Does parents’ economic, cultural, and social capital explain the social class effect on educational attainment in the Scandinavian mobility regime?

Social

Science

RESEARCH

Social Science Research 36 (2007) 719–744

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Does parents’ economic, cultural, and social capital explain the social class effect on educational attainment in the Scandinavian mobility regime? Mads Meier Jæger a

a,*

, Anders Holm

b

The Danish National Institute of Social Research, Herluf Trolles Gade 11, DK-1052 Copenhagen K, Denmark b Department of Sociology and Centre for Applied Microeconometrics, University of Copenhagen, Øster Farimagsgade 5, Postal Box 2099, DK-1014 Copenhagen K, Denmark Available online 2 January 2007

Abstract This paper analyzes how much of the effect of social class on children’s choice of secondary education in Denmark can be decomposed into the influence of parental economic, cultural, and social capital. Following mobility regime theory, we propose that in the Scandinavian mobility regime to which Denmark belongs, the effect of social class on educational attainment should be explained primarily by non-economic forms of capital. We use an extremely rich Danish longitudinal survey to construct empirical measures of economic, cultural, and social capital and an extended random effect framework for the statistical analysis. Our results are, first, that controlling for the three types of capital we explain a considerable part of the social class effect on educational attainment, and, second, that cultural and social capital are the key predictors of educational attainment.  2006 Elsevier Inc. All rights reserved. Keywords: Intergenerational educational mobility; Denmark; Mobility regimes; Bourdieu; Social class; Mixed logit model; Concomitant variables; Confirmatory factor analysis

1. Introduction Findings from recent comparative studies on intergenerational educational attainment (see Erikson and Jonsson, 1996; Mu¨ller et al., 1989, 1993; Shavit and Blossfeld, 1993) and *

Corresponding author. Fax: +45 33 48 08 33. E-mail addresses: mads@sfi.dk (M.M. Jæger), [email protected] (A. Holm).

0049-089X/$ - see front matter  2006 Elsevier Inc. All rights reserved. doi:10.1016/j.ssresearch.2006.11.003

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occupational mobility (see Breen, 2004; Erikson and Goldthorpe, 1992; Esping-Andersen, 1993a) highlight the persisting inequalities in educational opportunity across countries and over time. A paradoxical finding in this literature is that Western, industrialized countries, although highly diverse in terms of the scope of public social security, political economies, and historical and cultural backgrounds all display considerable levels of inequality in educational opportunity. Nowhere is this paradox more evident than in the Scandinavian countries which established comprehensive social security systems with the explicit aim of promoting equality of opportunity (Erikson and Hansen, 1987). Consequently, one would anticipate the relationship between social background and educational attainment to be considerably weaker here than in other countries. However, the empirical evidence tells a different story. While the Scandinavian countries display high levels of absolute educational mobility relative mobility rates, typically conceptualized through social class differences in educational outcomes, are of considerable magnitude in all Scandinavian countries (for empirical evidence on Sweden see Breen and Jonsson, 2000; Dryler, 1998; Erikson and Jonsson, 1996; Jonsson, 1993; Norway see Hansen, 1997; Lindbekk, 1998; Finland see Kivinen and Rinne, 1996; Kivinen et al., 2001; and Denmark see Davies et al., 2002; Jæger and Holm, 2004). From a theoretical perspective, the persisting social class differences in educational attainment in the Scandinavian countries are perplexing. The literature on social mobility ‘‘regimes’’ (DiPrete et al., 1997; DiPrete, 2002; Esping-Andersen, 1993a) suggests that in the Scandinavian regime, with its comprehensive social security coverage, redistributive policies, and state-financed post-compulsory education system, parental social class should be a much weaker predictor of children’s educational attainment than elsewhere. In particular, one would expect parents’ social class to be much more significant in the ‘‘liberal mobility regime’’ (e.g., the United States, Australia, and the UK) in which public welfare is much less comprehensive and where parents pay much more of children’s education. The empirical observation that the magnitude of social class inequality in educational attainment is similar across different mobility regimes begs the question of whether these class differences also mean the same thing. Studies on the Scandinavian countries offer one of two possible explanations of what the social class effect on educational attainment means. First, most studies follow a conventional approach to social class (as pertaining to labor market position or economic relationship to the market; see Scott, 2002; Sørensen, 2000) and interpret the social class effect as the outcome of differences in parents’ socioeconomic resources. Second, some studies interpret social class differences in educational attainment as arising from other social background factors also captured by social class variables (family cultural and social resources, class differences in educational aspirations etc.; Breen and Jonsson, 2000; Erikson and Jonsson, 1996; Hansen, 1997). However, these interpretations of what the social class effect on educational outcomes means in the Scandinavian context could be misleading. If, as the literature on mobility regimes implies, parents’ socioeconomic position is not the true source of educational inequality in Scandinavia, then the interpretation that social class differences in educational attainment are attributable to economic inequality is incorrect. Instead, the effect of social class on educational attainment might predominantly pertain to non-economic sources of stratification. Furthermore, although some studies interpret social class

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differences in educational attainment as proxies for non-economic forms of stratification these studies remain speculative because usually they offer no empirical tests to support these alternative interpretations. The aim of this paper is to test the extent to which social class inequalities in educational attainment in the Scandinavian mobility regime can be decomposed into economic and non-economic forms of stratification. Drawing on Bourdieu’s concept of ‘‘capital’’ and an extremely rich Danish database, we distinguish three types of economic and non-economic resources that might affect children’s educational attainment. These forms of capital are economic, cultural, and social. The analysis aims, first, to determine whether it is possible to account for the total social class effect on children’s educational attainment by disaggregating this effect into its economic, cultural, and social components. The second aim is to analyze the influence of each of the three types of capital with respect to children’s educational outcomes. Given mobility regime theory and the particular features of the Danish welfare and education system, we develop a set of empirical hypothesis of the impact of each of the three types of capital on children’s educational attainment. This type of analysis yields new insights into the particular features of educational stratification in the Scandinavian mobility regime but is also of significance to stratification research using social class as the explanatory framework. The paper also advances existing research at both the empirical and methodological levels. First, the richness of longitudinal database enables us to create variables pertaining to parents’ economic, cultural, and social capital. Most often, studies analyze either the effect of economic and cultural capital, or, the effect of social capital on educational attainment (see Wong, 1998). We simultaneously analyze the effect of all three types of capital. Second, the paper improves upon conventional methodological approaches in two key areas. In one, we allow for unobserved heterogeneity, thereby taking into account the presence of unobserved factors affecting children’s educational attainment. We propose a correlated random effect approach that explicitly models the relationship between the observed explanatory variables and unobserved parental characteristics also affecting children’s education. We use this approach because for each child in our dataset, we observe the social class position and possession of each of the three types of capital of only one parent in the child’s family (either the mother or the father) but never both parents simultaneously. Consequently, we are unable to control directly for the influence of the missing parent’s characteristics (social class, amount of capital, etc.) on the child’s educational attainment. The correlated random effect approach handles this limitation, first by allowing for the missing parent to be present in the empirical model (through the inclusion of random effects), and second by taking into account parental homogeneity with respect to socioeconomic traits (i.e., our approach allows the observed and missing parent to have similar socioeconomic characteristics, both of which may affect children’s educational attainment). As we will demonstrate, this methodological approach is superior to conventional approaches. Section 2 presents the theoretical approach, as well as the defining features of the Danish welfare state and education system. Section 3 presents the concept of capital as an alternative to social class, and Section 4 presents data, variables and our methodological approach. Section 5 analyzes the effect of the three types of parental capital on children’s educational attainment in Denmark. Finally, Section 6 discusses the empirical findings and provides suggestions for future research.

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2. Educational mobility in the Scandinavian welfare regime 2.1. Mobility regimes Recent studies have emphasized how cross-national variations in countries’ institutional arrangements affect social mobility in the Western, industrialized countries. In particular, the welfare state constitutes a key institution, in that it redistributes income and wealth in the population, delivers social security, and provides education and health care. The comparative literature on occupational (see Erikson and Goldthorpe, 1992; Esping-Andersen, 1993a; Sobel et al., 1998) and income mobility (see Bjo¨rklund and Ja¨nnti, 2001; Solon, 2002) has identified systematic cross-national differences in mobility rates. These differences are linked to institutional divergences among countries and have facilitated the identification of distinct ‘‘mobility regimes’’ (DiPrete et al., 1997; DiPrete, 2002; EspingAndersen, 1993b), which comprise empirical clusters of countries in which similar institutional arrangements tend to produce similar structural conditions for social mobility. These regimes resemble Esping-Andersen’s (1990, 1999) famous distinction between the social democratic, conservative, and liberal welfare regimes. Denmark largely conforms to the theoretical features of the Scandinavian welfare and mobility regime. Public expenditure on social security is around 30% of GDP and is very high compared to the rest of the OECD area (but roughly similar to Sweden and Norway). Levels of income inequality and poverty are very low compared to other OECD countries (Fo¨rster and d’Ercole, 2005). Cash benefits and social services are comprehensive and generous by international standards, and entitlement is based on social rights, not income and means testing. Incomes taxes finance the bulk of welfare programs. Finally, the education system is handled almost exclusively within the public sector. 2.2. Secondary education in Denmark Secondary education in Denmark consists of two main branches. Upon completion of nine years of elementary school at approximately age 15, students may choose to leave the education system, take up upper secondary education, or enter vocational secondary education. The state finances all forms of secondary education, with no tuition fees or other direct costs. Upper secondary education, the gymnasium (with a general, a mercantile, and a technical branch), normally takes three years to complete and comprises the ‘‘academic’’ track in Danish secondary education. About 50% of a cohort chooses this option, and drop-out rates are around 15%. Vocational secondary education is usually initiated after elementary school, and most types of vocational education take three to four years to complete. The Danish system of vocational secondary education resembles the German ‘‘dual system’’, in that the student shifts between school-based training in branch-specific schools (e.g., carpentry) and practical training as an apprentice with an employer. 35% of a cohort enters vocational secondary education, and about 35% of those who enroll in vocational education fail to complete. The remaining 15% does not pursue any type of education after elementary school (Andreasen et al., 1997). In Denmark, the choice of secondary education has important implications for the student’s future educational options. Having completed upper secondary education is a prerequisite for admission to any type of higher education. In contrast, vocational secondary education enables quick access to the labor market and a source of income. Consequently,

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when choosing vocational secondary education students effectively rule out the possibility of pursuing higher education. As Section 3.2 will discuss, the high degree of tracking in Danish secondary education (and, in particular, the clear-cut distinction between the academic and vocational tracks) suggests that the effect of the different types of parental capital (economic, cultural, and social) on children’s educational attainment will materialize in distinct ways. 3. Parental resources as capital 3.1. Capital Most studies on educational mobility in the Scandinavian countries analyze inequality in educational outcomes through the theoretical lens of social class, primarily the Erikson-Goldthorpe-Portocarero (EGP) or CASMIN (Comparative Analysis of Social Mobility in Industrial Nations) class schemes (Erikson and Goldthorpe, 1992), both of which distinguish social classes on the basis of hierarchically ordered occupational positions in the labor market (see Breen and Jonsson, 2000; Erikson and Jonsson, 1996). When used as an explanatory variable in empirical applications, social class could act as a proxy for a wide range of social background effects that do not necessarily relate to occupational or economic stratification. Since it follows from mobility regime theory that occupational and economic stratification is not likely to be a key generator of educational inequality in the Scandinavian mobility regime, we have reason to believe that social class differences in educational attainment reflects other, noneconomic forms of stratification. To introduce a more comprehensive conceptualization of the different types of social background resources that generate educational inequality in the Scandinavian context we turn to Pierre Bourdieu’s concept of capital (Bourdieu, 1986). Bourdieu delineates qualitatively different forms of economic and non-economic resources that parents may possess in different amounts and compositions and which they invest in promoting children’s educational success. Bourdieu distinguishes three major forms of capital. First, economic capital comprises wages or other form of monetary assets, e.g. capital, stock, property (see Bourdieu, 1984, pp. 114–115). Economic capital may promote children’s educational outcomes either through direct investment (e.g., payment of tuition fees, enrollment in prestigious educational institutions) or indirect investment (e.g., financial subsidization of children). Second, cultural capital comprises not only accumulation of education and knowledge, but also parents’ tastes, preferences, and general ‘‘know-how’’ of the education system (Bourdieu, 1977, 1984). This form of capital may affect children’s educational attainment because the home environment acts as a ‘‘learning lab’’ in the development of children’s educational preferences, knowledge of the normative codes of the education system, and cognitive skills. Third, social capital is defined as the total extent and quality of social networks and connections that one uses to promote one’s interests (Bourdieu, 1986). Social capital may be of direct importance to children’s educational attainment in Denmark because some types of education require students to find apprenticeship positions for successful completion. Parents may possess social connections that could facilitate the acquisition of such positions.

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3.2. Research hypotheses We do not expect parents’ economic capital to be an important stratification mechanism in Denmark. Here, public policies have curtailed income inequality and reduced the impact of many social risks (e.g., unemployment, single parenthood) (DiPrete et al., 1997; DiPrete, 2002; Esping-Andersen, 1990, 1999). In contrast, in countries belonging to the liberal welfare regime parents’ economic resources are important predictors of children’s educational attainment (see Conley, 2001; Duncan and Brooks-Gunn, 1997; Gregg and Machin, 2001; for evidence from the US and the UK). However, because funding of the education system in Denmark comes almost exclusively from the state, parents would find it difficult to use their economic capital to promote children’s education. Parents might send children to private elementary schools, which account for about 12% of Danish schoolchildren. Yet, research shows that students from private elementary schools do not get better grades than students from public schools (Rangvid, 2003), so sending children to private elementary schools does not ensure them success in post-elementary education. Another possibility for parents is to send children to school in wealthy municipalities. In Denmark local municipalities administer and finance elementary schools, and per capita expenditure on elementary schools is positively correlated with the average income of the inhabitants in the municipality. In theory, one might then expect that parents could improve children’s educational chances by sending them to schools in wealthy municipalities. However, recent evidence shows that per capita expenditure on elementary schools is only very weakly correlated with students’ subsequent educational attainment (Heinesen et al., 1999; Heinesen and Graversen, 2005). Therefore, sending children to school in wealthy neighborhoods offers no substantial educational benefit. Alternatively, parents’ non-monetary forms of capital may be significant for explaining children’s schooling in Denmark. First, we would expect parents’ cultural capital to be strongly linked to enrollment in upper secondary education. This type of secondary education comprises the ‘‘academic’’ track in Danish secondary education and is the direct and usually necessary pathway for those wanting to pursue higher education. Since parents with a lot of cultural capital are themselves likely to be highly educated, we would expect them to pass on to their children a preference for an academically oriented education (e.g., Aschaffenburg and Maas, 1997; De Graaf et al., 2000; de Graaf and Kalmijn, 2001). Furthermore, parents with a lot of cultural capital are also more likely to possess realistic information on the strategic importance of making the ‘‘right’’ choice of secondary education (i.e., that upper secondary education represents a long-term investment in educational and occupational status). Second, in Denmark social capital may be particularly important with respect to vocational secondary education. As successful completion of most types of vocational education depends on the student’s ability to find an apprenticeship position with an employer, knowing the ‘‘right’’ people is essential.1 Parents who have vocational educations or who

1 If students who enrol in vocational educations are not able to find an apprenticeship position with an employer, they are required to accept ‘‘in-school’’ apprenticeship positions organized by the school. However, both students and employers widely recognize in-school apprenticeships as second-rate alternatives. Consequently, students with in-school apprenticeships generally face poorer employment opportunities, especially since students are often offered their first job by the employer with whom they apprenticed.

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work in vocational professions are more likely to possess helpful social connections (see McNeal, 1999; Morgan and Sørensen, 1999; Sandefur et al., 2006). 4. Data, variables, and methods 4.1. Data The data for our analysis comes from the Danish Youth Longitudinal Study (DYLS). The DYLS consists of a nationally representative sample of 3151 Danish respondents, all of whom were born in or around 1954 (83% born in 1954, 12% in 1953, and 5% in 1955). First interviewed in 1968 at age 14, the primary respondents and their parents have since been interviewed in 1969, 1970, 1973, 1976, 1992, and again in 2001 when they were about 47 years old. The DYLS contains extremely useful longitudinal information on respondents’ life course, their socioeconomic position and possession of a comprehensive range of monetary and non-monetary resources throughout adulthood. Compared to other longitudinal surveys carried out in the Scandinavian countries, the DYLS has particularly rich information on monetary and non-monetary resources which makes the data wellsuited for an operationalization of economic, cultural, and social capital. Overall attrition is comparatively low: in 2001, 33 years after the first survey, 2507 respondents were successfully re-interviewed, yielding a response rate of 79.6%. In the analysis we focus on choice of secondary education for the oldest child of the primary DYLS respondents, provided that the child is at least 20 years old. This restriction results in a sample size of 1221 with non-missing observations on the primary respondents and their children. Note that this sample is not completely representative of the Danish population, because the criterion that the oldest child be at least 20 years old means that all respondents share the feature of having had their first child at a fairly young age. A comparison between the full DYLS data and the sample analyzed here suggests that respondents in the sample on average have somewhat lower educational qualifications, occupational status, and earnings, and furthermore, that there is an overrepresentation of women. The selectivity of the sample should be borne in mind when interpreting the findings. Notwithstanding these limitations, the richness of the data makes it well-suited for our purpose. 4.2. Variables The analysis contains two types of variables. First, we use a number of items from the DYLS to measure the three types of monetary and non-monetary parental capital (see also Jæger and Holm, 2004). We conceptualize the different types of capital as latent variables and use confirmatory factor analysis (CFA) to investigate the overall distribution of each of the three types. In the second step, we include these variables, along with a number of control variables, in the analysis of children’s educational attainment. Table 1 presents the items used for measuring parents’ economic, cultural, and social capital (see Appendix Table 1 for more information and descriptive statistics for each item). To measure respondents’ economic capital, we use four categorical items which represent both earnings and material possessions: (1) gross monthly income in DKK (Danish Kroner, where 6 DKK  1 USD), (2) ownership of home and its estimated value, (3)

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Table 1 Items used to estimate the three types of parental capital Form of capital

Item

Economic capital Cultural capital

(1) Gross monthly income, (2) home ownership and value of home, (3) car ownership and value of car, and (4) ownership of summerhouse. (1) Level of education, (2) number of foreign languages spoken, (3) number of newspaper subscriptions, (4) reads fictional books, and (5) interested in the visual arts. Respondent has social connections that might help children. . . (1) Find part- or full-time employment, (2) find an apprenticeship position, (3) find a place of residence, (4) get advice on educational choices, and (5) get help if the child wants to study abroad.

Social capital

ownership of a car and its estimated value, and finally (4) a dummy variable indicating ownership of a summerhouse. For cultural capital we use five categorical indicators: (1) the respondent’s level of education measured on a five-point ordered scale, (2) the number of foreign languages spoken, (3) the number of newspaper subscriptions, (4) a dummy variable indicating whether the respondent reads fictional books, and (5) a dummy variable indicating whether the respondent is interested in the visual arts. These items include both formal educational credentials and cultural preferences (see Aschaffenburg and Maas, 1997; De Graaf et al., 2000). For social capital we use five binary items indicating whether the respondent has social connections that might help children (1) find part- or full-time employment, (2) find an apprenticeship position, (3) find a place of residence, (4) get advice on educational choices, and (5) get help if the child wants to study abroad. Compared to the general social capital indicators typically available in survey data (social participation, involvement in schools, etc.), one advantage of our social capital items is that they pertain directly to social connections that may facilitate children’s education. Table 2 shows that our main dependent variable, children’s choice of secondary education, is measured through three discrete categories: (1) no education beyond elementary school, (2) vocational secondary education, and (3) upper secondary education. We also control for the child’s gender and age in years. In addition to the three variables measuring the observed parent’s economic, cultural, and social capital, we include a host of additional explanatory variables. First, we include the social class position of the observed DYLS parent. As mentioned in Section 3.1, the convention in the literature is to use the Erikson-Goldthorpe-Portocarero (EGP) social class scheme. We use the five-fold EGP classification (EGP-5) introduced by Halpin (1999) distinguishing among (1) professional and managerial employees and self-employed with 10 or more employees (service classes I and II); (2) routine non-manual professionals (class III); (3) self-employed and small employers (1–9 employees) (class IV); (4) skilled workers (classes V/VI); and (5) unskilled and semi-skilled workers (class VII). Second, we control for family status (with a dummy variable for single parent households) and number of siblings. Third, we control for the observed DYLS parent’s gender. Fourth, we also include two variables measuring the social class position of the observed respondent’s parents (i.e., the ‘‘grandparent’’ generation), again using the EGP-5 classification. Although we do not directly use these social class variables to explain children’s educational attainment, they serve as instrumental variables in the identification of the unobserved part of the statistical model.

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Table 2 Summary statistics Mean

SD

Dependent variable: Child’s secondary education None beyond elementary school Vocational secondary Upper secondary education

0.11 0.40 0.49

0.31 0.49 0.50

Explanatory variables: Parental capital Economic capital Cultural capital Social capital

0.04 0.05 0.08

0.02 0.02 0.02

1992/2001 1992 2001

0.50 23.83

0.50 2.67

2001 2001 2001

0.32 0.24 0.10 0.10 0.24 0.15 1.25 0.39

0.47 0.43 0.30 0.30 0.42 0.36 0.89 0.49

0.14 0.12 0.27 0.12 0.35

0.34 0.33 0.44 0.32 0.48

0.19 0.09 0.29 0.18 0.25

0.39 0.29 0.46 0.38 0.43

Control variables: Child’s gender (1 = male) Age of child Parent’s social class (EGP-5) I/II III IV V/VI VII Single parent household Number of siblings Parent’s gender (1 = male) Grandmother’s social class (EGP-5)* I/II III IV V/VI VII Grandfather’s social class (EGP-5) I/II III IV V/VI VII

Year of measurement 2001

2001 2001 1968 2001r

2001r

Note. *Grandfather’s social class was used if grandmother’s occupation was housewife, rretrospective information provided by respondent.

4.3. Methodology To analyze the extent to which we may decompose the effect of social class on children’s secondary schooling into economic and non-economic forms of stratification, we take a two-step approach. First, from the data we identify the three types of parental capital, the degree of empirical interrelationship between the different types of capital, and the ‘‘quantity’’ of each type of capital that the DYLS respondents hold. As previously mentioned, we use confirmatory factor analysis (CFA) to carry out this part of the analysis. Second, we develop a regression framework to analyze the effect of social class and parental capital on children’s educational attainment. The following subsections present this regression framework.

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4.4. An empirical model of educational attainment The aim of the empirical analysis is to explain children’s choice of secondary education as a function of social class, the three types of capital, and the control variables. The fundamental idea behind the analysis is that by controlling for parents’ possession of the three types of capital, we should be able to determine the extent to which observed social class differences in secondary education are attributable to economic and non-economic forms of capital. Since the dependent variable in our analysis consists of three unordered alternatives (no secondary education, vocational secondary education, and upper secondary education), we begin from the perspective of the multinomial logit model (MLM). To facilitate the extensions of the basic MLM that follow, we write the model in terms of latent propensities for choosing each of the three educational outcomes in the following form U j ¼ aj þ b j x þ e j ;

j ¼ 1; 2; 3:

ð1Þ

Here, Uj is a latent propensity for the child to choose alternative j (among the three alternatives), aj is a constant, and bj are regression coefficients for the vector of explanatory variables x (in our case the observed parent’s social class, the three types of capital, and the control variables). Furthermore, ej is an error term summarizing the effect of unobserved variables also affecting children’s educational choices but not included in x. To arrive at the standard MLM, in (1) we must assume that the three error terms (i.e., one for each j in the model) are independently extreme value distributed (Powers and Xie, 2000). Furthermore, we must assume that all error terms are independent across educational outcomes and independent of all observed variables in x. The assumptions behind the standard MLM entail several problems. First, the distributional assumptions pertaining to the error terms could be wrong. If so, maximum likelihood estimation of the model parameters will yield biased results (Abramson et al., 2000). Second, the assumption of independence between the error terms ej (j = 1, 2, 3) and the x’s has important substantive implications. In our application, this assumption implies that we must assume that the amount of economic, cultural, and social capital possessed by the observed parent in the DYLS is not correlated with the amount of capital possessed by the unobserved parent (which, by definition, is subsumed into the error term ej that is uncorrelated with x). Since it is well known that couples in most Western, industrialized countries tend to be homogenous with respect to socioeconomic characteristics (see Blossfeld and Timm, 2003; Smits et al., 1998), the assumption of no homogeneity among parents inherent in the MLM framework is very unrealistic. Moreover, if the assumption of no correlation between ej and x is violated, the estimated effects of the characteristics of the observed parent bj become inconsistent as they capture both the direct effect of this parent and any indirect effects from the unobserved parent affecting x through the correlation with ej. As 85% of the respondents in the DYLS sample report being married or cohabitating (see Table 2) we would expect the unobserved parent to influence children’s educational attainment in a magnitude similar to that of the observed parent. Clearly, if this scenario is true, the MLM framework could provide completely misleading results on the effects of the variables of the observed parent (since we would not be able to determine how much of the estimated effect is due to the ‘‘true’’ effect of the observed parent and how much is due to indirect effects from the unobserved parent).

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But how may we deal with these limitations in the MLM? Suppose that we decompose the error term in (1) into two parts e j ¼ e j þ vj :

ð2Þ

Here, ej is assumed to be independently identical extreme value distributed (as in the standard MLM) and captures random measurement error in the model. In contrast, vj summarizes the influence of the unobserved parent, i.e. a systematically unmeasured effect, on children’s educational attainment.2 For now, we leave the distribution of vj unspecified, but we will return to this problem shortly. Returning to the model in (1), we need to implement two extensions to accommodate our analytical requirements. First, we would like to include vj in the model in (1) as an explanatory variable to control for the influence of the unobserved parent. That is, if we could condition on vj in (1), the effects of the explanatory variables for the observed parent (bj) no longer become biased due to these parameters also picking up the effect of the unobserved parent. A modeling framework that accommodates unobserved variables is the random effect or mixture logit model (Train, 2003). We adopt this framework and, by including the random terms vj, obtain the random effect MLM P ðU j > U s ; 8s 6¼ jjv2 ; v3 Þ ¼

expðaj þ bj x þ vj Þ ; Ps¼J 1 þ s¼2 expðas þ bs x þ vs Þ

j ¼ 1; . . . ; J :

ð3Þ

Unlike the standard MLM, the probability that children choose educational outcome j (j = 1, . . . , J) now depends on both x and vj. Studies analyzing educational attainment have previously used random effect models to account for unobserved heterogeneity (e.g., Breen and Jonsson, 2000; Cameron and Heckman, 1998). However, an important drawback in the random effect model is that, to provide consistent estimates, we still need to assume that x and vj are uncorrelated. That is, while the random effect model allows for the effect of the unobserved parent to be present in the model, it still assumes that parents are not homogenous with respect to their socioeconomic characteristics. This problem leads to our second extension of the MLM framework. To accommodate the presence of a correlation between the characteristics of the observed parent, x, and the unobserved parent, v, we need to formulate a model that describes this correlation. However, first we need to discuss how we deal with the random effects vj which capture the effect of the unobserved parent on children’s educational attainment. As we have no prior information on the functional form of the v’s, we refrain from imposing any distributional assumptions. Instead, we attempt to approximate the true but unknown distribution by a discrete distribution with a fixed number of mass points or latent classes (Lindsay, 1983a,b; Wedel, 2002). With this approach, we capture unobserved parental characteristics (and their effect on children’s educational 2

In order to simplify the notation that follows we disregard gender-specific effects in (2) (i.e., the possibility that the effect of unobserved parental characteristics differs for mothers and fathers). This simplification of the model can be tested by allowing the effect of unobserved parental characteristics to depend on the gender of the observed parent, i.e., ej = ej + c Æ g Æ vj + vj, where g is a binary indicator of the observed parent’s gender and c is an effect parameter. A test of whether c is statistically significantly different from zero is then a test of whether the effect of the unobserved parent on children’s education is independent of the gender (but not necessarily other characteristics) of the observed parent. We carried out this test in the empirical analysis and found that c was not statistically different from zero. Consequently, we proceed with the framework outlined in (2).

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attainment) through a finite number of latent groups. As in traditional latent class analysis, these groups comprise latent clusters of (unobserved) parents who share similar traits (e.g., socioeconomic characteristics) and influences on children’s educational outcomes. The number of latent classes required to fully account for population heterogeneity is determined empirically. To accommodate a correlation between the characteristics of observed parents (the x’s) and the latent classes capturing unobserved parental characteristics (the v’s), we propose a concomitant variable model (Dayton and MacCready, 1988; Wedel, 2002). In this model, the latent classes are denoted k (k = 1, . . . , K), and we write the probability of membership of latent class k as P ðV ¼ v1k ; v2k Þ ¼

expðak þ bk zÞ ; PK 1 þ k¼2 expðak þ bk zÞ

k ¼ 1; . . . ; K;

ð4Þ

where a is a constant, and z comprises a vector of explanatory variables (with parameter vector bk) which affect the probability of belonging to a particular latent class, k. The concomitant variable model is then a logit model in which the dependent variable (latent class membership) is only indirectly observed. We introduce a correlation between observed and unobserved parental characteristics (which is equivalent to assuming marital homogamy) by allowing the variables in x pertaining to the observed parent to be included in z. Furthermore, we use the information on the observed parent to predict (at a probabilistic level) the socioeconomic characteristics of the unobserved parents and their influence on children’s educational attainment. Our strategy implies, first, that we account for any correlation between the characteristics of the observed and unobserved parent, and, second, that we use relevant variation in the data to obtain a more accurate identification of the unobserved part of the model. We label the model presented in this section the Latent Class Multinomial Logit Model (or LCMLM). However, to achieve non-parametric identification of the LCMLM, we still need instrumental variables in the z vector that affect latent class membership but have no direct effect on children’s educational choices (other than that going through the observed variables and the latent classes) (see Angrist et al., 1996; and Biblarz et al., 1996; and Warren and Hauser, 1997 for examples of studies demonstrating that grandparents’ socioeconomic characteristics have no direct effect on grandchildren’s outcomes). We apply the EGP-5 social class position of the observed DYLS parent’s father and mother as instruments. These social class variables capture variations in respondents’ socioeconomic origins, and we hypothesize that these variables may be used to predict both their current amounts of capital and the amount of capital possessed by their spouses (since we know that spouses resemble each other with respect to socioeconomic traits). Additional details concerning the estimation of the LCMLM appear in the Appendix. 5. Results This section is divided into two parts. First, we present the findings from the CFA model and discuss the distribution of economic, cultural, and social capital by social class. Second, we analyze the effect of social class and parental capital on children’s educational attainment.

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5.1. The distribution of capital In Fig. 1, we present the results of the confirmatory factor analysis (CFA). Since the input variables comprise a combination of categorical and continuous items, we estimated the model using polychoric correlations as the input matrix. The variance of each of the three factors was fixed at 1 so that the factor loadings for all items could be estimated freely. Finally, we imposed the constraint on the parameters that only items hypothesized as proxies for each of the three types of capital (economic, cultural, and social capital) were allowed to load on that factor. The fit indices (Comparative Fit Index/Tucker-Lewis Index, Root Mean Square of Approximation; see Bentler, 1990), also reported in Fig. 1, indicate that this model has an acceptable fit to the data. From Fig. 1, we find that the items load significantly and as anticipated on each of the three latent factors of parental capital. This suggests that the CFA model captures the three qualitatively different types of parental resources quite well. Fig. 2 plots mean values of economic, cultural, and social capital by respondents’ social class. With the exception of social class IV (self-employed), mean levels of economic, cultural, and social capital increase by social class from class VII (unskilled workers) to classes I/II (professional and managerial employees). Interestingly, respondents in class IV (rather than classes I/II) have the highest mean level of economic capital. However, since in Denmark a sizeable proportion of respondents in classes I/II are higher-level public officials (whose earnings levels are relatively modest compared to their social class position) this is not surprising. With a few exceptions, all social class differences in mean levels of economic, cultural, and social capital are statistically significant at the p < 0.05 level.

Fig. 1. Confirmative factor model for 3 types of capital.

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0,5

Factor score

0,3 0,1 -0,1 -0,3 -0,5 -0,7 I/II

III

IV

V/VI

VII

Social class Economic capital

Cultural capital

Social capital

Fig. 2. Mean economic, cultural and social capital by social class.

5.2. Determinants of educational attainment In this section, we present the empirical results of the decomposition of the social class effect on educational attainment into economic, cultural, and social capital. The section is divided into three subsections. In the first subsection we review the fit of different model specifications. In the second subsection we present the findings on the influence of social class, economic, cultural, and social capital on children’s educational attainment. Finally, in the third subsection we discuss the results of the concomitant variable model describing the influence of unobserved parental characteristics on children’s educational outcomes. Table 3 presents goodness-of-fit measures for different model specifications. We report the 2 log-likelihood, the small-sample corrected Akaike Information Criterion (AIC) (see Burnham and Anderson, 2002), and p-values for likelihood-ratio tests (LRT) comparing different model specifications. All models include the control variables, i.e. the observed parent’s gender, single parent household, number of siblings, and child’s age and gender. Model 1 is a baseline MLM in which we enter parental social class as dummy variables, as well as interactions between social class and the observed parent’s gender. This model comprises the most flexible parameterization of the social class effect on children’s educational attainment and furthermore (through the interaction terms) allows for the social class effect to differ for mothers and fathers. In model 2, we test if removing the interactions between social class and the observed parent’s gender significantly reduces the fit of the model. The p-value for the LRT comparing models 1 and 2 is 0.6374, thereby indicating that the effect of social class on children’s educational attainment does not differ for mothers and fathers. In model 3, we introduce an ordinal scaling of social class rather than the dummy coding used in model 1. This approach is theoretically preferable to dummy variables since it treats the categories of the social class variable as ordered rather than unordered. Furthermore, ordinal scaling expresses the social class effect on educational attainment in a single coefficient for each educational outcome which offers a more parsimonious approach by reducing the number of parameters to be estimated. In model 3, we use the estimated

#

1 2 3 4 5 6 7 8 9 10

Model

2 Loglikelihood

Small-sample Akaike information criterion

MLM: gender-specific parental social class dummies without parental capital variables MLM: parental social class dummies without parental capital variables MLM: parental social class ordinal without parental capital variables MLM: parental social class ordinal with gender-specific parental capital variables MLM: parental social class ordinal with parental capital variables 2-Class LCMLM without correlation 2-Class LCMLM with correlation 3-Class LCMLM with correlation 4-Class LCMLM with correlation 4-Class LCMLMwithout IV

2199

2138

2207

2166

0.6374

1

10

2204

2168

0.9543

1

12

2147

2094

0.0000

3

8

2153

2113

0.3990

4

6

2148 2111 2091 2074 2100

2095 2048 2004 1963 2009

9

— — — — 9

p-Value for likelihood-ratio test

Tested against model #

Difference in model degrees of freedom —

0.0018

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Table 3 Summary of goodness-of-fit measures for different model specifications

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regression coefficients from the dummy coding of social class in model 1 to scale the social class variable. That is, rather than assuming equidistant distances between the categories of the social class variable (e.g., 1, 2, 3, 4, and 5), we use the estimated regression coefficients from model 1 as measures of the distance between categories. The p-value from the LRT comparing model 3 (with social class as ordinal) to model 2 (with social class as dummy variables) is 0.9543, with deviations from 1.0000 (meaning identical fit of both models) only due to rounding errors. In the following models we use the ordinal scaling of the social class variable. In model 4, we introduce economic, cultural, and social capital into the model. In addition, we include interactions between the observed parent’s gender and the three forms of capital to allow for gender-specific effects of the different types of capital. As indicated by both the LRT and the AIC this model specification represents a strong improvement over model 1, thereby affirming that the three types of capital contribute significantly to explaining children’s educational attainment. In model 5, we remove the interaction terms to test if the gender-specific effects of economic, cultural, and social capital can be omitted from the model. The p-value for a LRT comparing model 4 to model 5 is 0.3990 indicating that no gender-specific effects of the different types of capital are significant. In models 6–9, we estimate the LCMLM with an increasing number of latent classes to approximate the distribution of unobserved parental influences. In addition, we estimate the LCMLM with and without correlations between observed and unobserved parental effects. We find that the 2-class LCMLM has a better fit to the data compared to model 5 that does not take unobserved parental influences into account. Note that since models 5 and 6 are not nested (and neither are models 6, 8, and 9 which have different numbers of latent classes) the LRT cannot be used to assess the relative fit of competing model specifications. Rather, we must now use the AIC. It turns out that using 3 and 4 latent classes to approximate unobserved parental effects yield gradually improving model fit, whereas with 5 and more latent classes we encountered convergence problems.3 Finally, in model 10, we estimate the 4-class LCMLM without the Instrumental Variables (IVs) to evaluate the performance of the IVs in terms of aiding in the identification of the latent classes of unobserved parental influences. The p-value of a LRT of model 10 against model 9 that includes the IVs (the LRT can be used here since models 9 and 10 are nested) is 0.0018 indicating that model 10 without IVs is significantly inferior to model 9. Consequently, in the following sections we report findings from the preferred model which is the 4-class LCMLM with IVs. Table 4 present the results from models 3, 5, and 9; i.e. the baseline model with the ordinal parameterization of the social class effect, the model including economic, cultural, and social capital, and finally the 4-class LCMLM with IVs. In model 3, we find strong ‘‘raw’’ effects of social class on children’s educational attainment. In this model, we have coded the social class variable such that higher values indicate a ‘‘higher’’ or more advantageous social class background. It is evident from model 3 that the probability of choosing no or vocational secondary education relative to upper secondary education is lower for children whose parents are in advantageous social class positions compared to children from less 3

The effects of the explanatory variables do not change much in the LCMLM with 5 or more latent classes. Rather, it seems that the main cause of the instability of the 5+ latent-class LCMLM is due to the increasingly fine-grained segmentation of the unobserved parental effects. With modest sample sizes such as the one analyzed here it may not be possible to identify more latent classes in the data.

Model

Social class Economic capital Cultural capital Social capital Parent’s gender (1 = father) Number of siblings Single parent household Child’s age in years Child’s gender (1 = male) Constant Latent class II effect Latent class III effect Latent class IV effect

MLM (3)

MLM (5)

LCMLM (9)

No education

Vocational education

No education

Vocational education

No education

Vocational education

1.00

0.99

0.55 0.11 0.80 0.01 0.60 0.14 0.36 0.13 0.66 5.67 — — —

0.56 0.26 0.85 0.25 0.02 0.02 0.04 0.13 0.77 3.77 — — —

0.83 0.04 1.03 0.11 0.83 0.15 0.35 0.13 0.66 5.76 0.11 1.43 2.44

0.54 1.21 1.23 0.59 0.48 0.05 0.05 0.14 0.85 5.32 1.75 2.48 1.55

0.63 0.13 0.43 0.16 0.61 6.71 — — —

(0.20)***

(0.21)*** (0.11) (0.26)* (0.04)*** (0.20)*** (0.65)***

0.08 0.02 0.05 0.15 0.73 4.50 — — —

(0.18)***

(0.14) (0.08) (0.19) (0.03)*** (0.13)*** (0.98)***

(0.22)** (0.26) (0.23)*** (0.16) (0.21)*** (0.11) (0.28) (0.04)*** (0.20)*** (0.10)***

(0.21)** (0.17) (0.15)*** (0.11)** (0.15) (0.08) (0.20) (0.03)*** (0.13)*** (0.67)***

Upper secondary education is the reference category. Log-odds estimates and standard errors in parenthesis. Note. ***p < .001, **p < .01., *p < .05. N = 1221.

(0.32)*** (0.57) (0.35)*** (0.24) (0.29)*** (0.12) (0.30) (0.04)*** (0.21)*** (1.16)*** (0.78) (0.83) (1.14)**

(0.41) (0.44)*** (0.33)*** (0.20)*** (0.30) (0.09) (0.23) (0.03)*** (0.16)*** (1.39)*** (0.69)*** (0.74)*** (1.79)

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Table 4 Results from multinomial logit models of children’s choice of secondary education

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advantaged class backgrounds. In model 3, the estimated log-odds coefficients (1.00 for no education and 0.99 for vocational education) should be interpreted as the average effect, expressed in log-odds, associated with a one-unit increase in class background (i.e., a move into the adjacent ‘‘higher’’ social class background) on the probability of attaining these types of education. Consequently, our initial findings are as expected and replicate those from other Scandinavian countries (e.g., Erikson and Jonsson, 1996; Hansen, 1997; Jæger and Holm, 2004; Kivinen et al., 2001). In model 5, we introduce the observed parent’s economic, cultural, and social capital as explanatory variables. The estimated log-odds coefficients of social class are attenuated considerably (from around 1.00 to around 0.55) but remain significant at the p < 0.01 level, and it appears that controlling for the observed parent’s economic, cultural, and social capital explains a significant part of the social class effect on educational attainment. The effects of economic, cultural, and social capital on children’s educational attainment are as expected. Economic capital is not significant, whereas the probability of choosing no or vocational secondary education decreases sharply with parental cultural capital. Furthermore, as hypothesized social capital is positively associated with the probability that children choose vocational secondary education. Model 9 is the 4-class LCMLM with IVs. Interestingly, in this model, the social class effect on vocational education is no longer significant, whereas the social class effect on the probability of not choosing any type of secondary education is somewhat stronger than in model 5. Our finding is then that in the LCMLM the combined influence of observed and unobserved parental economic, cultural, and social capital completely explains the total ‘‘raw’’ social class effect on vocational secondary education. On the other hand, the analysis shows that parental social class has a substantive effect on the probability of not acquiring any secondary education even when parental capital is taken into consideration (as we discuss below, cultural capital is the only one of the three types of capital that has a significant effect on the probability of acquiring no versus upper secondary education). Apparently, social class captures some important stratification mechanisms leading to poor educational outcomes that are not captured by our three capital measures. The effects of parental capital on children’s schooling are generally stronger in the LCMLM than in model 5. This is not surprisingly since the parameter estimates in model 5 pick up both observed and unobserved parental influences which may work in different directions. In contrast, in the LCMLM the effects of unobserved parental characteristics have been purged (but are approximated by the latent class effects shown at the bottom of Table 4) and the estimated coefficients represent the ‘‘pure’’ effect of observed parental capital. Interestingly, in the LCMLM economic capital has a highly significant positive effect on the probability of choosing vocational over upper secondary education. The analysis then shows that when the influence of cultural and social capital has been removed children whose parents possess high amounts of economic capital are particularly likely to pursue vocational secondary education. This result is in contrast to our initial expectations but may be attributable to the fact that the self-employed respondents in social class IV (the class with the highest mean level of economic capital, cf. Fig. 2) very often have vocational educations (around 50% of members of social class IV have vocational educations). Possibly, the combination of a self-employed parent and high economic capital makes children more likely to prefer vocational over upper secondary education. Another explanation might be that since earnings returns to higher education in Denmark are

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among the lowest in the OECD (OECD, 2005), children from families with high levels of economic capital may not consider upper secondary and, later higher education to be a worthwhile investment in securing future earnings and social status. In contrast to economic capital, children whose parents possess high levels of cultural capital are uniformly more likely to enter upper secondary education than any of the remaining alternatives. The effect of cultural capital is somewhat stronger in the LCMLM than in model 5. This fact substantiates the argument that non-monetary parental resources such as cultural capital (once other confounding effects are purged) is a key determinant of upper secondary education in the Scandinavian context. Given the institutional setting in the Scandinavian mobility regime with strong redistribution and comprehensive social security this finding is not surprising. In addition, in the LCMLM social capital is a positive predictor of vocational education. This finding corroborates our theoretical hypothesis that social connections, and particularly connections that may help children find apprenticeships and possibly later a job, are especially important for children who want to pursue vocational education. The effects of the control variables do not vary much across the different model specifications. First, if the observed parent is a father, children have a higher probability of not obtaining any type of secondary education. This result seems peculiar but could reflect sample selection such that the fathers in the sample possess unobserved traits associated with poor child outcomes that are not completely accounted for by the latent classes. Second, sibship size and family type has no independent effect on children’s educational attainment. Third, child’s age is a positive predictor of acquiring no or vocational secondary relative to upper secondary education. This effect could be a proxy for family socioeconomic traits since older children in the sample are born to relatively young parents (all primary respondents in the DYLS are born in or around 1954). Fourth, we find that boys are more likely than girls not to pursue any type of secondary education or to choose vocational secondary education. These gender differences in educational choices are well known in the Danish case (Jensen et al., 1997). As the last part of the empirical analysis we present the results of the concomitant variable model which are shown in Tables 4 and 5. To recall, the concomitant variable model states that the unobserved parental characteristics may be divided into four qualitatively different latent groups, each of which has a different effect (captured by v in (3) in Section 4.4) on children’s educational attainment (the latent class effects are shown in Table 4). Furthermore, because parents tend to resemble each other with respect to socioeconomic traits, we use the characteristics of the observed parent (i.e., the scores on the three different types of capital, social class, and gender) to predict which latent class the unobserved parent is more likely to belong to. The regression coefficients in Table 5 describe how the characteristics of the observed parent help discriminate between these four latent classes. The regression coefficients have the interpretation that positive coefficients signify a higher probability of belonging to latent class II–IV compared to latent class I (which is the reference category whose values are set to 0), and negative coefficients have the opposite interpretation. We observe in Table 4 that the latent class effects differ across educational outcomes. Children with latent class II and latent class III unobserved parents have a significantly higher probability of acquiring vocational secondary education but are not more likely to not obtain any secondary education. Although children with latent class II and III parents tend to have similar educational outcomes, what discriminates these latent classes? Interpretations of the effects in Table 5 are tentative since only few contrasts are statisti-

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Table 5 Latent class parameters for 4-class LCMLM Concomitant variables

Latent class II

Latent class III

Latent class IV

Social class Economic capital Cultural capital Social capital Gender Grandmother’s social class Grandfather’s social class Grandmother’s social class · grandfather’s social class Constant

1.62 6.55 3.09 2.41 1.54 0.81 1.81 3.52 2.06

1.62 1.02 4.52 1.27 2.35 1.76 3.54 1.14 1.26

3.60 2.75 2.54 3.44 3.01 0.32 2.27 2.21 1.37

(1.82) (2.79)*** (2.01) (1.60) (2.03) (3.99) (3.46) (4.91) (2.58)

(1.73) (2.11) (2.01)** (1.45) (2.29) (2.79) (3.86) (4.22) (2.35)

(1.97) (1.87) (2.20) (1.85) (2.19) (3.45) (3.05) (5.23) (2.05)

Log-odds estimates and standard errors in parenthesis. ***p < .001, **p < .01.

cally significant. However, it seems that membership of latent class II is likely if the observed parent has very high economic capital, some cultural capital, low social capital, and is male. In contrast, membership of latent class III is likely if the observed parent has little economic capital, high cultural capital, some social capital, and is female. One possible interpretation is that latent class II captures wives of husbands (wives because the observed parent has a high likelihood of being male, i.e. the husband, meaning that the latent class must capture the influence of the unobserved wife) with very high economic capital, whereas latent class III captures husbands of middle-class wives (who have relatively modest economic but relatively much cultural capital). In both latent classes children tend to favor vocational education, although for possibly different reasons. Maybe, latent class II captures how wives of high earners somehow exacerbate the observed positive effect of husbands’ economic capital on the probability that children choose vocational education. In contrast, latent class III appears to capture middle class characteristics where wives have relatively low economic but fairly high cultural capital (for example, nurses or schoolteachers, or other groups of public sector employees with medium-length education and relatively modest earnings). Often, wives with these socioeconomic characteristics will be married to husbands working in vocational occupations, and it may be the case that the positive effect of latent class III on the probability that children choose vocational education captures the influence of these unobserved fathers. Membership of latent class IV is especially probable if the observed parent has fairly high economic and cultural capital, little social capital, and is male. This latent class may then capture upper middle-class wives, and, as may be seen in Table 4, children whose unobserved parents belong to this class have a significantly lower probability of not obtaining any secondary education relatively to upper secondary education. In substantive terms, this latent class may capture middle-class families’ preferences for education as a means of securing their social position. Consequently, having a latent class IV unobserved parent ‘‘protects’’ children against not obtaining any secondary-level schooling. Finally, for the IVs (grandfather and grandmother’s social class) no individual parameters are significant in any of the latent class contrasts. Since grandfather and grandmother’s social class are correlated we have included an interaction term in the model which tends to improve the fit of correlated explanatory variables. However, as we demonstrated above when testing model 9 against model 10, the IVs significantly improve the overall fit of the model and should be included.

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6. Discussion This paper started from the puzzling observation that social class inequalities in children’s educational attainment are very similar across a range of otherwise different Western, industrialized countries. This finding is particularly paradoxical in the Scandinavian context in which social security and redistributive and educational policies have always been used as means to promote equality of educational opportunity. As a consequence, social class inequality in educational attainment would be expected to be smaller here than elsewhere. Most existing studies on educational attainment in the Scandinavian countries interpret social class differences in educational outcomes as pertaining to mainly economic forms of stratification. Alternatively, social class effects are sometimes argued to be manifestations of other mechanisms of social stratification, for example class-based differences in educational aspirations or ‘‘cultural’’ resources. The main argument in this paper is that conventional interpretations of the nature of the social class effect on educational attainment in the Scandinavian context might potentially be either incorrect or unsubstantiated. It follows from mobility regime theory that interpreting the social class effect as mainly an economic type of stratification is theoretically implausible. Given the high level of redistribution and comprehensive social security coverage in this mobility regime, it is not likely that the social class effect on educational attainment exclusively or even mainly captures economic forms of stratification. Similarly, suggesting that social class represents a proxy for a host of other forms of social stratification remains speculative since this argument offers little insights into what these forms of stratification are and how they operate. With Denmark as the test case this paper implements an empirical test of the hypothesis that the social class effect on educational attainment in the Scandinavian mobility regime can be decomposed into one monetary and two non-monetary dimensions. Building on Bourdieu’s notion of capital we distinguish between economic, cultural, and social capital as different types of social background resources into which the total effect of social class on educational attainment may be decomposed. Empirically, we use an extremely rich Danish survey to construct empirical indicators of the three different types of capital. In terms of methodology, we develop an extended random effect model which controls for the influence of unobserved parental characteristics on children’s educational attainment and which furthermore accommodates homogeneity with respect to observed and unobserved parental characteristics. In the empirical analysis we find that by controlling for parents’ economic, cultural, and social capital, we are able to fully explain the ‘‘raw’’ social class effect on choice of vocational secondary education but only some of the class effect on the risk of not obtaining any secondary education. Furthermore, we find, first, that parental economic capital is a predictor of vocational education, second, that cultural capital is the key predictor of upper secondary education relative to all other educational outcomes, and third that social capital predicts vocational education. Our findings underscore that the conventional interpretation of the social class effect on schooling as pertaining to mainly economic stratification is implausible in the Scandinavian mobility regime. When isolated, parents’ economic resources only predict why children choose the vocational track in secondary education. As we suggest, low rates of earnings returns to education may be the reason why children from economically advantaged backgrounds do not perceive upper secondary education as an attractive means of securing social status.

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What perspectives for future research does our analysis provide? First, our findings illustrate that social class may act as a proxy for a wide range of qualitatively different social background influences on children’s educational attainment. By disaggregating the social class effect into economic, cultural, and social capital, we gain new insights into understanding what social class differences in educational outcomes mean in the Scandinavian context. From our analysis of Denmark it appears that the economic dimension of the social class effect is only one among several dimensions that make up the total effect of social class on educational attainment. Cultural capital and social capital are also crucial aspects of this total effect. Our analysis then challenges the notion in the comparative literature that social class inequalities in educational inequality are substantively similar across countries (Erikson and Jonsson, 1996; Mu¨ller et al., 1989, 1993; Shavit and Blossfeld, 1993). Rather, our findings suggest that in the Scandinavian mobility regime cultural capital (or other non-monetary background factors) explains the majority of the social class effect on educational attainment, whereas in the liberal mobility regime parents’ economic capital explains most of the social class effect. Hence, maybe cross-national similarities in the quantitative (i.e., more/less) effects of social class on educational attainment conceal important qualitative differences among countries and mobility regimes? Obviously, our analysis of a single country cannot determine if this is the case. Nonetheless, we believe that our findings stress the need to carefully consider what stratification mechanisms may explain observed social class differences in educational attainment in different mobility regimes. Acknowledgments An earlier version of this paper was presented at the RC28 Spring Meeting ‘‘Welfare States and Social Inequality’’, May 5–8, 2005, University of Oslo, Norway. We thank Hanna Ayalon, Niels Ploug, Kim Sønderskov, Thomas Boje, Signe Hald Andersen, and three anonymous referees from Social Science Research for extremely generous comments on previous versions of the paper, and Lotte Rener and Natalie Reid for editorial assistance. Appendix A. Estimation of the latent class multinomial logit model In this section, we describe how the Latent Class Multinomial Logit Model (LCMLM) is estimated. As described in Section 4.4., the LCMLM comprises a mixed logit model of the form P ðU j > U s ; 8s 6¼ jjv2 ; v3 Þ ¼

expðaj þ bj x þ vj Þ ; Ps¼J 1 þ s¼2 expðas þ bs x þ vs Þ

j ¼ 1; . . . ; J ;

ða1Þ

where Uj is the probability that the child chooses educational outcome j, aj is the constant, x is the vector of explanatory variables with regression coefficients b, and vj captures the influence of unobserved variables particular to educational outcome j. For the distribution of vj we propose a finite mixture approach in which we approximate the unknown distribution of vj through a fixed number of mass points or latent classes (where the number of

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latent classes is V, with v = 1, . . . , V). In order to correct for any correlation between x and v we introduced the concomitant variable model P ðV ¼ v1k ; v2k Þ ¼

expðak þ bk zÞ ; PK 1 þ k¼2 expðak þ bk zÞ

k ¼ 1; . . . ; K;

ða2Þ

where the probability of membership of latent class k depends on the constant a and the vector of explanatory variables z (with parameter vector b). We now turn to the problem of estimating the model given that the v’s are unobserved. First, we form the joint probability of a choice of secondary education for a particular value of V P ðU j > U s ;8s 6¼ j; V ¼ v1k ;v2k Þ ¼ P ðU j > U s ;8s 6¼ jjV ¼ v1k ; v2k Þ  P ðV ¼ v1k ;v2k Þ ¼

expðaj þ bj xj þ vjk Þ expðak þ bk zÞ  ; PK Ps¼2 1 þ s¼1 expðas þ bs xs þ vsk Þ 1 þ k¼2 expðak þ bk zÞ ða3Þ

for the choices of no education beyond elementary school and vocational education, and where the choice of upper secondary education is obtained by substitution of the relevant terms from (a3). To construct a log-likelihood function based on the observed data we marginalize the joint distribution of educational outcomes and unobserved components by summing (a3) over all possible values of V. In order to arrive at the marginal distribution of all educational outcomes, take logs and sum across all observations to form the log-likelihood function Xi¼n Xk¼K expðaj þ bj xj þ vjk Þ expðak þ bk zÞ ln L ¼  ln : ða4Þ PK Ps¼2 i¼1 k¼1 1 þ 1 þ s¼1 expðas þ bs xs þ vsk Þ k¼2 expðak þ bk zÞ Maximizing this function with respect to the parameters of the model yields nonparametric maximum likelihood (ML) estimates. If the number of latent classes, K, is fixed and known, this model enjoys all the usual properties of ML. In practice, the parameters of the LCMLM are obtained by gradually increasing the number of latent classes until the point where the likelihood function no longer improves. At this point, a sufficient number of latent classes has been used to approximate the unknown distribution of vj and we obtain consistent non-parametric ML estimates of the model parameters. Appendix Table 1 Means and standard deviations for items used to calculate parental capital variables

Economic capital Gross monthly income 0–12,999 13,000–15,999 16,000–17,99 17,600–19,999 20,000 or more

Mean

SD

Description

0.23 0.22 0.15 0.23 0.17

0.42 0.41 0.36 0.42 0.38

Gross monthly income in DKK, 1992

(continued on next page)

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Appendix Table 1 (continued)

Homeowner Does not own home 0–800,000 801–1.100,000 1.101–1.500,000 1.501,000 or more Car ownership Does not own a car 0–50,000 51–95,000 96–150,000 151,000 or more Owns summerhouse Cultural capital Level of education Elementary school Vocational Lower tertiary Intermediate tertiary Higher tertiary Number of foreign languages spoken Does not speak any foreign languages 1 Foreign language 2 Foreign languages 3 or more foreign languages Number of newspaper subscriptions Does not subscribe to any newspapers Subscribes to 1 newspaper Subscribes to 2 or more newspapers Reads fictional books Interested in the visual arts Social capital Does parent have social connections in 2001 that might help children. . . Find full- or part-time employment Find an apprenticeship position Find a place of residence Get advice on educational choices Get help of the child wants to study abroad

Mean

SD

Description

0.21 0.21 0.22 0.22 0.14

0.41 0.41 0.42 0.41 0.35

Ownership (with spouse) and value of own home in DKK, 2001

0.13 0.25 0.17 0.27 0.18 0.14

0.34 0.44 0.37 0.44 0.39 0.34

Ownership (with spouse) and value of car in DKK, 2001

0.21 0.35 0.27 0.14 0.03

0.41 0.48 0.45 0.34 0.18

Educational attainment, 1992

0.36 0.31 0.28 0.05

0.48 0.47 0.45 0.21

Number of foreign languages spoken,1992

0.39 0.52 0.09 0.34 0.57

0.49 0.50 0.29 0.47 0.49

0.44 0.54 0.68 0.54 0.66

0.50 0.50 0.47 0.50 0.47

Owns summerhouse (with spouse), 2001

Number of newspaper subscriptions, 1992

Reads fictional books, 1992 Interested in the visual arts, 1992

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