Welfare State, Social Stratification, Democracy and Emigration Intentions

2011–2012

ERSTE Foundation Fellowship for Social Research Should we stay or should we go?

Migration and its effects on demographic and economic development in Central Eastern Europe

Welfare State, Social Stratification, Democracy and Emigration Intentions Alexi Gugushvili

WELFARE STATE, SOCIAL STRATIFICATION, DEMOCRACY AND EMIGRATION INTENTIONS Alexi Gugushvili PhD Researcher at the Department of Political and Social Sciences European University Institute Visiting Research Fellow at the Kennedy School of Government Harvard University alexi.gugushvili@eui.eu Prepared for ERSTE Foundation Fellowship for Social Research

According to ‘new economics’ of migration social stratification and social protection are important for emigration decisions and behaviour, but there is scarce evidence how welfare programmes independently correlate with emigration. In the first part of the project, using the recent UNDP/UNICEF Social Exclusion Survey for two former Soviet republics of Moldova and Ukraine and two former Yugoslav Republics of Macedonia and Serbia and employing multivariate regressions techniques, we find that social stratification in terms of occupational social class and subjective perception of wellbeing has statistically significant association with emigration, having social insurance correlates with lower propensity to leave the country, whereas the quality of jobs has significant effect on emigration intentions. The results vary between Balkan and former Soviet states suggesting that the effect of welfare provisions depend on macro context in which emigration decisions are being made.

The second part of the study tries to understand the modes of emigration from the South Caucasian countries and investigates new patterns emerging as a result of recent developments in the region. Since 2005 South Caucasian countries diverged in their socioeconomic models of development, which are reflected in different covariates of emigration intentions in these societies. Using micro-level survey data from the Caucasus Barometer for 2009-2010, this paper looks how various sets of variables associate with emigration intentions. We test a hypothesis that recent uneven economic and political developments are reflected in individuals’ intentions to leave these societies. Results indicate that, controlling for other covariates, political attitudes have significant associations with emigration intentions and the effect appears to be more important in Azerbaijan, while economic problems seem to be most relevant for emigration intentions from Georgia.

Florence, Italy Cambridge, MA February, 2012

Table of Contents

SOCIAL STRATIFICATION, WELFARE STATE AND EMIGRATION INTENTIONS: THE CASE OF LESS SUCCESSFUL TRANSITIONAL SOCIETIES...................................................2 Abstract ......................................................................................................................................................2 1. Introduction ..........................................................................................................................................2 2. Research framework and literature review ...................................................................................3 3. Hypothesis.............................................................................................................................................5 4. Method and data ..................................................................................................................................6 5. Results....................................................................................................................................................9 5.1. Control variables, gender and social stratification ...................................................................9 5.2. Welfare concerns, social protection and employment stratification....................................11 6. Conclusions .........................................................................................................................................12 Tables and figures..................................................................................................................................14 References................................................................................................................................................20 POLITICAL ECONOMY OF EMIGRATION IN THE SOUTH CAUCASUS: DO POLITICAL OPINIONS EXPLAIN EMIGRATION INTENTIONS? .........................................22 Abstract ....................................................................................................................................................22 1. Introduction ........................................................................................................................................22 2. Literature on democracy and emigration .....................................................................................23 3. Hypothesis...........................................................................................................................................25 4. Method and data ................................................................................................................................26 5. Results..................................................................................................................................................28 5.1. The baseline model.........................................................................................................................28 5.2. Material deprivation, economic attitudes and emigration intentions ................................29 5.3. Political attitudes and emigration intentions ..........................................................................30 6. Conclusions .........................................................................................................................................31 Tables and figures..................................................................................................................................33 References................................................................................................................................................38

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SOCIAL STRATIFICATION , W ELFARE STATE AN D EM IGRATION INTENTIONS: TH E CASE OF LESS SU CCESSFU L TRAN SITION AL SOCIETIES Abstract According to ‘new economics’ of migration social stratification and social protection are important for emigration decisions and behaviour, but there is scarce evidence how welfare programmes independently correlate with emigration. For the major emigrating societies, studies generally do not analyse social model as the covariate of emigration. At the same time, the difficulties in finding adequate data to study the characteristics of migrants have led to the growing interest in the indirect analysis of migration behaviour based on migration intentions. We use the similar approach and test the importance of social protection coverage and other proxy variables of welfare regime on emigration intentions, using the recent UNDP/UNICEF Social Exclusion Survey which contains questions on temporary and permanent emigration intentions among nationally representative samples in two former Soviet republics of Moldova and Ukraine and two former Yugoslav Republics of Macedonia and Serbia. Employing multivariate regressions techniques, we find that social stratification in terms of occupational social class and subjective perception of wellbeing has statistically significant association with emigration, having social insurance correlates with lower propensity to leave the country, whereas the quality of jobs has significant effect on emigration intentions. The results vary between Balkan and former Soviet states suggesting that the effect of welfare provisions depend on macro context in which emigration decisions are being made.

1. Introduction This paper studies associations between social stratification, welfare state programmes and emigration intentions in less successful transition societies. There are contradictory accounts on the role of emigration on the local communities, but most scholars agree that emigration, particularly of skilled population, is damaging the country.

The review of migration literature indicates that there is substantial evidence that human and social capital, along with labour market attachment and personality traits determine emigration intentions and behaviour. Much of the social stratification and welfare state scholarship addresses the effects of migration on destination countries’ social welfare developments in affluent welfare democracies, while less is known about the implications of the existing social model on migration from sending transitional or developing societies.

Based on the insights of ‘new economics’ of migration, it can be hypothesised that in line with market and family-related factors, a decision to emigrate is linked to the existing social stratification and welfare regime in the country. Even being relatively well off, individuals’ probability to emigrate can be affected by their expectations of public support in case on necessity. Emigration can be also understood as a vote of confidence for the existing social model, but the major problem for researching these associations is the difficulty of disentangling deprivation, inequality and welfare state from other covariates of emigration. This means that if we want to research how social stratification and welfare policies affect emigration we must control for already identified emigration determinants.

In the framework employed in this paper, migration causation encompass monetary and 2

non-monetary factors, where decision-making occurs within a broad context of factors at the micro-, meso- and macro-levels.

However, the comprehensive analysis of covariates of actual emigration behaviour is always curtailed by the partial information available to researchers. Good quality data on actual migrants and their current demographic and labour market attributes are accessible in some cases, but these surveys are generally poorer at providing accurate information on the various characteristics family, market and macro state characteristic from which migrants arrived. The opposite is true for the nationally representative household surveys in which only limited information if any tend to be available on already emigrated local population. These two fundamental constraints have prompted migration scholars to divert their attention towards the migration intentions (Castaldo et al., 2007). In an empirical analysis, we use recent UNDP/UNICEF (2010) Social Exclusion Survey to test whether welfare programmes have statistically significant associations with emigration intentions in four less successful transitional societies of Moldova, Ukraine, FYR Macedonia and Serbia. These countries can serve as the good test-cases of the role of welfare state in emigration as they still experience negative net migration, while existing research show the importance of human and social capital in their citizens’ emigration decisions. The chapter starts with the description of the research framework of the study; the section three outlines specific hypothesis of the expected relationships between dependent and independent variables; the next section reviews method and data used in the analysis; section four presents the results from statistical models; whereas the last section concludes.

2. Research fram ework and literature review As already emphasised, the empirical part of this paper is based on emigration intentions rather than on actual emigrational behaviour, but the research framework still derives mainly from standard emigration literature. Neoclassical economics provides the simplest, oldest and yet most influential theory to analyse migration, according to which international movement of labour is caused by geographic differences in the supply of and demand for labour and associated differentials in wages, while the latter is responsible for migration behaviour (Todaro and Maruszko, 1987). From the second half of 1980s ‘the new economics’ of labour migration was developed, which considers that actions are embedded within larger social context such as families, households and communities (Stark and Bloom, 1985), while agents aim not only to maximize expected income, but also to minimize risks and constraints stemming from a variety of failures beyond the labour market (Massey and Capoferro, 2006). Many families depend on wages earned by vulnerable workers. If a sudden shock, such as economic crisis or work injury, occurs the household's livelihood may be threatened, but the scale of effect will depend whether governments maintain insurance programmes such as unemployment and disability schemes. If such system is absent or incomplete in coverage families will have more incentives to send workers abroad to provide an alternative form of insurance (Massey et al., 1993).

Furthermore, according to ‘the new economics’ of migration motivation to increase income relative to other households can be one of the central determinants of cross-national movement of labour. People compare their welfare within their reference group, which generates feelings of relative deprivation or relative satisfaction. It is hypothesised that to change the relative position in the reference group, higher relative deprivation can lead to a stronger incentive to migrate by individuals (Stark and Taylor, 1991).

Probably the most relevant way of connecting emigration to prevailing social model is to see the action of migration itself as an informal social protection mechanism for people/families that seek risk reduction by migrating. Migration may be conceptualised as a 3

mean of informal coping strategy of reducing the probability of shocks before they happen, or mitigating strategy of adverse consequences once shocks have occurred (Holzmann and Jørgensen, 2001). In this framework emigration can be seen as fulfilling promotive, preventive and protective functions of welfare state (Sabates-Wheeler and Waite, 2003). In a promotive strategy individuals may migrate in order to enhance their life-chances regardless the level of wellbeing in the country of departure; in a preventive strategy, emigration can be employed as a risk diversification mechanism by which the family reduces vulnerability through both income diversification and informal insurance; whereas a protective strategy implies viewing emigration as a safety net for very vulnerable households in which decisions are made after alternative strategies are exhausted (Kabeer, 2002). In contrast to economic theories of migration, a livelihoods approach views migration as one strategy used to diversify and support well-being by households and communities.

Migration is arguably the most important social risk management instrument available to mankind, while the need to manage risk and secure livelihoods is the main driver of migration decisions (Sabates-Wheeler and MacAuslan, 2007). Within this narrative, the deprived are most likely to require migration as livelihood diversification strategy, but poor are a diverse group, with differential access to resources and institutions, and therefore different capacities to undertake strategies such as migration (Waddington and SabatesWheeler, 2003). Indeed, it has been shown that an increase in income, until it reaches a certain threshold, in a poor sending country has a positive impact on emigration intentions, even when the income differential with the receiving country is controlled for (Faini and Venturini, 2010).

Furthermore, based on well-known standard results of group research in social psychology and sociology, migration incentives can not only result from existing absolute income differentials, but from the income position relative to a reference group. The literature on social stratification has been investigating for a long time the importance of relative social positioning on actions of individuals (Vogler and Rotte, 2000). In this sense, emigration could be an exit from the prevailed social structures as a mean of upward or at least insurance from downward mobility. One of the dimensions of stratification which could be particularly interesting for migration decisions is unequal positioning within employment structures. In spite of literature on the subject the available studies address employment positioning a given without considering other driving forces of emigration intentions. While majority of studies deal with the question of positive or negative selfselection, this is generally applied to education, skills and incomes. Stratification within employment structures, as the main determinants of life-chances, is under-researched in emigration literature. Models typically control for education/skills, income and unemployment with an implicit assumption that they capture social stratification and relative distribution of resources. However, substantial literature on various aspects of life chance outcomes has consistently shown that after controlling for ‘usual suspects’ there is still strong stratification among classes in educational attainment, incomes, happiness, poverty, material deprivation, health and mortality (e.g. Lareau and Conley, 2008).

Therefore, it seems reasonable to assume that occupational stratification could be relevant also for migration intentions. On the one hand, varying employment conditions and jobs security could produce different incentives on migration among classes, but on the other hand supply and openness of positions in destination countries might also vary according to occupational structures.

If we assume that social stratification is important for emigration decisions and behaviour, we also can argue that social protection programmes intending to reduce social hardship and stratification affect incentives to emigrate. Social protection as a primarily 4

tool for reducing vulnerability and social risks of low-income households has become an important part of the development discourse at both national and international settings, but there is little literature linking migration to social protection policies (Sabates-Wheeler and MacAuslan, 2007). To integrate emigration into social protection framework, SabatesWheeler and Waite (2003) propose to add the ‘transformative’ element to the current discourse around social protection which refers to power imbalances in society that ‘encourage, create and sustain vulnerabilities over time and space.’ In this perspective, there is a strong need for implementing social protection policies to protect the potential migrants against the adverse economic and social consequences of their vulnerability.

Dalen and Henkens (2007) argue that the communities may accommodate the different types of individual preferences by individuals organising themselves or migrating into communities which will provide the public goods they want. Therefore the perception that a welfare state, as one of the central pillars of modern state, is of a low quality may trigger the desire to emigrate. It might appear that for less developed nations welfare institutions cannot be an important covariate of emigration when the incentives to emigrate are evidently associated with human and social capital, income-related characteristics, and other circumstances such as immigration regimes imposed on these countries. Nevertheless, poor welfare state with low educational and healthcare investments may create further incentives to move abroad in order to gain access to higher investments in their or their children’s human capital.1 3. H ypothesis As mentioned in the introduction, the employed dataset allows to analyse four transitional countries of Moldova, Ukraine, FYR Macedonia and Serbia. Existing studies for these countries prove the role of traditional explanations of emigration such as gender, age, marital status, education, unemployment, poverty and international social network (Görlich and Trebesch, 2008, Cipko, 2006, Tolstokorova, 2009, Danzer and Dietz, 2009, Nikolovska, 2004). It is clear that one of the key features of migration decisions is the presence of measures, such as quotas that restrict numbers or policies that select immigrants according to certain characteristics, which raise the costs of immigration and may prevent many from realising their intentions (Hatton and Williamson, 2002). Much can depend on the migration policies installed by the immediate and close neighbours of the analysed countries. Both regions have undergone through continuous economic and political transformations since the collapse of the Berlin Wall, which obviously affects the nature of migration intentions and decisions. In spite of sharing common communist past, in terms of emigration it is important to distinguish two former Soviet republics from Balkan countries. Moldova and Ukraine share the close cultural heritage and links, they are immediate neighbours and with close economic and political levels of development. Both largely experience the same migration regimes from the EU and other countries, but a major distinction comes from the ethnic similarities of Moldovans with Romanians which has lead to a large number of its citizens acquiring Romanian citizenship and the new prospects for migration to the West European countries (Gasca, 2010). In turn more residents of Ukraine are ethnically Russians and have corresponding social network, which gives them better prospects for migrating to the East. It has been demonstrated that Russia and other Commonwealth of Independent States’ (CIS) countries attract the majority of

1 Indeed, the dataset used in this paper shows that about 6 percent of sample names better education is a primary reason for emigration, while about 20 percent states that the main motivation for emigration is living in a more developed country which is itself associated with better public welfare provisions.

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temporary migrants in the case of Moldova and Ukraine (Danzer and Dietz, 2009). In turn the dominant destinations of migrants from Balkan countries are located in the Western Europe (Kupiszewski, 2009b). In either case emigration is associated with substantial costs which apparently prevents emigration activities among the most needy, but the effect might be different between the considered two regions. Besides, the welfare arrangements in destination countries might differ for immigrant from these two regions, which in turn can have varying effect on emigration intentions.

Building on the framework reviewed in the previous section, we expect that welfare programmes are associated with emigration intentions, but it is also reasonable to assume that this effect varies according to macro characteristics. As Serbia and FYR Macedonia are ahead in terms of establishing institutions of market economy than Moldova and Ukraine (EBRD, 2010), it is possible that individuals are more able to address their social risks without public intervention. Inferring from the standard explanations of emigration, if we assume that labour market is the most significant determinant of emigration, then state’s involvement through welfare programmes would be less efficient in preventing emigration in an environment where market is deficient to mitigate social risks. One way to test this assumption is to study how employment related characteristics, such as precariousness of jobs (Standing, 2009), is associated with emigration. Of course it can be also argued that in economically more troubled conditions, in our case in Moldova and Ukraine, welfare state can reduce emigration intentions, but in circumstances where welfare programmes are limited by the length and depth of entitlements, this causative relationship seems to be less likely. In terms of welfare states, all four of these countries in communist times maintained intensive social welfare systems which have been shrinking thereafter. The current comparison of national social security systems indicates that the welfare regimes maintained by Balkan and formers Soviet republics are qualitatively different, the former having superior schemes in terms of coverage and content of the programmes (ISSA, 2011, Bertelsmann Stiftung, 2010). Therefore, social protection system for individuals in FYR Macedonia and Serbia might generate higher incentives to remain in the country rather than to emigrate. Furthermore, not only actual coverage and quality of welfare programmes, but their perceived value might be important for emigration intentions. Dalen and Henkens (2007) find that controlling for other explanations of emigration in Netherlands, opinions about the quality of welfare state were negatively associated with intention to emigrate. Attitudes toward the welfare system also can be important covariate of emigration in transition countries because welfare systems were significantly amended with paradigmatic reforms, which introduced targeted and means-tested elements across welfare programmes, especially in less successful transitional societies (Orenstein, 2008).

Consequently, the central hypothesis of this paper is that the lower the coverage of welfare state programmes and their perceived quality, the higher the individuals’ intentions to emigrate, although the association is stronger in Balkan countries than in the former Soviet states.

FIGURE 1 ABOUT HERE 4. M ethod and data Migration economics has been long shown that migrants do not represent a random sample of the population in the source countries as certain form of selection drives the migration (Liebig and Sousa-Poza, 2004). The studies on determinants of emigration that rely on host-country data have obvious shortcomings because specific host-country characteristics such as migration restrictions, historical links and geographical proximity 6

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are likely to affect the observed trajectories. The difficulties in finding adequate data to study the characteristics of migrants in recent years lead to growing interest in studies based on intentions data which analyses migration behaviour indirectly (Avato, 2009) as a way to assess future migration flows and investigate the propensity for migration and its determinants. This approach is also employed in the present paper. As Boneva and Frieze (2001) claim it is necessary to study immigration desires before the actual immigration occurs because those who want to emigrate tend to be characterized with migrant personality syndrome as they call it and otherwise we would not be able to distinguish between effects of migration opportunities from the underlying desires to immigrate.

According to the theory of reasoned action, individuals’ behavioural intention of a particular act is both the immediate determinant and the single best predictor of their behaviour.

There is some evidence which suggests that intentions are good predictors of future behaviour in domestic (Van Dalen and Henkens, 2010) and international (Gardner et al., 1985, Van Dalen and Henkens, 2008) migration. However, the use of emigration intentions data as a proxy for actual emigration is not uncontested. The responses to the survey question concerning the willingness to emigrate can partly reflect personal frustration, without actually considering the actual ability of emigration (Kupiszewski, 2009a). Even if individuals have rational expectations of future behaviour, their decisions may be affected by intervening shocks (Burda et al., 1998). In addition, working on intentional data might posses a different kind of sample selection problem stemming from the absence of those in the sample who have already migrated (Litchfield and Reilly, 2009). Even though we have no evidence that intentions are associated with emigration behaviour in transition economies, the sound theoretical arguments, numerous studies on internal migration and some evidence from developing and developed world allow us to look for the valuable associations by analysing individual characteristics that are important in determining migration at a time when the migrants are still in the country of origin. Considering all these shortcomings we acknowledge that the results of the analysis are only applicable to the intentions to emigrate which might or might not be relevant to actual migration behaviour.

The empirical analyses is conducted with the help of multivariate regression methods. Because of binomial nature of the dependent variables we employ logistic regression and post-estimation predicted probabilities, while for the vivid illustration of results marginal effect from logit regression are calculated. Since the paper is built on cross-sectional data, only associational conclusions can be drawn from the findings presented bellow. We employ the UNDP/UNICEF (2010) Social Exclusion Survey which includes data for Moldova, Ukraine, FYR Macedonia and Serbia. The survey was conducted in 2009 and used nationally representative multi-stage random sampling, based on the list of voting stations, national census and territorial units, taking into account nationally representative weighting factors (TNS, 2010). Dependent variable derives from the following questions: ‘What is probability for you to go abroad to find employment? What is probability for you to go abroad to emigrate to live in another country?’ For answers on both questions we code probable=1, not probable=0. In addition to hypothesised variables, we control for explanatory factors which have previously shown to affect migration intentions.

What follows is the description or the wording of the survey questions for all independent variables used in the analysis.

• Gender: We code male=1, female=0.

• Age: For controlling of the age effect we create binary variables for seven cohorts born in 1985-1994, 1975-1984, 1965-1974, 1955-1964, 1945-1954 and before 1945 (reference category).

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Education: Three dummy variables are included into the models for the highest attained level of education: a) no degree, primary or basic educational attainment (reference category), b) secondary, gymnasium or vocational education, and c) higher education.2 Health: The survey includes subjective assessment of respondents health status which we recode into three dummies: a) Poor or fair health (reference category), b) good health, c) excellent or very good health.

Marital status: Four marital status are distinguished: a) single (reference category) b) married, c) cohabitating, d) separated or divorced.

Settlement type: a) rural areas, b) small town, c) regional centres and capital cities (reference category). We also include into the models dummies for regions to control for specific characteristics stemming from separate parts of country.

Social network abroad: The survey includes question on help received friends and relatives from abroad, yes=1, no=0. In addition the survey also provides information whether or not respondent have been outside the country for more than 3 months for employment, yes=1, no=0.

Social network within the country: Following questions have been used: ‘How often do you spend your free time with family/relatives?’ answers range from (1) never; to (5) almost every day. The variety of friends is measured from this question: ‘From close friends are there among them who…’ 12 answers range from having ‘friends with different ethnic identity’ to ‘having friends with political power’. The answers are added up, the higher is value, the richer is social network. Social activity is measured by: ‘In which form have you participated during the last 6 months in activities of the following associations, teams or clubs?’ 10 answer options vary from ‘a political party’ to ‘a women’s, citizens, student, pensioners or environmental association.’ The answers are added up, the higher value, the higher social activity of individuals.

Labour market status: In addition to a) unemployed and those who are b) out of employment (reference category), our models also control for c) enrolment in educational institution, d) being retired, e) homemaker and f) disable person.

Occupational social class: 4-class version of Erikson-Goldthorpe class schema is employed: a) Petty-bourgeoisie, b) service class c) intermediate class which includes routine non-manual workers, technicians and supervisors; and d) Manual class which comprises skilled manual and non-skilled manual workers.

Absolute standards of living: ‘How often your household could afford following items in the past 12 months?’ 9 items ranging from ‘buying food for three meals a day,’ to ‘paying for a week’s annual holiday away from home’ are listed. For each item answer scale is from (1) never, to (4) often. Values are summed up, the higher values mean better living standards. Household deprivation: ‘Can household afford the following item?’ 14 household items range from ‘a computer’ to ‘living room furniture’, no=1, yes=0, after summing up responses higher values indicate lower living standards.

Relative standards of living: We create 5 dummies for quintile personal incomes (bottom 20 percent as a reference category). Subjective relative welfare is measured by individuals’ assessment of their living standards compared to the majority in country, worse=1, otherwise=0.

Welfare concerns: Respondents were asked: ‘How worried are you about each of the following situations?’ a) denied access to health care practitioners, b) denied access to

2 We also attempted to have in the models the years of education, but the applying the dummies for the levels of education turn out to be more illustrative.

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education, c) lack of housing, d) hunger. For each item we code, very worried=1, otherwise=0.

Welfare programmes coverage: Respondents were asked: ‘Are you currently having social insurance/health insurance?’ For both questions we use no=1, yes=0.

Trust in welfare state: For the trust in welfare institutions variable we create composite index from three questions. ‘How much trust do you have in the ability of the health care/state pensions/social assistance system to deliver when you need it?’ The answers from (1) a great deal of trust; to (4) no trust at all are summed up. The higher the values of index, the lower trust in welfare state.

Job security: The following three questions were used: ‘During the last 5 years have you been unemployed and seeking work for any period?’ yes=1, no=0. ‘What is your formal status at your current main job?’ from having permanent contract=1, to informal employment=3. ‘How likely do you think is that you lose your job in the next 6 months?’

From Not very likely=1 to Very likely=3.

5. Results We begin with presenting findings from regression analysis for control variables across gender followed by material deprivation and inequality measures, while the second subsection tests how welfare coverage and job-quality indicators are associated with emigration intentions.

5.1. Control variables, gender and social stratification Together with country-level models, in table 1 we pool data and look on gender differences because of the previous studies on the visible gender segregation of emigration in the region (Tolstokorova, 2009, Danzer and Dietz, 2009). Overall, the models explain more than one-fifth of variation in outcome variable, which can be considered to be high in migration intentions research. All specified broad components do matter in explaining migration intentions, but education, health status and rural-urban differences appear to be less significant for temporary emigration. Across the gender samples the relations are largely maintained, however some specific characteristics emerge. Secondary education turns out to be significant for emigration intentions only for females, while good health – only for males. Marriage is hindering, while cohabitation is conducive factor for emigration aspirations among females, whereas for males this association is not significant. Social capital is also more relevant for females as having variety of friends as well as participating in public and community activities increases emigration intentions. On the other hand, belonging to ethnic minority is marginally significant covariate of emigration intentions only for men. The substantial gender differences are observed for labour market status.

Although retired and disable individuals have roughly the same negative effect on intentions for both gender, being a homemaker, student or employed reduces emigration aspirations only for females by 5, 5 and 11 percent, respectively. On the other hand, only among males unemployment strongly and positively increase emigration intentions by 9 percent. In order to look on the country level differences, we run separate regressions.

Gender difference are manifested in different forms across the countries (not shown).

Overall, we can conclude that age, social network and labour market do matter for emigration intentions, but the effects are manifested differently across the gender and countries. In addition we run the models with the same variable specifications and permanent emigration intentions as the different dependent variable. Generally, the scale of regression coefficients for birth cohorts decreases, but still age along with domestic and international social appears to be the most significant covariates of emigration intentions.

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TABLE 1 ABOUT HERE As the next stage of our analysis, we introduce in the models proxy indicators of material deprivation and social stratification in table 2. We do not present full regression output partly for reason of space, but mainly because this study is not primary concerned with the standard covariates of emigration intentions. Nevertheless, all relevant explanatory factors are controlled for in the models. For the effect of the material deprivation, we regress two composite indexes on living standards – unaffordability of the main household items and the ability to satisfy basic human needs – separately and together.3 In Ukraine and Serbia the less deprived individuals are more likely to be inclined for emigration, while the opposite appears to be the case in Macedonia. The interpretation of this results is difficult, however the scale of associations does not exceed 1 percent, which points out that the there is no strong relationship between deprivation and emigration intentions. At the same time, we cannot distinguish whether poorer people do not wish to emigrate or they just are not able to, or vice versa, whether richer people can emigrate but just do not want to. One of the alternatives to the measures of observable material conditions is the subjective perception of individuals’ life standards. This variable reveals strong inverse association with emigration intentions, believing that individuals economic conditions are worse than majority in the country increases temporary emigration intentions from 4 percent in Ukraine to 8 percent in Serbia. The model 1, we introduce into the analysis the quintile dummies for personal net monthly incomes which show the effect of earnings on emigration intentions. In comparison to individuals in the 1st quintile, those who are in the 4th quintile have 7 percent lower intentions to emigrate temporarily, but the effect weakens by moving up to the 5th quintile. One possible explanation could be that as income grows individuals are less willing to leave for employment in other countries, but at the same time are more able to do so in case they wish to.

We have to be cautious about personal incomes variable because it cannot adequately grasp the welfare of the respondents as it does not consider transfers between household members and are generally under- or misreported.4 Introduction in the models occupational social classes instead of the dummy variable for income quintiles does not affect the explanatory power of the model, although all dummy variable demonstrate statistically significant association with emigration intentions in comparison to those who stay out of employment. As can be seen from the regression coefficients those who belong to petty-bourgeoisie social class have the lowest inclinations for emigration in models 2 and 3.

Belonging to the intermediate class increases chances of considering emigration to -5 percent, while manual employers have the same propensity to emigrate as do those who stay out of the labour market. When in the model 3 we include social class and quintiles together the effect of earnings almost completely disappears, while social class maintains its statistical significance. The effect of occupational stratification on emigration is also observed in all country-level regressions, but the income stratification appears to be more important in FYR Macedonia where the individuals in the top three quintiles have 8, 9 and 14 percent lower emigration intentions than those in quintile 1. Social class effect also has significance for permanent emigration intentions in Moldova and Ukraine where service,

We also tried in the models the practice of borrowing money in last 12 month before the interview strongly and positively correlates with emigration intentions. This variable is not without problems because borrowing can be associated with various life-circumstances in upper as well as bottom distribution of life-chances.

4 We also employed variable on household expenditure per member, but no significant results were observed.

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intermediate and manual social classes have significantly lower permanent emigration intentions than those who stay out of labour market. We can conclude that subjective as well as objective inequalities in terms of income quintiles and occupational stratification significantly correlate with temporary and permanent emigration intentions.

TABLE 2 ABOUT HERE 5.2. W elfare concerns, social protection and em ploym ent stratification In the models of table 3 dummy variables control for those who are very concerned about access to education, health, housing and possible hunger. Model 1 shows that worrying about housing and hunger increases propensity to emigrate by 3 percent. To test the effect of welfare state coverage, dummy variables for not having social and health insurance are introduced. In addition, we also test how trust in welfare institutions, measured by a composite index of trust in social assistance, public pensions and health care system is correlated with emigration intentions. The results indicate that even after controlling for gender, age, education, marital status, social network, employment status, social class and subjective wellbeing, not having social insurance coverage is associated with higher migration intentions. Country-level regressions demonstrate the expected pattern of associations. As hypothesised, in FYR Macedonia and Serbia absence of social insurance coverage does increase emigration intentions by 12 and 8 percent, respectively.

Interestingly in the same countries individuals concern about the lack of housing is also associate positively with emigration intentions by 11 and 7 percent accordingly. On the other hand in former Soviet republics of Moldova and Ukraine social insurance coverage does not show significant effect (in Moldova), or shows the reversed relationship (in Ukraine). The same applies to welfare concerns. Healthcare concerns has negative association with emigration intentions, though it is statistically significant only in Moldova.

This relationship may come from the detrimental effect of health on mobility because those who are especially concerned about health could simultaneously be physically less mobile.

We could not find that having health insurance is important for emigration intentions in any country, which is another unexpected result related to health dimension of our analysis. 5 Contrary to our expectations, the results indicate that trust in welfare institutions is in Moldova and Serbia have significant, but opposite associations with emigration intentions, meaning that those who trust more in welfare system are more likely to consider emigration in Moldova and less likely in Serbia. When we compare the effect of this independent variable on permanent emigration intentions, as opposed to temporary emigration, welfare concerns and having social insurance appear to have smaller effect, but trust in welfare institutions becomes more significant in Moldova.

In the final part of our analysis, we restrict our sample to only employed individuals in order to test how job quality variables are associated with emigration intentions. This strategy reduces the number of available cases and we consequently limit our regressions with the covariates that demonstrated statistical relevance in the earlier models. The postestimation predicted probabilities are used to vividly illustrate how changes in job security correlates with emigration intentions. Logit models from which the predicted probabilities are derived, control for age, being abroad, international social network, and social class.6 Interestingly, when welfare state and employment related variables are introduced in the

5 Healthcare in transition societies might be the most complicated welfare state programme and disserves its own piece of research in relation to emigration.

6 The effects from these variables remain almost the same as in the previous models.

11

regressions simultaneously in pooled regression (not shown), the statistical significance of social insurance coverage diminishes, which indicates that what really matters for emigration intentions, at least for employed individuals, is the quality of employment rather than social insurance coverage as such. We cannot test whether for the whole sample employment characteristics are more important for emigration intentions than social insurance coverage because obviously we do not have employment characteristics for those who stay out of employment. Nevertheless, with pooled data all three variables on past unemployment history, present employment conditions and future labour market prospects demonstrate expected correlation with emigration intentions. Diagrams in figure 2 show how changes in independent variable on job security are associated with temporary emigration intentions, controlling for other important covariates. The overall pattern is clear – past unemployment experience, current worse employment conditions and adverse expectations about future employment prospects are associated with higher temporary emigration intentions. However, the figures reveal some interesting tendencies. In former Soviet republics of Moldova and Ukraine unemployment in past 5 years has much more significant effect on the dependent variable than in Balkan countries of FYR Macedonia and Serbia. In the former countries those who experienced unemployment are by 13 and 7 percentage points more likely to consider emigration, while in the latter societies these associations only amount to 4 and 3 percentage points. It is difficult to speculate why such differences occur between these two regions, but perhaps in ex-Soviet states prospects to find a new job after losing the previous one are lower.

On the other hand, the reversed tendencies occur in relation to the current employment status of the respondents. In FYR Macedonia and Serbia moving from permanent contract to informal employment increases predicted probability of emigration by almost twice, whereas changes are negligible in Moldova and Ukraine. These trends are hard to explain, but arguably in Balkan countries segregation between different types of employment, for instance in terms of social class, is more pronounced than in the new independent states of the Soviet Union. Finally, the effect of future expectations about the losing a job is strongly changes emigration intentions among employees in Moldova, FYR Macedonia and Serbia. In the latter society intention to emigrate temporarily increases from 13 percent among those who think that losing a job is not very likely to 29 percent among those who think that it unemployment is very likely to happen. Last but not least, when we run models of predicted probabilities with the same specifications except that the dependent variable is permanent emigration intention, the associations become nonsignificant for past unemployment history; for the formal status at the current job respondents in FYR Macedonia and Serbia maintain expected relationship; while for job expectation only FYR Macedonia and partially Moldova demonstrate statistical significance. Overall, it appears that not having social insurance has some positive association with emigration intentions, but for the sample with only employed individuals job-security variables have decisive importance on emigration intentions. Interestingly, for permanent emigration intentions, welfare state and job-security variables have only marginal effects.

FIGURE 2 ABOUT HERE 6. Conclusions This paper investigated the covariates of emigration intentions in four less successful transitional societies. The emphasis has been put on social stratification and welfare concerns rather than on the traditional explanations of emigration intentions such 12

Â

as human and social capital. We hypothesised that various aspects of emerging social model, namely social stratification and welfare state, have independent effect on emigration intentions. The analysis revealed that social inequalities in terms of social class differences and subjective perception of wellbeing are statistically significant covariates of migration intentions, even after controlling for age, education, incomes and living standards, while material deprivation apparently does not have linear relationship on emigration decisions.

We could not find evidence that welfare programmes determine welfare intentions in Moldova and Ukraine, although the results indicate that associations between having social insurance coverage and emigration intentions tend to be negative and significant in FYR Macedonia and Serbia. In the same countries those who are highly concerned with access to housing are more likely to consider emigration as an option. Furthermore what the current analysis reveals is that variables that describe quality of job are strong covariates of emigration intentions, which in turn emphasises the importance of labour market related welfare policies. The indicators measuring past experience, present conditions and future expectation in terms of job security have statistically significant effect on emigration intentions in all countries. These results could indicate that at a certain point of development in the developing and transitional countries the existence of social insurance programmes for whole population can reduce emigration intentions, whereas policies directed towards improving labour market conditions of workers might consequently reduce the levels of emigration. These effects might be especially relevant for countries which have only basic public welfare provisions. Overall, we can conclude that traditional explanations of emigration are not only significant covariates for explaining emigration intentions in less successful transitional societies and established social model also affects aspiration to leave these countries.

13

Â

Tables and figures

Figure 1: The levels of market economy advancement in Balkan and former Soviet republics, 2010

Moldova

Ukraine

FYR Macedonia

Serbia

Notes: Each criteria is measured from 1 (worst performance) to 10 (best performance) scale.

Source: Based on data from Bertelsmann Stiftung (2010)

14

Table 1: Marginal effects from logit regressions for temporary and permanent emigration intentions with human capital, social network and employment states variables

Permanent intentions

Temporary intentions

Permanent intentions

Temporary intentions

Permanent intentions

Temporary intentions

Permanent intentions

Temporary intentions

Permanent intentions

Temporary intentions

Permanent intentions

Variables Intercept Male Age Cohort born: After 1985 Cohort born: 1975–1984 Cohort born: 1965–1974 Cohort born: 1955–1964 Cohort born: 1945–1954 Completed education Secondary education Higher education Subjective health Good health Excellent health Marital status Married Cohabitating Divorced Domestic social capital/social network Time with family and relatives Having variety of friends Civil activity Belonging to minority International social capital/ social network Experience of being abroad Receiving remittances Employment status Employed Looking for a job

Country-level regressions on temporary and permanent emigration Moldova Ukraine FYROM Serbia

Temporary intentions

Pooled samples Males Females

–3.74 -

–3.97

–4.34 -

–3.82 -

–4.21 .01

–4.21 –.01

–4.82 .03**

–2.85 .03**

–3.32 .06***

–3.12 .02

–3.28 .06***

–2.15 .02

.43*** .38*** .31*** .26*** .11

.20*** .19*** .15*** .12** .05

.35*** .29*** .24*** .20*** .11*

.13*** .12*** .06 .05 .01

.54*** .46*** .40*** .35*** .19**

.25*** .23*** .21*** .16** .14**

.22*** .17** .13* .12* –.03

.05 .04 .02 .00 –.01

.02 .01

.04** .06***

.03** .02

.00 .01

.03 .00

.04** .07***

.05* .07**

.00 .02

.01 –.04

–.01 –.03

.02 .03

–.01 .04

.03* .03

.01 .02

–.02 –.03

.00 –.01

.00 –.01

.01 .05**

.03* –.02

.02 .01

–.03 –.02

–.04 –.05*

–.01 .02

–.04 –.03

–.01 .03 .01

.01 .08** –.03

–.02 .00 –.01

–.03 .00 .01

–.01 .01 .02

–.07*** .01 –.04

–.06*** .01 –.05

–.06** .05* .00

–.01 .01 .03

.01 .00 .00 –.08**

.01 .00 .01** –.03

–.02* .00 .01* .06**

-.02*** .01*** .00 .01

–.02 .02 –.05

–.04** –.02 .00

–.03** .09*** .01

–.01 .00 .00 .03*

–.01** .00** .00** .02*

.00 .01** .00*** –.01

.00 .01*** .01*** .01

–.01 .01** .01** .06***

–.01 .01** .01*** .04**

.00 .01*** .00 .02

.00 .01*** .00 .00

.22*** .07***

.07*** .03**

.24*** .09***

.07*** .05***

.18*** .05*

.04* .05**

.20*** .07*** –.04 .09***

.05*** .07*** –.02 .04*

–.05*** .03

–.04** –.04 –.04 –.01 .02 –.04 Continued on the next page

–.08*** –.01

–.04* –.01

.40*** .40*** .28** .22* .16

.21*** .03 .01 .11***

.25*** .26*** .16** .16** .00

.14*** .06* –.01 .05

.33*** .27** .23** .16 .03

.18** .16* .09 .04 .00

.17*** .06*

.03 .04*

–.09*** .06

–.05* .02

In education Pensioner Homemaker Disable Settlement type Rural areas Small towns N of observations Pseudo-R2

–.01 –.24*** –.05 –.28***

.00 –.05 –.02 –14*

Continued from the previous page –.05** –.03 –.01 –.01 –.26*** –.11*** –.18*** –.09* –.11*** –.09*** –.12*** –.07** –.15*** –.11*** –.19*** –.16***

–.08*** –.24*** –.10** –.18***

–.03 –.05 –.06 –.06*

.02 –.01

–.04*** –.05***

.01 –.01

.01 –.01

.01 –.01

3877 .23

3834 .16

4529 .24

–.03*** –.02* 4508 .16

–.02 –.08* 2554 .26

–.07*** .02 2517 .19

2328 .26

2336 .11

–.04 –.41*** –.05 –.38** .02 .03 1764 .21

–.05 –.15** –.05 –.11

.01 –.30*** –.07 –.09

–.01 –.12 –.09 –.05

–.01 –.05*

.06*** .02

.02 .01

1760 .25

1738 .20

1751 .15

Note: Control for country dummies are included in pooled regressions, for country regressions regional dummies are included, not shown. ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 levels, respectively, using two-tailed tests. Reference categories are cohort born before 1945, having primary education, poor health, being single, out of labour market, living is capital and regional centres.

Source: Author’s calculations based on data from UNDP/UNICEF (2010).

16

Table 2: Marginal effects from logit regressions for temporary and permanent emigration intentions with variables of material deprivation and social stratification

–.01*** .01

–.01** .02**

.01 –.03 –.05** –.02

.02 –.01 –.02 –.02

.05** .03 –.02 .12

.04* .02 .10** .01

–.01 –.04 –.04 .01

–.01 .00 –.01 .03

–.02 –.08* –.09* –.14**

–.01 –.09** –.09* –.14**

–.03 –.08* –.08 –.09

.01 –.01 .00 –.07

–.10*** –.04* –.05** –.03

–.07*** –.03 –.05*** –.05***

–.13* –.11** –.11*** –.07*

–.08 –.07** –.10*** –.08***

–.11** –.07* –.06 –.05

–.06* –.08** –.06** –.06**

–.02 .12** .04 .09**

–.05 .07 –.05 .02

–.19*** –.10* –.08 –.12**

–.19** –.06 –.04 –.07

.07***

.02***

.07***

.02

.04**

.01

.07***

.03

.08***

.04**

6279 .25

6260 .17

1993 .28

1972 .23

1644 .28

1653 .15

1361 .28

1360 .21

1281 .28

1275 .23

.01*** .02***

Permanent intentions

.00 –.01*

.00** .00

Temporary intentions

.00 –.01**

Temporary intentions

.00 –.01

Model 3

Permanent intentions

7358 .23

Temporary intentions

6955 .23

–.12*** –.06*** –.05** –.02

.00 –.01**

.00 .00

Permanent intentions

– – – –

– – – –

Permanent intentions

.00 –.04** –.07*** –.05**

.00 .00

Temporary intentions

N of observations Pseudo-R2

.00 .00

Country-level regressions on temporary and permanent emigration Moldova Ukraine FYROM Serbia Permanent intentions

Variables Living standards Basic human needs Household items Income quintiles 2nd 3rd 4th 5th Social class Petty-bourgeoisie Service class Intermediate class Manual class Subjective welfare Worse than majority

Model 2

Model 1

Pooled samples Temporary intentions

.01*** .01

Note: Control for age, education, domestic and external social networks, labour market characteristics and country dummies are included, not shown. For country regressions regional dummies are included. ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 levels, respectively, using two-tailed tests. Reference categories are being in the 1st income quintile and out of labour market.

Source: Author’s calculations based on data from UNDP/UNICEF (2010).

17

Table 3: Marginal effects from logit regressions for temporary and permanent emigration intentions with variables on welfare concerns and social protection

N of observations Pseudo-R2

Temporary intentions

Permanent intentions

Temporary intentions

Permanent intentions

Temporary intentions

Permanent intentions

Model 3 .02 –.02 .02* .03**

Permanent intentions

– – – –

Temporary intentions

.02 –.03** .03** .03***

Country-level regressions on temporary and permanent emigration Moldova Ukraine FYROM Serbia

Permanent intentions

Variables Very worried about...

Access to education Access to healthcare Lack of housing Possible hunger Welfare coverage No social insurance No health insurance Trust in welfare state

Model 2

Model 1

Pooled samples Temporary intentions

.01 .00 .01 .01

.06** –.06** .01 .05*

.01 .01 .02 .02

.03 .01 –.03 –.01

.01 –.02 –.02 .00

–.01 –.01 .11*** .04

–.01 .02 .03 .01

–.04 –.03 .07** .07**

.04 –.05 .04 .01

– – –

.03*** .00 .00

.03** .00 .00

.03** .00 .00**

.01 .03 .01*

.00 –.01 .01***

–.04** –.01 .00

7149 .24

6394 .24

6070 .24

6031 .15

1906 .27

1883 .19

1672 .26

–.02 .01 .00

.12*** .00 .00

.10*** .03 .01

1153 .26

1168 .18

1675 .10

.08*** .02 –.01**

.04 .02 –.01

1320 .23

Notes: sex, age, experience abroad, remittance and social class are controlled for. ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 levels, respectively, using two-tailed tests Source: Author’s calculations based on data from UNDP/UNICEF (2010).

18

1305 .18

Figure 2: Job quality variables and predicted probabilities of emigration intentions Temporary emigration intentions

Permanent emigration intentions

Temporary emigration intentions

Permanent emigration intentions

Temporary emigration intentions

Permanent emigration intentions

Note: Models control for gender, age, external social network and occupational social class Temp intentions: Moldova N=980, Pseudo-R2=.17; Ukraine N=929, Pseudo-R2=.10; FYROM N=732, Pseudo-R2=.13; Serbia N=849, Pseudo-R2=.13 Perm intentions: Moldova N=969, Pseudo-R2=.06; Ukraine N=943, Pseudo-R2=.05; FYROM N=720, Pseudo-R2=.15; Serbia N=845, Pseudo-R2=.11 Source: Author’s calculations based on data from UNDP/UNICEF (2010).

19

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21

POLITICAL ECON OM Y OF EM IGRATION IN TH E SOU TH CAU CASU S: DO POLITICAL OPIN ION S EXPLAIN EM IGRATION IN TEN TION S? Abstract This paper tries to understand the modes of emigration from the South Caucasian countries and studies new patterns emerging as a result of recent developments in the region. Since 2005 South Caucasian countries diverged in their socio-economic models of development, which are reflected in different covariates of emigration intentions in these societies. The traditional migration pattern of individuals with similar background, emigrating to the same countries as a result of the common economic hardship currently becomes less applicable. Using micro-level survey data from the Caucasian Barometer for 2009-2010, this paper looks how various sets of variables associate with emigration intentions. We test a hypothesis that recent uneven economic and political developments are reflected in individuals’ intentions to leave these societies. Results indicate that, controlling for other covariates, political attitudes have significant associations with emigration intentions and the effect appears to be more important in Azerbaijan, while economic problems seem to be most relevant for emigration intentions from Georgia.

1. Introduction So far much of the migration scholarship addresses the effects of migration on destination countries’ democratic developments in regard of political participation and emerging inequalities (Bäck and Soininen, 1998), while much less is known about the implications of existing democratic model on emigration in the sender countries. If the causative effect from political environment on emigration exist, it is also reasonable to assume the reverse causation also takes place. Since emigration changes the characteristics of electorate, it affects the democratic representation and policymaking practices, particularly in the policy areas in which beneficiary constituencies are weakened by emigration (Boerner and Uebelmesser, 2007). International migration affects the supply as well as demand side of domestic political institutions. Emigration could therefore be detrimental to the domestic political system by undermining demand for political accountability and by weakening the capacity to supply better quality institutions (Batista and Vicente, 2011). What this theoretical judgment might mean is that the lack of democratic practices can stimulate emigration, while the latter can contribute to further deteriorating political freedoms. Although, there is some evidence that emigration can actually stimulate democratic competition in the sending country (Pfutze, 2009), the main thesis of this paper is that even being relatively well off, individuals’ probability to emigrate can be affected by their perception of the quality and fairness of existing political system.

Academic literature on the issue is scarce, but anecdotal evidence indicates the links between those two. The New York Times recently reported that an announcement of the Russian Prime Minister to run again for a presidency in the late 2011 significantly raised emigration intentions among those who did not support his political stance (Mydans, 2011).

Emigration in the framework of this paper can be understood as a vote of confidence for the existing political model, but the major problem for researching this question is the difficulty of disentangling democratic values from other determinants of emigration. One way to mitigate this problem is employing multivariate approach to control for other explanation of emigration which have been demonstrated to significantly correlate with emigration (Van Dalen and Henkens, 2010). This means that if we want to research how 22

political attitudes correlate with emigration we must control for already identified emigration determinants in analysed societies. The current study indeed concentrates on micro-level relationships between democratic transformations and emigration intentions in South Caucasian countries of Armenia, Azerbaijan and Georgia, controlling for standard explanations of emigration. The South Caucasian region is particularly interesting because in the last two decades it maintained one of the worlds’ highest emigration rates (World Bank, 2010), while the available studies on the determinants of migration reveal that in the initial phase of transition the violent conflicts and ethnic distribution of population contributed to emigration, which gradually were superseded by economic motives of migration (Hofmann and Buckley, 2011). What is less understood about the region is that in recent years these countries substantially diverged in terms of their political, economic and social changes which must have implications for emigration patterns. More precisely, controlling for other factors economic conditions have to play more important role if sluggish economic growth and higher employment rates provide scare economic opportunities. On the other hand, the quality of democracy might be more significant explanation of emigration in more authoritarian environment. We start with a short literature review on democracy and emigration which will be followed by the outline of hypothesis. The methodology section describes the data and statistical techniques used in the analysis. After the presentation of results we provide a short discussion on the implications of the study.

2. Literature on dem ocracy and em igration Emigration can be viewed as a safety valve that allows individuals unhappy with their political institutions to abandon their home country (Batista and Vicente, 2011).

Tiebout (1956) argued that the communities may accommodate the different types of individual preferences by individuals organising themselves into communities which will provide the public goods they want. Perhaps the most influential approach to connect emigration and political attitudes stems from Hirschman’s (1970) seminal work. According to his theory, in unsatisfactory environment in one's country there are effectively two responses individuals can make. The first is ‘exit’ or emigrating from the country without attempting to improve the system. The second is ‘voice,’ which is attempting to fix the problems without abandoning the system. Idiosyncratic conditions in specific situations define the type of response causing some to stand and fight and others to cut and run.

During the enormous out-migration from Europe in the 19th and 20th centuries people who chose to leave were obviously dissatisfied in some way with the society they were part of and the availability of exit made them less likely to resort to voice and arguably the ships heading to the western shores of Atlantic contained many actual or potential reformers, socialists, revolutionaries and anarchists (Hirschman, 1978). The role of the sending and destination countries political systems is not straightforward but there some evidence which indicates on the importance of democratic environment in this process. On the macro level after assessing the relevance of economic and demographic forces in 1870–1910 migration to the new world, Bertocchi and Strozzi (2008) show that quality of institutions in destination countries mattered as well for emigration decisions, with more democratic societies proving to be more attracting destinations for migrants.

In more recent times, the links of political turmoil on emigration are indisputable when it comes to violent regimes. This is quite self-explanatory as when people are endangered for their lives and security their might move quite fast from emigration intentions to actual emigration behaviour (Stanton, 1992). However, it is less clear how democratic or authoritarian regimes affect intentions to emigrate. In extreme cases, states 23

may design restrictive emigration measures like in Mexico from 1900 to the early 1970s when government consistently attempted to control the volume, duration, skills, and geographic origin of emigrants (Fitzgerald, 2006). On the other hand, democracies almost implicitly imply that freedom of emigration is unequivocally granted to its citizens. The problem with this analysis is that the nature of political regimes is often correlated with specific levels of socioeconomic development which makes it difficult to disentangle the potential associations of the character of political system and aspirations to leave the country. Existing studies on emigration intentions show some associations between intentions and institutional attachment to the country. From the pespective of the developed world, in recent decades migration scholars understood that migration was growing among the countries with the same level of economic development in developing as well in developed world. For Netherlands Dalen and Henkens (2007) look at shortcomings in political institutions that deal with welfare and social problems, such as the educational system, social security, health care, and old-age pensions and find that controlling for other explanations of emigration, opinions about the quality of welfare state were negatively associated with intention to emigrate from the country. The authors conclude that the perception that institutions of democratic state is of a low quality may trigger the desire to emigrate.

There is also some evidence on links between political attitudes and emigration aspirations from non-European and transitional socieites. Hartman and Hartman (1995), investigating intentions to emigrate of over 2300 high school students, find that support of the government and especially right-wing political attitudes significantly and negatively correlate with intentions to leave the country. For Rominian case, Sandu and Gordon (1996), controlling for a large set of other determinants in the multilevel settings, demonstrate that market and democracy values are important predictors of intention to emigrate. Authors suggest that at least in the early stages of transition in Romania, migration to a significant degree was a search for places with greater political and economic freedoms. Papapanagos and Sanfey (2001), show that from Albania intentions for emigration can be partially explained by support for market reforms, suggesting positive association between those who believe in market economy and their intentions to leave the country. This might be explained in part by the fact that the money earned abroad allows returning migrants to exploit new opportunities which emerge in more market oriented and democratic societies as a result of the relaxation of state political and economic control. For the region analysed in this paper, we could not find any study which try explicitly connect political perceptions and emigration. Nevertheless, for Armenian sample Grigorian and Melkonyan (2011), in multilevel models, contrary to their expectation, find that corruption and rule-of-lawrelated problems are associated with a lower probability of emigration, which they tentatively explain as the result of lower ability of households to meet the costs of migration in most unlawful regions. Alternatively, residents of regions with more corruption cannot afford to migrate because potential migrants do not want to leave their family members in an insecure environment. Tchaidze and Torosyan (2010) analysing Georgia data indicate that political considerations were not found as important covariates for migration decisions, but the importance of this factor cannot be excluded as political development may indirectly affect emigration from the country. In Azerbaijan democracy in destination countries was on one of the most prevalent answers on the reasons of emigration in the recent public survey (Krylova-Mueller, 2011).

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3. H ypothesis In this paper we are interested how established political environment associates with the intentions to emigrate in the South Caucasus. Do such factors of freedom of speech, rule of law, fairness of elections, have independent effect on emigration intentions controlling for other relevant covariates? We argue that if in the earlier stages emigration was defined by ethnic considerations and security reasons, followed by the economic motivations, in more recent times due to higher information accessibility and growing awareness quality of political institutions play a role in decisions concerning emigration.

The survey data indicates that in all these countries individual value democratic institutions as one of the most important aspects of their socioeconomic life. Substantial share of residents might frequently find themselves facing the power of a state that has neither the desire nor ability to help them, with few if any protections provided by inefficient markets and incapable family institutions. Political structures are intrinsically connected to equality of opportunities and life chances. The higher levels of authoritarianism uses non-merit based selection procedure and therefore might aggravate social stratification (Titma and Roots, 2006). It seems reasonable to hypothesise that in environment where people are treated unfairly, elections are falsified, and people are not sure about the direction of development of their countries, many will want to leave their homelands. Even if high economic growth generates perception of higher prosperity those individuals will be more willing to leave the country who feel that political developments are not acceptable. To the contrary, if economic changes generate a feeling that country has right direction, while its economic prospects are not advancing then maybe the latter should become more important explanation for emigration than perception of political aspects of life.

The hypothesis of this paper take into account developments which have occurred in these countries from the second half of the 2000s. Before 2005 by level of GDP per capita, poverty levels and other socioeconomic indicators the South Caucasian countries came very close to each other. The major difference was that Georgia’s performed slightly better in terms of democratic reforms. However, the situation substantially changed since 2005 which was mainly conditioned by two interrelated political and economic shocks. On the political side, the peaceful revolution in the late 2003 brought into power new government in Georgia overthrowing corrupt and inefficient political regime. Worried by the democratic developments in the neighbouring Georgia, as well as peaceful changes in Ukraine and Kyrgyzstan, ruling elites in Armenia and Azerbaijan toughened their political systems against a strong electoral challenge to authoritarian rule vividly shown in the violence against the opposition, arrests, blackmail, and the murder of a journalist during the parliamentary elections in Azerbaijan in 2005 and presidential elections in Armenia in 2008 (Valiyev, 2006, Bunce and Wolchik, 2009). At the later stages democratic reforms were also shaken in Georgia (Mitchell, 2009), however experts agree that Armenia and Azerbaijan maintain more authoritarian state institutions. Based on abovementioned it is reasonable to think that if democracy has any effect on emigration it would be more vividly illustrated in societies where authoritarianism is a greater problem, while people in more democratic environment might assign less value to democracy in their intentions to emigrate.

These political developments were also correlated with substantial economic transformations. In Georgia government decided to follow neoliberal policies downsizing public sector and liberalising other areas of economy. The immediate result of these reforms was growing unemployment level and stagnant socioeconomic conditions. Ideological differences were a direct cause of dramatically worsening relationships with Russian 25

Federation, by far the most important economic partner which curtailed economic growth and especially harmed fragile agricultural sector, followed by a short but dramatic war between these countries in 2008 (World Bank., 2008). Although, increasingly worrying assessments have been made about the recent democratic development of the country, the stagnant socioeconomic conditions are continuously named as the major concern of the residents (NDI, 2011). Therefore, it can be hypothesised that factors defined by the economic theories of migration must be more important covariates of emigration in Georgia.

On the other hand, Azerbaijan has witnessed the opposite development, accelerated export of oil allowed this country to grow its GDP at almost two times higher rates than in Georgia.

Even with the problems of income redistribution, by all accounts socioeconomic situation has significantly improved in the second half of 2000s. This structural change had to reduce economically motivated emigration, but if democratic environment also correlates with emigration political attitudes must become important explanations of emigration intentions.

The ruling elites consolidated their authoritarian power with no expectations on possible democratic reforms. Armenia was less successful than Azerbaijan in terms of economic development, but with the stabile macroeconomic environment and foreign trade managed to supersede Georgian GDP per capital by 30 percent in 2008. Not much changes have occurred in political freedoms in the recent years but the country still maintains substantially open political system than does Azerbaijan. Therefore, the central hypothesis of this paper is that in an authoritative political system (Azerbaijan), political attitudes are more important for emigration than economic conditions, while in more troubled economic environment (Georgia) material conditions are more decisive for emigration than political attitudes.

4. M ethod and data To analyse emigration intentions in Armenia, Azerbaijan and Georgia, we employ the Caucasus Barometer (Caucasus Research Resource Centers, 2009, 2010) which includes data for all three South Caucasian countries. We pool data from these nationally representative surveys conducted in 2009 and 2010. The dependent variables derive from the following questions: ‘If you had a chance, would you leave country for a certain period of time to live somewhere else? Would you leave country forever to live somewhere else?’ The answers for both questions are coded as yes=1 and no=0. The empirical analyses is based on multivariate statistical techniques. As the dependent variable is binary with use logistical form of regression analysis and present regression coefficients with marginal effects. As one of the central goals of the paper is to test of the strength of association between individuals’ attitudes and on democracy and their intentions to emigrate, we use post-estimation predicted probabilities which depict how changes in scrutinised variables correlated with changes in the dependent variables. To compare importance of various sets of covariates we also run nested logistic models which indicate fit of the models with separate sets of variables. The independent variables on political system are based on the following three questions: 1. ‘There are different opinions regarding the direction in which country’s domestic politics are going. Which of the following would you personally agree with?’

Options vary from ‘Politics is definitely going in the wrong direction’=1 to ‘Politics is definitely going in the right direction’=5. 2. ‘Would you say that the most recent election was conducted?’ ‘Completely fairly=3, ‘to some extent fairly’=2, or ‘not at all fairly’=3.

3. ’Under the present system of government in country, do you completely agree=4, somewhat agree=3, somewhat disagree=2, or completely disagree=4 that people like yourself are treated fairly by the government?’ We also control for explanatory factors

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which have previously shown to affect migration intentions. What follows is the description or the wording of the survey questions for all control variables used in the analysis.

• Gender: We code male=1, female=0.

• Age groups: Binary variables are created for 18-27, 28-27, 38-47, 28-57 and 58-65 (Reference category) years old individuals.

• Education: Three dummy variables are included into the models for the highest attained level of education: a) no degree, primary or basic educational attainment (reference category), b) secondary, gymnasium or vocational education, and c) higher education.7 • Health: The survey includes subjective assessment of respondents health status which we recode into three dummies: a) Poor or fair health (reference category), b) good health, c) excellent or very good health.

• Marital status: Four marital status are distinguished: a) single (reference category) b) married, c) cohabitating, d) separated or divorced.

• Settlement type: The survey allows to distinguish between residents who live in a) rural areas (reference category), b) urban areas, c) capital city.

• Social capital abroad: We use several questions of the survey related to international social capital” Do you have a family member or close relative currently/any close friends currently living abroad, outside the borders of the country? Yes=1, no=0; We also create two dummy variables if a respondent had at least one trip outside the country within the past 5 years prior to interview, and if the household receives any remittances from abroad.

• Labour market status: In addition to a) unemployed and those who are b) out of employment (reference category), our models also control for c) enrolment in educational institution, d) being retired, e) homemaker and f) disable person.

• Social network within country: The following three statements are presented to respondents: 1. There are many people I can trust completely; 2. there are enough people to whom I feel close; 3. there are plenty of people I can rely on when I have problems. For all questions the answer options include: describes=3, more or less describes=2, does not describe=1. We sum the answers, where the higher values of the index indicates on the weaker domestic social network of the respondents.

• Language skills: The survey asks respondents about their language abilities in Russian and English with the following possible answers: a. no basic knowledge=1; b. beginner=2; c. intermediate=3; d. advanced=4.

• Debt: If a household currently has a debt, our dummy variable=1.

• Deprivation index: This variable is based on the following 2 questions: ‘Over the course of a typical month, does your household have to limit consumption or use of the following due to budget difficulties?’ Options include – Bread, milk, meat, fruit, potatoes, electricity, transportation. 2. Whether or not your household owns DVD, washing machine, refrigerator, car, cell phone and computer. Deprivation of each item is coded as 1 and summed up for every respondent.

• Relative wellbeing: The survey asks respondents how would they describe the current economic condition of their household relative to most of the households around them.

From all answer options we code dummy variable as 1 if respondents consider their households as poor or very poor.

• Employment type: The survey identifies the following employment status and the type of workplaces: 1. Self-employed without employees; 2. self-employed with employees; 3.

7

We also attempted to have in the models the years of education, but the applying the dummies for the levels of education turn out to be more illustrative.

27

employee in a small local family business; 4. employee in a medium-sized or big local private organisation, company, or enterprise; 5. employee in a state organisation, company, or enterprise; 6. employee in a foreign or international organisation, company, enterprise; 7. employee in a local or international non-governmental or non-profit organisation.

5. Results Table 1 shows marginal effects from the nested logit models for the control variables that have been earlier shown to have effect on emigration intentions. The values of intercept clearly indicate that emigration intentions are significantly higher in Armenia and Azerbaijan than in Georgia, and this is particularly true for permanent emigration intentions.

5.1. The baseline m odel The derived coefficients show that age explain the most of variation in temporary and permanent emigration intentions. Gender is important covariate of emigration only in Azerbaijan, the country characterised with more conservative views on women’s role in society. For temporary emigration intentions, Georgia stands out as the country where age and gender can explain two thirds of variation in the dependent variable. Human capital measured by educational attainment and subjective health status overall explains about 1 percent of variation in the outcome variable. In Georgia, both education and health appear to affect temporary but not permanent emigration intentions. Those who have excellent subjective health are 14 percent more likely to express their willingness to emigrate.

Positive selection in terms of education and health is only marginally significant in Azerbaijan, whereas for Armenia human capital does not appear to have association with temporary emigration intentions. Furthermore, in Armenia individuals with primary or lower levels of educations are significantly more likely to consider permanent emigration.

Domestic social capital measured by marital status, composite index of social network, being locally born and minority status reveal only weak association with the dependent variables. Marriage appears to discourage emigration intentions across countries shown by negative sign of the variable in most of the models, but it reaches statistical significance only for temporary emigration intentions in Armenia (–5%) and permanent emigration intentions in Azerbaijan (-4%). Two more variables stand out from this block of covariates: social network composite index has negative correlation with emigration intentions in all models, except for temporary emigration in Georgia. It indicates that better social relationships slightly but statistically significantly reduce emigration aspirations.

Belonging to ethnic minority is also important for explaining variation in intentions. In Armenia and Georgia minorities have generally higher intentions to leave, whereas in Azerbaijan the contrary effect is observed: belonging to minority groups reduces intentions by 7 percent for temporary emigration.

TABLE 1 ABOUT HERE The results also indicate that after demographic variables, international social capital is the second most important set of covariates of emigration intentions in Armenia and Azerbaijan. For temporary emigration intentions this block is most significant in Armenia where having family member and friend abroad, experiencing trip in foreign country and receiving remittances all appear to have strong associations with emigration 28

intentions. Overall, having a friend in a foreign country turns out to be the most significant covariate for emigration intentions in all our models. Proficiency in English also appear to have positive association with permanent emigration considerations in Armenia and Georgia and this factor is arguably more important for Western European and North American destinations. Last but not least, some labour market characteristics have significant associations with emigration intentions particularly in Georgia, where this set of variables explains most of variation in the dependent variables after demographic characteristics. Unemployed people in the later country are 12 percent more likely than employed to consider temporary emigration intentions. The same association reaches 8 percent both in Armenia and Azerbaijan. In the models with permanent emigration intentions the effect of unemployment is strongly significant but the coefficients are lower in scale. Most likely those who have difficulties in finding a job are equally willing to emigrate either for temporary or permanent emigration intentions. Expectedly retired and disable people have lower propensity to emigrate. Interestingly, students in Georgia are 33 percent more likely to consider emigration than those who are employed, the same association for permanent emigration intention amounts up to 8 percent. This may indicate on various possibilities which are available for foreign education in Georgia.

5.2. M aterial deprivation, econom ic attitudes and em igration intentions Table 2 shows the results of analysis with the variables describing stratification among those who identify themselves as employed and several variables indicating economic conditions of individuals. Employment status and type of job must have important associations with emigration intentions. The Caucasus barometer allows distinguishing employed individuals in several categories and in the models we use unemployed as the reference group. It is rational to expect differences in emigration intentions between unemployed on the one hand and various categories of employment on the other, due to varying job security and power relationships between occupations. However, the marginal effects from logit regressions only identify three types of occupations which have statistically significant negative correlation with emigration intentions, in comparison to unemployed. What this might mean is that the differences between employed and unemployed is not huge in the South Caucasus, but it looks more reasonable to assume that this is the result of aggregating all employees under broad form of employment types which compile people with radically different life chances. Nevertheless, in all countries employment in public sector negatively associates with temporary emigration intentions. In both Armenia and Azerbaijan this type of employment is associated with 10 percent lower chances of having emigration intentions. In Georgia self-employment and in Armenia family business also reduces emigration aspirations by 16 and 12 percent respectively.

Obviously, these results do not disprove that employment conditions are important covariates of emigration intentions but rather indicate a need to obtain more comprehensive data on employment categories.

TABLE 2 ABOUT HERE The results reveal that out of the four variables describing economic conditions, whether a household has debt is the best predictor of emigration intention. It is associated with 9, 8 and 5 percent higher emigration intentions in Azerbaijan, Armenia and Georgia respectively. The interpretation of these results is not straightforward. On the one hand, households may use emigration to cope with indebtedness, or debts can be used to finance emigration related costs. The later aspect is tested by the composite index of deprivation 29

which is introduced into the models along with the deprivation squared variable. This approach allows us to identify whether there is curvilinear, inverse U-shaped, relationship between material deprivation and emigration intentions. Indeed, in Georgia material deprivation index shows inverse U-shaped association with temporary emigration. It appears that in the bottom and top of deprivation distribution individuals express lower intentions to emigrate than those who are in the middle. Arguably, those with the highest levels of deprivation do not consider emigration because their lack of resources, while those who are best off are less keen to consider emigration because of their advantageous positions. In turn in both Armenia and Azerbaijan improving incomes in last two years prior to interview has negative impact on emigration intentions, and this relationship is held at 5 percent of significance levels. Interesting results also emerge from the models on permanent emigration intentions. It appears that in Azerbaijan material deprivation has inverse U-shaped relationship with permanent emigration intentions, which means that those who are the most and the least deprived are more likely to consider permanent emigration intentions than those who are in the middle of deprivation distribution. Last but not least, the subjective perception of relative economic positioning has statistically significant effect in all countries, increasing permanent emigration intentions by 6, 5 and 2 percent in Azerbaijan, Armenia and Georgia, respectively.

5.3. Political attitudes and em igration intentions After reviewing baseline and economic explanations, we test how political attitudes correlate with emigration intentions. We hypothesise that those who are unsatisfied with current political environment must be more willing to consider emigration as an exit option.

Indeed, variables describing political attitudes reveal significant association with emigration intentions, especially in Armenia and Azerbaijan. To demonstrate associations between political variables and emigration intentions, we use post-regression predicted probabilities. This method allows to observe vividly the changes in the dependent variable for each value of independent variables. Figure 2 indicates that the perceptions on direction of politics has the strongest association with emigration intentions in Azerbaijan increasing the dependent variable from probability of .46 to the probability of .74. The effect is much lower but statistically significant in Georgia and Armenia leading to 14.1 and 10.3 percentage points change, respectively. Perception of the general directions of the politics also has the highest impact on permanent emigration intentions in Azerbaijan, followed by Armenia. Graph 2 also present predicted probabilities of temporary and permanent emigration intentions by the perception of fairness of the most recent elections. As in the previous case, in Azerbaijan believing that the last elections were not fair increases emigration intentions up to 30 percentage points. In Georgia evaluation of elections fairness is also strong predictor of temporary emigration intentions with 18 percentage points change, while in Armenia this variables is statistically significant but is very small in scale. In terms of permanent emigration intentions and fairness of elections Armenia and Azerbaijan come very close to each other 16 and 15 percentage point income respectively, while in Georgian this variables makes only 7 percent points difference. Similar results are observed for perceptions how people are treated by the government. Even though the survey does not specify what is considered in fair treatment of citizens, it can be assumed to be a mix of welfare policies as well as other aspects of intervention in private lives through public means. Overall, we can conclude that in Azerbaijan political attitudes are more decisive for considering emigration than in Armenia and Georgia.

FIGURE 2 ABOUT HERE 30

After consecutively studying baseline, economic and political variable, we conclude with the comparison of the fits of different set of variables analysed in the models. Nested logistical regressions are run for temporary and permanent emigration with all sets of factors investigated in this paper. We derive Wald Chi2 for the nine block of emigration intentions covariates. It is important to understand that it makes more sense to compare Wald Chi2 for different sets of covariates within rather than across the countries. After integrating all variables, age still appears to be the most significant covariate of temporary emigration intentions in all countries. However for the next most significant variables substantial differences between countries are observed. In Armenia international social network are capable to explain the most of variation in the dependent variable for temporary emigration intentions. In line with our hypothesis Georgia temporary emigration is best explained by economic conditions in which respondent live, followed by the domestic and international social capital. In Azerbaijan perception of the political environment in the country is revealed to explain the most of variation in temporary emigration intentions with Wald Chi2 reaching 46.48 which confirms our hypothesis that in more authoritarian state people intentions to emigrate associate more with political freedoms. Slightly different results are revealed for permanent emigration intentions. In Armenia those who want to leave this country for good are most likely to be disappointed by the political conditions and this set of variables is even more significant than demographic parameters, which is followed by the economic conditions and international social network. In Azerbaijan, after demographic variables, permanent emigration intentions are best correlated with economic variables followed by democratic perceptions. Probably those who want to leave Azerbaijan permanently are economically excluded from the society. For permanent emigration intentions from Georgia, domestic social capital along with international social capital is the strongest explanations for migration intentions, which can be probably explained by diverse ethnic composition of Georgian society and by the first wave of emigration of ethnically non-Georgian residents in the beginning of 1990s.

TABLE 2 ABOUT HERE 6. Conclusions Since our analysis is based on cross-sectional data from the Caucasus barometer for 2009 and 2010, we are not in a position to insist on the causative nature of relationships which we identify, but, this does not prevent us from proposing and testing hypotheses of associative relationships. It appears that in the countries of South Caucasus emigration intentions can be mainly explained by demographic variables such as age and gender together with having extensive social network abroad and being unemployment. In addition to the standard variables, which we control, in our hypothesis we argued that there should be differences in political attitudes as a covariate of emigration intentions between more authoritarian Azerbaijan on the one hand and less economically prosperous Georgia on the other. The former two countries are characterised with unconsolidated democracy, but economic problems, especially in Georgia, seem to be the major concern for their populations. In accordance with our hypothesis we find that political attitudes, such as the fairness of elections, and general perception that politics goes to the right direction in the country, are more significant explanation of temporary emigration intentions than other sets of variables in Azerbaijan. It is obvious that not all social groups were lucky enough to benefit from the oil-based economic growth. Indeed, the only significant economic variable we find for Azerbaijan is U-shaped effect of consumption deprivation on permanent 31

emigration intentions, meaning that those who suffer the most and the least have statistically significant higher associations with emigration intentions than those who are in the middle of welfare distribution. The position of the most disadvantaged is understandable, but maybe the elites also realize that living in an authoritarian state in the long run cannot be the best option they have.

Even though economic considerations appear to dominate in temporary emigration intentions in Georgia and likely in Armenia, for the permanent emigration intentions political views appear important to explain variation in the dependent variable.

Unfortunately, we are not able to detect the changes in associations between political attitudes and emigration intentions due to unavailability of longitudinal data, but what our findings might imply is that adverse trends in terms of democracy building might even further stimulate emigration intentions and consequently emigration decisions and behaviour. In Georgian case, it appears that materially most deprived social group have more aspiration to emigrate once they have resources to do so, meaning that the higher economic growth without meaningful political change has a potential to backfire. Last but not least we also want to mention a few caveats of the analysis presented here. The specification of models used in might be case-sensitive depending on the selected country.

For instance, employed proxies for political variables could have different weights and meaning in all considered societies therefore their comparison across countries could be problematic. Like many other studies using regression analysis, we think that our models suffer from omitted variable problem, meaning that some unobserved characteristics could be associated with emigration intentions as well as the perception of democracy in these countries. Nevertheless, we think that findings of this paper might be valuable for the literature in political and economic sociology of democratic transition as well as for emigration intentions scholarship.

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Tables and figures Figure 1: Democratic and economic transformation in the South Caucasian countries

Armenia [Moderate Autocracy]

Armenia [Market Economy with flaws]

Georgia [Defective Democracy]

Azerbaijan [Autocracy]

Azerbaijan [Market Economy with flaws]

Georgia [Market Economy with flaws]

Source: Transformation index 2010 (Bertelsmann Stiftung, 2010).

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Table 2: Marginal effects from nested logit regressions for temporary and permanent emigration intentions in South Caucasus with human capital, social network and employment states variables, pooled data for 2009-2010

Intercept Settlement and year Year 2010 Urban Capital Gender and age Male Age group: 18-27 Age group: 28-37 Age group: 38-47 Age group: 48-57 Education and health Sec. education Higher education Good health Excellent health Domestic social capital Married Cohabitating Divorced Social network index Being locally born Belonging to minority International social capital Family member abroad Friend abroad Experience of being abroad Receiving remittances Proficiency in English Proficiency in Russian Labour status Unemployed Out of labour market Student Homemaker Retired Disable Number of observations Log pseudo likelihood Pseudo R2

Armenia Temporary Permanent emigration emigration .20(.33) –1.6(.36)***

Country-level regressions Azerbaijan Temporary Permanent emigration emigration –.75(.33)** -2.8(.46)***

Georgia Temporary Permanent emigration emigration –2.5(.38)*** -3.2(.63)***

.08(.02)*** .01(.02) .05(.02)**

.03(.02)* .07(.02)*** .07(.02)***

.01(.02) .00(.02) .05(.03)**

.02(.01) .04(.02)** .07(.02)***

.02(.02) .30(.04)*** .23(.04)*** .15(.03)*** .11(.03)***

.04(.02)* .18(.04)*** .17(.04)*** .14(.04)*** .08(.04)**

.11(.02)*** .17(.04)*** .16(.04)*** .10(.04)*** .04(.04)

.06(.02)*** .16(.04)*** .15(.03)*** .09(.03)** .03(.04)

–.01(.03) –.03(.04) –.01(.03) –.04(.03)

–.05(.03)* –.10(.04)*** –.06(.02)** –.02(.03)

.07(.03)** .07(.04)* .03(.03) .05(.03)*

–.05(.03)* –.01(.05) –.04(.04) –.02(.01)*** –.03(.02) .17(.10)*

–.01(.03) .07(.04)* .02(.04) –.02(.01)*** –.01(.02) .09(.07)

–.04(.03) .00(.20) –.02(.04) –.02(.01)*** –.06(.02)*** –.07(.04)*

–.04(.02)** .00(.10) .03(.03) –.01(.00)*** .01(.01) .04(.03)

.03(.02)* .07(.02)*** .04(.02)*

–.01(.02) .06(.02)*** .04(.02)**

.08(.02)*** .11(.02)*** .09(.03)***

.02(.02) .08(.02)*** .05(.02)***

.03(.02) .06(.02)** .03(.03)

.00(.01) .03(.01)* .06(.01)***

.03(.04) .02(.02) .02(.01)

.02(.03) .00(.01) .01(.01)

.07(.03)** .02(.01) .02(.01)

.06(.01)*** .02(.01)*** –.01(.01)

.12(.02)*** .02(.03) .33(.09)*** .01(.03) –.03(.05) .00(.06) 2490 -1530.57 0.1128

.03(.01)* .04(.02)* .09(.03)*** .06(.02)*** –.04(.03) .02(.04) 2486 -668.60 0.1551

.05(.02)* .01(.01) –.01(.02) .08(.02)*** –.04(.03) .03(.07) .02(.03) –.01(.04) –.12(.05)** 2807 -1741.78 0.0649

.02(.02) .03(.01)*** .00(.01) .07(.02)*** .01(.03) –.01(.05) .03(.03) .02(.05) .02(.05) 2779 -1553.76 0.0510

.08(.02)*** .02(.04) .11(.08) –.07(.03)** –.10(.04)** .01(.05) 2906 -1817.11 0.0955

.02(.02) .05(.03)* –.03(.02) –.05(.02)*

.07(.02)*** .04(.03) .03(.04) –.05(.03)* .00(.04) –.06(.05) 2876 -1183.65 0.1101

–.01(.02) .04(.02)* .02(.03)

–.04(.01)*** .01(.01) –.01(.02)

.05(.02)** .25(.04)*** .23(.04)*** .18(.03)*** .14(.03)***

.03(.01)** .03(.03) .03(.02) .03(.02) .02(.02)

.11(.04)*** .11(.05)** .09(.03)*** .14(.03)***

.05(.03)* .01(.03) .02(.02) .00(.02)

–.04(.03) –.01(.05) .02(.04) .00(.01) –.03(.02) .05(.03)

.00(.02) .01(.03) –.01(.02) –.01(.00)*** .00(.01) .08(.01)***

Note: ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 levels, respectively, using two-tailed tests. Robust delta-method standard errors are in parentheses. Reference categories are living in rural areas, 58-67 age group, individuals with primary education and bad health, singles, and being employed. Source: Author’s calculation based on data from the Caucasus Barometer (Caucasus Research Resource Centers, 2009, 2010).

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Table 3: Marginal effects from nested logit regressions for temporary and permanent emigration intentions in South Caucasus with economic and employment stratification variables, 2009-2010

Types of employment Public employment Self-employ, no employ.

Self-empl, with employ.

Family business Private business Foreign company, NGOs Other employment Economic conditions Deprivation index Deprivation index 2 Household has debt Relative conditions Changing incomes Number of observations Log pseudo likelihood Pseudo R2

Armenia Temporary Permanent emigration emigration

Azerbaijan Temporary Permanent emigration emigration

Georgia Temporary Permanent emigration emigration

–.10(.03)*** –.02(.03) .12(.03) –.16(.03)** –.03(.03) –.05(.03) –.02(.03)

–.08(.03)** –.03(.03) .07(.08) –.03(.08) –.02(.03) .02(.08) –.02(.08)

–.10(.04)*** .02(.05) –.14(.10) .02(.06) .03(.05) –.03(.07) –.08(.05)

–.04(.03) –.01(.04) –.12(.08) .00(.04) .00(.03) –.06(.05) –.07(.04)*

–.12(.04)*** –.12(.04)*** –.09(.09) –.01(.07) –.06(.04) –.09(.12) –.13(.10)

–.01(.03) –.03(.03) –.05(.07) .01(.04) .00(.03) .04(.05) –.05(.07)

.02(.03)* .00(.03) .08(.03)*** .00(.03) –.02(.03)** 2346 –1426.77 .0796

.01(.01) .00(.00) .02(.02) –.05(.01)** .00(.01) 2319 –1285.70 .0654

–.02(.01) .00(.00) .09(.02)*** –.03(.02) –.02(.01)** 2006 –1217.62 .1194

–.03(.01)** .00(.00)*** .03(.02) –.06(.01)*** –.01(.01)** 1995 –785.18 .1609

.04(.01)*** –.00(.00)** .05(.02)** –.02(.02) –.02(.01) 1622 –983.49 .1248

.01(.01) .00(.00) .00(.01) –.02(.01)* –.01(.01) 1622 –384.34 .2312

Note: ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 levels, respectively, using two-tailed tests. Robust delta-method standard errors are in parentheses. Models control for sex, age, education, health, domestic and international social capital, settlement type. Reference category for employment type is unemployed. Source: Author’s calculation based on data from the Caucasus Barometer (Caucasus Research Resource Centers, 2009, 2010).

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Figure 2: Political attitudes and predicted probabilities of emigration intentions

Note: Models control for all variable in table 1 and 2. Source: Author’s calculation based on data from the Caucasus Barometer (Caucasus Research Resource Centers, 2009, 2010).

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Table 3: Comparison of Wald Chi2 across different blocks of variables in explaining temporary and permanent emigration intentions

Block 1: Year and settlement Degree of Freedom=3 Block 2: Age and gender Degree of Freedom =5 Block 3: Human capital Degree of Freedom =4 Block 4: Domestic social capital Degree of Freedom =6 Block 5: Inter. social network Degree of Freedom =6 Block 6: Labour market status Degree of Freedom =5 Block 7: Employment type Degree of Freedom =5 Block 8: Economic conditions Degree of Freedom =5 Block 9: Political attitudes Degree of Freedom =3

Armenia Temporary Permanent emigration emigration 20.13(.000) 20.34(.000)

Azerbaijan Temporary Permanent emigration emigration 19.35(.000) 20.44(.000)

Georgia Temporary Permanent emigration emigration 12.16(.007) 8.06(.045)

62.07(.000)

26.13(.000)

72.99(.000)

63.57(.000)

49.63(.000)

6.64(.249)

3.94(.414)

8.42(.078)

16.35(.003)

14.52(.006)

7.84(.098)

7.96(.093)

20.10(.003)

13.98(.030)

26.94(.000)

18.80(.002)

16.61(.011)

54.51(.000)

24.69(.000)

16.16(.013)

31.13(.000)

17.67(.007)

14.96(.021)

41.33(.000)

15.38(.018)

4.34(.631)

6.30(.391)

13.86(.031)

13.37(.038)

6.48(.263)

13.98(.030)

6.80(.340)

12.35(.055)

3.16(.788)

0.98(.987)

2.25(.814)

18.35(.003)

21.67(.001)

21.85(.001)

57.01(.000)

20.16(.001)

18.06(.003)

11.22(.011)

41.60(.000)

46.48(.000)

47.05(.000)

11.56(.009)

18.66(.000)

Source: Author’s calculation based on data from the Caucasus Barometer (Caucasus Research Resource Centers, 2009, 2010).

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