Sociology of Health & Illness Vol. 35 No. 8 2013 ISSN 0141-9889, pp. 1242–1259 doi: 10.1111/1467-9566.12039

Overeducation and depressive symptoms: diminishing mental health returns to education Piet Bracke1, Elise Pattyn1 and Olaf von dem Knesebeck2 1

HeDeRa (Health and Demographic Research), Department of Sociology, Ghent University, Belgium 2 Department of Medical Sociology, University Medical Centre, Hamburg-Eppendorf, Germany

Abstract

In general, well-educated people enjoy better mental health than those with less education. As a result, some wonder whether there are limits to the mental health benefits of education. Inspired by the literature on the expansion of tertiary education, this article explores marginal mental health returns to education and studies the mental health status of overeducated people. To enhance the validity of the findings we use two indicators of educational attainment – years of education and ISCED97 categories – and two objective indicators of overeducation (the realised matches method and the job analyst method) in a sample of the working population of 25 European countries (unweighted sample N = 19,089). Depression is measured using an eight-item version of the CES-D scale. We find diminishing mental health returns to education. In addition, overeducated people report more depression symptoms. Both findings hold irrespective of the indicators used. The results must be interpreted in the light of the enduring expansion of education, as our findings show that the discussion of the relevance of the human capital perspective, and the diploma disease view on the relationship between education and modern society, is not obsolete.

Keywords: mental health, education, overeducation, cross-national comparative research

Introduction The association between education and mental health is a consistent finding in mental health epidemiology (Dohrenwend et al. 1992, Link et al. 1993, Mirowsky and Ross 2003, Von dem Knesebeck et al. 2011). Most studies, inspired by the human capital perspective (Mirowsky and Ross 2003), assume that there are few limits to the mental health benefits of education. An implicit assumption is that well-educated people end up in work contexts where they can put their learnt capacities to work. Nevertheless, there is indirect empirical evidence that the mental health returns to education diminish among well-educated people. Since the 1970s speculations about the limits to the benefits of educational attainment have arisen in the wake of the credential society theory (Collins 1971, 1979), as the worldwide expansion of higher education did not result in upgrading of the labour market (Burris 1983, Meyer 1977, Schofer and Meyer 2005). The mismatch between the supply of highly educated © 2013 The Authors. Sociology of Health & Illness © 2013 Foundation for the Sociology of Health & Illness/John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd., 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Maiden, MA 02148, USA

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people and the demand of educated labour led to the phenomenon of overeducation (Freeman 1976). At the population level the presence of overeducation is often inferred from the observation of diminishing returns to education; at the individual level it is defined as job– education mismatch. In the present study we take both approaches into account. We test whether there are diminishing mental health returns to education. In addition, we take a look at the mental health of overeducated people. Research on the consequences of overeducation is hampered by measurement issues, as the results partly depend upon the way it is measured. To enhance the validity of our findings we used two different indicators of educational attainment, as well as two objective measures of overeducation. Previous research on the economic (Trostel et al. 2002) and non-economic (Gesthuizen et al. 2008) returns of educational attainment showed substantial cross-national variation. So, instead of focusing on one country we use an European sample of employees from the European Social Survey (ESS n.d.) (third round, 2006) and model cross-national variation in the association between (over)education and depressive symptoms to enhance the generalisability of the findings. Education and mental health: constant or diminishing returns Several pathways link education to mental health (Aneshensel 1992, Pearlin 1989, Ross and Wu 1995, Turner and Noh 1983, Turner et al. 1995). Firstly, education generates better mental health because it leads to occupations that provide the economic resources and the social status that help to enhance an individual’s quality of life and sense of self-worth (the allocation function). More concretely, well-educated people face a lower risk of unemployment, have access to higher status occupations and are thus more likely to enjoy higher wages (Kettunen 1997, Mincer 1991). Secondly, education contributes to the development of capacities, knowledge, skills, attitudes and values (the socialisation function). Well-educated people are more likely to experience feelings of competence, mastery and self-efficacy that are useful for coping with work tasks and for tackling general life problems (Ross and Mirowsky 2006). Well-educated people also have more resources than the average to build and maintain supportive networks, and the mental health benefits of social support are well-documented (Aneshensel 1992, Pearlin 1989). Consequently, most social epidemiological studies implicitly assume that the higher the level of educational attainment, the better a person’s mental health status. They expect constant mental health returns to education in line with a common health sociological interpretation of the human capital perspective; namely the theory of learnt effectiveness (Mirowsky and Ross 1998, 2003). In other words, as a form of human capital education can accumulate and can be transferred into health benefits, almost without limit. A meta-analysis of 37 studies on education and depression has confirmed the linear nature of the relationship between years of education and the prevalence of major depression (Lorant et al. 2003). Nevertheless, even Gary Becker (2007), the founding father of human capital theory, makes no assumptions about the health production function of education. Moreover, there is indirect empirical evidence that the mental health benefits of education are limited. For instance, Mirowsky and Ross (2003) found smaller health benefits to emotional wellbeing in additional years of schooling beyond the master’s degree level among adults in the USA. In addition, a curvilinear relation between years of education and feelings of mastery was observed in 15 countries participating in the World Health Survey (OECD 2010). Furthermore, with regard to the frequency of depression and anxiety-related complaints, diminishing mental health returns to education were found among older adults in the USA (Goesling 2007). Smaller mental health returns among highly educated people were also observable in the data provided by Feinstein (2002), Alonso et al. (2004) and Link et al. (2008). Finally, the most straightforward © 2013 The Authors Sociology of Health & Illness © 2013 Foundation for the Sociology of Health & Illness/John Wiley & Sons Ltd

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test of the non-linear effect of education on depression was provided by Chevalier and Feinstein (2006), who demonstrated a decrease in returns to education among well-educated people, particularly among women. In fact, the theory of learnt effectiveness implicitly assumes that people on average end up in work contexts where they can put their learnt capacities to work. Others, mainly outside health sociology, question this optimistic viewpoint. Overeducation: diminishing mental health returns to educational attainment After 1960 there was an acceleration of the expansion of education across and beyond Europe (Groot and Maassen van den Brink 2000). When it became clear in the 1970s that the worldwide expansion of higher education had not resulted in an upgrading of the labour market, that is, an upgrading of production techniques in order to take advantage of a more educated labour force (McGuinness 2006), speculations about the limits to the benefits of educational attainment arose (Burris 1983, Collins 1979, Meyer 1977, Schofer and Meyer 2005). Labour market research started focusing on supply and demand explanations for the diminishing monetary and non-monetary returns to education (Trostel et al. 2002, Verdugo and Verdugo 1989). On the supply side, the labour market value of educational credentials inflated (Hannum and Buchmann 2005), whereas on the demand side, employers started to compete for employees with the highest credentials in order to reduce the costs of job training (Hirsch 1977, Thurow 1976). As a result, the fact that the supply of highly educated people outnumbered the demand for educated labour (Freeman 1976) led to some highly educated people ending up in jobs that actually required lower qualifications (Duncan and Hoffman 1981). This phenomenon of overeducation thus became a permanent condition for a substantial number of employees (Pritchett 2001, Rubb 2003, Vaisey 2006). At the population level the presence of overeducation is inferred from the observation of diminishing returns to tertiary education. For instance, Freeman (1976) defines overeducation as ‘a falling private rate of return to college education’ (Psacharopoulos, 1994: 1334). At the individual level it is defined as job–education mismatch, that is, when ‘the level of education acquired exceeds the level of education required to adequately perform the job’ (Wolbers 2003: 251). The idea that the benefits of higher education are limited is in line with the credential society theory (Collins 1971, 1979). In meritocratic societies credentialism leads people to value educational degrees irrespective of the skills and knowledge acquired, because the institutionalisation of education is a system of legitimation (Meyer 1977). Consequently, employers hire people based on their educational credentials instead of on their specific competences and capabilities. Meanwhile, for employees an increase in their investment in education becomes necessary to avoid the risk of downward social mobility. This process shifts the balance between the socialisation and the allocation function of education towards the latter. In the literature some negative consequences of overeducation are commonly discussed. Indirect evidence for this is provided by Trostel, Walker, and Trostel (2002) who demonstrated diminishing economic returns to education in 28 countries. Others studies more directly focus on overeducation and find that it is accompanied by lower earnings (Cohn and Kahn 1995, Hartog 2000, Verdugo and Verdugo 1989). Moreover, overeducation has non-monetary consequences also. For instance, it seems to diminish job satisfaction (Maynard et al. 2006, Vaisey 2006, Verhaest and Omey 2006, 2009). Furthermore, de Grip et al. (2008) found a decline of cognitive skills among overeducated people due to their lack of intellectual challenge (Link, Lennon, and Dohrenwend 1993; Kohn and Schooler 1983). Among overeducated clerical workers, Burris (1983) reported feelings of alienation and hopelessness, which are precursors or subdimensions of depression (Abramson et al. 1989, Mirowsky and Ross 1986). Other studies found that those who experienced inconsistencies in their status position due to their high education and low income reported a higher prevalence of mental disorders (Gal et al. 2008). © 2013 The Authors Sociology of Health & Illness © 2013 Foundation for the Sociology of Health & Illness/John Wiley & Sons Ltd

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In sum, many social epidemiological studies take for granted the linear association between mental health and education. We question this assumption and test two hypotheses. The first hypothesis, on the diminishing returns to education, states that the mental health benefits of educational attainment decrease at higher levels of acquired education. The second hypothesis, the overeducation hypothesis, states that the diminishing mental health returns to education could be linked to a job–education mismatch. The difference between these hypotheses is that the latter assumes that the mental health consequences of overeducation are limited to overeducated people, while the former assumes that there are diminishing mental health returns for all highly educated people. To test these hypotheses we need to take financial hardship into account. The mental health consequences of financial hardship are well-known (Butterworth et al. 2009, Lewis et al. 1998, Mirowsky and Ross 2001) and we want to distinguish them from the association between over education and mental health complaints. Lack of education is an important determinant of financial hardship and its impact on poverty works largely through the labour market (Van der Berg 2008). Apart from the fact that low-skilled workers are at risk, the literature on the job–education mismatch is consistent in showing the disadvantages of overeducation for the income of well-educated people (Nordin and Persson 2010). Other known correlates of education and depressive symptoms are not ignored. At the micro level, studies have shown that women report a higher prevalence of depressive symptoms (Bracke 2000, Van de Velde et al. 2010a). Moreover, women have only recently acquired a level of educational attainment equivalent to that of men (Breen et al. 2010). Furthermore, previous research has shown a curvilinear relationship between age and depression (L
epine et al. 1997, Levecque et al. 2011, Newmann 1989), and has found a relationship between overeducation and age (Clogg and Shockey 1984), as younger cohorts who are highly educated are more willing to accept jobs they are overqualified for. Additionally, we controlled for marital status, a known correlate of both education and mental health status (Bracke et al. 2010, Mirowsky and Ross 2003). Finally, we included information on number of working hours and the economic sector of employment. At the macro level, we controlled for GDP per capita, as both the level of educational expansion (Schofer and Meyer 2005) and the level of subjective well-being (Veenhoven 1991) are related to the level of economic development. Finally, to control for between-country differences in the level of educational attainment, we included country mean levels of educational attainment.

Methodology Population sample This study is based on data from the ESS Round 3. The ESS data are representative of the non-institutionalised population of 25 European countries aged 15 and older. Data were gathered by means of face-to-face interviews between the end of August 2006 and November 2007. Response rates ranged from 46.0 per cent in France to 73.2 per cent in Slovakia. The present sample was restricted to the employed population aged 24–60 years old. In addition, respondents for whom we lacked information on education (1,615 or 7.7 per cent) and gender, marital status or occupation were omitted from the sample. The final sample consisted of 19,089 respondents. Variables Depressive symptoms were measured by means of the shortened Center for Epidemiologic Studies Depression Scale (CES-D 8) (Radloff 1977). Previous research supported the © 2013 The Authors Sociology of Health & Illness © 2013 Foundation for the Sociology of Health & Illness/John Wiley & Sons Ltd

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measurement equivalence of the shortened eight-item CES-D scale among European respondents (Van de Velde et al. 2010b). The frequency and severity of the affective and somatic aspects of depression were calculated based on self-reports. The CES-D 8 total score ranged from 0 to 24, with higher scores indicating a higher amount of depressive symptoms. Missing values were handled by respondent mean substitution on condition that they had answered at least five items of the scale. The Cronbach’s alpha of the CES-D 8 scale ranged from 0.73 in Denmark to 0.88 in Hungary. Education was measured by years of full-time education completed and by highest educational degree attained (International Standard Classification of Education ISCED-97) (Hoffmeyer-Zlotnik and Warner 2007). In cross-country comparative research, years of education is often used as a proxy variable for education (Kunovich and Slomczynski 2007, Treiman and Yip 1989, cited in Kerckhoff and Dylan 1999, Treiman et al. 1998). To further enhance the comparability of the scores, the impact of outliers was restricted by equalising all scores greater than the mean plus three standard deviations. Schneider (2007), however, criticises the use of years of education to compare educational attainment across countries in Europe. We partially countered this criticism by using a second indicator of educational attainment: education as the highest educational degree completed. This indicator consists of four categories, ranging from less than lower secondary education (ISCED 0–1) to tertiary education completed (ISCED 5–6). We based our analyses on two objective indicators of overeducation (Groot and Maassen van den Brink 2000, Hartog 2000). The realised matches (RM) method (Clogg and Shockey 1984, Mendes de Oliveira et al. 2000) is based on the actual distribution of the educational attainment of the employees in each occupation. We computed the country-specific means of education in years for each minor category (first digit) of the International Standard Classification of Occupations (ISCO-88) occupational classification. The ISCO-88 groups occupations based on the similarity of skills required to fulfil the tasks and duties of a job. Following Verdugo and Verdugo (1989), we defined overeducated people as those whose educational attainment is more than one standard deviation above the mean amount of years of education within their occupational category. Under-educated people were defined in the same way. The job analyst (JA) method is based on a systematic evaluation by job analysts. It measures the level of discrepancy between the highest level of education completed and the level of education required to perform an occupation according to the ISCO-88 occupational classification (Ganzeboom and Treiman 1996). Those who are labelled overeducated have acquired a level of education that is at least one ISCED-97 score higher than the ISCED-97 level of education required for their occupational category. The under-educated label is determined similarly. The equivalent household income is used as an indicator of financial hardship. The net household income was weighted based on the modified OECD scale. This gives a weight of 1 to the first adult of the household, 0.5 to all other adults (> 14-years old), and 0.3 to children (  14-years old) (Atkinson et al. 2002). To enhance the comparability of the scores across high-income and middle-income countries in the sample we created five household income categories relative to each country’s median income: less than 50 per cent of the country’s median income, 50–80 per cent, 80–120 per cent (the reference category) and more than 120 per cent We included a separate missing category, since 24.9 per cent did not provide information about their household income. We controlled for work-related features by taking into account whether the respondents worked full-time (> 32 hours/week) or part-time (32 hours/week and less) and by classifying them according to the economic sector in which they are employed; ranging from agriculture, forestry and fishing (0) to the arts, entertainment and recreation (9); using the first digit of the © 2013 The Authors Sociology of Health & Illness © 2013 Foundation for the Sociology of Health & Illness/John Wiley & Sons Ltd

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Classification of Economic Activities in the European Community-NACE Rev.1. Finally gender, age and marital status were included as additional individual-level control variables. For gender, men formed the reference category. Age was measured in years and was centred around the overall sample mean. Its square term was added to control for the linearity of the relation between age and depressive symptoms. Marital status indicates whether someone is married or cohabiting (reference category), divorced or separated, or widowed or single. Country-level variables Two indicators were included as controls at the country level. First, the country mean level of education is an aggregated measure based on either the average amount of completed years of education of the country’s population or the mean score of the ISCED-97 indicator of education. Second, we took the gross national product – per capita in purchasing power parity (Eurostat 2011) – into account. To enlarge unstandardised parameter estimates the score was divided by a multiple of 10. Analysis procedure Our study used an SPSS (vers. 19.0) linear mixed models procedure to account for the clustering of the respondents in countries. The level of depressive symptoms was analysed as a metric variable with a normal distribution, although it has an oblique distribution – a Kolmogorov– Smirnov test confirms the deviation from normality. Nevertheless, the results of the analyses based on a logarithmically transformed dependent variable did not substantially differ from the results based on the non-transformed variable. The latter results are presented to enhance interpretability. We ran two parallel analyses; the first with years of education and the RM method and the second with the highest educational degree completed and the JA method. The variable years of education was centred on the country mean score. Centring helps to circumvent problems of multicollinearity when estimating the linearity of the relation between educational attainment and depressive symptoms (Cohen et al. 2003). The country mean scores in educational attainment were added to control for non-specified cross-country differences in educational systems that might lead to differences in average years of education or educational degrees. Additionally, we also estimated the variation in the association between education in years and depressive symptoms by including the random slope of education and over/under-education (see Albert and Davia 2007 for a similar strategy). Analyses showed significant random slopes for both indicators of education but failed to find significant random slopes for the indicators of a job–education mismatch. The models were therefore re-estimated including only random slopes for educational attainment. Model 1a examines the linearity of the effect of education on the mental health outcome. Education and its square term were entered in the equation. We added a random slope effect for both education and its square term to model between-countries variance. Similar results were obtained with or without the random slope effect of education squared. In order to preserve parsimoniousness, we dropped the random slope effect for the squared term. Model 1b examines the same effect taking the highest educational degree completed into account. In Models 2a and 2b the individual-level indicators of overeducation and under-education were added and in Models 3a and 3b financial hardship and working hours were included. In all models the type of economic activity (Statistical classification of economic activities in the European Community, NACE) was taken into account. For reasons of space, the estimates for the NACE categories were not reported. Design weights were used but we decided not to include population weights (Frohlich et al. 2001, Heck et al. 2010). Similar results were found when using the unweighted sample. © 2013 The Authors Sociology of Health & Illness © 2013 Foundation for the Sociology of Health & Illness/John Wiley & Sons Ltd

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Several sensitivity checks were performed (results available upon request). Since Portugal is an outlier in terms of number of overeducated people and the mean years of education, the models were also estimated while excluding the Portuguese sample. Similar results were obtained; therefore, only the models containing the Portuguese sample are presented. We also ran ordinary least squares regression models with country dummies and robust standard errors. These analyses confirmed the results presented below.

Results Descriptive statistics of the individual characteristics are given in Table S1 (available in Supporting Information online). When using the RM method the percentages of overeducated and under-educated people are estimated at 11.8 and 11.4 per cent, respectively. The JA method estimated the percentage of overeducated people at 51.6 per cent and the percentage of under-educated people at 10.0 per cent. In the overeducation literature the JA method commonly leads to higher estimates of overeducation. So, our findings are in line with those from the job mismatch literature. The association between educational attainment and depressive symptoms First, we turn our attention to the analyses based on the indicator of education as measured in years and on the RM method (Model 1a in Table 1). The association between education and depressive symptoms is clearly negative and curvilinear. We find significant effects for both educational attainment (B = 0.107, SE = 0.021, P < 0.001) and its square term (B = 0.005, SE = 0.001, P < 0.001). The number of years of education above which an increase in educational attainment is no longer associated with a decrease in depressive symptoms is estimated at 24.2 years [for the centred variable, 10.7 years = Beducation in years/ 2*Beducation in years² = 0.107/(2*0.005)] or approximately three standard deviations above the mean level, which is out of the observed range of variation in years of educational attainment. So there are diminishing mental health returns to educational attainment but even among well-educated people each additional year is beneficial for mental health. We also observe significant between-countries variation in the strength of the association between education and depressive symptoms (c = 0.009, SE = 0.003, P < 0.01). Additional calculations (available upon request) show that 67 per cent of the regression slopes fall between 0.012 and 0.202. So, while in most countries each additional year of education is associated with better mental health outcomes, in a minority of countries this association is absent. We draw similar conclusions from the models based on the ISCED-97 indicator of educational attainment and the JA method for measuring the job–education mismatch. We observe significant differences in depressive symptoms between people with different levels of educational attainment but the differences diminish as we move up the ladder of of educational attainment. For instance, between ISCED categories 5–6 and ISCED categories 3–4 there is a difference of 0.372 (SE = 0.105; P < 0.01; (Model 1b, Table 1), the difference between the latter category and ISCED 2 equals 0.357 (SE = 0.132; P > 0.05; data not shown), while the difference between the two lowest categories mounts up to 0.725 (SE = 0.200; P > 0.01; data not shown). Again, between-countries variation in the strength of the association between education and depressive symptoms is observed, limited to well-educated people (ISCED 5–6) (c = 0.170, SE = 0.076, P < 0.05). Also, we find an association between country mean level of educational attainment and the frequency and severity of depressive symptoms in Model 1a, but not in Model 1b (Model 1a: B SE = 0.414 [0.160], P < 0.05; Model 1b: B SE = 0.545 [0.355], P = NS). © 2013 The Authors Sociology of Health & Illness © 2013 Foundation for the Sociology of Health & Illness/John Wiley & Sons Ltd

9,903 0.626 0.165 0.052

(1.952)*** (0.049)*** (0.029)*** (0.027)

Marital status (married/civil partnership = ref.) Divorced/separated 1,177 (0.082)*** Widowed 2,280 (0.196)*** Single 0.721 (0.070)*** 0.107 (0.021)*** Education in years (centred) Education in years² 0.005 (0.001)*** (centred) Overeducation (RM method) Under-education (RM method) Education (ISCED97) (b) Less than lower secondary education (ISCED 0–1) Lower secondary education (ISCED 2) 3 Upper secondary education completed (ISCED 3) Upper sec. educ. or post-sec. non-tertiary educ. (ISCED 3–4) Overeducation (JA method) Under-education (JA method) Household income (80–120% country median income = ref.) 120% country median income Income data missing Work hours (>32 hours per week) Country level Education in years (country mean) Education (ISCED97) (country mean) GDP per capita (2006) Variance components and model fit Residual variance Intercept Education in years Education (ISCED97) (c) Less than lower secondary education (ISCED 0–1) Lower secondary education (ISCED 2) Tertiary education (ISCED 5–6) 2 log likelihood

Table 1 (continued)

0.568

103,769.3

(0.117)*** (0.078)** (0.003)**

( 0.069)***

(0.101)

(0.087) (0.070)

0.044 0.127

0.003

(0.084)

(SE) 0.154

11,571 0.243 0.008

B

Model 3a (a)

(0.125) (0.076)*

0.219 0.017

103,943.6

(0.144)

0.063

(0.056)***

(0.355)

(0.118)*** (0.068)**

0.522

0.545

(SE)

11,660 0.205

B

Model 1b (a)

0.517

0.529

(0.072)*

(0.118)

(0.144)

(0.118)*** (0.066)**

(0.054)***

(0.346)

(SE)

103,925.9

0.162

0.197

0.063

11,648 0.197

B

Model 2b (a)

0.551

0.475

0.017 0.100

0.157

(0.075)*

(0.117)

(0.144)

(0.117)*** (0.076)**

(0.059)***

(0.371)

(0.088) (0.070)

(0.084)

(SE)

103,820.2

0.170

0.192

0.051

11,583 0.227

B

Model 3b (a)

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Diminishing mental health returns to education

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Strong associations between population mental health and GDP per capita are present in both models. The role of overeducation and under-education Next, we add our indicators of overeducation and under-education. As can be seen in Model 2a in Table 1, overeducated people report substantially more symptoms of depression (B (SE) = 0.378 (0.092), P < 0.001). Under-educated people have levels of depressive symptoms similar to those who have the required level of education. Adding both dummy variables significantly improves the model fit (Table 2: Δ-2LL = 16.4 (df = 2), P > 0.001). After controlling for a job–education mismatch the curvilinearity in the association between years of education and depressive symptoms diminishes and the regression line becomes steeper. This is visualised in Figure 1. Between-country differences in the association between education and depressive symptoms are not related to the incidence of overeducation, as the random slope of education is not reduced. The findings based on the RM method are mostly similar to those based on the JA method. Again, higher levels of depressive symptoms are observed among overeducated people (Table 2, Model 2b: B (SE) = 0.174 (0.058), P < 0.01). Also, the introduction of the job–education mismatch indicators moderately enlarges the difference in depressive symptoms between well-educated and the less educated [people (see Figure 2). We do notice a significant reduction in log likelihood (Table 2: Δ-2LL = 17.7 (df = 2), P > 0.001) after introducing both indicators of a job–education mismatch. In both cases, neither the sector of employment nor working hours are associated with symptoms of depression. Financial hardship as mediating path As expected, the estimates in Models 3a and 3b (Table 2) reveal that the depressive symptoms are higher among those with an equivalent household income of 50–80 per cent of the country median income and is strongly elevated among those living in poverty with a household income of less than 50 per cent of the country median income. Additional analyses show that, on average, overeducated people have lower household incomes (not tabulated, Bovereducation/JAmethod (SE) = € 4298 (€267.8), P < 0.001; Bovereducation/RMmethod (SE) = € 4058.4 (€430.5), P < 0.001). Nevertheless, financial hardship statistically explains only a small part of the association between overeducation – determined by the RM method – and depressive symptoms. This association is substantially reduced when using the JA indicator. Finally, the strength of the curvilinear relationship between educational attainment and depressive symptoms is reduced but not completely eliminated when controlling for financial hardship (see Figures 1 and 2).

Discussion Before discussing the findings we want to illuminate the shortcomings of our study. Firstly, both objective measures of job–education mismatch have drawbacks (Halaby 1994, Hartog, 2000, Verhaest and Omey 2009). A drawback of the job analyst method is that it ignores large differences in complexity within occupations, which might have changed over time. Moreover, fine-grained differences in job–education mismatch among people with tertiary education have been missed as we could not differentiate between categories 5 and 6 of the ISCED-97. A disadvantage of the RM method is that it is less able to provide reliable information on the true incidence of overeducation and under-education because the use of the standard deviation as a cut-off standardises the scores. Nevertheless, by using both methods, we were able to © 2013 The Authors Sociology of Health & Illness © 2013 Foundation for the Sociology of Health & Illness/John Wiley & Sons Ltd

0.099 0.003 0.397 0.072

(0.019)*** (0.001)* (0.096)*** (0.100)

All other variables included The significance levels are *P < 0.05; **P < 0.01; ***P < 0.001.

Education in years (centred) Education in years² (centred) Overeducation (RM method) Under-education (RM method) Education (ISCED97) (b) Less than lower secondary education (ISCED 0–1) Lower secondary education (ISCED 2) 3 Upper secondary education completed (ISCED 3) Upper sec. or post-sec. nontertiary educ. (ISCED 3–4) Overeducation (JA method) Under-education (JA method)

0.097 0.003 0.340 0.139

(0.022) *** (0.001) * (0.094) *** (0.097)

(SE)

B

B

(SE)

Model 3a weighted sample

Model 3a unweighted sample B

(0.193)*** (0.135)***

(0.097)** (0.06)** (0.104)

1.360 0.680

0.270 0.173 0.189

(SE)

Model 3a unweighted sample

0.121 0.144

0.281

0.652

1,420

B

(0.059)* (0.101)

(0.107)*

(0.158)***

(0.198)***

(SE)

Model 3a weighted sample

Table 2 Linear mixed effect models of depressive symptoms regressed on education and job–education mismatch among the employed, aged 24–60 years (unweighted and weighted sample)

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Mean depression symptoms

7.0

6.5

Baseline (Model 1a)

6.0

5.5

5.0

4.5 –3SD

–2SD

–1SD

Mean

1SD

2SD

3SD

EducaƟon in years

Figure 1 The association between years of education and depression symptoms (estimated values based on models 1a–3a in Table 1.

7.5

Depression symptoms

Baseline (Model 1b) Over/undereducaƟon added (Model 2b) Household income added (Model 3b)

6.5

5.5

4.5

ISCED 0-1

ISCED 2 ISCED 3-4 EducaƟon (ISCED97)

ISCED 5-6

Figure 2 The association between level of educational attainment (based on ISCED97) and depression symptoms (estimated values based on models 1b–3b (in Table 1).

show the mental health implications of overeducation irrespective of the way it was operationalised. A similar conclusion is warranted for educational attainment. Second, the cross-sectional design of the study limits the interpretation of the findings in terms of causality, as we cannot ignore the fact that part of our findings might result from selection effects. The mental health status of adolescents and young adults might be related to both education and mental health outcomes in adulthood (Breslau et al. 2008, Fergusson and Woodward 2002). Nevertheless, prospective studies show that although causation and selection processes go hand in hand, the former are more important than the latter (Chevalier and Feinstein 2006, Dohrenwend et al. 1992) especially with regard to education (Smith 2004). Selection processes underlying the association between overeducation and depression symptoms are possible too, for instance, when prior feelings of incompetence or low self-esteem undermine one’s mental health as well as leading individuals to apply for or to hold onto jobs they are overskilled for. Nevertheless, Smith and Frank (2005), using longitudinal data to control for © 2013 The Authors Sociology of Health & Illness © 2013 Foundation for the Sociology of Health & Illness/John Wiley & Sons Ltd

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prior self-rated health status, confirm the detrimental health effects of over-qualification for well-educated people. Third, although we considered the curvilinear relationship between age and depressive symptoms, we did not take cohort or life-stage differences into account in the association between level of educational attainment, job–education mismatch and depressive symptoms. Research on economic returns to overeducation clearly show that these are dependent on career stage, as young employees are more willing to take jobs with a lower level of required education in their early careers in order to get work experience (Sicherman 1991). Finally, we limited our research to employees and questions could also be raised about the mental health returns of education for well-educated people in general or among unemployed individuals as well. Notwithstanding these limitations, our findings add to the discussion regarding the mental health benefits of educational attainment. We used two established objective indicators of job– education mismatch and found that overeducated people reported higher levels of depressive symptoms, irrespective of the indicator used. Previous research has demonstrated the detrimental consequences of overeducation for work-related wellbeing (Maynard et al. 2006, Vaisey 2006, Verhaest and Omey 2006, 2009). Our results add that overeducation has more general mental health consequences. The results were more pronounced when overeducation was operationalised using the RM method. We assume that this inconsistency is related to the fact that the JA method leads to an overestimation of the occurrence of overeducation among employees (Hartog 2000, Verhaest and Omey 2006). Moreover, our RM indicator was based on country-specific mean levels of education per occupational category; while the JA indicator was a more general measure, making it more difficult to grasp the cross-national variation in education–labour market fit. Therefore, we recommend the RM indicator for cross-national research on the mental health consequences of the job–education misfit in Europe. A second main finding is that we find clear evidence for diminishing mental health returns to education, irrespective of the indicator of educational attainment used. At higher levels of educational attainment, an additional increase in formal education is less beneficial for mental health. In other words, going from a first to a second stage of tertiary education is less significant for mental health than going from a primary to a secondary level of education. Our findings are in line with those of Goesling (2007) for mental health among aged people in the USA, for the European Study of the Epidemiology of Mental Disorders (Alonso et al. 2004), the World Health Survey (Subramanian et al. 2010) and among British (Chevalier and Feinstein 2006) and US adults (Mirowsky and Ross 2003). In addition, our results suggest that the occurrence of overeducation among well-educated people is a work-related condition that puts limits to the mental health benefits of educational attainment. The associations are moderate for the RM method and small for the JA method. We suppose that the latter is too inclusive and overestimates the proportion of overeducated people. Nevertheless, the findings are similar and they demonstrate that, for a substantial number of well-educated people, over-qualification on the job is related to mental health complaints. Also, we find higher than expected levels of depressive symptoms among welleducated people even when they personally do not hold jobs they are overqualified for. This finding remains tentative as long as we do not consider alternative explanations for the diminishing mental health returns to education. The present analyses are limited to demonstrating a diminishing mental health return to education and the mental health consequences of overeducation irrespective of the indicators used. We showed that a small part of the link between education, overeducation and the level of depressive symptoms is related to financial hardship, while the number of work hours and sector of employment do not contribute to the findings. Various studies have demonstrated the detrimental wage effects of overeducation © 2013 The Authors Sociology of Health & Illness © 2013 Foundation for the Sociology of Health & Illness/John Wiley & Sons Ltd

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(Hartog 2000, Nordin and Persson 2010). Yet our study indicates that the association between overeducation and depressive symptoms transcend these economic consequences. In other words, non-economic pathways – such as perceived social support, feelings of competence and self-esteem – offer promising socio-psychological explanations for both observations that should be further explored in future research. Concerning the job–education mismatch, we took a rather individualistic approach, as we depicted overeducation as an individual characteristic. Consequently, we assume that the mental health implications of a job–education mismatch are limited to overqualified employees and ignore the fact that a mismatch between education and the labour market is also a macro level phenomenon with potential health consequences for the general population, irrespective of their individual job situation. Furthermore, the present analyses show significant betweencountry variation in the mental health production function of education. In some countries there are substantive diminished mental health returns to education, while in others no limits to the mental health benefits of higher education are observed. So this observation is a good starting point for exploring in future research societal level concomitants of the mental health benefits of educational attainment that are possibly linked to the expansion of tertiary education (Collins 1979, Schofer and Meyer 2005, Vaisey 2006) or more cyclical determinants of the labour market–education fit on the contextual level (Verhaest and Van der Velden 2010). At the least, our findings show that the debate between the human capital perspective and the diploma disease view on the relationship between education and modern society is not obsolete (Baker 2009). Address for correspondence: Piet Bracke, Sociology, Ghent University, Korte Meer 5 Ghent 9000, Belgium e-mail: [email protected]

Supporting information Additional Supporting Information may be found in the online version of this article: Table S1. Descriptive statistics

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