As a result of the collapse of the Soviet Union, five million Russian and Russian-speaking people repatriated to Russia during 1990-2002. I use this natural experiment to study the effect of a large migration wave on the employment and wages of the local population. Taking into account the non-random choice of location by migrants within Russia, I find a negative effect of the inflows of immigrants on the local population’s employment but not on wages. The initial negative effects on employment are particularly large for local men, but they disappear after about ten years from the peak of the migration wave.
The effect of migration on the labor market of the host country is a long-standing question within economic literature and in public debate. In many cases, researchers try to estimate this effect using the data on large and unanticipated migration movements. The most famous study of this kind is probably Card (1990). Another case is the Russian migration to Russia resulting from the collapse of the Soviet Union. According to the 2002 Russian Census, 5.2 million of the people living in Russia in 2002 resided outside the country in 1989. That is, 3.6 percent of the 2002 population immigrated to Russia after 1989. Almost all of them (94.4 percent) immigrated from the former Soviet republics, most notably Kazakhstan, Ukraine and Uzbekistan.
The existing literature on migration flows in the former Soviet Union (fSU) since its collapse has emphasized the socio-political factors of migration. Locher (2002) finds that ethnic sorting was a major determinant of migration among the fSU countries, with the countries’ stage of transition and wealth level playing a minor role. Yerofeeva (1999) shows that ethnic repatriation was one of the main reasons behind migration from northern and eastern Kazakhstan.
In Lazareva (2015), I study two sides of the labor market effects of the immigration from fSU countries to Russia. The first side is the process of assimilation of migrants in the Russian labor market. The second side is the effect that inflows of immigrants had on the labor market position of the local population in Russia. Data used for estimation span a long period of time, which allows for tracing dynamic long-term effects of the influx of immigrants. This is the first comprehensive study of the labor market effects of one of the largest migration waves in Europe in recent history.
In order to estimate the effects of the inflow of immigrants on the employment and wages of the local population, I exploit variation in the share of immigrants across Russian regions. According to the Census in 2002, migrants were quite dispersed over Russia’s vast territory; their share in population varied from 0.42% in the Tyva region to 8.5% in the Kaliningrad region. A relatively large share of migrants is observed along the border to fSU countries as well as in the oil-rich regions of Western Siberia.
A major problem when using regional variation to estimate labor market effects is that the migrants’ choice of region may be affected by the condition of that region’s labor market. Naturally, migrants tend to choose locations with higher wages and more employment opportunities. If this is the case, the estimates of the labor market effects will be biased.
However, the immigrants’ choice of location was not completely unconstrained due to the costs of migration related to the distance and access to information. Given these constraints, there is a relative crowding of immigrants in the regions of Russia that are closer to the border with fSU countries. Hence, I use the variation in the share of migrants across regions, which depend on the geographical distance from the source countries. In other words, I obtain the estimates from the comparison of regions that are similar in all their characteristics except for the distance to the border with fSU.
Data and Results
I use panel data on households from the Russian Longitudinal Monitoring Survey for the period 1995-2009. In the 2009 survey, the respondents were asked since what year they live in the Russian Federation. I define as immigrants, people at the age of 18 and above who moved to Russia after 1989. Note that the RLMS sample, which consists of people residing in the same dwelling units in each round, is unlikely to include illegal migrants or temporary (seasonal) labor migrants. Rather these are mainly people who settled in Russia permanently at some point during the 1990s and 2000s.
In the RLMS sample, 3.6 percent of the respondents moved to Russia after 1989. This is consistent with the national-level statistical data on immigration flows. A majority of the immigrants arrived to Russia in the early and mid-1990s. Immigration peaked in 1994 when almost 1.2 million people moved to Russia. After that, immigration steeply declined; during the 2000s, the registered level of immigration was at about 200,000 people per year.
A majority of the immigrants (71.7%) in the RLMS sample are of Russian ethnicity, and there is a slightly higher share of males. Importantly, migrants are not significantly different from the locals in terms of their education levels. The statistics on marital status show that a higher share of migrants compared to locals have families and children. Apparently, family migration was a large part of this migration wave.
Using the methodology described above, I obtain an insignificant effect of the share of immigrants on the wages of the local population over the period of 1995-2009. The effect of immigrant share on the unemployment of the local population is also insignificant. In contrast, estimates for the labor force participation (LFP) show a significant negative effect of immigration on the LFP of the local population. The size of the effect is non-negligible: a one-percentage point increase in the share of immigrants in a region reduces the probability for a local person to be in the labor force by 0.6 percentage points. Thus, over the whole period of 1995-2009, Russian immigration is estimated to have had some displacement effect, but only in terms of the labor force participation of the local population.
Since the inflow of immigrants was mostly concentrated in the first half of 1990s, I estimate my model for three sub-periods: 1995-2000, 2001-2004, and 2005-2009. The results for the wages remain insignificant in all sub-periods. Immigration is shown to increase the unemployment among locals in the first half of 2000s, but this effect dissipated in the second half of 2000s. The effect of immigration on the labor force participation is negative and highly significant for the late 1990s, still negative and significant but smaller in magnitude in the early 2000s, and disappears in the late 2000s. This analysis suggests that the immigration wave had a quite significant displacement effect for the local population in terms of unemployment and labor force participation, but not in terms of wages. This effect slowly declined and had disappeared by the second half of 2000s. My results also suggest that the negative labor market effects were more significant for men than for women.
The results of this study have implications for the debate on the effect of immigration on local labor markets, in particular on wages and employment opportunities for the native population. The majority of existing studies find only minor negative effects of migration on the labor market position of locals. My results suggest that immigrants who are close substitutes to the local labor force, due to the common language and similar education, have more significant effects on the labor market outcomes of the local population.
The finding that displacement effects in Russia dissipated quite slowly may be related to the very low migration rates of the local population in Russia throughout the transition. In order to reduce negative labor market effects of large influxes of immigrants, policy measures are needed that improve labor mobility across regions. These may include moving or housing subsidies, retraining programs and policies ensuring equal access to jobs and public services for internal migrants across the regions of Russia.
- Card, David, 1990, The Impact of the Mariel Boatlift on the Miami Labor Market, Industrial and Labor Relations Review, Vol. 43, No. 2, pp. 245-257.
- Lazareva O. Russian Migrants to Russia: Assimilation and Local Labor Market Effects //IZA Journal of Migration. 2015. No. 4:20
- Locher, Lilo, 2002, “Migration in the Soviet Successor States,” Applied Economics Quarterly, 48 (1), 2002, 67-84
- Yerofeyeva, Irina, 1999, “Regional aspects of Slavic migration from Kazakhstan on the basis of examples from North Kazakhstan and East Kazakhstan provinces”. In: Vyatkin, Anatoly, Kosmarskaya, Natalya, Panarin, Sergei (Eds.), V Dvizhenii Dobrovoljnom i Vynuzhdennom [In Motion—Voluntary and Forced]. Natalis, Moscow, pp. 154–179
There was no inter-regional convergence in Russia during the 1990s but the situation changed dramatically after 2000. While interregional GDP per capita gaps still persist, the differentials in incomes and wages decreased substantially. Interregional fiscal redistribution has never played a major role in Russia, so understanding interregional convergence requires an analysis of internal capital and labor mobility. The capital market in Russia’s regions is integrated in a sense that local investment does not depend on local savings. Also, the barriers to labor mobility have come down. The situation is very different from the 1990s when many poor Russian regions were in a poverty trap: potential workers wanted to leave those regions but could not afford to finance their move. After 2000 (especially later in the first decade), these barriers were no longer binding. Overall economic development, as well as the development of financial and real estate markets, allowed even the poorest Russian regions to grow out of the poverty trap. This resulted in some convergence in the Russian labor market; the interregional gaps in incomes, wages and unemployment rates are now comparable to those in Europe.
Russia’s Regions are Finally Converging
Large interregional differences have always been an important feature of Russia’s transition to a market economy. This has been explained by the pre-transition geographical allocation of population and of physical capital that was determined by non-market forces. Soviet industrialization policies often pursued political or geopolitical goals. Even when they reflected economic realities, the economic decision-making was distorted substantially by central planning, price-setting and subsidies. In addition, the allocation of production was intended to serve a different country – the Soviet Union (or even the whole Council for Mutual Economic Assistance countries) rather than Russia alone. Moreover, believing in economies of scale rather than in competition, Soviet planners created many monotowns. These towns, cities or even regions relied on a single industry. Therefore economic restructuring and inter-sectoral reallocation implied not only moving workers or capital between employers in one town, but also required moving workers or capital between cities.
Despite the need for geographical reallocation during the transition to a market economy, the differentials between Russian regions remained high (and even increased!) throughout the 1990s. However, after 2000 (especially later in the first decade) there was substantial convergence in incomes and wages (Figure 1). By 2010, this resulted in reduction of the inter-regional differences in incomes in line with European levels. In Figure 2, while inter-regional differences in Russia are still substantially above those in the US and Western Europe, they are comparable to those in the EU.Figure 1. Differences among Russian Regions in Terms of Logarithms of Real Incomes, Real Wages, Unemployment, Real GDP Per Capita
Source: Guriev and Vakulenko (2012). Note: All variables measured as population-weighted standard deviations.
Figure 2. Income Differentials in Russia, Europe and the US
Interestingly, despite income convergence, there was no convergence in GDP per capita among Russia’s regions. Inter-regional dispersions in GDP per capita remain high not only by European standards, but also by standards of less developed countries. Indeed, in Figure 3, Russia is placed in the international context using the data recently developed by Che and Spilimbergo (2012).
Che and Spilimbergo calculate interregional differences for 32 countries in a compatible way and plot them against GDP per capita (averaged out for 1995-2005, in real PPP-adjusted dollars). Their main finding is that that there is a negative correlation between interregional differences and GDP per capita.
Since Russia was not in Che and Spilimbergo’s dataset, Guriev and Vakulenko (2012) reproduced their calculations for Russia, both for the 1995-2005 average (as they do for the other countries) but also for the individual years 1995, 2000, 2005 and 2010. It turns out that while Russia was “abnormally uniform” in the early 1990s, it did experience substantial divergence in the late 1990s. There was continuing, albeit weaker, divergence even in the early 2000s – so Russia became “abnormally unequal” given its GDP level. Even though there was some convergence late in the first decade, Russia is still “abnormally unequal”. Given the fast economic growth since 2000, Russia should have become substantially “more uniform” – at least given the downward-sloping relationship between income and inter-regional inequality in Che-Spilimbergo’s data.
Source: Che and Spilimbergo (2012). Note: The trend line is calculated without Russia.
Why didn’t income convergence happen in the 1990s and only start after 2000? Why hasn’t GDP convergence taken place? Large interregional differences are consistent with reduced income, wage, and unemployment differentials if the factors of production (labor and capital) have become more mobile while the productivity differences (due to geography, political and economic institutions, and inherited differences in infrastructure) remain in place. Therefore, in order to understand income convergence, an understanding of labor and capital mobility is needed.
Interregional Labor Mobility in Russia
Andrienko and Guriev (2004) studied internal migration flows in Russia in the 1990s and showed that the lack of convergence was explained by a “poverty trap”. In general, Russians did move from poorer to richer regions. However, in Russia’s very poor regions (in about 30% of the regions hosting about 30% of Russia’s population) the potential outgoing migrants wanted, but could not afford, to leave; so for these regions, an increase in income would have resulted in higher rather than lower outmigration.
What changed since 2000? Why did barriers to mobility come down? There are multiple potential explanations: (i) economic growth simply allowed most of Russia’s regions to grow out of the poverty trap; (ii) the development of financial and real estate markets reduced the transactions costs of moving therefore reducing the importance of the poverty trap; (iii) the development of capital markets increased capital mobility; (iv) federal redistribution reduced interregional differences.
According to Guriev and Vakulenko (2012), federal redistribution played a very minor role, while the other three explanations are consistent with the data. Our analysis of capital flows is, however, limited by the lack of detailed data, but our study of panel data on net capital inflows and investment shows that, first, capital does flow to regions with higher returns to capital and with lower wages and incomes, thus contributing to convergence. Second, investment in Russia’s regions is not correlated with savings which suggests that Russia’s capital market is not regionally segmented. As our data on capital are limited to the period after 2000, we cannot compare the recent years to those during the 1990s, but at least we can argue that recently, the capital market was functioning well and was contributing to convergence.
It is striking to what extent the poverty trap and liquidity constraints used to be, but are no longer, binding for labor mobility. Figure 4 is a graphical illustration of the poverty trap. Based on a semiparametric estimation with region-to-region fixed effects it shows the relationship between income in the origin region and migration (both in logarithm). Each dot on this graph represents migration from one region to another in a given year (during 1995-2010). As discussed above, the relationship is non-monotonic. If the sending region is poor, an increase in income results in higher out-migration; for richer regions, a further increase in income results in lower migration. The peak is at log income equal to 8.7 which amounts to average income equal to exp(8.7) ≈ 6003 in 2010 rubles and 1.02 of the Russian average subsistence levels in 2010. The regions to the left of the peak are in the poverty trap while the regions to the right are in a “normal mode” where liquidity constraints are not a substantial barrier to migration.
While in the 1990s tens of regions were below this threshold (and therefore were locked in the poverty trap), by 2010 only one region was below this threshold. In this sense, overall economic growth allowed Russian regions to overcome liquidity constraints by simply growing out of the poverty trap. We ran additional tests to show that financial development also contributed to relaxing liquidity constraints.Figure 4. Income in the Origin Region and Migration
Should we be worried about high interregional differentials in GRP per capita? Not necessarily. In order to ensure inter-regional convergence in incomes and wages, convergence in GDP per capita is not required. As long as barriers to labor and capital mobility are removed, mobility (or even a threat of mobility) protects workers. Therefore, the very fact of remaining large inter-regional dispersion in GDP per capita should not serve by itself as a justification for government intervention (e.g. region-specific government investment).
As reducing barriers to mobility is important for convergence, this is exactly where policies can contribute the most. Developing financial and housing markets and improving investor protection are better policies for reducing inter-regional differences in income; these factors have already reduced income differentials among Russian regions.
We should, however, provide an important caveat. Our analysis was done at the regional level. We therefore do not address the sub-regional level and have nothing to say on the need for town-level government interventions. There may well be many cases where individual towns (e.g. so called mono-towns) are locked in poverty traps. In those cases government intervention may be justified and desirable. Our results show that poverty traps did exist in Russia in the 1990s at the regional level. These may well still exist at the town level even now. We cannot extrapolate the quantitative value of the income threshold we identified for the poverty traps from regional level to the town level but our analysis provides very clear qualitative criteria for government intervention. If the average citizen of a town would benefit from moving out but cannot finance the move (e.g. because his/her real estate is worthless), then the government can and should step in through supporting financial intermediaries that could finance the move. Therefore our analysis is fully consistent with the rationale for the government’s mono-towns restructuring program.
- Andrienko, Yuri, and Sergei Guriev (2004). “Determinants of Interregional Mobility in Russia: Evidence from Panel Data.” Economics of Transition, 12 (1), 1-27.
- Che, Natasha, and Antonio Spilimbergo (2012). “Structural reforms and regional convergence.” CEPR Discussion Paper No. 8951.
- Guriev, Sergei and Elena Vakulenko (2012). “Convergence among Russian regions.” Background paper for the World Bank’s Eurasia Growth Project.
 Russian law defines monotowns as town where at least 25% employment is in a single firm. Even now, the Russian government’s Program for the Support of Monotowns lists 335 monotowns (out of the total of 1099 Russia’s towns and cities) with the total of 25% of Russia’s urban population.  EU (19): Belgium, Czech Republic, Germany, Estonia, Ireland, Greece, Spain, France, Italy, Latvia, Lithuania, Netherlands, Austria, Poland, Portugal, Slovakia, Finland, Sweden, United Kingdom. For EU (19) we consider only those NUTS-2 units for which there is data for each year. Western Europe: Austria, Belgium, Germany, Ireland, Greece, France, Italy, Netherlands, Norway, Portugal, Finland, Sweden, United Kingdom.  The graph shows the relationship between the logarithm of the real income in the sending region and the logarithm in migration controlling for income in the receiving region, unemployment and public goods in both sending and receiving, year dummies and other factors influencing migration. Moscow and Saint Petersburg are excluded.