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Abstract

Owing to their high political and social impact, studying elites is a growing strand of scholarship in sociology. In this research note, we investigate immigrants’ pathways into the income elite, that is, the top 1% of Germany’s income distribution. Using data from the German Microcensus from 2009 to 2018 covering more than three million observations, we examine the prevalence of immigrants in the elite and immigrants’ pathways to access the elite, that is, education, self-employment, country-of-origin effects and integration. Our results show that immigrants are underrepresented in the elite by 46%. Smaller returns to education and self-employment compared with natives drive this underrepresentation. Regarding immigrant-specific pathways, immigrants from EU countries, who face fewer legal, cultural and social barriers, have higher chances of being in the elite than immigrants from non-EU countries. Finally, we find no evidence of integration, that is, years since migration, affecting immigrants’ elite status.

Keywords

education elite immigrants integration self-employment

Introduction

Owing to the widening inequality between top-income earners and the rest of the population, the study of elites is an emerging field in sociology and related disciplines (Acemoglu and Robinson, 2008; Adamson and Johansson, 2021; Rahman Khan, 2012). Next to financial security, being in the elite offers substantial cultural and social influence, for example, through unparalleled access to policymakers compared with the general population (Keister and Lee, 2017). Existing research shows that minorities and immigrants are underrepresented among elites (Keister, 2014), indicating that these groups face substantial disadvantages in social and political influence. In the context of increasing immigration worldwide (Mcauliffe and Triandafyllidou, 2021), investigating the access to the elite for immigrants is crucial for sociological scholarship to understand societal processes contributing to inequalities between immigrants and natives. From a broader sociological perspective, understanding the contribution of societal elites to social stratification is important because their power structures and access to resources influence social inequality. This knowledge allows for analyses of power distribution, social mobility, the legitimization of inequality and contributes to explaining societal dynamics. Moreover, elites influence cultural identities, thereby shaping the structure of societies.

While previous scholarship shows differing pathways to access the elite for different groups, such as males and females (Collischon, 2023; Yavorsky et al., 2019), we are not aware of a single study investigating immigrants among the income elite. Following this strand of scholarship, we define elites as the top 1% of the income distribution. Our research question is: do individuals’ pathways to entering the top 1% differ between immigrants and natives?¹

Despite increasing scholarly interest in explaining ethnic stratification, research on immigrants and minorities in the elite is scarce and focuses on wealth, neglecting income. Scholars find that immigrants and minorities are underrepresented among high-wealth individuals (e.g. Bauer et al., 2011). For example, Collins and Hoxie (2015) find that the wealth of the richest 100 US households exceeds the total wealth of all Blacks and one-third of Hispanics. In the context of immigration, income could be a better indicator for ethnic stratification than wealth because inheritances affect wealth considerably and natives’ inheritances are usually substantially higher than immigrants’ inheritances (Muckenhuber et al., 2022).

In this article, we investigate first-generation immigrants to provide first evidence of immigrants’ pathways in accessing the top 1% of the income distribution² and contribute to the existing literature in at least two ways: first, we investigate how pathways to becoming part of the top 1% differ between immigrants and natives. In particular, we investigate the role of education and self-employment, which have been shown to be the main pathways to the 1% (Yavorsky et al., 2019). Additionally, we focus on immigrant-specific pathways and investigate the role of integration and immigrants’ countries of origin for achieving top-earner status.

Second, we show the prevalence of first-generation immigrants in the income elite in Germany. Germany is an ideal example to investigate immigrants’ access to the elite because it is one of the main immigration destination countries in Europe. In 2022, around 28% of the population living in Germany had an immigration background (Destatis, 2023a, 2023b). Around 64% are first-generation immigrants, mostly from European countries (62%), Asia (24%) and Africa (5%) (Destatis, 2023b). Overall, we provide a comprehensive picture of the top 1% income-earning immigrants in Germany.

To investigate our research question, we employ data from the German Microcensus, a representative sample of the German residential population. Our analysis is based on nearly three million observations, allowing analysis of small subpopulations such as first-generation immigrants in the top 1%. Since the Microcensus is a stratified random sample and participation is compulsory, the sample does not suffer from non-response bias, which is especially high for immigrants (Laganà et al., 2013) and individuals in the income elite (Blanchet et al., 2022). Thus, the Microcensus offers a unique opportunity to investigate immigrants in the top 1%.

Pathways to Access the Top 1%

One prominent pathway to access the top 1% is education (Yavorsky et al., 2019). In general, education can increase productivity (Goldthorpe, 2014) and signal productivity (Bol and van de Werfhorst, 2011). Because the transferability of immigrants’ human capital from their home country to the host country is limited (Lancee and Bol, 2017), education might be less valuable for immigrants than for natives. Consequently, we assume that educational attainment has a stronger effect on reaching a 1% status for natives than for immigrants.

Self-employment is the second major pathway into the elite (Yavorsky et al., 2019). While natives may choose self-employment for reasons of entrepreneurship and the pursuit of prestige, immigrants may choose self-employment predominantly to avoid unemployment (Dawson et al., 2009) and employer discrimination (Oskam et al., 2022). Immigrants might additionally face barriers as entrepreneurs, such as obtaining sufficient start-up capital and country-specific knowledge of the administrative processes related to self-employment. Thus, self-employment should not be positively associated with immigrants’ 1% status.

In addition to these general pathways into the 1%, we expect two immigrant-specific characteristics to influence the pathways of immigrants into the 1%. First, we expect integration to be a further pathway for immigrants to the elite. Integration refers to the process where immigrants gradually adopt the culture, language and social norms of their host country. Integration is beneficial for labour market success in the form of wages and is favoured by a variety of factors such as language acquisition (Bleakley and Chin, 2004) and the formation of social networks (Bills et al., 2017; Villarreal and Tamborini, 2018). Research on the association between integration and labour market outcomes reveals ambiguous findings. While some studies emphasize the importance of context for integration (e.g. Villarreal and Tamborini, 2018), most studies support the positive association between years since migration and labour market integration in the form of wages (e.g. Chiswick, 1978; Ebner and Helbling, 2016). Thus, we expect that years since migration have a positive effect on immigrants’ 1% status.

Besides integration, we argue that immigrants’ countries of origin may explain differences in immigrants’ memberships in Germany’s income elite. Specifically, we expect differences between EU and non-EU immigrants. First, while EU immigrants have unrestricted access to the German labour market, non-EU immigrants face legal barriers regarding residence and work permits. Second, being an EU immigrant is likely associated with a higher human capital quality. Higher-rated education systems in the country of origin for example can produce different opportunities for elite membership in Germany (Holbrow, 2020). Third, non-EU immigrants on average face higher language, social and cultural barriers compared with EU immigrants (Holbrow, 2020; Ebner and Helbling, 2016; Wang and Naveed, 2019). These factors jointly contribute to our expectation that immigrants from EU countries should, on average, be more often in the German income elite compared with immigrants from non-EU countries.

Data and Method

Data

We use data from the German Microcensus from 2009 to 2018 (DeStatis, 2018b). The Microcensus is an annual 1% sample of German households (i.e. around 400,000 households per year) collected by the Federal Statistical Agency. The data contain information on socio-demographic characteristics and household composition. Participation in the survey is mandatory.³ This ensures little non-response and high data quality (DeStatis, 2018a). Regarding immigration status, our analysis compares first-generation immigrants who do not possess a German citizenship with native Germans without a first- or second-generation immigration background.

Income is surveyed for individuals and households, respectively, as monthly net income from all sources in 25 categories (these categories range from ‘no income’ to above €18,000; Appendix Table A1 shows the distribution of individual incomes). The measure captures income not only from labour but also financial returns from assets or real estate. Although a continuous measure for income and gross income would be desirable (as, for example, from tax records as used by Bartels, 2019), the data provide sufficient information to classify the very top of incomes in Germany. In our sample, 1.58% of individuals report net incomes above €5500 per month. For simplicity, we refer to these individuals as being in the top 1% of individual incomes for readability.⁴

In addition to individual income, the survey collects information on the respondent’s marital status, age, education and employment status. Furthermore, we use information on children living in the household and whether the household is located in East or West Germany. After listwise deletion of cases that contain missing values in the analysis,⁵ our analysis sample consists of 3,011,855 observations.

Analytical Strategy

We begin the empirical analysis by comparing sample descriptives by top-earner status for immigrants and natives separately. This analysis provides information on the composition of high-earning natives and immigrants in Germany, which, in itself, is a novel contribution to the literature. In this analysis, we use the cross-sectional survey weights provided by the German statistical agency to allow for the generalizability of the findings to the residential population in Germany for the descriptive results only.

In our main analysis, we use regression analysis to investigate the correlates of being a top 1% income earner. To this end, we estimate the following linear probability model (LPM) separately for natives and immigrants:

$t o p e a r n e r_{i t} = β_{0} + β_{1} e d u c a t i o {n^{'}}_{i t} + β_{2} e m p s t a {t^{'}}_{i t} + β_{3} m i g c h a {r^{'}}_{i t} + γ Z^{'} t + δ X^{'} i t + ϵ_{i t}$

where education is a set of indicator variables for education, and empstat is the individual i’s employment status in year t. Additionally, we include a set of immigrant-specific characteristics migchar, consisting of years since migration and being from a non-EU country (reference: EU country) in the estimations for immigrants. Z is a set of survey year indicators, and X is a set of covariates, namely age (measured as categories: below 25, five-year steps to 65 and above 65), living in East Germany, having children and being married. This estimation allows us to disentangle the determinants of high-earner status while holding potentially confounding factors constant. Additionally, owing to the separate estimations for immigrants and natives, we allow covariate coefficients to differ by group, like in a fully interacted model, which is a more flexible approach than just interacting the variables of interest with the group indicator. We use heteroskedasticity-robust standard errors in the estimations.

Results

Descriptive Results

We begin by investigating average differences in socio-demographic variables between immigrants and natives and the bottom 99% and top 1% of individual monthly net incomes, respectively. Table 1, columns 1 and 2 descriptively show the differences between natives and immigrants among the bottom 99% of the income distribution. Immigrants are, on average, younger and less likely to be female in this group but are more likely to be married and have children. Even though low, the propensity to be self-employed without employees is higher among immigrants than natives.

Table 1.

Descriptive statistics of the 1% for natives and immigrants.

	99%		1%
	(1)	(2)	(3)	(4)
	Natives	Immigrants	Natives	Immigrants
Female (0/1)	0.53	0.50	0.13	0.13
Age	54.76	45.30	52.84	48.60
Married (0/1)	0.62	0.72	0.83	0.79
Child in HH (0/1)	0.29	0.47	0.51	0.52
Number children	0.49	0.94	0.99	1.03
East Germany (0/1)	0.25	0.22	0.22	0.17
Self-employed, w/o employees	0.03	0.04	0.08	0.10
Self-employed, w/employees	0.02	0.02	0.31	0.17
Non-self-employed	0.50	0.49	0.51	0.68
Non-working	0.44	0.44	0.09	0.05
Education: Vocational degree	0.57	0.27	0.20	0.09
Education: Meister	0.05	0.02	0.06	0.03
Education: Tertiary degree	0.16	0.18	0.52	0.65
Education: Doctoral degree	0.01	0.01	0.17	0.12
Education: Other	0.05	0.03	0.02	0.02
Education: No degree	0.16	0.50	0.03	0.10
Immigrant EU		0.40		0.68
Immigrant non-EU		0.60		0.32
Years since migration		19.33		18.17
Observations	2,730,841	232,730	46,148	2,125

Note: to ensure representativity, we employ cross-sectional survey weights for descriptive statistics. Owing to the weights, the weighted number of observations in the descriptive statistics deviates slightly from the unweighted number of observations in regression analyses.

Source: own calculations using the German Microcensus data.

Turning to the top 1%, differences between the groups substantially change: the share of women is low and now equal for immigrants and natives (13%) and the age gap declines. Notably, the share of individuals in self-employment is now drastically lower among immigrants (27%) compared with natives (39%) and immigrants have, on average, higher educational degrees than natives.

Regarding immigrant-specific characteristics, we observe that most immigrants in the income elite are immigrants from EU countries, whereas we find a higher share of immigrants from non-EU countries in the bottom 99%. Furthermore, the time that individuals spent in Germany since migration is smaller among the top 1% than the rest of the sample, even though the difference is small.

Next, we investigate the share of immigrants in the lower and upper parts of the income distribution over time, as displayed in Figure 1. This figure shows that immigrants are underrepresented among the income elite, because their share in this group is smaller than in the rest of the income distribution. Furthermore, the share of immigrants in the top 1% barely increased over time: from 6.1% in 2009 to 6.5% in 2018, while the overall share of immigrants increased nearly 50%, from 8.6% to 12.1%. Thus, immigrants are underrepresented by 46% in the income elite in Germany in 2018.

Figure 1.

Shares of immigrants among the top 1% and bottom 99% of the income distribution.

Regression Results

Pathways to the 1% by Immigration Status

In the next step, we turn to the regression results. Figure 2 presents the coefficients from the LPMs. We estimate these models separately for natives and immigrants and include immigrant-specific pathways only in the estimation for the immigrant sample. The results show that returns to education are larger for natives than for immigrants for tertiary education and especially for doctoral degrees. The differences in the coefficients for doctoral degrees are also statistically significant since the confidence intervals for natives and immigrants do not overlap. Thus, the results support our expectation that the link between education and 1% status is stronger for natives than for immigrants.

Figure 2.

Regression results from separate estimations for natives and immigrants.

The same applies to self-employment status compared with dependent employment. Again, returns to self-employment are larger for natives than for immigrants, especially in the case of self-employment with employees. Our expectations are also supported by the findings that self-employment without employees is not at all correlated with 1% status among immigrants. These results potentially support our expectations that immigrants could be pushed to self-employment due to a lack of other employment possibilities.

After analysing differences between immigrants and natives, we investigate the immigrant-specific factors of integration and country of origin as pathways to the 1%, which we can only investigate in the immigrant-specific estimation (Figure 2 and Appendix Table A2, column 2). Regarding integration, we find no association between years since migration and an immigrant’s 1% status in our main regressions (Figure 2). These results indicate that integration does not affect immigrants’ access to the income elite.

In line with our expectations regarding the importance of the country of origin, Figure 2 shows that non-EU immigrants have a lower propensity to have 1% status than EU immigrants (the reference group). This result indicates that immigrants benefit from country-of-origin effects, such as facilitated access to the labour market or lower cultural and social barriers.

Robustness Checks

As our results might be subject to the specification, we run several robustness checks. We begin by checking the robustness of the results regarding pathways into the 1% (corresponding to the baseline results in Figure 2). First, we further condition on industries (WZ08 three-digit codes) to investigate whether selection into industries drives the effect (Appendix Figure A1). Controlling for industries does not affect our results. Second, we investigate immigrants’ access to the top 1.58% in our main specification. To ensure that our results also apply to a more restrictive income specification, we investigate access to the very top income category (above €18,000 per month, the top 0.1%) in Appendix Figure A2. While the coefficients became smaller in magnitude, the relative differences between natives and immigrant remain comparable. Third, we additionally include second-generation immigrants (defined by a parent’s country of origin) in Appendix Figure A3. Again, the results mirror our baseline findings, showing that our results do not depend on the exact definition of immigration status. Fourth, we show marginal effects at the mean from logit regressions for the binary outcome in Appendix Figure A4. Again, overall coefficient magnitudes decrease, but the differences between immigrants and natives mirror the baseline results.

We also investigate the robustness of the results regarding integration (displayed in Appendix Figure A5). Since non-EU immigrants might especially benefit from integration due to higher cultural distance and social barriers (Ebner and Helbling, 2016), we repeat our estimation for EU and non-EU immigrants separately and use categories instead of a continuous measure for years since migration. Contrary to the expectations, Appendix Figure A5 shows virtually no difference in obtaining 1% status by years since migration between EU and non-EU immigrants. This finding is surprising, as we expected integration to especially matter for non-EU immigrants. Furthermore, we use a logit estimation and calculate marginal effects at the mean instead of an LPM. The results are displayed in Appendix Figure A6 and support the robustness of our main results. Overall, our results are not dependent on modelling decisions and the choice of specification.

Conclusion

The share of immigrants in western societies has increased substantially in recent decades (Mcauliffe and Triandafyllidou, 2021), highlighting the importance of immigration and ethnic stratification for sociological research. Despite making up around 12% of the population in Germany in 2018, only around 7% of first-generation immigrants are represented in the elite. Thus, immigrants are underrepresented by 46% in the elite in Germany in 2018, resulting in a potentially lower cultural, social and political influence of immigrants (Keister and Lee, 2017).

In this study, we find substantial differences between immigrants’ and natives’ pathways to access the income elite. While higher educational attainment and self-employment are associated with a 1% status in general (Yavorsky et al., 2019), we only find a small association between education and immigrants’ 1% status. Furthermore, self-employment without employees is not associated with the 1% status for immigrants. This finding could be due to a selection of immigrants into one-person businesses when they decide to be self-employed. Self-employed immigrants with employees, on the other hand, have a significantly higher propensity of belonging to the 1% compared with natives or immigrants without employees. In summary, we find substantial differences in achieving a 1% status between immigrants and natives, highlighting the importance of investigating group-specific pathways to access the elite.

In addition to education and self-employment, we investigated immigrant-specific pathways of integration and country-of-origin effects. Contrary to our expectations, our analyses show that, integration, that is, years since migration, does not affect the attainment of a 1% status. These results are in line with the ambivalence of existing studies on wage trajectories of immigrants, which produce different results.

Regarding country-of-origin effects, we find that immigrants from non-EU countries are more underrepresented in the 1% compared with immigrants from EU countries. These results underline the importance of immigrants’ countries of origin for attaining 1% status, that is, legal, economic and socio-cultural barriers to access the labour market. Moreover, we find no different effects for integration, that is, years since migration, between EU immigrants and non-EU immigrants. This finding is surprising since non-EU immigrants should face a greater challenge in overcoming legal, economic and socio-cultural barriers and should thus benefit more from integration.

Our study contributes to understanding ethnic stratification in a society, as we show that the access to the 1% is very different from the common positive influencing factors on social stratification. For example, education is known to have a positive impact on the labour market success of immigrants overall. In the case of the 1% status, however, education seems to be of minor relevance. Therefore, our findings make an important contribution to understanding the emergence and reproduction of social inequality within a society.

Although the Microcensus offers a unique opportunity to investigate immigrants’ pathways to the 1%, we have to address two major limitations of our study. First, we cannot consider the selectivity of immigrants compared with their home-country populations, for example, whether immigrants are positively or negatively selected with regards to their labour market characteristics compared with their home-country population. If we, for example, assumed that immigrants are already positively selected, then the true degree of underrepresentation among the income elite could be stronger. Second, we cannot separately investigate different dimensions of integration, such as language or culture.

Our findings lay the groundwork for future scholarship in this field, like disentangling the effects of language or culture. Another promising approach could be investigating gender differences within the group of immigrants with respect to the 1%. In this context, scholars could investigate whether female immigrants face a double penalty. Additionally, previously identified pathways for females to achieve the 1% (Yavorsky et al., 2019) can also be analysed for female immigrants.

As a further follow-up question, scholars could examine the role of social networks to achieve 1% status because networks play a vital role in determining immigrants’ labour market success (Mouw, 2003). In this context, immigrant–native partnerships might give immigrants access to natives’ social capital. Identifying the direct effects of partnership on 1% status could deepen our understanding of networks for ethnic stratification.

Footnotes

We thank Andreas Damelang,Michael Oberfichtner,Kevin Ruf and Silvia Schwanhäuser for their helpful comments.

ORCID iDs

Matthias Collischon

Florian Zimmermann

Supplemental Material

Supplemental material for this article is available online.

Matthias Collischon is a postdoctoral researcher at the Institute for Employment Research (IAB,Research Department Panel Study Labour Market and Social Security) in Nuremberg. His research interests include gender inequality,inequality in the labour market and quantitative methods. He has published in journals such as the European Sociological Review,Sociological Science and Social Forces .

Anja Wunder is a member of the joint graduate programme of the IAB and the FAU and a researcher at the IAB. Her research focuses on ethnic labour market inequalities and gender issues. She has published in Rationality and Society .

Florian Zimmermann is a researcher at the Institute for Employment Research (IAB). His research interests comprise gender issues,migration issues and social stratification. He has published in European Sociological Review,Sociological Science and European Societies,among others.

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