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Volume 52, September/October 2017, Number 5 · pp. 263-269

Forum

Large-Scale Immigration and Labour Market Integration: First
Lessons from the Recent Past in Germany

Yuliya Kosyakova, Steffen Sirries

Yuliya Kosyakova, Institute for Employment Research (IAB), Nuremberg, Germany.

Steffen Sirries, Institute for Employment Research (IAB), Nuremberg, Germany.

This article is part of the Forum Integration of Immigrants in European Labour Markets.

Over the last decade, Germany has become one of the major developed destination countries for immigrants. In addition to the medium-term trend with respect to regular immigration, the total number of immigrants in recent years has been heavily dominated by the immigration of refugees from countries suffering from tremendous conflicts and violations of human rights. As a result, the immigration numbers reached a historical peak in 2015-16 with a gross immigration flow of around 2 million people (1.1 million net), mainly driven by the arrival of 890,000 refugees and asylum seekers in 2015 and another 280,000 in 2016.1 These numbers equate to more than 50,000 regular first-time asylum applications per month between October 2015 and September 2016.2 Over this period, Germany was confronted with an administrative management crisis resulting in prolonged asylum procedures. This was tackled with several bureaucratic and political reforms with respect to the asylum system. With a sharp drop in monthly asylum applications since September 2016 and a recovery from the initial admission difficulties, the public and political debate has changed its focus. Although humanitarian reasons are by definition the most important consideration for refugee intake,3 comprehensive societal integration under potential economic constraints is of the utmost interest now. The main emphasis of German immigration policy became the fast and sustainable integration of the newly arrived into the German labour market, as this is seen as a successful way to reach the goal of societal integration.

In contrast to other labour market indicators, the available information, specifically on the performance of recently arrived refugees, is sparse. Specific monitoring in administrative statistics or the inclusion of a separate indicator for refugees in register data (e.g. at the level of the federal employment agency) has not been a common practice in Germany. This article relies on novel representative data from the IAB-BAMF-SOEP Survey of Refugees. The survey is an exception to the lack of reliable data regarding individuals who immigrated to Germany in the recent past, and it therefore serves as a highly important source of information.

For the first wave of the survey, the Institute for Employment Research (IAB), the research department of the Federal Office for Migration and Refugees (BAMF), and the German Socio-Economic Panel (SOEP) at the German Institute for Economic Research (DIW) cooperated to provide a new representative longitudinal dataset on the recently arrived refugees in Germany. For this purpose, a random sample of refugees who applied for asylum in Germany between 2013 and 2016 (irrespective of their actual residence status) was drawn from the Central Register of Foreigners (AZR) in 2016. Along with these respondents (anchor persons), all of their adult household members were also interviewed, resulting in a sample of 4,816 adult individuals. The sampling design of this survey allows us to draw a representative picture of the corresponding population.4 The high quality of the dataset was assured through personal face-to-face interviews conducted by professional interviewers, innovative and state-of-the-art survey tools, the availability of the survey in multiple languages, as well as through the careful design of a questionnaire that included more than 450 questions about relevant aspects of the past and present experiences of the asylum seekers. Moreover, an ongoing editing process ensured high standards. The IAB-BAMF-SOEP Survey of Refugees provides an impression of the current situation and the progress made in the labour market integration of the recently arrived adult refugees. Importantly, it allows us to explore which drivers lead to successful labour market integration.

Table 1 (back to the text)
Descriptive statistics (weighted percentage value)

Share

Women

0.26

Educational attainment and employment before migration

No school

0.14

Primary education attended or attained

0.19

Lower or upper secondary education attended or attained

0.45

Post-secondary non-tertiary education attended or attained

0.03

Tertiary education attended or attained

0.11

Missing on education before migration

0.08

Had job before migration

0.69

Self-reported German language proficiency before migration

Not at all

0.89

Very bad/Bad

0.10

Good/Very good

0.01

Year of arrival

2013

0.07

2014

0.17

2015

0.68

2016

0.08

Legal Status

No decision yet

0.40

Recognised

0.46

Rejected

0.08

Other

0.06

Country of origin

Syria

0.42

Afghanistan

0.14

Iraq

0.09

Eritrea

0.04

Former USSR

0.04

West Balkan

0.06

Other Africa

0.10

Other/Stateless

0.11

Note: The sample consists of 4,283 individuals, with a mean age of 30.47 years.

Source: IAB-BAMF-SOEP Survey of Refugees.

Since this study focuses on the labour market integration of the recently arrived refugees, the sample was restricted to individuals aged between 18 and 65 (to ensure their availability for the labour market). Individuals who arrived before 2013 or whose year of arrival was not available were excluded from the study, as were those who did not claim asylum and had legal status "other". After removing those with missing information in the variables of interest, the sample of the analyses includes 4,283 individuals.

The distribution of origin countries amongst the recent asylum seekers is in fact dominated by war and crisis countries (see Table 1). By far the biggest share of asylum seekers – 42% – come from Syria, followed by 14% from Afghanistan, 9% from Iraq and another 4% from Eritrea. These four countries already account for 69% of the refugees. At the same time, the age distribution is dominated by young individuals, with an average age of 30 years. The gender distribution is dominated by men, with a share of 74%.

Irrespective of their demographics, the labour market integration of refugees is in many dimensions not comparable to the labour market integration of natives or other types of migrants.5 First, refugees often suffer from major breaks in their labour market-related biographies (e.g. educational deficiencies, interruptions in work experience) due to the societal and economic situation in their home and transition countries. Second, since refugees' migration is often driven by exogenous shocks (e.g. war, genocide, persecution), they may lack opportunities to prepare for their migration and improve their integration prospects before they arrive in a safe destination country. At the same time, refugees face the same entry barriers to the labour market as others do. Obviously, this situation often results in delayed labour market entry. Integration measures such as language acquisition and/or integration courses, as well as the (lengthy) asylum procedure accompanied by uncertainty about their residence permit status, also impede the ability of refugees to quickly secure employment.

This unfavourable situation is mirrored by the results of the IAB-BAMF-SOEP Survey of Refugees. For instance, only one per cent of respondents self-reported having good or very good German language proficiency upon arrival (see Table 1). Their educational attainment before migration to Germany gives a more differentiated picture. At the bottom, 14% had no school degree at all, while 45% reported having received a lower or upper secondary education. The top 11% had at least attended or attained a tertiary education. More than two-thirds of refugees reported having worked before their migration.

Table 2 (back to the text)
Employment status and type of employment by arrival year

in %

Arrival year

Total

2013

2014

2015

Current employment status

Non-employed

72.47

75.73

86.97

84.27

Employed

27.53

24.27

13.03

15.73

Total

100

100

100

100

Type of employment

Full-/Part-time

56.33

52.43

41.50

46.13

Mini-jobs/Internship

21.17

39.02

40.80

37.30

Vocational training

22.50

8.05

17.70

16.56

Total

100

100

100

100

Source: IAB-BAMF-SOEP Survey of Refugees.

Our main variable of interest for this study is an indicator variable of whether or not the interviewee was employed at the interview date (second half of 2016). Following international official statistics on employment rates, employment includes all labour activities been which result in payment, including vocational training and internships, as well as irregular and so-called mini-jobs. Overall, the results suggest an employment rate of around 16% among refugees of working age who arrived between 2013 and 2016 (see Table 2). As expected, breaking down employment rates by year of arrival yields differences in favour of those who had alreadyin Germany for a longer time period: 28% of those who arrived in 2013 reported being employed, as did 24% of those who arrived in 2014 and 13% of the 2015 arrival cohort. While the latter number is quite small, the fact that 13% of refugee arrivals were participating in the German labour market roughly one year after the peak in total arrivals is nonetheless moderately impressive. Given the initial deficiencies with regard to language proficiency and educational attainment, these numbers provide first insights into the pace of the labour market integration of recently arrived refugees in Germany.

Comparable employment rates for refugees, conditional on the time since arrival, are found for other developed countries,6 as well as for past experiences in Germany.7 Building on these experiences and on the current numbers, Brücker, Hauptmann and Sirries judge an employment rate of 50% after five years to be a realistic estimate of the future development of the labour market participation of refugees.8 The recently well-performing German labour market in combination with ongoing investments in integration measures and human capital-building capacities may additionally contribute positively to the future path for the newly arrived.

In addition to the overall pace and progress of the labour market integration of the refugees, Table 2 reports the distribution of different types of employment. Overall, 46% of refugees were working full- or part-time, 37% were employed in internships or mini-jobs, and 16% were receiving vocational training. Breaking down these figures by arrival year indicates an increasing share of full- or part-time employment the longer one has been in Germany. While the full- or part-time share of total employment for the 2015 arrival cohort was 42%, the corresponding figure for those who arrived in 2013 was 56%. In turn, the share of internships and mini-jobs drops in favour of both vocational training and full- or part-time jobs when comparing the 2014 to the 2013 arrival cohorts. Hence, the observed shifts in the aggregate numbers suggest that the quality of employment rises with more time spent in Germany and, as might be expected, a shift from irregular jobs and internships to regular jobs or vocational training occurs.

The individual-level data of the IAB-BAMF-SOEP Survey of Refugees allows for a conventional specification of a multivariate analysis to explore the drivers behind the individual probability of being employed. Identified drivers may shed some light on potentially promising future investments for Germany as well as for other developed countries facing an intake of refugees. Moreover, the data partly allow for the presence of a degree of selectivity bias. Importantly, this study does not claim to identify causal effects of single variables but rather seeks to explore conditional correlations by controlling for crucial multicollinear co-drivers of the probability of being employed.

Table 3 outlines the estimation results on the determinants of employment using linear probability models. Here, the dependent variable is a dummy for being employed by the interview date (between June and December, 2016), and the point estimates refer to the average marginal effects. Model 1 includes all model covariates, whereas in Model 2 we additionally include origin-country fixed effects to absorb heterogeneity due to the specific characteristics linked to the country of origin. Models 3 and 4 replicate the two previous models while the dependent outcome is modified to an indicator of being employed only in full- and part-time jobs (equal to 1, whereas non-employment, mini-jobs, internships and vocational training are coded as 0).

The results of Model 1 imply that human capital already gained before migration is an important predictor for the successful labour market integration of recently arrived refugees. Compared to refugees who arrived without any schooling, having a lower or upper secondary degree is associated with a six percentage point increase in employment probability, and having a post-secondary non-tertiary degree (i.e. vocational education and training) is associated with a 15 percentage point increase.9 A tertiary degree seems to have no effect on employment probability. This is not very surprising, since higher-educated migrants usually take more time to find adequate employment, and they invest more often into the accreditation of their education certificates. Both may postpone labour market entry, yet they may improve the match between educational attainment and job quality, not to mention the increased wages this may entail.10 Likewise, German language proficiency and having worked prior to migration are positively associated with employment probability in Germany. Including origin-country fixed effects in Model 2 barely changes these results.

Table 3 (back to the text)
Estimation results of employment status on determinants of employment (linear probability model)

Any employment

Full-/Part-time employment

Model 1

Model 2

Model 3

Model 4

Woman

-0.04***

-0.04***

-0.05***

-0.05***

(0.01)

(0.01)

(0.01)

(0.01)

Age

0.00

0.01*

0.01***

0.01***

(0.00)

(0.00)

(0.00)

(0.00)

Age squared

-0.00**

-0.00**

-0.00***

-0.00***

(0.00)

(0.00)

(0.00)

(0.00)

Self-reported German language proficiency before migration

0.18***

0.17***

0.11***

0.10***

(0.02)

(0.02)

(0.01)

(0.01)

Had job before migration

0.08***

0.07***

0.06***

0.06***

(0.01)

(0.01)

(0.01)

(0.01)

Educational attainment before migration (Ref. No school)

Primary education attended or attained

0.00

0.02

-0.00

0.01

(0.01)

(0.02)

(0.01)

(0.01)

Lower or upper secondary education attended or attained

0.06***

0.07***

0.02***

0.03***

(0.01)

(0.01)

(0.01)

(0.01)

Post-secondary non-tertiary education attended or attained

0.15***

0.15***

0.10***

0.09***

(0.04)

(0.04)

(0.04)

(0.04)

Tertiary education attended or attained

0.00

0.02

0.00

0.01

(0.02)

(0.02)

(0.01)

(0.01)

Year of arrival (Ref. 2016)

2013

0.21***

0.19***

0.14***

0.13***

(0.02)

(0.03)

(0.02)

(0.02)

2014

0.15***

0.14***

0.08***

0.08***

(0.02)

(0.02)

(0.01)

(0.01)

2015

0.04**

0.03**

0.02*

0.01

(0.01)

(0.02)

(0.01)

(0.01)

Legal status (Ref. No decision yet)

Recognised

0.05***

0.08***

0.03***

0.04***

(0.01)

(0.01)

(0.01)

(0.01)

Rejected

-0.04

-0.06**

-0.05***

-0.07***

(0.02)

(0.02)

(0.02)

(0.02)

Other

0.03*

0.05**

0.03*

0.04**

(0.02)

(0.02)

(0.02)

(0.02)

Constant

-0.27***

-0.33***

-0.32***

-0.34***

(0.06)

(0.06)

(0.04)

(0.04)

Country of origin fixed effects

No

Yes

No

Yes

N

4283

4283

4283

4283

R2

0.17

0.20

0.14

0.17

Notes: Significance level: * p<0.1, ** p<0.05, *** p<0.01. All models control for missing values on educational attainment before migration.

Source: IAB-BAMF-SOEP Survey of Refugees.

Legal status also seems to be one of the notable determinants for labour market integration, particularly when we control for heterogeneity attributable to the origin country (Model 2).11 Here, recognised refugees are more likely to be employed than those still awaiting a decision. In turn, rejected refugees face stronger impediments to their labour market integration. Turning to the results for full- and part-time employment (Models 3 and 4), the patterns seem to be similar, although the marginal effects of the human capital covariates are reduced in size. Considering that entry into jobs of higher value is delayed (see Table 2), it is likely that the accumulation of human capital after migration (e.g. via participation in language courses) as well as the accumulation of networks is of higher importance for these type of jobs. To briefly sum up the estimation results, expected determinants like the human capital characteristics of individuals, their language proficiency, their length of stay in Germany and their legal status seem to be relevant for a successful transition to employment. These results also hold when absorbing the potentially unobserved heterogeneity at the origin-country level.

Accounting for the distribution of these relevant characteristics among the refugees, one can clearly detect investment needs for language and human capital formation to foster rapid and sustainable labour market integration. This is a common target of integration measures (integration and language courses). Several different programmes and courses are offered in Germany to newly arrived migrants and refugees.

The IAB-SOEP-BAMF Survey of Refugees makes it possible to assess whether and to what extent specific integration measures – the BAMF Integration course, the ESF-BAMF course, the BA introductory language programme, the BA Perspectives for Refugees and other German language courses – may have a favourable effect on the labour market integration of the newly arrived. Importantly, the selected measures differ in many ways, such as their primary goal, content, extent, target group and eligibility criteria.

The BAMF Integration course has been the standard integration course in Germany since 2005. It is obligatory for migrants who receive long-term permission to stay in Germany and do not have sufficient command of German. This integration course is a combination of language and "orientation" classes (ca. 700 lectures of 45 minutes each) and aims at a B1 German language proficiency level. The ESF-BAMF language course is aimed at people who are further along on their path to entering the labour market. Accordingly, it focuses on occupation-specific language teaching. Additional interesting components of the ESF-BAMF course include brief vocational training in the respective occupation, an internship to practice the German language in a daily job environment and guided visits in firms. Overall, the programme consists of 730 lectures spread across six months on a full-time basis (or 12 months part-time). Eligibility is restricted to registered unemployed people with a German language proficiency level of at least B1 (e.g. achieved via completion of the BAMF Integration course).

The next two programmes – the BA introductory language programme and the BA Perspectives for Refugees – are offered by the Federal Employment Agency (BA).12 The first one addresses asylum seekers who do not yet have a residence permit but have a high probability of receiving a positive result on their asylum application.13 The course aims at providing only basic language proficiency for participants with no or only negligible prior knowledge. The programme covers a total of 320 hours, preferably over a shorter time frame. The BA Perspectives for Refugees is a 12-week consulting and mentoring programme aimed at screening the labour market-relevant competencies of the participants. The main components of the programme include a six-week skill-screening internship, 96 hours of language training, a comprehensive overview of the German labour market and informational sources, and support in writing specific job applications. Eligibility is limited to refugees with a work permit or those with a high probability of being granted asylum. At the same time, a sufficient level of language proficiency is needed to join the programme, which is usually attained after the BAMF Integration course.

Finally, other language courses may be offered by private institutions or local organisations, possibly including private teachers, but these are not more precisely specified with respect to their content in this analysis. However, the data indicate that 95% of participants finished these courses with a language certificate (ranging from A1 to C2).

Against this background, the expectation is that the ESF-BAMF course and the BA Perspectives for Refugees should be the most valuable measures for successful labour market integration, due to their specific components of advanced language proficiency and labour market-related training. However, participants might be a highly selective group of individuals with unobserved characteristics (such as higher motivation and aspirations for employment), thereby driving higher returns to both measures, which in turn cannot be interpreted as causal effects. At the same time, the different programmes are not mutually exclusive and partly even build on each other to offer a sequence of programmes for some individuals. A reliable, disentangled evaluation of single programmes or sequences is further restricted since some of the programmes were only recently launched and the number of participants is still too low to conduct a comprehensive evaluation (e.g. only six per cent participated in and 4.5 per cent successfully completed the advanced programme BA Perspectives for Refugees).14

Table 4 compares the employment rates of refugees who had successfully finished a given integration course with those who had not yet participated in one, using linear probability models (Model 5). The correlations of all of the programmes with the employment indicator are strongly positive, suggesting that participation is linked with positive labour market returns. This qualitative conclusion also holds in a multivariate analysis where we control for the same set of regressors as in Table 3 (Model 6). Although such an explorative exercise cannot sort out all potential bias from self-selection into programmes, it may increase confidence in the targeted positive returns of integration measures.

Table 4 (back to the text)
Estimation results of integration measures on employment status (linear probability model)

Any employment

Model 5

Model 6

BAMF Integration course

0.13***

0.10***

(0.03)

(0.03)

ESF-BAMF language course

0.42***

0.35***

(0.03)

(0.03)

BA introductory language programme

0.19***

0.16***

(0.03)

(0.03)

BA Perspectives for Refugees

0.13***

0.15***

(0.03)

(0.03)

Other German language course

0.06**

0.04*

(0.02)

(0.02)

Constant

0.06***

-0.22***

(0.01)

(0.08)

Controls

No

Yes

Country of origin fixed effects

No

Yes

N

2501

2493

R2

0.33

0.41

Notes: Significance level: * p<0.1, ** p<0.05, *** p<0.01. Individuals who were still in any of the courses at the interview date are excluded. Likewise, individuals who dropped from courses or for whom the information is missing are excluded. Model controls include all model covariates from Model 2 in Table 3. Since multiple answers to language course participation are possible, all models additionally control for having successfully finished more than one course.

Source: IAB-BAMF-SOEP Survey of Refugees.

Net of the other model controls, successful completion of the BAMF Integration course is associated with a ten percentage point higher probability of being employed (Model 6, Table 4). Likewise, the BA introductory language programme may raise employment probability by 16 percentage points. Among the more labour market-oriented language courses, the ESF-BAMF language courses increase employment probability by 35 percentage points, while the BA Perspectives for Refugees increases the probability by 15 percentage points. In contrast, completion of other German language courses has the lowest correlation with employment on the probability scale, both in terms of statistical significance and the effect size.

In summary, because of their exogenously driven and often non-voluntary migration pace, refugees often face a disadvantageous starting position in a developed destination country with respect to their labour market integration. Employing the novel representative data of the IAB-BAMF-SOEP Survey of Refugees, this study depicts some of the disadvantages for recently arrived refugees in Germany. Nevertheless, some notable progress in terms of the labour market integration of refugees should be recognised. Furthermore, the results confirm and highlight the important roles which language proficiency and education – but also time and legal security – play for the successful integration of immigrants into the labour market. Indeed, integration measures which generally aim to support refugees' societal and labour market integration seem to increase the employment opportunities of the newly arrived in Germany. Successful labour market integration can be therefore seen as dependent on the ongoing efforts of all involved parties to minimise or even eliminate the detrimental starting points of refugees. At the same time, the results may be informative for other developed countries seeking to integrate refugees successfully, and they might motivate policy makers to implement reasonable integration measures.


  • 1 Bundesministerium des Innern (BMI), Federal Ministry of the Interior: 280.000 Asylsuchende im Jahr 2016, Press release, 11 January 2017, available at http://www.bmi.bund.de/SharedDocs/Pressemitteilungen/DE/2017/01/asylantraege-2016.

  • 2 Bundesamt für Migration und Flüchtlinge (BAMF), Federal Office for Migration and Refugees: Asylgeschäftsstatistik für den Monat Dezember 2016, Nuremberg 2016.

  • 3 The term "refugee" is not used in the legal sense in this study, but must be understood as a collective term for the group of 1) asylum seekers whose asylum procedures are still ongoing, 2) refugees who have already been granted protection, and 3) individuals whose asylum claims have been rejected but who are permitted to remain in the country temporarily with the status of Duldung ("toleration", a temporary stay before deportation).

  • 4 Since the sampling is stratified at several dimensions to collect enough observations of small subgroups, descriptive results in this report are weighted with the inverse drawing probability of individuals. See H. Brücker, N. Rother, J. Schupp (eds.): IAB-BAMF-SOEP-Befragung von Geflüchteten: Überblick und erste Ergebnisse, IAB-Forschungsbericht No. 14/2016, Institute for Employment Research (IAB), 2016; and H. Brücker, N. Rother, J. Schupp (eds.): IAB-BAMF-SOEP-Befragung von Geflüchteten 2016: Studiendesign, Feldergebnisse sowie Analysen zu schulischer wie beruflicher Qualifikation, Sprachkenntnissen sowie kognitiven Potenzialen, German Institute for Economic Research (DIW), 2017.

  • 5 B.R. Chiswick: Are Immigrants Favorably Self-Selected? An Economic Analysis, in C.B. Brettell, J.F. Hollifield (eds.): Migration Theory: Talking Across the Disciplines, New York 2008, Routledge, pp. 52-76.

  • 6 S. Aiyar et al.: The Refugee Surge in Europe: Economic Challenges, IMF Staff Discussion Note, SDN/16/02, International Monetary Fund, 2016, pp. 1-50; and OECD and European Commission: How are refugees faring on the labour market in Europe? A first evaluation based on the 2014 EU Labour Force Survey ad hoc module, Working Paper No. 1/2016, European Union, 2016.

  • 7 H. Brücker, P. Schewe, S. Sirries: Eine vorläufige Bilanz der Fluchtmigration nach Deutschland, IAB Aktuelle Berichte No. 19/2016, Institute for Employment Research (IAB), 2016; and T. Fendel, Y. Kosyakova: Ökonomische und soziale Integration von Geflüchteten in Deutschland, Mögliche Lehren aus vergangenen Erfahrungen, in: Geographische Rundschau, No. 3, 2017, pp. 30-37.

  • 8 H. Brücker, A. Hauptmann, S. Sirries: Arbeitsmarktintegration von Geflüchteten in Deutschland: Der Stand zum Jahresbeginn 2017, IAB Aktuelle Berichte No. 4/2017, Institute for Employment Research (IAB), 2017.

  • 9 Note that since educational variables are measured as either "attended or attained", these marginal effects might be interpreted as lower bounds for attaining a degree in the respective category.

  • 10 H. Brücker, E. Liebau, A. Romiti, E. Vallizadeh: Anerkannte Abschlüsse und Deutschkenntnisse lohnen sich, in: IAB-Kurzbericht, 21.3/2014, Institute for Employment Research (IAB), 2014, pp. 21-28; and I. Kogan: Potenziale nutzen! Determinanten und Konsequenzen der Anerkennung von Bildungsabschlüssen bei Zuwanderern aus der ehemaligen Sowjetunion in Deutschland, in: Kölner Zeitschrift für Soziologie und Sozialpsychologie, Vol. 64, No. 1, 2012, pp. 67-89.

  • 11 Due to the prioritised examination of certain caseloads (based on the origin country), the length of the asylum procedure varies considerably across countries of origin. For instance, in the 3rd quarter of 2015, asylum applications from asylum seekers stemming from Albania, Kosovo, Serbia, Macedonia, Bosnia and Herzegovina, Syria, Eritrea and Iraq (religious minorities) were being given priority. See Deutscher Bundestag: Antwort der Bundesregierung: Ergänzende Informationen zur Asylstatistik für das dritte Quartal 2015, Drucksache 18/6860, 18. Wahlperiode, 2015.

  • 12 The BA Perspectives for Young Refugees, aimed at refugees under 25 years old, was not considered for the analysis because of a very low number of participants within our data.

  • 13 At the time this programme was offered, origin countries which qualified people as eligible were Syria, Iraq, Eritrea and Iran.

  • 14 Future waves of the IAB-BAMF-SOEP Survey of Refugees will enable a more comprehensive evaluation of specific integration measures with regard to the labour market integration of refugees from a longitudinal perspective.


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