Location: Lithuania

Human Capital Loss Among Belarusian and Ukrainian Migrants to the EU

Posted on March 31, 2025 | Policy Brief

Silhouettes of construction workers on scaffolding at sunset, symbolizing underemployment among human capital migrants in the EU.

This policy brief examines the underutilization of human capital among involuntary migrants from Ukraine and Belarus in Poland and Lithuania. Focusing on those who migrated after 2020 (Belarus) and 2022 (Ukraine), the brief investigates the factors influencing the conversion of their pre-migration skills into gainful employment in their host countries. Our findings show that despite many migrants possessing high levels of education and professional qualifications, structural barriers and low convertibility of their skills, hinder their full labor market integration. This skill underutilization not only limits migrants’ professional growth and earning potential but also deprives the host countries of valuable skills and potential economic gains.

Effective labor market integration substantially benefits both host and sending countries and migrants themselves. For host nations, successful integration can alleviate critical skill shortages, boost productivity, and drive economic growth (Boubtane, Dumont, & Rault, 2016; Boubtane, 2019; Engler, Giesing, & Kraehnert, 2023; Bernstein et al., 2022). Conversely, inadequate integration leads to underemployment, diminished potential, and economic inefficiency. Countries of origin can benefit from remittances, the return of migrants with enhanced skills, and strengthened international economic ties. However, poor integration risks an uncompensated “brain drain” (Reinhold & Thom, 2009; Barrett & O’Connell, 2001; Iara, 2006; Barrett & Goggin, 2010; Co, Gang, & Yun, 2000). For migrants, the ability to continue their careers means higher earnings and less stress from the acquisition of a new profession, while the non-utilization of existing skills results in their depreciation, potentially causing permanent wage reductions even upon return to the home country (Bowman & Myers, 1967).

Migrants can be broadly categorized into voluntary migrants or forced migrants. Voluntary migrants assess labor market prospects beforehand and often possess convertible human capital – one that can be used in a new labor market. This group often includes professionals like IT specialists and scientists and those in low-skilled but highly transferable professions. Forced migrants, on the contrary, may be utterly unprepared for changes in jurisdiction and possess skills of limited transferability. For example, even highly specialized professions requiring extensive training and substantial human capital, such as lawyers, officials, and teachers, often prove “non-convertible“ (Duleep & Regets, 1999). These individuals’ skills are frequently country specific.

Low convertibility of skills generates significant negative consequences. Highly educated professionals, for instance, may find themselves relegated to low-paying, unskilled jobs, unable to leverage their expertise. This hinders their professional development and deprives host countries of valuable skills and potential contributions to economic growth. Addressing these mismatches is crucial for maximizing the benefits of migration for stakeholders in both home and host countries.

Forced Migration from Belarus and Ukraine

The political crisis in Belarus, starting with the contested 2020 presidential elections, led to widespread repression and significant forced migration. Belarus’s role in supporting Russia’s 2022 invasion of Ukraine exacerbated this situation, resulting in approximately 300,000 Belarusians seeking refuge in the European Union (Eurostat). This number accounts for a substantial proportion of the country’s 9 million population and its approximately 5 million-strong labor force (Belstat).

Russia’s full-scale invasion of Ukraine triggered the most significant wave of migration in Ukrainian history, with over 6 million of the pre-war 44 million population fleeing to the EU (UNHCR). About 90 percent of the initial refugees were women and children due to a mobilization law preventing most men aged 18 to 60 from leaving (UNHCR).

Online Survey and Migrant Differences

To better understand the situation of migrants, their integration into the EU labor market, and to develop data-driven recommendations for improving their conditions, the CIVITTA agency, in partnership with BEROC, conducted an online survey in the summer of 2024. This brief is based on the survey results. The survey includes responses from 616 Ukrainian nationals who migrated to Poland or Lithuania after Russia’s full-scale invasion of Ukraine in 2022, as well as 173 Belarusian migrants who left their home country after 2020. The research focuses on individuals aged 28 to 42, providing insights into their experiences and challenges in the labor market in their host countries. While we acknowledge the sample’s limitations in terms of representativeness, we believe the findings provide valuable insights into the specific challenges faced by involuntary migrants and their adaptation strategies in the new labor market.

Key differences characterize these migration waves. Ukrainian migration comprises of more women, while Belarusian migrants show a more balanced gender distribution, with 47 percent women in our sample versus 62 percent for Ukrainians. Family separation is also notable, as 91 percent of married Belarusians live with their spouses, compared to only 75 percent of Ukrainians (due to the mobilization law).

Survey respondents from both groups possess high levels of human capital with 60 percent of Ukrainians and 90 percent of Belarusians holding higher education degrees. Among Belarusians, 94 percent had over five years of work experience before migration, with and 79 percent of Ukrainians stating the same.

Ukrainian return intentions are split: 38 percent plan to return, 19 percent will not, and the rest are undecided. An end to the war and changes in Russian foreign policy would increase return rates to 70 percent. For Belarusians, 35 percent plan to return, 38 percent will not, and the rest are undecided. Education level is key, as less-educated Belarusians are more likely to stay abroad. An end to repression would increase the share of those Belarusians who want to return to 70 percent, and a regime change would increase this percentage to 82 percent.

Factors Conditioning Human Capital Loss

As expected, due to the involuntary nature of migration of the two groups in focus, a large fraction of survey participants reported losing their profession after migration. As Figure one shows, 48 percent of Belarusians and 63 percent of Ukrainians in our sample reported full loss of their prior careers. The lower percentage of Ukrainians fully retaining their careers (23 percent) compared to Belarusians (44 percent) could be attributed to several factors, including the more recent and disruptive nature of the Russo-Ukrainian war leading to more significant displacement and challenges in finding comparable work. The higher percentage of Ukrainians starting their careers from scratch (49 percent compared to 29 percent among Belarusians) also supports this idea.

Figure 1. Preservation of careers in the EU

Source: Authors’ computations based on survey data.

To foster an evidence-based discussions on the smooth integration of migrants into the EU labor market and the prevention of human capital loss, it is crucial to examine the individual factors that influence career continuity for Belarusian and Ukrainian migrants. We therefore utilize a logistic regression model to identify key predictors that increase the likelihood of migrants remaining in their profession after relocating to Poland and Lithuania.

In our quantitative analysis, an outcome binary variable for staying in the profession is equal to 1 if an individual either “continued career started in a home country (in the same position)” or “remained in the same profession but started working in a position lower than the one held before emigration.” As predictors, we consider a set of sociodemographic variables reasonably related to the probability of staying in the profession and dummy variables for the most common spheres of employment (see Table 1).

Table 1. Overview of model variables

Who Maintains Their Career After Emigration?

Based on the regression coefficients in Table 2, we can identify characteristics related to losing career-specific human capital. In our regression, we control for both home and host country factors. One noteworthy finding is that, while Ukrainian migrants in our sample report significantly higher rates of career loss than Belarusian migrants, nationality itself does not emerge as a significant predictor of career loss once other characteristics are accounted for.

Our results also show that the probability of staying in a profession is higher among men, those with more extended work experience and higher income before emigration, and those who were invited to a host country by an employer. The same holds for entrepreneurs, those who do not plan to return, and those employed in the fields of Architecture & Engineering and Information and Communication Technologies.

Table 2. Results of regression analysis

Note: *** Significant at the .001 level. ** Significant at the .01 level. * Significant at the .05 level.

Conclusion

Several conclusions and policy advice can be derived from the survey results.

The higher likelihood of entrepreneurs staying in their profession suggests that supporting migrant entrepreneurship can be a valuable strategy to retain human capital. This can be done, for example, by:

  • Providing access to resources, mentorship, and funding for migrant entrepreneurs.
  • Streamlining the procedures for migrants to start and operate businesses.
  • Facilitating access to capital for migrant-owned businesses.

The research highlights the disproportionate impact of human capital loss on women.  Therefore, policies should include gender-specific programs that address women’s unique challenges in integrating into new labor markets. This could include:

  • Skills retraining and certification programs: Designed to align women’s existing skills with the demands of the host country’s labor market, with consideration for childcare needs and other barriers women may face.
  • Connecting women migrants with established professionals in their fields to facilitate knowledge transfer and career guidance.
  • Language training programs: Tailored to the specific needs of women, potentially incorporating childcare support to enable participation.

The study highlights the positive role of international companies in supporting employee relocation. Respondents who were invited by an employer demonstrated the most successful integration into the new labor market. To enhance and strengthen these networks, policies may focus on:

  • Encouraging corporations to hire and train migrant workers, potentially through tax breaks or other incentives. This could include partnerships with migrant-serving organizations to connect companies with qualified candidates.
  • Developing digital platforms that connect migrants with diaspora networks, potential employers, and relevant resources.

In addition, policies should address the non-recognition of foreign qualifications, simplifying and expediting the procedures for recognizing foreign degrees and professional certifications. Initiatives to create targeted training programs could complement such policies and allow migrants to quickly acquire any missing skills or certifications required by the host country’s professional bodies. These policy measures would enhance the utilization of migrants’ human capital, benefiting both migrants and host countries while also supporting sending countries. This could be achieved by fostering a successful diaspora or facilitating productive reintegration in the case of return migration.

References

  • Barrett, A., & Goggin, J. (2010). Returning to the question of a wage premium for returning migrants. National Institute Economic Review, 213, R43–R51. https://doi.org/10.1177/0027950110389752
  • Barrett, A., & O’Connell, P. J. (2001). Does training generally work? The returns to in-company training. ILR Review, 54(3), 647–662. https://doi.org/10.1177/001979390105400403
  • Bernstein, S., Diamond, R., McQuade, T. J., & Pousada, B. (2022). The contribution of high-skilled immigrants to innovation in the United States (No. w30797). National Bureau of Economic Research. https://doi.org/10.3386/w30797
  • Boubtane, E. (2019). The economic effects of immigration for host countries. L’Economie politique, 84(4), 72–83. https://doi.org/10.3917/leco.084.0072
  • Boubtane, E., Dumont, J.-C., & Rault, C. (2016). Immigration and economic growth in the OECD countries 1986–2006. Oxford Economic Papers, 68(2), 340–360. https://doi.org/10.1093/oep/gpv024
  • Bowman, M. J., & Myers, R. G. (1967). Schooling, experience, and gains and losses in human capital through migration. Journal of the American Statistical Association, 62(319), 875–898. https://doi.org/10.2307/2283723
  • Co, C. Y., Gang, I. N., & Yun, M.-S. (2000). Returns to returning. Journal of Population Economics, 13, 57–79. https://doi.org/10.1007/s001480050121
  • Duleep, H. O., & Regets, M. C. (1999). Immigrants and human-capital investment. American Economic Review, 89(2), 186–191. https://doi.org/10.1257/aer.89.2.186
  • Engler, P., Giesing, Y., & Kraehnert, K. (2023). The macroeconomic effects of large immigration waves. IAB-Discussion Paper. https://doi.org/10.5167/uzh-239271
  • Iara, A. (2006). Skill diffusion in temporary migration? Returns to Western European working experience in the EU accession countries (Development Studies Working Paper No. 210). Centro Studi Luca d’Agliano. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=921492
  • Reinhold, S., & Thom, K. (2009). Temporary migration and skill upgrading: Evidence from Mexican migrants. University of Mannheim, unpublished manuscript.
  • UNHCR. (n.d.). Operational Data Portal. https://data.unhcr.org/

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Active Labor Market Policy in the Baltic-Black Sea Region

Posted on June 3, 2024 | Policy Brief

Image that shows an overhead view of a large, open pedestrian area with people walking and standing around representing active labour market policy.

This brief outlines the characteristics of active labor market policy (ALMP) in four countries in the Baltic-Black Sea region: Belarus, Lithuania, Poland, and Ukraine. An analysis of the financing expenditure structure within this framework reveals significant differences between the countries, even for Poland and Lithuania, where the policies are to be set within a common EU framework. Countries also differed in terms of their ALMP reaction to the economic challenges brought about by the Covid-19 pandemic, as Poland and Lithuania increased their ALMP spending, while Ukraine, and, especially, Belarus, lagged behind. Despite these differences, all four countries are likely to benefit from a range of common recommendations regarding the improvement of ALMP. These include implementing evidence-informed policymaking and conducting counterfactual impact evaluations, facilitated by social partnership. Establishing quantitative benchmarks for active labor market policy expenditures and labor force coverage by active labor market measures is also advised.

Introduction

This policy brief builds on a study aimed at conducting a comparative analysis of labor market regulation policies in Belarus, Ukraine, Lithuania, and Poland. In comparing the structure of labor market policy expenditures, the aim was to identify common features between Poland and Lithuania, both of which are part of the EU and employ advanced labor market regulation approaches. We also assessed Ukraine’s policies, currently being reformed to align with EU standards, contrasting them with Belarus, where economic reforms are hindered by the post-Soviet authoritarian regime.

The analysis of the labor market policies for the considered countries is based on an evaluation of the structure of pertinent measures between 2017 and 2020 (Mazol, 2022). We used the 2015 OECD systematization of measures of active labor market policy, as presented in the first column of Table 1.

Our study reveals substantial differences in active labor market policies within the four considered countries. Still, motivated by OECD’s approach to ALMP, we provide a range of common policy recommendations that are relevant for each country included in the study. Arguably, aligning with the OECD approach would have more value for current EU and OECD members, Poland and Lithuania, and the aspiring member, Ukraine. However, these recommendations also hold value when considering a reformation of the Belarusian labor market policy.

ALMP Expenditures in Belarus, Lithuania, Poland and Ukraine

Labor market policy comprises of active and passive components. Active labor market policy involves funding employment services and providing various forms of assistance to both unemployed individuals and employers. Its primary objective is to enhance qualifications and intensify job search efforts to improve the employment prospects of the unemployed (Bredgaard, 2015). Passive labor market policy (PLMP) encompasses measures to support the incomes of involuntarily unemployed individuals, and financing for early retirement.

Poland and Lithuania are both EU and OECD members, so one would expect their labor market policies to be driven by the EU framework, and, thus, mostly aligned. However, our analysis showed that the structure of their expenditures on active labor market policies in 2017-2019 differed (Mazol, 2022). In Lithuania, the majority of the funding was allocated to employment incentives for recruitment, job maintenance, and job sharing. From 2017 to 2019, the share for these measures was between 18 and 28 percent of all expenditures for state labor market regulation. In Poland, the majority of funding was allocated to measures supporting protected employment and rehabilitation. The spending on these measures fluctuated between 23 and 34 percent of all expenditures for state labor market regulation between 2017 and 2019.

The response to the labor market challenges during the Covid-19 pandemic in Poland and Lithuania resulted in a notable surge in state labor market policy spendings in 2020, amounting to 1.78 percent of GDP and 2.83 percent of GDP, respectively. Both countries sharply increased the total spending on employment incentives (see Table 1 which summarizes the expenditure allocation for 2020). Poland experienced a nine-fold increase in costs for financing these measures (29.4 percent of total expenditures on state labor market regulation). Meanwhile, in Lithuania, financing for employment incentives increased more than tenfold, amounting to 42.5 percent of all expenditures for state labor market regulation. In both countries it became the largest active labor market policy spending area.

Table 1. Financing of state labor market measures in Baltic-Black Sea region countries in 2020 (in millions of Euro).

Source: DGESAI, 2023. Author’s estimations based on World Bank data (World Bank, 2023), National Bank of Belarus data, National Bank of Ukraine data.

In Ukraine, the primary focus for active labor market policy expenditures was, from 2017 to 2020, directed towards public employment services, comprising 18 to 24 percent of total labor market policy expenditures. Notably, despite the Covid-19 pandemic, there were no significant changes in either the structure or the volume of active labor market policy expenditures in Ukraine in 2020. Despite Ukraine’s active efforts to align its economic and social policies with EU standards, the government has underinvested in labor market policy, with expenditures accounting for only 0.33-0.37 percent of GDP between 2017 and 2020. This is significantly below the levels observed in Lithuania and Poland.

In Belarus, labor market policy financing is one of the last priorities for the government. In 2020, financing accounted for about 0.02 percent of GDP, amounts clearly insufficient for having a significant impact on the labor market. Moreover, Belarus stood out as the sole country in the reviewed group to have reduced its funding for labor market policies, including both active and income support measures, during the Covid-19 pandemic. The majority of the financing for labor market policy has been directed towards protected and supported employment and rehabilitation, including job creation initiatives for former prisoners, the youth and individuals with disabilities.

ALMP Improvement Recommendations

As illustrated above, the countries under review do not have a common approach to active labor market policy spendings. Further, countries like Poland and Lithuania took a more flexible stance on addressing labor market challenges caused by the Covid-19 pandemic, by implementing additional financial support for active labor market policies. However, Ukraine and Belarus did not adjust their expenditure structures accordingly. Part of these cross-country differences can be attributed to differing legal framework: Poland and Lithuania are OECD and EU member states, and, thus, subject to corresponding regulations. Ukraine is in turn motivated by the prospects of EU accession, while Belarus currently has no such prosperities to take into account.

Another important source of deviation arises from the differences in current labor market and economic conditions in the respective countries, and the governments’ need to accommodate these. While such a market-specific approach is well-justified, aligning expenditure structures with current labor market conditions necessitates obtaining updated and reliable information about the labor market situation and the effectiveness of specific labor market measures or programs. An effective labor market policy thus requires establishing a reliable system for assessing the efficiency of government measures, i.e., deploying evidence-informed policy making (OECD, 2022).

To achieve this, it is crucial to establish a robust system for monitoring and evaluating the implementation of specific measures. This involves leveraging data from various centralized sources, enhancing IT infrastructure to support data management, and utilizing modern methodologies such as counterfactual impact evaluations (OECD, 2022).

Moreover, an effective labor market regulation policy necessitates the ability to swiftly adapt existing active measures and service delivery methods in response to changes in the labor market. This might entail rapid adjustments in the legal framework, underscoring the importance of close cooperation and coordination among key stakeholders, and a well-functioning administrative structure (Lauringson and Lüske, 2021).

To accomplish this objective, it is vital to foster close collaboration between the government and institutions closely intertwined with the labor market, capable of providing essential information to labor market regulators. One of the most useful tools in this regard appears to be so-called social partnerships – a form of a dialogue between employers, employees, trade unions and public authorities, involving active information exchange and interaction (OECD, 2022).

A reliable system to assess labor market policy and in particular to facilitate their targeting, is an essential component of this approach.

Ukraine and Belarus are underfunding their labor market policies, both in comparison to the levels observed in Poland and Lithuania, and in absolute terms. It is therefore advisable to establish quantitative benchmark indicators to act as guidance for these countries, in order to ensure that any labor market policy implemented is adequately funded. Here, a reasonable approach is to align the costs of implementing labor market measures with the average annual levels for OECD countries (which are 0.5 percent of GDP for active measures and 1.63 percent for total labor market policy expenditures (OECD, 2024). Furthermore, it’s essential to ensure a high level of labor force participation in active labor market regulation measures. A target standard could be set, based on the average annual coverage from active labor market measures, at 5.8 percent of the national economy labor force, as observed in OECD countries (OECD, 2024).

Conclusion

The countries under review demonstrate varying structures of active labor market expenditures. Prior to the Covid-19 pandemic, employment incentives received the most financing in Lithuania. In Poland the largest share of expenditures was instead directed to measures to support protected employment and rehabilitation. In Ukraine, the main expenditures were directed towards financing employment services and unemployment benefits while Belarus primarily allocated funds to protected and supported employment and rehabilitation. Notably, Lithuania and Poland responded to the economic challenges following Covid-19 by significantly increasing spending on employment incentives, while Ukraine and Belarus did not undertake such measures.

Part of the diverging patterns may be attributable to the countries varying legal framework and differences in the countries respective labor market and economic conditions.

While some of the differences in labor market policies are thus justified, ensuring funding at the OECD level for labor market measures, alongside adequate tools for monitoring and evaluating labor market policies, are likely to benefit all four Baltic-Black Sea countries.

References

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.