Educational sorting and ethnic inequality in occupational prestige among children of immigrants

Abstract

This study investigates the occupational prestige of children of immigrants compared to natives in Norway, focusing on the first job after graduation. Using high-quality registry data from 2003 to 2014 (N = 530,340), the analysis examines ethnic disparities in occupational prestige for six origin-based groups: African, Indian, Pakistani, Turkish, Vietnamese and European. The findings reveal that children of immigrants generally perform similarly to natives, with some notable differences: children of Indian origin experience a prestige advantage, while those of African origin face disadvantages. These differences are largely explained by detailed educational attainment, which plays a significant role in determining occupational status. Quantile regression shows that ethnic differences in occupational prestige are generally small and consistent across the prestige distribution. However, some groups, particularly those of Indian descent, exhibit a significant prestige advantage at the top of the distribution. This study contributes to understanding ethnic stratification in labour markets by showing how educational sorting influences occupational outcomes and challenges stereotypes about immigrant integration. It offers new insights into how children of immigrants navigate the occupational hierarchy, aligning with human capital theories while also addressing broader implications for integration and social mobility. The findings are of value to immigration and stratification research, providing a nuanced view of ethnic inequalities in the labour market.

Keywords

Immigration ethnic minorities occupational prestige labour market Norway

Introduction and research questions

In modern societies, occupational differences are key determinants of individuals’ positions within the broader social hierarchy (Chan and Goldthorpe, 2004; Lersch et al., 2020; Treiman, 1977). For immigrants and their descendants, access to positions across all levels of this hierarchy is crucial for long-term structural integration (Haller et al., 2011; Heath et al., 2008; Hermansen et al., 2023; Nee and Alba, 2012). While immigrants often occupy low-status, unskilled occupations, many children of immigrants have high professional aspirations and make more ambitious educational choices than their similarly performing native peers (Dollmann and Weißmann, 2020; Gabrielli and Impicciatore, 2022; Jonsson and Rudolphi, 2011). However, barriers such as hiring discrimination and less advantageous social networks may impede their advancement in the labour market (Quillian et al., 2019; Zschirnt and Ruedin, 2016). A key question, then, is whether these groups can convert their aspirations and educational qualifications into occupational positions comparable to those of similarly qualified natives.

In this study, I use comprehensive registry data to examine the occupational prestige attainment of the six largest origin-based groups of children of immigrants in Norway, compared to natives, based on parental country or region of origin. The groups include Pakistan, India, Turkey, Vietnam, Africa and Europe.

I address three research questions: first, whether ethnic differences in occupational prestige exist in individuals’ first jobs after graduation; second, to what extent such differences vary across the occupational prestige distribution; and third, how detailed educational sorting relates to ethnic differences in occupational prestige. Answering these questions contributes to and expands upon previous studies on the occupational attainment of children of immigrants, which have primarily used the EGP class scheme and focused on income differences (Heath et al., 2008; Hermansen, 2013). This study uses the SIOPS prestige scale (Treiman's Standard International Occupational Prestige Scale), a widely used, internationally standardised measure of occupational social standing (Ganzeboom and Treiman, 1996; Härkönen et al., 2016; Lersch et al., 2020; Treiman, 1977).

To address the first research question, children of immigrants’ occupational prestige relative to natives matters because achieving social standing on a par with, or higher than, the majority population may counteract negative stereotypes and contribute to a ‘blurring’ of the economic and social boundaries between groups, enabling social integration (Alba et al., 2011; Orupabo, 2014). Compared to other related measures, such as socio-economic status, the SIOPS scale is arguably better suited to making comparisons across countries and time, as well as for investigating gender and other categorical group differences – both of which are important when studying immigrant integration (Härkönen et al., 2016; Lersch et al., 2020).

Regarding the relevance of the second research question, in addition to providing a more comprehensive empirical picture through the use of quantile regression, there are also theoretical reasons to expect prestige differences to vary according to origin at different points along the prestige scale, as I will explore later. The third research question is specifically motivated by the close connection between educational credentials and prestige, and the crucial question of whether children of immigrants are able to convert their credentials into prestige at the same level as natives. Importantly, the available data allow for the inclusion of educational information at very high levels of detail, enabling an examination of the impact of nuanced prestige differences that can be associated with educational distinctions within broader fields.

On a more general level, socio-economic assimilation is considered a central form of structural integration in both contemporary and classical debates on assimilation and integration (Alba et al., 2011; Gordon, 1964; Haller et al., 2011). While proponents of neo-assimilation theories typically emphasise the average achievements of immigrants and their descendants, segmented assimilation theory highlights heterogeneity within and between groups (Alba et al., 2011; Haller et al., 2011). In this article, I contribute to both goals by examining ethnic disparities in occupational prestige, considering both average differences and variations across the prestige distribution for graduates from all levels of the education system.

Theory and previous research

Previous research on ethnic differences in employment, earnings and occupational attainment

While children of immigrants are more successful at obtaining employment than their parents, they generally fare worse than similarly qualified natives (Alba and Foner, 2015; Crul et al., 2012; Heath and Cheung, 2007; Heath et al., 2008; Hermansen, 2013; Lillehagen and Birkelund, 2022). However, there is significant heterogeneity between groups, with those originating from less developed non-European countries typically faring the worst. Once employment is secured, studies from Norway, Sweden, the Netherlands and Great Britain show that differences in earnings tend to be small, and some evidence even points to minority advantages (Birkelund and Mastekaasa, 2009; Dustmann and Theodoropoulos, 2010; Hällsten and Szulkin, 2009; Van Ours and Veenman, 2004).

Regarding occupational attainment, measured using the EGP class scheme in a comprehensive set of studies, the evidence reveals greater geographical variation. In Sweden and Norway, most ethnic minority groups enter the salariat (professional and managerial jobs) on a par with natives, and some origins are even associated with advantages when models are adjusted for educational variation (Heath et al., 2008, for an overview; Hermansen, 2013). The findings from Britain differ according to country of origin (Heath et al., 2008; Li, 2018; Li and Heath, 2010). Studies from continental European countries (including Austria, Belgium, France, Germany and the Netherlands) provide clear evidence of disadvantages for immigrants, almost regardless of their country of origin (Heath et al., 2008; cf. OECD, 2022).

Very few studies based on European data have examined the occupational attainment of immigrants and their children using metric measures of social stratification other than income. One notable exception is Helgertz (2011), who investigated the career progression of immigrants from 18 origins using Swedish data covering the period from 1970 to 1990. He used the International Socio-Economic Index (ISEI) to rank occupations and found clear evidence of immigrant disadvantage in the private sector, although the differences were somewhat smaller in the public sector.

Occupational prestige

An important distinction within sociological stratification research is between continuous and categorical approaches, and their associated outcome variables (Ganzeboom et al., 1992). In this study, the main measure of occupational status is the continuous SIOPS scale. The aim is to complement and extend previous research, which is typically based on the EGP class scheme or focuses on income differences, by emphasising a new and crucial dimension of social inequalities (Erikson and Goldthorpe, 1992; Ganzeboom et al., 1992).¹

The primary reason for focusing on the SIOPS scale, a widely used and internationally standardised measure, in this article is to gauge the evaluative and symbolic dimensions of the occupational hierarchy – deference and demeanour (Goldthorpe and Hope, 1972). Differences in this type of social standing are related to levels of deference, approval and respect ascribed to specific occupational positions, directly influencing processes of social inclusion and exclusion (Ganzeboom et al., 1992; Treiman, 1977: 16–22). I study children of immigrants, and the symbolic boundaries between groups suggested by this dimension are generally considered a key determinant of whether immigrant integration will be successful over time (e.g. Alba and Foner, 2015). This is my primary rationale for choosing the SIOPS rather than other commonly used measures, such as the ISEI, with which SIOPS is highly correlated (e.g. Lambert and Bihagen, 2014).

A key strength of SIOPS is its suitability for comparative research across countries and cohorts, which is vital for advancing cumulative empirical findings in this area and for future theoretical developments (e.g. Härkönen et al., 2016; Lersch et al., 2020, for two high-quality recent examples using the SIOPS scale). The scale was originally constructed from data at various levels, including cross-national surveys, and validated for cross-country comparisons. It has since been empirically demonstrated to exhibit high stability across social groupings within the same country, across countries and over historical time (Hout and DiPrete, 2006; Lersch et al., 2020; Treiman, 1977: 67). This stability makes comparisons between children of immigrants and natives, where cultural differences may be at play, all the more illuminating.

Another advantage of the SIOPS scale compared to socio-economic measures is that it is better suited to investigating categorical group differences, such as gender disparities (Härkönen et al., 2016; Lersch et al., 2020: 12).² Composite measures, such as socio-economic status indices, are calculated based on average educational and income levels of occupations, but these compositions are likely to vary across the relevant social categories (Härkönen et al., 2016; Lersch et al., 2020; Warren et al., 1998). In this study, both origin-based variation and gender differences are important, which again suggests that SIOPS is a good choice.

How, then, does the SIOPS scale depict the hierarchy of social life? A rough overview: occupations at the very top are typically highly exclusive positions, such as ministers, supreme court justices or bishops. Somewhat more common positions in the upper range include scientists, doctors and professors. The middle of the scale features occupations such as journalists, nurses, musicians and photographers. At the lower end of the scale, manual and unskilled occupations are the most prominent.

Theoretical mechanisms and expectations

Based on the extensive literature on differences between (children of) immigrants and natives in the labour market, several explanations have proven important (e.g. Heath et al., 2008). I now outline the most relevant explanations in turn, and, where feasible, specify empirical expectations that are pertinent to my research questions.

In classic human capital theory, education is linked to labour market performance either by serving as a direct measure of productivity or by functioning as a signal of unobserved productivity-enhancing attributes (Becker, 1993; Stiglitz, 1975). This family of theories encompasses variation in educational levels as well as fields (e.g. Gerber and Cheung, 2008). Newer empirical literature has further underscored the importance of fine-grained labour market sorting based on detailed, field-based skill profiles, in both the United States and Europe, including Norway (e.g. Borgen and Mastekaasa, 2018; Håkanson et al., 2021; Tomaskovic-Devey et al., 2020).

Turning to the first research question, given the close relationship between educational profiles and labour market outcomes, I expect natives and descendants of immigrants from different origins to end up at different prestige levels due to educational differences. It is well documented that descendants of immigrants tend to choose different fields of educational specialisation, often making more ambitious choices than otherwise comparable natives (Dollmann and Weißmann, 2020; Jonsson and Rudolphi, 2011; Midtbøen and Nadim, 2019; Schou, 2009). Norwegian studies have also documented a proclivity to move into high-prestige programmes, including health-related professional degrees, as well as degrees in the natural sciences, law and economics (Daugstad, 2007). These disparities are also visible in my dataset, and potential deeper explanations include differences in family background, ethnic-specific cultural traits and self-beliefs (e.g. Gabrielli and Impicciatore, 2022, for an overview). Here, I am mainly interested in the consequences of such choices.

In view of these arguments, and pertaining to the third research question, I expect that detailed educational choices, on which there is rich information in the registries, will account for a substantial share of the ethnic variation in occupational prestige (cf. Dollmann and Weißmann, 2020). I also anticipate that some groups of descendants of immigrants will display prestige advantages relative to natives because of their tendency to choose high-prestige (sub-)fields.

Still, there are important reasons to expect at least some origin-based prestige differences to remain salient even when educational differences are included in the models. First, children of immigrants may show advantages due to unmeasured differences in skills or ambitions, not all of which will be captured by including educational controls. One potentially important mechanism is that the (unobservable) positive and self-selected traits that make immigrants successful are likely to be transmitted to their children (Feliciano, 2005; Feliciano and Lanuza, 2017; Ichou, 2014), for example, by parents promoting and internalising higher levels of ambition in their children (Fekjær and Leirvik, 2011; Gabrielli and Impicciatore, 2022; Goyette and Xie, 1999; Kao and Tienda, 1995). Other studies have underscored the role of ethnic capital, or group-specific resources originating at least in part from interactions outside the family (Portes et al., 2009).

While at least some of the impact of such factors on prestige attainment is likely to be mediated through educational choices, there could also be more direct pathways between background and the outcome. Importantly, there is a potential impact of social origin. Parental educational level is a factor of general sociological interest (Breen, 2004) and has also been shown to matter empirically for several educational and labour market outcomes for children of immigrants (Heath et al., 2008; Hermansen, 2013). Parental education is included in this study as a control variable. Mechanisms such as family contacts, variation in career aspirations and favouritism could remain relevant as direct effects (e.g. Bernardi and Ballarino, 2016: 5). Still, it remains an open empirical question whether it has an independent impact on occupational prestige when highly detailed educational controls are introduced.

A large literature, predominantly based on field experiments, has convincingly documented labour market disadvantages for immigrants’ children (typically lower callback rates when applying for jobs) due to discrimination, using data from several countries, including Norway (e.g. Friberg and Midtbøen, 2018; Zschirnt and Ruedin, 2016, for overviews). The degree of discrimination faced by immigrants and their children has also been shown to vary across different sectors and types of occupations (Birkelund et al., 2017; Di Stasio and Larsen, 2020; Friberg and Midtbøen, 2018; Quillian et al., 2019; Riach and Rich, 2002).

While these factors cannot be measured directly using my data, they need to be taken into account when interpreting the findings. Theories of discrimination imply remaining disadvantages for descendants, net of education. First, difficulties in obtaining employment may lead descendants to lower their standards and accept positions of lower occupational prestige than similarly educated natives. Second, the presence of discrimination could imply that employers offer jobs with different occupational prestige to ethnic minorities and natives who are similarly qualified (Larsen et al., 2018). This relates particularly to the third research question.

Furthermore, several studies suggest that the highly educated face less discrimination, particularly in occupations associated with closure mechanisms (such as licensing). This is clearly relevant here, as pursuing professional education, including medicine and law, is more common among descendants than natives (Drange, 2013; Drange and Helland, 2019). These arguments imply that descendant disadvantages are likely to be less marked (or advantages more pronounced) at higher levels of the prestige hierarchy, though not necessarily at the very top, where more unique positions, such as political, legal or religious leaders, are more common. This speaks to my second research question.

The use of social networks also plays an important role in finding jobs for both natives and ethnic minorities (Frijters et al., 2005; Granovetter, 1995; Green et al., 1999; Sanders et al., 2002). Such networks are likely to be especially important for obtaining high-prestige jobs (Lin, 1999). More generally, studies report a high reliance on co-ethnic job-referral networks among immigrant workers (Åslund et al., 2014; Dustmann et al., 2016; Goel and Lang, 2019), persisting ethnic networks for descendants in schools and neighbourhoods (Leszczensky and Pink, 2019; Smith et al., 2016), and that immigrant managers often recruit employees from their own country of origin (Åslund et al., 2014; Kerr and Kerr, 2021). This could affect access to high-status jobs in particular and therefore be relevant for all three research questions.

Lastly, all analyses are conducted separately by gender. Based on previous studies, I expect to find greater disadvantages for female children of immigrants than for males, all else being equal. This is explained by sex-differentiated family-related obligations, higher levels of discrimination against women, and other cultural norms (e.g. Heath et al., 2008; Hermansen, 2013).

The Norwegian context

European societies are becoming increasingly demographically heterogeneous, and Norway is no exception to this trend (Dustmann and Frattini, 2013; OECD, 2022). The share of immigrants and their native-born descendants in the Norwegian population has grown from 1.5% in 1970 to nearly 20% today (Statistics Norway, 2022). The individuals included in this study are Norwegian-born children of immigrants who arrived in Norway between the early 1970s and the late 1980s.

The first post-war immigrants to Norway came from India, Pakistan and Turkey in the early 1970s. They were typically unskilled workers, later joined by family members through family reunification in subsequent decades, after the early liberal era of labour immigration had ended (Henriksen and Østby, 2007). Of these immigrant groups, Indian immigrants displayed the highest levels of education and the lowest unemployment rates. Vietnamese immigrants, on the other hand, were among the first groups of political refugees to arrive in Norway during the latter part of the Vietnam War in the mid-1970s. This group was characterised by a relatively low level of education but a high employment rate.

My study also includes children of immigrants originating from African countries, with Eritrea, Ethiopia and Somalia representing the majority. Most immigrants from these countries arrived as refugees due to war and conflict, later joined by family members through family reunification. Immigrant groups from these less developed origin countries generally exhibit low employment rates, tend to concentrate in manual and routine occupations, have low wages, and exhibit high dependency on social welfare assistance (Bratsberg et al., 2014; Orupabo and Nadim, 2020).

On the other hand, their descendants have made considerable intergenerational progress in terms of education and employment (Hermansen, 2016). Still, evidence suggests that children of immigrants tend to live in neighbourhoods characterised by economic disadvantage and are also segregated in terms of partner choice (Wiik et al., 2021). While the emphasis in this paper is on what is often referred to as non-Western groups, I also include children of immigrants from Europe. The reference group in the analysis consists of ‘natives’ (i.e. individuals born in Norway to two parents who were also born in Norway), and the inclusion of those of European origin provides an interesting comparison among the non-natives. Employment rates for the parental generation in this group are on a par with the native average. While the history of the parental generation provides context for understanding outcomes in the next generation, Norwegian-born descendants of immigrants have spent their childhood in Norway and have been educated within the Norwegian education system. This suggests that the importance of factors such as language proficiency and problems related to the portability of their human capital should be substantially reduced.

Sample and data

Data

I use individual-level panel data from Statistics Norway's comprehensive registries, which are linked using a unique and anonymised ID number for each person. The sample includes all individuals born between 1980 and 1997 who obtained their first job between 2003 and 2014. No reliable occupational codes are available for data prior to 2003, and 2014 is the last year for which I have access to information. Each individual is represented in the sample with information from the year in which this job was obtained (N = 530,340). Furthermore, only individuals who were between the ages of 17 (having graduated from lower secondary school) and 32 at the time of obtaining their first job were included in the final sample.

Definitions of variables

Occupational prestige is included as a continuous, metric dependent variable. This variable was derived from occupational information in several steps. First, Norwegian occupational codes in the registries were recoded into the international ISCO-88 standard based on the syntax provided by Mortensen (2007). Then, each code was assigned a prestige score based on Ganzeboom and Treiman (2019). This process was conducted in Stata using syntax from Hendrickx (2002).

Country/region of origin

I use information about the mother's country of birth ³ to define the country or region of origin for each individual, with natives serving as the reference category. Children of immigrants from Vietnam, Turkey, India and Pakistan are included as separate groups. Due to the low number of observations for certain countries, I merge descendants of African origin (primarily from Ethiopia and Eritrea) and European origin into broader groups. Children of mixed origin, that is those born to one native and one immigrant parent, are not included in the sample. The native reference category excludes grandchildren of immigrants, as this group is too small for meaningful analysis.

Detailed information on educational fields and levels

The National Education Database (NUDB) includes detailed educational information, recorded using four-digit codes. The first digit provides information on the highest completed level of education. Lower secondary education is mandatory and covers grades 8–10, and all individuals in my sample have completed grade 10. Upper secondary education (3 years) is not mandatory, but within each birth cohort the vast majority of individuals enter upper secondary school. Higher degrees at colleges and universities conform to European standards, with a 3-year bachelor's degree, a 5-year master's degree and a PhD-level degree.

The second digit provides information on broad educational fields, divided into the following categories: ‘general subjects’, ‘humanistic and aesthetic subjects’, ‘teaching and pedagogy’, ‘social sciences and law’, ‘economy and administration’, ‘science, crafts and technical subjects’, ‘health, social subjects and sports’, ‘agriculture, hunting and fishing’, ‘transportation, safety and service’ and ‘others’. I use ‘general subjects’ ⁴ as the reference category. By including the third and fourth digits of the educational codes, one can utilise information on 209 and 419 detailed degrees, respectively.⁵

Control variables

Year of birth is included in all models as a set of dummy variables, that is as a categorical variable, allowing for more flexible modelling than using a continuous variable. The reference category is 1986. To account for economic fluctuations, year of employment is also included as a set of dummy variables (with 2010 as the reference category). As a measure of social background, I include the highest level of education attained by either parent when the individual was 16 years old, also as a categorical variable (implemented as a set of dummy variables). When the two parents have the same level, I use the father's education.⁶

The categories are as follows: 0 indicates no education and pre-school education (reference), 1 represents primary education, 2 corresponds to lower secondary education, 3 indicates upper secondary education (basic education), 4 represents the final year of upper secondary education, 5 corresponds to post-secondary non-tertiary education, 6 denotes the first stage of tertiary education at the undergraduate level, 7 denotes the first stage of tertiary education at the graduate level, 8 corresponds to the second stage of tertiary education (postgraduate education) and 9 represents cases where the parental education level is unspecified.

Statistical models

I estimate two types of linear regression models: ordinary least squares (OLS) and unconditional quantile regression (UQR), a relatively new approach (Firpo et al., 2009; Killewald and Bearak, 2014). The main goal is to investigate differences in the prestige distribution associated with different origins relative to natives. While OLS estimates the average change in the outcome variable associated with a one-unit change in an explanatory variable, UQR allows estimation of the impact of such changes at different quantiles of the outcome variable (Firpo et al., 2009). Since I use unconditional rather than conditional quantile regression, the prestige distribution is defined prior to the regression and independently of the included independent variables (Killewald and Bearak, 2014). Consequently, the quantiles remain constant when control variables and fixed effects are included in the model. An origin coefficient at the xth percentile is interpreted as the difference in prestige associated with that origin compared with natives at the same percentile in the prestige distribution, adjusted for other independent variables.⁷

I estimate several models, incorporating statistical control variables for the completed level and type of education at progressively higher levels of detail. The main independent variable in all models is country/region of origin, with all coefficients estimated relative to the native population. All models are estimated separately by gender and adjusted for year of birth and year of employment. In the stepwise models with educational fixed effects, parental education is included in Models 2 to 5, but not in the baseline model (see Figures 2(a) and 2(b)).

These estimates are presented in four Figures 1(a), 1(b), 2(a), 2(b). The complete regression results are reported in the appendix. The first two figures show differences in occupational prestige for each origin group compared with natives, including both average differences and differences at the 10th to 90th percentiles (first to ninth deciles). The third and fourth figures present estimated average prestige differences, adjusting for increasingly detailed educational information using fixed effects estimation.

Results

Tables 1a and 1b provide full descriptive statistics for the male and female samples, separated by country or region of origin (quintiles for the dependent variable are shown here to simplify the presentation). The tables reveal variation in both the averages and distributions of prestige by origin. For both sexes, the average prestige advantage of children of Indian immigrants stands out, as does the disadvantage for those of African origin. The tables also highlight clear educational differences between the groups. Notably, individuals of Indian origin are overrepresented in higher education, while individuals from Africa, Turkey and Pakistan are overrepresented in lower secondary education relative to natives. Additionally, there is substantial variation between groups in broad fields of education. Descriptive information on the more detailed educational categories is not included due to the large number of categories. Finally, children of immigrants from most origins, and of both genders, are somewhat younger than natives.

Table 1a.

Descriptive statistics, men.

Variables
region/Country of origin	Norway	Europe	Africa	Vietnam	Turkey	India	Pakistan
Dependent Variable
Prestige (Avg. Std.dev)	37.1 (11.9)	35.8 (12.3)	33.3 (11.0)	36.4 (12.8)	34.1 (10.4)	43.0 (16.8)	35.9 (13.3)
Prestige (First quintile) %	21.9	26.5	36.1	24.0	25.0	19.1	23.0
Prestige (Second quintile) %	23.2	30.2	28.8	32.5	36.4	24.0	40.9
Prestige (Third quintile) %	21.0	11.7	11.5	10.0	15.6	5.2	7.5
Prestige (Fourth quintile) %	16.5	15.2	10.5	15.0	13.0	17.2	13.2
Prestige (Fifth quintile) %	17.4	16.4	13.2	18.5	10.0	34.6	15.5
Timing
Year of birth	1986.8 (4.4)	1988.5 (4.6)	1989.2 (4.3)	1988.7 (4.2)	1988.5 (4.2)	1987.3 (4.4)	1987.3 (4.2)
Year of employment	2008.9 (3.6)	2010.0 (3.5)	2010.4 (3.3)	2010.4 (3.2)	2010 (3.5)	2010.1 (3.2)	2009.2 (3.5)
Educational Level
Lower secondary (%)	25.7	36.9	49.6	35.5	53.4	22.2	47.6
Upper Secondary (%)	50.7	40.1	38.6	37.1	35.5	29.7	29.5
Short Higher Education %	16.8	16.0	9.6	19.5	8.5	26.9	14.7
Long Higher Education %	6.8	7.0	2.3	8.0	2.5	21.1	8.3
Region/Country of Origin	Norway	Europe	Africa	Vietnam	Turkey	India	Pakistan
Broad Field of Education
General Studies (%)	37.7	54.6	72.6	54.4	63.4	41.7	63.5
Humanistic and Esthetical (%)	2.8	4.0	0.1	2.8	0.8	1.1	0.9
Teaching and Pedagogy (%)	2.0	1.6	0.6	0.7	0.6	0.6	0.4
Social Science and Law (%)	3.6	3.7	1.5	2.4	1.0	5.6	2.9
Economics and Administration (%)	7.1	7.8	5.9	10.6	5.1	18.3	10.7
Science, crafts and technical (%)	36.3	20.7	11.2	19.6	24.7	16.9	13.6
Health, social and sports (%)	5.7	5.2	7.0	7.2	2.6	14.4	6.9
Agriculture and Fishing (%)	1.3	0.4	0.1	0.0	0.1	0.0	0.1
Transportation and Service (%)	3.5	2.0	1.0	2.2	1.8	1.1	1.0
Not assigned (%)	0.1	0.2	0.0	0.1	0.0	0.3	0.1
Number of observations	309 878	1228	843	956	726	360	2096

Table 1b.

Descriptive statistics, women.

Variables
region/Country of origin	Norway	Europe	Africa	Vietnam	Turkey	India	Pakistan
Dependent Variable
Prestige (Avg., Std.dev)	37.9 (12.6)	36.9 (13.45)	34.2 (11.4)	37.7 (13.74)	33.9 (10.84)	44.3 (16.7)	38.2 (13.8)
Prestige (First quintile) %	25.65	28.81	38.65	22.62	34.64	18.83	23.37
Prestige (Second quintile) %	26.62	33.17	29.32	37.92	36.69	21.43	36.33
Prestige (Third quintile) %	3.87	3.20	1.95	2.44	1.54	2.60	2.67
Prestige (Fourth quintile) %	20.98	14.84	15.94	15.30	13.31	21.75	16.55
Prestige (Fifth quintile) %	22.88	19.98	14.14	21.72	13.82	35.39	21.08
Timing
Year of birth	1986.6 (4.35)	1988.5 (4.51)	1988.7 (4.27)	1988.45 (4.17)	1987.9 (4.22)	1986.8 (4.42)	1986.9 (4.0)
Year of employment	2009.2 (3.5)	2010.3 (3.3)	2010.2 (3.4)	2010.4 (3.2)	2009.3 (3.7)	2009.9 (3.4)	2009.2 (3.5)
Educational Level
Lower secondary (%)	19.2	27.8	36.1	22.1	42.9	11.6	27.2
Upper Secondary (%)	37.4	37.8	36.1	38.9	35.0	26.7	37.3
Short Higher Education %	34.7	25.7	22.3	27.0	19.8	36.6	23.9
Long Higher Education %	8.7	8.7	5.5	12.0	2.3	25.0	11.5
Region/Country of Origin	Norway	Europe	Africa	Vietnam	Turkey	India	Pakistan
Broad Field of Education
General Studies (%)	33.8	47.7	57.0	43.0	55.2	30.8	43.0
Humanistic and Esthetical (%)	7.0	7.7	3.4	3.8	2.3	3.5	1.2
Teaching and Pedagogy (%)	8.2	3.6	2.2	3.0	5.8	2.9	3.1
Social Science and Law (%)	6.0	6.3	5.0	3.4	1.4	7.6	5.1
Economics and Administration (%)	9.0	8.3	7.6	11.9	6.5	16.3	13.8
Science, crafts and technical (%)	6.5	3.5	2.4	6.3	1.9	6.1	3.4
Health, social and sports (%)	24.7	19.3	20.7	22.6	24.3	32.0	28.6
Agriculture and Fishing (%)	1.1	0.4	0.0	0.0	0.2	0.0	0.0
Transportation and Service (%)	3.7	3.2	1.6	6.1	2.5	0.9	1.7
Not assigned (%)	0.1	0.2	0.1	0.0	0.0	0.0	0.1
Number of observations	208 716	1148	740	859	643	344	1803

Figures 1(a) (men) and 1(b) (women) show the average prestige difference compared with natives by country or region of origin, as well as the difference at all deciles, based on the regression set-up outlined above (full numerical results can be found in Appendix Tables A1a and A1b). For men, average prestige for children of immigrants from European, Vietnamese, Turkish and Pakistani origins is not statistically different from that of natives from the same birth cohort and employment year. For women, all origins show statistically significant differences from natives.

Some of these prestige differentials are quite similar in magnitude for both men and women. For both genders, children of immigrants of African origin display an average prestige score that is slightly more than one prestige point lower than that of natives. Conversely, having Indian-born parents is associated with a prestige level above the native average, with advantages of 5.163 and 5.221 for men and women, respectively. To put these numbers into perspective, 1–2 points on the 78-point prestige scale correspond to approximately 10% to 20% of a standard deviation.

The remaining significant differences for women are as follows: European, Vietnamese and Pakistani origins are associated with a prestige advantage of 0.87, 1.35 and 1.13 points on average, respectively. Conversely, having Turkish origins is associated with an average prestige score 1.35 points lower than that of natives. Figures 1(a) and 1(b) also show that prestige differentials across the distribution generally mirror the average difference. This is demonstrated by the substantial overlap between the confidence intervals of the OLS estimate (the larger blue line, with two smaller dotted blue lines indicating the confidence interval) and the results from the quantile regression models for both genders. However, there are some notable exceptions.

First, for descendants of Indian origin, the prestige advantage is most pronounced at the upper end of the distribution, especially among men. However, statistically significant advantages are also observed at some lower and middle percentiles. Second, male children of Pakistani immigrants display disadvantages in the middle of the distribution (50th to 60th percentiles), whereas they are not significantly different from natives at other levels of the prestige distribution. Third, a similar pattern (a difference only in the middle), with much smaller prestige disadvantages, is also found at the 50th percentile for male children of European and Vietnamese immigrants.

Figures 2(a) and 2(b) show how estimated differences in average prestige by origin change when adjusting for increasingly detailed educational information via fixed effects estimation (full results are found in Appendix Tables A2a and A2b). All models compare children of immigrants with natives from the same birth cohort and employment year.

For women, all but one of the prestige differences associated with the various non-native origins relative to natives become non-significant when adjusting for detailed educational differences (2–4 digits). The only statistically significant difference remaining after full educational controls is a slight advantage of 0.789 points associated with Pakistani origin. These changes indicate that accounting for detailed education reduced disadvantages for individuals of African and Turkish origin, as well as advantages for individuals of European, Vietnamese, Indian and Pakistani origins. It should be noted that these changes are generally congruent with the overall differences in educational level shown in Descriptive Tables 1a and 1b. Origins with higher educational levels compared with natives, such as Indian, start with advantages that disappear when adjustments are introduced, and vice versa. I return to a discussion of the mechanisms at play in the ‘Discussion’ section.

For men, when adjusting for 2–4 digits of educational information, children of African immigrants are no longer disadvantaged relative to natives, and those of Indian origin are no longer advantaged. With full four-digit adjustment, children of Pakistani immigrants display a small disadvantage of 0.792 prestige points on average. Vietnamese-origin men also display a small disadvantage in some of the detailed education models, including the four-digit specification.

It should again be noted that these differences are quite small in magnitude (typically 1–2 points on a 78-point scale, which corresponds to approximately 10% to 20% of the standard deviation). The largest ethnic difference is the prestige advantage observed for individuals of Indian descent, which is approximately 4–5 points, or 40% to 50% of the standard deviation. To get a somewhat less abstract sense of what such differences may amount to, here are some examples of pairs of occupations with differences in the relevant range (Ganzeboom and Treiman, 1996, 2019): astronomers (71) and physicists (76); veterinarians (61) and pharmacists (64); firefighters (35) and police officers (40); and nursing professionals (54) and teaching professionals (61).

Discussion

In this article, I use Norwegian administrative data to investigate ethnic disparities in occupational prestige for children of immigrants from six different origins, as well as natives, born between 1980 and 1997, and who obtained their first job between 2003 and 2014.

The first research question concerned whether there are ethnic differences in occupational prestige in individuals’ first job after graduation. I found that when children of immigrants were compared with natives from the same birth cohort and employment year (without other controls), one origin (Africa) was associated with a disadvantage and one (India) with a prestige advantage for both genders. Two gender differences were observed: for children of Pakistani immigrants, females showed an advantage, whereas males performed on a par with natives. For children of European immigrants, males were not significantly different from natives and females showed a small advantage, with the same pattern observed for those of Vietnamese origin. ⁸ As previously mentioned, the differences were generally small, with the exception of the advantage documented for those of Indian origin.

Pertaining to the second research question, results from the quantile regressions showed that origin-based disparities are generally similar across the occupational prestige distribution. However, prestige advantages for those of Indian origin are mainly found at the top of the distribution, while children of immigrants of Pakistani origin are disadvantaged compared with natives in the middle of the distribution. As noted in the results, there is a more general pattern of disadvantage in the middle (around the 50th or 60th percentile) for several origins.

This pattern is not fully explained by the current design. Two mechanisms are plausible: (i) outward-facing clerical and administrative roles that require culturally specific communication and other soft skills, and (ii) skilled-craft occupations where entry depends on apprenticeships and employer networks that may be less accessible to descendants of immigrants. Recruitment in these segments is often less formalised, increasing the role of discretionary screening. This account aligns with the lack of a mid-distribution trough for individuals of Indian origin, whose educational choices are concentrated in STEM and professional fields. Unobserved selection into employment and occupations may also contribute.

The third question concerned the relationship between educational sorting and ethnic differences in occupational prestige. The results were clear: when children of immigrants were compared to peers within the same level and field of education, most ethnic prestige differences (both advantages and disadvantages) were very small and not statistically significant. The only exceptions were small disadvantages of less than one prestige point for men of Vietnamese and Pakistani origin, and a slight advantage for women of Pakistani origin.

Taken at face value, the overall findings of the study align with the existence of a credential- and skill-based labour market, where detailed formal educational choices account for most ethnic differences in prestige. How can this be reconciled with the well-established findings in the literature regarding discrimination and social network disadvantages? A more complex set of processes and mechanisms needs to be considered. First, previous research has shown that ethnic differences in educational choices are driven, at least in part, by mechanisms such as resource limitations, discouragement due to anticipated discrimination, and other factors (see Ortiz-Gervasi, 2023; and Gabrielli and Impicciatore, 2022 for an overview). In other words, the production of inequalities begins at earlier stages in life.

Second, the sample only includes individuals who are employed, and prior research documents some disadvantages in the transition to employment for descendants of immigrants (e.g. Hermansen, 2013; Lillehagen and Birkelund, 2022). We also know that discrimination and social networks play a role in this process. The findings of this study, where origin-based disadvantages disappear once education is accounted for, suggest that discrimination is likely more relevant at earlier stages – such as in determining who gets a job and how long it takes to obtain one – thereby acting as a bottleneck into employment (Hermansen, 2013). If this selection process makes it more difficult for non-natives to enter the labour market, the findings of this study could be upwardly biased. ⁹

On a more detailed level, discrimination theory can be directly linked to specific empirical patterns. In the theory section, I argued that there is likely to be less scope for discrimination in some parts of the occupational structure due to closure mechanisms, such as licensing (Drange and Helland, 2019). This aligns with the Indian descendant prestige advantage, which is mainly observed at the top of the distribution (consistent with such arguments and, as shown in Tables 1a and 1b, this origin group is overrepresented in educational fields where these factors are highly relevant, including health, law, economics, and some technical subjects).

It is also possible that discrimination and/or a lack of relevant social contacts contribute to the remaining disadvantages observed for male descendants of Vietnamese and Pakistani origins, as well as to some of the disadvantages observed before detailed educational controls are introduced. This is partially consistent with Norwegian findings of an ‘ethnic hierarchy’, where individuals perceived as Muslim or with darker skin tones are the most likely to face discrimination (Larsen and Midtbøen, 2024). Clearly, more research is needed to shed light on this.

Lastly, there was little impact of parental education beyond detailed educational sorting. A supplementary model without parental education produced very similar results overall.¹⁰ A reasonable interpretation is that social origin effects on first-job prestige are largely mediated by children's detailed educational choices and possibly other factors that influence access to the labour market.

While this is, to the best of my knowledge, the first European study of the occupational prestige attainment of children of immigrants, some comparisons can be made with previous research using related outcomes. First, the findings of this study are broadly consistent with existing empirical evidence on occupational class attainment in Sweden and Norway, where class schemes are interpreted as representing hierarchical social positions. In these countries, most studies point to no ethnic differences in access to the salariat, and even advantages for some groups of children of immigrants when comparing individuals with the same educational qualifications (Heath et al., 2008; Hermansen, 2013). However, studies using similar measures for several continental European countries have pointed to disadvantages in service class attainment, particularly when comparing individuals with the same level of education (Heath et al., 2008).

The findings are also consistent with those of Larsen et al. (2018), who showed that Norwegian children of immigrants were no more likely than natives to work in jobs for which they were educationally overqualified. If descendants had shown a greater probability of being overqualified, I would likely have found prestige disadvantages when including controls for education.

In a comparative perspective, Norway has several institutional characteristics that could be relevant to the small differences in occupational prestige documented in this study. First, tracking in the Norwegian educational system begins relatively late, which likely reduces ethnic disparities in education and, in turn, may contribute to small prestige differences (Crul, 2015; Müller and Karle, 1993). Second, some aspects of the egalitarian social-democratic system could make it easier for children of immigrants to find and obtain employment suited to their qualifications (Hermansen, 2013). This reasoning is also consistent with the small differences in access to higher-class positions found for children of immigrants in Sweden (Heath et al., 2008). Third, the Norwegian labour market was relatively tight during the studied period, with low levels of unemployment and high demand for labour, which could reduce the relevance of factors such as discrimination (Birkelund, 2016; Statistics Norway, 2019). However, the broader importance of such institutional variation remains an important empirical question.

Limitations

In this study, I have used high-quality Norwegian registry data to document prestige differences between children of immigrants and natives. However, important limitations remain. First, the study is primarily descriptive, and the impact of origin on occupational prestige cannot be interpreted as causal in a straightforward manner. Furthermore, I was unable to directly measure the theoretical mechanisms discussed, except for detailed education. Several alternative explanations therefore remain plausible.

As mentioned in the text, the outcome is also contingent on having achieved employment. If the bar for achieving employment is higher for some or all minority groups (e.g. due to discrimination or a lack of information), the non-native individuals included in this study may represent a selected sample with higher skill levels than those who remain outside the labour market (see e.g. Heath and Brinbaum, 2007; Hermansen, 2013). Differential selection may also occur within the educational system. Several relevant measures are lacking, such as job search strategies, networks, educational preferences, as well as personality differences.

Finally, despite using full-population registry data, the relatively small number of observations in each origin category introduces some statistical uncertainty. Nevertheless, as shown in the results, the standard errors are generally small enough to rule out large differences between groups.

Conclusion

This study shows that ethnic differences in occupational prestige are generally small on average and across the prestige distribution, and that most of these differences can be accounted for by variations in educational choices. We thereby see that children of immigrants generally distribute themselves along the full spectrum of socially desirable (and less desirable) occupations in the same way as similarly qualified natives.

This could lead to a re-evaluation of cognitive perceptions of ethnic minorities, which are often stereotypically linked to low-status (typically unskilled) work (Orupabo, 2014), thereby contributing to the blurring of ethnic boundaries (Alba et al., 2011). However, questions regarding the underlying mechanisms remain, and further research is needed to explore prestige differences later in the career and to better understand the role of this form of ethnic social stratification within a broader assimilation or integration framework.

Footnotes

Acknowledgements

The author is grateful for comments from the participants in the Social inequalities and population dynamics-seminar at the University of Oslo, as well as participants in the annual seminar of the Norwegian Sociological Association.

ORCID iD

Mats Eirik Lillehagen

Funding

The author disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This work was supported by the Research Council of Norway [Grant 202479 and grant 287016].

Data availability statement

The data used for this study are available from Statistics Norway but strong restrictions apply to gain access to this information. They were used under license for the current study. Transfer of personal data outside Norwegian borders is not allowed according to the Norwegian Statistics Act. For information on how to gain access to Norwegian microdata and formal requirements, see .

A replication package is available at:

Notes

Author biography

Mats Eirik Lillehagen is an associate professor of sociology at OsloMet – Oslo Metropolitan University. He is a former postdoctoral fellow in the department of sociology and human geography at the University of Oslo, where he also completed his PhD. His main research interests include social inequalities, immigration, population dynamics and social theory. His work has been published in journals such as Social Forces, European Sociological Review, Demography and Population Studies.

Appendix

Appendix Table A2b.

Full regression results, education fixed effects models, women.

	No FE	EducationOne Digit	EducationTwo Digits	EducationThree Digits	EducationFour Digits
Norway (Ref.)
Europe	0.870*(0.354)	0.228(0.300)	0.125(0.292)	0.200(0.279)	0.304(0.276)
Africa	−1.125* (0.440)	−0.606(0.380)	−0.607(0.369)	−0.455(0.352)	−0.417(0.349)
Vietnam	1.350*** (0.407)	0.371(0.350)	−0.133(0.340)	−0.0410 (0.325)	−0.0802 (0.322)
Turkey	−1.345** (0.469)	0.543(0.435)	0.259(0.422)	0.349(0.403)	0.368(0.400)
India	5.221*** (0.646)	1.604** (0.543)	0.328(0.528)	−0.345(0.504)	0.170(0.500)
Pakistan	1.131*** (0.283)	1.480** (0.249)	0.754** (0.242)	0.634** (0.232)	0.789*** (0.230)
Constant	45.17***	37.91***	37.57***	37.73***	37.83***
	(0.114)	(0.0999)	(0.0975)	(0.0933)	(0.0926)
Observations	214,253	214,253	214,253	214,253	214,253

Notes: Standard errors in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001. All models include controls for year of birth and the year of employment in addition to the specified educational fixed effects. Models 2 to 5 include parental education.

Source: Author's calculations based on registry data.

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