TY - JOUR
AU - Gil-Hernández, Carlos, J
AB - Abstract Recent studies document a social-origins gap or direct effect of social origin (DESO) on labour market outcomes over and above respondents’ education, challenging the idea that post-industrial societies are education-based meritocracies. Yet, the literature offers insufficient explanations on DESO heterogeneity across education and different labour market outcomes. Little is also known about underlying mechanisms. We contribute by answering two questions: (i) How does DESO vary when comparing college-degree holders with non-holders? (ii) For which specific parental and children’s occupations is the largest DESO observed? We focus on Spain, using a large new dataset (n = 144,286). Firstly, we find a larger DESO on socioeconomic status among non-degree holders, and on income among degree holders. We propose the notions of compensatory advantage in occupational attainment and boosting advantage in income for high social-origin individuals to explain these opposite patterns, drawing from ‘downward mobility avoidance’ and ‘effectively maintained inequality’ theories. Secondly, we map origin and destination micro-classes where DESO is largest. High-grade managerial and professional parental occupations, characterized by social closure and influence in large organizations, are the origin micro-classes exerting the largest DESO. We also find that compensatory advantage for low-educated children from advantaged origins is related to their higher chances of accessing managerial occupations, while boosting advantage on income among college graduates is observed for high-grade managers and liberal professionals, suggesting that micro-class reproduction may partially account for boosting advantage. We conclude by discussing the generalizability of our findings to other countries and their implications for research on DESO, meritocracy and social mobility. Introduction Recent studies show that, in addition to the well-documented gender- and ethnic- gaps in labour market outcomes, subjects from socioeconomically advantaged families obtain better occupations and higher incomes compared to subjects with the same level of education who come from disadvantaged families. This effect has been termed as the social-origin gap in labour market outcomes or alternatively as the direct effect of social origin (DESO) (Torche, 2011; Bernardi and Ballarino, 2016; Laurison and Friedman, 2016; Gugushvili, Bukodi and Goldthorpe, 2017; Fiel, 2020). The study of DESO involves important implications for current debates on low levels of social mobility and policy to promote discussion about it (Eurofound, 2017; OECD, 2018), and for more general normative debates on meritocracy and social justice in contemporary societies (Markovits, 2019). If confirmed, the finding of a DESO ultimately undermines the notion that affluent post-industrial societies are education-based meritocracies (Bukodi and Goldthorpe, 2018), and puts into question the idea that the educational system functions as the great equalizer of opportunities. The research on DESO is also connected to two more specific debates in social mobility studies. A first debate focuses more narrowly on college education as the great equalizer (Torche, 2011; Karlson, 2019; Zhou, 2019; Witteveen and Attewell, 2020). It has long been noted that the association between social origin and destination is weaker or even disappears among college-degree holders (Hout, 1988). Recent studies, nonetheless, report that a strong intergenerational association resurfaces in the income prospects of advanced degree holders (Torche, 2018; Oh and Kim, 2020), thus calling for further research on DESO across different educational levels and labour market outcomes. A second debate revolves around the conceptualization of social origins and destinations in social mobility studies. Proponents of a ‘micro-classes’ approach have argued that the processes of transmission of (dis)advantages from one generation to the next are better understood at the level of specific occupations (Weeden and Grusky, 2005; Jonsson et al., 2009, 2011). In this approach, detailed occupations are the fundamental conduits of inequality reproduction. In this article, we engage with these debates and study the DESO on socioeconomic status and income in Spain, drawing from a new large dataset (n = 144,286). We address two research questions: How does the DESO on socioeconomic status and income vary depending on the respondents’ level of education, in particular when comparing college-degree holders with non-holders? For which specific parental and respondents’ occupations is the largest DESO on socioeconomic status and income observed? Our contribution with respect to previous studies on DESO is both theoretical and methodological and goes far beyond providing new findings for the Spanish case. First, we depart from the dominant theoretical interpretations in the debate on college education as the great equalizer. While the current debate focuses on the supposed ‘meritocratic nature’ of the labour market for the highly educated and the selection on unobserved characteristics of college-degree holders from disadvantaged backgrounds, we propose an alternative explanation based on the mobility strategies of the upper classes. We refer to the theories of downward mobility avoidance (Goldthorpe, 2007) and effectively maintained inequality (Lucas, 2001) to interpret different patterns of compensatory advantage for socioeconomic status of non-college degree holders, and boosting advantage for income of college degree holders, respectively. Second, to the best of our knowledge, we provide the first application of a micro-class approach to investigate the loci of DESO and map the social-origins gap at the level of detailed origin and destination occupations. We also show that there are close theoretical connections between the mechanisms outlined to explain micro-class reproduction and those proposed to explain the DESO. With regard to social origins, we identify which specific parental occupations provide the largest advantages to their children in terms of socioeconomic status and income. With regard to destinations, we rank the most common occupations among non-degree holders by social origins to identify those occupations in which compensatory advantage comes into being. We also rank the destination occupations with the largest DESO on income among college degree holders by social origin to ascertain the occupations where the boosting advantage is largest. This detailed occupational analysis enables us to more closely access the micro-processes and mechanisms that bring about the observed DESO. Substantively, Spain is an interesting case to address our research questions because it is one of the European countries with the largest DESO and intergenerational inequality (Bernardi and Ballarino, 2016; Hertel and Groh-Samberg, 2019). During the last decades, the country has experienced a steep educational expansion into college education that has not however been followed by an equivalent increase in highly qualified jobs (Marqués Perales and Gil-Hernández, 2015). Moreover, differently from the United Kingdom and United States, but similarly to most other European countries, in Spain, there is not a clear-cut differentiation between elite and non-elite universities. In this context of expansion and limited differentiation of college education, and the scant availability of highly qualified occupations, we can expect the social-origin gap in labour market outcomes to be particularly pronounced. We will take into account how various institutional factors may affect the magnitude of the social-origin gap when interpreting our findings for Spain and discussing their generalizability to other countries in the discussion. Theoretical Debates on the Social-Origins Gap Parallel to the concepts of gender gap and ethnic gap or penalty, the social origin gap or DESO can be defined as the advantage that subjects of the upper-class experience for occupational attainment and income compared with subjects of the lower class at the same level of education (Laurison and Friedman, 2016). Different mechanisms underlying the social-origins gap have been discussed in the literature (Erikson and Jonsson, 1998; Hällsten, 2013). First, the DESO can be produced by social background differences in productivity-enhancing cognitive and non-cognitive skills that are not adequately captured by formal educational qualifications (Karlson, 2019). Second, students coming from more advantaged social background might have higher aspirations and be more motivated to compete for higher status and better-paid occupations. Third, they might also benefit from a direct transfer of economic resources to pay off debts contracted during the studies, to accept starting jobs or internships that are lower paid but with better career prospects, or that can function as initial capital to start their own businesses (Zhou, 2019). Fourth, social networks might facilitate access to useful information regarding vacancies and can provide direct advantages to respondents from higher social background in the hiring processes (Lin, Cook and Burt, 2001). Finally, in the hiring process employers might discriminate in favour of candidates from higher social origins due to, for instance, differences in cultural capital or soft skills (Rivera, 2015). Against the backdrop of these explanations, we can now examine why DESO might vary by educational level and connect the study of DESO with that of the micro-classes. DESO Heterogeneity by Education: Compensation in Occupational Attainment and Boosting in Income There are two main explanations for the finding that the origin-destination association in terms of social class and socioeconomic status declines and almost vanishes among college graduates (Hout, 1988; Torche, 2011). On the one hand, it has been argued that the labour market for university graduates functions in a more meritocratic manner (Breen and Jonsson, 2007). With regard to the different explanations of DESO, this means that, among college graduates, social background discrimination by employers should be less common and that parental social networks should be less relevant. This might happen because graduates tend to work in sectors characterized by high bureaucratization, such as the public sector and multinational corporations. On the other hand, other authors have also stressed that individuals coming from disadvantaged backgrounds who manage to achieve a college degree are positively selected in terms of cognitive and non-cognitive abilities and motivation (Mare, 1993; Karlson, 2019). In order to reach the highest level of education, they must overcome the initial disadvantage associated with the lack of economic and cultural resources in the family of origin. They therefore have to be particularly talented and determined to succeed. These (usually unobserved) traits might offset other mechanisms that provide an advantage to those with high social origins. The low or null origin-destination association among college-degree holders would then reflect the selection of those individuals coming from low social origins based on traits that also are positively associated with success in the labour market (Zhou, 2019). At the same time, recent studies show that the DESO on income does not disappear and even becomes larger among those with university or postgraduate education (see Fiel, 2020; Witteveen and Attewell, 2020; see Torche, 2011, 2018; Oh and Kim, 2020 for the United States; Laurison and Friedman, 2016 for the United Kingdom; Falcon and Bataille, 2018 for France). This is particularly the case for men in countries such as Italy, Israel, the Netherlands, Norway, Russia, Spain, United States, and Switzerland (Bernardi and Ballarino, 2016). The findings of a larger social-origins gap in income among college-degree holders frontally challenge the idea of a more meritocratic labour market for graduates. These opposite patterns of social-origins gaps across educational levels for occupational attainment and income can, however, be reconciled if one interprets them as the result of different intergenerational mobility strategies (Lucas 2001; Bernardi and Ballarino, 2016; Erola and Kilpi-Jakonen, 2017). While previous research has referred to the positive selection of university graduates from low social origins and to the supposed meritocratic nature of the labour market for college graduates, in order to explain why the DESO mainly on occupational attainment (e.g. socioeconomic status and occupational class) is weaker, we propose to shift the analytical perspective and look at the same phenomenon from a different angle. The explanadum becomes in our view why there is a stronger DESO among non-college degree holders. Having framed the problem in these terms, our explanation brings strategies of inequality reproduction pursued by socioeconomically advantaged families to the centre of the scene. The core assumption of the rational action theory of social mobility is that intergenerational social mobility strategies are driven by the goal of avoiding social demotion (Goldthorpe, 2007). In case of low achievement in education, upper-class families will have strong motivation to support their adult children in the labour market and help them avoid or limit downward social mobility. Upper-class parents will mobilize their social, cultural, and economic resources to guarantee that their children avoid unskilled occupations and achieve a ‘decent’ social standing. This downward mobility avoidance of low educational achievers of the upper class is in line with the prediction of the compensatory advantage model, which argues that the consequences of an adverse event, low educational attainment in this case, for status-attainment are smaller for subjects of the upper class (Bernardi, 2014). At the same time, upper-class families may also boost the income opportunities of those students who have succeeded in the educational system. This prediction is in line with the core assumption of the effectively maintained inequality theory (EMI), positing that socioeconomically advantaged actors will ‘secure for their children some degree of advantage wherever advantages are commonly possible (Lucas, 2001: p. 1652).’ EMI also posits that all factors that are relevant for social inequality, for instance, educational attainment or access to health care, have both a quantitative and qualitative dimension (Lucas, 2017). When advantage in the quantitative dimension is not possible, socioeconomically advantaged individuals seek advantage in the qualitative one. In the case of inequalities in education attainment, the crucial insight of EMI is that a reduction in quantitative inequality, for instance in terms of years of schooling, can be undermined by an increase in qualitative inequality in the type of program or school attended. Although EMI has been mainly applied to studying educational inequalities, it can also inform the study of occupational attainment. In this case, we can consider that the socioeconomic status associated with a job represents the quantitative dimension of occupational attainment, while other characteristics of the job that also ensure income gains, such as the prestige of the firm, prospects of job promotion or earning benefits, are its qualitative component. When a quantitative advantage in terms of socioeconomic status is not possible, upper-class families can provide an extra advantage to their children in attaining a job with additional qualitative characteristics that guarantee higher income. In particular, certain college degrees are geared to specific occupations. Those who study law or finance, for instance, are likely to become lawyers or managers. Upper-class families might then not be able to provide a quantitative advantage to their children in terms of a higher socioeconomic status (or class) attainment. They can however help them in finding inter-ships as lawyers or entry jobs as managers in more prestigious firms that warrant higher income, thanks to their connections or specific knowledge of the sector. We label this process whereby college-degree holders from socioeconomically advantage families earn more on average as boosting advantage, and in the conclusions, we will discuss which institutional features make this specific form of DESO more likely. Seen through the conceptual lens of intergenerational mobility strategies, the findings of a larger DESO on both occupational attainment for low educational achievers and income among high achievers become less puzzling. They reflect two different strategies of inequality reproduction of socioeconomically advantaged families, namely, avoidance of downward mobility in case of low educational attainment of their children, and maximization of income in case of high educational attainment. Micro-Classes and DESO Advocates of an occupational or micro-class approach maintain that detailed occupations are the fundamental conduits of the reproduction of inequality from one generation to the next (Grusky and Weeden, 2006; Jonsson et al., 2009; 2011). They argue that parents transmit occupation-specific cognitive and non-cognitive skills to their children, favour the development of occupation-specific tastes and aspirations and give access to occupation-specific networks. Through these mechanisms children develop an interest for the same occupations as those of their parents and have access to key resources that provide an advantage for attaining those occupations. Moreover, if the parents are employers or self-employed they can also involve their children in the family business and pass it over to them. There are also demand-side mechanisms that can further strengthen micro-class reproduction. During the recruitment process, employers may privilege applicants with given individual traits (supposedly transmitted by the parents) that also match those already employed in the given occupation (Rivera, 2015). The mechanisms suggested to explain micro-class reproduction—parental transmission of human, cultural, social, and economic capital that are occupation-specific, and employers’ discrimination based on some occupational specific traits—closely resemble those discussed above to explain the DESO (Erikson and Jonsson, 1998; Hällsten, 2013). What is most relevant for the purpose of this article, and for the study of DESO in general, is that some of the mechanisms underlying micro-class reproduction operate beyond or regardless of educational attainment. Part of the micro-class reproduction occurs through the transmission of occupation-specific tastes and aspirations that lead to the choice of a given field of studies. This part of the micro-class reproduction is mediated by educational achievement. However, other channels of micro-class reproduction, such as privileged access to economic and social capital, are unrelated to educational achievement and contribute to the social-origins gap. What is also relevant for the study of the social-origin gap is that previous studies have shown that micro-class immobility is highest among liberal professionals, and managers and directors of large corporations (Jonsson et al., 2009; Ruggera and Barone, 2017). Institutional restrictions to entry into liberal professions (i.e. lawyers, medical doctors) make access to them more difficult and dependent on occupation-specific resources. Sons and daughters of liberal professionals are then advantaged in pursuing the same occupations as their parents (Ruggera and Barone, 2017). Relatedly, high-level managers and directors in large organizations can transmit firm-specific non-cognitive skills such as leadership and communication skills to their children. In addition, they command organizational resources (i.e. have authority) and can favour their children’s attainment in their same organization or in organizations similar or close to theirs. We can then expect that micro-class immobility at the top of the occupational ladder is responsible for part of the observed DESO. Children of liberal professionals and managers have an advantage in becoming liberal professionals or managers themselves, compared to other individuals with their same level of education but of lower social origins. The same factors (for instance access to social networks) that facilitate the attainment of the same parental occupation can also guarantee higher earnings. More in general, once we break down social origins and destinations into detailed occupational groups, we can investigate the direct associations between different origin and destination occupations, among respondents with the same educational level, and obtain a finer-grained picture of the loci of DESO. In addition to the direct micro-class reproduction at the top of the occupational ladder, we are also able to map other typical origin-destination routes of transmission of advantage. Moreover, parental occupations differ in the amount of economic, social, cultural, and organizational resources they command and destination occupations differ for the formal restriction to access them and in the type of skills required. If we further distinguish among low- and high-educational achievers, we can then gauge some indirect evidence of the micro-class mechanisms underlying compensatory and boosting advantage. Data, Variables and Methods Data We pooled data from 54 monthly barometers and two waves of the General Social Survey (GSS) carried out by the Spanish Centre for Sociological Research (CIS) between February 2013 and May 2018, adding up to 144,286 observations (see Supplementary Table A.1.). Both monthly barometers and GSS samples are representative of the Spanish population, being highly comparable in terms of sampling design and variables’ operationalization. We assessed the characteristics of the full pooled sample and the GSS subsample (see Supplementary Table A.2.), and replicated the analysis shown in Table 3 with the full sample (see Supplementary Table A.3.) and survey fixed effects, to generally conclude that the samples are highly comparable. Table 1 Distribution of educational attainment for working, unemployed, or retired men and women aged 28–65 (subsample) Educational attainment . 14 categories . 8 categories . 4 categories . % . (1) ≤Primary ≤Primary ≤Primary 10.22 (2) Lower Secondary Lower Secondary Lower Secondary 26.00 (3) Lower Vocational Training Vocational Training-I 9.88 (4) Upper Vocational Training Vocational Training-II 12.15 (5) Academic Upper-Secondary Academic Upper-Secondary Upper-Secondary 13.09 (6) STEM Short 3.41 (7) Health Short 1.26 (8) Social Sciences/Humanities Short 4.71 (9) Business/Law Short Short University Degree 2.10 (10) STEM Long 2.25 (11) Health Long 1.44 (12) Social Sciences/Humanities Long 5.96 (13) Business/Law Long Long University Degree 3.99 (14) Postgraduates (MA; PhD) Postgraduates University Degree 3.55 Total 100 n 6,137 Educational attainment . 14 categories . 8 categories . 4 categories . % . (1) ≤Primary ≤Primary ≤Primary 10.22 (2) Lower Secondary Lower Secondary Lower Secondary 26.00 (3) Lower Vocational Training Vocational Training-I 9.88 (4) Upper Vocational Training Vocational Training-II 12.15 (5) Academic Upper-Secondary Academic Upper-Secondary Upper-Secondary 13.09 (6) STEM Short 3.41 (7) Health Short 1.26 (8) Social Sciences/Humanities Short 4.71 (9) Business/Law Short Short University Degree 2.10 (10) STEM Long 2.25 (11) Health Long 1.44 (12) Social Sciences/Humanities Long 5.96 (13) Business/Law Long Long University Degree 3.99 (14) Postgraduates (MA; PhD) Postgraduates University Degree 3.55 Total 100 n 6,137 Notes: Subfields included in each category: STEM: Science, Engineering, Mathematics, Informatics and Architecture; Health: Health and Veterinary; Social Sciences: Social and Behavioural Sciences, Journalism and Education; Humanities: Humanities and Arts. Open in new tab Table 1 Distribution of educational attainment for working, unemployed, or retired men and women aged 28–65 (subsample) Educational attainment . 14 categories . 8 categories . 4 categories . % . (1) ≤Primary ≤Primary ≤Primary 10.22 (2) Lower Secondary Lower Secondary Lower Secondary 26.00 (3) Lower Vocational Training Vocational Training-I 9.88 (4) Upper Vocational Training Vocational Training-II 12.15 (5) Academic Upper-Secondary Academic Upper-Secondary Upper-Secondary 13.09 (6) STEM Short 3.41 (7) Health Short 1.26 (8) Social Sciences/Humanities Short 4.71 (9) Business/Law Short Short University Degree 2.10 (10) STEM Long 2.25 (11) Health Long 1.44 (12) Social Sciences/Humanities Long 5.96 (13) Business/Law Long Long University Degree 3.99 (14) Postgraduates (MA; PhD) Postgraduates University Degree 3.55 Total 100 n 6,137 Educational attainment . 14 categories . 8 categories . 4 categories . % . (1) ≤Primary ≤Primary ≤Primary 10.22 (2) Lower Secondary Lower Secondary Lower Secondary 26.00 (3) Lower Vocational Training Vocational Training-I 9.88 (4) Upper Vocational Training Vocational Training-II 12.15 (5) Academic Upper-Secondary Academic Upper-Secondary Upper-Secondary 13.09 (6) STEM Short 3.41 (7) Health Short 1.26 (8) Social Sciences/Humanities Short 4.71 (9) Business/Law Short Short University Degree 2.10 (10) STEM Long 2.25 (11) Health Long 1.44 (12) Social Sciences/Humanities Long 5.96 (13) Business/Law Long Long University Degree 3.99 (14) Postgraduates (MA; PhD) Postgraduates University Degree 3.55 Total 100 n 6,137 Notes: Subfields included in each category: STEM: Science, Engineering, Mathematics, Informatics and Architecture; Health: Health and Veterinary; Social Sciences: Social and Behavioural Sciences, Journalism and Education; Humanities: Humanities and Arts. Open in new tab Table 2 Descriptive statistics among men and women working, unemployed or retired aged 28–65 Variables . All sample . Subsamplea . n . Mean . Std. dev. . Min . Max . n . Mean . Std. Dev. . Min . Max . Age 87,528 45.47 10.34 28 65 6,312 45.29 10.12 28 65 Female 87,528 0.46 0 1 6,312 0.46 0 1 Double nationality 87,110 0.04 0 1 5,894 0.04 0 1 Parental ISEI 81,818 34.08 18.56 11.74 88.7 5,895 35.01 18.83 11.74 88.7 Parental top occupations 81,818 0.23 0 1 5,895 0.26 0 1 Respondents’ ISEI 86,051 40.13 21.14 11.74 88.7 6,210 40.62 21.47 11.74 88.7 Monthly personal income (€) 53,070 1,151.66 752.35 150 8,000 4,525 1,231.00 757.45 150 8,000 Variables . All sample . Subsamplea . n . Mean . Std. dev. . Min . Max . n . Mean . Std. Dev. . Min . Max . Age 87,528 45.47 10.34 28 65 6,312 45.29 10.12 28 65 Female 87,528 0.46 0 1 6,312 0.46 0 1 Double nationality 87,110 0.04 0 1 5,894 0.04 0 1 Parental ISEI 81,818 34.08 18.56 11.74 88.7 5,895 35.01 18.83 11.74 88.7 Parental top occupations 81,818 0.23 0 1 5,895 0.26 0 1 Respondents’ ISEI 86,051 40.13 21.14 11.74 88.7 6,210 40.62 21.47 11.74 88.7 Monthly personal income (€) 53,070 1,151.66 752.35 150 8,000 4,525 1,231.00 757.45 150 8,000 a Subsample: General Social Survey (MD2975 Study in July 2013; MD3123 Study in December 2015–April 2016). Open in new tab Table 2 Descriptive statistics among men and women working, unemployed or retired aged 28–65 Variables . All sample . Subsamplea . n . Mean . Std. dev. . Min . Max . n . Mean . Std. Dev. . Min . Max . Age 87,528 45.47 10.34 28 65 6,312 45.29 10.12 28 65 Female 87,528 0.46 0 1 6,312 0.46 0 1 Double nationality 87,110 0.04 0 1 5,894 0.04 0 1 Parental ISEI 81,818 34.08 18.56 11.74 88.7 5,895 35.01 18.83 11.74 88.7 Parental top occupations 81,818 0.23 0 1 5,895 0.26 0 1 Respondents’ ISEI 86,051 40.13 21.14 11.74 88.7 6,210 40.62 21.47 11.74 88.7 Monthly personal income (€) 53,070 1,151.66 752.35 150 8,000 4,525 1,231.00 757.45 150 8,000 Variables . All sample . Subsamplea . n . Mean . Std. dev. . Min . Max . n . Mean . Std. Dev. . Min . Max . Age 87,528 45.47 10.34 28 65 6,312 45.29 10.12 28 65 Female 87,528 0.46 0 1 6,312 0.46 0 1 Double nationality 87,110 0.04 0 1 5,894 0.04 0 1 Parental ISEI 81,818 34.08 18.56 11.74 88.7 5,895 35.01 18.83 11.74 88.7 Parental top occupations 81,818 0.23 0 1 5,895 0.26 0 1 Respondents’ ISEI 86,051 40.13 21.14 11.74 88.7 6,210 40.62 21.47 11.74 88.7 Monthly personal income (€) 53,070 1,151.66 752.35 150 8,000 4,525 1,231.00 757.45 150 8,000 a Subsample: General Social Survey (MD2975 Study in July 2013; MD3123 Study in December 2015–April 2016). Open in new tab Table 3 OLS regression models on the effect of parental ISEI on labour market outcomes (subsample) . M1 . M2 . M3 . M4 . M5 . Control for education . No control education . Education . Education . Education . Education . 4-category . 8-category . 14-category . 28-category* . ISEI  Parental ISEI 0.477*** (0.0163) 0.158*** (0.0155) 0.137*** (0.0153) 0.131*** (0.0152) 0.131*** (0.0150)  Constant 29.91*** (1.844) 29.18*** (1.737) 29.47*** (1.710) 29.51*** (1.661) 29.25*** (1.626)  Observations 5,269 5,269 5,269 5,269 5,269  R-squared 0.180 0.509 0.528 0.537 0.541 Monthly Personal Income (€)  Parental ISEI 12.37*** (0.805) 4.555*** (0.806) 3.529*** (0.800) 3.501*** (0.790) 3.508*** (0.786)  Constant 1,331*** (67.99) 1,198*** (72.70) 1,223*** (74.11) 1,237*** (74.68) 1,213*** (74.25)  Observations 3,842 3,842 3,842 3,842 3,842  R-squared 0.123 0.273 0.294 0.301 0.308 . M1 . M2 . M3 . M4 . M5 . Control for education . No control education . Education . Education . Education . Education . 4-category . 8-category . 14-category . 28-category* . ISEI  Parental ISEI 0.477*** (0.0163) 0.158*** (0.0155) 0.137*** (0.0153) 0.131*** (0.0152) 0.131*** (0.0150)  Constant 29.91*** (1.844) 29.18*** (1.737) 29.47*** (1.710) 29.51*** (1.661) 29.25*** (1.626)  Observations 5,269 5,269 5,269 5,269 5,269  R-squared 0.180 0.509 0.528 0.537 0.541 Monthly Personal Income (€)  Parental ISEI 12.37*** (0.805) 4.555*** (0.806) 3.529*** (0.800) 3.501*** (0.790) 3.508*** (0.786)  Constant 1,331*** (67.99) 1,198*** (72.70) 1,223*** (74.11) 1,237*** (74.68) 1,213*** (74.25)  Observations 3,842 3,842 3,842 3,842 3,842  R-squared 0.123 0.273 0.294 0.301 0.308 *** Notes: Robust standard errors in parentheses (calibration weights); Reference categories between parentheses; Controls: gender, age, and nationality; P < 0.001; *28-category education: short or long degrees in: Science; Math/Informatics; Engineering; Architecture; Health; Education; Arts/Humanities; Social Sciences; Business/Administration; Law. Open in new tab Table 3 OLS regression models on the effect of parental ISEI on labour market outcomes (subsample) . M1 . M2 . M3 . M4 . M5 . Control for education . No control education . Education . Education . Education . Education . 4-category . 8-category . 14-category . 28-category* . ISEI  Parental ISEI 0.477*** (0.0163) 0.158*** (0.0155) 0.137*** (0.0153) 0.131*** (0.0152) 0.131*** (0.0150)  Constant 29.91*** (1.844) 29.18*** (1.737) 29.47*** (1.710) 29.51*** (1.661) 29.25*** (1.626)  Observations 5,269 5,269 5,269 5,269 5,269  R-squared 0.180 0.509 0.528 0.537 0.541 Monthly Personal Income (€)  Parental ISEI 12.37*** (0.805) 4.555*** (0.806) 3.529*** (0.800) 3.501*** (0.790) 3.508*** (0.786)  Constant 1,331*** (67.99) 1,198*** (72.70) 1,223*** (74.11) 1,237*** (74.68) 1,213*** (74.25)  Observations 3,842 3,842 3,842 3,842 3,842  R-squared 0.123 0.273 0.294 0.301 0.308 . M1 . M2 . M3 . M4 . M5 . Control for education . No control education . Education . Education . Education . Education . 4-category . 8-category . 14-category . 28-category* . ISEI  Parental ISEI 0.477*** (0.0163) 0.158*** (0.0155) 0.137*** (0.0153) 0.131*** (0.0152) 0.131*** (0.0150)  Constant 29.91*** (1.844) 29.18*** (1.737) 29.47*** (1.710) 29.51*** (1.661) 29.25*** (1.626)  Observations 5,269 5,269 5,269 5,269 5,269  R-squared 0.180 0.509 0.528 0.537 0.541 Monthly Personal Income (€)  Parental ISEI 12.37*** (0.805) 4.555*** (0.806) 3.529*** (0.800) 3.501*** (0.790) 3.508*** (0.786)  Constant 1,331*** (67.99) 1,198*** (72.70) 1,223*** (74.11) 1,237*** (74.68) 1,213*** (74.25)  Observations 3,842 3,842 3,842 3,842 3,842  R-squared 0.123 0.273 0.294 0.301 0.308 *** Notes: Robust standard errors in parentheses (calibration weights); Reference categories between parentheses; Controls: gender, age, and nationality; P < 0.001; *28-category education: short or long degrees in: Science; Math/Informatics; Engineering; Architecture; Health; Education; Arts/Humanities; Social Sciences; Business/Administration; Law. Open in new tab Issues Related to the Measurement of Education A valid estimation of DESO requires an accurate measurement of respondents’ education (Hällsten, 2013). If education is measured using categories that are too broad, the DESO is likely to be overestimated (Sullivan et al., 2017). Consider, for instance, a classification for education with a single category for university graduates, not distinguishing between short- and long-degrees. If subjects from advantaged backgrounds are more likely to achieve long-degrees, and, if long-degrees lead to more favourable socioeconomic outcomes when compared with short-degrees, then, the DESO estimated using this classification will also capture part of the unmeasured effect of the distinction between short- and long-degrees and the ‘true’ DESO will be overestimated. One should, therefore, measure all observable and relevant aspects of a given educational qualification, including the distinction between private and public secondary education, final grades, field of study and prestige of the awarding institution. The relevance of these aspects of horizontal differentiation differs across countries. In principle, however, using the more detailed classification of education will provide a more valid estimate of the DESO. This detailed classification should not, however, include indicators of cognitive skills (as in Sullivan et al., 2017; Jacob and Klein, 2019), or non-cognitive skills.1 Cognitive and non-cognitive skills are mediators of the direct advantage provided by social origins and their inclusion likely implies an underestimation of DESO (Karlson and Birkelund, 2019). In our empirical analysis, we use four different classifications of respondents’ educational attainment. The most disaggregated educational classification comprises 28 categories, with a fine-grained distinction in short- and long-degrees and field of studies among university graduates. With regard to field of study, we consider Science, Engineering, Mathematics, Informatics, and Architecture (that are aggregated as STEM in the 14-fold classification); Health and Veterinary (Health); Social and Behavioural Sciences, Journalism, Education, Humanities and Arts, and Social Services (Social Sciences/Humanities); Law, Business and Administration (Business/Law). As shown in Table 1, the second most disaggregated categorization includes 14 categories differentiating between short- and long-university degrees and major fields of study (between parentheses above). In the third classification, education is recoded into eight categories, distinguishing primary or less (Category 1 in Table 1), lower secondary (2), lower vocational training (3), higher vocational training (4), academic upper-secondary (5), short-degree university education (6–9), long-degrees (10–13), and postgraduate education (Master and PhD) (14). Finally, the most aggregated classification measures education with four broad categories: primary or less (1), lower secondary (2), upper-secondary (3–5), and university (6–14). Variables We use two dependent variables to measure respondents’ labour market outcomes: the International Socio-Economic Index of Occupational Status (ISEI) (Ganzeboom and Treiman, 1996), and monthly net personal income. ISEI scores are defined based on the three-digit CNO-11 Spanish classification of occupations (national version of ISCO-08). Monthly net personal income is constructed from the original variable codified in 10 categories (≤€300; €301–600; €601–900; €901–1,200; €1,201–1,800; €1,801–2,400; €2,401–3,000; €3,001–4,500; €4,501–6,000; >€6,000) by computing the average for each interval. The resulting variable ranges from €150 to €8,000 on a continuous scale (truncated for the highest category), excluding those observations not reporting any kind of income (14 per cent). In the Supplementary Appendix, we carry out several robustness checks with income defined with the original ten categories. Parental social background is measured through the highest parental ISEI. Parental ISEI is constructed from the father’s or mother’s three-digit CNO-11. Unfortunately, parental educational attainment is not available; we also cannot measure parental occupational class because there is no information on parental employment status. In some analyses, social background is also codified into a dummy distinguishing between top (ISCO 1–3) and bottom occupations (ISCO 4–9) (see Supplementary Table A.4. and Figure A.1. for a replication of the main analyses). Top parental occupations include managerial, professional, and technical occupations that broadly correspond to the service class in the EGP-class schema, and are at the top 25 per cent of the parental ISEI distribution (Ganzeboom and Treiman, 1996). The detailed occupational or micro-class category ‘is a grouping of technically similar jobs that is institutionalized in the labour market’ (Grusky and Galescu, 2005: p. 66). We use a scheme that includes 140 detailed occupational categories based on the three-digit occupational classification CNO-11 which closely resembles ISCO-08. This classification organizes jobs into a clearly defined set of groups according to the tasks and duties undertaken in the job and captures boundaries in the division of labour that are socially recognized (International Labour Organization, 2011). To define the parental micro-class, we consider the occupation of the parent who has the highest ISEI. Table 4 Top-15 parental occupations with the strongest DESO on ISEI and personal income (all sample) Parental occupation . DESO . SE . n . ISEI (n = 80,179) .  Life science professionals (biologists) 11.7 2.4 38  Legal professionals (lawyers and judges) 10.2 0.8 388  Physical/earth science professionals (physicists, chemists) and mathematicians 9.9 1.4 110  University and higher education teachers 9.6 1.1 175  Managing directors and chief executives 9.3 0.9 274  Finance professionals (accountants, financial advisers, and analysts) 8.9 1.4 115  Economists 8.9 1.6 89  Physical and engineering science technicians 8.9 1.1 173  Other health professionals (dentists, physiotherapists, dieticians, audiologists, and optometrists) 8.9 1.5 90  Legal professionals not elsewhere classified (notary) 8.7 1.3 137  Social professionals (sociologists, psychologists, philosophers, historians…) 8.1 1.5 91  Legislators and senior officials 8.0 2.1 48  Medical doctors 7.9 0.6 651  Information and communications technology service managers 7.7 0.9 278  Retail and wholesale trade managers 7.6 0.7 449 Monthly personal income (€) (n = 49,722)  Sales, marketing, and development managers 454.2 101.4 42  Managing directors and chief executives 428.7 52.4 163  University and higher education teachers 424.0 61.4 118  Management and organization analysts 383.7 52.8 161  Legal professionals (lawyers and judges) 383.5 46.1 215  Authors, journalists and linguists 334.6 83.7 62  Legislators and senior officials 329.4 121.7 29  Physical and engineering science technicians 325.0 62.0 115  Legal professionals not elsewhere classified (notary) 317.2 68.0 95  Sales, marketing, and public relations professionals 304.9 119.8 30  Pharmaceutical professionals 293.8 83.7 62  Retail and wholesale trade managers 280.3 40.3 283  Commissioned armed forces officers 270.8 56.5 139  Business Services and Administration Managers 258.9 39.0 306  Life science professionals (biologists) 256.7 126.3 27 Parental occupation . DESO . SE . n . ISEI (n = 80,179) .  Life science professionals (biologists) 11.7 2.4 38  Legal professionals (lawyers and judges) 10.2 0.8 388  Physical/earth science professionals (physicists, chemists) and mathematicians 9.9 1.4 110  University and higher education teachers 9.6 1.1 175  Managing directors and chief executives 9.3 0.9 274  Finance professionals (accountants, financial advisers, and analysts) 8.9 1.4 115  Economists 8.9 1.6 89  Physical and engineering science technicians 8.9 1.1 173  Other health professionals (dentists, physiotherapists, dieticians, audiologists, and optometrists) 8.9 1.5 90  Legal professionals not elsewhere classified (notary) 8.7 1.3 137  Social professionals (sociologists, psychologists, philosophers, historians…) 8.1 1.5 91  Legislators and senior officials 8.0 2.1 48  Medical doctors 7.9 0.6 651  Information and communications technology service managers 7.7 0.9 278  Retail and wholesale trade managers 7.6 0.7 449 Monthly personal income (€) (n = 49,722)  Sales, marketing, and development managers 454.2 101.4 42  Managing directors and chief executives 428.7 52.4 163  University and higher education teachers 424.0 61.4 118  Management and organization analysts 383.7 52.8 161  Legal professionals (lawyers and judges) 383.5 46.1 215  Authors, journalists and linguists 334.6 83.7 62  Legislators and senior officials 329.4 121.7 29  Physical and engineering science technicians 325.0 62.0 115  Legal professionals not elsewhere classified (notary) 317.2 68.0 95  Sales, marketing, and public relations professionals 304.9 119.8 30  Pharmaceutical professionals 293.8 83.7 62  Retail and wholesale trade managers 280.3 40.3 283  Commissioned armed forces officers 270.8 56.5 139  Business Services and Administration Managers 258.9 39.0 306  Life science professionals (biologists) 256.7 126.3 27 Note: Results from OLS regressions with parental occupation fixed-effects, and controls for 8-category respondents’ education, gender, nationality, and age. Only parental occupations P-value < 0.001 are included. The reference category is the parental occupation construction workers. SE = standard error. Open in new tab Table 4 Top-15 parental occupations with the strongest DESO on ISEI and personal income (all sample) Parental occupation . DESO . SE . n . ISEI (n = 80,179) .  Life science professionals (biologists) 11.7 2.4 38  Legal professionals (lawyers and judges) 10.2 0.8 388  Physical/earth science professionals (physicists, chemists) and mathematicians 9.9 1.4 110  University and higher education teachers 9.6 1.1 175  Managing directors and chief executives 9.3 0.9 274  Finance professionals (accountants, financial advisers, and analysts) 8.9 1.4 115  Economists 8.9 1.6 89  Physical and engineering science technicians 8.9 1.1 173  Other health professionals (dentists, physiotherapists, dieticians, audiologists, and optometrists) 8.9 1.5 90  Legal professionals not elsewhere classified (notary) 8.7 1.3 137  Social professionals (sociologists, psychologists, philosophers, historians…) 8.1 1.5 91  Legislators and senior officials 8.0 2.1 48  Medical doctors 7.9 0.6 651  Information and communications technology service managers 7.7 0.9 278  Retail and wholesale trade managers 7.6 0.7 449 Monthly personal income (€) (n = 49,722)  Sales, marketing, and development managers 454.2 101.4 42  Managing directors and chief executives 428.7 52.4 163  University and higher education teachers 424.0 61.4 118  Management and organization analysts 383.7 52.8 161  Legal professionals (lawyers and judges) 383.5 46.1 215  Authors, journalists and linguists 334.6 83.7 62  Legislators and senior officials 329.4 121.7 29  Physical and engineering science technicians 325.0 62.0 115  Legal professionals not elsewhere classified (notary) 317.2 68.0 95  Sales, marketing, and public relations professionals 304.9 119.8 30  Pharmaceutical professionals 293.8 83.7 62  Retail and wholesale trade managers 280.3 40.3 283  Commissioned armed forces officers 270.8 56.5 139  Business Services and Administration Managers 258.9 39.0 306  Life science professionals (biologists) 256.7 126.3 27 Parental occupation . DESO . SE . n . ISEI (n = 80,179) .  Life science professionals (biologists) 11.7 2.4 38  Legal professionals (lawyers and judges) 10.2 0.8 388  Physical/earth science professionals (physicists, chemists) and mathematicians 9.9 1.4 110  University and higher education teachers 9.6 1.1 175  Managing directors and chief executives 9.3 0.9 274  Finance professionals (accountants, financial advisers, and analysts) 8.9 1.4 115  Economists 8.9 1.6 89  Physical and engineering science technicians 8.9 1.1 173  Other health professionals (dentists, physiotherapists, dieticians, audiologists, and optometrists) 8.9 1.5 90  Legal professionals not elsewhere classified (notary) 8.7 1.3 137  Social professionals (sociologists, psychologists, philosophers, historians…) 8.1 1.5 91  Legislators and senior officials 8.0 2.1 48  Medical doctors 7.9 0.6 651  Information and communications technology service managers 7.7 0.9 278  Retail and wholesale trade managers 7.6 0.7 449 Monthly personal income (€) (n = 49,722)  Sales, marketing, and development managers 454.2 101.4 42  Managing directors and chief executives 428.7 52.4 163  University and higher education teachers 424.0 61.4 118  Management and organization analysts 383.7 52.8 161  Legal professionals (lawyers and judges) 383.5 46.1 215  Authors, journalists and linguists 334.6 83.7 62  Legislators and senior officials 329.4 121.7 29  Physical and engineering science technicians 325.0 62.0 115  Legal professionals not elsewhere classified (notary) 317.2 68.0 95  Sales, marketing, and public relations professionals 304.9 119.8 30  Pharmaceutical professionals 293.8 83.7 62  Retail and wholesale trade managers 280.3 40.3 283  Commissioned armed forces officers 270.8 56.5 139  Business Services and Administration Managers 258.9 39.0 306  Life science professionals (biologists) 256.7 126.3 27 Note: Results from OLS regressions with parental occupation fixed-effects, and controls for 8-category respondents’ education, gender, nationality, and age. Only parental occupations P-value < 0.001 are included. The reference category is the parental occupation construction workers. SE = standard error. Open in new tab Methods We use ordinal least squares (OLS) regression models to assess the DESO on ISEI and income. In the Supplementary Appendix, we replicate the analyses using quantile regressions for income and generalized ordinal logistic models for the original ten income categories. In the main analyses based on OLS regressions, we first, estimate a sequence of models with different classifications of education, from the most aggregate to the most disaggregated one that includes fields of study. Secondly, to answer our first research question on the possible heterogeneity of the DESO, we include an interaction-term between parental ISEI and respondents’ education, measured with the 8-fold classification. Finally, to answer the second research question, we estimate parental occupation fixed-effects to identify which specific parental occupations provide the largest DESO, net of respondents’ educational attainment (8-category). The reference category for the DESO is the parental occupation ‘construction workers’, which is one of the most common low-ISEI parental occupations. As we will see, we further explore heterogeneity in the DESO by respondents’ education and rank the parental occupations that show the largest DESO on ISEI among non-degree holders and on income among degree holders. We also identify: (i) the most common occupational destinations for low educational achievers, distinguishing low and high social origins. In this way we can determine in which occupations the occupational compensatory advantage in ISEI comes about; and (ii) those respondents’ occupations with largest DESO on income among degree holders. We can therefore scrutinize in which occupations the largest income boosting advantage by social origins occurs. In all models, we control for gender (see Supplementary Table A.6. and Figures A.4. and A.5. for separate analysis by gender), age groups (28–37; 38–47; 48–57; 58–65) and nationality, distinguishing Spanish or foreigners with double nationality. Table 6 Bottom-panel: Top-15 respondents’ occupation with the largest DESO on personal net monthly income (€) among university graduates Respondent’s occupation . DESO (€) . Std. err. . n . Managing directors and chief executives 21.02 4.76 67 Ship and aircraft controllers and technicians 20.36 7.97 27 Restaurant managers 14.65 6.87 41 Sales, marketing and development managers 13.43 4.70 60 Production managers in agriculture, foresty and fisheries 12.72 5.05 50 Legal professionals (lawyers and judges) 12.31 2.07 283 Finance professionals (accountants, financial advisers, and analysts) 10.54 3.35 134 Tellers, money collectors and related clerks 10.35 3.99 103 Pharmaceutical professionals 7.32 3.48 87 Business services and administration managers 7.01 2.02 330 Professional services managers not classified elsewhere 6.86 2.70 216 Other health professionals (dentists, physiotherapists, dieticians, audiologists, optometrists) 6.28 2.69 178 Business services agents (i.e. real state) 5.55 2.19 318 Physical and engineering science technicians 5.26 2.48 220 Management and organization analysts 4.91 1.67 463 Respondent’s occupation . DESO (€) . Std. err. . n . Managing directors and chief executives 21.02 4.76 67 Ship and aircraft controllers and technicians 20.36 7.97 27 Restaurant managers 14.65 6.87 41 Sales, marketing and development managers 13.43 4.70 60 Production managers in agriculture, foresty and fisheries 12.72 5.05 50 Legal professionals (lawyers and judges) 12.31 2.07 283 Finance professionals (accountants, financial advisers, and analysts) 10.54 3.35 134 Tellers, money collectors and related clerks 10.35 3.99 103 Pharmaceutical professionals 7.32 3.48 87 Business services and administration managers 7.01 2.02 330 Professional services managers not classified elsewhere 6.86 2.70 216 Other health professionals (dentists, physiotherapists, dieticians, audiologists, optometrists) 6.28 2.69 178 Business services agents (i.e. real state) 5.55 2.19 318 Physical and engineering science technicians 5.26 2.48 220 Management and organization analysts 4.91 1.67 463 Notes: In bold those respondents’ occupations equal to parental occupations with the largest DESO on income (Table 4 and Supplementary A.7. bottom-panels); controls for respondents’ education (8-fold), gender, nationality, and age groups; coefficients with P-value < 0.05; n = 13,249. Open in new tab Table 6 Bottom-panel: Top-15 respondents’ occupation with the largest DESO on personal net monthly income (€) among university graduates Respondent’s occupation . DESO (€) . Std. err. . n . Managing directors and chief executives 21.02 4.76 67 Ship and aircraft controllers and technicians 20.36 7.97 27 Restaurant managers 14.65 6.87 41 Sales, marketing and development managers 13.43 4.70 60 Production managers in agriculture, foresty and fisheries 12.72 5.05 50 Legal professionals (lawyers and judges) 12.31 2.07 283 Finance professionals (accountants, financial advisers, and analysts) 10.54 3.35 134 Tellers, money collectors and related clerks 10.35 3.99 103 Pharmaceutical professionals 7.32 3.48 87 Business services and administration managers 7.01 2.02 330 Professional services managers not classified elsewhere 6.86 2.70 216 Other health professionals (dentists, physiotherapists, dieticians, audiologists, optometrists) 6.28 2.69 178 Business services agents (i.e. real state) 5.55 2.19 318 Physical and engineering science technicians 5.26 2.48 220 Management and organization analysts 4.91 1.67 463 Respondent’s occupation . DESO (€) . Std. err. . n . Managing directors and chief executives 21.02 4.76 67 Ship and aircraft controllers and technicians 20.36 7.97 27 Restaurant managers 14.65 6.87 41 Sales, marketing and development managers 13.43 4.70 60 Production managers in agriculture, foresty and fisheries 12.72 5.05 50 Legal professionals (lawyers and judges) 12.31 2.07 283 Finance professionals (accountants, financial advisers, and analysts) 10.54 3.35 134 Tellers, money collectors and related clerks 10.35 3.99 103 Pharmaceutical professionals 7.32 3.48 87 Business services and administration managers 7.01 2.02 330 Professional services managers not classified elsewhere 6.86 2.70 216 Other health professionals (dentists, physiotherapists, dieticians, audiologists, optometrists) 6.28 2.69 178 Business services agents (i.e. real state) 5.55 2.19 318 Physical and engineering science technicians 5.26 2.48 220 Management and organization analysts 4.91 1.67 463 Notes: In bold those respondents’ occupations equal to parental occupations with the largest DESO on income (Table 4 and Supplementary A.7. bottom-panels); controls for respondents’ education (8-fold), gender, nationality, and age groups; coefficients with P-value < 0.05; n = 13,249. Open in new tab Sample Selection The analytic sample is restricted to those individuals aged 28–65 who, at the time of the survey, were working, unemployed, or retired (having worked previously). For the unemployed or retired individuals at the time of the survey, we consider their last available occupation or income. We have replicated the analyses keeping only those individuals who were employed at the time of the surveys and the results were highly consistent (results available upon request). Previous longitudinal studies have showed that the DESO on socioeconomic status (ISEI), prestige (SIOPS) and class attainment remains fairly stable over the employment career (Barone, Lucchini and Schizzerotto, 2011; Manzoni, Härkönen and Mayer, 2014; Passaretta et al., 2018; Ballarino, Cantalini and Panichella, 2020), although some studies report that it weakens for university graduates (Bukodi and Goldthorpe, 2011; Jacob, Klein and Iannelli, 2015; Jacob and Klein, 2019). We are not aware of any longitudinal study of DESO on income. As a robustness check, we have replicated the analyses separately for the age groups 28–47 and 48–65. The estimates of DESO are very similar for the two age groups, suggesting that our cross-sectional estimates on the broad age interval 28-to-65 are not overly biased (see Supplementary Table A.6. for details). We use two samples in the empirical analysis. First, we use the large data set that pools 54 monthly barometers (n = 133,902) and GSS (2013/n = 5,094; 2015/n = 5,290) to estimate the more complex models with the parental background-respondents’ education interaction and with parental and children-occupation fixed effects. Second, to assess possible bias in DESO measurement, we use the GSS subsample (n = 10,384) that enables us to control for the most detailed classifications of education (14–28 categories), which are only available in the GSS surveys. After we apply the age and labour status restrictions and list-wise deletion, the full pooled sample ranges between 80,179 cases for predicting ISEI and 49,722 for personal income, while the GSS subsample ranges from 5,269 cases for predicting ISEI to 3,842 cases for personal income (see Supplementary Appendix A for comparability issues between ISEI and income analytic samples). Table 2 displays summary statistics for all the variables included in the regression analyses in the full pooled sample and GSS subsample. Results The upper panel in Table 3 shows the estimates of the DESO on respondent’s ISEI, while the lower panel presents the results for respondent’s income. In Model 1, we estimate the total effect of parental ISEI without controlling for education. In the next models, we control for respondents’ educational attainment from the lowest (4-category in Model 2) to highest disaggregated categorization (28-category in Model 5). The first important finding is that, if one compares the estimate for parental ISEI between models 1 and models 5, education attainment accounts for about 75 per cent of the total origin-destination and is the main channel of inequality reproduction. The DESO represents then the remaining 25 per cent of the total association. The size of the DESO is also substantively relevant. For instance, considering the estimates of Models 5, a respondent whose father and/or mother is a medical doctor (parental ISEI = 88) is expected to have an eight points-larger ISEI than those respondents with the same level of education whose father and/or mother is an unskilled blue collar worker (parental ISEI = 20).2 The respondent with the medical parent also earns €239 larger monthly personal income, on average. The size of these estimates is in line with previous findings for Spain and supports the claim that education does not fully function as the great equalizer of opportunities in the Spanish labour market. The type of families one comes from makes a marked difference in terms of socioeconomic success among subjects with the same level of education. Next, we can compare the effect size of the parental ISEI coefficients across models in each panel. When the most aggregated educational classification of education is used (Models 2), the effect of parental ISEI is overestimated by 21 per cent (ISEI) and 30 per cent (income) when compared with our most detailed educational classification with 28 categories (Models 5).3 With the 8-fold classification that disaggregates education horizontally in upper-secondary and between short- and long-degrees university degree (Models 3), the overestimation of DESO is considerably lower, at about 5 per cent (ISEI) and 1 per cent (income), in comparison to Models 5. Finally, when using the 14-category classification accounting for major fields of study (Models 4), the effect of DESO does not change with respect to Models 5. Our analysis shows that, if one does not take into account horizontal differences at upper-secondary education levels (vocational training and academic upper-secondary) and vertical differences at the university level (short vs. long degrees and postgraduate studies), DESO is seriously overestimated. However, an 8-fold classification works reasonably well and the bias is limited in comparison to a 28-fold classification that considers educational level, length of degree (short/long) and fields of study in Spain. Regarding our first research question on the heterogeneity of DESO by respondents’ educational level, Figure 1 illustrates the average marginal effects (AMEs) of parental ISEI on labour market outcomes by respondents’ education in eight categories. These AMEs are derived from models with interaction terms between parental ISEI and respondents’ education, controlling for age groups, gender and nationality. The left-hand graph in Figure 1 shows that, in line with the compensatory advantage hypothesis discussed above, the influence of DESO on respondents’ ISEI is considerably larger for those individuals with lower-medium educational levels than for those who have achieved long and advanced university degrees (linear combination test of coefficients: β = 0.15 primary or less vs. long-degree holders, P-value < 0.01; β = 0.10 lower secondary vs. long-degree holders, P-value < 0.01; β = 0.07 upper-secondary versus long-degree holders, P-value < 0.01). Figure 1. Open in new tabDownload slide Interaction models: average marginal effects of parental ISEI on labour market outcomes by respondents’ education with 95% confidence intervals (all sample) Note: Sample size: ISEI = 80,179; personal income = 49,722. Controls: age groups, gender, nationality, and 8-category education. Figure 1. Open in new tabDownload slide Interaction models: average marginal effects of parental ISEI on labour market outcomes by respondents’ education with 95% confidence intervals (all sample) Note: Sample size: ISEI = 80,179; personal income = 49,722. Controls: age groups, gender, nationality, and 8-category education. In the case of income (right-hand graph in Figure 1), the pattern of heterogeneity of DESO by respondents’ education is different, following a U-shape. The largest DESO on income is found among those individuals with low education and among those who attained long university degrees and postgraduate studies. There are few respondents with primary education coming from high socioeconomic status families (see Supplementary Tables A.9 and A.10) and, for this reason, the confidence intervals are extremely large. A more informative comparison, not based on extreme values, is made between those with upper-secondary education (vocational or academic) and those with long university degrees. The resulting pattern is opposite to the one observed for ISEI; in comparison to individuals with upper-secondary education, the marginal effect of parental ISEI is around €1.5–1.8 (P-value < 0.05) larger among long-degree holders. The boosting advantage in terms of income among long university degree holders is also confirmed when we estimate a quantile regression and non-proportional ordered logit model on the original ten income categories (see Supplementary Figure A.3. and Table A.5.). Table 5 Top-15 most common respondents’ occupations and ISEI for individuals with lower-secondary education Top parental occupations (ISCO 1–3) (n = 1,985) . Bottom parental occupations (ISCO 4–9) (n = 20,231) . Respondent’s occupation . n . % . ISEI . Respondent’s occupation . n . % . ISEI . Shop salespersons 131 6.6 29.47 Domestic, hotel and office cleaners and helpers 1,711 8.46 14.64 Domestic, hotel and office cleaners and helpers 111 5.59 14.64 Shop salespersons 1,283 6.34 29.47 Waiters and bartenders 103 5.19 24.53 Bricklayers and related workers 1,161 5.74 25.94 Administrative and specialized secretaries 80 4.03 57.99 Waiters and bartenders 981 4.85 24.53 Sales and purchasing agents and brokers 67 3.38 57.97 Heavy truck drivers 656 3.24 25.71 Bricklayers and related workers 66 3.32 25.94 Market gardeners and crop growers 601 2.97 16.34 Cooks 57 2.87 24.53 Agricultural labourers 556 2.75 11.74 Heavy truck drivers 51 2.57 25.71 Cooks 514 2.54 24.53 Professional services managers not classified elsewhere 44 2.22 65.01 Domestic workers 500 2.47 11.64 Car, van and motorcycle drivers 41 2.07 30.11 Other sales workers 463 2.29 29.47 Transport and storage labourers 38 1.91 19.66 Manufacturing labourers 377 1.86 17.55 Restaurant managers 37 1.86 43.85 Food and related products machine operators 373 1.84 29.84 Protective services workers 37 1.86 36.86 Administrative and specialized secretaries 351 1.73 57.99 Retail and wholesale trade managers 35 1.76 51.56 Car, van and motorcycle drivers 344 1.7 30.11 Other sales workers 35 1.76 29.47 Transport and storage labourers 338 1.67 19.66 Top parental occupations (ISCO 1–3) (n = 1,985) . Bottom parental occupations (ISCO 4–9) (n = 20,231) . Respondent’s occupation . n . % . ISEI . Respondent’s occupation . n . % . ISEI . Shop salespersons 131 6.6 29.47 Domestic, hotel and office cleaners and helpers 1,711 8.46 14.64 Domestic, hotel and office cleaners and helpers 111 5.59 14.64 Shop salespersons 1,283 6.34 29.47 Waiters and bartenders 103 5.19 24.53 Bricklayers and related workers 1,161 5.74 25.94 Administrative and specialized secretaries 80 4.03 57.99 Waiters and bartenders 981 4.85 24.53 Sales and purchasing agents and brokers 67 3.38 57.97 Heavy truck drivers 656 3.24 25.71 Bricklayers and related workers 66 3.32 25.94 Market gardeners and crop growers 601 2.97 16.34 Cooks 57 2.87 24.53 Agricultural labourers 556 2.75 11.74 Heavy truck drivers 51 2.57 25.71 Cooks 514 2.54 24.53 Professional services managers not classified elsewhere 44 2.22 65.01 Domestic workers 500 2.47 11.64 Car, van and motorcycle drivers 41 2.07 30.11 Other sales workers 463 2.29 29.47 Transport and storage labourers 38 1.91 19.66 Manufacturing labourers 377 1.86 17.55 Restaurant managers 37 1.86 43.85 Food and related products machine operators 373 1.84 29.84 Protective services workers 37 1.86 36.86 Administrative and specialized secretaries 351 1.73 57.99 Retail and wholesale trade managers 35 1.76 51.56 Car, van and motorcycle drivers 344 1.7 30.11 Other sales workers 35 1.76 29.47 Transport and storage labourers 338 1.67 19.66 Notes: In bold non-shared occupations across parental occupations’ groups. Open in new tab Table 5 Top-15 most common respondents’ occupations and ISEI for individuals with lower-secondary education Top parental occupations (ISCO 1–3) (n = 1,985) . Bottom parental occupations (ISCO 4–9) (n = 20,231) . Respondent’s occupation . n . % . ISEI . Respondent’s occupation . n . % . ISEI . Shop salespersons 131 6.6 29.47 Domestic, hotel and office cleaners and helpers 1,711 8.46 14.64 Domestic, hotel and office cleaners and helpers 111 5.59 14.64 Shop salespersons 1,283 6.34 29.47 Waiters and bartenders 103 5.19 24.53 Bricklayers and related workers 1,161 5.74 25.94 Administrative and specialized secretaries 80 4.03 57.99 Waiters and bartenders 981 4.85 24.53 Sales and purchasing agents and brokers 67 3.38 57.97 Heavy truck drivers 656 3.24 25.71 Bricklayers and related workers 66 3.32 25.94 Market gardeners and crop growers 601 2.97 16.34 Cooks 57 2.87 24.53 Agricultural labourers 556 2.75 11.74 Heavy truck drivers 51 2.57 25.71 Cooks 514 2.54 24.53 Professional services managers not classified elsewhere 44 2.22 65.01 Domestic workers 500 2.47 11.64 Car, van and motorcycle drivers 41 2.07 30.11 Other sales workers 463 2.29 29.47 Transport and storage labourers 38 1.91 19.66 Manufacturing labourers 377 1.86 17.55 Restaurant managers 37 1.86 43.85 Food and related products machine operators 373 1.84 29.84 Protective services workers 37 1.86 36.86 Administrative and specialized secretaries 351 1.73 57.99 Retail and wholesale trade managers 35 1.76 51.56 Car, van and motorcycle drivers 344 1.7 30.11 Other sales workers 35 1.76 29.47 Transport and storage labourers 338 1.67 19.66 Top parental occupations (ISCO 1–3) (n = 1,985) . Bottom parental occupations (ISCO 4–9) (n = 20,231) . Respondent’s occupation . n . % . ISEI . Respondent’s occupation . n . % . ISEI . Shop salespersons 131 6.6 29.47 Domestic, hotel and office cleaners and helpers 1,711 8.46 14.64 Domestic, hotel and office cleaners and helpers 111 5.59 14.64 Shop salespersons 1,283 6.34 29.47 Waiters and bartenders 103 5.19 24.53 Bricklayers and related workers 1,161 5.74 25.94 Administrative and specialized secretaries 80 4.03 57.99 Waiters and bartenders 981 4.85 24.53 Sales and purchasing agents and brokers 67 3.38 57.97 Heavy truck drivers 656 3.24 25.71 Bricklayers and related workers 66 3.32 25.94 Market gardeners and crop growers 601 2.97 16.34 Cooks 57 2.87 24.53 Agricultural labourers 556 2.75 11.74 Heavy truck drivers 51 2.57 25.71 Cooks 514 2.54 24.53 Professional services managers not classified elsewhere 44 2.22 65.01 Domestic workers 500 2.47 11.64 Car, van and motorcycle drivers 41 2.07 30.11 Other sales workers 463 2.29 29.47 Transport and storage labourers 38 1.91 19.66 Manufacturing labourers 377 1.86 17.55 Restaurant managers 37 1.86 43.85 Food and related products machine operators 373 1.84 29.84 Protective services workers 37 1.86 36.86 Administrative and specialized secretaries 351 1.73 57.99 Retail and wholesale trade managers 35 1.76 51.56 Car, van and motorcycle drivers 344 1.7 30.11 Other sales workers 35 1.76 29.47 Transport and storage labourers 338 1.67 19.66 Notes: In bold non-shared occupations across parental occupations’ groups. Open in new tab It could be argued, though, that these larger inequalities in income among long-degree holders reflect the fact that individuals from advantaged families are more likely to obtain a degree in more lucrative fields of study. In order to rule out this possibility, we have also estimated an interaction between the 14-fold educational classification including major fields of study and parental ISEI (Supplementary Figure A.2.). This additional analysis confirms the key finding that the DESO on income is larger among long-degree holders even controlling for field of study, in particular for those with a degree in law, business, and health. These observed different patterns of DESO heterogeneity by respondents’ education and labour market outcomes—larger DESO among low-medium educated individuals for socioeconomic status and among the highly-educated for income—suggest that the classic explanations accounting for the lower effect of DESO among graduates, those which emphasize either the meritocratic characteristics of the labour market or the positive selection of disadvantaged individuals, do not suffice. As we pointed out above, these opposite patterns can be reconciled if one focuses on intergenerational mobility strategies of the upper classes. We argue that the larger DESO observed among non-university degree holders in terms of ISEI and the larger DESO among university degree holders in terms of income may be explained by compensatory strategies followed by socioeconomically advantaged families to avoid downward social mobility in case of ‘failure’ to obtain a university degree and by income-maximization strategies when a university degree is achieved. Lastly, we address our second research question, on which specific parental occupations provide the largest DESO to their children, and which destination occupations cash in the largest rewards. Firstly, in Table 4, we present the results of a parental occupation fixed-effect model presenting the estimates for the top-15 parental occupations with the largest DESO for each labour market outcome. We present estimates only for occupations with more than 25 observations (140 out of 170 occupations) in order to focus on reliable findings that do not depend on few extreme cases (detailed results are available upon request). The ranking in each of the two panels of the table is not very important as there is considerable variation around the point estimates. It is relevant, however, that we tend to find similar occupations in the two panels of the table. The occupations listed in Table 4 can be generally categorized into two groups of high-grade professional and managerial positions. The first group includes prestigious sociocultural and scientific professions (university professors, journalists, medical doctors, biologists, and engineers) and liberal professionals (economists, lawyers, and notaries) with high levels of cultural capital and job security (Laurison and Friedman, 2016). These occupations are also characterized by processes of social closure that limit access to them (Grusky and Sørensen, 1998). The second group consists of high-grade managers and administrators, with high levels of economic and social capital and, therefore, considerable power and influence in large organizations, such as big private firms and corporations but also in the public administration, political parties, special interest organizations (legislators and senior officials), and the army (commissioned officers). This ranking of parental occupations exerting the largest DESO does not seem to vary considerably when stratifying this analysis by respondents’ education, as shown in Supplementary Table A.7. Secondly, to shed more light on possible mechanisms accounting for the observed patterns of compensatory and boosting advantage discussed above, in Table 5, we show the most common destination occupations by social origins among low educational achievers, and the occupations with the largest DESO in income among high educational achievers. The upper panel of Table 5 shows that individuals with lower-secondary education (see Supplementary Table A.8. for upper-secondary) coming from managerial or professional families (ISCO 1–3) are more likely to work in managerial occupations with higher socioeconomic status (restaurant or retail-wholesale trade managers; business and administration associate professionals) than individuals from more disadvantaged families (ISCO 4–9), who are over-represented in bottom occupations such as agricultural labourers, unskilled industrial workers, and domestic workers. This descriptive evidence suggests that individuals from advantaged families who failed in the educational system may experience compensatory advantage and land in managerial jobs in retail and hospitality that typically require non-cognitive skills. They can also be favoured by the transmission of economic capital to start their own business in those industries or by finding employment in the family business. We also find that retail/wholesale trade managers (micro-class with one of the largest inter-generational reproduction rate) and business and administration managers/professionals are the parental occupations with the largest DESO on ISEI (see Table 4 and Supplementary Table A.7.). Regarding individuals with college education, Table 6 illustrates the top-15 destination occupations with the largest DESO on income. The largest within-occupation inequality in income is found among high-grade managers and administrators (CEO, managers in administration, sales, research and development, marketing), and liberal professionals in law and finance (lawyers, accountants). This social-origin gap in income within-occupations is substantial. For instance, legal professionals whose parents themselves are legal professionals (ISEI = 85) are expected to make about €700 per month more than legal professionals whose parents are construction workers (ISEI = 26). Among the top-15 destination occupations with largest social-origin gap, eight are the same parental occupations with largest DESO on income shown in Table 4 and Supplementary Table A.7. Particularly, high-grade managers and lawyers are the micro-classes with the largest levels of intergenerational reproduction in Spain. This analysis suggests that boosting advantage may be partially driven by micro-class reproduction among lawyers and managers, so that privileged children work in the same occupations as their parents and cash in larger rewards. Though this exploratory analysis takes a step forward in identifying where the DESO originates at the level of parental micro-classes and which destination occupations accrue larger rewards, we can only speculate about the actual mechanisms that underlie the observed DESO in terms of income. Overall, among the mechanisms discussed above (i.e. direct inheritance of family business; social networks; cognitive and non-cognitive skills; career aspirations; and favouritism), it seems that lawyers may use their networks to help their children attain starting jobs or internships with better career prospects or directly involve them as partners. Parents who work at high managerial occupations may use their networks and influence to grant access to their children to better paid jobs or directly transmit the family business to their offspring, as in the case, for instance, of hospitality and retail managers who are also owners of their own firm. Robustness Checks We have performed additional analyses separately for men and women, for two broad age groups (28–47 and 48–65), and using a more detailed combination of educational level and field of studies. We have also performed quantile regressions on income and estimated ordered logistic models on the original income categories, relaxing the proportionality assumption. The rationale of these supplementary analyses are discussed in the Supplementary Appendix B. The results of these robustness checks confirm the core findings presented in the body of article. Conclusions In this article, we addressed two main research questions related to the social-origins gap in socioeconomic status and income: (i) how the DESO varies depending on the respondents’ level of education; and (ii) for which specific parental and respondents’ occupations the DESO is larger. With regard to the first question, we have found a larger DESO among the low educated in respect to socioeconomic status (ISEI) and a larger DESO among the highly educated in respect to income. We argue that low-educated individuals from advantaged families manage to minimize downward occupational mobility and benefit from a compensatory advantage in their socioeconomic status. At the same time, highly educated individuals from high social origins enjoy a boosting advantage in income. Within high socioeconomic status occupations, they tend to earn higher income compared to similarly highly-educated individuals who come from lower social origins. To visually summarize these findings, advantaged individuals show a double movement of ‘lift and push’. Upper-class parents manage to lift up their children who have failed to achieve a college degree in order to avoid unskilled occupations, and provide an extra push to earn larger incomes to those who have earned the degree. With regard to the second question, we have identified two broad clusters of parental and respondent’s occupations where the DESO is largest: high prestige liberal professions, on the one hand, and managerial occupations in large private companies, interest organizations, and public administration, on the other. We have found that the largest compensatory advantage for occupational attainment comes about in low-middle ranked managerial or administrative positions in the service sector (restaurant, retail, and business). These occupations do not require high educational credentials of their applicants. They do however, require, economic resources to launch small businesses and the non-cognitive skills (communication skills, interpersonal skills, and foreign languages) to successfully manage them. The boosting advantage on income is most common among liberal professionals in law and finance (lawyers and accountants) and high-grade managers and administrators. Subjects from socioeconomically advantaged families can receive economic support from their parents and thus can accept starting jobs or internships that are lower paid but with better career prospects. Parental networks can also facilitate access to better paid jobs. In the case of liberal professionals there is also very high occupational immobility. For instance, in Spain, some 25 per cent of the children of lawyers are lawyers themselves, so that they can work in partnership with their parents and have access to their portfolio of clients. These findings might reflect some specificities of the Spanish educational system and labour market. Still, we believe that the scope of our results goes beyond the particular national case considered in this article. Our results are suggestive of a general model of inequality reproduction beyond educational attainment, one that combines key ideas from risk aversion to social demotion and maximally maintained inequality theories together with a micro-class approach. In this general model, the reproduction of inequality is the result of parental strategies whose primary goal is to avoid downward social mobility (Goldthorpe, 2007). Beyond this first-order concern, parents strive to provide an advantage to their children whenever that is possible (Lucas, 2001). Education remains the primary channel of inequality reproduction (Hout and DiPrete, 2006), as our findings also clearly indicate. In some instances, however, education does not function effectively as a transmission chain of inequality, simply because some children of socioeconomically advantaged families fail to achieve higher education, or because educational credentials do not sufficiently screen among college-degree holders. Compensatory advantage in occupational attainment of low educational achievers is one instance of a successful strategy of downward mobility avoidance, possibly one of the most successful, since, due to failure in education, the risk of downward mobility is particularly severe. Conversely, the boosting advantage in income among those who are highly educated is an example of effectively maintained inequality. If these are the mobility strategies when education does not suffice, parental occupations are the micro conduits of inequality reproduction. Parental occupations provide the specific cultural, human, social, and economic capital through which parental mobility strategies are pursued. With regard to future research in this area, we think there are three priorities. First, we would need to consolidate and replicate the findings on compensatory and boosting advantage in labour market outcomes in other countries. That would also be a first step towards a better understanding of how various institutional factors affect DESO and its heterogeneity by level of educational attainment on different labour market outcomes. We cannot develop a theory of cross-country variation in DESO and its heterogeneity here, but we can sketch out some key elements of it. First of all, we can expect that the compensatory advantage in occupational attainment (socioeconomic status and class) among non-college degree holders will be a cross-country regularity, relatively unaffected by institutional factors. Available evidence seems to bear out this expectation (Goldthorpe and Jackson, 2008; Bernardi and Ballarino, 2016; Fiel, 2020). Compensatory advantage can be seen as an inherent component of the logic of class reproduction. Socioeconomically advantaged families can always find solutions to limit the negative consequences of educational failure through their social networks and economic resources. They can, for instance, help their children to find middle ranked jobs in sales and commerce or lend them the money to open up their own (small) business. On the other hand, we can expect that the social-origins gap in income among university graduates varies, depending on country-specific institutional features that affect supply and demand of college degree holders (Bol et al., 2019). The social-origins gap in income among graduates is actually a less well-established finding across countries. Tentatively, we can conjecture that (i) the more selective and differentiated tertiary education is and (ii) the smaller the within-occupation variation in income is, the smaller the social-origin gap in income among graduates will be. In such a scenario, education is the main channel that determines occupational attainment and income, leaving little room for other factors over and above education where social origins is likely to make a difference. Second, it would be interesting to employ the detailed occupational approach present in this article using longitudinal data. In particular, we are not aware of any longitudinal study of DESO on income and we suspect that the social origin gap on income might widen over the employment career (Ballarino et al., 2020). Third, we need in-depth research on mechanisms underlying the social-origins gap. The role that cognitive and non-cognitive skills, and economic, cultural, social resources linked to the family of origin play is not clear. A detailed occupational approach such as the one we have used in this article allows us to map specific parental and respondent’s occupations where the social-origin gap occurs and provide some indications on where to look to shed further light on those mechanisms, possibly via a qualitative approach (Rivera, 2015). In the case of Spain, for instance, based on our results, we would focus on lawyers by comparing earnings trajectories of first- and second-generation lawyers to identify which resources are most consequential to access higher earning trajectories. In order to investigate mechanisms underlying compensatory advantage in occupational status, one might focus on low educational achievers from the upper class and study how and when they manage to achieve better than elementary occupations. Finally, with respect to the current debate on low levels of social mobility in OECD countries and the policy to promote it (OECD, 2018), our study indicates that education is not enough to equalize the chances of success in the labour market. Individuals who come from socioeconomically advantaged families tend to get better jobs and higher income when compared to equally qualified individuals who come from disadvantaged families. In addition to providing an ultimate critique to the notion that affluent post-industrial societies are education-based meritocracies, this study’s evidence of a substantial social-origin gap in income among college graduates also casts doubt on the idea that expansion of tertiary education can be the most-effective policy to bring about more social mobility. Supplementary Data Supplementary data are available at ESR online. Notes Fabrizio Bernardi is Chair of Sociology at the Department of Political and Social Sciences of the European University Institute. His current projects are on Compensatory Advantage as a Mechanism producing intergenerational Inequality, the Direct Effect of Social Origins, Consequences of Non-Intact Families and Long Term trends in Educational Inequality. His work has been published in journals such as Sociology of Education, Demography, and Journal of Marriage and Family. Carlos J. Gil-Hernández is PhD Researcher at the Department of Political and Social Sciences of the European University Institute, Italy. His research interests include educational inequalities, intergenerational social mobility, and social policy. His work has been published in journals and editorials such as Sociology of Education, Research in Social Stratification and Mobility, Demographic Research, and Stanford University Press. Footnotes 1 Sullivan et al. (2017)’s claim of ‘no DESO’ is based on an incorrect interpretation of statistical significance testing that equates a non-statistically significant effect to a zero effect (Bernardi, Chakhaia and Leopold, 2017). Their claim is based on the absence of statistical significance of an odds ratio = 1.3 for parental education (M3, Table 6) which, however, seems substantively relevant and probably also statistical significant at P ≤ 0.10%. The key point here is that, based on the same finding, one could reach the exactly opposite conclusions of Sullivan et al. (2017): despite controlling for different dimensions of educational attainment, one still observes a sizeable DESO. 2 This estimate is based on the formula (88–20) × 0.13, where 0.13 is the estimates for parental ISEI on children’s ISEI. For monthly income, the formula is: (88–20) × 3.508. 3 After applying tests for coefficients’ differences across non-nested models, parental ISEI coefficients’ differences are only statistically significant between Model 1 and Models 2-to-5 for ISEI and income, between Model 2 and Model 3 for ISEI, and between Model 2 and Models 3-to-5 for income. Acknowledgements We thank the participants in the International and Interdisciplinary Seminars of the Network for the Advancement of Social and Political Studies (NASP) at the Università degli Studi di Milano Statale in 13 April 2018, and the 2nd Research Committee on Social Stratification and Inequality Seminar organized by the Spanish Federation of Sociology celebrated at the University of Cádiz, Spain, in 25–26 June 2018. We are also grateful to Macarena Ares for the codes provided to construct the occupational variables. References Ballarino G. , Cantalini S. , Panichella N. ( 2020 ). Social origin and compensation patterns over the occupational career in Italy . Acta Sociologica , doi:10.1177/0001699320920917. Google Scholar OpenURL Placeholder Text WorldCat Barone C. , Lucchini M. , Schizzerotto A. ( 2011 ). Career Mobility in Italy . European Societies , 13 , 377 – 400 . Google Scholar Crossref Search ADS WorldCat Bernardi F. ( 2014 ). Compensatory advantage as a mechanism of educational inequality: a regression discontinuity based on month of birth . Sociology of Education , 87 , 74 – 88 . Google Scholar Crossref Search ADS WorldCat Bernardi F. , Ballarino G. (Eds.) ( 2016 ). Education, Occupation and Social Origin: A Comparative Analysis of the Transmission of Socio-Economic Inequalities , Cheltenham, UK; Northampton, MA : Edward Elgar Publishing Limited . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Bernardi F. , Chakhaia L. , Leopold L. ( 2017 ). ‘ Sing Me a Song with Social Significance’: the (Mis)Use of Statistical Significance Testing in European Sociological Research . European Sociological Review , 33 , 1 – 15 . Google Scholar OpenURL Placeholder Text WorldCat Bol T. et al. ( 2019 ). School-to-work linkages, educational mismatches, and labor market outcomes . American Sociological Review , 84 , 275 – 307 . Google Scholar Crossref Search ADS WorldCat Breen R. , Jonsson J. O. ( 2007 ). Explaining change in social fluidity: educational equalization and educational expansion in twentieth-century Sweden . American Journal of Sociology , 112 , 1775 – 1810 . Google Scholar Crossref Search ADS WorldCat Bukodi E. , Goldthorpe J. ( 2018 ). Social Mobility and Education in Britain: research, Politics and Policy . Cambridge : Cambridge University Press . Google Scholar Crossref Search ADS Google Preview WorldCat COPAC Bukodi E. , Goldthorpe J. ( 2011 ). Social class returns to higher education: chances of access to the professional and Managerial Salariat for men in three British birth cohorts . Longitudinal and Life Course Studies , 2 , 185 – 201 . Google Scholar OpenURL Placeholder Text WorldCat Erikson R. , Jonsson J. O. ( 1998 ). Social origin as an interest-bearing asset: family background and labour-market rewards among employees in Sweden . Acta Sociologica , 41 , 19 – 36 . Google Scholar Crossref Search ADS WorldCat Erola J. , Kilpi-Jakonen E. ( 2017 ) (Eds). Social Inequality Across the Generations. The Role of Compensation and Multiplication in Resource Accumulation . Cheltenham, UK; Northampton, MA : Edward Elgar Publishing Limited . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Eurofound ( 2017 ). Social Mobility in the EU . Luxembourg : Publications Office of the European Union . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Falcon J. , Bataille P. ( 2018 ). Equalization or reproduction? Long-term trends in the intergenerational transmission of advantages in higher education in France . European Sociological Review , 34 , 335 – 347 . Google Scholar Crossref Search ADS WorldCat Fiel J. E. ( 2020 ). Great equalizer or great selector? Reconsidering education as a moderator of intergenerational transmissions . Sociology of Education , doi:10.1177/0038040720927886. Google Scholar OpenURL Placeholder Text WorldCat Ganzeboom H. B. G. , Treiman D. ( 1996 ). Internationally comparable measures of occupational status for the 1988 ISCO . Social Science Research , 25 , 201 – 239 . Google Scholar Crossref Search ADS WorldCat Goldthorpe J. H. ( 2007 ). On Sociology . Stanford, CA : Stanford University Press . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Goldthorpe J. H. , Jackson M. ( 2008 ). Education-based meritocracy: the barriers to its realisation. In Lareau A. , Conley D. (Eds.), Social Class: How Does it Work? New York : Russell Sage Foundation, pp. 93–117. Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Grusky D. B. , Galescu G. ( 2005 ). Foundations of a neo-durkheimian class analysis. In Wright E. O. (Ed.), Approaches to Class Analysis . Cambridge : Cambridge University Press , pp. 51 – 81 . Google Scholar Crossref Search ADS Google Preview WorldCat COPAC Grusky D. , Weeden K. ( 2006 ). Does the sociological approach to studying social mobility have a future? In Morgan S. , Grusky D. , Fields G. (Eds.), Mobility and Inequality . Stanford : Stanford University Press , pp. 85 – 108 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Grusky D. B. , Sørensen J. B. ( 1998 ). Can class analysis be salvaged? American Journal of Sociology , 103 , 1187 – 1234 . Google Scholar Crossref Search ADS WorldCat Gugushvili A. , Bukodi E. , Goldthorpe J. H. ( 2017 ). The direct effect of social origins on social mobility chances: “glass floors” and “glass ceilings” in Britain . European Sociological Review , 33 , 305 – 316 . Google Scholar Crossref Search ADS WorldCat Hällsten M. ( 2013 ). The class-origin wage gap: heterogeneity in education and variations across market segments . The British Journal of Sociology , 64 , 662 – 690 . Google Scholar Crossref Search ADS PubMed WorldCat Hertel F. R. , Groh-Samberg O. ( 2019 ). The relation between inequality and intergenerational class mobility in 39 countries . American Sociological Review , 84 , 1099 – 1133 . Google Scholar Crossref Search ADS WorldCat Hout M. ( 1988 ). More universalism. Less structural mobility: the American Occupational Structure in the 1980s . American Journal of Sociology , 93 , 1358 – 1400 . Google Scholar Crossref Search ADS WorldCat Hout M. , DiPrete T. A. ( 2006 ). What we have learned: RC28’s contributions to knowledge about social stratification . Research in Social Stratification and Mobility , 24 , 1 – 20 . Google Scholar Crossref Search ADS WorldCat International Labour Organization ( 2011 ). International Standard Classification of Occupations. Geneva: International Labour Office. Jacob M. , Klein M. ( 2019 ). Social origin, field of study and graduates’ career progression: does social inequality vary across fields? British Journal of Sociology , 70 , 1850 – 1873 . doi: 10.1111/1468-4446.12696 . Google Scholar Crossref Search ADS WorldCat Jacob M. , Klein M. , Iannelli C. ( 2015 ). The impact of social origin on graduates’ early occupational destinations—an Anglo-German comparison . European Sociological Review , 31 , 460 – 476 . Google Scholar Crossref Search ADS WorldCat Jonsson J. et al. ( 2009 ). Microclass mobility: social reproduction in four countries . American Journal of Sociology , 114 , 977 – 1036 . Google Scholar Crossref Search ADS WorldCat Jonsson J. et al. ( 2011 ). It’s a decent bet that our children will be professor too. In Grusky D. B. , Szelenyi S. (Eds.), The Inequality Reader: Contemporary and Foundational Readings in Race, Class, and Gender . Boulder CO : Westview Press , pp. 499 – 516 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Karlson K. B. ( 2019 ). College as equalizer? Testing the selectivity hypothesis . Social Science Research , 80 , 216 – 229 . Google Scholar Crossref Search ADS PubMed WorldCat Karlson K. B. , Birkelund J. F. ( 2019 ). Education as a mediator of the association between origins and destinations: the role of early skills . Research in Social Stratification and Mobility , 64 , 100436 . Google Scholar Crossref Search ADS WorldCat Laurison D. , Friedman S. ( 2016 ). The class pay gap in higher professional and managerial occupations . American Sociological Review , 81 , 668 – 695 . Google Scholar Crossref Search ADS WorldCat Lin N. , Cook K. , Burt R. S. ( 2001 ). Social Capital: Theory and Research . New York : Aldine de Gruyter . Google Scholar Crossref Search ADS Google Preview WorldCat COPAC Lucas S. R. ( 2001 ). Effectively maintained inequality: education transitions, track mobility, and social background effects . American Journal of Sociology , 106 , 1642 – 1690 . Google Scholar Crossref Search ADS WorldCat Lucas S. R. ( 2017 ). An archaeology of effectively maintained inequality theory . American Behavioral Scientist , 61 , 8 – 29 . Google Scholar Crossref Search ADS WorldCat Manzoni A. , Härkönen J. , Mayer K. U. ( 2014 ). Moving on? A growth-curve analysis of occupational attainment and career progression patterns in West Germany . Social Forces , 92 , 1285 – 1312 . Google Scholar Crossref Search ADS WorldCat Marqués Perales I. , Gil-Hernández C. J. ( 2015 ). Social origins and over-education of Spanish University Graduates: is access to the service class merit-based? Revista Española de Investigaciones Sociológicas , 150 , 89 – 112 . Google Scholar OpenURL Placeholder Text WorldCat Mare R. ( 1993 ). Educational stratification on observed and unobserved components of family background. In Shavit Y. , Blossfeld H. P. (Eds.), Persistent Inequality. Changing Educational Attainment in Thirteen Countries . Boulder : Westview Press , pp. 351 – 394 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Markovits D. ( 2019 ). The Meritocracy Trap . New York : Penguin Random House . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Oh B. , Kim C. ( 2020 ). Broken promise of college? New educational sorting mechanisms for intergenerational association in the 21st century . Social Science Research , 86 , 102375 . Google Scholar Crossref Search ADS PubMed WorldCat OECD ( 2018 ). A Broken Social Elevator? How to Promote Social Mobility . Paris : OECD Publishing . doi: 10.1787/9789264301085-en . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Crossref Passaretta G. et al. ( 2018 ). The direct effect of social origin on men’s occupational attainment over the early life course: an Italian-Dutch comparison . Research in Social Stratification and Mobility , 56 , 1 – 11 . Google Scholar Crossref Search ADS WorldCat Rivera L. ( 2015 ). Pedigree: How Elite Students Get Elite Jobs . Princeton : Princeton University Press . Google Scholar Crossref Search ADS Google Preview WorldCat COPAC Ruggera L. , Barone C. ( 2017 ). Social closure, micro-class immobility and the intergenerational reproduction of the upper class: a comparative study . British Journal of Sociology , 68 , 194 – 214 . Google Scholar Crossref Search ADS WorldCat Sullivan A. et al. ( 2017 ). The path from social origins to top jobs: social reproduction via education . The British Journal of Sociology , 69 , 776 – 798 . Google Scholar Crossref Search ADS PubMed WorldCat Torche F. ( 2011 ). Is a college degree still the great equalizer? Intergenerational mobility across levels of schooling in the US . American Journal of Sociology , 117 , 763 – 807 . Google Scholar Crossref Search ADS WorldCat Torche F. ( 2018 ). Intergenerational mobility at the top of the educational distribution . Sociology of Education , 91 , 266 – 289 . Google Scholar Crossref Search ADS WorldCat Weeden K. , Grusky D. ( 2005 ). The case for a new class map . American Journal of Sociology , 111 , 141 – 212 . Google Scholar Crossref Search ADS WorldCat Witteveen D. , Attewell P. ( 2020 ). Reconsidering the ‘meritocratic power of a college degree ’. Research in Social Stratification and Mobility , 66 , 100479 . Google Scholar Crossref Search ADS PubMed WorldCat Zhou X. ( 2019 ). Equalization or selection? Reassessing the “meritocratic power” of a college degree in intergenerational income mobility . American Sociological Review , 84 , 459 – 485 . Google Scholar Crossref Search ADS WorldCat © The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)
TI - The Social-Origins Gap in Labour Market Outcomes: Compensatory and Boosting Advantages Using a Micro-Class Approach
JF - European Sociological Review
DO - 10.1093/esr/jcaa034
DA - 2021-01-30
UR - https://www.deepdyve.com/lp/oxford-university-press/the-social-origins-gap-in-labour-market-outcomes-compensatory-and-NI0Bc3bDq6
SP - 32
EP - 48
VL - 37
IS - 1
DP - DeepDyve
ER -