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BEYOND BORDERS
Azamat Valei, Suleiman O. Mamman
The Impact of Socioeconomic Factors on BRICS Migrants in the Russian Federation
VALEI, Azamat —
researcher at the Graduate School of Economics and Management of the Ural Federal University. Address: 19, Mira str., Ekaterinburg, 620002, Russia.
Email: azamat.valey@ urfu.ru
Abstract
Within the last decade, the Russian Federation has witnessed an unprecedented growth in migrants' inflow placing it amongst the top destinations for transnational migrants. This trend includes members of the BRICS (Brazil, Russia, India, China, and South Africa) economic bloc whose obvious increase started in the post-2010 era. Thus, it is unclear whether the economic cooperation has facilitated migration flow or other socioeconomic factors which could be explained by the Pull-Push theory are responsible. The study carried out an empirical assessment of the socioeconomic factors that determine BRICS migrants to Russia on a macro scale using data from the member states that include Brazil, India, China, and South Africa. The heterogeneous panel model was adopted as the analytical method. The result reveals a negative effect of Russian wages on immigrants' inflow while GDP per capita had a positive effect. For the push factors, unemployment had a positive and significant effect in the short run but not so in the long run. Also, the population had a negative and insignificant effect in the short run but a positive and significant effect in the long run. The income differentials were also found to be positive and significant in the model. Lastly, there was evidence of policy effect on the migrants' movement; however, concerning BRICS countries, there was rather a negative effect of migration policy on the immigrants mobility.
Keywords: migration; heterogeneous panel; BRICS; pull-push theory; income differentials; migration policy; economic development.
Introduction
Over the years, the concept of migration has been one of the subjects of discussion, particularly over its implications in both the receiving and the country of origin. For instance, while the country of origin suffers from the loss of talented people, which diminishes the human capital component crucial for any country's economic progress, it stands to benefit from remittances sent back and a less competitive labor market. On the other hand, the recipient country also suffers from imbalances in its balances as there is an outflow of funds in the form of remittances from the host countries. In some instances, recipient countries also suffer from social and demographic issues such as cultural and religious conflicts and may even experience population growth. Furthermore, it is assumed that wealth and resources of host workers are redistributed with the influx of migrant workforce, particularly impacting unskilled labour in the informal sector. However, such countries stand to enjoy cheap and available labour supply which could also help restore the declining labour supply.
MAMMAN, Suleiman Onimisi — PhD fellow and research engineer at the Graduate School of Economics and Management of the Ural Federal University. Address: 19, Mira str., Ekaterinburg, 620002, Russia.
Email: onimisism@gmail. com
Another topical issue discussed in the context of migration is what motivates people to migrate. Some studies have tried to categorize such motivations into macro, meso and macro factors (see [Castelli 2018]). Factors such as political, economic, social, and demographic are considered as macro factors that instigate people to migrate. Migration networks and communication technology fall under the meso category, while factors such as age, education, religion, among others, are considered as micro factors. Another issue of contention is which of these factors strongly and significantly determines migrant movement. Whereas studies like [De Haas 2011b; Vakulenko 2016] have stressed that economic and political factors generally seem to prevail over demographic and environmental factors in explaining international migration, studies like [Hugo 2013] attribute the movement of people to technological advancement and labour market liberalisation which increases the rate of circular migration. [Reuveny 2007; Simonelli 2008; Susan 2013] have argued that environmentally induced factors can serve as a driving force for migration. However, [Carling, Collins 2018], while recognizing that environmental change could shape the migration pattern, stressed that it could be an overestimation to assume climate change as a major driver of migration given the intricate relations of socioeconomic diversity. But [Lille0r, Van den Broeck 2011] has indicated that climate change could affect migration through economic channels. That is, changes and variation in climate may widen income differentials and increase its variation among countries. This could spur an outflow of migrants from regions more affected by climate change to those less affected if there is an indication of a negative effect on economic performance.
As a consequence of the inflow of migrants, most countries have imposed strict border laws and policies. But the Economic Union such as the European Union has identified the widening imbalances between the developed and developing countries in the region. To address the gap and foster prospective economic growth in the region, a labour migration policy was introduced to allow unrestricted movement of migrants from the developing countries to developed ones within the region. However, not all economic unions have the same migration policy. Given that the goal of economic cooperation is to promote economic growth and development among member states, there is no specific policy on the movement of migrants among the BRICS member countries.
Notwithstanding, countries like Russia have made significant strides in terms of receiving migrants. According to data from [MPI 2019], Russia was ranked as the 4th most popular destination among the top 25 countries for international migrants, following the USA, Germany, and Saudi Arabia. The approximate number of immigrants in Russia is about 12 million which form over 8.2 percent of the total population. In the report, India was ranked 13 th, South Africa 15th and China (Hong Kong) 22nd, with 5.2 million migrants (less than 1 percent of the total population), 4.2 million (about 7 percent of the total population), and 2.9 million (40 percent) respectively. More so, prior to 2014, Russia is assumed to have a more open labor migration policy which could have resulted in the influx of migrants into the region (see Fig. 1). Thus, this could also indicate the improvement in the ranks of being amongst the top countries host migrants in Europe and top in the CIS region. As outlined by [Ivakhnyuk 2013], Russian migration policy has
transitioned from an open policy based on free-market principles, especially in the early 1990s, to a more restrictive policy in the early 2000s and beyond. However, these policies are a bit more relaxed for CIS migrants.
8 Net migration rate (per 1,000 population)
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Source: Author's computation using data from the United Nations—World Population Prospects.
Fig. 1. Net Migration in Russia
Russia receives a larger number of migrants amongst the BRICS states. Despite this enormous figure, a cursory look at data from the Russian Federal State Statistics Service (Rosstat) indicates that the number of migrants from other BRICS countries remains low given the established economic cooperation amongst them (see Fig. 2).
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□ India
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1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Source: Federal State Statistic Service, n. d.
Fig. 2. Migrants' Inflows to Russia Federation from the BRICS States
However, the numbers have improved as compared to prior 2006 when BRICS bloc was founded. Concerns have been raised that despite acknowledging the impact of migration on the social and economic development and demographic situation in the BRICS countries as outlined in the Sochi 2015 declaration, the migration flows within the BRICS member states are restricted in scope and scale. Nonetheless, the motivation for the selection comes from the fact that the migration policy of Russia towards BRICS countries is based on the principles of mutual benefit, cooperation, and respect. Russia views BRICS as a strategic partnership that can
enhance its economic, political, and cultural ties with the emerging economies of the world. Russia also sees BRICS as a platform to promote a multipolar world order that respects the sovereignty and diversity of nations. According to the Russian Ministry of Foreign Affairs, Russia's migration policy towards BRICS countries aims to facilitate the legal and orderly movement of people, especially in the areas of labor, education, tourism, and humanitarian assistance [Srinivas 2022]. Thus, the broad questions are now raised: Firstly, are BRICS migrants to Russia impelled by pull or push socioeconomic factors in their choice of migration? Secondly, what is the effect of migration policy on the BRICS migrants amid the absence of migration movement agreement for BRICS countries?
Literature Review
Brief Overview of BRICS Objectives
At the first ministerial meeting held in September 2006, the representative of Russia, Brazil, India, and China expressed their interest in expanding their economic horizon and multilateral cooperation. The term BRIC was coined by Jim O'Neill from Goldman Sachs in 2001 to refer to the large, fast-growing market economies in emerging or newly industrialized countries. The creation of the BRIC was initiated by Russia. Several communiques were issued addressing current economic and developmental issues, along with suggested coping strategies towards the impending economic and financial crisis. In 2009, the first BRIC summit was held in Yekaterinburg where the goals of the economic bloc were set forth. These goals included promotion of dialogue and cooperation among member countries in an incremental, pragmatic, proactive, open, and transparent way. They also included building a harmonious world of lasting peace and common prosperity.
The economic bloc has outlined key priority areas which include Trade & Investment; Manufacturing & Mineral processing; Energy; Agricultural cooperation; Science, Technology & Innovation, Financial cooperation; Connectivity and ICT cooperation1. The economic bloc has grown tremendously with conspicuous relevance and influence in the global economy attributed to their abundant natural resources and extensive population. According to a BRICS strategic brief in 2015, the economic bloc accounted for 30 percent of global land, 43 percent of the global population and 21 percent of the world's Gross Domestic Product (GDP), 17.3 percent of global merchandise trade, 12.7 percent of global commercial services and 45 percent of world's agriculture production2. In 2013, BRICS accounted for about 27 percent of the global GDP (in terms of the purchasing power parity of their national currencies). The total BRICS population is 2.88 billion (42 percent of the total global population), and the five countries cover 26 percent of the planet's land.
The economic bloc countries are influential members of leading international organisations and agencies, including the UN, the G20, the Non-Aligned Movement and the Group of 77. They are also members of various regional associations. For instance, the Russian Federation is a member of the Commonwealth of Independent States, the Collective Security Treaty Organisation, and the Eurasian Economic Union. Russia and China are members of the Shanghai Cooperation Organisation and the Asia Pacific Economic Cooperation. Brazil is a member of the Union of South American Nations, MERCOSUR and the Community of Latin American and the Caribbean States. The Republic of South Africa is a member of the African Union and the Southern African Development Community. India is a member of the South Asian Association for Regional Cooperation. Thus, the relations between BRICS partners are built on the UN Charter, universally recognised principles and norms of international law and the principles established by member countries at their 2011 Summit: openness, pragmatism, solidarity, non-alignment, and neutrality towards third parties. BRICS work is based on action plans approved during annual summits starting from 2010.
1 See http://en.brics2015.ru/load/381830 for details.
2 See http://en.brics2015.ru/load/381830
However, despite BRICS evolution, a significant gap remains in the socio-economic structures and the dynamics and goals of the economic development of its member states. This is because each member state has their different priority plans which may not be harmonious and further obscure the formulation of comprehensible mutual policies. One of the areas identified is facilitating the movement of people within the region. That is, migration represents one of the areas in which BRICS countries can have mutually complementary structural characteristics and goals of development, allowing for the formulation of coherent shared policies. In particular, it is desirable to establish a framework to regulate various types of legal labour migration (highly skilled, educational, and low skilled) within BRICS, to prevent illegal migration as well as to develop unified policies regarding migration exchanges with third countries.
Theoretical Framework
There is a consensus that a singular economic theory cannot fully explain the complexities of international migration. For instance, the prevalent neoclassical theory has faced criticism for assuming that people migrate based on rational decision-making and perfect knowledge of host labour markets. However, studies such as [De Haas 2011a] have stressed that rationality of human actors is not sufficient enough to push one to migrate. There are also key determinants such as social, economic, political and environmental factors amongst other factors [De Haas 2011a]. In line with this argument, the current study is anchored in the neoclassical theory and Pull-Push theory as the conceptual framework guided by the presumption that it is rather inadequate to explain migration with reliance on one theory. Furthermore, despite the argument against the use of simplistic theories to explain migration, it is also important not to dismiss the effectiveness of such models.
In a recent scholarly contribution, [De Haas 2021] puts forth the aspirations-capabilities theory of migration. The theory, in its essence, offers an insightful comprehension of human mobility as an inherent component of more extensive mechanisms of societal transformation and progress. The theory postulates that migration is influenced by individual aspirations and abilities to move, taking into account the perceived opportunities available in different geographical contexts. The theory utilises [Berlin 1969] concepts of positive and negative liberty in order to theorize the ways in which macro-structural transformations influence individual desires and abilities to migrate. Additionally, it seeks to establish novel categories of human mobility and migration that are derived from theoretical frameworks. Positive liberty, in essence, pertains to the freedom to exercise one's will, encompassing the possession of necessary resources, abilities, and prospects to facilitate such autonomy. On the contrary, negative liberty pertains to the absence of external interference or coercion, thereby encompassing the possession of rights, protections, and assurances to exercise one's freedom. According to the aspiration-capabilities theory, it is posited that the intricate and non-linear effects of social transformation and development processes can have an impact on individuals' positive and negative liberty [De Haas 2021]. Consequently, these influences can shape their aspirations and capabilities, ultimately influencing their decision to migrate or remain in a particular location. The concept further differentiates between the instrumental aspect, which refers to the means used to achieve a particular goal, and the intrinsic aspect, which directly affects one's well-being, when considering human mobility. Aspirations, in this context, pertain to the desires or wishes individuals have in order to attain specific objectives, such as enhancing their income, education, security, or overall quality of life. Capabilities, on the other hand, refer to individuals' aptitudes or prospects for attaining said objectives, contingent upon a multitude of factors encompassing their assets, proficiencies, connections, entitlements, and liberties. Opportunity structures refer to the distinct sets of opportunities and constraints that individuals encounter in various geographical areas. These structures encompass a range of factors, including economic, social, political, and environmental conditions.
The framework serves as a valuable tool for comprehending the multifaceted nature and ever-changing patterns of migration across various contexts and scales. It enables the unification of the analysis of almost every form of migratory movements under a single meta-conceptual framework [De Haas 2021]. It also presents a
challenge to certain widely held assumptions and misconceptions surrounding migration, such as the notion that poverty is the sole driving force behind migration, that development inevitably curbs migration, or that migration is a predicament in need of a solution. Instead, it provides a more nuanced and holistic perspective on migration as a customary and potentially advantageous occurrence that showcases human initiative and adjustment. In this context, human mobility is characterised as people's capability to choose where to live, including the option to stay, rather than as the act of moving or migrating itself.
The neoclassical theory assumes that labour markets and economies move towards equilibrium in the long run through trade and migration. It further assumes rationality among migrants such that they move from societies where labour is abundant and wages are low, to societies where labour is scarce, and wages are high. The study of [Borjas 1989; Harris, Todaro 1970] argued that actual and expected wage differentials, as well as the difference in the standard of living between sending and host communities, appears to be a strong determinant that controls the movement of people domestically and internationally. However, the work of [De Haas 2010] argued that the neo-classical migration theory is the most prevalent and the most erudite in its application to migration studies. Also, at the macro-level, neoclassical economic theory explains migration by geographical differences in the supply and demand for labour. The Pull-Push theory, on the other hand, postulates that people migrate because of factors that push them out of their existing State/Origin and factors that pull them into another. This is guided by the human desire to improve on its welfare status. The decision to migrate here is informed by three factors which include macro (such as demographic, socioeconomic factors), meso (which include information and communication technology), and micro (such as education, religion marital status, among others).
[Hunter, Simon 2023] contend that failing to take into account the natural environment could lead to migration models that are not accurately specified. These models may overemphasise the role of social and economic factors, especially in the face of climate change in today's world. On the contrary, the study posits that neglecting the incorporation of migration theory within climate scenarios may result in overly simplistic projections and interpretations, as exemplified by the idea of "climate refugees." In a comparative analysis by [Klocker, Daumann 2023], it is observed that climate change and life expectancy play a prominent role in migration, while factors such as economic development, social assistance, and education level do not exhibit significant impacts. [Bhardwaj, Sharma 2023] provides a thorough analysis that lends support to various factors, such as wage differentials, employment opportunities, higher earnings, and improved family life, as key drivers of human migration. This aligns with findings from previous studies on the subject. [Brandhorst 2023] elucidates the intricate interplay between the migrants' origin, their transnational engagement, and the remittances they send back home. The study additionally observes that the socioeconomic transformation process, influenced by emigration and reliance on migrants' economic remittances, presents a particularly intriguing regional framework for investigating the effects of migration on social changes.
Studies such as [Peridy 2006; Ortega, Peri, Drive 2009; De Haas 2011b; Ruyssen, Everaert, Rayp 2012; Doc-quier, Peri, Ruyssen 2014; Ruyssen, Rayp 2014; Simpson 2022] have provided empirical evidence on the drivers of migration. The focal point of this studies emphasizes that income differentials and income variability are the significant factors which explain migration flow. Based on this argument, it implies developed (high income) countries receive more migrants from developing (low income) countries as people's decisions are guided by seeking for better safety nets. In light of this, the study of [De Haas 2011b; Peridy 2006] revealed some evidence regarding the pattern of migration flows to Europe from a large set of countries. Also [Clark, Hatton, Williamson 2007] found similar evidence concerning migration flows to the United States. Some have attributed the movement of people from one region to another due to climatic and environmental factors [Reuveny 2007; Susan 2013; Nawarathna 2019]. Because some regions are prone and vulnerable to natural disaster, most people tend to move to a region with less disaster risk and better climatic condition. [Yu, Zhang, Wu 2020] used Eigen-based Spatial Function approach to access the impact of socioeconomic and environ-
mental factors on migration destination choices. The outcome indicates that better economic opportunities, road accessibility and better climatic condition such as cooler temperature entice migrants.
[Vakulenko 2016] have identified higher incomes as a specific factor that attracts migrants, whereas high unemployment level deters them. The study argues that improved public goods, such as good infrastructure, often have a positive effect on migration inflows and discourage outflow. Also, the study stressed that demographic and economic factors are the most significant migration factors in Russia. On the demographic effect on migration, [Pradhan 2008] noted the importance of population growth for domestic environmental variations. The study contended that rapid population growth is one of the causes of the growing number of people migrating for their livelihood. Conversely, migration has also been a major determinant of rapid population growth in urban areas. [Wang et al. 2019] used spatial panel econometrics to analyse the impact of socioeconomic factors on migration and found evidence of a spillover effect. The outcome identifies factors such as per capita GRP (Gross Regional Product) and life expectancy to have both direct and indirect significant effect on migration. But the unemployment rate and urban scale only have a direct significant effect on migration. This provides evidence that regions with higher economic status, including income levels and larger urban populations, entice people while unemployment discourages them. [Pissarides 1992; Rutkowski 2006] have noted that some of the main consequences of long-term unemployment are corrosion of human capital, poverty, and social exclusion. Because individuals experience idleness for a long term, they are driven by this factor to relocate from their current location to places or regions with better and active economic activities.
Another contextual issue is the pattern of migration, determining whether it is transitory or permanent. Most migration policy sometimes does not control the number of people migrating but rather redirect migrants towards alternative routes and making them undocumented resulting in an irregular or circular movement of migrants [De Haas 2007; Mbaye 2014]. It is further argued that people prefer transitory or circular3 migration as they see it as a survival strategy rather than permanent migration [Bell, Ward 2000; Hu, Xu, Chen 2011]. In a related study, [Keshri, Bhagat 2013] identified the prevalence of temporary migration over permanent migration, stressing that temporary migration is predominantly a rural phenomenon. [Nawarathna 2019] have argued that circular migration can have a more positive developmental effect on the sending country compared to the host country.
Methodology
The study sought to achieve its objectives through a panel data analysis anchored in the neoclassical theory, Pull-Push theory, and the proposed aspirations-capabilities theory of migration. There seems to be a dissen-tion as to whether people are receptive to pull factors or push factors especially amongst an economic bloc with similar characteristics like the BRICS member countries. Though each member state had their peculiar objectives and varying growth rate, they are still considered to be fast emerging economies. Hence, it becomes imperative to determine what factors are more influential in inspiring BRICS migrants to move to Russia. In evaluating this relationship, it becomes essential to adopt a technique that corrects for endogeneity and autocorrelation which is theoretically informed. Thus, the panel is characterized by long T(time-dimension) and short N (cross-section dimension), that is T > N. Thus, the dynamic panel of the heterogeneous non-stationary model will be employed with the adoption of the Mean Group estimation technique by [Pesaran, Smith 1995], along with the Pool Mean Group estimation technique by [Pesaran, Shin, Smith 1999]. Hence, the underlying baseline model is given below as
Mgu = a + $fus\t + PfulLt + pjncDht + ^4Migpolht + PsEmi.gi,t + ^........+1
3 By definition, circular migration is a movement for a short term period with the intend of returning back to place of origin [Bilsborrow, Oberai, Standing 1984] (cited in [Keshri, Bhagat 2013]).
Where
Img.t is BRICS migrants to Russia;
Pull,t is a vector of Pull factors such as Economic growth and Wage level; Push.t is a vector of Push factors such as unemployment, population growth;
IncD.t is the Income differential which will be calculated using the difference in GDP per capita of Russia and other individual BRICS member states;
MigPolit is a dummy variable introduced to capture the periods of changes in labour migration policy; Emig.t is the BRICS emigrants from Russia which is introduced as a control variable in the model.
Data Source
Data, such as migrants flow (Arrivals and Departures), Wage level, Economic growth, will be sourced from Rosstat while the other components, such as Unemployment rate, Population growth, GDP per capita, will be sourced from the World Bank, World Development Indicators across the BRICS countries (Brazil, Russia, India, China, and South Africa). The current project tried to analyse the dynamic changes in the migrants' flows from BRICS countries to Russia. Despite the signing of the BRICS agreement in 2006, there was a slow growth rate in the migration flow into Russia from the BRICS countries. Using the Pull-Push theory of migration and the classical theory of migration, the study tried to evaluate the determinants of migrants' inflow in Russia using some socioeconomic factors embedded in the Pull-Push theory. Besides, the study also introduces two dummies; one to capture the period before and after the BRICS agreement, and the other to assess the impact of the current migration policy, even though the BRICS still do not have a migration policy within the union. The baseline model functional relationship is given below as
Immigrants = fPull + Push).
Here, Pull is a vector of Socioeconomic Pull factors which includes the wage levels in Russia, economic progress indicated by GDP per capita. The Push factors are the factors that spur the immigrants to leave their home countries which include an increase in population size and unemployment rate. Apart from these two factors, another strong factor identified in the literature is Income differentials. This implies that the wider the income gap, the more motivated the migrants are to leave their home countries and to go to host countries.
Immigrants = fPull + Push + Income diff.).
With the understanding of the crucial role that policies play in the migrant movement, the study as earlier stated introduced two dummies that were used to capture the BRICS policy and Russian migration policy.
Immigrants = f(Pull + Push + Income diff. + Policy).
Data
For the Immigrants data, the immigrant's inflow (arrivals) from Brazil, India, China, and South Africa were obtained from the data repository of the Russian Federal State Statistical Service (Rosstat). Average accrued annual nominal wages were also obtained from the Rosstat. However, GDP per capita (GDP per capita (con-
stant 2010 US$)), Unemployment rate (Unemployment, total (per cent of the total labour force)) and Total population were obtained from World Bank, World Development Indicator. For the Dummy variables, in Migration policy, 0 was used to capture a period of less restrictive migration policy from 1997 to 2012 and 1 for the period of restrictive migration policy from 2013 to 20184. Also, for BRICS countries, 1 was used to indicate the period before BRICS agreement (1997-2005) and 0 for the period after the BRICS agreement (2006-2018). The Income differential between the Host country (Russia) and the sending countries was computed using the difference in their GDP per capita given as
Incomedifferential = GDP per capitah - GDP per capitaz
The GDP per capita term is chosen because it takes account of each citizen's contribution to the overall economic output. This is often regarded as a good proxy for measuring economic progress.
Results and Discussion
Immigration inflow, Wages, GDP per capita, and Total population were logged for the analysis. Also, a summary statistic was taken for both the logged and unlogged variables as shown in Tables 1 and 2.
Table 1
Summary Statistics
Variable Obs Mean Std. Dev. Min Max
Immigration 74 1385.64 2574.26 0.00 9043.00
Wages 88 17325.60 13758.24 950.20 43724.00
GDPpc 88 5579.83 3576.01 727.04 11993.48
Population 88 686000000.00 576000000.00 43000000.00 1390000000.00
Unemployment 88 10.89 10.11 2.27 33.47
Income differential 88 3815.55 3637.32 -3135.58 10155.35
Table 2
Logged Summary Statistics
Variable Obs Mean Std. Dev. Min Max
Immigration 73 4.77 2.66 0.00 9.11
Wages 88 9.24 1.21 6.86 10.69
GDPpc 88 8.32 0.88 6.59 9.39
Population 88 19.67 1.37 17.58 21.05
Unemployment 88 10.89 10.11 2.27 33.47
Income differential 88 3815.55 3637.32 -3135.58 10155.35
Further to summary statistics, the variables were subjected to panel unit root tests using Im-Pesaran-Shin and Fisher-type (Choi). The result indicates an identical outcome for the two tests as presented in Table 3 revealing a mixture of I (1) and I (0) for the variables.
The categorization was done based on the State Migration Policy concept that was enacted into law in June 2012.
4
Table 3
Panel Unit Root Test
Variable Im_Pesaran_Shin (IPS) Hadri
Immigration — 2 73*** I (1) 6.23*** I (1)
Wages - 5.16*** I (0) 20.46*** I (0)
GDPpc - 2.20** I (1) 3 94*** I (1)
Population - 3.67*** I (0) 45.76*** I (0)
Unemployment — 2 99*** I (1) 5.22*** I (1)
Income differential — 3 34*** I (1) 6.89*** I (1)
*** p < 0.01, ** p < 0.05, * p < 0.1
The result from the Unit root informed the study to take a further step of cointegration using the Kao and Pe-droni type panel cointegration test. The result reveals evidence of cointegration.
Table 4
Panel Cointegration Test
Cointegration Test Value
Kao (Modified Dickey-Fuller t) - 1.86**
Pedroni (Modified Phillips-Perron t) 1.46*
*** p < 0.01, ** p < 0.05, * p < 0.1
In an attempt to control for endogeneity in line with the panel characteristics of long T > N and non-stationary of some series, the study adopted the Mean Group of [Pesaran, Smith 1995], and Pool Mean Group of [Pesaran, Shin, Smith 1999], heterogeneous panel estimation technique. However, seven different levels of estimation were carried out for both the Pool Mean Group and Mean Group. The pairwise Hausman test was used to determine the most efficient and consistent estimates at each level of estimation. The results indicate that Models 1 and 2 preferred the use of PMG rather than MG, whereas Model 3 preferred the use of the MG to the PMG. But the rest of the model was indeterminate, and the PMG was preferred due to the sign, size, and significance of the estimates. The estimates are presented in Tables 5 to 8, with Tables 5 and 6 displaying the short-run estimates and Table 7 and 8 presenting the long-run estimates for both techniques.
Short Run
From Model 1, Table 5 (as PMG is preferred to MG), only wage level is included as the independent variable. The result shows that in the short run, a percentage increase in wages reduces immigrant inflow by 2.72 percent. While one had expected a positive effect between wages levels as a factor meant to pull immigrants to Russia, we could also view the change from the generality of the increase in wage levels. For instance, if the change is limited to the native labour force and exclusive of the migrants, then it could be plausible to assume that this effect might have a negative impact on immigrant's labour force as that will also prevent intense competition between migrant and native labour forces. Similarly, the constant term implies that given the absence of wage level, migration will increase by 0.75 percent. Model 2 also reveals a similar outcome between wages and immigrants but a diverse relationship between GDP per capita and immigrant's inflow. This is a positive but statistically insignificant relationship. For instance, a percentage increase in GDP per capita increases the number of migrants by 3.2 percent. This is much expected as the economic progress is a strong pull factor because migrants seek countries with improved safety nets and the prospect of enhanced economic opportunities. Thus, they are deemed to move to a better country especially if their economic performance is higher than that in their home country. Incidentally, when keeping both pull factors fixed, the constant term indicates that migrant's inflow is reduced by approximately 8 percent.
Table 5
PMG Short-Run Estimates
VARIABLES mod 1 mod 2 mod 3 mod 4 mod 5 mod 6 mod 7
Ect - 0.220*** - 0.183** - 0.426* - 0.432 - 1.060** - 1.028*** - 0.442
(0.0234) (0.0896) (0.247) (0.271) (0.443) (0.377) (0.404)
D.log(wages) - 2.718*** - 2.800*** - 2.837 0.963 2.795 1.601* - 5.255*
(0.498) (0.858) (2.596) (2.663) (2.180) (0.970) (2.720)
D.log(gdppc) 3.220 5.324 14.67 13.15 10.53 3.847
(7.014) (10.99) (9.579) (11.50) (9.116) (17.99)
D.log(pop) - 392.6 - 171.5 - 116.6 - 105.3 - 1.200
(351.9) (271.9) (377.3) (328.8) (1.035)
D.Unemployment 0.0603 0.930* 0.728 0.885*
(0.517) (0.544) (0.487) (0.507)
D.Income diff. 0.00131*
(0.000743)
Constant 0.751** - 8.030** - 296.5 - 2.338 - 1.645*** - 1.504*** 771.3
(0.340) (3.726) (182.1) (1.495) (614.6) (487.4) (723.7)
Observations 61 61 61 61 61 61 61
Standard errors in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.
In Model 3, the MG estimates of Table 6 were used as indicated by the Hausman test to be the most preferred estimate. The estimates in this model are not plausible compared to the previous model, with both GDP per capita and population growth showing a negative effect which is counter-intuitive to theory. However, in cases where migrants originate from countries with high population growth rates like China and India, such outcome could be expected. Also, the wage level in this model is positive. Nonetheless, none of the estimates in this model are statistically significant.
Table 6
MG Short-Run Estimates
VARIABLES mod 1 mod 2 mod 3 mod 4 mod 5 mod 6 mod 7
ect - 0.216*** - 0.391*** - 1.125*** - 1.103*** - 1.322*** - 1.267*** - 1.201**
(0.0264) (0.0975) (0.199) (0.170) (0.251) (0.316) (0.494)
D.log(wages) - 3.405** - 1.142 1.464 5.315* 4.643 3.123 4.989
(1.385) (1.441) (1.772) (3.031) (2.956) (3.042) (15.87)
D.log(gdppc) 2.849 - 7.547 6.460 5.352 5.367 68.31
(11.75) (6.685) (12.13) (11.24) (11.97) (50.84)
D.log(pop) - 1,351 - 2,090 - 1,454 - 2,501 7,069
(1,277) (1,343) (1,468) (1,764) (6,856)
D.Unemployment 0.251 0.365 0.411 2.420**
(0.372) (0.379) (0.266) (1.134)
D.Income diff. 0.000744
(0.000625)
Constant 1.627 -12.31 2,444 1,820 1,617 3,231 - 19,795
(1.713) (54.77) (2,399) (2,863) (2,831) (3,351) (21,730)
Observations 61 61 61 61 61 61 61
Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.
From Model 4 (PMG), Table 5, the estimates reveals that the population increase in immigrants home country has a negative and insignificant effect on the immigrant's movement to Russia. But the most revealing thing is that unemployment rate, wages, and GDP per capita are now positively related to immigrants' inflow. This implies, given all the pull and push factors in the short run, the immigrants' movement to Russia is determined by unemployment rate in their home country, the wage levels and economic progress in Russia. Models 5 and 6 contain the policy terms but because they are dummy and because of non-convergence in the model in the short run, they were excluded and included in the long-run component. However, there was a partial effect of their impact; for example, unemployment levels were significant in Model 5 with the inclusion of migration policy, whereas wage levels were significant in Model 6 with the inclusion of BRICS policy. Another interesting finding emerged in Model 7, where income differentials were included. Such inclusion changed the sign of wage level from positive to negative, while the unemployment level remains positive and significant. This indicates that migrants from BRICS countries to the Russian Federation are more responsive to income differentials (difference in GDP per capita) than wage levels (especially when these wage levels are specific to the native workforce); unemployment remains positive and significant. The implication from this last model indicates that in the short run migrants from BRICS countries are more responsive to Push factors than Pull factors. This could imply that while the Pull factors are exogenous to migrants', it becomes difficult for them to be responsive to them; for instance, in the case of wages if seen from a specific rather than a generic point of view. The migrants may not move if they have full information on the wage structure and that they may earn less than natives of the same skills and qualification level. The error correction terms for all the models were negative and statistically significant, lending evidence to the cointegration test of a common trend between the dependent variable and explanatory variables. However, the error correction term for Models 5 and 6 appears insignificant, with the value looking explosive with over 100 percent of adjustment.
Following the sequence of the short run, the long-run estimates are presented in Table 7 (PMG) and Table 8 (MG). Model 1 indicates a positive but not significant impact of wages on immigrants' inflow into Russia.
Long Run
Table 7
PMG Long-Run Estimates
VARIABLES
mod 1 mod 2 mod 3 mod 4 mod 5 mod 6 mod 7
log(wages)
0.407 - 0.166 - 4.067*** - 12.81*** - 5.778*** - 5.550*** - 2.596*** (0.492) (0.974) (0.540) (2.580) (0.684) (0.715) (0.956)
Migpol
Income diff.
BRICS
log(gdppc)
log(pop)
Unemployment
5.922 - 0.986 - 34.82*** 11.30*** 10.39*** 7.904* (4.248) (3.176) (11.43) (1.597) (1.514) (4.494) 38.31*** 289.4*** 80.22*** 75.38*** - 85.27*** (13.22) (74.91) (7.826) (8.545) (13.48) - 1.927*** - 0.303*** - 0.184* - 0.507 (0.546) (0.0824) (0.110) (0.619) - 1.716*** - 1.707*** 1.450 (0.407) (0.391) (0.910) - 0.491* 0.672 (0.277) (0.468) 0.00151*** (0.000552)
Observations
61 61 61 61 61 61 61
Standard errors in parenthese; *** p < 0.01, ** p < 0.05, * p < 0.1.
That is, the percentage increase in wage level increases the migrants' inflow into Russia by 0.47 percent (see Table 7, column 2). This means that in the long run the changes (increase) in wage levels could spur migrant's inflow but not significant, implying that this effect is not salient. Yet the short-run effect was negative and significant. This means, as mentioned in the short-run analysis, changes could be seen as specific to natives and not having a significant effect on the migrants' labour force. Model 2 indicates that wage levels do have a negative but insignificant effect on immigrants' inflow. But the GDP per capita introduced in the second model is positive though not significant. This also shows that given the wage levels as GDP per capita is not sufficient to spur migrants' inflow to Russia. Model 3 in Table 8 demonstrates that upon incorporating population growth in the model, wages remain negative, while GDP per capita now is positive and significant, even though population growth is less likely to increase the immigrant inflow into Russia, considering wages and economic development. Though it is expected that population growth in sending countries could spur the inflow of migrants' (especially labour migrants) into Russia with control such as wage which might be seen as general increase, the growth might not have any significant effect on the migrants' movement. This could also lend evidence to the fact that the wage level is viewed as only specific to the native labour force rather than the whole workforce.
Table 8
MG Long-Run Estimates
VARIABLES mod 1 mod 2 mod 3 mod 4 mod 5 mod 6 mod 7
log(wages) - 0.152 - 3.781 - 0.967 - 5.149 - 1.809 - 1.312 - 16.78
(0.874) (3.897) (3.537) (6.017) (3.927) (5.547) (16.62)
log(gdppc) 11.37 22.64*** 13.92 20.33*** 29.60*** - 39.37
(18.49) (6.042) (12.99) (6.493) (9.802) (45.15)
log(pop) - 104.2 - 55.58 - 70.45 - 148.8 245.6
(104.1) (142.5) (112.4) (128.9) (474.4)
Unemployment 0.104 -0.0293 -0.0670 0.780
(0.789) (0.523) (0.456) (1.013)
Migpol 0.923 2.126 3.183
(1.710) (2.642) (3.208)
BRICS 0.765 - 0.235
(0.733) (0.834)
Income diff. 0.00253
(0.00331)
Observations 61 61 61 61 61 61 61
Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.
Model 4 in Table 7 indicates that unemployment has a negative effect on immigrants' inflow given population growth, wage levels and economic progress in the long run. However, population growth is now positive and has a significant effect on migrants' inflow. This is much expected especially for countries like China and India, which have large working populations that are more likely to move to Russia. Their decision to move may not be caused by the increasing unemployment rate in their home countries but rather by the pursuit of better opportunities considering the competitiveness of their services in their respective home countries. While it is understandable that an increase in population growth could push them to Russia, it is implausible to suggest that GDP per capita in the host country would reduce migrants' inflow. With the introduction of migration policy into Model 5, we saw that more restrictive policy is more likely to negatively affect the migrants' inflow to Russia. This could stem from the absence of a formal agreement on labour migration within the BRICS, unlike the established frameworks in other economic unions such as the European Union. However, other indicators such as population growth and GDP per capita are now significant and influence positively the influx of migrants into
Russia. But indicators such as wages and unemployment has remained still negative indicating that these do not positively stimulate immigrants into Russia. The BRICS agreement was introduced in Model 6 to account for the effect of the BRICS agreement. The coefficient indicates a negative effect on migrants' inflow. This could be attributed to the fact that the objective of the economic cooperation is to complement and strengthen the existing bilateral and multilateral relationship through trade, research and development, and food security among the member states. There is still no legislation facilitating the ease of movement of migrants from member states into Russia. In the last model, income differential was introduced, and as seen in the short-run effect, the income differential is positive and significant. This suggests the resilience of the factor amidst both pull and push factors.
Conclusion
There seem to be several factors responsible for the migration of people from their home countries to a foreign destination. These factors include economic, environmental, technological, age-related and others. They were categorized into micro (age, education), meso (networking, communication) and macro (social, economic) perspectives. From a theoretical perspective, these factors are explained by the push-pull theory as well as the neoclassical theory of migration. Since not a single theory could explain the complexity of migration process, the current study adopted a combination of the two theories.
Over the last decade, Russia has experienced an overwhelming growth in immigration flow from both the European and BRICS member states, placing it among the top four destinations for migrants. There was also an upsurge of migrants from the BRICS member states within the last decade, leading to uncertainty regarding the primary factors responsible for the dynamic changes. The current study analyzed the impact of socioeconomic factors on immigrants from the other BRICS member states. The first finding indicates evidence on cointegration between the dependent and independent variables suggesting a common trend between immigrants' inflow to Russia and socioeconomic factors outlined in the Pull-Push theory. This is also an indication of the sustainability of the immigration inflow based on these pull-push factors.
The study showed that in the short run, wages negatively affect immigrant's inflow against the dominant argument that better wages are meant to pull immigrants from their home countries. However, the study has argued that if changes in wage level are not general and only restricted to the native workforce (such as when minimum wage exclusively applies to only native migrants), such outcome is plausible. This effect persisted across the long-run estimates as well. Another pull factor, which is the economic progress represented by GDP per capita, shows a positive effect on immigrants' inflow as expected, though it was not significant. But the long-run estimates looked promising, with most of the models having a positive effect on migration flow, which indicates that BRICS member states migrants are influenced by the economic progress of the Russian Federation, especially in the post-2010 era. The push factors showed some interplay between two periods; for instance, while unemployment was a strong significant factor in the short run that influences economic migration to Russia, population growth (in migrants' home countries) was seen to be negative and non-significant factor. But this effect reversed in the long run, with unemployment no longer playing a role in the movement of migrants, while population growth became a significant factor. This is an interesting observation as it suggests that people move in the short run because of unemployment shocks, but in the long run they may be influenced by the motive to avoid competitiveness in their home country, resulting in low income due to the presence of workforce with similar skills.
The introduction of income differential aligns with the argument in the literature that migration flow is influenced by income differential, meaning that migrants from low-income countries are pushed to move to countries with higher income value as the income gap between countries widens. This was the finding of the current study. Lastly, the introduction of policy components (migration policy and BRICS agreement) revealed that these policies hindered migration flow. This was attributed to the absence of specific laws governing migration movement among the member states since the focus was mainly on fostering trade, research & development,
among other areas. One limitation of the study is its inability to incorporate some critical factors that must
have resulted in changes in the volume of migration flows, namely changes in the criteria for determining migration (the inclusion of temporary arrivals in statistics for periods of nine months or more).
Acknowledgement
The article has been prepared with the support of the Ministry of Science and Higher Education of the Russian
Federation (Ural Federal University Program of Development within the Priority-2030 Program).
References
Bell M., Ward G. (2000) Comparing Temporary Mobility with Permanent Migration. Tourism Geographies, vol. 2, no 1, pp. 97-107.
Berlin I. (1969) Four Essays on Liberty, London; New York: Oxford University Press.
Bhardwaj B., Sharma D. (2023) Migration of skilled Professionals across the Border: Brain Drain or Brain Gain? European Management Journal, vol. 41, no 6, pp. 1021-1033.
Bilsborrow R. E., Oberai A. S., Standing G. (eds.). (1984)Migration Surveys in low Income Countries: Guidelines for Survey and Questionnaire Design, London: Croom Helm.
Borjas G. J. (1989) Economic Theory and International Migration. International Migration Review, vol. 23, no 3, pp. 457-485.
Brandhorst R. (2023) The Migration Motive and Transnational Engagement Nexus. A Case Study of Transnational Families between Cuba and Germany. Journal of Ethnic and Migration Studies, vol. 49, no 7, pp.1803-1825.
Carling J., Collins F. (2018) Aspiration, desire and drivers of migration. Journal of Ethnic and Migration Studies, vol. 44, no 6, pp. 909-926.
Castelli F. (2018) Drivers of Migration: Why Do People Move? Journal of Travel Medicine, vol. 25, no 1, tay040.
Clark X., Hatton T. J., Williamson J. G. (2007) Explaining U.S. Immigration, 1971-1998. Review of Economics and Statistics, vol. 89, no 2, pp. 359-373.
De Haas H. (2007) Turning the Tide? Why Development Will Not Stop Migration. Development and Change, vol. 38, no 5, pp. 819-841.
De Haas H. (2010) Migration and Development: A Theoretical Perspective. International Migration Review, vol. 44, no 1, pp. 227-264.
De Haas H. (2011a) The Determinants of International Migration: Conceptualising Policy, Origin and Destination Effects. IMI Working Paper Series, no 32, pp. 1-35.
De Haas H. (2011 b) Mediterranean Migration Futures: Patterns, Drivers and Scenarios. Global Environmental Change, vol. 21, no 1, pp. S59-S69.
De Haas H. (2021) A Theory of Migration: The Aspirations-Capabilities Framework. Comparative Migration Studies, vol. 9, no 8, art. 8 (2021).
Docquier F., Peri G., Ruyssen I. (2014) The Cross-Country Determinants of Potential and Actual Migration. International Migration Review, vol. 48, pp. S37-S99.
Federal State Statistic Service (n. d.). Available at: https://eng.gks.ru/ (accessed 25 October 2020).
Harris J. R., Todaro M. P. (1970) Migration, Unemployment Anddevelopment: A Two-Sector Analysis. American Economic Review, vol. 60, no 1, pp. 126-142. Available at: https://www.aeaweb.org/aer/ top20/60.1.126-142.pdf (accessed 21 March 2024).
Hu F., Xu Z., Chen Y. (2011) Circular Migration, or Permanent Stay? Evidence from China's Rural-Urban Migration. China Economic Review, vol. 22, no 1, pp. 64-74.
Hugo G. (2013) What We Know about Circular Migration and Enhanced Mobility. Migration Policy Institute Policy Brief, vol. 7, no 8, pp. 1-10.
Hunter L. M., Simon D. H. ( 2023)Time to Mainstream the Environment into Migration Theory? International Migration Review, vol. 57, no 1, pp. 5-35.
Ivakhnyuk I. (2013)The Russian Migration Policy and its Impact on Human Development. Human Development Reports. Available at: http://hdr.undp.org/en/content/russian-migration-policy-and-its-impact-hu-man-development (accessed 25 October 2020).
Keshri K., Bhagat R. B. (2013) Socioeconomic Determinants of Temporary Labour Migration in India: A Regional Analysis. Asian Population Studies, vol. 9, no 2, pp. 175-195.
Klocker J. A., Daumann F. (2023) What Drives Migration to Germany? A Panel Data Analysis. Research in Economics, vol. 77, no 2, pp. 251-264.
Lille0r H. B., Van den Broeck K. (2011) Economic Drivers of Migration and Climate Change in LDCs. Global Environmental Change, vol. 21, no S1, pp. S70-S81.
Mbaye L. M. (2014) "Barcelona or Die": Understanding Illegal Migration from Senegal. IZA Journal of Migration, vol. 3, iss. 1, pp. 1-19.
MPI (2019) Top 25 Destinations of International Migrants. Available at: https://www.migrationpolicy.org/ programs/data-hub/charts/top-25-destinations-international-migrants (accessed 21 March 2024).
Nawarathna B. (2019) Factors Influencing Migration. Available at: https://www.researchgate.net/publica-tion/330496325_Factors_Influencing_Migration (accessed 26 October 2020).
Ortega F., Peri G., Drive O. S. (2009) The Causes and Effects of International Migrations: Evidence from OECD Countries 1980-2005. NBER Working Paper, no w14833.
Peridy N. (2006) The European Union and Its New Neighbors: An Estimation of Migration Potentials. Economics Bulletin, vol. 6, no 2, pp. 1 -11.
Pesaran M. H., Smith R. (1995) Estimating Long-Run Relationships from Dynamic Heterogeneous Panels. Journal of Econometrics, vol. 68, no 1, pp. 79-113.
Pesaran M. H., Shin Y., Smith R. P. (1999) Pooled Mean Group Estimation of Dynamic Heterogeneous Panels. Journal of the American Statistical Association, vol. 94, no 446, pp. 621-634.
Pissarides C. A. (1992) Loss of Skill During Unemployment and the Persistence of Employment Shocks. Quarterly Journal of Economics, vol. 107, no 4, pp. 1371-1391.
Pradhan P. K. (2008) Population Growth, Migration and Urbanisation. Environmental Consequences in Kath-mandu Valley, Nepal. Environmental Change and its Implications for Population Migration. Advances in Global Change Research (eds. J. D. Unruh, M. S. Krol, N. Kliot), vol. 20, Dordrecht: Springer, pp. 177-199.
Reuveny R. (2007) Climate Change-Induced Migration and Violent Conflict. Political Geography, vol. 26, no 6, pp. 656-673.
Rutkowski J. (2006) Labor Market Developments During Economic Transition, vol. 3894, Washington, DC: World Bank.
Ruyssen I., Rayp G. (2014) Determinants of Intraregional Migration in Sub-Saharan Africa 1980-2000. Journal of Development Studies, vol. 50, no 3, pp. 426-443.
Ruyssen I., Everaert G., Rayp G. (2012) Determinants and Dynamics of Migration to OECD Countries in a Three-Dimensional Panel Framework. Empirical Economics, vol. 46, no 1, pp. 175-197.
Simonelli A. C. (2008) Migration and Climate Change. OIM MigrationReserch Series (ed. l. Pinto-Dobernig), no 31, Geneve: International Organization for Migration.
Simpson N. (2022) Demographic and economic Determinants of Migration. IZA World of Labor, no 373.
Srinivas J. (2022) Russia and BRICS. Future of the BRICS and the Role of Russia and China, Singapore: Palgrave Macmillan, pp. 77-113.
Susan M. (2013) Environmental Change and Migration: What We Know. Available at: https://www.migra-tionpolicy.org/research/environmental-change-and-migration-what-we-know (accessed 26 October 2020).
Vakulenko E. S. (2016) Econometric Analysis of Factors of Internal Migration in Russia. Regional Research of Russia, vol. 6, no 4, pp. 344-356.
Wang L., Huang J., Cai H., Liu H., Lu J., Yang L. (2019) A Study of the socioeconomic Factors Influencing Migration in Russia. Sustainability, vol. 11, no 6, art. 1650.
Yu D., Zhang Y., Wu X. (2020) How Socioeconomic and Environmental Factors Impact the Migration Destination Choices of Different Population Groups in China: An Eigenfunction-Based Spatial Filtering Analysis. Population and Environment, vol. 41, no 3, pp. 372-395.
Received: April, 24, 2023
Citation: Valei A., Mamman S. O. (2024) The Impact of Socioeconomic Factors on BRICS Migrants in the
Russian Federation. Journal of Economic Sociology = Ekonomicheskaya sotsiologiya, vol. 25, no 2, pp. 160176. doi: 10.17323/1726-3247-2024-2-160-176 (in English).
