,580
1,573
1,546
1,545
4.87
3.73
2.54
4.09
2.93
2.96
5
4
3
4
3
3
0.49
1.09
1.18
0.94
1.11
1.09
1
1
1
1
1
1
5
5
5
5
5
5
Institutional trust
the Presidency (trust1 )
cabinet of ministers (trust2 )
regional government (trust3 )
local government (trust4 )
Duma (trust5 )
courts (trust6 )
political parties (trust7 )
armed forces (trust8 )
the police (trust9 )
banks and the financial system (trust10 )
foreign investors (trust11 )
NGO’s (trust12 )
trade unions (trust13 )
religious institutions (trust14 )
1,573
1,576
1,572
1,572
1,573
1,573
1,571
1,571
1,573
1,566
1,567
1,567
1,549
1,562
3.42
3.11
2.88
2.81
2.79
2.72
2.51
3.23
2.66
2.81
2.59
2.66
2.92
2.94
4
3
3
3
3
3
3
3
3
3
3
3
3
3
1.28
1.24
1.18
1.26
1.09
1.21
1.09
1.31
1.28
1.22
0.99
0.98
1.09
1.14
1
1
1
1
1
1
1
1
1
1
1
1
1
1
5
5
5
5
5
5
5
5
5
5
5
5
5
5
work (dummy)
income (1 to 10)
age (in years)
female (dummy)
1,580
1,529
1,580
1,580
0.589
3.93
47
0.667
4
47
-
0.49
1.66
17.52
0.47
0
1
13
0
1
10
96
1
Educational dummies
no education (ed1 )
primary education (ed2 )
lower secondary education (ed3 )
upper secondary (ed4 )
post-secondary (ed5 )
bachelor’s degree (ed6 )
master’s degree or PhD (ed7 )
1,580
1,580
1,580
1,580
1,580
1,580
1,580
0.005
0.016
0.080
0.184
0.415
0.289
0.009
-
0.07
0.13
0.27
0.39
0.49
0.45
0.09
0
0
0
0
0
0
0
1
1
1
1
1
1
1
Marital status dummies
never married
married
divorced
separated
widowed
1,560
1,560
1,560
1,560
1,560
0.146
0.524
0.146
0.017
0.167
-
0.35
0.50
0.35
0.13
0.37
0
0
0
0
0
1
1
1
1
1
Religion affiliation dummies
Orthodox
Muslim
Jewish
Buddhist
Protestant
Atheistic
Other
1,580
1,580
1,580
1,580
1,580
1,580
1,580
0.80
0.03
0.0006
0.0006
0.005
0.09
0.06
-
0.39
0.18
0.025
0.025
0.07
0.29
0.25
0
0
0
0
0
0
0
1
1
1
1
1
1
1
32
Table 10: Descriptive statistics. Regional level data
Obs
Mean
Median
Std. Dev.
Min
Max
Ethnolinguistic diversity
ELF, 2002
ELF(2), 2002
ELF(1), 2002
POL, 2002
ELF, 1959
POL, 1959
ELF, 1926
POL, 1926
1,580
1,580
1,580
1,580
1,580
1,580
1,580
1,580
0.27
0.21
0.17
0.44
0.27
0.75
0.34
0.77
0.25
0.19
0.15
0.43
0.22
0.74
0.32
0.84
0.13
0.13
0.14
0.18
0.13
0.18
0.19
0.22
0.08
0.04
0.02
0.16
0.04
0.17
0.01
0.07
0.72
0.53
0.52
0.9
0.71
0.99
0.76
0.99
Deportations
inward (dummy)
German deportees (number of people, outflow)
1,584
1,584
0.47
11,929
0
0.5
23,005.91
0
0
1
99,990
crimes (per 100,000 people, 2010)
Gini index (2009)
1,584
1,584
1,855
0.4
1,695
0.4
476.3
0.05
1174
0.35
2784
0.52
33
Table 11: IV Estimations. Dependent variable: general trust
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
Panel A: First stage for Ethnolinguistic Fragmentation
inward
R2
-0.091**
(0.037)
-0.091**
(0.037)
-0.091**
(0.037)
-0.091**
(0.036)
-0.091**
(0.037)
-0.090**
(0.037)
-0.088**
(0.037)
-0.086**
(0.042)
-0.117*
(0.065)
-0.086**
(0.035)
0.122
0.122
0.123
0.132
0.124
0.125
0.117
0.119
0.132
0.167
0.137
(1.655)
0.008***
(0.003)
-0.076
(1.815)
0.008***
(0.003)
-0.782
(2.044)
0.008**
(0.003)
0.037
(1.676)
0.008***
(0.003)
0.127***
(0.034)
0.124***
(0.034)
1.057
(1.402)
0.126***
(0.033)
0.127***
(0.032)
-0.000
(0.000)
0.139*
(0.081)
0.033
(0.097)
-0.082
(0.189)
0.122
(0.120)
0.156*
(0.090)
0.070
(0.110)
-0.103
(0.195)
0.134
(0.129)
Panel B: Second stage for General Trust
ELF
0.718
(1.610)
age
0.555
(1.597)
0.006**
(0.002)
female
0.556
(1.598)
0.006**
(0.002)
0.012
(0.069)
0.594
(1.589)
0.007**
(0.003)
0.643
(1.624)
0.006**
(0.003)
work
0.617
(1.629)
0.005*
(0.003)
-0.082
(0.073)
income
Gini
crime
married
0.172*
(0.092)
0.042
(0.115)
-0.200
(0.204)
0.084
(0.125)
divorced
separated
widowed
ed2
0.176*
(0.091)
0.059
(0.114)
-0.183
(0.208)
0.073
(0.123)
0.160*
(0.088)
0.069
(0.112)
-0.109
(0.200)
0.140
(0.128)
0.153*
(0.087)
0.053
(0.112)
-0.117
(0.193)
0.133
(0.129)
-0.186
(0.388)
-0.378
(0.360)
-0.181
(0.357)
-0.276
(0.350)
-0.235
(0.354)
-0.392
(0.425)
ed3
ed4
ed5
ed6
ed7
Muslim
0.002
(0.116)
-1.042***
(0.169)
1.630***
(0.524)
-0.130
(0.547)
0.223
(0.341)
-0.185
(0.293)
Jewish
Buddhist
Protestant
Atheistic
Other
N
1,578
1,578
1,578
1,578
1,558
1,558
1,510
1,510
1,510
1,510
Equations are estimated by 2SLS. Robust standard errors clustered by region are in parentheses. Significance levels are
as follows: *** p<0.01, ** p<0.05, * p<0.1. The corresponding educational dummies are named as follows: primary
education (ed2 ), lower secondary education (ed3 ), upper secondary (ed4 ), post-secondary (ed5 ), Bachelor’s degree (ed6 ),
Master’s degree or PhD (ed7 ).
34
Table 12: OLS estimations. Dependent variable: general trust
ELF
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
0.170
(0.576)
0.160
(0.556)
0.006**
(0.002)
0.161
(0.556)
0.006**
(0.002)
0.009
(0.068)
0.186
(0.561)
0.006**
(0.003)
0.174
(0.558)
0.006**
(0.003)
0.160
(0.559)
0.005*
(0.003)
0.100
(0.573)
0.008**
(0.003)
0.063
(0.610)
0.008***
(0.003)
0.053
(0.600)
0.008**
(0.003)
0.026
(0.602)
0.008**
(0.003)
0.127***
(0.034)
0.124***
(0.035)
1.016
(1.142)
0.125***
(0.034)
0.127***
(0.032)
-0.000
(0.000)
0.156*
(0.081)
0.060
(0.105)
-0.095
(0.188)
0.131
(0.129)
0.156*
(0.083)
0.070
(0.106)
-0.103
(0.196)
0.134
(0.132)
age
female
work
-0.088
(0.067)
income
Gini
crime
married
0.164*
(0.086)
0.028
(0.112)
-0.195
(0.196)
0.080
(0.126)
divorced
separated
widowed
ed2
0.169*
(0.085)
0.047
(0.113)
-0.177
(0.199)
0.068
(0.123)
0.160*
(0.081)
0.068
(0.108)
-0.108
(0.198)
0.140
(0.130)
0.155*
(0.080)
0.058
(0.106)
-0.118
(0.193)
0.134
(0.131)
-0.173
(0.393)
-0.344
(0.334)
-0.164
(0.342)
-0.260
(0.339)
-0.223
(0.345)
-0.362
(0.397)
ed3
ed4
ed5
ed6
ed7
Muslim
0.002
(0.118)
-1.043***
(0.137)
1.633***
(0.214)
-0.129
(0.575)
0.224
(0.217)
-0.185
(0.292)
Jewish
Buddhist
Protestant
Atheistic
Other
Observations
R-squared
1,578
0.000
1,578
0.009
1,578
0.009
1,578
0.011
1,558
0.014
1,558
0.015
1,510
0.046
1,510
0.048
1,510
0.048
1,510
0.051
Robust standard errors clustered by region are in parentheses. Significance levels are as follows: *** p<0.01, ** p<0.05,
* p<0.1. The corresponding educational dummies are named as follows: primary education (ed2 ), lower secondary
education (ed3 ), upper secondary (ed4 ), post-secondary (ed5 ), Bachelor’s degree (ed6 ), Master’s degree or PhD (ed7 ).
35