Introduction

Against the background of the migration processes of the last decades of the twentieth century, at present, migration (cross-border and internal) has reached a historically unique level, forming a special model of globalization, which includes the relatively free movement of people, money and goods. As one of the manifestations of the globalization process, migration in general has a positive impact on the socio-economic and political development of states, contributes to the integration of the international community, cultural rapprochement of peoples, and the development of productive forces (Ivanus et al., 2020; Papastergiadis, 2018). At the same time, if migration processes are uncontrollable, the level of security of states is sharply reduced, in addition, uncontrolled migration does not allow protecting the rights of migrants themselves (Castles & Davidson, 2020). In the context of public administration and the development of effective migration policy, the study of the relationship between factors such as unemployment, poverty risk, education and crime on the basis of statistical data is extremely important. Since migration, on the one hand, is an easy way to reproduce the missing labor resources, and on the other hand, entails adaptation problems, each state should develop and implement a competent migration policy to extract the maximum public benefit from the processes of displacement.

The article is devoted to the problem of determining the current state and structure of migration flows and factors predicting their occurrence in two very large and closely related migration regions of the world: the EU and the Russian Federation. Without claiming to be exhaustive, this work offers a view on the possibility of identifying the most important factors affecting migration through correlation analysis of statistical data from these regions. Taking into account the existing theories of migration and their weaknesses, this study is devoted to finding new ways in the study of migration phenomena.

Globalization prompts millions of economic migrants from developing countries of origin to seek work in economically better recipient countries, including through educational migration, despite tightened border controls. For example, the Italian island of Lampedusa, acts as a key transit site for migrants crossing the Mediterranean Sea on their way to Europe. Indignant and dissatisfied with this situation, the local population associates this with the weakness of migration state policy (Franceschelli, 2019).

Recently, scientists have been actively exploring issues of inequality in the legal status of migrants, the neoliberal logic of migration management and classification policies (Bauder, 2020). As part of migration studies, an analysis of inequality between migrants and host groups is also regularly conducted. Migration policy in itself is a source of inequality between migrants, but understanding other sources of inequality is also important in informing state and non-state institutions about the results of policies regarding migrants (Friesen, 2020).

Inequality manifests itself in various and localized forms affecting access to income, wealth, opportunities, welfare, social and political capital. Diversification of modern migration policies in English-speaking countries accepting labor creates inequality between groups of migrants, which is manifested in the complex relationships of race, class, gender, legal status, sexuality, age and history of settlement (Collins, 2020; Collins et al., 2020). There is a direct correlation between migration policy and inequality by focusing on labor migration and the ways in which it is possible to assess the professional and competence value of migrants. This conclusion is also confirmed by Biavaschi et al. (2020), which believes that global migration is highly dependent on skill levels, with workers with higher education four times more likely to migrate than workers with lower education.

Observing a sharp increase in the flow of migrants from the 2000s, it can be assumed that emergent access to the Internet and mobile communications contributed to this. According to Kotyrlo (2020), in the medium and long term, the growth rate of migration is lower in countries with higher ICT growth rates, this negative relationship can also be explained by the development of immigration policies to solve the problems associated with the increase in the amount of information required for migration, and also to reduce the risk of uncontrolled migration.

State border crossing and cyclic migration policies shape and often impede the implementation of development plans, welfare strategies for ways and means to meet care needs within the family life cycle (Bryceson, 2019). An important direction of modern researchers is empirical research of the results of integration in the labor market for family migrants (Eggebo & Brekke, 2019).

Of course, risks can arise during any movement of a person. In the study of risks and uncertainties associated with migration processes, six theoretical approaches can be applied: 1) risks in terms of the quality of human capital formation, 2) risk attitude and tolerance, 3) new economic aspects of migration, 4) cultural risks, 6) social risks and statehood (Bijak & Czaika, 2020; Zinn, 2016). The issues of the influence of the level of education on the incomes of the economically active population and the prospects for educational migration in the countries of the European Union and in the Russian Federation were considered in our previous works (Kuznetsova, 2019), factors and reasons for the successful adaptation of migrants to the labor market in the European Union, as well as the impact of migration on the labor market (Kuznetsova et al., 2019). We completely agree with the opinion of scientists that existed theories are not quite useful for specialists in the field of migration, because of migration theories are fragmented, belong to different spatial scales, they are difficult to practically implement and cover a number of distant disciplines (Wickramasinghe & Wimalaratana, 2016; Willekens et al., 2016).

Within the framework of this study, there is raised the question whether there is a direct dependence between the selected indicators related to issues of social well-being, and whether the factors confirming such a relationship can be used as predictors (prognostic parameters, predictors) of migration processes. Specifically, it comes about such factors as the relationship between the risk of poverty and the indicator of public spending on social protection, the unemployment rate among people with a level of education 0–2 (according to the scale of the International Standard Classification of Education) and the indicator “risk of poverty”, the indicator “the number of people who received refugee status in the host country per 100 thousand people” and the indicator “number of thefts”. This study focuses on filling a gap in the research literature on migration issues, devoted to a quantitative assessment of real migration situations in certain regions and countries. A related task is the search for methods for assessing factors that are predictors of migration phenomena outside the existing migration theories, which are recognized as insufficiently applicable in practice.

Literature Overview

The first scientist who developed a multifaceted and integrated approach to the study of migration factors was Ernst Georg Ravenstein, who expounded his views in the fundamental work "The Laws of Migration" (1876) (Greenwood, 2019; Popova & Sheryazdanova, 2018). As a result, eleven Laws of Migration have been identified (Greenwood, 2019; Rees & Lomax, 2019). A large body of scientific literature by both foreign and domestic scientists is devoted to the issues of the formation and implementation of migration policy. In foreign literature, theoretical and methodological aspects of the study of migration are also studied in the concepts of “attraction – pushing” by Lee (1996), the mobile transition of Zelinsky (1971) and are presented in modern approaches of neoclassical theory migration (Castles & Miller, 1993; Castles et al., 2005), the new economic theory (Massey, 2002) and other concepts.

In foreign historiography, the problems of modern migration policy in Europe and the USA are widely considered. Modern scientists, concerned about the migration crisis of 2014–2015, study migration processes mainly fragmentary in relation to the classical theory of migration Ravenshtein. For example, that the main causes of migration are precisely economic reasons, they state in their works Allen et al. (2018), Arif (2020), Gibson et al. (2020), Kone et al. (2020), and Murard (2020).

Some scholars believe that economic, political and, to a lesser extent, social institutions are key factors in attracting migrants, and economic and social factors are push factors (Arif, 2020). According to others, refugees have the greatest desire to get paid work than locals (by 6%) than those who migrated because of a desire to find a job, are two percentage points less likely to find a job than local residents (Kone et al., 2020).

Currently, there are many studies examining the relationship between migration processes, social well-being, the state of the economy, and other socio-economic indicators. This particulary applies to issues such as a interconnection between immigration, unemployment, wages and gross domestic product per capita in host countries (AboElsoud et al., 2020; Bijak & Czaika, 2020; Zinn, 2016). The consequences of migratory movements are also that leisure spending reduces the net welfare gain from migration by one quarter relative to what consumption growth implies (Murard, 2020).

Modern Russian studies related to the transformation of migration processes are devoted to such issues as the analysis of migration processes in assessing the economic security of Russia (Dianov & Dolbilov, 2021), factors influencing the intensity of migration processes in Russia (Yukish, 2020), consideration of the impact of migration on the crime situation (Muzenitov & Potekhina, 2018), migration intentions of graduates of schools of small and medium-sized cities in Russia (Karachurina, & Florinskaya, 2019), correlation analysis of the dependence of the level of emigration from Russia on the average price of primary housing (Mindiyarova, 2018). In particular, on the basis of statistical data and multivariate regression analysis, there was made an attempt to trace the relationship between the growing influx of migrants to the Russian Federation and such factors as low unemployment, an increase in the number of jobs, an attractive tax system, earning opportunities, improved living conditions in Russia, and other factors that are the reasons for migration as a result lead to the constant arrival of migrants in the regions of the Russian Federation (Dianov & Dolbilov, 2021). It is noted that the intensity of migration processes is regionally associated with the development of oil and gas, energy industrial spheres (Yukish, 2020).

Nevertheless, existing studies describing correlation models rarely consider such factors as the risk of poverty, especially in combination with data on spending on social protection, in the context of migration processes. Few relevant studies focus on issues such as examining the German experience in reducing the risk of poverty (Ibragimova, 2017), study of European practices for measuring poverty and social exclusion based on the AROPE index (Korchagina et al., 2019), comparative analysis of the concepts of absolute and relative poverty in relation to the rural and urban population of Russia (Shabanov, 2019).

Foreign scientific sources in this regard, as a rule, are more extensive and informative, and the correlation of various social factors and indicators in the context of migration processes has been studied more widely. Despite the fact that the study of the correlation of factors such as those considered in this work has not been carried out, nevertheless, there are currently many studies that consider the relationship of related factors in the relationship between social well-being, adaptation policy, economy and migration processes. In particular, this applies to studies confirming that the income level of migrants directly depends on their knowledge of the language of the host country (Schuss, 2018), lack of language barrier contributes to the optimal work of migrants (Lundborg & Skedinger, 2016). Migration from a poor country to a rich one affects economic beliefs, preferences and the effectiveness of household decisions (Gibson et al., 2020). It is also important that migration improves the welfare of the country of destination in the presence of flexible wages, but it has a detrimental effect in the presence of unemployment (Gabszewicz & Zanaj, 2019). The migration of people from villages to cities is accompanied by patterns of economic growth of GDP per capita, an increase in life expectancy and an increasing share of the population living in cities (Grafeneder-Weissteiner et al., 2020).

Materials and Methods

The information base of the study was secondary official data. For the European Union, the source of information was data from the official website of Eurostat (2018). For Russia, the research information base was made up of official data from the Federal State Statistics Service of the Russian Federation for the period from 2009 to 2018 years. For the purposes of the study, data on the actual number of emigrants and immigrants in the EU and the Russian Federation are used; on the structure of immigration resettlement in a number of European Union countries, which were identified in the course of the study as the most attractive for immigration and similar data for the Russian Federation; and a prediction of the expected structure of migration flows for the EU according to Eurostat data. The work used the methods of statistical research, methods of economic analysis, tabular, graphical methods. The review of relevant studies was carried out using the method of thematic analysis, keyword analysis.

The time frame of the study covers the years 2009–2018, since in both cases the relevant statistical information from Eurostat and Rosstat in the field of migration is limited to this period. This period is of research interest as it covers data since the start of the World Economic Crisis, includes data on European countries taking into account the last major EU enlargement in 2006–2007, and covers a three-year period since the beginning of the migration crisis in 2015. This study is based on the processing and analysis of summary statistical data of the Federal State Statistics Service of the Russian Federation (Rosstat, 2021) and the Statistical Service of the European Union (Eurostat, 2021a).

The used statistical data was processed in order to identify factors that serve as possible predictors of migration processes. To determine the factors, the Pearson correlation coefficient method was used for three separate correlation models. To clarify the results of applying each of the models, two values were determined: R—Pearson’s correlation coefficient, and R2—the proportion of variability in Y, which is due to a linear relationship with X1. The second indicator allows to partially eliminate the weakness of Pearson’s correlation in the presence of abnormal values or pronounced subgroups in the observed sequence of values.

In order to determine the first correlation model were set the relationship between factors that can affect the “Y” variable – an indicator of social protection expenditures per person in 28 countries of the European Union, we selected the poverty risk indicator (in percent) – X1. The presence or absence of correlations was established between the sets of statistical data on Y and X1 variables for each of the countries.

In the second correlation model, the poverty risk indicator (in percent) was chosen as the dependent variable “Y”; X1 – unemployment rate among people with education level of 0–2 (in percent).Footnote 1

In the third correlation model, the indicator of the number of persons who received refugee status in the host country per 100 thousand people was chosen as the dependent variable “Y”; X1 is the number of thefts (in percent).

Results

EU Statistics Analysis Results

In the last decade, there has been a new surge in migration transformations, covering the countries of the European Union and the Russian Federation (Fig. 1).

Fig. 1
figure 1

Emigration and immigration in the countries of the European Union and the Russian Federation (thousand people). Source: developed by authors based on the Official website of the statistics of the European Union (Eurostat, 2018)

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European Union countries are extremely attractive to migrants from many countries and continents. According to official European statistics, over the period from 2009 to 2018, over 38 million migrants arrived in EU countries, about 28 million left (European Commission, 2020; Eurostat, 2021b). The official population of the EU countries 28 for the period from 2009 to 2018 was replenished by more than 10 million people due to migration growth. As can be seen from the data in Fig. 1, the largest surge in the influx of migrants was observed in 2014–2018, while from year to year the number of migrants arriving in the European Union grew by 13–23%.

According to the official data of the Federal State Statistics Service of Russia, 4.7 million people arrived in the Russian Federation, and 2.2 million migrants left during the same period. The Syrian crisis of 2014 did not significantly affect migration growth in the Russian Federation. This was facilitated by strict migration legislation, adopted and successfully implemented in Russia.

It is important to note that among the countries of the European Union the most attractive for migrants are only 11 countries: Germany, Great Britain, France, Spain, Romania, Poland, Italy, the Netherlands, Sweden, Greece and Belgium. The proportion of the influx of migrants to these countries throughout the studied dynamics was 82–86%.

The largest share of the influx of migrants since 2014 belonged to Germany (especially in 2014 – 23.4%, in 2015 – 33.1%). A significant proportion of the influx of migrants was noted in the UK in the range of 14.6–18.3%. If during the analyzed period the influx of migrants in the UK, France, Italy, Poland, Romania, Sweden decreased, in Germany, Spain, the Netherlands, Belgium, on the contrary, its growth was significant.

Consider the structure of migration inflows in five countries of the European Union, where over 50% of migrants migrate (Fig. 2).

Fig. 2
figure 2

The structure of immigration resettlement to the Germany, Spain, UK, France, Italy (in percent). Source: developed by authors based on the Official website of the statistics of the European Union (Eurostat, 2018)

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Among the other countries considered above, five are particularly popular among migrants: Germany, France, Spain, UK, Italy. In 2009, 11.2% migrated to Germany, 12.7% to Spain, 18.3% to the UK, 9.6% to France, and 14.3% to Italy. In 2018, 19.7% migrated to Germany, 14.2% to Spain, 13.3% to the UK, 8.6% to France, and 7.3% to Italy. Considering the other five countries, also attractive for migrants, we should highlight Poland (4.7%), the Netherlands (4.3%), Romania (3.8%), Belgium (3%), Sweden (2.9%). In 2018, the percentage of migrant inflows to Germany was 19.7%, to Spain – 14%, to the UK – 13.3%, to France – 8.3%, to Italy – 7.3%.

Between 2009 and 2018, Germany (4.2 million people), the United Kingdom (2.5 million people), Italy (2.3), Sweden (0.73), France, Austria and the Netherlands experienced the largest migration growth balance (0.5), Belgium (0.39), Hungary (0.16). Romania (-0.5), Poland (0.43), Lithuania (0.28), Greece (0.2), Latvia (0.16), Croatia and Portugal (0.11 each) experienced serious migration outflows, Bulgaria (26 thousand people), Ireland (4.5 thousand people). Thus, nine EU countries are not attractive in terms of quality of life and not only for migrants, but also for the local population.

Eurostat has developed a forecast of the level of migration in the EU countries (28) for the coming 80 years, until 2100 (Fig. 3).

Fig. 3
figure 3

Forecast of net migration in the countries of the European Union until 2100 (people). Source: developed by authors based on the Official website of the statistics of the European Union (Eurostat, 2018)

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According to the current Eurostat forecast, the number of migration flows will be halved by 2100 compared to 2020, by 57% compared to 2030.

Russia Statistics Analysis Results

The main wave of migration outflow of qualified specialists from Russia occurred in 1990–1995. Then there was an influx of migrants from the countries of the former Soviet Union. In the dynamics from 2009 to 2018, transformational migration processes in Russia took place in waves, characterized by the influx of low-skilled and the outflow of highly skilled labor. Consider the situation with the influx and outflow of migrants in the Russian Federation in Fig. 4.

Fig. 4
figure 4

The structure of the inflow and outflow of migrants to the Russian Federation from non-CIS countries and from the countries of the Commonwealth of Independent States (in percent). Source: developed by authors based on the Official website of Rosstat (2021)

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From non-CIS countries, including from European Union countries, to the Russian Federation, the proportion of migrant inflows during the studied dynamics was in the range of 6.6–13%, and from countries of the former Soviet republics the influx of migrants was more significant – within 87–93%. At the same time, 62.6–86.6% of people migrated from the Russian Federation to the countries of the former Soviet republics, and from 37.4% in 2009 to 13.4% in 2018 migrated to foreign countries. It is quite obvious that a high influx of people from the countries of the former Soviet republics was accompanied by a periodic outflow (there was a pendulum labor migration). Mostly people arrived in search of a better paid job than in their home countries. Plus, the migration legislation of the Russian Federation since 2011 has been focused on setting and maintaining strict quotas for the influx of labor migrants. Since 2011, approximately 1.7 million permits per year have been issued. Thus, the restriction of migration inflows to the Russian Federation occurs primarily by administrative means, by restricting the issuance of quotas, patents and some other measures (Fig. 5).

Fig. 5
figure 5

The number of the inflow and outflow of migrants to the Russian Federation from non-CIS countries and from the countries of the Commonwealth of Independent States (the number of people). Source: developed by authors based on the Official website of Rosstat (2021)

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As we have already noted, from 2009 to 2018, 4.7 million migrants arrived in Russia, and about 2.2 million left. Mostly citizens from the countries of the Commonwealth of Independent States arrive here. From the countries of the Commonwealth of Independent States during this period 4.1 million people arrived, and 1.8 million people left. The balance of migration flows is 2.3 million people over the past ten years. As far as foreign countries are concerned, over the past ten years 381.9 thousand people have arrived in Russia, and 503.8 thousand people have left Russia for other countries, the balance of migration outflows has made 121.9 thousand people (Fig. 6).

Fig. 6
figure 6

The structure of the inflow and outflow of migrants to the Russian Federation from non-CIS countries and from the countries of the Commonwealth of Independent States (in percent). Source: developed by authors based on the Official website of Rosstat (2021)

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The largest share of migrants came to the Russian Federation from Ukraine (17–27%), Kazakhstan (14–15%), Tajikistan (10–13%), Uzbekistan (11–16%), Armenia (913%), Kyrgyzstan (8–9%), Moldova (6.3%), Azerbaijan (5–8.7%), Belarus (3%), Turkmenistan (2%). These processes are primarily due to the low level and quality of life in donor countries.

The structure of the migration flow to the Russian Federation from non-CIS countries is as follows: from China in 2018, 13% of migrants arrived in the country, from Georgia – 11.6%, from India – 9.2%, from Vietnam – 7.3%, from Germany – 5.9%, from Abkhazia – 3.6%, from Korea – 3.3%, from Turkey – 3.2%, from Morocco – 3.1%, from the Syrian Arab Republic – 2.3%, from Latvia – 2.3%, from Afghanistan – 2.2%, from Egypt – 2.1%, from the USA – 1.8%, from Estonia – 1.6%.

Thus, the balance of migration inflow to Russia from the countries of the former Soviet republics is extremely positive, and to the countries of the far abroad – negative. As a rule, low-skilled labor arrives in the country, and highly qualified specialists drop out. The natural population decline has long been smoothed out by migration growth. However, in recent years, tightening of migration legislation has led to a curbing of the influx of migrants.

Correlation Models Results

The results of evaluating correlation models for detecting factors affecting migration processes showed the following. For the first correlation model as a result of the influence of factor X1 on Y, it was found that the closeness of the relationship between the factors is R = -0.48, R2 = 0.23,Footnote 2 which indicates that there is an average closeness of the relationship, but it is the inverse between the poverty risk indicator and an indicator of government spending on social protection.

For the second correlation model as a result of a regression analysis of the influence of factor X1 on Y, we found that the connection tightness is R = 0.27, R2 = 0.07, which indicates that there is a direct low correlation between the indicator "unemployment rate among people with educational level 0–2" and indicator “poverty risk”. This conclusion is also very logical, the higher the unemployment rate among people with a level of education 0–2, the higher the risk of poverty. The relatively low tightness of the relationship between the studied factors indicates the presence of other hidden factors.

For the third correlation model as a result of a regression analysis of the influence of factor X1 on Y, we found that the closeness of the connection was R = 0.22, R2 = 0.03, which indicates that there is an average relationship between the indicator “the number of persons who received refugee status in the host country per 100 thousand people” and indicator “the number of thefts”. This conclusion is also very logical, the greater the number of persons who received refugee status in the host country per 100 thousand people, the higher the likelihood of theft. The relatively low tightness of the connection between the studied factors indicates the presence of other hidden factors, the search for which will be devoted to other studies.

Discussion

The formation of the structure of migration flows is largely associated with the domestic and international policy of the target country of migration (Gabszewicz & Zanaj, 2019). The lack of research on this topic does not allow us to draw strict conclusions, but there is evidence that allows us to hypothesize that the international policy of a particular country has a greater impact on attracting migration flows than domestic policy. Migrants are not inclined to take into account the internal policies of the countries in which they arrive, and often get acquainted with this policy only after arriving at the place, and the state policy does not always satisfy them (Biavaschi et al., 2020). The results of the study of three correlation models in the case of our study also partially support this hypothesis. For example, an increase in theft after obtaining official refugee status, as well as a significant migration of people with a low educational status (the second correlation model) with no hope of getting a job in the target country of migration. This statement is consistent with existing research (Souzas et al., 2020; Vargová, 2018). The example of Greece and Turkey during the manifestation of the refugee crisis is especially indicative (Souzas et al., 2020). In general, migration can be argued that migration is driven by rational factors and sober economic calculation to an insignificant extent and more dependent on expectations and irrational hopes and unverified information (Gibson et al., 2020; Kone et al., 2020).

Within the current framework the study faced a lack of relevant works that use a similar methodology to assess the relationship between the risk of poverty in the EU countries and the indicator of public spending on social protection. However, it should be noted that according to information for 2019, Eurostat data show that among people living in the EU-27 countries, 20% of citizens, 26% of citizens of other EU member states and 45% of citizens of countries outside the EU in the EU living in the EU faced the risk of poverty or social exclusion. It is argued that the risk of monetary poverty in the EU-27 countries in 2019 was about twice as high for foreign citizens (32%) as for nationals (15%), and was especially concentrated among citizens of non-EU countries (38%) (Eurostat, 2021c). Nevertheless, based on statistical data on people aged 20–64 (including EU citizens, foreigners for the period 2010–2019), it can be noted that the dynamics associated with exposure to the risk of poverty or social exclusion is negative (Eurostat, 2021c).

International politics is formed and adjusted depending on the socio-economic, political situation in each country, or in a number of countries. For example, in Germany, in France, in the USA and in Switzerland there is a policy of assimilation and integration (Franceschelli, 2019). In Canada, Australia, Finland, the emphasis is on recruiting migration policies with the involvement of highly qualified specialists (Balaz & Williams, 2018; Bijak & Czaika, 2020). In the Russian Federation, the lack of labor resources until 2016–2017 compensated for the influx of people from the countries of the former Soviet Union: Kazakhstan, Uzbekistan, Tajikistan (Kuznetsova, 2019). The process of successful integration depends on the quality of migration policy, strict compliance with the Laws, knowledge of the language of the receiving parties and the level of qualification of arriving migrants (Theoharides, 2020). However, unemployment, the inability to earn money ultimately lead to increased crime and deviant behavior of migrants. This leads to social tension, a feeling of rejection and antipathy of the local population in relation to arriving migrants and other socially significant problems. There are problems that migrants could solve in their countries of birth to build their own better lives, at the same time there are problems (for example, climate change, biological wars (Ebola, Coronavirus, etc.)) that can be solved without a science-based approach impossible.

The study of the structure of migration flows, and especially their changes over relatively long periods of time, can provide reference data for determining evidence-based predictors of migration changes (Allen et al., 2018). Thus, our study traces a correlation between significant government social protection expenditures and a reduction in the risk of poverty. It can be argued that there is a connection between this factor and the formation of a pool of EU countries that are most attractive for migration flows. This pool includes countries with increased social security that pursue the most flexible policy of multiculturalism and the involvement of migrants in the life of the local community (Collins et al., 2020; Kuznetsova et al., 2019). Similar conclusions require further research in order for them to become strictly evidence-based, but the significance of the factors identified in our work has something in common with the results obtained by other researchers (Arif, 2020; Bauder, 2020). The increase in the number of people with refugee status and the increase in theft obviously have additional operating factors associated with specific policies in different countries. This explains the relatively weak correlation level of 0.22 against the background of an equally low share of variability. The data of a number of studies and conclusions from the theories of migration used today suggest that the level of social support of migrants from the state, the possibilities of medical support, the availability of education, the degree of cultural integration and proficiency in the language of the host country (AboElsoud et al., 2020; Collins et al., 2020; Lundborg & Skedinger, 2016).

Conclusion

The research is considered to the problem of determining the current state and structure of migration flows and factors predicting their occurrence in related migration regions of the EU and the Russian Federation. The research partially fills the gap in the research literature, devoted to an assessment of migration situations in certain regions and countries. For the purposes of the study, data on the actual number of emigrants and immigrants in the EU and the RF are used; on the structure of immigration resettlement in a number of European Union countries, which were identified in the course of the study as the most attractive for immigration and similar data for the Russian Federation. Statistical data were processed in order to identify factors that serve as possible predictors of migration processes. The Pearson correlation coefficient method was used for three separate correlation models with two values resulted: Pearson’s correlation coefficient (R), and the proportion of variability (R2) to partially eliminate the weakness of Pearson’s correlation in the presence of abnormal values or pronounced subgroups in the observed sequence of values. Correlation study demonstrated mean inverse correlation between the poverty risk indicator and an indicator of government spending on social protection; low correlation between the indicator "unemployment rate among people with educational level 0–2" and indicator “poverty risk” and average correlation between the indicator “the number of persons who received refugee status in the host country per 100 thousand people” and indicator “the number of thefts”. The relatively low tightness between the studied factors indicates the presence of other hidden factors, the search for which will be devoted to further studies.

Based on the hypothesis of the study about the presence of a dependence between the indicators considered in the work (the risk of poverty and government spending on social protection, etc.), in the context of the countries and interstate entities considered, it should be noted that, despite the apparent obviousness, the study does not confirm the existence of such a dependence in all three cases simultaneously. At the same time, there exists a correlation between significant public spending on social protection and a reduction in the risk of poverty. The study gives grounds to say that the factors considered can be used as prognostic parameters of migration processes. So, in particular, such a parameter as an increase in the number of people with refugee status and an increase in theft, obviously, has additional operational factors associated with specific policies in different countries.

Taking into account the existing limitations, the work did not consider the issue of the influence of corruptiogenic factors on the correlation between the factors under consideration. In particular, the influence of corruption-generating factors on the correlation between public spending and reducing the risk of poverty is of research interest. This issue is subject for further study.