»Poverty Risks of Ethnic Minorities«(1990-2020) – Examining trends and explaining poverty risks across ethnic origins and generation status

»Poverty Risks of Ethnic Minorities«(1990-2020) – Examining trends and explaining poverty risks across ethnic origins and generation status | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article »Poverty Risks of Ethnic Minorities«(1990-2020) – Examining trends and explaining poverty risks across ethnic origins and generation status Sascha-Christopher Geschke This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7688061/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract While previous research gained a detailed understanding of ethnic inequalities in education and the labor market, much less is known about ethnic poverty risks. In particular, there is little knowledge about the heterogeneity of trends and underlying determinants of poverty risks across many specific ethnic origin groups. This study complements the literature by providing a long-term, large-scale study, examining poverty trends over three decades (1991–2019) and determinants of poverty risks across 20 ethnic origins and two generations in Germany. With its long migration history and considerable ethnic heterogeneity, Germany is a valuable case study to understand ethnic poverty risks in Europe. Results based on long-term, large-scale, nationally representative Microcensus data indicate that third-country guest workers from Turkey and Morocco, Post-Soviet repatriates, EU asylum seekers from Kosovo/ Macedonia, and refugees from North Africa and Yugoslavia have high poverty risks, while refugees from Afghanistan, West Africa, the Middle East, Vietnam, and Iran experience exceptionally high risks. These disparities are also partially evident in the second-generation. Logistic regressions of pooled repeated cross-sections in nested, stepwise models—analyzing mediation processes and cumulative disadvantages and using average marginal effects to compare key theoretical perspectives—provide new detailed insights into the determinants of ethnic poverty risks. Lower education accounts largely for ethnic poverty differences, yet higher education does not offset risks, indicating inadequate occupational placement, especially among third-country immigrants and refugees, with disadvantages partly transmitted to their children. Precarious employment stands out as the most influential factor across generations. Household composition matters particularly for Middle Eastern, Afghan, and West African families. Citizenship is beneficial for most immigrants, especially refugees. poverty migration education and occupation precarious employment household composition legal status Figures Figure 1 Figure 2 1 Introduction Poverty is a persistent social problem although many policies target at reducing inequality and poverty. Relative poverty is currently at a comparatively high level and continues to rise in many regions, including Europe (Eurostat 2024), especially among populations with a migration history (Eurostat 2021 ). Research shows that immigrants (first-generation) experience much higher poverty risks compared to the autochthonous population (e.g., Heady et al. 1994 ; Blume et al. 2005 ; Álvarez-Miranda 2011 ; Finseraas 2012 ; Brady et al. 2025 ). Descendants (second-generation) also experience higher poverty risks than natives, although to a lesser extent (Giesecke et al. 2017 ). However, despite the considerable ethnic heterogeneity, research on poverty risks across ethnic origin groups is limited for immigrants (Blume et al. 2005 ; Kesler 2015 ) and largely lacking for descendants. In addition, only a few studies moved beyond the descriptive level, addressing the underlying determinants of ethnic poverty risks in explanatory research using micro-data (e.g., Crettaz 2011 ; Kesler 2015 ; Brady et al. 2025 ). While migration research has advanced our understanding of ethnic inequalities in education and the labor market, much less is known about the drivers of ethnic poverty risks. Because resources are shared within households, poverty is best conceptualized at the household level. Explanatory factors must therefore go beyond individual education and labor market positions, also taking household characteristics and context factors, particularly the legal status of immigrants, into account. Among the few studies that have examined a wide range of ethnic origins, Blume et al. ( 2005 ) found increasing poverty disparities between natives and nine immigrant groups (1984 to 1997), largely due to the concentration in low-wage sectors and lengthier labor market integration, while family and welfare policies mitigated these gaps. They used administrative data from Statistics Denmark and the Swedish Income Panel. Kesler ( 2015 ) studied 17 immigrant groups and found that low-education households with children and welfare receipt were associated with lower female employment, contributing to poverty risks. Kesler used pooled cross-sectional data from the German Microcensus (1996 and 2000), the British Labour Force Survey (1996 to 2004), and also Swedish Longitudinal Individual Data (2002). Crettaz ( 2011 ), using Luxembourg Income Study data from 2004, found that native-born individuals had 40% lower odds of in-work poverty, while non-EU15 and Turkish citizens experienced much higher risks. Welfare states, like Germany and Sweden, reduced these risks relative to liberal regimes, such as the United States. Brady et al. ( 2025 ) analyzed 12 immigrant groups in the U.S. using Luxembourg Income Study data (1993 to 2023), showing poverty disparities by citizenship and national origin, influenced significantly by joblessness, low-education, and single parenthood among Central and also Latin American groups, while Asian groups benefited from educational selectivity. The study stressed that structural factors outweighed immigrant-specific traits. For Germany, Giesecke et al. ( 2017 ) used Socio-Economic Panel (SOEP) and Microcensus data (2013) to describe and decompose poverty risks for immigrants and descendants. They found significant poverty gaps for both generations across age, education and occupation, household compositions, and regions. However, these factors explained only a small share of the migrant–native gap. Heady et al. ( 1994 ), using SOEP data (1984 to 1989), found that immigrant guest workers were, on average, 2.5 times more likely to be at risk of poverty and remained in poverty for over half a year longer, compared to natives in West Germany. Against this background, this contribution complements existing research by: (1) examining poverty risks for immigrants from 20 different countries of origin (e.g., Gresch/Kristen 2011), rather than treating them as a homogenous group or selecting a few groups, and (2) disentangling their ethnic heterogeneity across the first and the second generation. In this regard, Germany provides a valuable case study, given its decades-long migration history, one of the highest shares of migrants in Europe, and a large number of sizable ethnic origin groups (EU Country Report 2023). Germany is also a valuable case from the perspective of poverty research. While Germany is among the wealthiest countries in Europe, poverty risks have been rising and peaked in 2020, affecting 16,2% of the total population. For the population with a migration background, poverty risks were nearly three times as high: 28,0% were at-risk-of-poverty 1 , compared to 11,8% among the autochthonous population 2 (Destatis 2020 ). In addition to providing new detailed descriptions, (3) this study empirically tests and compares different theoretically derived explanations of the heterogeneity of ethnic poverty risks. In multivariate and nested analyses factors at the individual, household, and legal level are investigated stepwise to account for differences in poverty risks across origins and generation status. (4) An auxiliary contribution is that the multivariate explanatory analyses are based on pooled long-term data (27 time points between 1990–2020), because cross-sectional ‘snapshots’ are embedded into the specific context of a given period and are unaware of time-dependent poverty risks. This means that some individuals or households are vulnerable “only” during economic recessions but they have the resources to overcome poverty afterwards (cyclical risks), whereas others remain vulnerable even in times of economic growth (long-term risks). Accordingly, restricting the analyses to a specific year would lead to misleading conclusions (Foster et al. 2010 ; Gradín et al. 2012 ). Although individual-level panel data, such as the Socio-Economic Panel, could provide deeper insights into poverty dynamics, they often lack sufficient observations for most ethnic origins. Against this trade-off and given the focus on ethnic heterogeneity in poverty risks, this study draws on the German Microcensus, a large-scale, nationally representative survey that provides detailed information on a wide range of ethnic origins, covering first- and second-generation migrants, within a repeated cross-sectional design over a long time span. Accordingly, this study, examines the following research questions: (RQ1): How do poverty risks differ across ethnic origins, compared to the autochthonous population? (RQ2): How do poverty risks differ between first and second generation migrants across ethnic origins? (RQ3): How can these ethnic and generational differences in poverty risks be explained at the individual-level by education, occupation, and precarious employment, and how are they further influenced by household composition and citizenship? This paper is structured as follows: The next section gives an overview of poverty trends across ethnic origins to identify long-term poverty risk groups, addressing RQ1 and RQ2 (Section 2). This is followed by a theoretical discussion of poverty across migrant groups and generations, addressing RQ3 (Section 3). Subsequently, the data and methodology are outlined (Section 4). All outcomes are described and analyzed accordingly (Section 5). Findings and limitations will be discussed in the final section (Section 6). 2 Poverty Trends Adding to the limited knowledge on poverty risks across a wide range of ethnic origins, this research begins by describing poverty trends: over time (1991–2019) and the duration of stay (0–30 years), both across generations, to identify ethnic long-term poverty risk groups. The analyses include ethnic minorities from 32 countries, with some grouped into larger regions, based on their shared political history and comparable social contexts, to identify broader trends and issues relevant to Europe. The following description covers 20 ethnic groups (as indicated by the italics ) in addition to the autochthonous (“German”) population, which serves as the reference point. 3 The so-called guest workers (“Gastarbeiter”) arrived in West Germany under bilateral recruitment agreements between 1955 and 1973. These agreements stopped in 1973, but the migrant population continued to grow due to family reunification policies in the 1970s and 1980s (Münz et al. 1997 ). These are immigrants from Turkey, Italy, Greece, Spain/Portugal, Morocco , and Yugoslavia (now: Bosnia-Herzegovina, Serbia, Croatia, and Montenegro). Late repatriates (“Spät-Aussiedler”) are ethnic German resettlers who mainly arrived in the 1990s after the end of the Soviet Union (Panagiotidis 2021 ). They originated from Post-Soviet States (now: Russia, Kazakhstan, and Ukraine), as well as from Romania / Bulgaria , and Poland . EU migrants from Central and Eastern Europe moved to Germany with the EU enlargement in 2004 and 2007. It is possible to distinguish between migrants from Central EU countries (Czech Republic, Hungary, and Slovakia) and Poland , who arrived since 2004, and migrants from Romania/Bulgaria , who came since 2007. Kosovan/Macedonian EU asylum seekers arrived after the independence from Serbia in 2008 and the lift of visa requirements in 2009. Refugees who migrated to Germany originated from a wide range of regions around the world. They include early refugees from Iran , who arrived between 1979–2009, and Vietnam , who entered between 1975–2009, from Yugoslavia , who came between 1991–2002; And more recent refugees, who fled since 2010, from the Middle East (Iraq, Syria, Jordan, Arab Emirates, Lebanon, and Yemen), North Africa (Egypt, Algeria, Libya, and Tunisia), West Africa (Nigeria, Ghana, Niger, Mali, Senegal, Guinea, Benin, Togo, and Sierra Leone) and Afghanistan . Time – Fig. 1 4 depicts the overall trend and shows that poverty risks are significantly higher for all ethnic groups across generations at almost every point in time compared to autochthonous Germans. While Germans maintain lower and relatively stable poverty risks, 13 out of 20 ethnic groups face medium to high risks and substantial fluctuations over time. Overall, guest workers from Turkey and Morocco, Post-Soviet late repatriates, EU asylum seekers from Kosovo/Macedonia and refugees from Yugoslavia and North Africa faced large (20 to 30 p.p.) and all other refugees even very large poverty gaps (> 30 p.p., Afghanistan and Middle East > 50 p.p.). EU migrants from Romania/Bulgaria and Poland encountered medium gaps (up to 25 p.p.). All other ethnic origins have lower poverty gaps (up to 10 p.p.). Over time, comparing the first and the last given value, guest workers from Yugoslavia, Turkey, Italy, Greece, Spain/Portugal, and Afghan refugees faced the highest poverty risk increase (+ 11% to + 16%), indicating long-term risks. The strongest decrease was observed for Post-Soviet late repatriates (–26%) and refugees from Yugoslavia (–17%), while a medium decrease was observed for refugees from Vietnam, EU immigrants from Poland and Romania/Bulgaria, and guest workers from Morocco (–11% to − 13%). 5 A convergence of poverty risks with those of autochthonous Germans was found for immigrants from Romania/Bulgaria and Poland (LR), and descendants of Spanish/Portuguese, Italian and Greek (GW), and Polish (LR) origin. Some immigrants reduced their poverty gaps to low differences (up to 10 p.p.), those from Spain/Portugal, Italy and Greece (GW), Yugoslavia (GW and RG), Central EU and Poland (EU), and Post-Soviet States (LR). Whereas refugees continue to face large to very large poverty gaps: Vietnam, Iran, North Africa (> 25 p.p.), West Africa (> 35 p.p.), Middle East and Afghanistan (> 50 p.p.). Generation status – Fig. 1 also presents a comparison between first- and second-generation migrants. Poverty risks are significantly higher for immigrants compared to descendants at almost any point in time (up to 10 p.p.). Exceptions are rare and typically temporary. Additionally, poverty gaps remain relatively high over time for many immigrants, whereas they decline more substantially for most descendants. Duration of stay – Fig. 2 shows that poverty risks fall largely for almost all ethnic origins with increasing time of residence. The largest decrease appears during the first 10 years (up to − 33%), but poverty risks also drop significantly between 10 and 30 years of residence (up to − 13%). During the first 10 years of residence, high poverty risk reductions ( \(\:\approx\:\:\) –30%) are observed for Romanian/Bulgarian repatriates and refugees from West Africa. Medium poverty reductions ( \(\:\approx\:\:\) –20%) are observed for Post-Soviet repatriates and EU asylum seekers from Kosovo/Macedonia, and refugees from the Middle East. Smaller, though significant and substantial poverty reductions ( \(\:\approx\:\:\) –10%) are observed for all other repatriates, EU migrants and refugees. Exceptions are refugees from Yugoslavia (± 0%) and guest workers. Over 30 years of residence, the highest poverty risk reductions are observed for Romanian/Bulgarian (–30%) and Post-Soviet repatriates (–33%), followed by Polish repatriates (–18%) with medium reductions. All other ethnic minorities experience only smaller reductions in poverty risk or reach a plateau. Guest workers either show growing poverty risks over time: during the first 10 years (up to + 20%) and over 30 years of stay (up to + 10%); or they remain at almost the same poverty risk level as in the beginning. An increase is observed for guest workers from Greece, Turkey, and Yugoslavia; and stagnation for those from Italy and Spain/Portugal – both indicating long-term risks. Only Moroccans showed substantial poverty reductions (–12%), but with high uncertainty (as indicated by the 95% CI). 6 In summary, despite a trend towards convergence with the poverty risk level of autochthonous Germans, significant differences remain for most ethnic origins, often also across generations. The analyses (in Section 5) will therefore focus on explaining the remaining overall poverty gaps. 3 Theoretical Approach The explanation of ethnic differences in poverty risks across generations is based on general theoretical considerations about possible origins of poverty and their application to migrants. I will also draw on migrant-specific arguments, such as legal status. All explanations are elaborated for specific immigration groups. 3.1 Educational resources and labor market positioning According to human capital theory, investments in formal education contribute to higher labor market positions (e.g., occupational status) associated with higher wages (Becker 1993). The level of formal education is, with differences between ethnic origins, overall lower for most immigrants (e.g., Kristen/Granato 2007), particularly immigrants from non-EU countries and refugees, compared to native Germans (Spörlein et al. 2020). This may be due to the educational systems in immigrants’ countries of origin, leading to different—often lower—educational levels compared to Germany (MIPEX 2011, 2020). Thus, observed differences in educational levels should translate into varying occupational outcomes and wages, thereby contributing to poverty risks. Yet, certain immigrant-specific conditions may affect this translation, such that labor market outcomes to foreign qualifications may be lower than those obtained by the autochthonous population, even with the same level of formal education. In support of this, previous research has found that immigrants are unemployed more often (e.g., Kogan 2011 ) and that their achieved occupational status is frequently lower compared to natives (e.g., Chiswick et al. 2005 ) with substantial variation across ethnic origins (Brücker et al. 2012 ). An important argument in this context is that immigrants’ foreign qualifications may not be fully transferable to the new context (Friedberg 2000 ; Chiswick/Miller 2009); another is that the recognition of foreign qualifications could be limited (e.g., Kogan 2016). In addition to explanations based on educational resources, further arguments may explain immigrants' lower labor market positions. These include ethnic penalties (Heath/Cheung 2006), for example, statistical discrimination by employers who are uncertain about the productivity of individuals of certain ethnic groups (Phelps 1972; Arrow 1973 ), or taste-based discrimination (Becker 1957 ). Ethnic differences in occupational attainment may also be a result of self-selection on unobserved traits, such as skills, ambition, or preferences (Borjas 1987 ). These conditions could contribute to observed differences in occupational status, which translate into varying wages, thereby explaining poverty risks. Guest workers originated from countries where manual labor skills were often prioritized over formal education (Herbert 1990 ). In addition, they were mainly selected from lower socio-economic groups in their home countries, with limited access to formal education before migration (OECD 2015 ). Both factors may contribute to their lower educational attainment overall. Recruitment policies primarily targeted guest workers with lower levels of formal education to address labor shortages in lower-status occupations (Heizmann et al. 2015) and as a result, they were more frequently employed in unskilled occupations than other immigrants; even when their formal qualifications were recognized, this may indicate ethnic penalties (e.g., Constant/Massey 2005; Heath/Cheung 2006). Late repatriates have lower levels of formal education overall compared to native Germans, partly because manual and technical trainings prevailed in their countries of origin (Flake 2013 ). In addition, struggles with the recognition of foreign qualifications (Dietz 1999 ) and language barriers have complicated their labor market positioning, relegating many, including those with higher education, to lower-status occupations (Brück-Klingberg et al. 2007 ). Ethnic penalties in the German labor market appear to be smaller for repatriates than for other ethnic groups, especially those from third countries (Antidiskriminierungsstelle 2012). Many EU migrants , particularly those from Eastern Europe (e.g., Romania and Bulgaria), have a lower level of formal education on average compared to Germans (OECD 2017). Despite the mutual recognition of qualifications in the EU, job positions are tied to specific qualifications in the German labor market (Allmendinger 1989). This could contribute to the devaluation of qualifications attained abroad, because they appear to be less tailored to certain occupations. Germany restricted the full freedom of movement for immigrants from certain newer EU member states, such as Romania and Bulgaria. Yet, to address labor shortages in low-wage sectors, seasonal and temporary work permits were issued to them. This policy contributed to a selection with lower formal education on average relative to natives (Dietz/Kaczmarczyk 2008). EU migrants face ethnic penalties less often than non-EU immigrants, indicated by better initial labor market positioning and faster status convergence (Berbée/Stuhler 2023). Refugees often originate from fragile, conflict-affected or developing countries where educational systems are less developed and stratified. Thus, they are less frequently equipped with educational resources applicable to the host society compared to other migrants and have lower levels of formal education overall compared to natives (e.g., Eisnecker et al. 2016 ). Their foreign qualifications are often not (fully) recognized, contributing to devaluation and limited employment prospects (Sommer 2019 ). Ethnic penalties are likely for refugees, indicated by the highest initial occupational status differences and the slowest convergence overall (e.g., Berbée/Stuhler 2023). They tend to be positively selected in terms of educational resources compared to non-migrants. This is particularly the case for earlier refugees (e.g., Iran and Vietnam) compared to more recent refugees (e.g., Afghanistan and the Middle East) (Spörlein et al. 2020). Hypothesis a (education) : The poverty gaps of immigrants compared to autochthonous people, which vary across ethnic groups, can partly be explained by their levels of formal education. Hypothesis b (occupation) : The poverty gaps of immigrants compared to autochthonous people, which vary across ethnic groups — even after accounting for differences in their level of formal education — can partly be explained by their attained occupational status. Parents’ educational resources and occupational attainment are often passed on to their children, influencing their opportunities, choices, and outcomes in life (e.g., Marx 1867; Bourdieu 1984 ). Intergenerational transmission thus provides a link between immigrants and their descendants. While low levels of formal education in the first-generation contribute to less favorable prospects for the second-generation (Borjas 1999 ), ‘immigrant optimism’ could counteract this (Kao/Tienda 2002). It refers to a tendency of immigrant families to have higher aspirations than autochthonous peers, endorsing upward mobility (Salikutluk 2016). Ethnic penalties may also persist in the second-generation, especially for those with non-EU origin, affecting the attainment of higher-status occupations (Berbée/Stuhler 2023). Hypothesis c (Generation status) : The remaining poverty gap between descendants and autochthonous peers, which varies across ethnic groups, can partly be explained by their level of formal education and attained occupational status, as in the case of Hypothesis 1a and 1b. 3.2 Precarious employment Precarious employment includes different forms of ‘non-standard’ employment, such as part-time, temporary, agency, and marginal work, and full-time jobs with lacking social security. Precarious employment is often characterized by lower earnings, increased risks of job loss (more frequent and/or enduring shortfalls in earnings), and lower upward mobility, contributing to lower wages and higher poverty risks (Gebel/Giesecke 2011). Immigrants tend to be over-represented in the unskilled, low-wage labor market, experiencing precarious employment more often (Kogan 2011 ), with significant variation across ethnic groups. Refugees and non-EU immigrants often face the greatest barriers to ‘standard’ employment (Heath/Cheung 2007). This could be due to ethnic labor market segmentation, referring to the division of the workforce into an autochthonous in-group, who encounter ‘standard’ employments more often, and an ethnic out-group, who face ‘precarious’ employments more frequently (Doeringer/Piore 1971; Gebel 2015 ). This pattern may result from recruitment practices and/or immigration policies gaining migrants for lower-status employments commonly avoided by natives (Heckmann 1992). Otherwise, it could reflect another form of ethnic penalties, where employers tend to hire migrants for temporary employments to maintain workforce flexibility and cost efficiency (Gebel/Giesecke 2011). Migrants accept precarious employment conditions more often due to their fewer or lacking job alternatives (Eichhorst/Marx 2011). Therefore, ethnic differences in precarious employment may translate into varying wages, thereby contributing to poverty risks. Guest workers were initially recruited into temporary residence and work permits that tied them to specific employers and restricted their labor market mobility. This dependency frequently led to precarious work in un- or low-skilled jobs, offering minimal employment security and upward mobility, and poor working conditions in low-paid industries, for example cleaning, agriculture, or manufacturing (Zimmermann 2005). Late repatriates encountered difficulties in obtaining ‘standard’ employment, despite their supportive integration policies, as they arrived during periods of economic downturn (Panagiotidis 2021 ). This frequently led to a relegation to low-paid and unstable jobs, contributing to limited upward mobility and a cycle of precarious work and unemployment (BAMF 2014). EU migrants from certain newer EU member states, such as Romania and Bulgaria, did not receive full freedom of movement till 2014 (Eurofound 2014 ). Their labor market access was limited to temporary working permits to fill labor shortages in low-paid industries or to replace non-EU immigrants, frequently contributing to precarious work and unemployment (Barrett/Maître 2011). Refugees’ precarious work is often linked to uncertainties such as temporary residence and work permits, making employment less likely (Liebau/Salikutluk 2016). Some engage in voluntary work, receiving minimal compensation, during the asylum process, which aims to facilitate integration but often risks confining them to low-wage sectors characterized by precarious work, unemployment, and limited mobility (Salikutluk et al. 2016 ; Brücker et al. 2016 ). Hypothesis a (Precarity) : The poverty gaps of immigrants compared to autochthonous people, which vary across ethnic groups — even after accounting for differences in their level of formal education and occupational status — can partly be explained by precarious employment. Parental experiences of precarious and unstable employment can influence their children’s career trajectories (e.g., Kalter/Granato 2007). Intergenerational transmission thus, again, provides the link between immigrants labor market experiences and the experiences of their descendants (e.g., Yuksel 2009 ). The transmission of labor market disadvantages was strong among migrants families compared to autochthonous ones (e.g., Groh-Samberg/Voges 2014). Ethnic labor market segmentation could contribute to the perpetuation of these disadvantages across generations (Aradhya et al. 2023). All these experiences vary significantly across ethnic origins and generations (Heath/Cheung 2007), some descendants are catching up with autochthonous peers, others continue to face disadvantages (Luthra 2013 ). Hypothesis b (Generation status) : The remaining poverty gap between descendants and autochthonous peers, which varies across ethnic groups — net of differences in level of formal education and occupational status — can partly be explained by precarious employment, as in the case of Hypothesis 2a. 3.3 Household resources and needs The financial situation of a household is based on (a) household resources, such as income from wages, benefits, or social transfers, and (b) household needs, such as expenses on different forms of consumption (e.g., Jenkins 2000 ). According to resource dilution theory, each dependent individual added to a household leads to a decrease in available resources (Blake 1981 ). Household resources and needs are related to the number of its dependents. Households with a larger number of dependents relative to earners, encounter higher poverty risks (Smith et al. 2017 ). Increasing the number of working hours can partially offset poverty risks associated with more dependents. At the same time, immigrants work fewer hours than native Germans, may be due to their lower labor force participation and the lack of participation of certain household members, such as immigrant women (e.g., Sprengholz et al. 2021 ), or adult siblings and grandparents within extended families (e.g., Glick 2010 ). Moreover, the presence of children reduces the time available for paid work, thereby reducing household income and increasing poverty risks (Lenze 2019 ). Immigrants tend to have higher fertility rates (Milewski 2010 ) and, therefore, higher dependent–earner ratios than autochthonous households. Natives, by contrast, live in single-parent households more frequently than immigrants. This may limit their working hours, thereby contributing to poverty risks (Cribb et al. 2017). In addition, marriage may improve incomes for many couples due to joint taxation (“Ehegattensplitting”), providing tax advantages. As immigrants are more often married than autochthonous Germans (Kuhnt/ Krapf 2020), they may benefit from this regulation. Therefore, ethnic differences in the household composition may contribute to different poverty risks. First-generation guest workers were often employed in labor-intensive jobs, working longer hours than autochthonous Germans to maximize their earnings, yet there was a considerable decline in the average working hours over time due to lowering employment rates of immigrant women (e.g., Sprengholz et al. 2021 ). At the same time, the vast majority of guest workers are married, whereas the share among autochthonous people is substantially smaller (Kuhnt/Krapf 2020). The fertility rates of guest worker women are, on average, higher compared to those of autochthonous women and other immigrant women (Münz et al. 1997 ). Higher fertility rates and lower labor market participation should contribute to high dependents-earner ratios. Late repatriates did not differ much from autochthonous Germans regarding their working hours. They were only slightly lower due to the lower employment of repatriate women. However, compared to other migrant women, they had the highest number of hours worked (Friedrichs/Graf 2022). In terms of marriage, repatriates are married more often than natives on average, also in comparison with most other immigrant groups (BAMF 2014). Fertility rates of repatriate women are slightly below those of other immigrant groups, but largely above those of autochthonous women (ibid.). Late repatriates usually migrated as an unit with an extended family, this should contribute to high dependents-earner ratios (Dietz 1999 ). EU migrants have the highest employment rates and working hours, even exceeding those of native Germans (Clemens/Hart 2018). They are forerunners of unmarried cohabitation, leaving room for economic vulnerability (Kostova 2007 ). EU immigrant women had the lowest fertility rates in Germany overall (Wolf/Kreyenfeld 2020). Therefore, lower fertility rates and higher labor market participation, should contribute to low dependents-earner ratios. Employment rates and working hours among refugees are considerably lower than among other immigrant groups, and far below the level of autochthonous Germans (Salikutluk et al. 2016 ). Refugees tend to have higher fertility rates than natives and other immigrant groups (Newsham/Rowe 2019). Therefore, higher fertility rates and lower labor market participation, particularly among recent refugees from African or Arabian countries (ibid.), may contribute to high dependents-earner ratios. Hypothesis a (Household resources) : The poverty gaps of immigrants compared to autochthonous Germans, which vary across ethnic groups — even after accounting for differences in their level of formal education, occupational status, and precarious employment — can partly be explained by their household resources. Hypothesis b (Household needs) : The poverty gaps of immigrants compared to autochthonous Germans, which vary across ethnic groups — even after accounting for differences in their level of formal education, occupational status, precarious employment, and household resources — can partly be explained by their household needs. Parents’ family values and their household division of labor are expected to contribute to their children’s practices (Hochschild/Machung 2012). Descendants need time to adapt to host society family norms (Huschek et al. 2011 ). This shows the link between generations, again. Descendants’ working hours tend to decrease as their educational attainment and occupational mobility improves (Dustmann/Glitz 2011). At the same time, the labor force participation of second-generation women increases, contributing to a higher household income (BAMF 2019). Descendants shows partnership and fertility patterns that shift towards native peers—such as a preference for cohabitation over marriage and delayed marriage—that lie roughly between patterns of their parents and autochthonous couples (Nauck 2007 ). Thus, higher labor market participation and lower fertility rates, may contribute to lower dependents-earner ratios across generations. Hypothesis c (Generation status) : The remaining poverty gap between descendants and autochthonous peers, which varies across ethnic groups — even after accounting for differences in their level of formal education, occupational status, and precarious employment — can partly be explained by their household resources and needs, as in the case of Hypothesis 3a and 3b. 3.4 Legal arrangements Legal arrangements tied to citizenship are important for immigrants’ integration into the labor market, as citizenship can provide opportunities for labor market access (Gathmann/Garbers 2023), occupational mobility as well as employment security (Brücker et al. 2016 ), thereby contributing to wages. In addition to citizenship regulations, legislation may also promote integration in other ways, such as through the provision of language or integration courses. The access to German citizenship and naturalization rates differed largely for non-EU migrants and refugees, compared to EU migrants (Diehl/Blohm 2002). This could be due to stricter conditions to obtain German citizenship and lengthy and complex Bureaucratic procedures (ibid.). Thus, observed differences in the citizenship status may translate into varying labor market access and integration, thereby contributing to poverty risks. Legal arrangements to support the labor market integration of guest workers in West Germany between 1955 and 1989 developed slowly, because they were expected to leave. Their legal residence began with temporary employment tied to specific jobs and evolved into repeatedly renewed work permits, permanent residence, as well as family reunification for millions of recruited guest workers after 1973 (Goddar/Huneke 2011). This allowed many to stay and settle, creating an incentive for citizenship (Constant/Massey 2002), especially for third-country immigrants (Diehl/Blohm 2002). After the collapse of the Soviet Union, late repatriates migrated to Germany under the Federal Expellees Act (“Bundesvertriebenengesetz”). This law provided supportive legal conditions (e.g., language and integration courses), facilitating their integration into the education system and labor market (Dietz 2000). Between 1991 and 2009, approximately 2.1 million late repatriates arrived with German descent, receiving their citizenship upon arrival, distinguishing them from other immigrants (Kalter/Kogan 2014). EU migrants benefit from free movement and supportive legal arrangements, which have been a core component of EU law since 1993. Acquiring citizenship is therefore less relevant for EU migrants, who have full access to the German labor market by virtue of their EU citizenship. After several EU enlargements, restrictions were imposed between 2004 and 2014 on some newer EU member states, such as Romania and Bulgaria. Their access to the labor market was limited to sectors experiencing shortages (BAMF 2014). Prior to this restriction, EU migrants had fewer incentives than non-EU migrants, due to their full access and easier integration. Refugees have relatively easy access to integration courses nowadays and receive thorough language training and other forms of support (Brücker et al. 2016 ). At the same time, most refugees experienced lengthy asylum procedures, and they are prohibited from working until their legal status is decided. For this reason, their transition to employment takes longer compared to other migrants, slowing down or even delaying their economic integration (Kosyakova/Brenzel 2020). Taken together, refugees have an high incentive for citizenship to gain access to legal residency and full access to the labor market. Hypothesis a (Citizenship) : The poverty gaps of immigrants relative to autochthonous people, which vary across ethnic groups — even after accounting for differences in their level of formal education, occupational status, precarious employment, household resources and needs — can partly be explained by their citizenship status. 4 Data and Methodology 4.1 Data and target population Data – The German Microcensus (MC) is a representative household survey by the Federal Statistical Office that covers 1% of the total population in Germany. All citizens must provide information, so the data are highly representative and allow to produce detailed subgroup analyses for a wide range of ethnic origins. The analyses in this paper are based on 27 Microcensus datasets: 1991, 1993, 1995, and 1996 to 2019, which are prepared and harmonized by own routines (see Appendix B). The data documentation, the Microcensus trend files for different survey years were used to be aware of pitfalls and peculiarities (Lengerer et al. 2010 ), as well the GESIS-MISSY pages of the German Microdata Lab (GML 2023). From these initial datasets, relevant variables are selected and converted into a coherent form and then combined into a single longitudinal file. Target population – The population with a migration history, differentiated by their ethnic origin and generation, is of main interest, while the autochthonous population is the reference point. To determine a migration history, I follow the concept introduced in by the Federal Statistical Office in 2005: Immigrants (first-generation) are all people born outside of Germany that migrated after 1949. Descendants (second-generation) are all people born in Germany with at least one parent born abroad. For migrants with a German passport, the definition of origin is primarily based on their own nationality held before naturalization, or on the current or former citizenship of one of their immigrant parents. I restrict the analysis to prime-age individuals (25–54) living in private households. Table A1 shows the pooled poverty risks between 1991 and 2019, along with the number of observations (in brackets below). The poverty risks were already described in detail in Section 2, with further elaborations provided in Section 5. The smallest sample sizes are observed for the population from Spain (472) and Portugal (541) at-risk-of-poverty, but even these lower observation numbers are sufficient. A minimum of 30–50 observations per group is recommended for basic analysis and larger samples for multivariate models (Cohen 2013 ), as shown. 4.2 Operationalizations Research subject – The empirical assessment of relative income poverty is largely based on the approach of the European Statistical Office (2021) in order to allow comparability with other studies. Financial resources are a suitable indicator for studying inequality in prosperous countries, since poverty is often relative (poor with respect to a country-specific social context), whereas absolute poverty and material deprivation (poor regarding basic human needs) are rare. A person is at-risk-of-poverty if the equivalized annual disposable income 7 is below the threshold of 60% of the country-specific median (Eurostat 2021 ). The actual “disposable income” (net income) is only apparent after deducting all taxes and compulsory insurances (e.g., social security, unemployment, and health) and it depends on the size and composition of the household. So, the net household income is weighted using an equivalence scale (OECD 2015 ), resulting in a needs-weighted per capita income. The implicit assumption is that all members of the household benefit from joint income, but the consumption utility of the household grows with increasing members. The household head (person with the highest age and or income) receives a weight of 1, further members over the age of 14 receive a weight of 0.5, and children up to the age of 14 receive a weight of 0.3. The operationalization follows the GESIS Poverty Measurement (Boehle 2015 ). Key covariates – The at-risk-of-poverty rate will be studied by 20 ethnic origins, their first- and partly their second generation (identifiable for 17 ethnic groups). Appendix B provides a concise overview of the operationalization and coding for all theoretically selected models. 4.3 Methods The poverty risks will be examined in a pooled model for repeated cross-sections (tsset pid syear, yearly; Cox/Donnelly 2011) for the period 1991 to 2019. The dependent variable is a dummy that identifies individuals at-risk-of-poverty, so a ‘Logistic Regression’ is applied, clustered for households (vce(cluster hid)) to account for dependencies. ‘Average Marginal Effects’ (AME) will be used, as they allow to compare coefficients across models (Mood 2010). Coefficients show the poverty risks of different ethnic origins compared to autochthonous Germans, accordingly poverty gaps are estimated. To balance group- and design-specific selection probabilities across different interview years, I use probability weighting (svyset pid [pw = weight]; Solon et al. 2015 ) with standard sampling weights of the Microcensus (GML 1991–2019). 5 Results The analysis begins with baseline effects across ethnic origins and generations as a reference model (M0: only controls), accounting for gender, federal territory, age, and survey year. Subsequent models (M1–M4) are introduced stepwise and nested, according to the theoretical explanations and corresponding hypotheses (3.1–3.4). This modeling strategy separates variance across multiple levels and addresses mediator–confounder constellations (‘causes of effects’) and cumulative disadvantages, while avoiding overcomplication (‘obscuring interaction terms’). After clarifying individual-level factors, the model then isolates residual variation at the household and structural level, disentangling how each factor contributes to each specific ethnic poverty gap. 5.1 Educational resources and labor market positioning Based on the descriptives in Section 2, substantial and significant poverty gaps are expected for most first-generation guest workers, repatriates, EU migrants, and all refugees. 8 Table 1 (M0) shows that very high poverty gaps are faced by refugees from the Middle East, Afghanistan, West Africa, Vietnam, and Iran; and guest workers from Morocco (all sorted by magnitude). High poverty gaps are experienced by refugees from Yugoslavia and North Africa, Post-Soviet late repatriates, EU immigrants from Kosovo/Macedonia and Romania/Bulgaria, and guest workers from Turkey. Lower, but still substantial and significant poverty gaps are observed for Polish repatriates and EU immigrants, and guest workers from Italy, Greece, and Yugoslavia. For the second-generation , a few substantial and significant poverty gaps are observed across generations, especially among refugees of Afghan, Middle Eastern, Vietnamese, and Iranian origin, repatriates of Post-Soviet origin, and guest workers of Moroccan and Turkish origin. After identifying long-term poverty risk groups across generations, Model 1 in Table 1 examines the effect of educational resources and occupational status. Adding education first shows that lower levels of formal education contribute substantially to the observed poverty gaps. This is particularly true for guest workers from Morocco, Tukey, and Yugoslavia (immigrants and descendants), Greece and Italy (immigrants), as well as refugees from Yugoslavia (immigrants). Conversely, some ethnic groups, such as Polish EU migrants (both generations) and refugees from North Africa (immigrants and descendants), have high levels of formal education––this becomes evident as poverty gaps were underestimated before accounting for education––yet they face medium to high poverty gaps. Secondly, adding the occupational status reveals that the translation of educational resources into occupational attainment is affected for all refugees (except Vietnamese), EU immigrants from Romania/Bulgaria, and descendants of Post-Soviet, Middle Eastern, and Iranian origin, as it significantly accounts for their poverty gaps. An intergenerational transmission of lower educational resources is observed among Moroccan, Turkish, Yugoslavian (GW), Post-Soviet, Middle Eastern, and Vietnamese descendants, as education contributes substantially to their poverty gaps. Conversely, Polish, Kosovan/Macedonian and Romanian/Bulgarian (EU), Iranian, West and North African descendants tend to attain higher educational levels, overcoming parental experiences. Polish and Romanian/Bulgarian (EU) descendants also frequently attain a higher occupational status, whereas lower occupational attainments tend to be reproduced partially across generations for Post-Soviet, Middle Eastern, and Iranian descendants. 5.2 Precarious employment The second model in Table 2 accounts additionally for precarious employment. The results support the argument that differences in employment can account for substantial parts of the observed poverty gaps. This is particularly true for all late repatriates, EU migrants, and refugees in the first generation. Even all refugees, EU migrants and guest workers from third countries in the second generation tend to be affected largely by precarious work. This explanation, thus, appears to be of greater relevance than educational resources for most ethnic origins—while accounting for Model 0 and Model 1. Parental experiences of precarious employment are partly passed on across generations, impacting their children’s career trajectories as well. Only guest workers (immigrants and descendants) from EU member states tend to attain ‘standard’ employments, as indicated by fairly stable poverty gaps. 5.3 Household resources and needs Considering household resources in Model 3a does not change the poverty gaps much. However, moderate poverty reductions could be achieved by increasing the work intensity of the household for refugees from Middle East (immigrants and descendants) and smaller poverty reductions are possible for refugees from Afghanistan and West Africa (immigrants and descendants), Iran (immigrants) and Vietnam (descendants). Adding household needs in Model 3b contributes to smaller changes in the poverty gaps of Afghan immigrants and Middle Eastern immigrants and descendants (see Table 3). Thus, parental practices regarding family values and the household division of labor seem to be of minor relevance and tend to contribute to their children’s practices only in the case of Middle Eastern and West African families. 5.4 Legal arrangements Adding citizenship in Model 4 supports the argument that legal arrangements contribute to opportunities for better labor market access, because it affects the poverty gaps of almost all ethnic immigrant groups significantly (see Table 4). This is especially true for most refugees in the first generation and EU migrants from Poland and Romania/Bulgaria, and to a lower extent for West and North African refugees, all guest workers, late repatriates, and other EU immigrants. Under the same legal conditions—while accounting for Model 0 to Model 3— guest workers from Spain, Portugal and Yugoslavia even experience significantly lower poverty risks than autochthonous Germans. Descendants, being socialized and educated in Germany, do not benefit from naturalization efforts. 6 Discussion and Conclusion This study provides a detailed analysis of poverty risks across 20 ethnic origins and two generations in Germany from 1990 to 2020, based on Microcensus data. By identifying poverty risk groups and comparing different poverty explanations, this study provides new, detailed insights into ethnic and generational differences, emphasizing their relevance and origin-specific conditions for poverty. The results indicate that immigrants face substantially higher poverty risks than autochthonous Germans. These disparities and their underlying conditions are also partially found in the second-generation. Among the most affected ethnic groups are refugees from the Middle East, Afghanistan, West and North Africa, Vietnam, and Iran, guest workers from Morocco and Turkey, Post-Soviet late repatriates and EU asylum seekers from Kosovo/Macedonia. The origin-specific factors contributing to these poverty gaps will be discusses now. Educational resources play a crucial role in explaining poverty risks across both generations, especially for third-country guest workers and refugees, whose lower levels of formal education contribute largely to their poverty gaps. This result shows that formal education often explains labor market prospects of immigrants and descendants as well, indicating that disadvantages are partially passed on to their children. Conversely, ‘immigrant optimism’ may explain why descendants of all EU migrants and some refugees, especially those of Iranian, African and Yugoslavian origin, tend to have higher levels of formal education. An additional finding is that, even when third-country immigrants and refugees attain the same level of formal education, the translation of their educational resources into a suitable labor market position is less effective, probably due to their foreign qualifications or maybe ethnic penalties. Accordingly, additional educational and/or vocational training, better skill recognition, and fast-track adaptation programs could facilitate transitions into suitable occupations and career advancement. Precarious employment appears to be the second crucial factor in explaining poverty risks across generations, particularly for refugees, late repatriates, EU and third-country migrants. This finding supports considerations that low-wage and unstable jobs with limited upward mobility contribute largely to economic vulnerabilities, even across generations. This highlights long-term consequences of precarious employment and the importance of stable, ‘standard’ employment opportunities. Ensuring this—by strengthening labor laws, social security, and career development—could be one of the keys to reduce poverty risks. Household resources and needs have limited influence in explaining poverty gaps, suggesting that individual and structural factors are more influential. Yet, refugees and their descendants living in households with multiple dependents could benefit from opportunities to increase their work intensity—for example through childcare, elderly support, or flexible work arrangements. This may help to reduce their financial struggles. Legal arrangements tied to citizenship appear to be influential in explaining labor market access and poverty risks. Immigrants with restricted access, such as temporary workers, or with stricter citizenship requirements, such as asylum seekers and third-country nationals, seem to be especially vulnerable to poverty. This finding suggests that policies facilitating labor market integration, such as skill recognition programs to access work permits or residency, may help mitigate poverty risks faced by immigrants. Naturalization efforts are likely to improve the economic well-being of immigrants, but descendants, socialized and educated in Germany, do not benefit from citizenship. Declarations Author Contribution Single-author contributions for all 14 CRediT Acknowledgement I would like to express the deepest gratitude to my brilliant and kind supervisors, Michael Gebel and Cornelia Kristen, all the excellent teachers and scholars from Humboldt University and DIW Berlin, and all supportive colleagues from the University of Bamberg, whose guidance, expertise, and encouragement greatly contributed to this research and my personal development.I am also deeply thankful for all the grants by the Bamberg Graduate School of Social Sciences and my scholarship granted by the Hans-Böckler-Foundation, which provided all resources and facilities to conduct this study.Finally, I am deeply grateful for my family and friends and their unwavering support and encouragement. This work, dedicated to greater social equality, was only possible thanks to the kindness of all of you! Data Availability The Microcensus scientific use files (1991-2019) are available under the condition of a data usage agreement (see Appendix C).– Link: https://www.forschungsdatenzentrum.de/de/haushalte/mikrozensus– Link: https://www.gesis.org/missy/metadata/MZ/The data processing is available under:– FAIRsharing.org: PRoEM-ET-ER; PRoEM-Exploring-Trends-and-Explaining-Risks, FAIRsharing ID: https://fairsharing.org/6362 References Álvarez-Miranda, B. (2011). In-work poverty among immigrants. Working poverty in Europe, 250–277. Palgrave Macmillan. Allison, P. D. (2009). Fixed effects regression models . SAGE. Antidiskriminierungsstelle des Bundes (2012). Diskriminierung in Deutschland. Bundesministerium für Familie, Senioren, Frauen und Jugend. Arrow, K. J. (1973). The theory of discrimination. Discrimination in labor markets , 3–33. Princeton University Press. BAMF Bundesamt für Migration und Flüchtlinge (2014). Zuwanderung aus den neuen EU-Mitgliedstaaten Bulgarien und Rumänien (Forschungsbericht 24). – (2019). Integration in the job market by women of foreign nationality in Germany. Working Paper 20. Barrett, A., & Maître, B. (2011). Immigrant welfare receipt across Europe. Institute of Labor Economics Discussion Papers 5515. Becker, G. S. (1957). The economics of discrimination . University of Chicago Press. – (1993). Human capital: A theoretical and empirical analysis, with special reference to education. University of Chicago Press. Berbée, F., & Stuhler, J. (2023). Immigration and occupational mobility: Evidence from Europe. Labour Economics , 81 , 101–139. Blake, J. (1981). Family size and the quality of children. Demography , 18 (4), 421–442. Blume, K., Gustafsson, B., Pedersen, P. J., & Verner, M. (2005). Determinants of poverty among immigrants to Denmark and Sweden. Journal of Ethnic and Migration Studies , 31 (3), 511–528. Boehle, A. (2015). GESIS Poverty Measurement Report. Borjas, G. J. (1987). Self-selection and the earnings of immigrants. American Economic Review , 77 (4), 531–553. Borjas, G. J. (1999). The Economic Analysis of Immigration. Handbook of Labor Economics (Vol. 3A, pp. 1697–1760). Elsevier. Bourdieu, P. (1984). Distinction: A social critique of the judgement of taste . Harvard University Press. Brady, D., Bocanegra, A., Cervantes, D., Macy, L., & Saravia, N. R. (2025). The Heterogeneities of Immigrant Poverty in the US. Population Research and Policy Review , 44 (4), 41. Brücker, H., Hauptmann, A., Jahn, E. J., & Upward, R. (2012). Migration and Imperfect Labor Markets. Institute of Labor Economics Discussion Papers 6713. Brücker, H., Rother, N., & Schupp, J. (2016). IAB-BAMF-SOEP Survey of Refugees: Overview and First Results . IAB. Brück-Klingberg, A., Heineck, G., & Siedler, T. (2007). The labor market performance of ethnic Germans: a natural experiment. Economics of Transition , 15 (4), 689–709. Chiswick, B. R., Lee, Y. L., & Miller, P. W. (2005). Immigrant earnings: A longitudinal analysis. Review of Economics of the Household , 3 (2), 171–193. Chiswick, B. R., & Miller, P. W. (2009). The international transferability of immigrants’ human capital. Economics of Education Review , 28 (2), 162–169. Clemens, M., & Hart, J. (2018). EU immigration has increased Germany's economic growth. DIW weekly report , 8 (44), 441–449. Constant, A., & Massey, D. S. (2002). Return migration by German guestworkers. International Migration , 40 (4), 5–38. – (2005). Labor market segmentation and the earnings of German guestworkers. Population Research and Policy Review , 21(5), 489–512. Cox, D. R., & Donnelly, C. A. (2011). Principles of applied statistics . Cambridge University Press. Crettaz, E. (2011). Why are immigrants and ethnic minorities more affected by working poverty? LIS Working Paper Series, 564. Cohen, J. (2013). Statistical power analysis for the behavioral sciences . Routledge. Destatis, S., & Bundesamt (2020). A2 Armutsgefährdungsquoten, Bundesländer (Bundesmedian) ab 2020 . Statistikportal. Diehl, C., & Blohm, M. (2002). A comparison of the socio-economic situation of naturalized and non-naturalized immigrants in Germany. European Sociological Review , 19 (3), 249–266. Dietz, B. (1999). Ethnic German Immigration from Eastern Europe and the Former Soviet Union to Germany. Institute of Labor Economics Discussion Papers 68. – (2000). German and Jewish migration from the former Soviet Union to Germany. Journal of Ethnic and Migration Studies , 26(4), 635–652. Doeringer, P. B., & Piore, M. J. (1971). Internal Labor Markets and Manpower Analysis . Heath Lexington Books. Dustmann, C., & Glitz, A. (2011). Migration and education. Handbook of the Economics of Education (Vol. 4, pp. 327–359). Elsevier. Eichhorst, W., & Marx, P. (2011). Reforming German labor market institutions. Journal of Labor Market Research , 44 (1), 49–68. Eisnecker, P., et al. (2016). Integrating refugees: Insights from the past. DIW Economic Bulletin , 6 (34/35), 387–390. Eurofound (2014). Labour mobility in the EU. Eurostat (2021). Non-EU citizens at higher risk of poverty or social exclusion. – (2023). Country Report – Germany. – (2024). Living conditions in Europe – income distribution and income inequality. Finseraas, H. (2012). Poverty, ethnic minorities among the poor, and preferences for redistribution in European regions. Journal of European Social Policy , 22 (2), 164–180. Flake, R. (2013). Educational attainment and economic assimilation of immigrant families. International Migration Review , 47 (1), 150–185. Fleischmann, F., & Höhne, J. (2013). Immigrant women's labor market participation in Germany. Journal of Marriage and Family , 75 (5), 1081–1097. Friedberg, R. M. (2000). You can't take it with you? Immigrant assimilation and the portability of human capital. Journal of Labor Economics , 18 (2), 221–251. Friedrichs, N., & Graf, J. (2022). Integration gelungen? Lebenswelten und gesellschaftliche Teilhabe von (Spät­)Aussiedler:innen. SVR­Studie 2022­1, Berlin. Foster, G., Greer, J., & Thorbecke, E. (2010). The Foster-Greer-Thorbecke (FGT) poverty measures: 25 years later. Journal of Economic Inequality , 8 (4), 491–524. Gangl, M. (2004). Welfare states and the scar effects of unemployment. American Journal of Sociology , 109 (6), 1319–1364. Gathmann, C., & Garbers, K. (2023). Citizenship and economic integration. Journal of Economic Perspectives , 37 (1), 89–112. Gebel, M. (2015). Labor market instability, labor market entry, and early career development. Emerging trends in the social and behavioral sciences , 1–16. Gebel, M., & Giesecke, J. (2011). The changing skill-based temporary employment and unemployment risks in Europe. Social Forces , 90 (1), 17–39. Giesecke, J., Kroh, M., Tucci, I., Baumann, A., & El-Kayed, N. (2017). Armutsgefährdung bei Personen mit Migrationshintergrund. SOEPpapers , 907. DIW Berlin. Glick, J. E. (2010). Connecting complex processes: A decade of research on immigrant families. Journal of Marriage and Family , 72 (3), 498–515. Gradín, C., Cantó, O., & del Río, C. (2012). Measuring poverty accounting for time. Review of Income and Wealth , 58 (2), 330–354. Gresch, C., & Kristen, C. (2011). Staatsbürgerschaft oder Migrationshintergrund? Ein Vergleich unterschiedlicher Operationalisierungsweisen am Beispiel der Bildungsbeteiligung. Journal for Educational Research Online , 3 (2), 1–18. Goddar, A., & Huneke, T. (2011). Legal frameworks and policies for guest workers in Germany. International Migration Review , 45 (3), 772–801. Groh-Samberg, O., & Voges, W. (2014). Precursors and consequences of youth poverty in Germany. Longitudinal and Life Course Studies , 5 (2), 151–172. Heath, A., & Cheung, S. Y. (2006). Ethnic penalties in the labor market: Employers and discrimination. – (2007). The comparative study of ethnic minority disadvantage. In Proceedings-British Academy (137, p.1). Oxford University Press. Heady, B., Krause, P., & Habich, R. (1994). Long and Short Term Poverty: Is Germany a Two-Thirds Society? Social Indicators Research , 31 , 1–25. Heizmann, B., Busch-Heizmann, A., & Holst, E. (2017). Sorting or devaluation? International Migration Review , 51 (2), 475–505. Herbert, U. (1990). A history of foreign labor in Germany, 1880–1980 . University of Michigan Press.​. Huschek, D., de Valk, H. A. G., & Liefbroer, A. C. (2011). Timing of first union among second generation Turks in Europe. Demographic Research , 25 , 437–466. Jenkins, S. P. (2000). Modelling household income dynamics. Journal of Population Economics , 13 (4), 529–567. Kalter, F., & Granato, N. (2007). Educational hurdles on the way to the labor market. Journal of Education and Work , 20 (1), 21–38. Kalter, F., Granato, N., & Kristen, C. (2007). Disentangling trends of second generation structural assimilation in Germany. Proceedings of the British Academy (137, 271–319). Kalter, F., & Kogan, I. (2014). Migrant networks and labor market integration of immigrants from the Former Soviet Union in Germany. Social Forces , 92 (4), 1435–1456. Kao, G., & Tienda, M. (1998). Educational aspirations of minority youth. American Journal of Education , 106 (3), 349–384. Kesler, C. (2015). Welfare states and immigrant poverty. Acta Sociologica , 58 (1), 39–61. Kogan, I. (2011). Labour market flexibility and immigrants' employment paths in Germany. International Journal of Comparative Sociology , 52 (4), 264–283. – (2016). Integration policies and immigrants’ labor market outcomes in Europe. Sociological Science , 3, 335–358.​. Kostova, D. (2007). The emergence of cohabitation in a transitional socio-economic context. Demografia , 50 (5), 135–162. Kuhnt, A. K., & Krapf, S. (2020). Partnerships and fertility among immigrants in Germany. Advances in Life Course Research , 44.​. Kosyakova, Y., & Brenzel, H. (2020). Labour market entry of refugees in Germany. Journal of Ethnic and Migration Studies , 46 (9), 1713–1737. Lengerer, A., Lechert, Y., & Riede, T. (2010). Data documentation of the German Microcensus. GESIS Technical Reports , 2010/11.​. Lenze, A. (2019). Childcare availability and maternal employment in Germany. Journal of European Social Policy , 29 (5), 635–649. Liebau, E., & Salikutluk, Z. (2016). Gender differences in the labor market integration of refugees. Soziale Welt , 67 (4), 377–409. Luthra, R. R. (2013). Explaining ethnic inequality in the German labor market. European Sociological Review , 29 (5), 1095–1107. Milewski, N. (2010). Is there a socialization effect in transitions to second and third births? European Journal of Population , 26 (3), 297–323. MIPEX: Migrant Integration Policy Index (2011). 2020). CIDOB and MPG. Münz, R., Seifert, W., & Ulrich, R. (1997). Zuwanderung nach Deutschland: Strukturen, Wirkungen, Perspektiven . Campus. Nauck, B. (2007). Value of children and the social production of welfare. Demographic Research , 17 , 669–700. Newsham, A., & Rowe, K. (2019). Fertility patterns among refugees in Europe: A comparative study. Population Studies , 73 (2), 221–237. OECD (2015). 2017). Bildung auf einen Blick: OECD-Indikatoren. OECD-Publishing . – (2018). Settling In 2018: Indicators of Immigrant Integration. Orrenius, P. M., & Zavodny, M. (2009). How Immigrants Fare in Good and Bad Economic Times . Migration Policy Institute. Panagiotidis, P. (2021). Late Repatriates in Germany: Integration and Identity . Springer. Salikutluk, Z., Giesecke, J., & Kroh, M. (2016). Refugees' labor market integration in Germany: The role of education and training. European Sociological Review , 32 (6), 756–769. Smith, N. A., Phillips, D., Simpson, P., Cribb, J., Hood, A., Joyce, R., & Keiller, A. N. (2017). In-work poverty among families with children . IFS Report. Solon, G., Haider, S. J., & Wooldridge, J. M. (2015). What are we weighting for? Journal of Human Resources , 50 (2), 301–316. Sommer, M. (2019). Recognition of foreign qualifications in Germany. Journal of Education and Work , 32 (5), 487–501. Sprengholz, M., Diehl, C., Giesecke, J., & Kreyenfeld, M. (2021). From guest workers to EU migrants. Journal of Family Research , 33 (2), 252–283. UNESCO. (2012). International Standard Classification of Education: ISCED 2011 . Montreal. Wolf, K., & Kreyenfeld, M. (2020). Demographic differences in fertility patterns of EU immigrant women in Germany. Comparative Population Studies , 45 , 171–200. Yuksel, M. (2009). Moving with natives or stuck in their neighborhoods? SOEPpapers 230.​ DIW Berlin. Footnotes A person is at-risk-of-poverty if the needs-adjusted net household income (after deductions of tax, social security, unemployment and health insurance) is below 60% of the median in the national distribution (Eurostat 2021 ). If both parents were born in Germany, the term autochthonous is applied. The majority population is a suitable reference to identify significant ethnic differences against the common German social context. Methodological Effects – Since 2016, the Microcensus has been restructured, limiting comparability with earlier years. Additionally, high immigration after 2015, especially of refugees, inflated results (Lengerer et al. 2020). To address these issues, mean correction was applied to smooth artificial spikes (see Appendix B). Remarks to Fig. 1 : (i) Immigrants' poverty risks are plotted with solid lines, and descendants with dashed lines in a matching color for each origin. For some origins, descendants cannot be identified due to limited observations or because children of recent immigrants are still underage or have not yet spent sufficient time (≥ 10 years) in the labor market. (ii) Confidence intervals (CIs) are shown around point estimates to indicate the expected range of the true value with 95% certainty. Additional remarks to Fig. 2 : (iii) The observation window is restricted for EU migrants and refugees to a period of 10 to 15 years, because most of them did not exist in significant numbers before 2009. (iv) The observation window for guest workers starts after two years, because their recruitment was limited to two years in the beginning. A high share left (Münz et al. 1997 ) and only stayers are observed. Averaged annual income is more resistant to fluctuations and thus somewhat more robust in empirical implementation (Jenkins 2000 ). Effects sizes (in p.p.) – are distinctions relative to the observed poverty gaps found in this representative sample and its context. Poverty gaps (> 5 p.p.) are considered as substantial, (5–10 p.p.) as low, (10–20 p.p.) as medium, (20–30 p.p.) as high, and (> 30 p.p.) of very high magnitude. Tables Tables 1 to 4 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Tables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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01:39:43","extension":"html","order_by":43,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":173262,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7688061/v1/8a33d4229d338e6d01edefab.html"},{"id":94806705,"identity":"0fbf6888-fa7e-4c0e-9940-e5121dd1399e","added_by":"auto","created_at":"2025-10-31 01:39:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":169586,"visible":true,"origin":"","legend":"\u003cp\u003eAt-risk-of-poverty rate (in %) over time (1990-2020), by generations\u003cem\u003e.\u003c/em\u003e\u003ca href=\"#RemarksFig1\"\u003e\u003csup\u003e5\u003c/sup\u003e\u003c/a\u003e \u003cem\u003eSource\u003c/em\u003e: Microcensus 1991-2019 – own calculations, weighted.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7688061/v1/6cae5ee7a627833a9091d306.png"},{"id":94826314,"identity":"a794d2eb-a5c7-4c4a-a530-6e3c96e60b5b","added_by":"auto","created_at":"2025-10-31 06:51:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":144761,"visible":true,"origin":"","legend":"\u003cp\u003eAt-risk-of-poverty rate (%) associated to the duration of stay\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003e(in years).\u003ca href=\"#RemarksFig2\"\u003e\u003csup\u003e6\u003c/sup\u003e\u003c/a\u003e \u003cem\u003eSource\u003c/em\u003e: Microcensus 1991-2019 – own calculations, weighted.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7688061/v1/53146bb770aabaf9d723bf1f.png"},{"id":94985008,"identity":"fea9995a-41b9-4f1e-9226-4a528a1e0c47","added_by":"auto","created_at":"2025-11-03 06:57:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1213440,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7688061/v1/fa4e264c-b75f-482c-a576-7c2b5b16f418.pdf"},{"id":94806633,"identity":"32ed508a-84c4-438d-a51b-c4185891f081","added_by":"auto","created_at":"2025-10-31 01:39:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2231183,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7688061/v1/1cd40b8650cb366b0c781ce4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"»Poverty Risks of Ethnic Minorities«(1990-2020) – Examining trends and explaining poverty risks across ethnic origins and generation status","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003ePoverty is a persistent social problem although many policies target at reducing inequality and poverty. Relative poverty is currently at a comparatively high level and continues to rise in many regions, including Europe (Eurostat 2024), especially among populations with a migration history (Eurostat \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Research shows that immigrants (first-generation) experience much higher poverty risks compared to the autochthonous population (e.g., Heady et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Blume et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; \u0026Aacute;lvarez-Miranda \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Finseraas \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Brady et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Descendants (second-generation) also experience higher poverty risks than natives, although to a lesser extent (Giesecke et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, despite the considerable ethnic heterogeneity, research on poverty risks across ethnic origin groups is limited for immigrants (Blume et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Kesler \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and largely lacking for descendants. In addition, only a few studies moved beyond the descriptive level, addressing the underlying determinants of ethnic poverty risks in explanatory research using micro-data (e.g., Crettaz \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Kesler \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Brady et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). While migration research has advanced our understanding of ethnic inequalities in education and the labor market, much less is known about the drivers of ethnic poverty risks. Because resources are shared within households, poverty is best conceptualized at the household level. Explanatory factors must therefore go beyond individual education and labor market positions, also taking household characteristics and context factors, particularly the legal status of immigrants, into account.\u003c/p\u003e\u003cp\u003eAmong the few studies that have examined a wide range of ethnic origins, Blume et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) found increasing poverty disparities between natives and nine immigrant groups (1984 to 1997), largely due to the concentration in low-wage sectors and lengthier labor market integration, while family and welfare policies mitigated these gaps. They used administrative data from Statistics Denmark and the Swedish Income Panel. Kesler (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) studied 17 immigrant groups and found that low-education households with children and welfare receipt were associated with lower female employment, contributing to poverty risks. Kesler used pooled cross-sectional data from the German Microcensus (1996 and 2000), the British Labour Force Survey (1996 to 2004), and also Swedish Longitudinal Individual Data (2002). Crettaz (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), using Luxembourg Income Study data from 2004, found that native-born individuals had 40% lower odds of in-work poverty, while non-EU15 and Turkish citizens experienced much higher risks. Welfare states, like Germany and Sweden, reduced these risks relative to liberal regimes, such as the United States. Brady et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) analyzed 12 immigrant groups in the U.S. using Luxembourg Income Study data (1993 to 2023), showing poverty disparities by citizenship and national origin, influenced significantly by joblessness, low-education, and single parenthood among Central and also Latin American groups, while Asian groups benefited from educational selectivity. The study stressed that structural factors outweighed immigrant-specific traits.\u003c/p\u003e\u003cp\u003eFor Germany, Giesecke et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) used Socio-Economic Panel (SOEP) and Microcensus data (2013) to describe and decompose poverty risks for immigrants and descendants. They found significant poverty gaps for both generations across age, education and occupation, household compositions, and regions. However, these factors explained only a small share of the migrant\u0026ndash;native gap. Heady et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), using SOEP data (1984 to 1989), found that immigrant guest workers were, on average, 2.5 times more likely to be at risk of poverty and remained in poverty for over half a year longer, compared to natives in West Germany.\u003c/p\u003e\u003cp\u003eAgainst this background, this contribution complements existing research by: (1) examining poverty risks for immigrants from 20 different countries of origin (e.g., Gresch/Kristen 2011), rather than treating them as a homogenous group or selecting a few groups, and (2) disentangling their ethnic heterogeneity across the first and the second generation. In this regard, Germany provides a valuable case study, given its decades-long migration history, one of the highest shares of migrants in Europe, and a large number of sizable ethnic origin groups (EU Country Report 2023). Germany is also a valuable case from the perspective of poverty research. While Germany is among the wealthiest countries in Europe, poverty risks have been rising and peaked in 2020, affecting 16,2% of the total population. For the population with a migration background, poverty risks were nearly three times as high: 28,0% were at-risk-of-poverty\u003csup\u003e1\u003c/sup\u003e, compared to 11,8% among the autochthonous population\u003csup\u003e2\u003c/sup\u003e (Destatis \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In addition to providing new detailed descriptions, (3) this study empirically tests and compares different theoretically derived explanations of the heterogeneity of ethnic poverty risks. In multivariate and nested analyses factors at the individual, household, and legal level are investigated stepwise to account for differences in poverty risks across origins and generation status.\u003c/p\u003e\u003cp\u003e(4) An auxiliary contribution is that the multivariate explanatory analyses are based on pooled long-term data (27 time points between 1990\u0026ndash;2020), because cross-sectional \u0026lsquo;snapshots\u0026rsquo; are embedded into the specific context of a given period and are unaware of time-dependent poverty risks. This means that some individuals or households are vulnerable \u0026ldquo;only\u0026rdquo; during economic recessions but they have the resources to overcome poverty afterwards (cyclical risks), whereas others remain vulnerable even in times of economic growth (long-term risks). Accordingly, restricting the analyses to a specific year would lead to misleading conclusions (Foster et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Grad\u0026iacute;n et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Although individual-level panel data, such as the Socio-Economic Panel, could provide deeper insights into poverty dynamics, they often lack sufficient observations for most ethnic origins. Against this trade-off and given the focus on ethnic heterogeneity in poverty risks, this study draws on the German Microcensus, a large-scale, nationally representative survey that provides detailed information on a wide range of ethnic origins, covering first- and second-generation migrants, within a repeated cross-sectional design over a long time span.\u003c/p\u003e\u003cp\u003eAccordingly, this study, examines the following research questions:\u003c/p\u003e\u003cp\u003e(RQ1): How do poverty risks differ across ethnic origins, compared to the autochthonous population?\u003c/p\u003e\u003cp\u003e(RQ2): How do poverty risks differ between first and second generation migrants across ethnic origins?\u003c/p\u003e\u003cp\u003e(RQ3): How can these ethnic and generational differences in poverty risks be explained at the individual-level by education, occupation, and precarious employment, and how are they further influenced by household composition and citizenship?\u003c/p\u003e\u003cp\u003eThis paper is structured as follows: The next section gives an overview of poverty trends across ethnic origins to identify long-term poverty risk groups, addressing RQ1 and RQ2 (Section 2). This is followed by a theoretical discussion of poverty across migrant groups and generations, addressing RQ3 (Section 3). Subsequently, the data and methodology are outlined (Section 4). All outcomes are described and analyzed accordingly (Section 5). Findings and limitations will be discussed in the final section (Section 6).\u003c/p\u003e"},{"header":"2 Poverty Trends","content":"\u003cp\u003eAdding to the limited knowledge on poverty risks across a wide range of ethnic origins, this research begins by describing poverty trends: over time (1991\u0026ndash;2019) and the duration of stay (0\u0026ndash;30 years), both across generations, to identify ethnic long-term poverty risk groups.\u003c/p\u003e\u003cp\u003eThe analyses include ethnic minorities from 32 countries, with some grouped into larger regions, based on their shared political history and comparable social contexts, to identify broader trends and issues relevant to Europe. The following description covers 20 ethnic groups (as indicated by the \u003cem\u003eitalics\u003c/em\u003e) in addition to the autochthonous (\u0026ldquo;German\u0026rdquo;) population, which serves as the reference point.\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eThe so-called guest workers (\u0026ldquo;Gastarbeiter\u0026rdquo;) arrived in West Germany under bilateral recruitment agreements between 1955 and 1973. These agreements stopped in 1973, but the migrant population continued to grow due to family reunification policies in the 1970s and 1980s (M\u0026uuml;nz et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThese are immigrants from \u003cem\u003eTurkey, Italy, Greece, Spain/Portugal, Morocco\u003c/em\u003e, and \u003cem\u003eYugoslavia\u003c/em\u003e (now: Bosnia-Herzegovina, Serbia, Croatia, and Montenegro).\u003c/p\u003e\u003cp\u003eLate repatriates (\u0026ldquo;Sp\u0026auml;t-Aussiedler\u0026rdquo;) are ethnic German resettlers who mainly arrived in the 1990s after the end of the Soviet Union (Panagiotidis \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). They originated from \u003cem\u003ePost-Soviet States\u003c/em\u003e (now: Russia, Kazakhstan, and Ukraine), as well as from \u003cem\u003eRomania\u003c/em\u003e/\u003cem\u003eBulgaria\u003c/em\u003e, and \u003cem\u003ePoland\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eEU migrants from Central and Eastern Europe moved to Germany with the EU enlargement in 2004 and 2007. It is possible to distinguish between migrants from \u003cem\u003eCentral EU\u003c/em\u003e countries (Czech Republic, Hungary, and Slovakia) and \u003cem\u003ePoland\u003c/em\u003e, who arrived since 2004, and migrants from \u003cem\u003eRomania/Bulgaria\u003c/em\u003e, who came since 2007. \u003cem\u003eKosovan/Macedonian\u003c/em\u003e EU asylum seekers arrived after the independence from Serbia in 2008 and the lift of visa requirements in 2009.\u003c/p\u003e\u003cp\u003eRefugees who migrated to Germany originated from a wide range of regions around the world. They include early refugees from \u003cem\u003eIran\u003c/em\u003e, who arrived between 1979\u0026ndash;2009, and \u003cem\u003eVietnam\u003c/em\u003e, who entered between 1975\u0026ndash;2009, from \u003cem\u003eYugoslavia\u003c/em\u003e, who came between 1991\u0026ndash;2002; And more recent refugees, who fled since 2010, from the \u003cem\u003eMiddle East\u003c/em\u003e (Iraq, Syria, Jordan, Arab Emirates, Lebanon, and Yemen), \u003cem\u003eNorth Africa\u003c/em\u003e (Egypt, Algeria, Libya, and Tunisia), \u003cem\u003eWest Africa\u003c/em\u003e (Nigeria, Ghana, Niger, Mali, Senegal, Guinea, Benin, Togo, and Sierra Leone) and \u003cem\u003eAfghanistan\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTime\u003c/b\u003e \u0026ndash; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003csup\u003e4\u003c/sup\u003e depicts the overall trend and shows that poverty risks are significantly higher for all ethnic groups across generations at almost every point in time compared to autochthonous Germans. While Germans maintain lower and relatively stable poverty risks, 13 out of 20 ethnic groups face medium to high risks and substantial fluctuations over time.\u003c/p\u003e\u003cp\u003eOverall, guest workers from Turkey and Morocco, Post-Soviet late repatriates, EU asylum seekers from Kosovo/Macedonia and refugees from Yugoslavia and North Africa faced large (20 to 30 p.p.) and all other refugees even very large poverty gaps (\u0026gt;\u0026thinsp;30 p.p., Afghanistan and Middle East\u0026thinsp;\u0026gt;\u0026thinsp;50 p.p.). EU migrants from Romania/Bulgaria and Poland encountered medium gaps (up to 25 p.p.). All other ethnic origins have lower poverty gaps (up to 10 p.p.).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOver time, comparing the first and the last given value, guest workers from Yugoslavia, Turkey, Italy, Greece, Spain/Portugal, and Afghan refugees faced the highest poverty risk increase (+\u0026thinsp;11% to +\u0026thinsp;16%), indicating long-term risks. The strongest decrease was observed for Post-Soviet late repatriates (\u0026ndash;26%) and refugees from Yugoslavia (\u0026ndash;17%), while a medium decrease was observed for refugees from Vietnam, EU immigrants from Poland and Romania/Bulgaria, and guest workers from Morocco (\u0026ndash;11% to \u0026minus;\u0026thinsp;13%).\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eA convergence of poverty risks with those of autochthonous Germans was found for immigrants from Romania/Bulgaria and Poland (LR), and descendants of Spanish/Portuguese, Italian and Greek (GW), and Polish (LR) origin. Some immigrants reduced their poverty gaps to low differences (up to 10 p.p.), those from Spain/Portugal, Italy and Greece (GW), Yugoslavia (GW and RG), Central EU and Poland (EU), and Post-Soviet States (LR). Whereas refugees continue to face large to very large poverty gaps: Vietnam, Iran, North Africa (\u0026gt;\u0026thinsp;25 p.p.), West Africa (\u0026gt;\u0026thinsp;35 p.p.), Middle East and Afghanistan (\u0026gt;\u0026thinsp;50 p.p.).\u003c/p\u003e\u003cp\u003e\u003cb\u003eGeneration status\u003c/b\u003e \u0026ndash; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e also presents a comparison between first- and second-generation migrants. Poverty risks are significantly higher for immigrants compared to descendants at almost any point in time (up to 10 p.p.). Exceptions are rare and typically temporary. Additionally, poverty gaps remain relatively high over time for many immigrants, whereas they decline more substantially for most descendants.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDuration of stay\u003c/b\u003e \u0026ndash; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that poverty risks fall largely for almost all ethnic origins with increasing time of residence. The largest decrease appears during the first 10 years (up to \u0026minus;\u0026thinsp;33%), but poverty risks also drop significantly between 10 and 30 years of residence (up to \u0026minus;\u0026thinsp;13%).\u003c/p\u003e\u003cp\u003eDuring the first 10 years of residence, high poverty risk reductions (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\approx\\:\\:\\)\u003c/span\u003e\u003c/span\u003e\u0026ndash;30%) are observed for Romanian/Bulgarian repatriates and refugees from West Africa. Medium poverty reductions (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\approx\\:\\:\\)\u003c/span\u003e\u003c/span\u003e\u0026ndash;20%) are observed for Post-Soviet repatriates and EU asylum seekers from Kosovo/Macedonia, and refugees from the Middle East. Smaller, though significant and substantial poverty reductions (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\approx\\:\\:\\)\u003c/span\u003e\u003c/span\u003e\u0026ndash;10%) are observed for all other repatriates, EU migrants and refugees. Exceptions are refugees from Yugoslavia (\u0026plusmn;\u0026thinsp;0%) and guest workers.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOver 30 years of residence, the highest poverty risk reductions are observed for Romanian/Bulgarian (\u0026ndash;30%) and Post-Soviet repatriates (\u0026ndash;33%), followed by Polish repatriates (\u0026ndash;18%) with medium reductions. All other ethnic minorities experience only smaller reductions in poverty risk or reach a plateau.\u003c/p\u003e\u003cp\u003eGuest workers either show growing poverty risks over time: during the first 10 years (up to +\u0026thinsp;20%) and over 30 years of stay (up to +\u0026thinsp;10%); or they remain at almost the same poverty risk level as in the beginning. An increase is observed for guest workers from Greece, Turkey, and Yugoslavia; and stagnation for those from Italy and Spain/Portugal \u0026ndash; both indicating long-term risks. Only Moroccans showed substantial poverty reductions (\u0026ndash;12%), but with high uncertainty (as indicated by the 95% CI). \u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eIn summary, despite a trend towards convergence with the poverty risk level of autochthonous Germans, significant differences remain for most ethnic origins, often also across generations. The analyses (in Section 5) will therefore focus on explaining the remaining overall poverty gaps.\u003c/p\u003e"},{"header":"3 Theoretical Approach","content":"\u003cp\u003eThe explanation of ethnic differences in poverty risks across generations is based on general theoretical considerations about possible origins of poverty and their application to migrants. I will also draw on migrant-specific arguments, such as legal status. All explanations are elaborated for specific immigration groups.\u003c/p\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Educational resources and labor market positioning\u003c/h2\u003e\u003cp\u003eAccording to human capital theory, investments in formal education contribute to higher labor market positions (e.g., occupational status) associated with higher wages (Becker 1993). The level of formal education is, with differences between ethnic origins, overall lower for most immigrants (e.g., Kristen/Granato 2007), particularly immigrants from non-EU countries and refugees, compared to native Germans (Sp\u0026ouml;rlein et al. 2020). This may be due to the educational systems in immigrants\u0026rsquo; countries of origin, leading to different\u0026mdash;often lower\u0026mdash;educational levels compared to Germany (MIPEX 2011, 2020). Thus, observed differences in educational levels should translate into varying occupational outcomes and wages, thereby contributing to poverty risks.\u003c/p\u003e\u003cp\u003eYet, certain immigrant-specific conditions may affect this translation, such that labor market outcomes to foreign qualifications may be lower than those obtained by the autochthonous population, even with the same level of formal education. In support of this, previous research has found that immigrants are unemployed more often (e.g., Kogan \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and that their achieved occupational status is frequently lower compared to natives (e.g., Chiswick et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) with substantial variation across ethnic origins (Br\u0026uuml;cker et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). An important argument in this context is that immigrants\u0026rsquo; foreign qualifications may not be fully transferable to the new context (Friedberg \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Chiswick/Miller 2009); another is that the recognition of foreign qualifications could be limited (e.g., Kogan 2016). In addition to explanations based on educational resources, further arguments may explain immigrants' lower labor market positions. These include ethnic penalties (Heath/Cheung 2006), for example, statistical discrimination by employers who are uncertain about the productivity of individuals of certain ethnic groups (Phelps 1972; Arrow \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1973\u003c/span\u003e), or taste-based discrimination (Becker \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1957\u003c/span\u003e). Ethnic differences in occupational attainment may also be a result of self-selection on unobserved traits, such as skills, ambition, or preferences (Borjas \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). These conditions could contribute to observed differences in occupational status, which translate into varying wages, thereby explaining poverty risks.\u003c/p\u003e\u003cp\u003e\u003cem\u003eGuest workers\u003c/em\u003e originated from countries where manual labor skills were often prioritized over formal education (Herbert \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). In addition, they were mainly selected from lower socio-economic groups in their home countries, with limited access to formal education before migration (OECD \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Both factors may contribute to their lower educational attainment overall. Recruitment policies primarily targeted guest workers with lower levels of formal education to address labor shortages in lower-status occupations (Heizmann et al. 2015) and as a result, they were more frequently employed in unskilled occupations than other immigrants; even when their formal qualifications were recognized, this may indicate ethnic penalties (e.g., Constant/Massey 2005; Heath/Cheung 2006).\u003c/p\u003e\u003cp\u003e\u003cem\u003eLate repatriates\u003c/em\u003e have lower levels of formal education overall compared to native Germans, partly because manual and technical trainings prevailed in their countries of origin (Flake \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In addition, struggles with the recognition of foreign qualifications (Dietz \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) and language barriers have complicated their labor market positioning, relegating many, including those with higher education, to lower-status occupations (Br\u0026uuml;ck-Klingberg et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Ethnic penalties in the German labor market appear to be smaller for repatriates than for other ethnic groups, especially those from third countries (Antidiskriminierungsstelle 2012).\u003c/p\u003e\u003cp\u003eMany \u003cem\u003eEU migrants\u003c/em\u003e, particularly those from Eastern Europe (e.g., Romania and Bulgaria), have a lower level of formal education on average compared to Germans (OECD 2017). Despite the mutual recognition of qualifications in the EU, job positions are tied to specific qualifications in the German labor market (Allmendinger 1989). This could contribute to the devaluation of qualifications attained abroad, because they appear to be less tailored to certain occupations. Germany restricted the full freedom of movement for immigrants from certain newer EU member states, such as Romania and Bulgaria. Yet, to address labor shortages in low-wage sectors, seasonal and temporary work permits were issued to them. This policy contributed to a selection with lower formal education on average relative to natives (Dietz/Kaczmarczyk 2008). EU migrants face ethnic penalties less often than non-EU immigrants, indicated by better initial labor market positioning and faster status convergence (Berb\u0026eacute;e/Stuhler 2023).\u003c/p\u003e\u003cp\u003e\u003cem\u003eRefugees\u003c/em\u003e often originate from fragile, conflict-affected or developing countries where educational systems are less developed and stratified. Thus, they are less frequently equipped with educational resources applicable to the host society compared to other migrants and have lower levels of formal education overall compared to natives (e.g., Eisnecker et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Their foreign qualifications are often not (fully) recognized, contributing to devaluation and limited employment prospects (Sommer \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Ethnic penalties are likely for refugees, indicated by the highest initial occupational status differences and the slowest convergence overall (e.g., Berb\u0026eacute;e/Stuhler 2023). They tend to be positively selected in terms of educational resources compared to non-migrants. This is particularly the case for earlier refugees (e.g., Iran and Vietnam) compared to more recent refugees (e.g., Afghanistan and the Middle East) (Sp\u0026ouml;rlein et al. 2020).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis\u003c/strong\u003e\u003cp\u003e\u003cb\u003ea (education)\u003c/b\u003e: \u003cem\u003eThe poverty gaps of immigrants compared to autochthonous people, which vary across ethnic groups, can partly be explained by their levels of formal education.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis\u003c/strong\u003e\u003cp\u003e\u003cb\u003eb (occupation)\u003c/b\u003e: \u003cem\u003eThe poverty gaps of immigrants compared to autochthonous people, which vary across ethnic groups\u003c/em\u003e\u0026mdash;\u003cem\u003eeven after accounting for differences in their level of formal education\u003c/em\u003e\u0026mdash;\u003cem\u003ecan partly be explained by their attained occupational status.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003eParents\u0026rsquo; educational resources and occupational attainment are often passed on to their children, influencing their opportunities, choices, and outcomes in life (e.g., Marx 1867; Bourdieu \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). Intergenerational transmission thus provides a link between immigrants and their descendants. While low levels of formal education in the first-generation contribute to less favorable prospects for the second-generation (Borjas \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), \u0026lsquo;immigrant optimism\u0026rsquo; could counteract this (Kao/Tienda 2002). It refers to a tendency of immigrant families to have higher aspirations than autochthonous peers, endorsing upward mobility (Salikutluk 2016). Ethnic penalties may also persist in the second-generation, especially for those with non-EU origin, affecting the attainment of higher-status occupations (Berb\u0026eacute;e/Stuhler 2023).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis\u003c/strong\u003e\u003cp\u003e\u003cb\u003ec (Generation status)\u003c/b\u003e: \u003cem\u003eThe remaining poverty gap between descendants and autochthonous peers, which varies across ethnic groups, can partly be explained by their level of formal education and attained occupational status, as in the case of Hypothesis 1a and 1b.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Precarious employment\u003c/h2\u003e\u003cp\u003ePrecarious employment includes different forms of \u0026lsquo;non-standard\u0026rsquo; employment, such as part-time, temporary, agency, and marginal work, and full-time jobs with lacking social security. Precarious employment is often characterized by lower earnings, increased risks of job loss (more frequent and/or enduring shortfalls in earnings), and lower upward mobility, contributing to lower wages and higher poverty risks (Gebel/Giesecke 2011). Immigrants tend to be over-represented in the unskilled, low-wage labor market, experiencing precarious employment more often (Kogan \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), with significant variation across ethnic groups. Refugees and non-EU immigrants often face the greatest barriers to \u0026lsquo;standard\u0026rsquo; employment (Heath/Cheung 2007). This could be due to ethnic labor market segmentation, referring to the division of the workforce into an autochthonous in-group, who encounter \u0026lsquo;standard\u0026rsquo; employments more often, and an ethnic out-group, who face \u0026lsquo;precarious\u0026rsquo; employments more frequently (Doeringer/Piore 1971; Gebel \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This pattern may result from recruitment practices and/or immigration policies gaining migrants for lower-status employments commonly avoided by natives (Heckmann 1992). Otherwise, it could reflect another form of ethnic penalties, where employers tend to hire migrants for temporary employments to maintain workforce flexibility and cost efficiency (Gebel/Giesecke 2011). Migrants accept precarious employment conditions more often due to their fewer or lacking job alternatives (Eichhorst/Marx 2011). Therefore, ethnic differences in precarious employment may translate into varying wages, thereby contributing to poverty risks.\u003c/p\u003e\u003cp\u003e\u003cem\u003eGuest workers\u003c/em\u003e were initially recruited into temporary residence and work permits that tied them to specific employers and restricted their labor market mobility. This dependency frequently led to precarious work in un- or low-skilled jobs, offering minimal employment security and upward mobility, and poor working conditions in low-paid industries, for example cleaning, agriculture, or manufacturing (Zimmermann 2005).\u003c/p\u003e\u003cp\u003e\u003cem\u003eLate repatriates\u003c/em\u003e encountered difficulties in obtaining \u0026lsquo;standard\u0026rsquo; employment, despite their supportive integration policies, as they arrived during periods of economic downturn (Panagiotidis \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This frequently led to a relegation to low-paid and unstable jobs, contributing to limited upward mobility and a cycle of precarious work and unemployment (BAMF 2014).\u003c/p\u003e\u003cp\u003e\u003cem\u003eEU migrants\u003c/em\u003e from certain newer EU member states, such as Romania and Bulgaria, did not receive full freedom of movement till 2014 (Eurofound \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Their labor market access was limited to temporary working permits to fill labor shortages in low-paid industries or to replace non-EU immigrants, frequently contributing to precarious work and unemployment (Barrett/Ma\u0026icirc;tre 2011).\u003c/p\u003e\u003cp\u003e\u003cem\u003eRefugees\u0026rsquo;\u003c/em\u003e precarious work is often linked to uncertainties such as temporary residence and work permits, making employment less likely (Liebau/Salikutluk 2016). Some engage in voluntary work, receiving minimal compensation, during the asylum process, which aims to facilitate integration but often risks confining them to low-wage sectors characterized by precarious work, unemployment, and limited mobility (Salikutluk et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Br\u0026uuml;cker et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis\u003c/strong\u003e\u003cp\u003e\u003cb\u003ea (Precarity)\u003c/b\u003e: \u003cem\u003eThe poverty gaps of immigrants compared to autochthonous people, which vary across ethnic groups\u003c/em\u003e\u0026mdash;\u003cem\u003eeven after accounting for differences in their level of formal education and occupational status\u003c/em\u003e\u0026mdash;\u003cem\u003ecan partly be explained by precarious employment.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003eParental experiences of precarious and unstable employment can influence their children\u0026rsquo;s career trajectories (e.g., Kalter/Granato 2007). Intergenerational transmission thus, again, provides the link between immigrants labor market experiences and the experiences of their descendants (e.g., Yuksel \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The transmission of labor market disadvantages was strong among migrants families compared to autochthonous ones (e.g., Groh-Samberg/Voges 2014). Ethnic labor market segmentation could contribute to the perpetuation of these disadvantages across generations (Aradhya et al. 2023). All these experiences vary significantly across ethnic origins and generations (Heath/Cheung 2007), some descendants are catching up with autochthonous peers, others continue to face disadvantages (Luthra \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis\u003c/strong\u003e\u003cp\u003e\u003cb\u003eb (Generation status)\u003c/b\u003e: \u003cem\u003eThe remaining poverty gap between descendants and autochthonous peers, which varies across ethnic groups\u003c/em\u003e\u0026mdash;\u003cem\u003enet of differences in level of formal education and occupational status\u003c/em\u003e\u0026mdash;\u003cem\u003ecan partly be explained by precarious employment, as in the case of Hypothesis 2a.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Household resources and needs\u003c/h2\u003e\u003cp\u003eThe financial situation of a household is based on (a) household resources, such as income from wages, benefits, or social transfers, and (b) household needs, such as expenses on different forms of consumption (e.g., Jenkins \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). According to resource dilution theory, each dependent individual added to a household leads to a decrease in available resources (Blake \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). Household resources and needs are related to the number of its dependents. Households with a larger number of dependents relative to earners, encounter higher poverty risks (Smith et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIncreasing the number of working hours can partially offset poverty risks associated with more dependents. At the same time, immigrants work fewer hours than native Germans, may be due to their lower labor force participation and the lack of participation of certain household members, such as immigrant women (e.g., Sprengholz et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), or adult siblings and grandparents within extended families (e.g., Glick \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMoreover, the presence of children reduces the time available for paid work, thereby reducing household income and increasing poverty risks (Lenze \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Immigrants tend to have higher fertility rates (Milewski \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and, therefore, higher dependent\u0026ndash;earner ratios than autochthonous households.\u003c/p\u003e\u003cp\u003eNatives, by contrast, live in single-parent households more frequently than immigrants. This may limit their working hours, thereby contributing to poverty risks (Cribb et al. 2017). In addition, marriage may improve incomes for many couples due to joint taxation (\u0026ldquo;Ehegattensplitting\u0026rdquo;), providing tax advantages. As immigrants are more often married than autochthonous Germans (Kuhnt/ Krapf 2020), they may benefit from this regulation. Therefore, ethnic differences in the household composition may contribute to different poverty risks.\u003c/p\u003e\u003cp\u003eFirst-generation \u003cem\u003eguest workers\u003c/em\u003e were often employed in labor-intensive jobs, working longer hours than autochthonous Germans to maximize their earnings, yet there was a considerable decline in the average working hours over time due to lowering employment rates of immigrant women (e.g., Sprengholz et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAt the same time, the vast majority of guest workers are married, whereas the share among autochthonous people is substantially smaller (Kuhnt/Krapf 2020). The fertility rates of guest worker women are, on average, higher compared to those of autochthonous women and other immigrant women (M\u0026uuml;nz et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Higher fertility rates and lower labor market participation should contribute to high dependents-earner ratios.\u003c/p\u003e\u003cp\u003e\u003cem\u003eLate repatriates\u003c/em\u003e did not differ much from autochthonous Germans regarding their working hours. They were only slightly lower due to the lower employment of repatriate women. However, compared to other migrant women, they had the highest number of hours worked (Friedrichs/Graf 2022). In terms of marriage, repatriates are married more often than natives on average, also in comparison with most other immigrant groups (BAMF 2014). Fertility rates of repatriate women are slightly below those of other immigrant groups, but largely above those of autochthonous women (ibid.). Late repatriates usually migrated as an unit with an extended family, this should contribute to high dependents-earner ratios (Dietz \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cem\u003eEU migrants\u003c/em\u003e have the highest employment rates and working hours, even exceeding those of native Germans (Clemens/Hart 2018). They are forerunners of unmarried cohabitation, leaving room for economic vulnerability (Kostova \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). EU immigrant women had the lowest fertility rates in Germany overall (Wolf/Kreyenfeld 2020). Therefore, lower fertility rates and higher labor market participation, should contribute to low dependents-earner ratios.\u003c/p\u003e\u003cp\u003eEmployment rates and working hours among \u003cem\u003erefugees\u003c/em\u003e are considerably lower than among other immigrant groups, and far below the level of autochthonous Germans (Salikutluk et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Refugees tend to have higher fertility rates than natives and other immigrant groups (Newsham/Rowe 2019). Therefore, higher fertility rates and lower labor market participation, particularly among recent refugees from African or Arabian countries (ibid.), may contribute to high dependents-earner ratios.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis\u003c/strong\u003e\u003cp\u003e\u003cb\u003ea (Household resources)\u003c/b\u003e: \u003cem\u003eThe poverty gaps of immigrants compared to autochthonous Germans, which vary across ethnic groups\u003c/em\u003e\u0026mdash;\u003cem\u003eeven after accounting for differences in their level of formal education, occupational status, and precarious employment\u003c/em\u003e\u0026mdash;\u003cem\u003ecan partly be explained by their household resources.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis\u003c/strong\u003e\u003cp\u003e\u003cb\u003eb (Household needs)\u003c/b\u003e: \u003cem\u003eThe poverty gaps of immigrants compared to autochthonous Germans, which vary across ethnic groups\u003c/em\u003e\u0026mdash;\u003cem\u003eeven after accounting for differences in their level of formal education, occupational status, precarious employment, and household resources\u003c/em\u003e\u0026mdash;\u003cem\u003ecan partly be explained by their household needs.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003eParents\u0026rsquo; family values and their household division of labor are expected to contribute to their children\u0026rsquo;s practices (Hochschild/Machung 2012). Descendants need time to adapt to host society family norms (Huschek et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This shows the link between generations, again. Descendants\u0026rsquo; working hours tend to decrease as their educational attainment and occupational mobility improves (Dustmann/Glitz 2011). At the same time, the labor force participation of second-generation women increases, contributing to a higher household income (BAMF 2019). Descendants shows partnership and fertility patterns that shift towards native peers\u0026mdash;such as a preference for cohabitation over marriage and delayed marriage\u0026mdash;that lie roughly between patterns of their parents and autochthonous couples (Nauck \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Thus, higher labor market participation and lower fertility rates, may contribute to lower dependents-earner ratios across generations.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis\u003c/strong\u003e\u003cp\u003e\u003cb\u003ec (Generation status)\u003c/b\u003e: \u003cem\u003eThe remaining poverty gap between descendants and autochthonous peers, which varies across ethnic groups\u003c/em\u003e\u0026mdash;\u003cem\u003eeven after accounting for differences in their level of formal education, occupational status, and precarious employment\u003c/em\u003e\u0026mdash;\u003cem\u003ecan partly be explained by their household resources and needs, as in the case of Hypothesis 3a and 3b.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Legal arrangements\u003c/h2\u003e\u003cp\u003eLegal arrangements tied to citizenship are important for immigrants\u0026rsquo; integration into the labor market, as citizenship can provide opportunities for labor market access (Gathmann/Garbers 2023), occupational mobility as well as employment security (Br\u0026uuml;cker et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), thereby contributing to wages. In addition to citizenship regulations, legislation may also promote integration in other ways, such as through the provision of language or integration courses. The access to German citizenship and naturalization rates differed largely for non-EU migrants and refugees, compared to EU migrants (Diehl/Blohm 2002). This could be due to stricter conditions to obtain German citizenship and lengthy and complex Bureaucratic procedures (ibid.). Thus, observed differences in the citizenship status may translate into varying labor market access and integration, thereby contributing to poverty risks.\u003c/p\u003e\u003cp\u003eLegal arrangements to support the labor market integration of \u003cem\u003eguest workers\u003c/em\u003e in West Germany between 1955 and 1989 developed slowly, because they were expected to leave. Their legal residence began with temporary employment tied to specific jobs and evolved into repeatedly renewed work permits, permanent residence, as well as family reunification for millions of recruited guest workers after 1973 (Goddar/Huneke 2011). This allowed many to stay and settle, creating an incentive for citizenship (Constant/Massey 2002), especially for third-country immigrants (Diehl/Blohm 2002).\u003c/p\u003e\u003cp\u003eAfter the collapse of the Soviet Union, \u003cem\u003elate repatriates\u003c/em\u003e migrated to Germany under the Federal Expellees Act (\u0026ldquo;Bundesvertriebenengesetz\u0026rdquo;). This law provided supportive legal conditions (e.g., language and integration courses), facilitating their integration into the education system and labor market (Dietz 2000). Between 1991 and 2009, approximately 2.1\u0026nbsp;million late repatriates arrived with German descent, receiving their citizenship upon arrival, distinguishing them from other immigrants (Kalter/Kogan 2014).\u003c/p\u003e\u003cp\u003e\u003cem\u003eEU migrants\u003c/em\u003e benefit from free movement and supportive legal arrangements, which have been a core component of EU law since 1993. Acquiring citizenship is therefore less relevant for EU migrants, who have full access to the German labor market by virtue of their EU citizenship. After several EU enlargements, restrictions were imposed between 2004 and 2014 on some newer EU member states, such as Romania and Bulgaria. Their access to the labor market was limited to sectors experiencing shortages (BAMF 2014). Prior to this restriction, EU migrants had fewer incentives than non-EU migrants, due to their full access and easier integration.\u003c/p\u003e\u003cp\u003e\u003cem\u003eRefugees\u003c/em\u003e have relatively easy access to integration courses nowadays and receive thorough language training and other forms of support (Br\u0026uuml;cker et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). At the same time, most refugees experienced lengthy asylum procedures, and they are prohibited from working until their legal status is decided. For this reason, their transition to employment takes longer compared to other migrants, slowing down or even delaying their economic integration (Kosyakova/Brenzel 2020). Taken together, refugees have an high incentive for citizenship to gain access to legal residency and full access to the labor market.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis\u003c/strong\u003e\u003cp\u003e\u003cb\u003ea (Citizenship)\u003c/b\u003e: \u003cem\u003eThe poverty gaps of immigrants relative to autochthonous people, which vary across ethnic groups\u003c/em\u003e\u0026mdash;\u003cem\u003eeven after accounting for differences in their level of formal education, occupational status, precarious employment, household resources and needs\u003c/em\u003e\u0026mdash;\u003cem\u003ecan partly be explained by their citizenship status.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Data and Methodology","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Data and target population\u003c/h2\u003e\u003cp\u003e\u003cem\u003eData\u003c/em\u003e \u0026ndash; The German Microcensus (MC) is a representative household survey by the Federal Statistical Office that covers 1% of the total population in Germany. All citizens must provide information, so the data are highly representative and allow to produce detailed subgroup analyses for a wide range of ethnic origins. The analyses in this paper are based on 27 Microcensus datasets: 1991, 1993, 1995, and 1996 to 2019, which are prepared and harmonized by own routines (see Appendix B). The data documentation, the \u003cem\u003eMicrocensus trend files\u003c/em\u003e for different survey years were used to be aware of pitfalls and peculiarities (Lengerer et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), as well the GESIS-MISSY pages of the German Microdata Lab (GML 2023). From these initial datasets, relevant variables are selected and converted into a coherent form and then combined into a single longitudinal file.\u003c/p\u003e\u003cp\u003e\u003cem\u003eTarget population\u003c/em\u003e \u0026ndash; The population with a migration history, differentiated by their ethnic origin and generation, is of main interest, while the autochthonous population is the reference point. To determine a migration history, I follow the concept introduced in by the Federal Statistical Office in 2005: Immigrants (first-generation) are all people born outside of Germany that migrated after 1949. Descendants (second-generation) are all people born in Germany with at least one parent born abroad. For migrants with a German passport, the definition of origin is primarily based on their own nationality held before naturalization, or on the current or former citizenship of one of their immigrant parents. I restrict the analysis to prime-age individuals (25\u0026ndash;54) living in private households.\u003c/p\u003e\u003cp\u003eTable A1 shows the pooled poverty risks between 1991 and 2019, along with the number of observations (in brackets below). The poverty risks were already described in detail in Section 2, with further elaborations provided in Section 5. The smallest sample sizes are observed for the population from Spain (472) and Portugal (541) at-risk-of-poverty, but even these lower observation numbers are sufficient. A minimum of 30\u0026ndash;50 observations per group is recommended for basic analysis and larger samples for multivariate models (Cohen \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), as shown.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Operationalizations\u003c/h2\u003e\u003cp\u003e\u003cem\u003eResearch subject\u003c/em\u003e \u0026ndash; The empirical assessment of relative income poverty is largely based on the approach of the European Statistical Office (2021) in order to allow comparability with other studies. Financial resources are a suitable indicator for studying inequality in prosperous countries, since poverty is often relative (poor with respect to a country-specific social context), whereas absolute poverty and material deprivation (poor regarding basic human needs) are rare.\u003c/p\u003e\u003cp\u003eA person is \u003cem\u003eat-risk-of-poverty\u003c/em\u003e if the equivalized annual disposable income\u003csup\u003e7\u003c/sup\u003e is below the threshold of 60% of the country-specific median (Eurostat \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The actual \u0026ldquo;disposable income\u0026rdquo; (net income) is only apparent after deducting all taxes and compulsory insurances (e.g., social security, unemployment, and health) and it depends on the size and composition of the household. So, the net household income is weighted using an \u003cem\u003eequivalence scale\u003c/em\u003e (OECD \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), resulting in a needs-weighted per capita income. The implicit assumption is that all members of the household benefit from joint income, but the consumption utility of the household grows with increasing members. The household head (person with the highest age and or income) receives a weight of 1, further members over the age of 14 receive a weight of 0.5, and children up to the age of 14 receive a weight of 0.3. The operationalization follows the GESIS Poverty Measurement (Boehle \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cem\u003eKey covariates\u003c/em\u003e \u0026ndash; The at-risk-of-poverty rate will be studied by 20 ethnic origins, their first- and partly their second generation (identifiable for 17 ethnic groups). Appendix B provides a concise overview of the operationalization and coding for all theoretically selected models.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Methods\u003c/h2\u003e\u003cp\u003eThe poverty risks will be examined in a pooled model for repeated cross-sections (tsset pid syear, yearly; Cox/Donnelly 2011) for the period 1991 to 2019. The dependent variable is a dummy that identifies individuals at-risk-of-poverty, so a \u0026lsquo;Logistic Regression\u0026rsquo; is applied, clustered for households (vce(cluster hid)) to account for dependencies. \u0026lsquo;Average Marginal Effects\u0026rsquo; (AME) will be used, as they allow to compare coefficients across models (Mood 2010). Coefficients show the poverty risks of different ethnic origins compared to autochthonous Germans, accordingly \u003cem\u003epoverty gaps\u003c/em\u003e are estimated. To balance group- and design-specific selection probabilities across different interview years, I use probability weighting (svyset pid [pw\u0026thinsp;=\u0026thinsp;weight]; Solon et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) with standard sampling weights of the Microcensus (GML 1991\u0026ndash;2019).\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Results","content":"\u003cp\u003eThe analysis begins with baseline effects across ethnic origins and generations as a reference model (M0: only controls), accounting for gender, federal territory, age, and survey year. Subsequent models (M1\u0026ndash;M4) are introduced stepwise and nested, according to the theoretical explanations and corresponding hypotheses (3.1\u0026ndash;3.4). This modeling strategy separates variance across multiple levels and addresses mediator\u0026ndash;confounder constellations (\u0026lsquo;causes of effects\u0026rsquo;) and cumulative disadvantages, while avoiding overcomplication (\u0026lsquo;obscuring interaction terms\u0026rsquo;). After clarifying individual-level factors, the model then isolates residual variation at the household and structural level, disentangling how each factor contributes to each specific ethnic poverty gap.\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Educational resources and labor market positioning\u003c/h2\u003e\u003cp\u003eBased on the descriptives in Section 2, substantial and significant poverty gaps are expected for most \u003cem\u003efirst-generation\u003c/em\u003e guest workers, repatriates, EU migrants, and all refugees. \u003csup\u003e8\u003c/sup\u003e Table\u0026nbsp;1 (M0) shows that very high poverty gaps are faced by refugees from the Middle East, Afghanistan, West Africa, Vietnam, and Iran; and guest workers from Morocco (all sorted by magnitude). High poverty gaps are experienced by refugees from Yugoslavia and North Africa, Post-Soviet late repatriates, EU immigrants from Kosovo/Macedonia and Romania/Bulgaria, and guest workers from Turkey. Lower, but still substantial and significant poverty gaps are observed for Polish repatriates and EU immigrants, and guest workers from Italy, Greece, and Yugoslavia. For the \u003cem\u003esecond-generation\u003c/em\u003e, a few substantial and significant poverty gaps are observed across generations, especially among refugees of Afghan, Middle Eastern, Vietnamese, and Iranian origin, repatriates of Post-Soviet origin, and guest workers of Moroccan and Turkish origin.\u003c/p\u003e\u003cp\u003eAfter identifying long-term poverty risk groups across generations, Model 1 in Table\u0026nbsp;1 examines the effect of educational resources and occupational status. Adding education first shows that lower levels of formal education contribute substantially to the observed poverty gaps. This is particularly true for guest workers from Morocco, Tukey, and Yugoslavia (immigrants and descendants), Greece and Italy (immigrants), as well as refugees from Yugoslavia (immigrants). Conversely, some ethnic groups, such as Polish EU migrants (both generations) and refugees from North Africa (immigrants and descendants), have high levels of formal education\u0026ndash;\u0026ndash;this becomes evident as poverty gaps were underestimated before accounting for education\u0026ndash;\u0026ndash;yet they face medium to high poverty gaps.\u003c/p\u003e\u003cp\u003eSecondly, adding the occupational status reveals that the translation of educational resources into occupational attainment is affected for all refugees (except Vietnamese), EU immigrants from Romania/Bulgaria, and descendants of Post-Soviet, Middle Eastern, and Iranian origin, as it significantly accounts for their poverty gaps.\u003c/p\u003e\u003cp\u003eAn intergenerational transmission of lower educational resources is observed among Moroccan, Turkish, Yugoslavian (GW), Post-Soviet, Middle Eastern, and Vietnamese descendants, as education contributes substantially to their poverty gaps. Conversely, Polish, Kosovan/Macedonian and Romanian/Bulgarian (EU), Iranian, West and North African descendants tend to attain higher educational levels, overcoming parental experiences. Polish and Romanian/Bulgarian (EU) descendants also frequently attain a higher occupational status, whereas lower occupational attainments tend to be reproduced partially across generations for Post-Soviet, Middle Eastern, and Iranian descendants.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e5.2 Precarious employment\u003c/h2\u003e\u003cp\u003eThe second model in Table\u0026nbsp;2 accounts additionally for precarious employment. The results support the argument that differences in employment can account for substantial parts of the observed poverty gaps. This is particularly true for all late repatriates, EU migrants, and refugees in the first generation. Even all refugees, EU migrants and guest workers from third countries in the second generation tend to be affected largely by precarious work. This explanation, thus, appears to be of greater relevance than educational resources for most ethnic origins\u0026mdash;while accounting for Model 0 and Model 1.\u003c/p\u003e\u003cp\u003eParental experiences of precarious employment are partly passed on across generations, impacting their children\u0026rsquo;s career trajectories as well. Only guest workers (immigrants and descendants) from EU member states tend to attain \u0026lsquo;standard\u0026rsquo; employments, as indicated by fairly stable poverty gaps.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e5.3 Household resources and needs\u003c/h2\u003e\u003cp\u003eConsidering household resources in Model 3a does not change the poverty gaps much. However, moderate poverty reductions could be achieved by increasing the work intensity of the household for refugees from Middle East (immigrants and descendants) and smaller poverty reductions are possible for refugees from Afghanistan and West Africa (immigrants and descendants), Iran (immigrants) and Vietnam (descendants). Adding household needs in Model 3b contributes to smaller changes in the poverty gaps of Afghan immigrants and Middle Eastern immigrants and descendants (see Table\u0026nbsp;3).\u003c/p\u003e\u003cp\u003eThus, parental practices regarding family values and the household division of labor seem to be of minor relevance and tend to contribute to their children\u0026rsquo;s practices only in the case of Middle Eastern and West African families.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e5.4 Legal arrangements\u003c/h2\u003e\u003cp\u003eAdding citizenship in Model 4 supports the argument that legal arrangements contribute to opportunities for better labor market access, because it affects the poverty gaps of almost all ethnic immigrant groups significantly (see Table\u0026nbsp;4). This is especially true for most refugees in the first generation and EU migrants from Poland and Romania/Bulgaria, and to a lower extent for West and North African refugees, all guest workers, late repatriates, and other EU immigrants. Under the same legal conditions\u0026mdash;while accounting for Model 0 to Model 3\u0026mdash; guest workers from Spain, Portugal and Yugoslavia even experience significantly lower poverty risks than autochthonous Germans. Descendants, being socialized and educated in Germany, do not benefit from naturalization efforts.\u003c/p\u003e"},{"header":"6 Discussion and Conclusion","content":"\u003cp\u003eThis study provides a detailed analysis of poverty risks across 20 ethnic origins and two generations in Germany from 1990 to 2020, based on Microcensus data.\u003c/p\u003e\u003cp\u003eBy identifying poverty risk groups and comparing different poverty explanations, this study provides new, detailed insights into ethnic and generational differences, emphasizing their relevance and origin-specific conditions for poverty.\u003c/p\u003e\u003cp\u003eThe results indicate that immigrants face substantially higher poverty risks than autochthonous Germans. These disparities and their underlying conditions are also partially found in the second-generation. Among the most affected ethnic groups are refugees from the Middle East, Afghanistan, West and North Africa, Vietnam, and Iran, guest workers from Morocco and Turkey, Post-Soviet late repatriates and EU asylum seekers from Kosovo/Macedonia. The origin-specific factors contributing to these poverty gaps will be discusses now.\u003c/p\u003e\u003cp\u003eEducational resources play a crucial role in explaining poverty risks across both generations, especially for third-country guest workers and refugees, whose lower levels of formal education contribute largely to their poverty gaps. This result shows that formal education often explains labor market prospects of immigrants and descendants as well, indicating that disadvantages are partially passed on to their children. Conversely, \u0026lsquo;immigrant optimism\u0026rsquo; may explain why descendants of all EU migrants and some refugees, especially those of Iranian, African and Yugoslavian origin, tend to have higher levels of formal education. An additional finding is that, even when third-country immigrants and refugees attain the same level of formal education, the translation of their educational resources into a suitable labor market position is less effective, probably due to their foreign qualifications or maybe ethnic penalties. Accordingly, additional educational and/or vocational training, better skill recognition, and fast-track adaptation programs could facilitate transitions into suitable occupations and career advancement.\u003c/p\u003e\u003cp\u003ePrecarious employment appears to be the second crucial factor in explaining poverty risks across generations, particularly for refugees, late repatriates, EU and third-country migrants. This finding supports considerations that low-wage and unstable jobs with limited upward mobility contribute largely to economic vulnerabilities, even across generations. This highlights long-term consequences of precarious employment and the importance of stable, \u0026lsquo;standard\u0026rsquo; employment opportunities. Ensuring this\u0026mdash;by strengthening labor laws, social security, and career development\u0026mdash;could be one of the keys to reduce poverty risks.\u003c/p\u003e\u003cp\u003eHousehold resources and needs have limited influence in explaining poverty gaps, suggesting that individual and structural factors are more influential. Yet, refugees and their descendants living in households with multiple dependents could benefit from opportunities to increase their work intensity\u0026mdash;for example through childcare, elderly support, or flexible work arrangements. This may help to reduce their financial struggles.\u003c/p\u003e\u003cp\u003eLegal arrangements tied to citizenship appear to be influential in explaining labor market access and poverty risks. Immigrants with restricted access, such as temporary workers, or with stricter citizenship requirements, such as asylum seekers and third-country nationals, seem to be especially vulnerable to poverty. This finding suggests that policies facilitating labor market integration, such as skill recognition programs to access work permits or residency, may help mitigate poverty risks faced by immigrants. Naturalization efforts are likely to improve the economic well-being of immigrants, but descendants, socialized and educated in Germany, do not benefit from citizenship.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSingle-author contributions for all 14 CRediT\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eI would like to express the deepest gratitude to my brilliant and kind supervisors, Michael Gebel and Cornelia Kristen, all the excellent teachers and scholars from Humboldt University and DIW Berlin, and all supportive colleagues from the University of Bamberg, whose guidance, expertise, and encouragement greatly contributed to this research and my personal development.I am also deeply thankful for all the grants by the Bamberg Graduate School of Social Sciences and my scholarship granted by the Hans-B\u0026ouml;ckler-Foundation, which provided all resources and facilities to conduct this study.Finally, I am deeply grateful for my family and friends and their unwavering support and encouragement. This work, dedicated to greater social equality, was only possible thanks to the kindness of all of you!\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe Microcensus scientific use files (1991-2019) are available under the condition of a data usage agreement (see Appendix C).\u0026ndash; Link: https://www.forschungsdatenzentrum.de/de/haushalte/mikrozensus\u0026ndash; Link: https://www.gesis.org/missy/metadata/MZ/The data processing is available under:\u0026ndash; FAIRsharing.org: PRoEM-ET-ER; PRoEM-Exploring-Trends-and-Explaining-Risks, FAIRsharing ID: https://fairsharing.org/6362\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e\u0026Aacute;lvarez-Miranda, B. (2011). In-work poverty among immigrants. Working poverty in Europe, 250\u0026ndash;277. Palgrave Macmillan.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAllison, P. D. (2009). \u003cem\u003eFixed effects regression models\u003c/em\u003e. SAGE.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAntidiskriminierungsstelle des Bundes (2012). Diskriminierung in Deutschland. \u003cem\u003eBundesministerium f\u0026uuml;r Familie, Senioren, Frauen und Jugend.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArrow, K. J. (1973). The theory of discrimination. \u003cem\u003eDiscrimination in labor markets\u003c/em\u003e, 3\u0026ndash;33. Princeton University Press.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBAMF \u003cem\u003eBundesamt f\u0026uuml;r Migration und Fl\u0026uuml;chtlinge\u003c/em\u003e (2014). Zuwanderung aus den neuen EU-Mitgliedstaaten Bulgarien und Rum\u0026auml;nien (Forschungsbericht 24).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e\u0026ndash; (2019). Integration in the job market by women of foreign nationality in Germany. Working Paper 20.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarrett, A., \u0026amp; Ma\u0026icirc;tre, B. (2011). Immigrant welfare receipt across Europe. \u003cem\u003eInstitute of Labor Economics\u003c/em\u003e Discussion Papers 5515.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBecker, G. S. (1957). \u003cem\u003eThe economics of discrimination\u003c/em\u003e. University of Chicago Press.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e\u0026ndash; (1993). Human capital: A theoretical and empirical analysis, with special reference to education. University of Chicago Press.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBerb\u0026eacute;e, F., \u0026amp; Stuhler, J. (2023). Immigration and occupational mobility: Evidence from Europe. \u003cem\u003eLabour Economics\u003c/em\u003e, \u003cem\u003e81\u003c/em\u003e, 101\u0026ndash;139.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBlake, J. (1981). Family size and the quality of children. \u003cem\u003eDemography\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(4), 421\u0026ndash;442.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBlume, K., Gustafsson, B., Pedersen, P. J., \u0026amp; Verner, M. (2005). Determinants of poverty among immigrants to Denmark and Sweden. \u003cem\u003eJournal of Ethnic and Migration Studies\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e(3), 511\u0026ndash;528.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBoehle, A. (2015). GESIS Poverty Measurement Report.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBorjas, G. J. (1987). Self-selection and the earnings of immigrants. \u003cem\u003eAmerican Economic Review\u003c/em\u003e, \u003cem\u003e77\u003c/em\u003e(4), 531\u0026ndash;553.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBorjas, G. J. (1999). The Economic Analysis of Immigration. \u003cem\u003eHandbook of Labor Economics\u003c/em\u003e (Vol. 3A, pp. 1697\u0026ndash;1760). Elsevier.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBourdieu, P. (1984). \u003cem\u003eDistinction: A social critique of the judgement of taste\u003c/em\u003e. Harvard University Press.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrady, D., Bocanegra, A., Cervantes, D., Macy, L., \u0026amp; Saravia, N. R. (2025). The Heterogeneities of Immigrant Poverty in the US. \u003cem\u003ePopulation Research and Policy Review\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e(4), 41.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBr\u0026uuml;cker, H., Hauptmann, A., Jahn, E. J., \u0026amp; Upward, R. (2012). Migration and Imperfect Labor Markets. \u003cem\u003eInstitute of Labor Economics\u003c/em\u003e Discussion Papers 6713.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBr\u0026uuml;cker, H., Rother, N., \u0026amp; Schupp, J. (2016). \u003cem\u003eIAB-BAMF-SOEP Survey of Refugees: Overview and First Results\u003c/em\u003e. IAB.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBr\u0026uuml;ck-Klingberg, A., Heineck, G., \u0026amp; Siedler, T. (2007). The labor market performance of ethnic Germans: a natural experiment. \u003cem\u003eEconomics of Transition\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(4), 689\u0026ndash;709.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChiswick, B. R., Lee, Y. L., \u0026amp; Miller, P. W. (2005). Immigrant earnings: A longitudinal analysis. \u003cem\u003eReview of Economics of the Household\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(2), 171\u0026ndash;193.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChiswick, B. R., \u0026amp; Miller, P. W. (2009). The international transferability of immigrants\u0026rsquo; human capital. \u003cem\u003eEconomics of Education Review\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(2), 162\u0026ndash;169.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eClemens, M., \u0026amp; Hart, J. (2018). EU immigration has increased Germany's economic growth. \u003cem\u003eDIW weekly report\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(44), 441\u0026ndash;449.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eConstant, A., \u0026amp; Massey, D. S. (2002). Return migration by German guestworkers. \u003cem\u003eInternational Migration\u003c/em\u003e, \u003cem\u003e40\u003c/em\u003e(4), 5\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e\u0026ndash; (2005). Labor market segmentation and the earnings of German guestworkers. \u003cem\u003ePopulation Research and Policy Review\u003c/em\u003e, 21(5), 489\u0026ndash;512.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCox, D. R., \u0026amp; Donnelly, C. A. (2011). \u003cem\u003ePrinciples of applied statistics\u003c/em\u003e. Cambridge University Press.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCrettaz, E. (2011). Why are immigrants and ethnic minorities more affected by working poverty? \u003cem\u003eLIS\u003c/em\u003e Working Paper Series, 564.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCohen, J. (2013). \u003cem\u003eStatistical power analysis for the behavioral sciences\u003c/em\u003e. Routledge.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDestatis, S., \u0026amp; Bundesamt (2020). \u003cem\u003eA2 Armutsgef\u0026auml;hrdungsquoten, Bundesl\u0026auml;nder (Bundesmedian) ab 2020\u003c/em\u003e. Statistikportal.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDiehl, C., \u0026amp; Blohm, M. (2002). A comparison of the socio-economic situation of naturalized and non-naturalized immigrants in Germany. \u003cem\u003eEuropean Sociological Review\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(3), 249\u0026ndash;266.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDietz, B. (1999). Ethnic German Immigration from Eastern Europe and the Former Soviet Union to Germany. \u003cem\u003eInstitute of Labor Economics\u003c/em\u003e Discussion Papers 68.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e\u0026ndash; (2000). German and Jewish migration from the former Soviet Union to Germany. \u003cem\u003eJournal of Ethnic and Migration Studies\u003c/em\u003e, 26(4), 635\u0026ndash;652.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDoeringer, P. B., \u0026amp; Piore, M. J. (1971). \u003cem\u003eInternal Labor Markets and Manpower Analysis\u003c/em\u003e. Heath Lexington Books.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDustmann, C., \u0026amp; Glitz, A. (2011). Migration and education. \u003cem\u003eHandbook of the Economics of Education\u003c/em\u003e (Vol. 4, pp. 327\u0026ndash;359). Elsevier.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEichhorst, W., \u0026amp; Marx, P. (2011). Reforming German labor market institutions. \u003cem\u003eJournal of Labor Market Research\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e(1), 49\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEisnecker, P., et al. (2016). Integrating refugees: Insights from the past. \u003cem\u003eDIW Economic Bulletin\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(34/35), 387\u0026ndash;390.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEurofound (2014). Labour mobility in the EU.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEurostat (2021). Non-EU citizens at higher risk of poverty or social exclusion.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e\u0026ndash; (2023). Country Report \u0026ndash; Germany.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e\u0026ndash; (2024). Living conditions in Europe \u0026ndash; income distribution and income inequality.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFinseraas, H. (2012). Poverty, ethnic minorities among the poor, and preferences for redistribution in European regions. \u003cem\u003eJournal of European Social Policy\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(2), 164\u0026ndash;180.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFlake, R. (2013). Educational attainment and economic assimilation of immigrant families. \u003cem\u003eInternational Migration Review\u003c/em\u003e, \u003cem\u003e47\u003c/em\u003e(1), 150\u0026ndash;185.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFleischmann, F., \u0026amp; H\u0026ouml;hne, J. (2013). Immigrant women's labor market participation in Germany. \u003cem\u003eJournal of Marriage and Family\u003c/em\u003e, \u003cem\u003e75\u003c/em\u003e(5), 1081\u0026ndash;1097.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFriedberg, R. M. (2000). You can't take it with you? Immigrant assimilation and the portability of human capital. \u003cem\u003eJournal of Labor Economics\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(2), 221\u0026ndash;251.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFriedrichs, N., \u0026amp; Graf, J. (2022). Integration gelungen? Lebenswelten und gesellschaftliche Teilhabe von (Sp\u0026auml;t\u0026shy;)Aussiedler:innen. \u003cem\u003eSVR\u0026shy;Studie\u003c/em\u003e 2022\u0026shy;1, Berlin.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFoster, G., Greer, J., \u0026amp; Thorbecke, E. (2010). The Foster-Greer-Thorbecke (FGT) poverty measures: 25 years later. \u003cem\u003eJournal of Economic Inequality\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(4), 491\u0026ndash;524.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGangl, M. (2004). Welfare states and the scar effects of unemployment. \u003cem\u003eAmerican Journal of Sociology\u003c/em\u003e, \u003cem\u003e109\u003c/em\u003e(6), 1319\u0026ndash;1364.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGathmann, C., \u0026amp; Garbers, K. (2023). Citizenship and economic integration. \u003cem\u003eJournal of Economic Perspectives\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e(1), 89\u0026ndash;112.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGebel, M. (2015). Labor market instability, labor market entry, and early career development. \u003cem\u003eEmerging trends in the social and behavioral sciences\u003c/em\u003e, 1\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGebel, M., \u0026amp; Giesecke, J. (2011). The changing skill-based temporary employment and unemployment risks in Europe. \u003cem\u003eSocial Forces\u003c/em\u003e, \u003cem\u003e90\u003c/em\u003e(1), 17\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGiesecke, J., Kroh, M., Tucci, I., Baumann, A., \u0026amp; El-Kayed, N. (2017). Armutsgef\u0026auml;hrdung bei Personen mit Migrationshintergrund. \u003cem\u003eSOEPpapers\u003c/em\u003e, 907. DIW Berlin.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGlick, J. E. (2010). Connecting complex processes: A decade of research on immigrant families. \u003cem\u003eJournal of Marriage and Family\u003c/em\u003e, \u003cem\u003e72\u003c/em\u003e(3), 498\u0026ndash;515.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrad\u0026iacute;n, C., Cant\u0026oacute;, O., \u0026amp; del R\u0026iacute;o, C. (2012). Measuring poverty accounting for time. \u003cem\u003eReview of Income and Wealth\u003c/em\u003e, \u003cem\u003e58\u003c/em\u003e(2), 330\u0026ndash;354.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGresch, C., \u0026amp; Kristen, C. (2011). Staatsb\u0026uuml;rgerschaft oder Migrationshintergrund? Ein Vergleich unterschiedlicher Operationalisierungsweisen am Beispiel der Bildungsbeteiligung. \u003cem\u003eJournal for Educational Research Online\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(2), 1\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGoddar, A., \u0026amp; Huneke, T. (2011). Legal frameworks and policies for guest workers in Germany. \u003cem\u003eInternational Migration Review\u003c/em\u003e, \u003cem\u003e45\u003c/em\u003e(3), 772\u0026ndash;801.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGroh-Samberg, O., \u0026amp; Voges, W. (2014). Precursors and consequences of youth poverty in Germany. \u003cem\u003eLongitudinal and Life Course Studies\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(2), 151\u0026ndash;172.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHeath, A., \u0026amp; Cheung, S. Y. (2006). Ethnic penalties in the labor market: Employers and discrimination.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e\u0026ndash; (2007). The comparative study of ethnic minority disadvantage. In \u003cem\u003eProceedings-British Academy\u003c/em\u003e (137, p.1). Oxford University Press.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHeady, B., Krause, P., \u0026amp; Habich, R. (1994). Long and Short Term Poverty: Is Germany a Two-Thirds Society? \u003cem\u003eSocial Indicators Research\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e, 1\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHeizmann, B., Busch-Heizmann, A., \u0026amp; Holst, E. (2017). Sorting or devaluation? \u003cem\u003eInternational Migration Review\u003c/em\u003e, \u003cem\u003e51\u003c/em\u003e(2), 475\u0026ndash;505.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHerbert, U. (1990). \u003cem\u003eA history of foreign labor in Germany, 1880\u0026ndash;1980\u003c/em\u003e. University of Michigan Press.​.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuschek, D., de Valk, H. A. G., \u0026amp; Liefbroer, A. C. (2011). Timing of first union among second generation Turks in Europe. \u003cem\u003eDemographic Research\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e, 437\u0026ndash;466.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJenkins, S. P. (2000). Modelling household income dynamics. \u003cem\u003eJournal of Population Economics\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(4), 529\u0026ndash;567.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKalter, F., \u0026amp; Granato, N. (2007). Educational hurdles on the way to the labor market. \u003cem\u003eJournal of Education and Work\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(1), 21\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKalter, F., Granato, N., \u0026amp; Kristen, C. (2007). Disentangling trends of second generation structural assimilation in Germany. \u003cem\u003eProceedings of the British Academy\u003c/em\u003e (137, 271\u0026ndash;319).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKalter, F., \u0026amp; Kogan, I. (2014). Migrant networks and labor market integration of immigrants from the Former Soviet Union in Germany. \u003cem\u003eSocial Forces\u003c/em\u003e, \u003cem\u003e92\u003c/em\u003e(4), 1435\u0026ndash;1456.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKao, G., \u0026amp; Tienda, M. (1998). Educational aspirations of minority youth. \u003cem\u003eAmerican Journal of Education\u003c/em\u003e, \u003cem\u003e106\u003c/em\u003e(3), 349\u0026ndash;384.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKesler, C. (2015). Welfare states and immigrant poverty. \u003cem\u003eActa Sociologica\u003c/em\u003e, \u003cem\u003e58\u003c/em\u003e(1), 39\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKogan, I. (2011). Labour market flexibility and immigrants' employment paths in Germany. \u003cem\u003eInternational Journal of Comparative Sociology\u003c/em\u003e, \u003cem\u003e52\u003c/em\u003e(4), 264\u0026ndash;283.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e\u0026ndash; (2016). Integration policies and immigrants\u0026rsquo; labor market outcomes in Europe. \u003cem\u003eSociological Science\u003c/em\u003e, 3, 335\u0026ndash;358.\u0026amp;#8203.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKostova, D. (2007). The emergence of cohabitation in a transitional socio-economic context. \u003cem\u003eDemografia\u003c/em\u003e, \u003cem\u003e50\u003c/em\u003e(5), 135\u0026ndash;162.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKuhnt, A. K., \u0026amp; Krapf, S. (2020). Partnerships and fertility among immigrants in Germany. \u003cem\u003eAdvances in Life Course Research\u003c/em\u003e, 44.​.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKosyakova, Y., \u0026amp; Brenzel, H. (2020). Labour market entry of refugees in Germany. \u003cem\u003eJournal of Ethnic and Migration Studies\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(9), 1713\u0026ndash;1737.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLengerer, A., Lechert, Y., \u0026amp; Riede, T. (2010). Data documentation of the German Microcensus. \u003cem\u003eGESIS Technical Reports\u003c/em\u003e, 2010/11.\u0026amp;#8203.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLenze, A. (2019). Childcare availability and maternal employment in Germany. \u003cem\u003eJournal of European Social Policy\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(5), 635\u0026ndash;649.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiebau, E., \u0026amp; Salikutluk, Z. (2016). Gender differences in the labor market integration of refugees. \u003cem\u003eSoziale Welt\u003c/em\u003e, \u003cem\u003e67\u003c/em\u003e(4), 377\u0026ndash;409.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLuthra, R. R. (2013). Explaining ethnic inequality in the German labor market. \u003cem\u003eEuropean Sociological Review\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(5), 1095\u0026ndash;1107.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMilewski, N. (2010). Is there a socialization effect in transitions to second and third births? \u003cem\u003eEuropean Journal of Population\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(3), 297\u0026ndash;323.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMIPEX: \u003cem\u003eMigrant Integration Policy Index\u003c/em\u003e (2011). 2020). CIDOB and MPG.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eM\u0026uuml;nz, R., Seifert, W., \u0026amp; Ulrich, R. (1997). \u003cem\u003eZuwanderung nach Deutschland: Strukturen, Wirkungen, Perspektiven\u003c/em\u003e. Campus.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNauck, B. (2007). Value of children and the social production of welfare. \u003cem\u003eDemographic Research\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e, 669\u0026ndash;700.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNewsham, A., \u0026amp; Rowe, K. (2019). Fertility patterns among refugees in Europe: A comparative study. \u003cem\u003ePopulation Studies\u003c/em\u003e, \u003cem\u003e73\u003c/em\u003e(2), 221\u0026ndash;237.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOECD (2015). 2017). Bildung auf einen Blick: OECD-Indikatoren. \u003cem\u003eOECD-Publishing\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e\u0026ndash; (2018). Settling In 2018: Indicators of Immigrant Integration.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOrrenius, P. M., \u0026amp; Zavodny, M. (2009). \u003cem\u003eHow Immigrants Fare in Good and Bad Economic Times\u003c/em\u003e. Migration Policy Institute.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePanagiotidis, P. (2021). \u003cem\u003eLate Repatriates in Germany: Integration and Identity\u003c/em\u003e. Springer.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSalikutluk, Z., Giesecke, J., \u0026amp; Kroh, M. (2016). Refugees' labor market integration in Germany: The role of education and training. \u003cem\u003eEuropean Sociological Review\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(6), 756\u0026ndash;769.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmith, N. A., Phillips, D., Simpson, P., Cribb, J., Hood, A., Joyce, R., \u0026amp; Keiller, A. N. (2017). \u003cem\u003eIn-work poverty among families with children\u003c/em\u003e. IFS Report.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSolon, G., Haider, S. J., \u0026amp; Wooldridge, J. M. (2015). What are we weighting for? \u003cem\u003eJournal of Human Resources\u003c/em\u003e, \u003cem\u003e50\u003c/em\u003e(2), 301\u0026ndash;316.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSommer, M. (2019). Recognition of foreign qualifications in Germany. \u003cem\u003eJournal of Education and Work\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(5), 487\u0026ndash;501.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSprengholz, M., Diehl, C., Giesecke, J., \u0026amp; Kreyenfeld, M. (2021). From guest workers to EU migrants. \u003cem\u003eJournal of Family Research\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(2), 252\u0026ndash;283.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUNESCO. (2012). \u003cem\u003eInternational Standard Classification of Education: ISCED 2011\u003c/em\u003e. Montreal.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWolf, K., \u0026amp; Kreyenfeld, M. (2020). Demographic differences in fertility patterns of EU immigrant women in Germany. \u003cem\u003eComparative Population Studies\u003c/em\u003e, \u003cem\u003e45\u003c/em\u003e, 171\u0026ndash;200.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYuksel, M. (2009). Moving with natives or stuck in their neighborhoods? \u003cem\u003eSOEPpapers\u003c/em\u003e 230.​ DIW Berlin.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e A person is \u003cem\u003eat-risk-of-poverty\u003c/em\u003e if the needs-adjusted net household income (after deductions of tax, social security, unemployment and health insurance) is below 60% of the median in the national distribution (Eurostat \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e If both parents were born in Germany, the term autochthonous is applied.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The majority population is a suitable reference to identify significant ethnic differences against the common German social context.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cem\u003eMethodological Effects\u003c/em\u003e \u0026ndash; Since 2016, the Microcensus has been restructured, limiting comparability with earlier years. Additionally, high immigration after 2015, especially of refugees, inflated results (Lengerer et al. 2020). To address these issues, mean correction was applied to smooth artificial spikes (see Appendix B).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cem\u003eRemarks to\u003c/em\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: (i) Immigrants' poverty risks are plotted with \u003cem\u003esolid\u003c/em\u003e lines, and descendants with \u003cem\u003edashed\u003c/em\u003e lines in a matching color for each origin. For some origins, descendants cannot be identified due to limited observations or because children of recent immigrants are still underage or have not yet spent sufficient time (\u0026ge;\u0026thinsp;10 years) in the labor market. (ii) Confidence intervals (CIs) are shown around point estimates to indicate the expected range of the true value with 95% certainty.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cem\u003eAdditional remarks to\u003c/em\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: (iii) The observation window is restricted for EU migrants and refugees to a period of 10 to 15 years, because most of them did not exist in significant numbers before 2009. (iv) The observation window for guest workers starts \u003cem\u003eafter\u003c/em\u003e two years, because their recruitment was limited to two years in the beginning. A high share left (M\u0026uuml;nz et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) and only stayers are observed.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Averaged annual income is more resistant to fluctuations and thus somewhat more robust in empirical implementation (Jenkins \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cem\u003eEffects sizes (in p.p.)\u003c/em\u003e \u0026ndash; are distinctions relative to the observed poverty gaps found in this representative sample and its context. \u003cem\u003ePoverty gaps\u003c/em\u003e (\u0026gt;\u0026thinsp;5 p.p.) are considered as substantial, (5\u0026ndash;10 p.p.) as low, (10\u0026ndash;20 p.p.) as medium, (20\u0026ndash;30 p.p.) as high, and (\u0026gt;\u0026thinsp;30 p.p.) of very high magnitude.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 4 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"poverty, migration, education and occupation, precarious employment, household composition, legal status","lastPublishedDoi":"10.21203/rs.3.rs-7688061/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7688061/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWhile previous research gained a detailed understanding of ethnic inequalities in education and the labor market, much less is known about ethnic poverty risks. In particular, there is little knowledge about the heterogeneity of trends and underlying determinants of poverty risks across many specific ethnic origin groups. This study complements the literature by providing a long-term, large-scale study, examining poverty trends over three decades (1991\u0026ndash;2019) and determinants of poverty risks across 20 ethnic origins and two generations in Germany. With its long migration history and considerable ethnic heterogeneity, Germany is a valuable case study to understand ethnic poverty risks in Europe. Results based on long-term, large-scale, nationally representative Microcensus data indicate that third-country guest workers from Turkey and Morocco, Post-Soviet repatriates, EU asylum seekers from Kosovo/ Macedonia, and refugees from North Africa and Yugoslavia have high poverty risks, while refugees from Afghanistan, West Africa, the Middle East, Vietnam, and Iran experience exceptionally high risks. These disparities are also partially evident in the second-generation. Logistic regressions of pooled repeated cross-sections in nested, stepwise models\u0026mdash;analyzing mediation processes and cumulative disadvantages and using average marginal effects to compare key theoretical perspectives\u0026mdash;provide new detailed insights into the determinants of ethnic poverty risks. Lower education accounts largely for ethnic poverty differences, yet higher education does not offset risks, indicating inadequate occupational placement, especially among third-country immigrants and refugees, with disadvantages partly transmitted to their children. Precarious employment stands out as the most influential factor across generations. Household composition matters particularly for Middle Eastern, Afghan, and West African families. Citizenship is beneficial for most immigrants, especially refugees.\u003c/p\u003e","manuscriptTitle":"»Poverty Risks of Ethnic Minorities«(1990-2020) – Examining trends and explaining poverty risks across ethnic origins and generation status","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-31 01:39:29","doi":"10.21203/rs.3.rs-7688061/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cda235c3-06ed-4fbd-bbd6-5d64fc25aea8","owner":[],"postedDate":"October 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T11:24:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-31 01:39:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7688061","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7688061","identity":"rs-7688061","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}