Abstract In this paper, I argue that migration responses to push factors can differ along ethnic lines. To arrive at migration as an adaptive response in which minorities engage, two processes are necessary. First, an individual making the decision to migrate must interpret ethnic tensions as a threat to her life chances, and she must evaluate her future prospects in this ethnically charged framework. Second, the option of migration must be a viable one. That is, an individual must consider them self the plausible target of the threat of diminishing life chances, conclude that an adaptive response is required, and determine that the benefits of migrating outweigh the costs. In order to explain these processes, the relational theory of ethnic politics (Hale 2008) and demographic theories of migration are employed. To test this hypothesis, an event history model is estimated using regional, household, and individual-level data from Russian censuses and the Russia Longitudinal Monitoring Survey. The relationship between out-migration and regional nationalist vote share is examined as well as regional hate crimes. The findings suggest that political push factors affect minority groups differently from the ethnic majority, supporting the hypothesis that the success of ethno nationalist politics in a region signals vulnerability to ethnic minorities, influencing migration decisions. 1. Introduction The rise of xenophobic nationalism in Europe has piqued the attention of scholars over the last two decades (Betz and Immerfall 1998; Brubaker 1998; Björgo 2000; Eatwell 2000; Hechter 2000; Eatwell and Mudde 2004; Mudde 2007; Rydgren 2007; Semyonov, Raijman, and Gorodzeisky 2006; Eger and Valdez 2015). Similar attention has been paid to the rise of this particular brand of nationalism in Russia and Eastern Europe after the fall of the Soviet Union (Tishkov 1997; Beissinger 2002; Alexseev 2006; Kuhelj 2011; Gorodzeisky, Glikman, and Maskileyson 2015). Nationalist political parties are characterized by populist (anti-establishment) platforms paired with xenophobia (Rydgren 2007; Eger and Valdez 2015), and have had varying levels of mainstream success across countries and regions. The European Parliament elections in 2014 assigned seats to the French Front National, Germany’s National Democratic Party, Golden Dawn in Greece, and the Danish People’s Party, among others, stirring up media attention to the trend. However, despite the interest in the factors contributing to the rise of nationalism in Europe, few studies have examined the outcomes of such a shift. In this paper, I argue that the success of these nationalists has demographic consequences. Particularly, I argue that nationalist politics, even when non-violent, can influence migration decision-making and contribute to increased out-migration by ethnic minorities. In order to establish this relationship, I examine the case of Russia using individual level panel data and an event history model. I find that, controlling for regional economic indicators, the regional vote share of the mainstream nationalist party is associated with a higher probability of out-migration of ethnic minorities from that region, particularly after an election year. These findings have implications for migration research, particularly in heterogeneous societies. Studies of migration have only incorporated ethnic group membership on a case-by-case basis. This research suggests that more theoretical development is needed around the dynamics of heterogeneous societies and the effect of group membership on migration decision-making. 2. Background 2.1 Migration as an adaptive response In this paper, I focus on migration as one of the venues for response to ethnic nationalism. Certainly, in many contexts, collective action, interest group formation, and voting can be undertaken as a substitute or alongside migration. However, political action is not always a viable option, and indeed in many contexts, government crackdown on protest and the perceived efficacy of political action can substantially reduce the likelihood of taking such an action (Dowding et al. 2000; Pfaff 2006). Exit, or migration, can then be utilized as a response to unfavorable political and economic climates. Migration is a costly and complex event, carried out by individuals or households for a wide range of reasons, generally responding to push factors at the origin and pull factors at the destination, with consideration of both personal factors and intervening obstacles (Lee 1966). Demographic theories of migration have suggested mechanisms at every level of motivation. Macro neoclassical economic theory explains labor migration through wage differentials caused by variations in supply and demand between origin and destination countries, while micro neoclassical economic theory focuses on individual decision-making in which a rational actor weighs the costs and benefits of migration (Massey et al. 1993). The new economics of migration pivots from the individual to the household unit as the primary location of decision-making in order to diversify risk and increase exposure to favorable markets as a whole unit, thus characterizing migration as a family-unit strategy to increase household resources (Stark and Bloom 1985). While much research has been done to examine the proximate determinants of migration, the role of political conflict in migration decision-making has been underdeveloped. Most research that has considered political determinants of migration has focused on armed conflict in the origin countries (Stanley 1987; Jones 1989; Williams et al. 2012). Violent intrastate conflict has been linked to changes in migration decision-making in Nepal (Bohra-Mishra and Massey 2011; Williams et al. 2012), Nicaragua (Lundquist and Massey 2005), Colombia (Engel and Ibáñez 2007), and El Salvador (Stanley 1987; Jones 1989). Micro-level violence has also been tied to out-migration. Tolnay and Beck (1992) find that, in the early 20th century, lynching significantly contributed to the out-migration of the black population from southern US states. Importantly, the authors conclude that while white lynching did occur, and while white migration did occur, the relationship between the two was not significant. Thus, ‘[t]he threat of violence was salient only for blacks in the South, and represented a motivation for migration only for them’ (Tolnay and Beck 1992: 112). Their findings suggest that in some cases, violence signals a group-based threat that contributes to migration decision-making. However, non-violent conflict events may also affect migration. Quebec’s nationalist policies geared to provide economic advantages to Francophones over the previously dominant Anglophones were significantly related to out-migration of Anglophones from Quebec to other provinces of Canada (Pettinicchio 2012). Speaking English was a significant predictor of migration, as was the interaction of speaking English and having a professional occupation, providing evidence that this policy that explicitly favored Francophones contributed to the worsening prospects of Anglophones in Quebec and influenced migration decision-making. Research in Kyrgyzstan has focused on ethnic violence as a contributor to intentions to migrate among Asian and European residents. The authors focus on differences between ethnic groups, noting that ‘the collapse of the Soviet Union brought to the fore and rearranged ethnic identities and because ethnicity has been a major factor in post-Soviet migration’ (Agadjanian, Nedoluzhko and Kumskov 2008: 621). The authors find few differences among members of ethnic groups. However, they note the limitations of using rare incidents of ethnic violence as their explanatory variable. Further, intentions to migrate may change over time and, more often than not, do not result in subsequent migration. Other research in Central Asia has complicated the idea of a relationship between political conflict and out-migration. Radnitz (2006) examines the political and economic factors of out-migration from post-Soviet Uzbekistan. The author finds that when both political and economic factors were negative, Uzbeks were more likely to out-migrate, and that there seemed to be no effect of political factors alone (Radnitz 2006). When faced with a faltering economy and ethnic tensions, Uzbeks were more likely to migrate. Researchers linking ethnicity and migration in Eurasia have repeatedly and convincingly argued that ethnic group membership alone cannot account for variations in migration trajectories in the region (Buckley 2008). While ethnicity alone surely cannot account for migration decisions in the region, ethnic group membership may render some political signals more salient than others. 2.2 Ethnic nationalism as a ‘push’ factor Ethnic nationalism, as I conceptualize it here, has been understood as ‘nativist’ (Mudde 2007, 2010), ‘neo-nationalist’ (Eger and Valdez 2015), ‘ethno-nationalist’ (Rydgren 2007). Mudde and Rydgren argue that these parties are ideologically defined by their desire to preserve the native group of a nation from cultural threat or even extinction from non-native threats. Eger and Valdez further describe the economics of these parties as adhering not to traditionally right-wing neo-liberal economics, but to a platform supporting a welfare state with benefits exclusive to the native-born ethnic majority. Ethnic nationalism matters for migration decision-making when it sends a signal to individuals in the community. Individuals receive the signal, interpret it as potentially threatening, and decide to take action. An individual will interpret political expression as a threat to his or her own life chances when the activity is framed in terms of ethnic group membership. Life chances, as defined by Hale (2008), are material well-being, physical security, power, and status. Material well-being can be threatened through barriers to employment and discrimination, where physical security can be threatened by acts or threats of violence. Power is a fungible good that may lead to other gains, such as economic prosperity or physical security. Status and self-esteem may be desires that, if left unfulfilled, lead to resentment and ethnic tension. Power and status may be threatened when individuals in certain groups face barriers to political representation and the ability to translate capital into other goods. If these life chances are threatened, individuals can take a number of actions, including migration. Because ethnic nationalism may be expressed in many different ways, the effect on migration decision-making may vary. I focus here on the difference between mainstream parties and isolated violence. Mainstream political parties are well established and visible to the public. The platforms are well known. When successful, these parties can affect local and national policies that threaten life chances of minorities, by creating structural barriers to employment and diminishing political representation of minorities. While mainstream political parties may send a wide signal to minorities about worsening prospects in the region, political rhetoric tends to emerge cyclically—that is, during election cycles, the salience of mainstream political parties should increase. I argue that the greater the success of such parties, the greater the probability of a minority to migrate out of that region. I further posit that this should be especially true immediately following an election cycle, when political rhetoric is especially salient. Isolated violence, on the other hand, may be attributed to ‘fringe’ radical groups that are less well organized. These acts of violence are typically not state-sponsored or party condoned, but can be empowered by mainstream political parties. Isolated violence can threaten physical security, but cannot set up structural barriers to employment or political representation. Violence may be rationalized and normalized, with minorities taking precautions to prevent contact with violent actors. In this way, isolated violence sends a more localized signal and may have a smaller influence on migration decision-making than mainstream political parties. This theoretical linkage between ethnic nationalism and migration applies to international or internal migration. Politics are often local or regional, and because of this, I argue that politics can affect migration even when the migrant does not cross international borders. For instance, rural to urban migrants may find cities to be safer; or other regions with more co-ethnics to provide more power or status. Likewise, this theoretical framework applies to both individuals making independent migration decisions as well as households making migration decisions to ‘send’ individuals out. For instance, in certain contexts of armed conflict, such as Nepal, migration as a response to conflict events did not necessarily entail the entire household leaving. Men often migrated while women stayed behind to manage households and farms (Williams 2013). Individuals may choose to establish their adult households or attend university outside their origin region because of the political climate. 3. Setting: Contemporary Russia Scholars and the media, both in the West and in Russia, have noted the rise of anti-immigrant, anti-minority nationalists in the country for over a decade (Alexseev 2006; Bidder 2008; Englund 2010; Verkhovsky, Kozhevnikova, and Sibireva 2010; Bennetts 2011; Conant 2014; Verkhosky 2014; Gorodzeisky et al. 2015). In this case, both the aforementioned theoretical expressions of ethnic nationalism exist. The mainstream ethnic nationalist party is the fourth largest political party in Russia, Vladimir Zhirinovsky’s Liberal Democratic Party of Russia. A stable ideological party, with varying regional success at the polls, it is rooted in deep-seated xenophobia. Far from a marginalized party, the LDPR has been one of the most stable parties in Russia since the mid-1990s, with organizational capacity in many of Russia’s regions (Hanson 2010). LDPR is a widely recognized party that has gained not only traction in legislative elections, but also media attention. This is important, because it indicates that Russians know what this party stands for, and are familiar with the platform on which it runs. Its success or failure in a region signals information to individuals living in that region, upon which these individuals may evaluate their future prospects in the region and determine the need for adaptation (Lohmann 1993). The signal that the LDPR sends is clear and consistent. Four predominant characteristics define LDPR: ‘anti-Western, anti-poverty, anti-communist, and xenophobic’ (Alexseev 2006: 215). Those features have changed little from LDPR leader Vladimir Zhirinovsky’s first publications in the early 1990s to the party’s published programs in 2015. The political creed includes an aim to ‘return to the [ethnic] Russian people the status of nation-state. … What’s good for Russians is good for all. For [ethnic] Russians, along with all those indigenous people of Russia, we will build our common Russian home.’ The party platform further claims that its leaders will ‘defend the country from migrants,’ and that the kinds of things that migrants bring to Russia, like ‘disease, narcotics, ethnic gangs,’ will cause national tension, and eventually ‘blow Russia apart from the inside out,’ (Zhirinovsky 2011). This familiar nationalist rhetoric places the ethnic Russian on top of an ethnic hierarchy and demands for the restoration of land and status to the population, while defending it from the threats of immigration and foreign politics. Because LDPR is explicitly neo-nationalist and pro-ethnic Russian, it serves as a proximate signal of a broader anti-minority sentiment. The signal that it sends is one of threatened prospects for ethnic minorities. Thus, ethnic minorities receiving this signal update their information about their status in the regional environment and determine that adaptation is necessary, while ethnic Russians may not. The second form of ethnic nationalist expression, isolated violence, is not an uncommon occurrence in Russia. Reports from the SOVA Center for Information and Analysis include contextual analyses as well as quantitative data drawn from newspaper reports across Russia. During the period of this study (2009 to 2012), anti-Caucasian and anti-Muslim violence occurred in high profile incidents such as the Moscow metro riots in 2010 (Kozhevnikova 2010; Bidder 2013). The year 2010 also ushered in increased violence and threats that were religiously motivated, such as those against Jehovah’s Witnesses and Jews. By 2012, the SOVA Center reported that xenophobic violence had not decreased as expected, and instead had intensified. By this time, the number of LGBT targets had begun to increase (Yudina and Alperovich 2012). I hypothesize that the ethnic nationalist signals sent by either the mainstream party or the isolated violence should be interpreted differently by ethnic Russians and ethnic minorities. However, specific ethnic groups may not have their own interpretation of broader anti-minority sentiment. For instance, a policy of returning the Russian Federation to its ‘rightful’ ethnic Russian owners will likely be threatening to minority individuals, regardless of specific ethnic group identification. H1: In regions with greater vote share awarded to the mainstream ethnic nationalist party, an ethnic minority individual will have a greater probability to migrate out. H2: The effect of the region’s mainstream ethnic nationalist party vote share will be greater in the year following an election. H3: In regions with more isolated violence, an ethnic minority individual will have a greater probability to migrate out. H4: The effect of the region’s mainstream ethnic nationalist party vote share will be greater than the effect of the region’s isolated violence on the probability to migrate out. It should be noted that this study focuses on regional variation in political and economic indicators in Russia. Migration is likewise measured as individuals leaving a region. This focus on regions is intentional, for two reasons. First, administrative regions, as opposed to neighborhoods or to the nation as a whole, are the most important component of national identity to ethnic Russians. Indeed, ‘the identity threats against which Zhirinovsky wants Russians to defend themselves would be most resonant with the most salient type of group identification by ethnic Russians—the region (province)’ (Alexseev 2006: 219). Second, the real implications of LDPR regional leadership result from the political process itself. During the period studied, seats in the Duma were allocated based on a proportional representation system (State Duma 2005). In all regional parliaments, either a proportional representation or mixed system is in place, meaning that at least some of the parliament seats are awarded due to the party’s proportion of vote share (Ross 2014). I discuss specific measurement decisions in further detail below. 4. Data For this research, I use panel data from the Russia Longitudinal Monitoring Survey 2009–12, combined with regional data from the Russian Census and the SOVA Center for Information and Analytics, data from the Russian Census, and the Central Election Commission of Russia into one unique dataset. The Russia Longitudinal Monitoring Survey – HSE, is conducted by the National Research University Higher School of Economics and ZAO ‘Demoscope’ together with Carolina Population Center, University of North Carolina at Chapel Hill and the Institute of Sociology RAS, and is henceforth referred to as the RLMS. The RLMS is considered the only nationally representative survey in Russia. The sampled units in the RLMS map on to 25 distinct regions, out of 87 total administrative regions. For the sake of brevity, I use the term ‘region’ to refer to administrative regions known as ‘oblasts,’ ‘krai,’ ‘republics,’ and ‘federal cities.’ With the exception of the federal cities Moscow and St Petersburg, regions are roughly equivalent to US states. They vary in size, population, and autonomy. The RLMS sampled regions in and around Moscow and St Petersburg, as far south as Dagestan, and as far east as Vladivostok. Scarcely populated regions in northern Siberia and the Kamchatka peninsula were excluded from the data collection effort. Data are collected at both the household and individual level, with a unique household and ‘site’ identified for each observation. Household-level variables include a detailed roster, reasons that members left the household, income and expenditures (among many others). Variables at the individual level include birthplace, ethnicity, language(s) spoken, and labor force participation (among many others). For each year, I merge the household and individual files, matching each individual to his or her corresponding record in the household roster. With the exception of one-member households, none of the households had individual surveys for all members of the household. This requires that some members of the household receive an ethnicity indicator based on the other members of the household. This decision relies on the important assumption that while only one individual in the household may be an ethnic minority, even an inter-ethnic married couple will interpret anti-minority sentiment as a threat to their collective future prospects. Similar migration decision-making and heightened ethnic awareness may occur for households in which only one ethnic minority resides. Using this method, approximately 19,000 observations with missing ethnicity information are deemed ethnic Russians, roughly 2,600 are deemed ethnic minorities, and a little over 2,000 cannot be determined, and are dropped from the analysis. I have structured the data in a person-year event history file in which individuals are nested within households within the 25 regions contained in the RLMS. The RLMS provides repeated observations of households and individuals over time, in which respondents may enter or exit the dataset at any time due to new sampling that year or to non-response. The repeated household roster is a significant asset to the RLMS. Interviewers surveying a household in the first year in which the household enters the survey take detailed notes about every person living in the household, including birth date, gender, and relationship to others on the roster. In subsequent years, the interviewer returns to the household with the roster and updates it. If a member of a household leaves, the survey captures a general annual timeline of migration as well as the reason for absence and the distance the individual moved. Reasons why an individual left a household are categorized as ‘lives in this building, but in a different household,’ ‘lives in this region, but at another address,’ ‘lives outside of this region,’ ‘moved for university,’ ‘died,’ and ‘other.’ This nuanced coding of individual moves was new to the RLMS in 2009. Because of the introduction of this nuanced measurement, I restrict my analysis to the period of 2009–12. 4.1 Dependent variable I define migration as an individual migrating by T2 given that the independent variables are measured at T1. Consistent with the theory presented in this paper, I expect that ethnic nationalist politics will contribute to the probability of ethnic minorities moving out of a region. Individuals moving within a region would continue to be subject to the same regional political conditions. Thus, I create a dependent variable with three options—the individual does not move (0), the individual moves within the region (1), or the individual moves outside of the region (2). Only moves coded as leaving the region are considered migration for this study. These moves are coded using the response for why an individual has left the household, described above. 4.2 Variables of interest 4.2.1 Nationalist Party vote share To each person-year, I attach the official regional vote share data for the Liberal Democratic Party of Russia, which was collected from the Central Election Committee of Russia and compiled by Electoral Geography. The data are time-lagged, so that each person-year receives the regional score of the most recent legislative election that occurred at least one year ago. An individual record in survey year 2011 receives the vote share for 2007, and the same individual in survey year 2012 receives the vote share for the election 2011. In this way, the vote share is both time-lagged, and time-varying, although it does not change annually. I use only legislative elections and exclude presidential elections. Presidential elections in Russia during this time period are problematic to measure, as recurring favorite Vladimir Putin carries the bulk of the vote share in these elections, reducing variation in the vote share for the other candidates. Legislative elections allow participation from more parties and smaller parties may receive a more meaningful share of the votes. Table 1 summarizes the vote share for each of the sampled regions in the two legislative elections during this time period. Table 1. LDPR vote share in 2007 and 2011 legislative elections in RLMS-sampled regions Regions 2007 2011 Altai 6.45% 10.65% Amurskaya 10.13% 20.99% Chelyabinskaya 9.45% 11.77% Chuvashia 8.49% 10.67% Kabardino-Balkaria 0.41% 0.08% Kaliningradskaya 10.17% 14.10% Kaluzhskaya 8.23% 14.36% Khanty-Mansiisky AD 13.19% 22.53% Komi 11.42% 11.91% Krasnodarsky krai 8.07% 10.45% Krasnoyarsky krai 10.56% 16.99% Leningradskaya oblast 8.64% 14.78% Lipeckaya 9.65% 14.40% Moscow city 7.14% 9.45% Novgorodskaya 9.55% 11.48% Penzenskaya 5.86% 10.12% Rostovskaya 5.36% 10.15% Saint-Petersburg city 7.48% 10.30% Saratovskaya 6.22% 7.24% Smolenskaya 11.99% 14.75% Tambovskaya 7.68% 7.09% Tatarstan 3.88% 3.48% Tomskaya 13.20% 17.85% Tulskaya 7.13% 9.21% Volgogradskaya 9.03% 13.28% All Russia 8.15% 11.68% Regions 2007 2011 Altai 6.45% 10.65% Amurskaya 10.13% 20.99% Chelyabinskaya 9.45% 11.77% Chuvashia 8.49% 10.67% Kabardino-Balkaria 0.41% 0.08% Kaliningradskaya 10.17% 14.10% Kaluzhskaya 8.23% 14.36% Khanty-Mansiisky AD 13.19% 22.53% Komi 11.42% 11.91% Krasnodarsky krai 8.07% 10.45% Krasnoyarsky krai 10.56% 16.99% Leningradskaya oblast 8.64% 14.78% Lipeckaya 9.65% 14.40% Moscow city 7.14% 9.45% Novgorodskaya 9.55% 11.48% Penzenskaya 5.86% 10.12% Rostovskaya 5.36% 10.15% Saint-Petersburg city 7.48% 10.30% Saratovskaya 6.22% 7.24% Smolenskaya 11.99% 14.75% Tambovskaya 7.68% 7.09% Tatarstan 3.88% 3.48% Tomskaya 13.20% 17.85% Tulskaya 7.13% 9.21% Volgogradskaya 9.03% 13.28% All Russia 8.15% 11.68% Source: Central Election Commission of Russia. Table 1. LDPR vote share in 2007 and 2011 legislative elections in RLMS-sampled regions Regions 2007 2011 Altai 6.45% 10.65% Amurskaya 10.13% 20.99% Chelyabinskaya 9.45% 11.77% Chuvashia 8.49% 10.67% Kabardino-Balkaria 0.41% 0.08% Kaliningradskaya 10.17% 14.10% Kaluzhskaya 8.23% 14.36% Khanty-Mansiisky AD 13.19% 22.53% Komi 11.42% 11.91% Krasnodarsky krai 8.07% 10.45% Krasnoyarsky krai 10.56% 16.99% Leningradskaya oblast 8.64% 14.78% Lipeckaya 9.65% 14.40% Moscow city 7.14% 9.45% Novgorodskaya 9.55% 11.48% Penzenskaya 5.86% 10.12% Rostovskaya 5.36% 10.15% Saint-Petersburg city 7.48% 10.30% Saratovskaya 6.22% 7.24% Smolenskaya 11.99% 14.75% Tambovskaya 7.68% 7.09% Tatarstan 3.88% 3.48% Tomskaya 13.20% 17.85% Tulskaya 7.13% 9.21% Volgogradskaya 9.03% 13.28% All Russia 8.15% 11.68% Regions 2007 2011 Altai 6.45% 10.65% Amurskaya 10.13% 20.99% Chelyabinskaya 9.45% 11.77% Chuvashia 8.49% 10.67% Kabardino-Balkaria 0.41% 0.08% Kaliningradskaya 10.17% 14.10% Kaluzhskaya 8.23% 14.36% Khanty-Mansiisky AD 13.19% 22.53% Komi 11.42% 11.91% Krasnodarsky krai 8.07% 10.45% Krasnoyarsky krai 10.56% 16.99% Leningradskaya oblast 8.64% 14.78% Lipeckaya 9.65% 14.40% Moscow city 7.14% 9.45% Novgorodskaya 9.55% 11.48% Penzenskaya 5.86% 10.12% Rostovskaya 5.36% 10.15% Saint-Petersburg city 7.48% 10.30% Saratovskaya 6.22% 7.24% Smolenskaya 11.99% 14.75% Tambovskaya 7.68% 7.09% Tatarstan 3.88% 3.48% Tomskaya 13.20% 17.85% Tulskaya 7.13% 9.21% Volgogradskaya 9.03% 13.28% All Russia 8.15% 11.68% Source: Central Election Commission of Russia. In some regions the LDPR has consistently low support, such as Kabardino-Balkaria, while other regions show a consistently high turnout for LDPR, such as Smolenskaya. Others demonstrate a dynamic increase between the 2007 and 2011 results, such as Khanty-Mansiisky. For the period studied, none of the regions produced a vote share of 0% for LDPR, nor did any of the regions report 0% turnout. 4.2.2 Isolated violence I include the number of hate crimes committed in the region that year as an indicator of isolated racial violence. I use annual hate crime count data from the SOVA Center for Information and Analysis, time-lagged by one year. The SOVA Center is a Moscow-based Russian non-profit non-governmental organization that was founded in 2002. The center regularly publishes articles and books in addition to monthly counts of hate crime data, classified by region and victim identity. Systematic counts of incidents of violence and vandalism were collected every month between 2009 and 2012. Incidents of violence include murder, beating, stabbing, wounding, or threats of murder. Incidents of vandalism include breaking windows, arson, and graffiti. An incident is classified as a hate crime based on victim membership to a minority group. This includes ethnic and religious minorities in addition to LGBT individuals. I include hate crimes regardless of specific minority group classification, which is consistent with the theoretical concept that violence against a minority sends a signal of worsening prospects to all minorities. Hate crime figures should be considered conservative estimates, as they are subject to gross underreporting. 4.2.3 Minority status I operationalize minority status as non-Russian ethnicity. As with many European contexts, often the terms nationality and ethnicity are used interchangeably in Russia. Nearly always ethnicity is self-identified, including in the RLMS survey. This is also true on the Russian census, which typically returns hundreds of ethnic group responses. I have constructed a variable called ‘minority’ in which I give a ‘0’ outcome to individuals who self-identified as having a Russian ethnicity. I give respondents a ‘1’ if they self-identify as a member of one of the ethnic minority groups represented in the data. Ethnic Russians make up the majority of the dataset, but with some significant categories of other ethnic groups. As previously mentioned, I was unable to identify ethnicity for approximately 2% of respondents, and these individuals were removed from my analysis. I create a household level variable representing minority households. Previously reported ethnicity is assumed constant for all other observations of the household. Recognizing the large number of ethnic groups in Russia, I focus on this dichotomous measure of ethnicity for a number of reasons. I have argued that signals from the nationalist party conveys the idea of ethnic Russian superiority, and that ethnic minorities interpret this signal as threatening to their future prospects in a certain region. Targeted violence that appears to focus on phenotypical differences, specific ethnic groups, or particular symbols of minority status (such as attacking Muslims wearing the hijab) may create acute fear within that minority group. However, the regional environment of anti-minority sentiment may have more aggregated effects and, I argue, that the signal of worsening prospects is sent to all minorities, even those who are not members of targeted groups. This is not to claim that phenotypical differences are unimportant in a practical way, particularly when those differences are highlighted by the media and political platforms. Attacks against Chechens and migrants from Central Asia receive media and political attention, but anti-minority sentiment against Jews, Muslims, Westerners, Roma, and even Slavic minorities such as Ukranians and Belarussians continues to be widespread. In a subsequent analysis of these data, I divided the data into specific targeted ethnic groups such as those from Central Asia and the North Caucasus. These data represent individuals from over 80 ethnic groups, so that such a division greatly reduced the power of the model and made interpretation nearly impossible. Thus, dividing ethnic groups not only reduces explanatory power, but also forces the researcher to make too many arbitrary decisions about which minority groups perceive which signals. In this way, the dichotomous representation of ethnicity, while collapsing the variation among ethnic groups, allows the model to delineate between Russians and non-Russians in an interpretable way. 4.3 Control variables I control for demographic and economic conditions at the individual, household, and regional level. Each individual exists within a household in one of the 25 sampled regions in the RLMS. The data are not restricted by a maximum age, but all children under the age of 18 are removed. I include gender in the model, with female respondents receiving a 1 and male respondents receiving a 0 on the indicator. Gender previously reported for the same individual is assumed to continue through to subsequent years. Individual respondents report their monthly income, and not the income of the entire household. I attach this indicator to each household member record. Although this means that the wages reported are not ‘real’ household wages, they are useful in generating relative income versus other respondents. For use in the model, I standardize the income figures, so that one unit increase is equivalent to one standard deviation increase. This allows for the coefficient to be large enough to be interpretable in the model. This income level may vary by time. At the regional level, I include a standardized measure of the regional Gross Domestic Product (GDP) per capita in 2009. I also include a standardized measure of the regional population in 2010 according to the Russian Census. For both of these standardized regional levels, a unit increase is equivalent to one (sample) standard deviation increase. Neither variable is time varying, but both provide regional variability. I omit individuals who are reported as already gone from their households at the beginning of the case study time. The resulting panel data contain over 30,000 ethnic Russians and 4,600 ethnic minorities, contributing over 87,000 person-years total. Table 2 summarizes the characteristics of these two groups. In many ways, the groups are descriptively similar, with close median ages, household incomes, and proportion female. Table 2. Descriptive statistics of explanatory and control variables Ethnic Russians Ethnic non-Russians Number of individuals 30,623 4,646 Number of person-years 76,085 11,385 Percent female 57.31% 54.55% Ethnic Russians SD Ethnic non-Russians SD Median age 43 17.82 45 17.69 Median monthly income (rubles) 10,000 19160.9 9,723 33176.19 Median 2009 GDP in region of residence 15,098 9649.47 15,290 8472.78 Median 2010 population in region of residence 2,521,892 2,968,800 1,521,420 2,549,721 Median LDPR vote share in region of residence 0.0945 0.0336 0.0849 0.0492 Median count of hate crimes in region of residence 4 42.76 3 33.82 Ethnic Russians Ethnic non-Russians Number of individuals 30,623 4,646 Number of person-years 76,085 11,385 Percent female 57.31% 54.55% Ethnic Russians SD Ethnic non-Russians SD Median age 43 17.82 45 17.69 Median monthly income (rubles) 10,000 19160.9 9,723 33176.19 Median 2009 GDP in region of residence 15,098 9649.47 15,290 8472.78 Median 2010 population in region of residence 2,521,892 2,968,800 1,521,420 2,549,721 Median LDPR vote share in region of residence 0.0945 0.0336 0.0849 0.0492 Median count of hate crimes in region of residence 4 42.76 3 33.82 SD, standard deviation. Table 2. Descriptive statistics of explanatory and control variables Ethnic Russians Ethnic non-Russians Number of individuals 30,623 4,646 Number of person-years 76,085 11,385 Percent female 57.31% 54.55% Ethnic Russians SD Ethnic non-Russians SD Median age 43 17.82 45 17.69 Median monthly income (rubles) 10,000 19160.9 9,723 33176.19 Median 2009 GDP in region of residence 15,098 9649.47 15,290 8472.78 Median 2010 population in region of residence 2,521,892 2,968,800 1,521,420 2,549,721 Median LDPR vote share in region of residence 0.0945 0.0336 0.0849 0.0492 Median count of hate crimes in region of residence 4 42.76 3 33.82 Ethnic Russians Ethnic non-Russians Number of individuals 30,623 4,646 Number of person-years 76,085 11,385 Percent female 57.31% 54.55% Ethnic Russians SD Ethnic non-Russians SD Median age 43 17.82 45 17.69 Median monthly income (rubles) 10,000 19160.9 9,723 33176.19 Median 2009 GDP in region of residence 15,098 9649.47 15,290 8472.78 Median 2010 population in region of residence 2,521,892 2,968,800 1,521,420 2,549,721 Median LDPR vote share in region of residence 0.0945 0.0336 0.0849 0.0492 Median count of hate crimes in region of residence 4 42.76 3 33.82 SD, standard deviation. 5. Methods To examine the effect of ethnic nationalist politics on migration as hypothesized in H1, I estimate a discrete-time event history model (Allison 1984, 2010; Box-Steffensmeier and Jones 2004) using multinomial regression. To examine the effect of an election year, as hypothesized in H2, I use the same model for the year immediately following an election, which in this sample is 2012. To test H3, I include isolated violence in the multinomial event history model. I focus on a respondent’s first instance of migration and subsequent moves are not considered in this model. I restrict my analysis to those at risk of migrating. Respondents marked ‘absent’ at the beginning of the period examined, in 2009, are removed from the data. After any individual’s first migration, she is also removed from the analysis. Right censoring occurs here if an individual does not migrate within this observation period. The event history model allows for time varying explanatory and control variables. Because each observation is a person-year, indicators may change over time, as subsequent years for the same individual become additional observations. In this model, any individual may have between one and four observations, depending on when they enter and/or exit the sample. 6. Results I consider evidence from the multinomial event history models reported in Table 3. By including the individual-level, household-level, and contextual control variables the likelihood of drawing conclusions based on confounding, spurious or suppressor relationship is reduced. Table 3. Multinomial event–history regression results for all sample years and for the year after an election only. Standard errors clustered at the individual level Sample 2009–12 2012 only N = 65,714 18,841 Dependent variable (base = no move) Migrates within oblast Migrates out of oblast Migrates within oblast Migrates out of oblast Ethnic nationalist variables Minority household 0.2704 0.8059* –0.154 –1.91* LDPR vote share 9.98* 6.76* 5.11* –2.32 Hate crimes count (oblast level) –0.0079* –0.0075* –0.0145* –0.0054 Minority * LDPR –1.87 –4.76 1.38 11.06* Minority * hate crimes Count –0.0087 0.0119* –0.0157 0.0304* Individual and household controls Age –0.027† –0.094* –0.008 –0.076† Age squared –0.00008 –0.0005† –0.0004 0.0002 Sex –0.3112* –0.2316* –0.271* –0.350† Standardized Income –0.2616* –0.4759* –0.440* –0.719* Oblast-level controls Standardized GDP –0.038 –0.171† 0.108 0.161 Standardized population 0.5403* 0.1497 0.602* –0.232 Intercept –4.2599* –3.079* –3.73* –1.79* Sample 2009–12 2012 only N = 65,714 18,841 Dependent variable (base = no move) Migrates within oblast Migrates out of oblast Migrates within oblast Migrates out of oblast Ethnic nationalist variables Minority household 0.2704 0.8059* –0.154 –1.91* LDPR vote share 9.98* 6.76* 5.11* –2.32 Hate crimes count (oblast level) –0.0079* –0.0075* –0.0145* –0.0054 Minority * LDPR –1.87 –4.76 1.38 11.06* Minority * hate crimes Count –0.0087 0.0119* –0.0157 0.0304* Individual and household controls Age –0.027† –0.094* –0.008 –0.076† Age squared –0.00008 –0.0005† –0.0004 0.0002 Sex –0.3112* –0.2316* –0.271* –0.350† Standardized Income –0.2616* –0.4759* –0.440* –0.719* Oblast-level controls Standardized GDP –0.038 –0.171† 0.108 0.161 Standardized population 0.5403* 0.1497 0.602* –0.232 Intercept –4.2599* –3.079* –3.73* –1.79* Note: * p < 0.05; † p < 0.10. Table 3. Multinomial event–history regression results for all sample years and for the year after an election only. Standard errors clustered at the individual level Sample 2009–12 2012 only N = 65,714 18,841 Dependent variable (base = no move) Migrates within oblast Migrates out of oblast Migrates within oblast Migrates out of oblast Ethnic nationalist variables Minority household 0.2704 0.8059* –0.154 –1.91* LDPR vote share 9.98* 6.76* 5.11* –2.32 Hate crimes count (oblast level) –0.0079* –0.0075* –0.0145* –0.0054 Minority * LDPR –1.87 –4.76 1.38 11.06* Minority * hate crimes Count –0.0087 0.0119* –0.0157 0.0304* Individual and household controls Age –0.027† –0.094* –0.008 –0.076† Age squared –0.00008 –0.0005† –0.0004 0.0002 Sex –0.3112* –0.2316* –0.271* –0.350† Standardized Income –0.2616* –0.4759* –0.440* –0.719* Oblast-level controls Standardized GDP –0.038 –0.171† 0.108 0.161 Standardized population 0.5403* 0.1497 0.602* –0.232 Intercept –4.2599* –3.079* –3.73* –1.79* Sample 2009–12 2012 only N = 65,714 18,841 Dependent variable (base = no move) Migrates within oblast Migrates out of oblast Migrates within oblast Migrates out of oblast Ethnic nationalist variables Minority household 0.2704 0.8059* –0.154 –1.91* LDPR vote share 9.98* 6.76* 5.11* –2.32 Hate crimes count (oblast level) –0.0079* –0.0075* –0.0145* –0.0054 Minority * LDPR –1.87 –4.76 1.38 11.06* Minority * hate crimes Count –0.0087 0.0119* –0.0157 0.0304* Individual and household controls Age –0.027† –0.094* –0.008 –0.076† Age squared –0.00008 –0.0005† –0.0004 0.0002 Sex –0.3112* –0.2316* –0.271* –0.350† Standardized Income –0.2616* –0.4759* –0.440* –0.719* Oblast-level controls Standardized GDP –0.038 –0.171† 0.108 0.161 Standardized population 0.5403* 0.1497 0.602* –0.232 Intercept –4.2599* –3.079* –3.73* –1.79* Note: * p < 0.05; † p < 0.10. For all years, the likelihood of migrating within an oblast is increased by the LDPR vote share and the population of the region. This likelihood is decreased by the number of hate crimes. Women are less likely to migrate within the oblast, and those with less income are also less likely. After an election year, the factors contributing to migrating within an oblast are substantively equivalent. The likelihood of migrating out of the region is increased when the individual resides in a minority household, when the LDPR vote share is higher. The effect of hate crimes is significantly positive when the individual resides in a minority household. The likelihood of moving out of a region is decreased by the hate crimes count in general and by the age of the respondent. Women are less likely to migrate out of the region; after an election year, the likelihood of migrating out of the region changes. Consistent with H2, during this time, the effect of a higher LDPR vote share on the likelihood of migrating out is increased when the individual is in a minority household. The likelihood of migrating out is decreased when an individual is in a minority household, generally, and the effects of age and sex are marginally significant. These findings shed light on the differential migration decision-making process for ethnic minorities in contemporary Russia. Ethnic minorities are more likely to migrate out of a region in the presence of higher hate crimes, regardless of election year. This effect does not hold for moves within a region, as these individuals are still exposed to the same regional political conditions.1 Ethnic minorities are more likely to migrate out of a region in the presence of higher nationalist party vote share in the year following an election. This is consistent with the idea that the signals that a party sends will be intensified after an election. The findings suggest that the effects of electoral politics on migration may indeed follow the electoral cycle. As such, I fail to reject the null hypothesis for H1. In most years, ethnic minority individuals do not appear to have a greater probability of migrating out of a region with greater vote share awarded to the ethnic nationalist party. I find support to reject the null hypothesis for H2, in that the effect of the vote share on ethnic minorities’ probability to migrate out of the oblast is significantly positive in the year immediately following an election. Consistent with H3, in all years, ethnic minorities have a higher probability to migrate out of the region where more hate crimes occur. The signal sent by isolated violence is consistent through the years in this sample, regardless of electoral cycle. I find partial support for H4, in that the effect of the vote share, after an election only, is greater than the effect of the region’s isolated violence on the probability of migrating out. Although they are not the primary focus of this study, other results reported in Table 3 warrant brief mention. In both models, a statistically significant negative relationship exists between age and migration, with younger respondents more likely to migrate out of the region. Women are less likely to migrate out of the region or within the region, although the significance in the year after an election is marginal. Those with higher income are less likely to migrate within the region or outside of the region in all years. Individuals in regions with higher population are more likely to migrate within the region, while GDP appears to have almost no significant effect in any of the models. 7. Discussion The implications of these findings are two-fold. First, non-violent signals sent by ethno-nationalist parties may have a different influence on decision-making for ethnic minorities, but it is truly meaningful when it is salient—this signal is closely related to the electoral cycle, and is intensified after an election year. The effects become less important in other years. Second, that isolated violence consistently contributes to migration decision-making for ethnic minorities—in all years, but only in regard to migrating out of the region. Few other studies have analyzed the relationship between electoral processes and migration, but the findings of this research support the notion that electoral processes may indeed have practical ramifications for social processes, such as migration. The results from this line of inquiry have implications for the way that social scientists analyze the political determinants of migration in heterogeneous societies. Migration decision-making may differ along ethnic lines, particularly in response to political factors. Formally including political conditions in statistical models may further reveal mechanisms underlying decisions to migrate. Researchers may find that certain contextual factors exhibit a different relationship with migration based on group membership. Further, different types of political expression may influence migration decision-making differently. This is consistent with the literature on armed conflict and migration, which suggests that different types of events affect migration differently—sometimes even in opposite directions (Williams et al. 2012). These findings contribute to our understanding of the relationship between political conflict and migration, supporting the notion that even in the absence of armed conflict, symbolic measures of anti-minority sentiment, such as the success of ethno-nationalist parties, can contribute to the out-migration of members of minority groups. 7.1 Limitations While the RLMS is a detailed and appropriate data source, it is not perfect, and carries with it some of the common limitations in survey data used in the study of migration. For instance, the RLMS only has nuanced measures of migration from 2009 on, which limits the scope of the study. Additionally, individual economic factors should be interpreted with care, due to the necessity of carrying forward information over an entire household. The household survey contains the dependent variable of out-migration, as well as birth date and gender of the household members. The ethnicity and monthly income indicators are measured in the individual interviews, and must be assumed for the rest of the household. Finally, the RLMS is not designed to follow individual migrants, and does not collect information on specific destinations, and thus it is impossible to say what political conditions await the individual upon arrival. Unfortunately, this is a limitation of many datasets used by migration scholars, and survey design taking destination into account would improve our understanding of migration streams. 7.2 Future research The presence of ethnic nationalism is not unique to Russia, and indeed, has been a concern for European scholars for some time. However, few studies consider the consequences of ethnic nationalism. The main contribution of this research is to demonstrate that non-violent manifestations of nationalist politics—such as vote share—have signaling effects that push individuals out of regions. This research has suggested that electoral processes may influence migration decisions made by ethnic minorities. Further research is needed to better conceptualize and measure these political indicators, particularly those for which official statistics are unreliable. An interesting interplay may exist between nationalist party voting and rates of hate crimes. While vote share and hate crimes are weakly correlated in the RLMS dataset, future research might consider different expressions of ethnic nationalism, both violent and non-violent, and how these expressions may interact, including spatially. Migration scholarship would benefit from a more developed understanding of the relationship between this type of political conflict and demographic outcomes. As scholars have seen in other case studies of the demographic consequences of armed conflict, the growing anti-immigrant and xenophobic political influences in Europe may well have demographic consequences in the coming years. Acknowledgements The author would like to thank Steve Pfaff, Stew Tolnay, Nathalie Williams, and Sara Curran for their guidance and careful reading of this manuscript. Funding This research was supported by a Eunice Kennedy Shriver National Institute of Child Health and Human Development research infrastructure grant, R24 HD042828, to the Center for Studies in Demography and Ecology at the University of Washington. Conflict of interest The author is not aware of any conflict of interest. Notes Although we do not have destination information for migrants in the RLMS, we do know when they leave a region and when they move within the same administrative boundary. This incomplete information is likely consistent with the incomplete information individuals have when they migrate—that is, individuals rarely have perfect information about their intended destination before they move. References Agadjanian V., Nedoluzhko L., Kumskov G. ( 2008) ‘ Eager to Leave? Intentions to Migrate Abroad among Young People in Kyrgyzstan’, International Migration Review , 42/ 3: 620– 51. Google Scholar CrossRef Search ADS Alexseev M. A. ( 2006) ‘ Ballot-Box Vigilantism? Ethnic Population Shifts and Xenophobic Voting in Post-Soviet Russia’, Political Behavior , 28/ 3: 211– 40. Google Scholar CrossRef Search ADS Allison P. D. ( 1984) Event History Analysis: Regression for Longitudinal Event Data . Thousand Oaks, CA: Sage. Allison P. D. ( 2010) ‘Survival Analysis’, in The Reviewer’s Guide to Quantitative Methods in the Social Sciences , p. 413. Oxford: Routledge. Beissinger M. R. ( 2002) Nationalist Mobilization and the Collapse of the Soviet State . Cambridge: Cambridge University Press. Bennetts M. ( 2011) ‘Russia’s Nationalists Go to the Polls’, Ria Novosti (pubd online 12 February 2011) <http://en.ria.ru/features/20111202/169255329.html> accessed 24 July 2014. Betz H.-G., Immerfall S., eds ( 1998) The New Politics of the Right: Neo-Populist Parties and Movements in Established Democracies . New York: St. Martin’s Press. Bidder B. ( 2008) ‘Zhirinovsky’s Follies: Nuclear Threats and Busty Ladies in the Race for Second-Place in Russia’, Spiegel (pubd online 28 February 2008) <http://www.spiegel.de/international/world/zhirinovsky-s-follies-nuclear-threats-and-busty-ladies-in-the-race-for-second-place-in-russia-a-538403.html> accessed 24 April 2013. Bidder B. ( 2013) ‘ Anti Immigrant Riots in Moscow Highlight Tensions’, Spiegl (pubd online 14 October 2013) <http://www.spiegel.de/international/europe/anti-immigrant-riots-in-moscow-highlight-tensions-a-927792.html> accessed 14 March 2014. Björgo T. ( 2000) ‘Xenophobic Violence and Ethnic Conflict at the Local Level: Lessons from the Scandinavian Experience’, in Koopmans R., Statham P. (eds) Challenging Immigration and Ethnic Relations Politics, Comparative European Perspectives , pp. 368– 86. Oxford: Oxford University Press. Bohra-Mishra P., Massey D. S. ( 2011) ‘ Individual Decisions to Migrate During Civil Conflict.’ Demography , 48/ 2: 401– 24. Google Scholar CrossRef Search ADS PubMed Box-Steffensmeier J., Jones B. ( 2004) Event History Modeling: A Guide for Social Scientists . Cambridge: Cambridge University Press. Brubaker R. ( 1998) ‘ Migrations of Ethnic Unmixing in the “New Europe”’, International Migration Review , 32/ 4: 1047. Google Scholar CrossRef Search ADS Buckley C. ( 2008) Migration, Homeland and Belonging in Eurasia . Washington, DC: Woodrow Wilson Center Press. Conant E. ( 2014) ‘ Ethnic Russians: Pretext for Putin’s Ukraine Invasion?’ National Geographic , 2 May 2014. Dowding K., John P., Mergoupis T., Vugt M. ( 2000) ‘ Exit, Voice and Loyalty: Analytic and Empirical Developments’, European Journal of Political Research , 37/ 4: 469– 95. Eatwell R. ( 2000) ‘Ethnocentric Party Mobilization in Europe: The Importance of the Three-Dimensional Approach’, in Koopmans R., Statham P. (eds) Challenging Immigration and Ethnic Relations Politics, Comparative European Perspectives , pp. 348– 67. Oxford: Oxford University Press. Eatwell R., Mudde C., eds. ( 2004) Western Democracies and the New Extreme Right Challenge . Oxford: Routledge. Eger M. A., Valdez S. ( 2015) ‘ Neo-Nationalism in Western Europe’, European Sociological Review , 31/ 1: 115– 30. Google Scholar CrossRef Search ADS Engel S., Ibáñez A. M. ( 2007) ‘ Displacement Due to Violence in Colombia: A Household-Level Analysis’, Economic Development and Cultural Change , 55/ 2: 335– 65. Google Scholar CrossRef Search ADS Englund W. ( 2010) ‘ Riots in Russia Rooted in Nationalism, Hatred of Immigrants’, The Washington Post , 14 December 2010. Gerber T. P. ( 2006) ‘ Regional Economic Performance and Net Migration Rates in Russia, 1993–20021’, International Migration Review , 40/ 3: 661– 97. Google Scholar CrossRef Search ADS Gorodzeisky A., Glikman A., Maskileyson D. ( 2015) ‘ The Nature of Anti-Immigrant Sentiment in Post-Socialist Russia’, Post-Soviet Affairs , 31/ 2: 115– 35. Google Scholar CrossRef Search ADS Hale H. E. ( 2008) The Foundations of Ethnic Politics: Separatism of States and Nations in Eurasia and the World . Cambridge: Cambridge University Press. Hanson S. E. ( 2010) Post-Imperial Democracies: Ideology and Party Formation in Third Republic France, Weimar Germany, and Post-Soviet Russia . New York: Cambridge University Press. Hechter M. ( 2000) Containing Nationalism . Oxford: Oxford University Press. Jones R. C. ( 1989) ‘ Causes of Salvadoran Migration to the United States’, Geographical Review , 79/ 2: 183. Google Scholar CrossRef Search ADS Kozhevnikova G. ( 2010) Manifestations of Radical Nationalism and Efforts to Counteract it in Russia during the First Half of 2010. Moscow: Sova Centre for Information and Analysis. <http://www.sova-center.ru/en/xenophobia/reports-analyses/2010/07/d19436/> accessed 7 August 2014. Kuhelj A. ( 2011) ‘ Rise of Xenophobic Nationalism in Europe: A Case of Slovenia’, Communist and Post-Communist Studies , 44/ 4: 271– 82. Google Scholar CrossRef Search ADS Lee E. S. ( 1966) ‘ A Theory of Migration’, Demography 3/ 1: 47– 57. Google Scholar CrossRef Search ADS Lohmann S. ( 1993) ‘ A Signaling Model of Informative and Manipulative Political Action’, The American Political Science Review , 87/ 2: 319. Google Scholar CrossRef Search ADS Lundquist J., Massey D. S. ( 2005) ‘ Politics or Economics? International Migration during the Nicaraguan Contra War’, Journal of Latin American Studies , 37/ 1: 29– 53. Google Scholar CrossRef Search ADS PubMed Massey D. S. et al. ( 1993) ‘ Theories of International Migration: A Review and Appraisal’, Population and Development Review , 19/ 3: 431– 66. Google Scholar CrossRef Search ADS Mudde C. ( 2007) Populist Radical Right Parties in Europe . Cambridge: Cambridge University Press. Mudde C. ( 2010) ‘ The Populist Radical Right: A Pathological Normalcy’, West European Politics , 33/ 6: 1167– 86. Google Scholar CrossRef Search ADS Pettinicchio D. ( 2012) ‘ Migration and Ethnic Nationalism: Anglophone Exit and the ‘decolonisation’ of Québec: Migration and Ethnic Nationalism’, Nations and Nationalism , 18/ 4: 719– 43. Google Scholar CrossRef Search ADS Pfaff S. ( 2006) Exit-Voice Dynamics and the Collapse of East Germany: The Crisis of Leninism and the Revolution of 1989 . Durham, NC: Duke University Press. Radnitz S. ( 2006) ‘ Weighing the Political and Economic Motivations for Migration in Post-Soviet Space: The Case of Uzbekistan’, Europe-Asia Studies , 58/ 5: 653– 77. Google Scholar CrossRef Search ADS Ross C. ( 2014) Russian Regional Politics Under Putin and Medvedev . Oxford: Routledge. Rydgren J. ( 2007) ‘ The Sociology of the Radical Right’, Annual Review of Sociology , 33/ 1: 241– 62. Google Scholar CrossRef Search ADS Semyonov M., Raijman R., Gorodzeisky A. ( 2006) ‘ The Rise of Anti-Foreigner Sentiment in European Societies, 1988–2000’, American Sociological Review , 71/ 3: 426– 49. Google Scholar CrossRef Search ADS Stanley W. D. ( 1987) ‘ Economic Migrants or Refugees from Violence? A Time-Series Analysis of Salvadoran Migration to the United States’, Latin American Research Review , 22/1: 132– 54. Stark O., Bloom D. E. ( 1985) ‘ The New Economics of Labor Migration’, The American Economic Review , 75/ 2: 173– 78. State Duma. ( 2005) On the Election of Deputies of the State Duma of the Federal Assembly of the Russian Federation. Tishkov V. A. ( 1997) Ethnicity, Nationalism and Conflict in and After the Soviet Union: The Mind Aflame . Thousand Oaks, CA: Sage. Tolnay S., Beck E. M. ( 1992) ‘ Racial Violence and Black Migration in the American South, 1910 to 1930’, American Sociological Review , 57/ 1: 103–16. Google Scholar CrossRef Search ADS Verkhosky A. ( 2014) Ukraine Upsets the Nationalist Apple-Cart: Xenophobic Nationalism and Efforts to Counteract it in Russia During the First Half of 2014 . Moscow: Sova Centre for Information and Analysis. Verkhosky A., Kozhevnikova G., Sibireva O. ( 2010) Xenophobia, Freedom of Conscience and Anti-Extremism in Russia in 2009 . Moscow: Sova Centre for Information and Analysis. White A. ( 2007) ‘ Internal Migration Trends in Soviet and Post-Soviet European Russia’, Europe-Asia Studies , 59/ 6: 887– 911. Google Scholar CrossRef Search ADS Williams N. E. ( 2013) ‘ How Community Organizations Moderate the Effect of Armed Conflict on Migration in Nepal’, Population Studies , 67/ 3: 353– 69. Google Scholar CrossRef Search ADS PubMed Williams N. E., Ghimire D. J., Axinn W. G., Jennings E. A., Pradhan M. S. ( 2012) ‘ A Micro-Level Event-Centered Approach to Investigating Armed Conflict and Population Responses’, Demography , 49/ 4: 1521– 46. Google Scholar CrossRef Search ADS PubMed Yudina N., Alperovich V. ( 2012) Summer 2012: Back to Lessons Learned . Moscow: Sova Centre for Information and Analysis. Zhirinovsky V. (2011) ‘A Practical Program for the Liberal Democratic Party’, <ldpr.ru/party/Program_LDPR/A_practical_program_for_the_Liberal_Democratic_Party/> accessed 30 July 2014. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: firstname.lastname@example.org
Migration Studies – Oxford University Press
Published: May 17, 2017
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