Current Study

We study genetic effects on educational achievement (school grades) and educational attainment (years of education). In light of the conceptual framework of primary and secondary effects of social background on education (Boudon 1974; Breen and Goldthorpe 1997; Erikson and Jonsson 1996), we expect that the role of genes differs for educational achievement and educational attainment. Primary effects describe parents’

efforts to improve children’s educational performance. Parents actively foster the development of cognitive and noncognitive skills and provide various goods and services to enhance school-related skills, such as extra learning material and/or private tutoring.

Secondary effects, by contrast, refer to stratified schooling choices over and above children’s educational performance. Parents’ educational decisions are determined by the anticipated costs, benefits, likelihood of success, and importantly, the intention to avoid downward mobility (Breen and Goldthorpe 1997). Consequently, parents from higher social backgrounds opt more often for higher educational tracks than parents from lower

social backgrounds who maintain their status with lower levels of education. Since parents’ schooling decisions are to some extent independent of children’s genetic potential for educational success, genetic effects should be stronger for educational achievement compared to educational attainment.

Our expectations about cross-country differences in genetic influences on educational success and their social stratification are rooted in different types of educational systems, their welfare states, and the related degrees of social inequality (Esping-Andersen 1990).

These macro-level differences have also been used to explain international differences in the heritability of educational outcomes and to motivate comparative studies on the Scarr–

Rowe hypothesis for cognitive skills (Selita and Kovas 2019; Tucker-Drob and Bates 2016). Following these criteria, we selected a sample of three advanced, industrialized societies for our cross-national analysis: Germany, Sweden, and the United States.

First, differences in the effects of genes and their stratification may be a consequence of differently structured educational systems. Here, we focus on tracking, which comprises the formal selection of students, based on their academic ability, and placing them in different schools, classes or set of courses. Tracking is a common characteristic of Western educational systems. However, differences exist in regard to the timing. The German educational system assigns children as young as 10 to 12 years of age to one of three hierarchically structured secondary schooling tracks. By contrast, Sweden and the United States have longer periods of comprehensive schooling and less strict tracking (Bol et al. 2014). In the United States, there is, however, a high degree of internal tracking (Lucas 1999). Different secondary school tracks represent different learning environments, since children are grouped by early ability, which is more closely related to social origin than ability at a later age. Since tracking in Germany takes place at an exceptionally young age of the children and is strongly linked to social background (e.g., Breen and Jonsson 2005; Dustmann 2004; Hillmert and Jacob 2010; Müller et al. 1993;

Shavit and Blossfeld 1993), we expect that genetic effects on educational success are comparably small in Germany, while the social stratification of genetic effects should be comparably strong.

Second, differences in genetic effects on education may be rooted in the welfare state and, particularly, the way social security is institutionalized. Liberal welfare states such as the United States provide only limited social security structures (DiPrete 2002; DiPrete and McManus 2000; Esping-Andersen 1990). Disadvantaged parents in liberal welfare states may face more severe economic hardship and are exposed to higher levels of stress

compared to Germany and Sweden where individuals are protected against major life risks (Diewald 2016b). Both resource restrictions and stress may lower parents’ capacity to provide rearing environments and inputs tailored to their children’s genetic endowments. This, in turn, decreases children’s chances to develop their genetic potential. In consequence, we expect genetic effects on educational success in the United States to be comparably small. Since access to relevant resources is dependent on individuals’ socioeconomic standing, we also expect the stratification of genetic influences to be comparably strong in the United States.

Overall, we hypothesize that the genetic effects on educational success are smaller in Germany and the United States than in Sweden. Furthermore, we expect that the impact of parents’ socioeconomic status on children’s chances to realize genetic potential relevant for education is stronger in Germany and in the United States than in Sweden. In Germany, the social stratification of genetic effects should be more pronounced because of the early tracking system, and in the United States, because of the meager role of the welfare state.

To test these expectations, we use large-scale observational twin data for Germany (German Twin Family Panel [TwinLife]) (Diewald et al. 2018) and for the United States (National Longitudinal Study of Adolescent Health [Add Health]) (Harris et al. 2013), as well as register data on twins for Sweden (Statistics Sweden 2011). The birth cohorts of the twins in the different samples range from years 1975 to 1993. The datasets are described in greater detail in Appendix section 4.A, and Table 4.D.1 provides an overview of the analytical samples.

Our outcomes of interest are measured as follows: As measure for school grades we use grade point averages at age 16 in Sweden (i.e., the end of comprehensive schooling).

In Germany and the United States we use final grade point average from secondary schooling. For years of education, we use a harmonized measure across countries (see Appendix Table 4.D.2 for a description). We differentiate in all countries between basic education, upper secondary education (vocational track), upper secondary education (academic track), post-secondary non-tertiary education, and tertiary level, and assign 9, 11, 12, 14, and 15.5 years for the corresponding educational levels. Since less differentiated measures of outcomes tend to lower estimates of genetic influences in behavioral genetic variance decompositions (Freese and Jao 2017), such a harmonization across countries, is necessary for the substantive interpretation of our results. We z-standardize all outcomes used in our analyses. Further details on the variables are reported

in Appendix 4.B. Summary statistics on the variables are provided in the Appendix Tables 4.D.3–4.D.5.

We analyze twin data from the different countries using genetically sensitive variance decomposition models (ACE models) based on the classical twin design (CTD) (Lang 2017; Plomin et al. 2008; Rabe-Hesketh, Skrondal, and Gjessing 2008). Twins are born at the same time; dizygotic (DZ) twins share 50% of the DNA, while monozygotic (MZ) twins are genetically identical. This information can be used to divide the total variance of an outcome into variances attributable to additive genetic influences (A), to shared environmental influences (C), and to unique environmental influences including measurement error (E). For Sweden twins’ zygosity (whether a twin is mono- or dizygotic) was unknown. Here we use twins’ sex to approximate zygosity. Twin pairs who are of opposite sex are dizygotic. Same-sex twin pairs can be both –monozygotic or dizygotic. For our analysis, we classify all same-sex twins as MZ twins. Due to the over classification of MZ twins in the Swedish sample, we apply an adjustment for using sex as a proxy (see section 4.C).

The standard ACE model assumes that spouses mate randomly in regard to the outcome under study. Given that assortative mating based on education is a well-established phenomenon across Western societies (e.g., Blossfeld 2009), we adjust our estimates for assortative mating (Loehlin, Harden, and Turkheimer 2009) (see Appendix section 4.C). To test for systematic differences in genetic effects, we estimate ACE models separately for different groups by parents’ social background.¹ This analytical strategy is known as nonparametric gene–environment interaction analysis (Guo and Wang 2002). ACE models have a long research tradition in studies on SRIs (Asbury et al. 2005; Baier and Lang 2019; Bates et al. 2013; Figlio et al. 2017; Fischbein 1980; Grant et al. 2010; Guo and Stearns 2002; Harden, Turkheimer, and Loehlin 2007;

Kirkpatrick et al. 2015; Schwartz 2015; van der Sluis et al. 2008; Tucker-Drob et al. 2011;

Turkheimer et al. 2003). Applying these techniques makes our analyses comparable to those in this body of literature. Further details on the methods are reported in the Appendix section 4.C.

1 The subgroup analyses are based on the assumption of random mating because the stratification accounts already for a large part of assortative mating (see also Baier and Lang (2019)).