Genetic Fortune: Winning or Losing Education, Income, and Health

Kweon, Hyeokmoon; Burik, Casper A.P.; Linnér, Richard Karlsson; Vlaming, Ronald de; Okbay, Aysu; Martschenko, Daphne Oluwaseun; Harden, K. Paige; DiPrete, Thomas A.; Koellinger, Philipp

doi:10.2139/ssrn.3682041

Cited by 22 publications

(24 citation statements)

References 105 publications

(127 reference statements)

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“…See the Methods section for more details about the assumptions. 5 To the best of our knowledge, only Kweon et al (29) have previously applied IV in a within-family design.…”

Section: /30mentioning

confidence: 99%

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Overcoming Attenuation Bias in Regressions using Polygenic Indices: A Comparison of Approaches

Kippersluis

Biroli²,

Galama

et al. 2021

Preprint

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Polygenic scores have become the workhorse for empirical analyses in social-science genetics. Because a polygenic score is constructed using the results of finite-sample Genome-Wide Association Studies (GWASs), it is a noisy approximation of the true latent genetic predisposition to a certain trait. The conventional way of boosting the predictive power of polygenic scores is to increase the GWAS sample size by meta-analyzing GWAS results of multiple cohorts. In this paper we challenge this convention. Through simulations, we show that Instrumental Variable (IV) regression using two polygenic scores from independent GWAS samples outperforms the typical Ordinary Least Squares (OLS) model employing a single meta-analysis based polygenic score in terms of bias, root mean squared error, and statistical power. We verify the empirical validity of these simulations by predicting educational attainment (EA) and height in a sample of siblings from the UK Biobank. We show that IV regression between-families approaches the SNP-based heritabilities, while compared to meta-analysis applying IV regression within-families provides a tighter lower bound on the direct genetic effect. IV estimation improves the predictive power of polygenic scores by 12% (height) to 22% (EA). Our findings suggest that measurement error is a key explanation for hidden heritability (i.e., the difference between SNP-based and GWAS-based heritability), and that it can be overcome using IV regression. We derive the practical rule of thumb that IV outperforms OLS when the correlation between the two polygenic scores used in IV regression is larger than √(10 / (N+10)), with N the sample size of the prediction sample.

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“…See the Methods section for more details about the assumptions. 5 To the best of our knowledge, only Kweon et al (29) have previously applied IV in a within-family design.…”

Section: /30mentioning

confidence: 99%

“…Hence, we assume that the standardized outcome EA and the standardized true latent PGS * are drawn from a bivariate standard normal distribution with correlation 0.5. 6 To the best of our knowledge, only Kweon et al (38) have previously applied IV in a within-family design.…”

Section: Simulation Studymentioning

confidence: 99%

Overcoming Attenuation Bias in Regressions using Polygenic Indices: A Comparison of Approaches

Kippersluis

Biroli²,

Galama

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

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“…During the interview, respondents answered a series of questions about their job (or last job if respondent had retired recently), which the interviewer used to classify the respondent's occupation among more than 400 detailed categories. We use the 2009 Annual Survey of Hours and Earnings (ASHE) to match such categories to median wages for each occupation (Kweon et al 2020). 8 We view this measure of occupational wages as complementary to our measure of household income: on one hand, income was reported in brackets and occupational wages is a better measure of permanent individual income; on the other, household income is available for a larger fraction of the sample.…”

Section: Data and Proxies For Early-life Advantagesmentioning

confidence: 99%

“…Because of Mendelian inheritance, one's PGI is randomly assigned conditional on the PGI of the parents. We exploit this variation to study a gene-by-environment interaction: how a compulsory schooling change can modify how much of an advantage is conferred by one's luck in the "genetic lottery" (Kweon et al 2020).…”

Section: Introductionmentioning

confidence: 99%

The Effect of Education on the Relationship between Genetics, Early-Life Disadvantages, and Later-Life SES

Barcellos

Carvalho

Turley

2021

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This paper investigates whether education weakens the relationship between early-life disadvantages and later-life SES. We use three proxies for advantage that we show are independently associated with SES in middle-age. Besides early, favorable family and neighborhood conditions, we argue that the genes a child inherits also represent a source of advantages. Using a regression discontinuity design and data for over 110,000 individuals, we study a compulsory schooling reform in the UK that generated exogenous variation in schooling. While the reform succeeded in reducing educational disparities, it did not weaken the relationship between early-life disadvantages and wages. This implies that advantaged children had higher returns to schooling. We exploit family-based random genetic variation and find no evidence that these higher returns were driven by genetically-influenced individual characteristics such as innate ability or skills.

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“…The genetic lottery is a primary source of inequalities. In fact, the genetic endowment affects health, income and education outcomes even in pair of siblings (Fletcher and Lehrer, 2011;Kweon et al, 2020). One of the most evident results of the inequalities arising from the genetic lottery is beauty.…”

Section: Introductionmentioning

confidence: 99%

Beautiful Inequality: Are Beautiful People More Willing To Redistribute?

Fazio

2020

SSRN Journal

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As winners of the "genetic lottery", beautiful people have several advantages over their life course: they earn more, find better jobs, and are happier than their peers. We investigate whether good-looking individuals are willing to share their fortune by supporting income redistribution. Using German data, we show that beauty is associated with lower support for redistribution. Furthermore, we show that attractive individuals are more likely to believe that income should be based solely on individual achievements. To check whether the set of questions used to measure taste for redistribution captures the underlying preferences, we then investigate a possible correlation between voting behavior and beauty. We show that beauty correlates with voting for the Free Democratic Party (FDP) thus suggesting that the correlation between beauty and preferences for redistribution is channeled through party affiliation. Our findings resist to a broad set of robustness checks. A plausible mechanism that drives our findings is the self-serving bias, that is to say: people tend to attribute success to their own merit and failure to external factors. Overall, our results suggest that benefiting from random luck, such as winning the "genetic lottery", reduces taste for redistribution.

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Genetic Fortune: Winning or Losing Education, Income, and Health

Cited by 22 publications

References 105 publications

Overcoming Attenuation Bias in Regressions using Polygenic Indices: A Comparison of Approaches

Overcoming Attenuation Bias in Regressions using Polygenic Indices: A Comparison of Approaches

The Effect of Education on the Relationship between Genetics, Early-Life Disadvantages, and Later-Life SES

Beautiful Inequality: Are Beautiful People More Willing To Redistribute?

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