On the labour market integration of refugees from Ukraine: A simulation study

Kosyakova, Yuliya; Brücker, Herbert Research Report On the labour market integration of refugees from Ukraine: A simulation study IAB-Forschungsbericht, No. 9/2024en Provided in Cooperation with: Institute for Employment Research (IAB) Suggested Citation: Kosyakova, Yuliya; Brücker, Herbert (2024) : On the labour market integration of refugees from Ukraine: A simulation study, IAB-Forschungsbericht, No. 9/2024en, Institut für Arbeitsmarktund Berufsforschung (IAB), Nürnberg, https://doi.org/10.48720/IAB.FB.2409EN This Version is available at: https://hdl.handle.net/10419/301252 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-sa/4.0/deed.de IAB Forschungsbericht 9|2024en 2 IAB RESEARCH REPORT Results from the project work of the IAB 9|2024en On the labour market integration of refugees from Ukraine: A simulation study Yuliya Kosyakova, Herbert Brücker On the labour market integration of refugees from Ukraine: A simulation study Yuliya Kosyakova (IAB, University of Bamberg) Herbert Brücker (IAB, Humboldt-Universität zu Berlin, BIM) The IAB Research Reports (IAB-Forschungsberichte) series publishes larger-scale empirical analyses and project reports, often with heavily dataand method-related content. In der Reihe IAB-Forschungsberichte werden empirische Analysen und Projektberichte größeren Umfangs, vielfach mit stark datenund methodenbezogenen Inhalten, publiziert. IAB Research Report 9|2024en 3 In brief • The Ukrainian population in Germany has increased from 156,000 to 1,240,000 by the end of 2023 since the onset of the Russian war of aggression. Given the prolonged conflict, an increasing number of Ukrainian refugees are considering longer or permanent stays in Germany, underscoring the importance of their labour market integration. • This research report develops scenarios based on data from refugees who moved to Germany before 2022 and other migrants from the former Soviet Union to project potential employment rate developments for Ukrainian refugees. • In the baseline scenario, Ukrainian refugees achieve an average employment rate of 45 percent after five years of residence in Germany, and 55 percent after ten years. • The employment rate disparity between genders is notable. Five years post-arrival, men achieve an employment rate of 58 percent, while women reach 41 percent. After ten years, these rates increase to 68 percent for men and 52 percent for women. • The family constellations of Ukrainian refugees, particularly the high proportion of single mothers, and their comparatively poor health negatively impact employment rate development. Conversely, their high level of education, anticipated improvement in language skills, and favorable institutional conditions – especially the abolition of the asylum procedure – positively influence employment outcomes. • Language support and completion of language courses are beneficial to employment rate development. • As the labour market tightens, the employment rates of Ukrainian refugees significantly increase. IAB Research Report 9|2024en 4 Contents In brief ................................................................................................................................................... 3 Contents ............................................................................................................................................... 4 Summary .............................................................................................................................................. 5 1 Introduction ................................................................................................................................. 7 2 Parallels and differences ............................................................................................................ 10 2.1 Demographic factors .......................................................................................................... 10 2.2 Education, training, language skills and employment status .......................................... 13 2.3 Institutional and other framework conditions .................................................................. 15 3 Method ........................................................................................................................................ 18 4 Simulation results ...................................................................................................................... 20 4.1 Baseline scenario ............................................................................................................... 20 4.2 Family constellations ......................................................................................................... 21 4.3 Education and training ...................................................................................................... 23 4.4 German language skills and German language courses ................................................... 24 4.5 Asylum procedure .............................................................................................................. 26 4.6 Health ................................................................................................................................. 27 4.7 Cumulative effect of German language course participation, asylum application and health status ................................................................................................................................... 29 4.8 Role of local labour market conditions ............................................................................. 30 5 Conclusion .................................................................................................................................. 32 References .......................................................................................................................................... 35 Appendix ............................................................................................................................................. 39 Figures ................................................................................................................................................ 42 Tables ................................................................................................................................................. 42 IAB Research Report 9|2024en 5 Summary Since the onset of Russia's war against Ukraine, the number of Ukrainian nationals in Germany has risen from 156,000 to 1,240,000. Although many plan to return to Ukraine after the war, the duration of the conflict is leading an increasing number of them to consider staying in Germany for an extended period or permanently. Against this backdrop, this research report simulates various scenarios of labour market integration for Ukrainian refugees, based on the employment trajectories of previous refugees and migrants from the former Soviet Union. The scenarios aim to establish realistic expectations about the progress of labour market integration for Ukrainian refugees and to quantitatively assess the impact of specific factors. However, these conditional scenarios should not be misunderstood as forecasts, as they are based on strong assumptions and do not fully control for many relevant factors. In the baseline scenario, which is based on what we believe are the most realistic assumptions regarding demographic factors, family configurations, education, language skills, and institutional and economic conditions, the average employment rate for Ukrainian refugees is projected to be 45 percent after five years and 55 percent after ten years. Gender composition and family configuration have a dampening effect on employment rates, while education level and expected language skills development have a positive impact. The comparatively poor health of Ukrainian refugees also dampens employment rates. Conversely, institutional conditions, particularly the absence of asylum procedures, have a positive impact. Economic conditions, measured by labour market tightness, also have a strong influence. The current economic downturn has a negative effect, but the picture could quickly improve with an economic recovery due to increasing labour market tension amid demographic changes, which would likely increase employment rates compared to the baseline scenario. A key finding for integration policy is that language courses not only enhance language development but also significantly boost employment rates. Zusammenfassung Seit Beginn des russischen Angriffskriegs auf die Ukraine ist die Anzahl ukrainischer Staatsangehörigen in Deutschland von 156.000 auf 1.240.000 angestiegen. Obwohl ein erheblicher Teil dieser Menschen nach dem Kriegsende plant, in die Ukraine zurückzukehren, zeigt sich, dass mit zunehmender Kriegsdauer immer mehr einen längeren oder dauerhaften Aufenthalt in Deutschland in Betracht ziehen. Vor diesem Hintergrund simuliert dieser Forschungsbericht verschiedene Szenarien der Arbeitsmarktintegration ukrainischer Geflüchteter. Grundlage bilden die Erwerbsverläufe früherer Geflüchteter in Deutschland sowie von Migrantinnen und Migranten aus der ehemaligen Sowjetunion. Die Szenarien zielen darauf ab, realistische Erwartungen über die Entwicklung der Arbeitsmarktintegration von Geflüchteten aus der Ukraine zu bilden, und die Einflüsse spezifischer Faktoren quantitativ zu analysieren. Diese konditionalen Szenarien sind allerdings nicht als Prognosen misszuverstehen, da sie davon IAB Research Report 9|2024en 6 abhängig sind, ob die zugrunde gelegten Annahmen zutreffen und viele relevante Faktoren nicht vollständig berücksichtigt werden können. In dem Basisszenario, dem nach unserer Einschätzung die realistischsten Annahmen über demografische Faktoren, Familienkonstellationen, Bildung, Sprache, institutionelle und wirtschaftliche Faktoren zu Grunde liegen, ergibt sich nach einer Aufenthaltsdauer von fünf Jahren eine durchschnittliche Erwerbstätigenquote von 45 Prozent, nach zehn Jahren von 55 Prozent. Insbesondere der hohe Anteil von Alleinerziehenden sowie der vergleichsweise schlechte Gesundheitszustand der ukrainischen Geflüchteten wirken sich dämpfend auf die Entwicklung der Erwerbstätigenquoten aus, während das relativ hohe Bildungsund Ausbildungsniveau sowie die zu erwartende Entwicklung der Sprachkenntnisse einen positiven Einfluss haben. Dies gilt auch für die institutionellen Rahmenbedingungen, insbesondere den Verzicht auf ein Asylverfahren. Die wachsende Arbeitsmarktanspannung hat ebenfalls einen starken positiven Einfluss. Die gegenwärtige Eintrübung der Konjunktur wirkt sich deshalb nachteilig aus, allerdings kann sich das Bild bei einer konjunkturellen Erholung aufgrund der demografiebedingt steigenden Arbeitsmarktanspannung schnell verbessern. Dann dürften die Erwerbstätigenquoten im Vergleich zum Basisszenario steigen. Ein zentraler Befund für die Integrationspolitik ist, dass Sprachkurse nicht nur die Sprachentwicklung, sondern auch die Erwerbstätigenquoten signifikant steigern können. IAB Research Report 9|2024en 7 1 Introduction Since the onset of Russia's military aggression against Ukraine in February 2022, the population of Ukrainian nationals in Germany has surged from 156,000 to 1,240,000 by the end of 2023 (BAMF, 2024). These people are entitled to temporary protection in Germany in accordance with Section 24 of the Residence Act (AufenthG). Given the uncertain future and duration of the conflict in Ukraine, the long-term intentions and prospects of these refugees remain unclear. Nevertheless, as their duration of stay extends, an increasing number of Ukrainians in Germany are considering longer or permanent settlement (Brücker et al., 2023c; Kosyakova et al., 2023). In this context, the challenges of labour market integration and social inclusion are gaining prominence, compounded by the humanitarian obligation to offer protection. To facilitate faster integration into the labour market, the German government has launched initiatives collectively referred to as "Job Turbo."1 Effective implementation and evaluation of these measures require realistic expectations about the potential trajectory of integration. This report contributes to forming those expectations. Germany has extensive experience in integrating refugees into the labour market, particularly following the significant influx of refugees in 2015 and 2016, as well as migrants from Ukraine and other former Soviet states. This historical context provides valuable insights for anticipating the labour market integration of Ukrainian refugees today. However, there are notable differences and similarities between the current Ukrainian refugees and earlier groups of refugees and migrants. Like other refugees, Ukrainian refugees often arrive poorly prepared for migration and integration into the labour market, typically lacking German language skills, professional connections, and immediate job opportunities (Brücker et al., 2023a; Kosyakova and Kogan, 2022). Furthermore, many are burdened with psychological stress and health issues stemming from the war and their subsequent flight (Ambrosetti et al., 2021; Brücker et al., 2019, 2023c). At the same time, significant differences distinguish Ukrainian refugees from other groups. Notably, a high proportion of Ukrainian refugees are women, due to bans on military-age men from leaving the country. Many arrive with children, placing additional burdens on single parents, and they tend to be older on average upon arrival compared to other refugee groups (Brücker et al., 2023a, 2016; Fendel et al., 2023). These demographic and family dynamics can complicate their integration into the labour market. However, Ukrainian refugees generally possess a higher average level of education compared to other refugee groups (Fendel et al., 2023), which can facilitate language acquisition – a crucial skill for integrating into the labour market and broader society (Kosyakova et al., 2022). The institutional conditions for Ukrainian refugees also differ significantly: they are permitted to work immediately upon arrival, bypassing the standard asylum procedure (Fendel et al., 2023). However, their temporary protection under Section 24 of the Residence Act (AufenthG) is timelimited, impacting their legal status and planning certainty. This uncertainty can deter investments in country-specific skills and stable employment relationships. Starting in June 1 https://www.bmas.de/DE/Arbeit/Migration-und-Arbeit/Flucht-und-Aysl/Turbo-zur-Arbeitsmarktintegration-vonGefluechteten/turbo-zur-arbeitsmarktintegration-von-gefluechteten.html https://www.bmas.de/DE/Arbeit/Migration-undArbeit/Flucht-und-Aysl/Turbo-zur-Arbeitsmarktintegration-von-Gefluechteten/turbo-zur-arbeitsmarktintegration-vongefluechteten.html IAB Research Report 9|2024en 8 2022, Ukrainian refugees became eligible for benefits under Germany's Social Code II (Bürgergeld) where necessary, receiving slightly higher rates than those available to asylum seekers.2 This early integration into the job center support structures, likely playing a key role in facilitating their entry into the labour market. The initial conditions in their countries of origin also significantly influence refugees' chances for integration. Most refugees come from countries affected by war, civil unrest, and persecution, such as Afghanistan and Syria. These conditions drive many to seek permanent residency in Germany (Brücker et al., 2020b, 2023d). In contrast, Ukrainians are not fleeing their government but are escaping the Russian military aggression. Many plan to return to Ukraine post-conflict, while others remain undecided about whether to stay in Germany or return home (Brücker et al., 2023c). Additionally, transnational living models are becoming more prevalent, affecting their integration into the labour market and other societal areas (Kosyakova et al., 2023). Against this backdrop, this research report develops potential scenarios for labour market integration of refugees over the coming years, drawing on data regarding refugees and populations from former Soviet states in Germany. It accounts for demographic characteristics such as gender, age, and parental status, as well as observable human capital factors including education, work experience, and German language proficiency. The simulation also considers some institutional differences, like asylum procedures and residency status, although these are subject to limitations. Further limitations arise due significant variability among groups, influenced by unobservable factors such as values, cultural attitudes, motivation, and personal experiences, also plays a role in integration outcomes. Additionally, some institutional frameworks, such as temporary protection under Section 24 of the Residence Act and immediate inclusion in the SGB II benefits system, lack historical precedents, limiting our ability to fully gauge their impact. Moreover, the changing nature of framework conditions over time means that the influence of specific factors is not constant. The current economic landscape is less favorable than in the previous decade, and predicting future economic conditions remains challenging. Therefore, the simulations presented in this report should not be interpreted as forecasts or predictions. Instead, they are conditional scenarios based on specific, often simplified, assumptions. The purpose is not to predict future outcomes but to explore plausible developments under various assumptions about refugee characteristics and the institutional and political framework conditions. The subsequent section leverages data from the Socio-Economic Panel (SOEP), the integrated studies of the IAB-SOEP Migration Sample and IAB-BAMF-SOEP Survey of Refugees in Germany along with the IAB-BIB/FReDA-BAMF-SOEP Survey of Refugees from Ukraine (cf. Info box 1). This section also briefly outlines parallels and differences between the populations represented in our samples and the Ukrainian refugees (Section 2). Following this, we detail the methodology and assumptions underpinning the individual simulations (Section 3). Section 4 presents the main findings from the simulations, which explore various scenarios based on the skills of the refugees and the institutional and integration policy framework. The report concludes with Section 5, where we draw key conclusions from the findings. 2 Since January 1, 2024, the standard benefit rate for basic security for single or single-parent adults under SGB II has been Euro 563, compared to Euro 460 under the Asylum Seekers Benefits Act. However, the difference in benefit rates is only temporary - refugees with a recognized protection status also receive benefits under SGB II II. IAB Research Report 9|2024en 15 working full-time). However, it is important to consider their shorter duration of stay when evaluating these figures. The employment rate among Ukrainian refugees is the lowest at just 18 percent at the time of the survey, of which 40 percent were in full-time employment. This lower rate is particularly due to their very short length of stay – less than one year – compared to the other two groups. In summary, Ukrainian refugees show similarities to migrants from the former Soviet Union in terms of their educational levels, notably with both groups having comparably high proportions of individuals with tertiary education. In terms of employment prior to immigration, Ukrainian refugees resemble refugees who came to Germany before 2022, as both groups have relatively high employment rates before arriving in Germany. Additionally, like the refugees who arrived before 2022, Ukrainian refugees typically have little knowledge of German upon arrival, a common characteristic of refugee migration processes. However, Ukrainian refugees are distinguished by poorer health compared to the other two groups. 2.3 Institutional and other framework conditions The various groups differ not only in their initial conditions for labour market integration, influenced by family status, gender, age, education level, language proficiency, and other factors, but also in the institutional and integration policy conditions they face. As illustrated in Table 3, these conditions vary significantly between refugees, migrants from the former Soviet Union, and Ukrainian refugees. Key differences include residence status, employment restrictions, access to the labour market, residential requirements, other limitations on spatial mobility, the extent of social transfer benefits in cases of need, access to language courses and other integration measures, as well as job placement and support. These variations can significantly influence their integration into the labour market and participation in other societal areas, as will be explored in the subsequent discussion. The asylum procedure is a critical factor for refugees who are not directly accepted by Germany. Throughout the asylum process, refugees face considerable uncertainty regarding their prospects of remaining in Germany, which can hinder investments in human capital by the individuals concerned and in their employment by companies. Thus, the duration of asylum procedures can have adverse effects on labour market integration in both the medium and long term (Åslund et al., 2024; Hainmueller et al., 2016; Hvidtfeldt et al., 2020; Kosyakova and Brenzel, 2020). After completion of the asylum procedure, the resultant residence status varies depending on whether an individual’s application for protection is approved or if they are designated as tolerated, latent, or obligated to leave the country. The duration of the right to reside also varies based on the protection status granted: those entitled to asylum and refugees recognized under the Geneva Refugee Convention are issued a residence permit for three years with the possibility of extension, while those granted subsidiary protection receive a one-year residence permit that can be renewed for two years at a time. Individuals under a national deportation ban are granted a residence right for at least one year. A permanent settlement permit, which is an unlimited right of residence, is achievable after three years for those entitled to asylum and recognized refugees under the Geneva Convention, or after five years for those with subsidiary protection and those under a national deportation ban (§ 9, § 25, paragraphs 1-3, § 26 AufenthG). IAB Research Report 9|2024en 16 Table 3: Institutional and other framework conditions Empty cell Refugees 1) Former Soviet Union 2) Ukrainian refugees 3) Asylum procedure Yes Usually no (depending on the legal status at arrival) no Residence status depending on the outcome of the asylum procedure 4) depending on the legal status at arrival Temporary protection according to § 24 AufenthG 5) Employment ban Absolute employment ban 3 months, up to 9 months restricted access to the labour market, since 2024 6 months 6) Usually no (depending on the legal status at arrival) no Residence requirements Yes, from 2016 also for refugees with recognized protection status 7) Usually no (depending on the legal status at arrival) possible, is not usually applied when finding private accommodation Access to integration courses and other BAMF language programs since 10/2015 for asylum seekers with good prospects of staying, after the end of the asylum procedure for all yes (depending on the legal status at arrival) Yes Benefit system in case of need initially Asylum Seekers Benefits Act, with recognized protection status SGB II usually SGB II (depending on the legal status at arrival) SGB II Support structure of the employment agencies Employment agencies for asylum seekers, change of legal status to job center after asylum decision 8) Employment agencies and job centers as required Job center Notes: 1) Persons who have applied for asylum in Germany or have been accepted directly by Germany on humanitarian grounds. 2) Persons who arrived as nationals of the successor states of the former Soviet Union (excluding Ukrainian nationals who arrived since 22.02.2022). 3) Ukrainian nationals who arrived since 22.02.2022. 4) The group includes persons who are still in the asylum process, who have been granted protection status and who are in Germany on a tolerated, latent or enforceable obligation to leave the country following the rejection of protection status. 5) Temporary protection under Section 24 AufenthG was initially valid until June 2023 and was extended until June 2025. 6) The absolute employment ban applies for three months after an asylum application has been submitted, after which employment during the asylum procedure can be permitted by the immigration offices with the approval of the Federal Employment Agency. This restricted access to the labour market applied for a maximum of 9 months (parents of underage children: 6 months), since 2024 for 6 months. 7) Asylum seekers are initially assigned a place of residence and are generally obliged to live in reception centers during the asylum procedure. After recognition of protection status, residence must be taken in the federal state of the place of residence; the federal states can also restrict the choice of place of residence to districts or municipalities. 8) During the asylum procedure, the employment agencies are responsible, but contact is voluntary. Only after the asylum decision has been made is there a change in legal status, at which point the job centers are responsible for granting benefits and at the same time for job placement and support. Source: Own compilation. Since 2013, the legal framework governing access to the labour market for refugees in Germany has undergone several changes. Initially, employment was entirely prohibited for asylum seekers for the first 12 months after their arrival, but this waiting period was reduced to three months in fall 2014 (Grote, 2018). During the asylum procedure, access to the labour market remained restricted for up to nine months, although it could be granted by immigration offices with approval from the Federal Employment Agency. This period was further reduced to six months by the end of 2023. Requirement to reside in reception facilities or shared accommodations and specific residence requirements restrict spatial mobility, thus hindering opportunities for labour market integration (Brücker et al., 2020a; Cardozo Silva et al., 2023). Refugees are generally required to live in these IAB Research Report 9|2024en 17 facilities for up to 18 months during their asylum process (six months for parents with underage children). From August 2015, they have also been subject to a residence obligation for an additional three years after their protection status is recognized (Section 12a Residence Act). Proficiency in the German language is crucial for social participation and integration into the labour market (Kosyakova et al., 2022). Until October 2015, asylum seekers were excluded from participating in integration courses and other BAMF language programs. Since then, asylum seekers from countries with a high probability of remaining in Germany have been permitted to participate in these courses, a policy that has since been extended to all asylum seekers as well as those with recognized protection and tolerated status. Participation in these courses can be mandatory if they receive benefits under the Asylum Seekers Benefits Act (§ 44 AufenthG). Asylum seekers are eligible for benefits under the Asylum Seekers Benefits Act, which are lower than those of the basic income support provided by the German Social Security Code II (Bürgergeld). If housed in reception facilities, benefits are primarily provided as in-kind services; if in private accommodation, they are mainly given as cash benefits. After recognition of their protection status, there is a change in legal status and refugees then receive benefits in accordance with SGB II if necessary. The system of benefit provision is closely linked to job placement and support. While the employment agencies of the Federal Employment Agency are primarily responsible for asylum seekers, benefits are disbursed by local authorities. This division often means that asylum seekers are not integrated into the placement and support structures of the employment agencies and must take the initiative to visit these agencies themselves, which is a practical challenge. Once their protection status is recognized, job centers take over responsibility, thereby institutionally unifying the granting of benefits, job placement, and support under one roof. Empirical evidence indicates that placement and training services are often utilized only after the recognition of protection status (based on the IAB-BAMF-SOEP survey of refugees). The population from the former Soviet Union and its successor states arrived in Germany through various channels, including as ethnic German repatriates, quota refugees, family reunification, and labour and educational migration, with a smaller number coming through the asylum system (Brücker, 2022; Kalter and Kogan, 2014; Liebau, 2011). These diverse routes have led to different institutional conditions for residency and labour market integration. Typically, individuals from this group received either a temporary residence permit or a permanent settlement permit, and ethnic German repatriates often obtained German citizenship directly. Unlike other refugees, they generally were not subject to employment restrictions. However, ethnic German repatriates and Jewish quota refugees sometimes faced a residence requirement. Following the labour market reforms of 2005, they were integrated into the SGB II benefits system (formerly social assistance) in the same manner as German nationals when necessary and had access to the employment agencies’ and job centers’ placement and support structures. Integration courses and other BAMF language programs were also made available to these groups in a manner similar to other migrant groups. For Ukrainian refugees who have moved to Germany since February 24, 2022, the conditions differ due to the granting of temporary protection under Section 24 of the Residence Act (AufenthG). This regulation immediately grants them a right of residence without an asylum procedure, and the Dublin procedure does not apply. They are allowed to take up employment or IAB Research Report 9|2024en 18 self-employment without restrictions. Although this protection is time-limited – initially until March 5, 2024, with an extension to March 5, 2025 – it provides greater legal and planning security than for those in the asylum procedure, though less than for recognized refugees or individuals entitled to protection under Article 16a of the Basic Law. In principle, individuals receiving temporary protection under Section 24 AufenthG are also subject to geographical restrictions when choosing their place of residence. However, in practice, there is no spatial distribution if the individuals are privately accommodated with friends or relatives or have found their own private accommodation. Consequently, less than a tenth live in shared accommodation immediately after arrival (Brücker et al., 2023a; Kosyakova et al., 2023). Access to integration courses and other BAMF language programs is open to all those with temporary protection, resulting in a higher participation rate than for other refugees (Fendel et al., 2023). Participation in language courses also depends on the availability of programs but is higher than for other refugees at the beginning of their stay (Brücker et al., 2023a; Kosyakova et al., 2023). Since June 2022, they have also received benefits under the German Social Code II (Bürgergeld), which are higher than those provided under the Asylum Seekers Benefits Act. Due to their inclusion in the SGB II system in June 2022, Ukrainian refugees are integrated into job center structures at an early stage, facilitating rapid access to placement and support service. 3 Method The simulations draw on a comprehensive database, which includes data from the SocioEconomic Panel, the integrated studies of the IAB-SOEP Migration Sample, the IAB-BAMF-SOEP Survey of Refugees in Germany, and the IAB-BIB/FReDA-BAMF-SOEP Survey of Refugees from Ukraine (Info box 1). The core of these simulations are regression analyses that model employment as a dependent binary variable, influenced by a range of observable sociodemographic factors along with selected institutional and economic framework conditions. Employment probability models used in this analysis are weighted and based on a probit model, with standard errors clustered at the individual level. The coefficients derived from these models (see Table 1) for various explanatory variables are utilized to construct conditional scenarios. These scenarios are built upon specific assumptions about the evolution of these factors for the Ukrainian refugee population in Germany. By altering these assumptions, the simulations not only explore the potential effects of different integration policies but also assess the robustness of the results relative to the assumptions made. The analyses and subsequent simulations consider the following explanatory factors: • gender (0 = man; 1 = woman), • children in the household (0=no children under 17; 1=youngest child between 0-6; 2=youngest child between 7-17), • partner in the household (0 = no; 1 = yes), • age at immigration, age at immigration squared, IAB Research Report 9|2024en 19 • highest level of education achieved (1 = less than primary level, 2 = primary level, lower secondary level (middle school), 3 = upper secondary level (high school) / post-secondary non-tertiary education, 4 = tertiary education), • employed before immigration (0 = no; 1 = yes), • asylum application on arrival (0 = no; 1 = yes), • good to very good health at the time of the survey (0 = no; 1 = yes), • language course participation before or after immigration (0 = no; 1 = yes), • good or very good German language skills at the time of the survey (0 = no; 1 = yes) • intention to stay in Germany forever (0 = no; 1 = yes), • east Germany (0 = no; 1 = yes), • regional labour market tension (ratio of vacancies to unemployed) at the time of the survey, • nationwide unemployment rate at the time of the survey, • duration of stay (indicator variables for 1 to 15 years and more since moving in). Due to data limitations, it is not possible to precisely determine the current residence status of all individuals in the samples – for example, whether they are in an asylum procedure – as well as their participation in integration courses, other language courses, and qualification programs at the time of the survey. Consequently, the simulations distinguish whether a person (i) has migrated to Germany as someone seeking protection and thus has undergone an asylum procedure, or (ii) has completed an integration course, vocational language course, or other German language course before or after moving to Germany. These conditions are assumed to be constant in the simulations, while the duration of stay is treated as a variable over time. German language proficiency is recognized as a crucial factor for successful labour market integration. Therefore, in a separate regression, the development of German language skills over time – or with an increasing length of stay – is estimated using the same explanatory variables as in the employment probability regression (see above) (see Table 2). The coefficients derived from this analysis are used to simulate the progression of Ukrainian refugees’ German language skills throughout their stay in Germany. These simulated language skills are then applied to model labour market integration. The employment rates were simulated over a 10-year period following the influx. As outlined in the introduction, the results of these simulations should not be viewed as forecasts. They are conditional scenarios based on strong assumptions. It is important to highlight that the outcomes of the simulations are highly dependent on the populations included in the samples, particularly for all non-observable factors or factors not included in the regressions. If the Ukrainian refugees differ significantly from these populations in terms of these unobservable characteristics, it could lead to divergent trends in labour market integration. Moreover, the estimated coefficients may not remain constant over time. Additionally, the institutional framework conditions differ from all prior historical experiences, which could skew the simulation results in various directions. IAB Research Report 9|2024en 20 4 Simulation results This section outlines different scenarios for the development of employment rates among Ukrainian refugees. Initially, a baseline scenario is presented, deemed the most realistic based on the given assumptions. This scenario posits that language skills will improve over the duration of the stay. Given that women make up a significant portion of the Ukrainian refugee population, the employment rates are further differentiated by gender and family constellation. Additionally, various scenarios are simulated to highlight the effects of several factors on employment rates. These factors include the level of education, participation in German language courses, involvement in asylum procedures, health status, and regional labour market conditions. Each scenario is designed to provide insights into how these elements might influence the labour market integration of Ukrainian refugees under different conditions. 4.1 Baseline scenario Figure 1 illustrates the simulated trend in employment rates for Ukrainian refugees since their arrival in Germany, utilizing a two-stage simulation method detailed in Chapter 4. The process begins by simulating the development of German language skills over time for Ukrainian refugees. This is based on data derived from refugees who arrived before 2022 and individuals from the former Soviet Union. Factors such as the presence of children and a partner in the household, age at arrival, socioeconomic factors (including education level and work experience before immigration), legal status at arrival (e.g., asylum application), intention to stay, health, participation in language courses, as well as the federal state of residence and the local economic situation are all considered in estimating German language proficiency. These variables reflect the average characteristics of the Ukrainian population in 2023. Subsequently, the probability of employment is calculated, leveraging the simulated German language skills by duration of stay and gender. The estimation of employment integrates the same observable demographic factors used in the language skills simulation, in addition to the newly acquired language competencies. The results demonstrate a steady increase in employment rates for Ukrainian refugees: starting at approximately 8 percent in the first year of residence and rising to 45 percent after five years. In this conditional scenario, the employment rate reaches 55 percent a decade after immigration. The dynamics vary by gender: men begin with an employment rate of 15 percent in the first year post-immigration, which increases to 58 percent after five years and 68 percent after ten years. Women start with a lower initial employment rate of 7 percent in the first year. However, their employment rates also rise steadily over time, reaching 41 percent after five years and 52 percent ten years post-immigration. Given that nearly 80 percent of the Ukrainian adult population of working age in Germany are women, their progress significantly influences the overall average employment rate. IAB Research Report 9|2024en 21 Figure 1: Baseline scenario: Simulation of the employment rate of Ukrainian refugees by gender and duration of stay Percentage of people of working age (18 to 64 years) Notes: Only people aged between 18 and 64 at the time of the survey. Gainfully employed persons are all persons who receive remuneration for their work (definition of the Federal Statistical Office). This also includes trainees, interns and marginally employed persons. Legend: The expected employment rate of Ukrainian refugees 10 years after arrival in Germany is 55 percent if the coefficients obtained from the analysis of German language skills and employment are used. Source: Own calculations based on data from SOEP-CORE, IAB-SOEP-MIG, IAB-BAMF-SOEP-REF and IAB-BiB/FReDA-BAMFSOEP-UA. 4.2 Family constellations Given that women heavily influence the average employment rate (cf. Table 1), Table 4 offers a detailed look at the simulated employment rates of Ukrainian refugees, broken down by household composition. There are noticeable differences, influenced by family circumstances and whether there is a partner or underage children in the household. In the first year after their arrival, men without minor children living with their partner have an employment rate of 24 percent, while women in the same situation start with an employment rate of 13 percent. After a decade, these rates increase to 79 percent for men and 65 percent for women. IAB Research Report 9|2024en 22 Table 4: Simulated employment rate of refugees from Ukraine by children and partner in the household Percentage of people of working age (18 to 64 years) Empty cellEmpty cell Length of stay <= 1 year 2 3 4 5 6 7 8 9 10 Families No children M 24 47 60 65 71 74 74 76 76 79 No children F 13 30 43 48 55 59 59 61 60 65 Children 0-6 years M 15 33 46 51 58 62 62 65 64 68 Children 0-6 years F 7 20 30 35 41 45 45 48 47 51 Children 6-17 M 23 46 59 64 70 74 73 75 75 78 Children 6-17 F 12 29 42 47 54 58 58 60 59 64 Single parents Children 0-6 years M 9 23 34 39 46 50 50 53 52 56 Children 0-6 years F 4 12 20 24 30 34 33 36 35 39 Children 6-17 M 15 34 47 52 58 63 62 65 64 68 Children 6-17 F 7 20 30 35 41 46 46 48 47 52 Single person M 16 35 48 53 59 64 63 66 65 69 Single person F 7 21 31 36 42 47 47 49 48 53 Notes: Only people aged between 18 and 64 at the time of the survey. Gainfully employed persons are all persons who receive remuneration for their work (definition of the Federal Statistical Office). This also includes trainees, interns and marginally employed persons. Source: Own calculations based on data from SOEP-CORE, IAB-SOEP-MIG, IAB-BAMF-SOEP-REF and IAB-BiB/FReDA-BAMFSOEP-UA. The presence of children aged 0 to 6 significantly alters employment dynamics. Men in households with young children have a first-year employment rate of 15 percent, which increases to 68 percent after ten years. For women in similar family situations, the employment rate starts at 7 percent, rising to 51 percent after a decade. If the children are aged 6 to 17, initial employment rates are higher for both genders. In this family setup, after ten years, rates reach 78 percent for men and 64 percent for women. Single-parent men with children aged 0 to 6 and 6 to 17 begin with employment rates of 9 percent and 15 percent, respectively, which increase to 56 percent and 68 percent after ten years. Single-parent women in these categories start with lower rates of 4 percent and 7 percent, respectively, achieving 39 percent and 52 percent after a decade. For single individuals without minor children, the employment rate for men is 16 percent in the first year, rising to 69 percent after ten years. Women start at 7 percent and reach 53 percent by the end of the simulation period. These scenarios highlight the crucial influence of family constellations on labour market integration, especially pertinent for Ukrainian refugees, among whom the proportion of single mothers in Germany is notably high at 36 percent (Brücker et al., 2023a). They begin with comparatively low employment rates upon arrival and consistently lag behind the rates of men throughout the simulation. This disparity underscores the importance of targeted support measures to enhance labour market opportunities for single mothers, such as childcare services, flexible working hours, and vocational training. These interventions would help promote economic independence and stability for this vulnerable group. IAB Research Report 9|2024en 23 4.3 Education and training Access to the German labour market is significantly influenced by educational and vocational qualifications (Müller and Shavit, 1998). In this context, Figure 2 demonstrates how labour market participation for Ukrainian refugees evolves based on their education level. It is evident that higher educational qualifications are crucial for labour market integration: Individuals with tertiary education qualifications consistently exhibit higher employment rates, reaching nearly 60 percent after a decade. Conversely, Ukrainian refugees with only elementary schooling or no formal education at all have employment rates slightly over 30 percent ten years after their arrival. The high average level of education among Ukrainian refugees provides a strong foundation for successful integration into the German labour market (Brücker et al., 2023a), as further illustrated by other scenarios. For instance, a hypothetical scenario that assumes an educational structure similar to that of refugees who immigrated before 2022 – while maintaining the same conditions for Ukrainian refugees in terms of gender, family status, age, etc. – shows that the employment rates would be only 38 percent five years after immigration and 49 percent after ten years (cf. Figure 1). The possession of a professional qualification from abroad is often insufficient in Germany, particularly in regulated professions where recognition of foreign qualifications is critical for securing appropriate employment. The recognition of professional qualifications is key to bridging information gaps in the labour market, thus enhancing labour market integration (Brücker et al., 2021; Damelang and Kosyakova, 2021). Empirical studies indicate that recognition of professional qualifications can boost the employment chances of migrants by up to 25 percentage points and increase their income by 20 percent over the long term (Brücker et al., 2021). The scenarios presented here suggest that targeted educational and training measures, which facilitate the recognition of foreign qualifications and enhance educational skills, can play a decisive role in accelerating labour market integration. IAB Research Report 9|2024en 24 Figure 2: Simulated employment rate of refugees from Ukraine according to the highest level of education attained before immigration Percentage of people of working age (18 to 64 years) Notes: Only people aged between 18 and 64 at the time of the survey. Gainfully employed persons are all persons who receive remuneration for their work (definition of the Federal Statistical Office). This also includes trainees, interns and marginally employed persons. Source: Own calculations based on data from SOEP-CORE, IAB-SOEP-MIG, IAB-BAMF-SOEP-REF and IAB-BiB/FReDA-BAMFSOEP-UA. 4.4 German language skills and German language courses German language skills are crucial to improving employment rates. This importance is highlighted in a counterfactual scenario where language skills remain constant at the level they were upon arrival. Under this assumption, while the employment rate still rises over time, it only reaches about 38 percent after five years and approximately 45 percent after ten years. This is 7 percentage points lower after five years and 10 percentage points lower after ten years compared to the baseline scenario, which assumes increasing German language proficiency (cf. Figure 3). IAB Research Report 9|2024en 31 although it dropped to 0.30 in 2023. Notably, labour market tension varies considerably between the federal states; for example, in 2022, it was 0.1 in Berlin and 0.57 in Bavaria.3 Figure 8: Simulated employment rate of refugees from Ukraine according to labour market tension Percentage of people of working age (18 to 64 years) Notes: Only people aged between 18 and 64 at the time of the survey. Gainfully employed persons are all persons who receive remuneration for their work (definition of the Federal Statistical Office). This also includes trainees, interns and marginally employed persons. Source: Own calculations based on data from SOEP-CORE, IAB-SOEP-MIG, IAB-BAMF-SOEP-REF and IAB-BiB/FReDA-BAMFSOEP-UA. The simulation results demonstrate a distinct increase in the employment rate over the duration of stay across all levels of labour market tension, with lower tension values generally correlating with a slower increase in the employment rate. With a high labour market tension (0.66), the employment rate in the first year after immigration is just under 15 percent and continuously rises to around 70 percent after ten years. In contrast, with a low labour market tension (0.01), the employment rate starts at only 4 percent and reaches about 40 percent ten years after immigration. 3 The federal state-specific labour market tension was calculated using data from DESTATIS, GENESIS table: 13211-0007 (https://www-genesis.destatis.de/genesis//online?operation=table&code=132110007&bypass=true&levelindex=0&levelid=1715610929324#abreadcrumb). IAB Research Report 9|2024en 32 Two key conclusions regarding the labour market integration of Ukrainian refugees can be drawn from these simulations: First, the general economic situation in Germany significantly influences labour market integration. The increase in labour market tension over the past decade has likely been beneficial for the labour market integration of refugees. Although the current economic situation remains more favorable than in the 2000s, the recent economic slowdown has decreased labour market tension, which is likely to negatively affect the labour market integration of Ukrainian refugees. Second, regional differences are crucial. Labour market tension varies significantly across regions in Germany, which substantially impacts integration opportunities. Unlike other refugee groups, many Ukrainian refugees were able to freely choose their place of residence in Germany (section 2.3). This likely resulted in a higher proportion of Ukrainian refugees settling in prosperous regions with high labour market tension, thus facilitating their labour market integration. However, a third of Ukrainian refugees reside in federal states such as Berlin, Bremen, Hamburg, North Rhine-Westphalia, and Saxony-Anhalt (Mediendienst-integration, 2024), where labour market tension is significantly lower than the national average (between 0.10-0.25), and significant regional differences in employment rates can be expected depending on the local labour market situation. 5 Conclusion Against the backdrop of the ongoing Russian war of aggression against Ukraine and the resultant significant influx of Ukrainian refugees into Germany, this research report has simulated various scenarios for the labour market integration of this group. These simulations leverage a comprehensive database of refugees who moved to Germany under different historical conditions and migrants from the former Soviet Union. The experiences of these groups provide a foundation for creating conditional scenarios for the future labour market integration of Ukrainian refugees. This serves two purposes: firstly, to form expectations about the future trajectory of labour market integration for Ukrainian refugees based on past refugee and migration episodes; and secondly, to demonstrate how various factors such as family constellations, German language skills, education, health, institutional framework conditions, integration policy measures, and labour market conditions can shape the course of integration. It should be emphasized that these conditional scenarios are contingent on specific assumptions and should not be misconstrued as forecasts. The population of Ukrainian refugees is markedly different from other groups due to observable factors like gender, education, training, institutional, and economic conditions. Furthermore, numerous unobservable factors or factors such as the Russian war of aggression or temporary protection in Germany influence the integration process but have no historical precedent. As a result, these factors cannot be fully captured in the scenarios, or only imperfectly. In our baseline scenario, which is considered the most likely development, Ukrainian refugees achieve an employment rate of around 45 percent with a duration of stay of five years, increasing to about 55 percent after ten years. The gender composition along with family constellations, particularly the high proportion of single mothers in Germany, negatively impacts the employment rates compared to other refugee and migrant groups in the sample, while the higher IAB Research Report 9|2024en 33 levels of education and training have beneficial effects. The poorer health of this group compared to other refugee groups also adversely affects labour market integration. The results are also dependent on labour market developments. Although the current labour market situation is much more favorable than the average over the sample period, the economic slowdown has reduced labour market tension, which is likely to negatively impact labour market integration in the short term. In the medium to long term, this situation may quickly improve, particularly against the backdrop of increasing labour market bottlenecks caused by demographic changes. This would result in higher employment rates for Ukrainian refugees compared to our baseline scenario. The potential effects of the ongoing war in Ukraine were not factored into the baseline scenario. Compared to other refugee and migrant groups, a high proportion of Ukrainian refugees in Germany plan to return to Ukraine after the war ends. Even though this proportion decreases with increasing length of stay, it has significant implications for investments in country-specific human capital – such as German language skills, education and further training, and recognition of professional qualifications – and employment relationships in Germany. Those granted temporary protection under Section 24 of the Residence Act have been treated on par with those who have not applied for protection in Germany.4 However, the legal prospects for Ukrainian refugees are more uncertain than for other migrant groups with longer or permanent residence rights, which can also negatively impact integration. The various other scenarios in this study not only illuminate the connections between different factors and labour market integration, but also allow several conclusions to be drawn for integration policy. One central finding is that family constellation has a considerable influence on labour market integration, especially given the high proportion of single mothers in Germany compared to other refugee and migrant groups. Effective childcare support and seamless integration of Ukrainian refugees’ children into the German education system are thus crucial factors that not only promote the individual labour market integration of mothers but also contribute to the long-term social integration of the entire family. Important insights can also be gleaned with respect to language support. The data available did not make it possible to identify temporary lock-in effects resulting from participation in language courses. However, key findings provide clear indications of the long-term positive influence of language support on labour market integration. It is evident that employment rates significantly increase as German language proficiency improves. Additionally, completing German language courses is not only associated with improved language skills but also significantly enhances opportunities in the labour market. These results confirm that targeted language support measures not only improve language skills in the short term but can also contribute to increasing employment rates in the medium to long term, thereby reducing the receipt of social benefits. Ukrainian refugees have a high level of education and training, at least formally, which generally facilitates labour market integration. However, it should be noted that the education systems are different, and many qualifications obtained in the dual training system in Germany were obtained at universities or comparable educational institutions in Ukraine. Therefore, the educational content often differs, and human capital acquired in Ukraine is often difficult to 4 This was not possible in the scenarios because $24 AufenthG was applied to Ukrainian refugees for the first time and therefore there is no historical precedent. IAB Research Report 9|2024en 34 transfer to the German labour market. Adaptation qualifications, other further training measures, or the acquisition of educational and training qualifications in Germany are therefore often necessary to enable employment with appropriate qualifications. In view of the high level of formal education, the recognition of these foreign qualifications is also crucial to reduce uncertainty in the labour market about the qualifications acquired in Ukraine. The state of health also plays an important role in labour market integration. Compared to other refugee and migrant groups, Ukrainian refugees report a poorer state of health, which is closely linked to the separation from partners, children, and other members of the nuclear family. Even if these war-related circumstances cannot be influenced by integration policy, good healthcare is likely to have a positive impact on the labour market integration of Ukrainian refugees. The simulation results also show that filing an asylum application and the associated asylum procedures are negatively related to labour market integration. In this respect, the granting of temporary protection and the associated facilitation of access to the labour market is likely to have significantly improved the integration opportunities of Ukrainian refugees. Finally, the economic framework conditions, measured in the simulations by the labour market tension, play a decisive role in the integration of Ukrainian refugees. This applies both nationally and locally. 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In: Social Forces 89, p. 515–534. IAB Research Report 9|2024en 39 Appendix Table A1: Determinants of the probability of employment Dependent variable: paid employment (employed = 1, not employed = 0) Empty cell Average marginal effect in percentage points Woman -14.0*** Children in the household at the time of the survey (reference: no children under 17) Children between 0-6 years -10.1*** Children between 7-17 years -0.5 Partner in the household 10.1*** Age at immigration 0.5 Age at immigration squared -0.0*** Highest level of education achieved (reference: less than primary level) Primary level 1.6 Lower secondary level (middle school) 15.3*** Upper secondary level (high school) / post-secondary non-tertiary education 18.2*** Tertiary education 18.6*** Employed before immigration 10.6*** Asylum application on arrival -8.0*** Good to very good health 9.1*** Participated in a language course before or after immigration 6.0** Good or very good German language skills 10.6*** Intention to stay (forever in Germany) 0.3 Labour market tension 35.1*** Unemployment rate 0.2 East Germany -2.1 Duration of stay (reference: 1 year or less) 2 years 16.6*** 3 years 27.3*** 4 years 31.3*** 5 years 36.7*** 6 years 40.1*** 7 years 39.6*** 8 years 41.8*** 9 years 41.1*** 10 years 44.7*** N 51,037 Notes: ***, **, * significant at the 1-, 5and 10-percent level. Standard errors grouped at person level. The table shows the average marginal effects in percentage points of a multivariate regression analysis using the probit method. The dependent variable is an indicator variable that has a value of 1 if the person is employed and a value of 0 if the person is not employed. All persons who receive remuneration for their work are employed (definition of the Federal Statistical Office). This also includes trainees, interns and marginally employed persons. Regressions also control for regression constant, missing values in the control variables, and for further indicator variables for duration of stay (one indicator each for the 11th to 30th year since arrival). Only people who were between 18 and 64 years old at the time of the survey. Source: Own calculations based on data from SOEP-CORE, IAB-SOEP-MIG, IAB-BAMF-SOEP-REF and IAB-BiB/FReDA-BAMFSOEP-UA. IAB Research Report 9|2024en 40 Table A2: Determinants of the probability of having good to very good German language skills Dependent variable: good to very good German language skills (good to very good German language skills = 1, poor to no German language skills = 0) Empty cell Average marginal effect in percentage points Woman 1.3 Children in the household at the time of the survey (reference: no children under 17) Children between 0-6 years -6.5*** Children between 7-17 years -2.2 Partner in the household -0.0 Age at immigration -3.8*** Age at immigration squared 0.0*** Highest level of education achieved (reference: less than primary level) Primary level 6.1* Lower secondary level (middle school) 9.5*** Upper secondary level (high school) / post-secondary non-tertiary education 10.7*** Tertiary education 18.0*** Employed before immigration 3.4 Asylum application on arrival -10.7*** Good to very good health 6.9*** Participated in a language course before or after immigration 4.9** Intention to stay (forever in Germany) 1.1 Labour market tension -1.5 Unemployment rate -0.8 East Germany 6.5*** Duration of stay (reference: 1 year or less) 2 years 14.9*** 3 years 22.5*** 4 years 26.2*** 5 years 29.0*** 6 years 32.7*** 7 years 35.2*** 8 years 35.3*** 9 years 34.1*** 10 years 35.7*** N 49,425 Notes: ***, **, * significant at the 1-, 5and 10-percent level. Standard errors grouped at person level. The table shows the average marginal effects in percentage points of a multivariate regression analysis using the probit method. The dependent variable is an indicator variable that has a value of 1 if the person has very good or good German language skills and a value of 0 if the person has poor to no German language skills. Regressions additionally control for further indicator variables for duration of stay (one indicator each for the 11th to 30th year since moving in), regression constant, and missing values in the control variables. Only people who were between 18 and 64 years old at the time of the survey. Source: Own calculations based on data from SOEP-CORE, IAB-SOEP-MIG, IAB-BAMF-SOEP-REF and IAB-BiB/FReDA-BAMFSOEP-UA.