Only the Tip of the Iceberg: Occupational Prestige Inequalities for First-Generation Academics in Germany

Only the Tip of the Iceberg: Occupational Prestige Inequalities for First-Generation Academics in Germany | 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 Only the Tip of the Iceberg: Occupational Prestige Inequalities for First-Generation Academics in Germany Christine Sälzer, Matthias Roth, Raphael Heiberger This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9368706/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 This study examines whether higher education equalizes labor-market outcomes for graduates from different social backgrounds, focusing on first-generation academics (FGAs) in Germany. Using nationally representative data from PIAAC Cycle 2, we analyze occupational prestige differences between FGAs and continuing-generation academics (CGAs) and test a set of theoretically grounded mechanisms that may reproduce or mitigate social-origin inequalities after graduation. Descriptive analyses reveal a substantial FGA disadvantage in occupational prestige despite equivalent tertiary degrees. Linear regression models show that this prestige gap remains robust when accounting for sociodemographic characteristics traditionally associated with labor-market disparities. Indicators of work–skill match partly attenuate the FGA penalty, suggesting that FGAs are more often allocated to positions in which their skills are underutilized, yet mismatches do not fully explain the observed gap. In contrast, job satisfaction significantly moderates the association between social origin and occupational prestige: the FGA disadvantage is considerably larger among individuals with low job satisfaction and diminishes at higher satisfaction levels. Taken together, the findings indicate that tertiary education in Germany does not fully eliminate inequalities related to social origin. Even among graduates with otherwise comparable credentials, FGAs face persistent structural and psychosocial barriers that limit the translation of educational attainment into occupational prestige. first generation academics PIAAC occupational prestige Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Whether and to what extent tertiary education functions as a major social equalizer or rather as an additional mechanism of selection has been widely examined in empirical educational and social research. The findings suggest that both dynamics are at play—albeit with certain limitations and contextual nuances. While the notion of the “great equalizer” (Bernardi & Ballarino, 2016) implies that higher education graduates enjoy equal conditions and opportunities in the labor market regardless of their socio-economic background, a substantial body of research indicates that this promise does not hold true for all graduates to the same extent (Fiel, 2020; Fujihara & Ishida, 2024; Karlson, 2019; Kratz & Klein, 2025; Zhou, 2019). While first generation students have received quite some attention (Harackiewicz et al., 2014; Ramos‐Sánchez & Nichols, 2007; Stephens et al., 2014), there is a noticeable lack of empirical evidence concerning first generation academics (FGAs) as graduates in the labor market. Considering the time after graduation from tertiary education, evidence on different careers of FGAs and continuing-generation academics (CGAs) remains comparatively limited and often focus on academia itself (Roscigno et al., 2023). Our study addresses this post-tertiary research gap by examining occupational prestige among FGAs relative to CGAs. Differences in occupational prestige between FGAs and CGAs—namely, after graduation from tertiary education and entry into the labor market—may provide valuable insights into glass ceilings and hidden mechanisms that constrain the role of education as a great equalizer. In our study, we focus on Germany due to the specific characteristics of its education system. Limiting the scope to a single country avoids confounding effects arising from cross-national differences in tertiary education systems, labor market structures, and prestige hierarchies, thereby enabling a more precise examination of within-country mechanisms. At the same time, this focus allows for a contextually grounded interpretation of the findings. The German higher education system differs from that of many other OECD countries in terms of its institutional structure, degree organization, and pathways into the labor market. For instance, undergraduate and graduate programs typically follow a 3+2 year model, and the system comprises both research-oriented universities (Universitäten) and practice-oriented universities of applied sciences (Fachhochschulen) (German Rectors’ Conference, n.d.). These institutional specificities shape graduates’ educational trajectories and transitions into employment, making a single-country analysis both analytically coherent and substantively meaningful. In addition, our study benefits from the unique analytical potential of the data used. By drawing on PIAAC Cycle 2, we combine detailed information on educational attainment and social origin with direct assessments of cognitive skills and rich indicators of labor market outcomes within a nationally representative sample. This allows us to examine the mechanisms linking social origin and occupational prestige beyond educational credentials alone, providing a more comprehensive perspective on post-tertiary inequalities. Our guiding research question therefore is: To what extent does tertiary education compensate for social origin in occupational prestige attainment, and how do job–skill match, job satisfaction, and cognitive skills shape this relationship among German graduates? First-Generation Academics and Labor-Market Outcomes A subgroup that has received comparatively little attention in research on intergenerational mobility in the labor market is the population of so-called first-generation academics (FGA), i.e. graduates whose parents did not attend tertiary education and have therefore no academic background. Whereas early research predominantly focused on the academic outcomes of first-generation students in comparison to continuous generation students, more recent studies have also begun to examine the academic processes that may account for and help explain resulting differences in outcomes, but also group-specific differences in the pathways of their study (Ives & Castillo-Montoya, 2020; Jenkins et al., 2013; Jury et al., 2015; López et al., 2023; Reinhold et al., 2022; Wright et al., 2023). For example, Wright and colleagues (2023) find that first generation students are more likely to choose majors that are considered occupationally specific and applied than continuing-generation students. In addition, Jury et al. (2015) demonstrated that first generation students, compared to their continuous generation counterparts, are significantly more concerned with avoiding underperformance relative to other students. They tend to worry about failure and strive to prevent mistakes, whereas continuous generation students are more strongly motivated by the desire to develop their competence and to demonstrate high ability. In line with Elliot and Harackiewicz’s Achievement Goal Theory (Elliot & Harackiewicz, 1996; Harackiewicz et al., 2002), the two groups thus differ substantially in their dominant motivational orientations. In addition, numerous studies have consistently demonstrated that first generation students are less likely than their continuous generation peers to complete their intended degree; their graduation rates are systematically lower (Weisen et al., 2024). The evidence currently available on differences in labor market outcomes points in a similar direction as for students and indicates that FGAs are disadvantaged in several respects when compared to CGAs (Furlong & Cartmel, 2005; Manzoni & Streib, 2019). Most studies conclude that FGAs experience systematic disadvantages in the labor market compared to CGAs. For example, FGAs face disadvantages because they are less able to leverage cultural, social, and identity capital and tend to engage less in competitive career self-management than CGAs (Schepper et al., 2024). Even after controlling for academic achievement, FGAs remain markedly underrepresented in prestigious occupational fields (Barsegyan & Maas, 2024). This persistent inequality cannot be explained by differences in cognitive ability, but rather by structural and cultural barriers, such as limited access to information channels and a lack of cultural and social capital that facilitate educational and career advancement (Ma & Shea, 2019; Engle, 2007). Together, these findings underscore that social background continues to exert influence well beyond initial access to tertiary education, shaping both educational trajectories and subsequent occupational opportunities. However, existing evidence on labor-market outcomes is not entirely consistent. While a substantial body of research points to persistent disadvantages for FGAs, other studies suggest that tertiary education may attenuate or even offset these differences under certain conditions. This indicates that the extent to which higher education compensates for social origin in the labor market remains an open empirical question. For example, Ford (2018) finds that, despite gaps in mathematical literacy, first-generation graduates may attain labor market positions comparable to those of continuing-generation graduates. This suggests that differences in cognitive skills do not necessarily translate into unequal labor-market outcomes. It therefore remains to be examined in greater depth and at scale how the systematic differences identified between FGAs and CGAs extend beyond educational institutions into the labor market, and through which mechanisms these disparities are reproduced or mitigated. The present study Building on prior research, the present study addresses the inconclusive evidence on whether and how tertiary education equalizes labor-market outcomes for first-generation academics. Specifically, we examine how structural job allocation, subjective job evaluation, and cognitive skills jointly shape occupational prestige among tertiary graduates. By focusing on the translation of educational attainment into occupational positioning, the study moves beyond access-based perspectives on social inequality and investigates the mechanisms through which social origin continues to matter after graduation. Drawing on nationally representative PIAAC Cycle 2 data, we are able to integrate information on social origin, educational attainment, cognitive skills, and detailed labor-market outcomes within a single analytical framework. From the perspective of labor-market assignment, occupational prestige reflects how educational credentials and skills are translated into positions within the occupational hierarchy (Becker, 1964; Sattinger, 1993). Because FGAs lack inherited social and cultural resources that facilitate navigation of professional career pathways (Rivera, 2012; Schepper et al., 2024), they face a higher risk of being allocated to positions below their formal qualification level. Overqualification thus represents a structural mechanism through which social origin continues to shape occupational outcomes even after tertiary completion. At the same time, labor-market trajectories are influenced by individuals’ subjective evaluations of their occupational situation (Judge & Kammeyer-Mueller, 2012). Job satisfaction captures perceived quality of work, career prospects, and perceived fit in professional environments. Lower job satisfaction is associated with weaker career persistence and slower advancement (Judge et al., 2001), which are reflected in lower occupational prestige. Given differences in career expectations and access to career-relevant information, first-generation academics may experience lower job satisfaction, with consequences for occupational attainment. Beyond educational degrees, cognitive skills represent an independent source of labor-market advantage. Higher literacy and numeracy proficiency are associated with greater productivity and improved access to complex and high-prestige occupations (Ford, 2018). However, previous research suggests that social-origin differences may persist even at comparable skill levels, as signaling processes, network access, and opportunity structures condition how skills are rewarded in the labor-market. Bringing these mechanisms together, we formulate the following hypotheses: Hypothesis 1 . FGAs hold jobs with lower occupational prestige than CGA (ceteris paribus). Hypothesis 2 . FGAs who report overqualification have jobs with lower occupational prestige than overqualified CGAs. Hypothesis 3 . FGAs who are dissatisfied with their jobs have lower occupational prestige than dissatisfied CGAs. Hypothesis 4a . Among individuals with high cognitive skills, FGAs report lower job satisfaction and hold jobs with lower occupational prestige than CGAs. Hypothesis 4b . Among individuals with high cognitive skills, FGAs are more likely to be overqualified and hold jobs with lower occupational prestige than CGAs. Taken together, these hypotheses examine whether structural allocation mechanisms (overqualification), subjective occupational evaluation (job satisfaction), and cognitive skills constitute distinct and potentially interacting channels through which social origin continues to shape occupational prestige after tertiary education. Analytical Strategy By centering attention on the translation of tertiary credentials into occupational prestige, our study advances the conversation from “who gets in” to “who gets ahead”, and under which conditions social origin continues to matter even after higher education has been successfully completed. To examine the mechanisms outlined above, we estimate a series of regression models that are structured in theoretically motivated blocks corresponding to the hypotheses. This stepwise modeling strategy allows us to assess both baseline differences between first-generation academics (FGAs) and continuing-generation academics (CGAs) and the extent to which these differences are shaped by structural, subjective, and skill-related factors. The baseline model (M1) tests Hypothesis 1 by estimating the association between FGA status and occupational prestige, controlling for key sociodemographic characteristics, including gender, migration background, parental employment at age 14, and cultural capital (number of books at home). These variables represent established axes of inequality that are known to shape educational and occupational trajectories and to co-occur with social origin (Engle, 2007; Ma & Shea, 2019; Schepper et al., 2024). In Model 2, we introduce perceived job–skill match to test Hypothesis 2, focusing on structural allocation processes. Drawing on human capital and assignment theories (Becker, 1964; Sattinger, 1993; McGuinness, 2006), we examine whether the relationship between social origin and occupational prestige varies depending on whether individuals are adequately matched, overqualified, or underqualified. This is implemented via interaction terms between FGA status and job-skill match. Model 3 incorporates job satisfaction to test Hypothesis 3, capturing subjective evaluations of occupational situations. We assess whether the association between job satisfaction and occupational prestige differs between FGAs and CGAs by including an interaction term between FGA status and job satisfaction. Finally, Model 4 introduces cognitive skills (numeracy and literacy) to examine Hypotheses 4a and 4b. Cognitive skills represent a distinct source of labor-market advantage that may attenuate or reinforce social-origin inequalities (Ford, 2018). To test whether the effects of job–skill mismatch and job satisfaction vary by skill level, we estimate three-way interaction terms between FGA status, cognitive skills, and (a) job–skill match and (b) job satisfaction. This allows us to assess whether higher cognitive skills mitigate or reproduce differences in occupational prestige between FGAs and CGAs. Methods Data We focus on Germany using the Programme for the International Assessment of Adult Competencies (PIAAC) Cycle 2 Scientific Use File (SUF) (Zabal et al., 2025), which provides a probability-based sample of the 16 to 65 year old inhabitants of Germany, complete with internationally harmonized measures that are well suited to large-scale, cross-sectional analyses of adult skills and labor-market outcomes.We restrict the analytical sample to individuals with tertiary education who have completed their studies and are active in the labor market excluding individuals currently enrolled in education. We operationalize occupational prestige using the Standard International Occupational Prestige Scale (SIOPS) available in the Scientific Use File and cross-validate our results with the International Socio-Economic Index of Occupational Status (ISEI). This allows us to test whether any first-generation penalty is robust across conceptually related status indicators. Analysis We analyze the SUF following recommended practice by the OECD (OECD, 2025), using replicate weights to account for the sampling design. Analysis involving cognitive skills as measured in PIAAC Cycle 2 were conducted using plausible values in accordance with OECD guidelines. We estimate multiple regression models to analyze the impact of FGA status on SIOPS. Marginal means and predicted probabilities were estimated using the models reported in the results or appendix. In the result section we report analysis using numeracy as a proxy of cognitive skill. As a robustness check we report the same analyses using literacy in the appendix. Missing values For analyses concerning CGA/FGA, we removed 50 respondents for which FGA status could not be determined. For regression models we used casewise deletion and report the number of observations together with the model parameters. Reproducibility All analyses were conducted in R. Replication materials and software packages used are available at https://osf.io/fdevn/overview?view_only=e94ca5877415413cbf2096914f8ef092. Results The results are presented in line with the hypotheses outlined above. We begin by reporting descriptive statistics to provide an overview of the sample and the distribution of occupational prestige and key explanatory variables. Descriptives Table 1 presents descriptive statistics for the German PIAAC Cycle 2 sample. Academics constitute 33.5% of the sample (n = 1,609), of whom 1,000 are first-generation academics (FGAs) and 559 are continuing-generation academics (CGAs). Individuals still enrolled in higher education were excluded from the analyses. Gender is evenly distributed across both groups. Among FGAs, 85.9% were born in Germany, compared to 79.6% among CGAs. Table 1 Descriptives Variable German PIAAC Sample Academics* First-Generation Academics* Continuing-Generation Academics* Total N 4793 1609 1000 559 Male 2356 (49.2%) 812 (50.5%) 509 (50.9%) 276 (49.4%) Female 2457 (50.8%) 797 (49.5%) 491 (49.1%) 283 (50.6%) Born in Germany 3868 (80.1%) 1345 (83.6%) 859 (85.9%) 445 (79.6%) Not born in Germany 807 (19.9%) 264 (16.4%) 141 (14.1%) 114 (20.4%) *Respondents in education are excluded (n = 819 = 692 + 127 missing values). All counts are weighted. First, we examined to what extent FGAs and CGAs differ in occupational prestige in Germany descriptively. Figure 1 reveals a clear and substantial gap in occupational prestige between FGAs and CGAs. On average, FGAs attain significantly lower occupational prestige than CGAs, despite holding equivalent tertiary degrees. On average, FGAs score approximately 6 prestige points lower than CGAs (weighted means: FGA = 50; CGA = 56), corresponding to roughly one third of one standard deviation of the prestige scale. The distribution further indicates that the FGA disadvantage is not driven by outliers, but reflects a consistent shift in the overall prestige distribution toward lower-ranked occupations. CGAs are more likely to be found in occupations situated in the upper segment of the prestige scale, whereas FGAs are overrepresented in mid-prestige and lower-prestige occupational categories. Table 2 presents the results of the regression models predicting occupational prestige (SIOPS), estimated in a stepwise manner corresponding to Hypotheses 1–4. Model 1 tests whether FGA differ from CGA in occupational prestige, net of known confounders. In line with H1, FGAs exhibit significantly lower occupational prestige than CGAs. The coefficient of –4.24 (SE = 1.09) indicates a substantial and statistically meaningful prestige penalty associated with being the first academic in a family. In substantive terms, this corresponds to approximately one third of the average prestige return associated with holding a tertiary degree, highlighting that the first-generation penalty offsets a considerable share of the educational advantage. This gap persists despite the strong and positive association between holding higher education and occupational prestige (b = 13.09, SE = .95), suggesting that tertiary credentials alone do not fully compensate for differences in social origin . The inclusion of parental higher education as a separate control also does not attenuate the FGA penalty, underscoring that the observed disadvantage is not reducible to simple parental educational differences but reflects a distinct first-generation effect. Table 2 Regressing SIOPS on FGA status and covariates Model 1 Model 2 Model 3 Model 4a Model 4b Intercept Intercept 19.46 (2.29) 19.37 (2.26) 19.46 (2.27) 20.65 (2.60) 19.90 (2.39) Education (Ref. cat.: Continuing academic & no higher education & parent no higher education) FGA -4.24 (1.09) -3.29 (1.20) -3.98 (1.08) -5.05 (5.45) -7.53 (4.49) Higher Education 13.09 (.95) 13.16 (.95) 13.17 (.95) 13.31 (.95) 13.23 (.95) Parent(s) have higher education -.24 (.83) -.25 (.83) -.27 (.83) -.23 (.83) -.22 (.83) FGA X Overqualified -2.56 (1.27) -4.90 (9.37) FGA X Underqualified -1.52 (1.66) -15.69 (13.15) FGA X Job dissatisfaction -6.87 (2.24) -2.79 (17.25) FGA X Numeracy .01 (.02) .01 (.01) FGA X Overqualified X Numeracy .01 (.03) FGA X Underqualified X Numeracy .05 (.04) FGA X Job dissatisfaction X Numeracy -.01 (.06) Age (Ref. cat.: 16-24) 25-34 .29 (1.14) .24 (1.13) .27 (1.14) .17 (1.13) .22 (1.15) 35-44 .83 (1.08) .85 (1.08) .79 (1.08) .80 (1.08) .75 (1.09) 45-54 .95 (1.03) .95 (1.03) .91 (1.03) .90 (1.05) .86 (1.05) 55+ 2.19 (1.16) 2.19 (1.16) 2.19 (1.16) 2.14 (1.15) 2.17 (1.16) Gender (Ref. cat.: Male) Female .07 (.45) .04 (.45) .02 (.45) .05 (.45) .06 (.45) Migration (Ref. cat: Native language, native born, both native language and native born) Foreign Language 2.41 (1.81) 2.34 (1.85) 2.58 (1.73) 2.45 (1.83) 2.65 (1.71) Foreign Born .33 (1.21) .32 (1.21) .33 (1.20) .32 (1.22) .34 (1.20) Foreign Language X Foreign Born -4.02 (2.40) -4.00 (2.43) -4.15 (2.32) -4.11 (2.43) -4.25 (2.28) Parents (Ref. cat.: Mother with job, Father with job) Mother no job .59 (.42) .64 (.41) .58 (.42) .63 (.41) .56 (.41) Father no job -.10 (.88) -.13 (.88) -.15 (.88) -.09 (.89) -.09 (.88) Books (Ref. cat.: 10 Books or less) 11 to 25 Books 1.20 (.79) 1.15 (.80) 1.21 (.79) 1.22 (.79) 1.26 (.79) 26 to 100 Books 2.61 (.81) 2.54 (.83) 2.71 (.81) 2.59 (.83) 2.75 (.81) 101 to 200 Books 2.85 (.90) 2.82 (.91) 2.95 (.89) 2.86 (.90) 2.98 (.89) 201 to 500 Books 3.95 (.89) 3.90 (.90) 4.05 (.89) 3.97 (.89) 4.10 (.88) More than 500 Books 4.64 (1.05) 4.52 (1.07) 4.77 (1.05) 4.63 (1.06) 4.82 (1.05) Perceived job match (Ref. cat.: Adequate job match) Overqualified -2.28 (.48) -1.56 (.55) -2.26 (.48) -3.76 (3.06) -2.23 (.48) Underqualified .76 (.60) 1.18 (.73) .78 (.59) 1.72 (4.28) .79 (.59) Overqualified X Numeracy .01 (.01) Underqualified X Numeracy -.00 (.01) Job satisfaction (Ref. cat.: Satisfied) Dissatisfied -.26 (1.24) -.19 (1.24) 2.12 (1.22) -.13 (1.24) 7.77 (7.89) Dissatisfied X Numeracy -.02 (.03) Cognitive Skills Numeracy .06 (.01) .06 (.01) .06 (.01) .06 (.01) .06 (.01) R squared R squared .35 (.02) .35 (.02) .35 (.02) .35 (.02) .35 (.02) N = 2999 Turning to Model 2, we test the interaction between perceived job–skill match and FGA status. The interaction term between FGA and overqualification is negative and statistically significant (b = –2.56, SE = 1.27). This indicates that the prestige penalty of overqualification is considerably larger for FGAs than for CGAs, what is also clearly visible in Figure 2 reporting the average marginal effects (AME) of the interaction. FGAs who report that their skills exceed job requirements occupy particularly low-prestige positions relative to adequately matched CGAs. Predicted margins indicate that overqualified FGAs experience a total prestige penalty of approximately –7.41 points compared to adequately matched CGAs, whereas the corresponding penalty among CGAs is only –1.56 points. Thus, overqualification multiplies the first-generation penalty, more than quadrupling the prestige loss observed among continuing-generation graduates. The interaction between FGA and underqualification, however, is negative but not statistically robust. These findings support H2: structural misallocation in the form of overqualification disproportionately disadvantages FGAs in the translation of educational credentials into occupational prestige. The results suggest that job–skill mismatch is one important mechanism through which social-origin inequalities persist after tertiary completion. Figures 2 and 3 present predicted marginal means of occupational prestige derived from the regression models, with 95% confidence intervals. Model 3 incorporates job satisfaction and its interaction with FGA status. While job dissatisfaction alone is not significantly associated with prestige in the baseline category, the interaction between FGA and job dissatisfaction is negative and statistically significant (b = –6.87, SE = 2.24; see also Figure 3 for AME). Predicted values indicate that dissatisfied FGAs experience a total prestige penalty of –8.73 points compared to satisfied CGAs. In contrast, dissatisfied CGAs do not exhibit a comparable prestige loss (b = 2.12, n.s.). Among FGAs, dissatisfaction thus more than doubles the baseline first-generation penalty (–3.98 vs. –8.73). This finding indicates that the FGA disadvantage in occupational prestige is amplified among individuals who are dissatisfied with their jobs. Among satisfied workers, the FGA–CGA gap is smaller; among dissatisfied workers, it widens considerably — while prestige for dissatisfied CGA workers is close to 50 (and even a bit higher than for CGA who are satisfied with their jobs), FGAs’ occupations rank on only at 39 (all else fixed at adequate statistical means). These results support H3 and point to the importance of subjective job evaluations. They suggest that psychosocial dimensions of work are closely intertwined with structural allocation processes: FGAs who perceive their occupational situation negatively are particularly likely to be located in low-prestige positions (and again, among the peer-group of academics). Finally, we turn to H4a and H4b by including three-way interaction terms (numeracy X FGA X overqualified / dissatisfied). While numeracy is positively and significantly associated with occupational prestige (b ≈ .06, SE = .01) across all models, the interaction between FGA and numeracy is not statistically significant, nor are the three-way interactions. Substantively, the estimated difference in occupational prestige between FGAs and CGAs at a given level of numeracy is captured by the coefficient for FGAs plus the interaction term with numeracy. Even at higher proficiency levels, predicted prestige differences remain negative for FGAs. For instance, at an above-average numeracy level, the first-generation penalty is reduced only marginally and does not disappear. The non-significant interaction terms indicate that cognitive skills increase occupational prestige similarly for both groups rather than compensating for social-origin disadvantage. Moreover, the comparatively large standard errors of the higher-order interaction terms suggest limited statistical power for detecting complex moderation effects. However, the absence of even weak directional patterns provides little evidence that cognitive skills substantially buffer the first-generation penalty. This indicates that higher cognitive skills do not eliminate the prestige gap between FGAs and CGAs, nor do they substantially alter the differential penalties associated with overqualification or dissatisfaction. Accordingly, the results provide only limited support for H4a and H4b. While cognitive skills are positively rewarded in the labor market, they do not meaningfully moderate the association between social origin and occupational prestige. To further examine whether FGAs differ systematically in their likelihood of experiencing job–skill mismatch, we estimated a multinomial regression model with “adequate job match” as the reference category. The predicted probabilities indicate that FGAs are significantly more likely than CGAs to report being overqualified, net of controls. The effect for underqualification is positive but smaller and less precisely estimated (Figure 4). These findings complement the linear regression results by demonstrating that FGAs are not only more strongly penalized when overqualified but are also more likely to experience overqualification in the first place (Figure 4). In contrast, higher education as such reduces the probability of overqualification (b = –.45, SE = .18), highlighting again that the disadvantage is specific to first-generation status rather than to tertiary education per se. Discussion The present study set out to examine whether tertiary education equalizes occupational prestige outcomes between first-generation academics (FGAs) and continuing-generation academics (CGAs) in Germany. The findings based on nationally representative data provide clear evidence that this is not the case. Even after controlling for sociodemographic background characteristics traditionally associated with labor-market inequalities, FGAs exhibit a substantial and statistically robust prestige penalty. This result qualifies the “great equalizer” thesis (Bernardi & Ballarino, 2016 ) in an important way. While tertiary education strongly increases occupational prestige overall, it does not fully compensate for differences in social origin. The persistent FGA disadvantage suggests that educational credentials alone are insufficient to guarantee equal labor-market positioning. In this sense, degrees may indeed represent only the visible part of a deeper stratification process. The second major finding concerns the role of job–skill mismatch. Overqualification is negatively associated with occupational prestige for all graduates, but this penalty is significantly larger for FGAs. Moreover, complementary analyses show that FGAs are more likely to experience overqualification in the first place. These findings align closely with assignment theory (Sattinger, 1993 ) and research on cultural matching in hiring processes (Rivera, 2012 ). They suggest that labor-market allocation mechanisms operate in socially differentiated ways. Even when FGAs possess tertiary credentials and comparable skill levels, they appear less successful in translating these resources into positions commensurate with their qualifications. Importantly, overqualification does not simply reflect individual skill deficits. Rather, it may signal differential access to information channels, professional networks, and informal career guidance—resources that are more readily available to CGAs. Structural misallocation thus emerges as a central mechanism through which social-origin inequalities persist beyond graduation. Another striking finding concerns job satisfaction. While dissatisfaction is not systematically associated with lower prestige among CGAs, it dramatically amplifies the prestige penalty among FGAs. Dissatisfied FGAs occupy substantially lower-prestige positions than both satisfied FGAs and dissatisfied CGAs. This pattern suggests that psychosocial dimensions of occupational experience are closely intertwined with structural inequalities. The findings resonate with research on belonging uncertainty (Walton & Cohen, 2007 ), impostor feelings (Clance & Imes, 1978 ; Feenstra et al., 2020 ), and identity-based stress among first-generation individuals (Cokley et al., 2013 ). FGAs may interpret workplace challenges differently, perceive fewer advancement opportunities, or experience weaker institutional support, which in turn may influence career mobility. Rather than being a mere correlate of low prestige, job dissatisfaction appears to interact systematically with social origin. This interaction points toward a cumulative disadvantage process in which structural positioning and subjective occupational evaluation reinforce one another. Contrary to expectations derived from human capital theory (Becker, 1964 ), higher cognitive skills do not eliminate the prestige gap between FGAs and CGAs. Numeracy proficiency is positively associated with occupational prestige overall, but it does not significantly moderate the first-generation penalty. Even at higher proficiency levels, FGAs do not converge toward the prestige levels of CGAs. These findings provide only limited support for H4. While skills are clearly rewarded in the labor market, their returns appear to be socially conditioned. This pattern is consistent with research suggesting that signaling processes, social capital, and institutional gatekeeping shape how competencies are recognized and rewarded (Rivera, 2012 ; Schepper et al., 2024 ). The absence of a compensatory effect of skills underscores that social-origin inequalities cannot be fully reduced to differences in measurable competencies. Structural and relational mechanisms appear to play an independent role. Taken together, the results shift the analytical focus from access to higher education toward the translation of educational attainment into occupational positioning. While much prior research has examined whether first-generation students enter and complete tertiary education, our findings demonstrate that inequalities persist even after successful graduation. Three interrelated mechanisms emerge: A baseline structural disadvantage (H1), differential exposure to overqualification (H2) and psychosocial amplification via dissatisfaction (H3). In contrast, cognitive skills function as a general resource but not as an equalizing force (H4). This constellation suggests that the persistence of inequality operates less through skill deficits and more through socially structured allocation processes and differential returns to similar credentials. Limitations Several limitations warrant consideration. First, the cross-sectional design does not permit causal inference. The observed associations should therefore be interpreted as correlational rather than causal, and longitudinal data would be required to examine career dynamics and mobility trajectories over time. Second, key explanatory variables such as job satisfaction and perceived job-skill match rely on self-reports and may reflect subjective evaluations rather than objective working conditions. While these perceptions are theoretically meaningful, they may introduce reporting bias and limit comparability across individuals. Third, the operationalization of first-generation status is based on parental educational attainment and does not capture the full range of social-origin differences, such as variations in cultural, social, or economic capital. As a result, heterogeneity within the group of first-generation academics may remain unobserved. Fourth, although PIAAC provides high-quality measures of cognitive skills, the use of plausible values introduces additional complexity and uncertainty in estimation, and results depend on appropriate handling of these measures. Moreover, occupational prestige (SIOPS) captures the relative social standing of occupations but does not directly reflect income, job quality, or career progression, which may follow different patterns. Fifth, the higher-order interaction models involve relatively small subgroups, limiting statistical power for detecting complex moderation effects, particularly in three-way interactions. Despite these limitations, the study benefits from nationally representative data, direct assessments of cognitive skills, and the integration of structural and subjective mechanisms within a unified analytical framework, allowing for a comprehensive examination of how social origin shapes labor-market outcomes after tertiary education. Finally, although the analyses are restricted to Germany, the PIAAC data offer the potential for cross-national extensions. However, such comparisons require careful harmonization of tertiary education categories, as the German system with its distinction between research-oriented universities and universities of applied sciences differs from many other countries. Ensuring comparability of tertiary degrees across contexts is therefore a key challenge for future research. Conclusions The findings carry important implications for both research and policy. Our results show that, even after successful completion of tertiary education, first-generation academics continue to be disadvantaged in terms of occupational prestige. This disadvantage is not primarily explained by differences in cognitive skills, but is instead associated with structural allocation processes and subjective evaluations of occupational situations. In particular, overqualification and lower job satisfaction emerge as relevant channels through which social origin continues to shape labor-market outcomes. For research, these findings highlight the need to move beyond access-based models of educational inequality and to more closely examine post-tertiary labor-market processes. Future studies should further investigate how institutional arrangements, recruitment practices, and signaling mechanisms shape the translation of educational credentials into occupational positions. In addition, extending this line of research to cross-national contexts using large-scale assessment data such as PIAAC would provide valuable insights, provided that tertiary education is operationalized in a comparable way across countries. For policy, the results suggest that widening participation in higher education is a necessary but not sufficient condition for promoting social mobility. Policies should therefore not only focus on access to tertiary education, but also address inequalities in the transition from education to employment. Measures such as targeted career guidance, transparent recruitment procedures, and mentoring structures may help to ensure that tertiary credentials translate into comparable occupational opportunities for graduates from different social backgrounds. Taken together, the findings underline that higher education does not fully neutralize social-origin inequalities, but rather shifts them to later stages of the life course, where they operate through more subtle structural and institutional mechanisms. Abbreviations FGAs First-generation academics CGAs Continuing-generation academics Declarations Availability of data and materials The datasets analyzed during the current study are available from the Programme for the International Assessment of Adult Competencies (PIAAC), conducted by the OECD. The German Scientific Use File can be obtained via GESIS. Acknowledgements This study uses data from the Programme for the International Assessment of Adult Competencies (PIAAC), conducted by the OECD. We thank GESIS for providing access to the German Scientific Use File. Author contribution statements Removed for anonymization, will be inserted in the published manuscript. Author information Removed for anonymization, will be inserted in the published manuscript. Competing interests The authors declare that they have no competing interests. Funding Not applicable. Ethics declaration Not applicable Author Contribution Author 1 conceptualized the study and led the writing of the manuscript. Author 2 conducted the data analysis. Author 1 and Author 3 contributed to the study design and all authors contributed to the interpretation of the results. All authors reviewed and approved the final manuscript. References Barsegyan, V., & Maas, I. (2024). 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Exploring choices in higher education: Female and male first-generation students’ trajectories from study aspiration to study satisfaction in Germany. Frontiers in Education , 7 , Article 964703. https://doi.org/10.3389/feduc.2022.964703 Rivera, L. A. (2012). Hiring as cultural matching: The case of elite professional service firms. American Sociological Review, 77 (6), 999–1022. https://doi.org/10.1177/0003122412463213 Roscigno, V. J., Lee, E. M., Hurst, A. L., Brady, D., King, C. R., Abraham Jack, A., Delaney, K. J., McDermott, M., Muñoz, J., Johnson, W., Francis, R. D., Warnock, D., & Weigers Vitullo, M. (2023). Mobility and Inequality in the Professoriate: How and Why First-Generation and Working-Class Backgrounds Matter. Socius: Sociological Research for a Dynamic World, 9 , Article 23780231231181859. https://doi.org/10.1177/23780231231181859 Sattinger, M. (1993). Assignment Models of the Distribution of Earnings. Journal of Economic Literature, 31 (2), 831–880. http://www.jstor.org/stable/2728516 Schepper, A. de, Kyndt, E., & Clycq, N. (2024). Developing an understanding of the labor market: the value of social, cultural and identity capital according to first- and continuing-generation graduates. Journal of Education and Work , 37 (1-4), 48–66. https://doi.org/10.1080/13639080.2024.2310267 Stephens, N. M., Hamedani, M. G., & Destin, M. (2014). Closing the social-class achievement gap: a difference-education intervention improves first-generation students' academic performance and all students' college transition. Psychological Science , 25 (4), 943–953. https://doi.org/10.1177/0956797613518349 Vergauwe, J., Wille, B., Feys, M., De Fruyt, F., & Anseel, F. (2015). Fear of being exposed: The trait-relatedness of the impostor phenomenon and its relevance in the work context. Journal of Business and Psychology, 30 (3), 565–581. https://doi.org/10.1007/s10869-014-9382-5 Walton, G. M., & Cohen, G. L. (2007). A question of belonging: Race, social fit, and achievement. Journal of Personality and Social Psychology, 92 (1), 82–96. https://doi.org/10.1037/0022-3514.92.1.82 Weisen, S., Do, T., Peczuh, M. C., Hufnagle, A. S., & Maruyama, G. (2024). How are first‐generation students doing throughout their college years? An examination of academic success, retention, and completion rates. Analyses of Social Issues and Public Policy , 24 (3), 1274–1287. https://doi.org/10.1111/asap.12413 Wright, A. L., Roscigno, V. J., & Quadlin, N. (2023). First-Generation Students, College Majors, and Gendered Pathways. The Sociological Quarterly , 64 (1), 67–90. https://doi.org/10.1080/00380253.2021.1989991 Zhou, X. (2019). Equalization or Selection? Reassessing the “Meritocratic Power” of a College Degree in Intergenerational Income Mobility. American Sociological Review , 84 (3), 459–485. https://doi.org/10.1177/0003122419844992 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9368706","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":637959601,"identity":"21043903-e71f-439c-af55-e51bbc675ca4","order_by":0,"name":"Christine Sälzer","email":"data:image/png;base64,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","orcid":"","institution":"University of Stuttgart","correspondingAuthor":true,"prefix":"","firstName":"Christine","middleName":"","lastName":"Sälzer","suffix":""},{"id":637959602,"identity":"3743522e-0291-489a-906f-5a7ba915bb28","order_by":1,"name":"Matthias Roth","email":"","orcid":"","institution":"Leibniz Institute for the Social Sciences","correspondingAuthor":false,"prefix":"","firstName":"Matthias","middleName":"","lastName":"Roth","suffix":""},{"id":637959605,"identity":"b193ba76-71a8-41ab-9970-454c94d076d1","order_by":2,"name":"Raphael Heiberger","email":"","orcid":"","institution":"University of Stuttgart","correspondingAuthor":false,"prefix":"","firstName":"Raphael","middleName":"","lastName":"Heiberger","suffix":""}],"badges":[],"createdAt":"2026-04-09 12:39:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9368706/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9368706/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109341578,"identity":"7afd19c8-a15f-424d-9370-9726a8df7f35","added_by":"auto","created_at":"2026-05-15 19:05:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":64238,"visible":true,"origin":"","legend":"\u003cp\u003eOccupational Prestige among FGAs and CGAs in Germany\u003c/p\u003e\n\u003cp\u003ePoints show mean SIOPS for CGA (left) and FGA (right). Error bars show standard deviations.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9368706/v1/c1754b8ecaa23dfee10fd3f9.png"},{"id":109405665,"identity":"e3d51d0f-1ad3-4a98-b015-c4edc9026884","added_by":"auto","created_at":"2026-05-17 13:19:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":103388,"visible":true,"origin":"","legend":"\u003cp\u003eMarginal means of SIOPS by perceived job match and FGA status. Based on Model H2 in Table 2. N = 2999. Error bars show 95% CI.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9368706/v1/6284b6d509f99506388a2c10.png"},{"id":109405573,"identity":"862b5f8b-921c-407e-a937-10e430358bc1","added_by":"auto","created_at":"2026-05-17 13:19:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":64432,"visible":true,"origin":"","legend":"\u003cp\u003eMarginal means of SIOPS by perceived job satisfaction and FGA status. Based on Model H3 in Table 2. N = 2999. Error bars show 95% CI.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9368706/v1/e6c58e116cc220c478b10c88.png"},{"id":109341579,"identity":"ff0245ce-a6dd-488b-b67f-bd251adfda02","added_by":"auto","created_at":"2026-05-15 19:05:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":136183,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted probabilities of perceived job match by FGA status („adequate job“ as reference category). Based on the multinominal model predicting perceived skill match reported in Appendix Table 2. N = 3027. Error bars show 95% CI.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9368706/v1/6f169e387789d16ebb85be87.png"},{"id":109406195,"identity":"b7e6f164-a847-4be6-901d-9ea02768fbbe","added_by":"auto","created_at":"2026-05-17 13:26:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":645464,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9368706/v1/b955076b-bb95-4e1e-8f23-b24e77ad2cf8.pdf"},{"id":109341577,"identity":"38a8e000-845a-449c-b992-2d3df0a0a078","added_by":"auto","created_at":"2026-05-15 19:05:21","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":46742,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-9368706/v1/b2ab892885fa531ceaca2c57.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Only the Tip of the Iceberg: Occupational Prestige Inequalities for First-Generation Academics in Germany","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWhether and to what extent tertiary education functions as a major social equalizer or rather as an additional mechanism of selection has been widely examined in empirical educational and social research. The findings suggest that both dynamics are at play—albeit with certain limitations and contextual nuances. While the notion of the “great equalizer” (Bernardi \u0026amp; Ballarino, 2016) implies that higher education graduates enjoy equal conditions and opportunities in the labor market regardless of their socio-economic background, a substantial body of research indicates that this promise does not hold true for all graduates to the same extent (Fiel, 2020; Fujihara \u0026amp; Ishida, 2024; Karlson, 2019; Kratz \u0026amp; Klein, 2025; Zhou, 2019). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile first generation \u003cem\u003estudents\u003c/em\u003e have received quite some attention (Harackiewicz et al., 2014; Ramos‐Sánchez \u0026amp; Nichols, 2007; Stephens et al., 2014), there is a noticeable lack of empirical evidence concerning first generation \u003cem\u003eacademics\u003c/em\u003e (FGAs) as graduates in the labor market. Considering the time \u003cem\u003eafter\u003c/em\u003e graduation from tertiary education, evidence on different careers of FGAs and continuing-generation academics (CGAs) remains comparatively limited and often focus on academia itself (Roscigno et al., 2023). Our study addresses this post-tertiary research gap by examining occupational prestige among FGAs relative to CGAs. Differences in occupational prestige between FGAs and CGAs—namely, after graduation from tertiary education and entry into the labor market—may provide valuable insights into glass ceilings and hidden mechanisms that constrain the role of education as a great equalizer. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn our study, we focus on Germany due to the specific characteristics of its education system. Limiting the scope to a single country avoids confounding effects arising from cross-national differences in tertiary education systems, labor market structures, and prestige hierarchies, thereby enabling a more precise examination of within-country mechanisms. At the same time, this focus allows for a contextually grounded interpretation of the findings. The German higher education system differs from that of many other OECD countries in terms of its institutional structure, degree organization, and pathways into the labor market. For instance, undergraduate and graduate programs typically follow a 3+2 year model, and the system comprises both research-oriented universities (Universitäten) and practice-oriented universities of applied sciences (Fachhochschulen) (German Rectors’ Conference, n.d.). These institutional specificities shape graduates’ educational trajectories and transitions into employment, making a single-country analysis both analytically coherent and substantively meaningful.\u003c/p\u003e\n\u003cp\u003eIn addition, our study benefits from the unique analytical potential of the data used. By drawing on PIAAC Cycle 2, we combine detailed information on educational attainment and social origin with direct assessments of cognitive skills and rich indicators of labor market outcomes within a nationally representative sample. This allows us to examine the mechanisms linking social origin and occupational prestige beyond educational credentials alone, providing a more comprehensive perspective on post-tertiary inequalities. Our guiding research question therefore is: To what extent does tertiary education compensate for social origin in occupational prestige attainment, and how do job–skill match, job satisfaction, and cognitive skills shape this relationship among German graduates?\u003c/p\u003e\n\u003ch1\u003eFirst-Generation Academics and Labor-Market Outcomes\u003c/h1\u003e\n\u003cp\u003eA subgroup that has received comparatively little attention in research on intergenerational mobility in the labor market is the population of so-called first-generation academics (FGA), i.e. graduates whose parents did not attend tertiary education and have therefore no academic background. Whereas early research predominantly focused on the academic outcomes of first-generation \u003cem\u003estudents\u003c/em\u003e in comparison to continuous generation students, more recent studies have also begun to examine the academic processes that may account for and help explain resulting differences in outcomes, but also group-specific differences in the pathways of their study (Ives \u0026amp; Castillo-Montoya, 2020; Jenkins et al., 2013; Jury et al., 2015; López et al., 2023; Reinhold et al., 2022; Wright et al., 2023). For example, Wright and colleagues (2023) find that first generation students are more likely to choose majors that are considered occupationally specific and applied than continuing-generation students. In addition, Jury et al. (2015) demonstrated that first generation students, compared to their continuous generation counterparts, are significantly more concerned with avoiding underperformance relative to other students. They tend to worry about failure and strive to prevent mistakes, whereas continuous generation students are more strongly motivated by the desire to develop their competence and to demonstrate high ability. In line with Elliot and Harackiewicz’s Achievement Goal Theory (Elliot \u0026amp; Harackiewicz, 1996; Harackiewicz et al., 2002), the two groups thus differ substantially in their dominant motivational orientations. In addition, numerous studies have consistently demonstrated that first generation students are less likely than their continuous generation peers to complete their intended degree; their graduation rates are systematically lower (Weisen et al., 2024).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe evidence currently available on differences in labor market outcomes points in a similar direction as for students and indicates that FGAs are disadvantaged in several respects when compared to CGAs (Furlong \u0026amp; Cartmel, 2005; Manzoni \u0026amp; Streib, 2019). Most studies conclude that FGAs experience systematic disadvantages in the labor market compared to CGAs. For example, FGAs face disadvantages because they are less able to leverage cultural, social, and identity capital and tend to engage less in competitive career self-management than CGAs (Schepper et al., 2024). Even after controlling for academic achievement, FGAs remain markedly underrepresented in prestigious occupational fields (Barsegyan \u0026amp; Maas, 2024). This persistent inequality cannot be explained by differences in cognitive ability, but rather by structural and cultural barriers, such as limited access to information channels and a lack of cultural and social capital that facilitate educational and career advancement (Ma \u0026amp; Shea, 2019; Engle, 2007). Together, these findings underscore that social background continues to exert influence well beyond initial access to tertiary education, shaping both educational trajectories and subsequent occupational opportunities.\u003c/p\u003e\n\u003cp\u003eHowever, existing evidence on labor-market outcomes is not entirely consistent. While a substantial body of research points to persistent disadvantages for FGAs, other studies suggest that tertiary education may attenuate or even offset these differences under certain conditions. This indicates that the extent to which higher education compensates for social origin in the labor market remains an open empirical question. For example, Ford (2018) finds that, despite gaps in mathematical literacy, first-generation graduates may attain labor market positions comparable to those of continuing-generation graduates. This suggests that differences in cognitive skills do not necessarily translate into unequal labor-market outcomes. It therefore remains to be examined in greater depth and at scale how the systematic differences identified between FGAs and CGAs extend beyond educational institutions into the labor market, and through which mechanisms these disparities are reproduced or mitigated.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch1\u003eThe present study\u003c/h1\u003e\n\u003cp\u003eBuilding on prior research, the present study addresses the inconclusive evidence on whether and how tertiary education equalizes labor-market outcomes for first-generation academics. Specifically, we examine how structural job allocation, subjective job evaluation, and cognitive skills jointly shape occupational prestige among tertiary graduates.\u003c/p\u003e\n\u003cp\u003eBy focusing on the translation of educational attainment into occupational positioning, the study moves beyond access-based perspectives on social inequality and investigates the mechanisms through which social origin continues to matter after graduation. Drawing on nationally representative PIAAC Cycle 2 data, we are able to integrate information on social origin, educational attainment, cognitive skills, and detailed labor-market outcomes within a single analytical framework.\u003c/p\u003e\n\u003cp\u003eFrom the perspective of labor-market assignment, occupational prestige reflects how educational credentials and skills are translated into positions within the occupational hierarchy (Becker, 1964; Sattinger, 1993). Because FGAs lack inherited social and cultural resources that facilitate navigation of professional career pathways (Rivera, 2012; Schepper et al., 2024), they face a higher risk of being allocated to positions below their formal qualification level. \u003cem\u003eOverqualification\u003c/em\u003e thus represents a structural mechanism through which social origin continues to shape occupational outcomes even after tertiary completion.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt the same time, labor-market trajectories are influenced by individuals’ subjective evaluations of their occupational situation (Judge \u0026amp; Kammeyer-Mueller, 2012). \u003cem\u003eJob satisfaction\u0026nbsp;\u003c/em\u003ecaptures perceived quality of work, career prospects, and perceived fit in professional environments. Lower job satisfaction is associated with weaker career persistence and slower advancement (Judge et al., 2001), which are reflected in lower occupational prestige. Given differences in career expectations and access to career-relevant information, first-generation academics may experience lower job satisfaction, with consequences for occupational attainment.\u003c/p\u003e\n\u003cp\u003eBeyond educational degrees, cognitive skills represent an independent source of labor-market advantage. Higher literacy and numeracy proficiency are associated with greater productivity and improved access to complex and high-prestige occupations (Ford, 2018). However, previous research suggests that social-origin differences may persist even at comparable skill levels, as signaling processes, network access, and opportunity structures condition how skills are rewarded in the labor-market. Bringing these mechanisms together, we formulate the following hypotheses:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHypothesis 1\u003c/em\u003e. FGAs hold jobs with lower occupational prestige than CGA (ceteris paribus).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHypothesis 2\u003c/em\u003e. FGAs who report overqualification have jobs with lower occupational prestige than overqualified CGAs.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHypothesis 3\u003c/em\u003e. FGAs who are dissatisfied with their jobs have lower occupational prestige than dissatisfied CGAs.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHypothesis 4a\u003c/em\u003e. Among individuals with high cognitive skills, FGAs report lower job satisfaction and hold jobs with lower occupational prestige than CGAs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHypothesis 4b\u003c/em\u003e. Among individuals with high cognitive skills, FGAs are more likely to be overqualified and hold jobs with lower occupational prestige than CGAs.\u003c/p\u003e\n\u003cp\u003eTaken together, these hypotheses examine whether structural allocation mechanisms (overqualification), subjective occupational evaluation (job satisfaction), and cognitive skills constitute distinct and potentially interacting channels through which social origin continues to shape occupational prestige after tertiary education.\u003c/p\u003e\n\u003ch1\u003eAnalytical Strategy\u003c/h1\u003e\n\u003cp\u003eBy centering attention on the translation of tertiary credentials into occupational prestige, our study advances the conversation from “who gets in” to “who gets ahead”, and under which conditions social origin continues to matter even after higher education has been successfully completed. To examine the mechanisms outlined above, we estimate a series of regression models that are structured in theoretically motivated blocks corresponding to the hypotheses. This stepwise modeling strategy allows us to assess both baseline differences between first-generation academics (FGAs) and continuing-generation academics (CGAs) and the extent to which these differences are shaped by structural, subjective, and skill-related factors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe baseline model (M1) tests Hypothesis 1 by estimating the association between FGA status and occupational prestige, controlling for key sociodemographic characteristics, including gender, migration background, parental employment at age 14, and cultural capital (number of books at home). These variables represent established axes of inequality that are known to shape educational and occupational trajectories and to co-occur with social origin (Engle, 2007; Ma \u0026amp; Shea, 2019; Schepper et al., 2024).\u003c/p\u003e\n\u003cp\u003eIn Model 2, we introduce perceived job–skill match to test Hypothesis 2, focusing on structural allocation processes. Drawing on human capital and assignment theories (Becker, 1964; Sattinger, 1993; McGuinness, 2006), we examine whether the relationship between social origin and occupational prestige varies depending on whether individuals are adequately matched, overqualified, or underqualified. This is implemented via interaction terms between FGA status and job-skill match.\u003c/p\u003e\n\u003cp\u003eModel 3 incorporates job satisfaction to test Hypothesis 3, capturing subjective evaluations of occupational situations. We assess whether the association between job satisfaction and occupational prestige differs between FGAs and CGAs by including an interaction term between FGA status and job satisfaction.\u003c/p\u003e\n\u003cp\u003eFinally, Model 4 introduces cognitive skills (numeracy and literacy) to examine Hypotheses 4a and 4b. Cognitive skills represent a distinct source of labor-market advantage that may attenuate or reinforce social-origin inequalities (Ford, 2018). To test whether the effects of job–skill mismatch and job satisfaction vary by skill level, we estimate three-way interaction terms between FGA status, cognitive skills, and (a) job–skill match and (b) job satisfaction. This allows us to assess whether higher cognitive skills mitigate or reproduce differences in occupational prestige between FGAs and CGAs.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eData\u003c/h2\u003e\n\u003cp\u003eWe focus on Germany using the \u003cem\u003eProgramme for the International Assessment of Adult Competencies\u0026nbsp;\u003c/em\u003e(PIAAC) Cycle 2 Scientific Use File (SUF) (Zabal et al., 2025), which provides a probability-based sample of the 16 to 65 year old inhabitants of Germany, complete with internationally harmonized measures that are well suited to large-scale, cross-sectional analyses of adult skills and labor-market outcomes.We restrict the analytical sample to individuals with tertiary education who have completed their studies and are active in the labor market excluding individuals currently enrolled in education.\u003c/p\u003e\n\u003cp\u003eWe operationalize occupational prestige using the \u003cem\u003eStandard International Occupational Prestige Scale\u0026nbsp;\u003c/em\u003e(SIOPS) available in the Scientific Use File and cross-validate our results with the \u003cem\u003eInternational Socio-Economic Index of Occupational Status\u0026nbsp;\u003c/em\u003e(ISEI). This allows us to test whether any first-generation penalty is robust across conceptually related status indicators.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAnalysis\u003c/h2\u003e\n\u003cp\u003eWe analyze the SUF following recommended practice by the OECD (OECD, 2025), using replicate weights to account for the sampling design. Analysis involving cognitive skills as measured in PIAAC Cycle 2 were conducted using plausible values in accordance with OECD guidelines. We estimate multiple regression models to analyze the impact of FGA status on SIOPS. Marginal means and predicted probabilities were estimated using the models reported in the results or appendix. In the result section we report analysis using numeracy as a proxy of cognitive skill. As a robustness check we report the same analyses using literacy in the appendix.\u003c/p\u003e\n\u003ch2\u003eMissing values\u003c/h2\u003e\n\u003cp\u003eFor analyses concerning CGA/FGA, we removed 50 respondents for which FGA status could not be determined. For regression models we used casewise deletion and report the number of observations together with the model parameters.\u003c/p\u003e\n\u003ch2\u003eReproducibility\u003c/h2\u003e\n\u003cp\u003eAll analyses were conducted in R. Replication materials and software packages used are available at https://osf.io/fdevn/overview?view_only=e94ca5877415413cbf2096914f8ef092.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe results are presented in line with the hypotheses outlined above. We begin by reporting descriptive statistics to provide an overview of the sample and the distribution of occupational prestige and key explanatory variables.\u003c/p\u003e\n\u003ch2\u003eDescriptives\u003c/h2\u003e\n\u003cp\u003eTable 1 presents descriptive statistics for the German PIAAC Cycle 2 sample. Academics constitute 33.5% of the sample (n = 1,609), of whom 1,000 are first-generation academics (FGAs) and 559 are continuing-generation academics (CGAs). Individuals still enrolled in higher education were excluded from the analyses. Gender is evenly distributed across both groups. Among FGAs, 85.9% were born in Germany, compared to 79.6% among CGAs.\u003c/p\u003e\n\u003cp\u003eTable 1 Descriptives\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"602\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eGerman PIAAC Sample\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eAcademics*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eFirst-Generation Academics*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eContinuing-Generation Academics*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eTotal N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e4793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e559\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e2356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e(49.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e(50.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e(50.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e(49.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e2457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e(50.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e(49.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e(49.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e(50.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eBorn in Germany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e3868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e(80.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e(83.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e(85.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e(79.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eNot born in Germany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e(19.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e(16.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e(14.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e(20.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" valign=\"top\" style=\"width: 602px;\"\u003e\n \u003cp\u003e*Respondents in education are excluded (n = 819 = 692 + 127 missing values). All counts are weighted.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFirst, we examined to what extent FGAs and CGAs differ in occupational prestige in Germany descriptively. Figure 1 reveals a clear and substantial gap in occupational prestige between FGAs and CGAs. On average, FGAs attain significantly lower occupational prestige than CGAs, despite holding equivalent tertiary degrees. On average, FGAs score approximately 6 prestige points lower than CGAs (weighted means: FGA = 50; CGA = 56), corresponding to roughly one third of one standard deviation of the prestige scale. The distribution further indicates that the FGA disadvantage is not driven by outliers, but reflects a consistent shift in the overall prestige distribution toward lower-ranked occupations. CGAs are more likely to be found in occupations situated in the upper segment of the prestige scale, whereas FGAs are overrepresented in mid-prestige and lower-prestige occupational categories.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2 presents the results of the regression models predicting occupational prestige (SIOPS), estimated in a stepwise manner corresponding to Hypotheses 1\u0026ndash;4. Model 1 tests whether FGA differ from CGA in occupational prestige, net of known confounders. In line with H1, FGAs exhibit significantly lower occupational prestige than CGAs. The coefficient of \u0026ndash;4.24 (SE = 1.09) indicates a substantial and statistically meaningful prestige penalty associated with being the first academic in a family. In substantive terms, this corresponds to approximately one third of the average prestige return associated with holding a tertiary degree, highlighting that the first-generation penalty offsets a considerable share of the educational advantage. This gap persists despite the strong and positive association between holding higher education and occupational prestige (b = 13.09, SE = .95), suggesting that tertiary credentials alone \u003cem\u003edo not\u003c/em\u003e \u003cem\u003efully compensate for differences in social origin\u003c/em\u003e. The inclusion of parental higher education as a separate control also does not attenuate the FGA penalty, underscoring that the observed disadvantage is not reducible to simple parental educational differences but reflects a distinct first-generation effect.\u003c/p\u003e\n\u003cp\u003eTable 2 Regressing SIOPS on FGA status and covariates\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"602\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 4a\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 4b\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 602px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntercept\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e19.46 (2.29)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e19.37 (2.26)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e19.46 (2.27)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e20.65 (2.60)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e19.90 (2.39)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 602px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation (Ref. cat.: Continuing academic \u0026amp; no higher education \u0026amp; parent no higher education)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eFGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-4.24 (1.09)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-3.29 (1.20)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-3.98 (1.08)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-5.05 (5.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-7.53 (4.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eHigher Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.09 (.95)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.16 (.95)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.17 (.95)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.31 (.95)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.23 (.95)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eParent(s) have higher education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-.24 (.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-.25 (.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-.27 (.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-.23 (.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-.22 (.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eFGA X Overqualified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.56 (1.27)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-4.90 (9.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eFGA X Underqualified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-1.52 (1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-15.69 (13.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eFGA X Job dissatisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-6.87 (2.24)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-2.79 (17.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eFGA X Numeracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e.01 (.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e.01 (.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eFGA X Overqualified X Numeracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e.01 (.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eFGA X Underqualified X Numeracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e.05 (.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eFGA X Job dissatisfaction X Numeracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-.01 (.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 602px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (Ref. cat.: 16-24)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003e25-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e.29 (1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e.24 (1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e.27 (1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e.17 (1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e.22 (1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003e35-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e.83 (1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e.85 (1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e.79 (1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e.80 (1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e.75 (1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003e45-54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e.95 (1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e.95 (1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e.91 (1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e.90 (1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e.86 (1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003e55+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e2.19 (1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e2.19 (1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e2.19 (1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.14 (1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e2.17 (1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 602px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender (Ref. cat.: Male)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e.07 (.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e.04 (.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e.02 (.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e.05 (.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e.06 (.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 602px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMigration (Ref. cat: Native language, native born, both native language and native born)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eForeign Language\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e2.41 (1.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e2.34 (1.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e2.58 (1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.45 (1.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e2.65 (1.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eForeign Born\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e.33 (1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e.32 (1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e.33 (1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e.32 (1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e.34 (1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eForeign Language X Foreign Born\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-4.02 (2.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-4.00 (2.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-4.15 (2.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-4.11 (2.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-4.25 (2.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 602px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParents (Ref. cat.: Mother with job, Father with job)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eMother no job\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e.59 (.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e.64 (.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e.58 (.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e.63 (.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e.56 (.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eFather no job\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-.10 (.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-.13 (.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-.15 (.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-.09 (.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-.09 (.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 602px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBooks (Ref. cat.: 10 Books or less)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003e11 to 25 Books\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.20 (.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.15 (.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.21 (.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.22 (.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e1.26 (.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003e26 to 100 Books\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.61 (.81)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.54 (.83)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.71 (.81)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.59 (.83)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.75 (.81)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003e101 to 200 Books\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.85 (.90)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.82 (.91)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.95 (.89)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.86 (.90)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.98 (.89)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003e201 to 500 Books\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.95 (.89)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.90 (.90)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.05 (.89)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.97 (.89)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.10 (.88)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eMore than 500 Books\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.64 (1.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.52 (1.07)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.77 (1.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.63 (1.06)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.82 (1.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 602px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePerceived job match (Ref. cat.: Adequate job match)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eOverqualified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.28 (.48)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.56 (.55)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.26 (.48)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-3.76 (3.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.23 (.48)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eUnderqualified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e.76 (.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.18 (.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e.78 (.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.72 (4.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e.79 (.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eOverqualified X Numeracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e.01 (.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eUnderqualified X Numeracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-.00 (.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 602px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJob satisfaction (Ref. cat.: Satisfied)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eDissatisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-.26 (1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-.19 (1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e2.12 (1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-.13 (1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e7.77 (7.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eDissatisfied X Numeracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-.02 (.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 602px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCognitive Skills\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eNumeracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.06 (.01)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.06 (.01)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.06 (.01)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.06 (.01)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.06 (.01)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 602px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR squared\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eR squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.35 (.02)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.35 (.02)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.35 (.02)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.35 (.02)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.35 (.02)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eN = 2999\u003c/p\u003e\n\u003cp\u003eTurning to Model 2, we test the interaction between perceived job\u0026ndash;skill match and FGA status. The interaction term between FGA and overqualification is negative and statistically significant (b = \u0026ndash;2.56, SE = 1.27). \u0026nbsp;This indicates that the prestige penalty of overqualification is considerably larger for FGAs than for CGAs, what is also clearly visible in Figure 2 reporting the average marginal effects (AME) of the interaction. FGAs who report that their skills exceed job requirements occupy particularly low-prestige positions relative to adequately matched CGAs. Predicted margins indicate that overqualified FGAs experience a total prestige penalty of approximately \u0026ndash;7.41 points compared to adequately matched CGAs, whereas the corresponding penalty among CGAs is only \u0026ndash;1.56 points. Thus, overqualification multiplies the first-generation penalty, more than quadrupling the prestige loss observed among continuing-generation graduates. The interaction between FGA and underqualification, however, is negative but not statistically robust. These findings support H2: structural misallocation in the form of overqualification disproportionately disadvantages FGAs in the translation of educational credentials into occupational prestige. The results suggest that job\u0026ndash;skill mismatch is one important mechanism through which social-origin inequalities persist after tertiary completion.\u003c/p\u003e\n\u003cp\u003eFigures 2 and 3 present predicted marginal means of occupational prestige derived from the regression models, with 95% confidence intervals.\u003c/p\u003e\n\u003cp\u003eModel 3 incorporates job satisfaction and its interaction with FGA status. While job dissatisfaction alone is not significantly associated with prestige in the baseline category, the interaction between FGA and job dissatisfaction is negative and statistically significant (b = \u0026ndash;6.87, SE = 2.24; see also Figure 3 for AME). Predicted values indicate that dissatisfied FGAs experience a total prestige penalty of \u0026ndash;8.73 points compared to satisfied CGAs. In contrast, dissatisfied CGAs do not exhibit a comparable prestige loss (b = 2.12, n.s.). Among FGAs, dissatisfaction thus more than doubles the baseline first-generation penalty (\u0026ndash;3.98 vs. \u0026ndash;8.73). This finding indicates that the FGA disadvantage in occupational prestige is amplified among individuals who are dissatisfied with their jobs. Among satisfied workers, the FGA\u0026ndash;CGA gap is smaller; among dissatisfied workers, it widens considerably \u0026mdash; while prestige for dissatisfied CGA workers is close to 50 (and even a bit higher than for CGA who are satisfied with their jobs), FGAs\u0026rsquo; occupations rank on only at 39 (all else fixed at adequate statistical means). These results support H3 and point to the importance of subjective job evaluations. They suggest that psychosocial dimensions of work are closely intertwined with structural allocation processes: FGAs who perceive their occupational situation negatively are particularly likely to be located in low-prestige positions (and again, among the peer-group of academics).\u003c/p\u003e\n\u003cp\u003eFinally, we turn to H4a and H4b by including three-way interaction terms (numeracy X FGA X overqualified / dissatisfied). While numeracy is positively and significantly associated with occupational prestige (b \u0026asymp; .06, SE = .01) across all models, the interaction between FGA and numeracy is not statistically significant, nor are the three-way interactions. Substantively, the estimated difference in occupational prestige between FGAs and CGAs at a given level of numeracy is captured by the coefficient for FGAs plus the interaction term with numeracy. Even at higher proficiency levels, predicted prestige differences remain negative for FGAs. For instance, at an above-average numeracy level, the first-generation penalty is reduced only marginally and does not disappear. The non-significant interaction terms indicate that cognitive skills increase occupational prestige similarly for both groups rather than compensating for social-origin disadvantage. Moreover, the comparatively large standard errors of the higher-order interaction terms suggest limited statistical power for detecting complex moderation effects. However, the absence of even weak directional patterns provides little evidence that cognitive skills substantially buffer the first-generation penalty. This indicates that higher cognitive skills do not eliminate the prestige gap between FGAs and CGAs, nor do they substantially alter the differential penalties associated with overqualification or dissatisfaction.\u003c/p\u003e\n\u003cp\u003eAccordingly, the results provide only limited support for H4a and H4b. While cognitive skills are positively rewarded in the labor market, they do not meaningfully moderate the association between social origin and occupational prestige.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo further examine whether FGAs differ systematically in their likelihood of experiencing job\u0026ndash;skill mismatch, we estimated a multinomial regression model with \u0026ldquo;adequate job match\u0026rdquo; as the reference category. The predicted probabilities indicate that FGAs are significantly more likely than CGAs to report being overqualified, net of controls. The effect for underqualification is positive but smaller and less precisely estimated (Figure 4).\u003c/p\u003e\n\u003cp\u003eThese findings complement the linear regression results by demonstrating that FGAs are not only more strongly penalized when overqualified but are also more likely to experience overqualification in the first place (Figure 4). In contrast, higher education as such reduces the probability of overqualification (b = \u0026ndash;.45, SE = .18), highlighting again that the disadvantage is specific to first-generation status rather than to tertiary education per se.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study set out to examine whether tertiary education equalizes occupational prestige outcomes between first-generation academics (FGAs) and continuing-generation academics (CGAs) in Germany. The findings based on nationally representative data provide clear evidence that this is not the case. Even after controlling for sociodemographic background characteristics traditionally associated with labor-market inequalities, FGAs exhibit a substantial and statistically robust prestige penalty. This result qualifies the \u0026ldquo;great equalizer\u0026rdquo; thesis (Bernardi \u0026amp; Ballarino, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) in an important way. While tertiary education strongly increases occupational prestige overall, it does not fully compensate for differences in social origin. The persistent FGA disadvantage suggests that educational credentials alone are insufficient to guarantee equal labor-market positioning. In this sense, degrees may indeed represent only the visible part of a deeper stratification process.\u003c/p\u003e \u003cp\u003eThe second major finding concerns the role of job\u0026ndash;skill mismatch. Overqualification is negatively associated with occupational prestige for all graduates, but this penalty is significantly larger for FGAs. Moreover, complementary analyses show that FGAs are more likely to experience overqualification in the first place. These findings align closely with assignment theory (Sattinger, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) and research on cultural matching in hiring processes (Rivera, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). They suggest that labor-market allocation mechanisms operate in socially differentiated ways. Even when FGAs possess tertiary credentials and comparable skill levels, they appear less successful in translating these resources into positions commensurate with their qualifications. Importantly, overqualification does not simply reflect individual skill deficits. Rather, it may signal differential access to information channels, professional networks, and informal career guidance\u0026mdash;resources that are more readily available to CGAs. Structural misallocation thus emerges as a central mechanism through which social-origin inequalities persist beyond graduation.\u003c/p\u003e \u003cp\u003eAnother striking finding concerns job satisfaction. While dissatisfaction is not systematically associated with lower prestige among CGAs, it dramatically amplifies the prestige penalty among FGAs. Dissatisfied FGAs occupy substantially lower-prestige positions than both satisfied FGAs and dissatisfied CGAs. This pattern suggests that psychosocial dimensions of occupational experience are closely intertwined with structural inequalities. The findings resonate with research on belonging uncertainty (Walton \u0026amp; Cohen, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), impostor feelings (Clance \u0026amp; Imes, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1978\u003c/span\u003e; Feenstra et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and identity-based stress among first-generation individuals (Cokley et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). FGAs may interpret workplace challenges differently, perceive fewer advancement opportunities, or experience weaker institutional support, which in turn may influence career mobility.\u003c/p\u003e \u003cp\u003eRather than being a mere correlate of low prestige, job dissatisfaction appears to interact systematically with social origin. This interaction points toward a cumulative disadvantage process in which structural positioning and subjective occupational evaluation reinforce one another.\u003c/p\u003e \u003cp\u003eContrary to expectations derived from human capital theory (Becker, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1964\u003c/span\u003e), higher cognitive skills do not eliminate the prestige gap between FGAs and CGAs. Numeracy proficiency is positively associated with occupational prestige overall, but it does not significantly moderate the first-generation penalty. Even at higher proficiency levels, FGAs do not converge toward the prestige levels of CGAs. These findings provide only limited support for H4. While skills are clearly rewarded in the labor market, their returns appear to be socially conditioned. This pattern is consistent with research suggesting that signaling processes, social capital, and institutional gatekeeping shape how competencies are recognized and rewarded (Rivera, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Schepper et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The absence of a compensatory effect of skills underscores that social-origin inequalities cannot be fully reduced to differences in measurable competencies. Structural and relational mechanisms appear to play an independent role.\u003c/p\u003e \u003cp\u003eTaken together, the results shift the analytical focus from access to higher education toward the translation of educational attainment into occupational positioning. While much prior research has examined whether first-generation students enter and complete tertiary education, our findings demonstrate that inequalities persist even after successful graduation. Three interrelated mechanisms emerge: A baseline structural disadvantage (H1), differential exposure to overqualification (H2) and psychosocial amplification via dissatisfaction (H3). In contrast, cognitive skills function as a general resource but not as an equalizing force (H4). This constellation suggests that the persistence of inequality operates less through skill deficits and more through socially structured allocation processes and differential returns to similar credentials.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eSeveral limitations warrant consideration. First, the cross-sectional design does not permit causal inference. The observed associations should therefore be interpreted as correlational rather than causal, and longitudinal data would be required to examine career dynamics and mobility trajectories over time.\u003c/p\u003e \u003cp\u003eSecond, key explanatory variables such as job satisfaction and perceived job-skill match rely on self-reports and may reflect subjective evaluations rather than objective working conditions. While these perceptions are theoretically meaningful, they may introduce reporting bias and limit comparability across individuals.\u003c/p\u003e \u003cp\u003eThird, the operationalization of first-generation status is based on parental educational attainment and does not capture the full range of social-origin differences, such as variations in cultural, social, or economic capital. As a result, heterogeneity within the group of first-generation academics may remain unobserved.\u003c/p\u003e \u003cp\u003eFourth, although PIAAC provides high-quality measures of cognitive skills, the use of plausible values introduces additional complexity and uncertainty in estimation, and results depend on appropriate handling of these measures. Moreover, occupational prestige (SIOPS) captures the relative social standing of occupations but does not directly reflect income, job quality, or career progression, which may follow different patterns.\u003c/p\u003e \u003cp\u003eFifth, the higher-order interaction models involve relatively small subgroups, limiting statistical power for detecting complex moderation effects, particularly in three-way interactions.\u003c/p\u003e \u003cp\u003eDespite these limitations, the study benefits from nationally representative data, direct assessments of cognitive skills, and the integration of structural and subjective mechanisms within a unified analytical framework, allowing for a comprehensive examination of how social origin shapes labor-market outcomes after tertiary education. Finally, although the analyses are restricted to Germany, the PIAAC data offer the potential for cross-national extensions. However, such comparisons require careful harmonization of tertiary education categories, as the German system with its distinction between research-oriented universities and universities of applied sciences differs from many other countries. Ensuring comparability of tertiary degrees across contexts is therefore a key challenge for future research.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe findings carry important implications for both research and policy. Our results show that, even after successful completion of tertiary education, first-generation academics continue to be disadvantaged in terms of occupational prestige. This disadvantage is not primarily explained by differences in cognitive skills, but is instead associated with structural allocation processes and subjective evaluations of occupational situations. In particular, overqualification and lower job satisfaction emerge as relevant channels through which social origin continues to shape labor-market outcomes.\u003c/p\u003e \u003cp\u003eFor research, these findings highlight the need to move beyond access-based models of educational inequality and to more closely examine post-tertiary labor-market processes. Future studies should further investigate how institutional arrangements, recruitment practices, and signaling mechanisms shape the translation of educational credentials into occupational positions. In addition, extending this line of research to cross-national contexts using large-scale assessment data such as PIAAC would provide valuable insights, provided that tertiary education is operationalized in a comparable way across countries.\u003c/p\u003e \u003cp\u003eFor policy, the results suggest that widening participation in higher education is a necessary but not sufficient condition for promoting social mobility. Policies should therefore not only focus on access to tertiary education, but also address inequalities in the transition from education to employment. Measures such as targeted career guidance, transparent recruitment procedures, and mentoring structures may help to ensure that tertiary credentials translate into comparable occupational opportunities for graduates from different social backgrounds.\u003c/p\u003e \u003cp\u003eTaken together, the findings underline that higher education does not fully neutralize social-origin inequalities, but rather shifts them to later stages of the life course, where they operate through more subtle structural and institutional mechanisms.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eFGAs\u0026nbsp; \u0026nbsp;First-generation academics\u003c/p\u003e\n\u003cp\u003eCGAs \u0026nbsp; Continuing-generation academics\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available from the Programme for the International Assessment of Adult Competencies (PIAAC), conducted by the OECD. The German Scientific Use File can be obtained via GESIS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study uses data from the Programme for the International Assessment of Adult Competencies (PIAAC), conducted by the OECD. We thank GESIS for providing access to the German Scientific Use File.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRemoved for anonymization, will be inserted in the published manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRemoved for anonymization, will be inserted in the published manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor 1 conceptualized the study and led the writing of the manuscript. Author 2 conducted the data analysis. Author 1 and Author 3 contributed to the study design and all authors contributed to the interpretation of the results. All authors reviewed and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBarsegyan, V., \u0026amp; Maas, I. (2024). First-generation students\u0026rsquo; educational outcomes: The role of parental educational, cultural, and economic capital \u0026ndash; A 9-years panel study. \u003cem\u003eResearch in Social Stratification and Mobility, 91\u003c/em\u003e, 100939, https://doi.org/10.1016/j.rssm.2024.100939\u003c/li\u003e\n\u003cli\u003eBecker, G. S. (1964). \u003cem\u003eHuman capital: A theoretical and empirical analysis, with special reference to education.\u003c/em\u003e University of Chicago Press. https://doi.org/10.7208/chicago/9780226041223.001.0001\u003c/li\u003e\n\u003cli\u003eBernardi, F., \u0026amp; Ballarino, G. (2016). 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Equalization or Selection? Reassessing the \u0026ldquo;Meritocratic Power\u0026rdquo; of a College Degree in Intergenerational Income Mobility. \u003cem\u003eAmerican Sociological Review\u003c/em\u003e, \u003cem\u003e84\u003c/em\u003e(3), 459\u0026ndash;485. https://doi.org/10.1177/0003122419844992\u003c/li\u003e\n\u003c/ol\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":"first generation academics, PIAAC, occupational prestige","lastPublishedDoi":"10.21203/rs.3.rs-9368706/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9368706/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines whether higher education equalizes labor-market outcomes for graduates from different social backgrounds, focusing on first-generation academics (FGAs) in Germany. Using nationally representative data from PIAAC Cycle 2, we analyze occupational prestige differences between FGAs and continuing-generation academics (CGAs) and test a set of theoretically grounded mechanisms that may reproduce or mitigate social-origin inequalities after graduation. Descriptive analyses reveal a substantial FGA disadvantage in occupational prestige despite equivalent tertiary degrees. Linear regression models show that this prestige gap remains robust when accounting for sociodemographic characteristics traditionally associated with labor-market disparities. Indicators of work\u0026ndash;skill match partly attenuate the FGA penalty, suggesting that FGAs are more often allocated to positions in which their skills are underutilized, yet mismatches do not fully explain the observed gap. In contrast, job satisfaction significantly moderates the association between social origin and occupational prestige: the FGA disadvantage is considerably larger among individuals with low job satisfaction and diminishes at higher satisfaction levels. Taken together, the findings indicate that tertiary education in Germany does not fully eliminate inequalities related to social origin. Even among graduates with otherwise comparable credentials, FGAs face persistent structural and psychosocial barriers that limit the translation of educational attainment into occupational prestige.\u003c/p\u003e","manuscriptTitle":"Only the Tip of the Iceberg: Occupational Prestige Inequalities for First-Generation Academics in Germany","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-15 19:05:16","doi":"10.21203/rs.3.rs-9368706/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":"eb9028dc-3cbe-4f3c-a1a8-b743657847af","owner":[],"postedDate":"May 15th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-11T06:26:22+00:00","index":20,"fulltext":""},{"type":"reviewerAgreed","content":"284145440317077031438312533589577810638","date":"2026-05-08T14:32:14+00:00","index":19,"fulltext":""},{"type":"reviewerAgreed","content":"13762111202817655337165155926019648561","date":"2026-05-07T06:04:33+00:00","index":18,"fulltext":""},{"type":"reviewersInvited","content":"5","date":"2026-05-06T14:17:41+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T19:05:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-15 19:05:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9368706","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9368706","identity":"rs-9368706","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}