The association between education and voter turnout is well-established in almost a century of research. The causal status of this correlation, however, is still subject to debate. Results in the previous literature differ substantially, and this may reflect both methodological differences and heterogeneous effects across populations or types of elections. This study addresses the question using a discordant twin design and variance decomposition methods with validated turnout data for both firstand second-order elections in a large sample of Swedish twins, paired with population-wide sibling data. Results show that education does not have an effect on national electoral turnout, but does have an effect on turnout in the European elections. Furthermore, the association between education and turnout is shown to be affected by substantial genetic confounding, which leaves a non-trivial amount of bias even in sibling based designs. This underscores the importance of taking genetic confounding seriously in observational research.
A substantial literature in political psychology has emphasized the importance of personality traits for understanding differences in political participation. One such trait is extraversion. However, the causal status of this relationship is complicated by a number of issues, not least genetic confounding stemming from the heritability of both personality traits and political participation. This study confirms the well-established naive relationship between extraversion and participation, but goes on with (a) a discordant MZ twin design and (b) a new approach using measured genetic variation, or a polygenic index, in the given trait (extraversion) to assess the causal nature of this relationship. First, utilizing variation in extraversion and participation within identical twin pairs shows that twins with higher extraversion do not participate more. Second, random variation within fraternal twin pairs in a polygenic index of extraversion does predict trait extraversion, but does not predict political participation. In summary, previously identified associations between extraversion and political participation are not likely to be causal, but instead reflect common underlying familial factors.
The boom in wealth inequality seen in recent decades has generated a steep rise in scholarly interest in both the drivers and the consequences of the wealth gap. In political science, a pertinent questionregards the political behavior across the wealth spectrum. A common argument is that the wealthy practice patrimonial voting, i.e. voting for right-wing parties to maximize returns on their assets. While thispattern is descriptively well documented, it is less certain to what extent this reflects an actual causal relationship between wealth and political preferences. In this study, we provide new evidence by exploitingwealth variation within identical twin pairs. Our findings suggest that while more wealth is descriptivelyconnected to more support for right-wing parties, the causal impact of wealth on policy preferences islikely highly overstated. For several relevant policy areas these effects may not exist at all. Furthermore,the bias in naive observational estimates seems to be mainly driven by environmental familial confoundersshared within twin pairs, rather than genetic confounding.
A core part of political research is to identify how political preferences are shaped. The nature of these questions is such that robust causal identification is often difficult to achieve, and we are not seldom stuck with observational methods that we know have limited causal validity. The purpose of this paper is to measure the magnitude of bias stemming from both measurable and unmeasurable confounders across three broad domains of individual determinants of political preferences: socio-economic factors, moral values, and psychological constructs. We leverage a unique combination of rich Swedish registry data for a large sample of identical twins, with a comprehensive battery of 34 political preference measures, and build a meta-analytical model comparing our most conservative observational (naive) estimates with discordant twin estimates. This allows us to infer the amount of bias from unobserved genetic and shared environmental factors that remains in the naive models for our predictors, while avoiding precision issues common in family-based designs. The results are sobering: in most cases, substantial bias remains in naive models. A rough heuristic is that about half of the effect size even in conservative observational estimates is composed of confounding.
Benjamin et al. construct polygenic indexes (DNA-based predictors) for 47 phenotypes and make them available to researchers in 11 datasets. They also present a theoretical framework and estimator to help interpret analyses using polygenic indexes. Polygenic indexes (PGIs) are DNA-based predictors. Their value for research in many scientific disciplines is growing rapidly. As a resource for researchers, we used a consistent methodology to construct PGIs for 47 phenotypes in 11 datasets. To maximize the PGIs' prediction accuracies, we constructed them using genome-wide association studies-some not previously published-from multiple data sources, including 23andMe and UK Biobank. We present a theoretical framework to help interpret analyses involving PGIs. A key insight is that a PGI can be understood as an unbiased but noisy measure of a latent variable we call the 'additive SNP factor'. Regressions in which the true regressor is this factor but the PGI is used as its proxy therefore suffer from errors-in-variables bias. We derive an estimator that corrects for the bias, illustrate the correction, and make a Python tool for implementing it publicly available.
We estimate the effect of genetic variants that are associated with differences in cognitive and non -cognitive skills on labor market and education outcomes by linking genetic data from individuals in the Swedish Twin Registry to government registry data. Genes are fixed over the life cycle and genetic differences between full siblings are random, making it possible to establish the causal effects of within -family genetic variation. We show that polygenic indices associated with cognitive skills and personality traits significantly affect income, occupation, and educational attainment. By comparing estimates that use only within -family variation to OLS estimates with and without socioeconomic controls, our results also provide indications of the degree of (residual) confounding, which can be useful for research conducted in datasets that do not contain sibling pairs. Overall, our results indicate that education and labor market outcomes are partially the result of a genetic lottery.
Estimates from genome-wide association studies (GWAS) of unrelated individuals capture effects of inherited variation (direct effects), demography (population stratification, assortative mating) and relatives (indirect genetic effects). Family-based GWAS designs can control for demographic and indirect genetic effects, but large-scale family datasets have been lacking. We combined data from 178,086 siblings from 19 cohorts to generate population (between-family) and within-sibship (within-family) GWAS estimates for 25 phenotypes. Within-sibship GWAS estimates were smaller than population estimates for height, educational attainment, age at first birth, number of children, cognitive ability, depressive symptoms and smoking. Some differences were observed in downstream SNP heritability, genetic correlations and Mendelian randomization analyses. For example, the within-sibship genetic correlation between educational attainment and body mass index attenuated towards zero. In contrast, analyses of most molecular phenotypes (for example, low-density lipoprotein-cholesterol) were generally consistent. We also found within-sibship evidence of polygenic adaptation on taller height. Here, we illustrate the importance of family-based GWAS data for phenotypes influenced by demographic and indirect genetic effects. Within-sibship genome-wide association analyses using data from 178,076 siblings illustrate differences between population-based and within-sibship GWAS estimates for phenotypes influenced by demographic and indirect genetic effects.
We investigate the role of information about tax incentives for the labour-leisure choice. We randomize 37,000 leaflets about the Swedish EITC, and then study the effects with pre-registered analyses and administrative data. Our focus is on the household decision to allocate between labour income and parental leave payments. The EITC and its interactions with the parental leave system is not well-known. Despite the substantial incentives involved, and the flexibility with which a person may earn labour income, we find that information about the EITC has a precisely estimated zero impact on labour supply on the extensive and the intensive margin.
We use population-wide Swedish data with information on adopted children’s biological and adoptive parents to assess the importance of prebirth factors (measured by biological parents’ voting) and postbirth socialization factors (as captured by adoptive parents’ voting) for generating intergenerational associations in voter turnout. We find that both prebirth and postbirth factors explain the parent-child similarity in turnout behavior. More importantly, we show that the conditions that strengthen the social pathways to intergenerational transmission—such as youth and exposure to consistent parental behavior—at the same time weaken the biological mechanisms and vice versa. Follow-up analyses based on US and UK samples suggest that these results are externally valid. Our findings are important for understanding how political inequality is reproduced across generations.