Integrating Genetic Data Into Economic Research

Paper Session

Friday, Jan. 6, 2017 2:30 PM – 4:30 PM

Hyatt Regency Chicago, Plaza B
Hosted By: American Economic Association
  • Chair: David Laibson, Harvard University

Polygenic Scores That Can Be Usefully Incorporated Into Economic Research

Daniel J. Benjamin
,
University of Southern California
David Cesarini
,
New York University
David Laibson
,
Harvard University

Abstract

Polygenic scores, which are constructed as indexes of genetic variants, are variables that can be used as genetic predictors of traits of interest. The results of genome-wide association studies (GWAS) can be used to estimate the weights needed for constructing polygenic scores. We discuss how the results of several large-scale GWAS that have been completed in the past few years, or are currently underway, enable constructing polygenic scores for height, BMI, educational attainment, subjective well-being, fertility, and other traits, in datasets that also contain rich economic data. We summarize some recent economics applications and discuss future uses of polygenic scores in economics.

Polygenic Scores as Proxies for Unobservables: An Instrumental Variables Approach and an Application to the Returns to Schooling

Casper Burik
,
VU University Amsterdam
Philipp Koellinger
,
VU University Amsterdam
Thomas A. DiPrete
,
Columbia University

Abstract

In recent years, polygenic scores have become the favored tool for summarizing the influence of genetic predispositions on phenotypic characteristics and behavior when the genetic effect arises from the accumulation of small effects from a potentially very large number of genetic markers. In principle, such polygenic scores could be useful as proxies for unobserved characteristics in applied economics. Even though the explanatory power of polygenic scores continues to improve due to the combination of improved statistical techniques and larger sample sizes for genetic discovery, polygenic scores typically capture less variance than implied by the heritability of a trait. This attenuation of the predictive accuracy of polygenic scores creates well known problems in applied contexts; it biases downward the estimated effect of genetic markers on the outcome of interest, and it biases the effect of behavioral and environmental variables that are entered into the same specification as the genetic variables. Instrumental variables regression methods can correct for such bias under a set of assumptions, mostly notably the exclusion restriction, but the practical value of these strategies depends upon the strength of the instruments and the size of the sample used to carry out the estimation. This paper explores the potential use of polygenic scores as proxies for unobservables in the context of a returns to schooling estimation. Specifically, we approximate unobserved ability by a polygenic score for educational attainment. Using data from the Health and Retirement Survey, we employ a series of alternative plausible instrumental variables along with sensitivity analyses of the consequences of imperfect instruments to examine the potential improvement of model estimates.

Gene-by-SES Interplay in Health Behavior: Theory and Empirics

Laura Bierut
,
Washington University-St. Louis
Pietro Biroli
,
University of Zurich
Titus J. Galama
,
University of Southern California
Kevin Thom
,
New York University

Abstract

We present a lifecycle theory of gene-by-socioeconomic status (SES) interplay in unhealthy behavior. We start with the canonical theory for health due to Grossman (1972) and add genetic heterogeneity in preferences and health production, and potentially addictive behavior. The theory provides a conceptual framework for analyzing the role of genetics in explaining health behaviors, such as smoking, overeating and excessive alcohol consumption, and for predicting how SES moderates this process. The theory predicts rich interactions between genes, SES, health behavior, health and longevity. For example, SES increases the value of health, thereby increasing the demand for health investment and reducing the demand for unhealthy consumption. Withdrawal, tolerance, and reinforcement, however drive unhealthy behaviors forward. These competing effects suggest SES protects individuals from detrimental genetic endowments by raising the value of health, reducing consumption and addiction. We also present empirical evidence of gene by SES interactions in smoking and overeating (obesity). We find evidence of substantial protective effects of SES for those who possess detrimental genetic endowments.

Genetic and Environmental Influences on Schooling and Lifetime Earnings

Lauren L. Schmitz
,
University of Michigan
David Weir
,
University of Michigan

Abstract

This study exploits administrative earnings records matched to detailed genetic and sociodemographic data in the Health and Retirement Study (HRS) to estimate whether the educational environment, as captured by state-level differences in average years of schooling, modify the associations between genetic propensity for educational attainment and individual schooling, and genetic propensity for educational attainment and lifetime earnings. To capture the complex genetic architecture that underlies the bio-developmental pathways, behavioral traits, and evoked environments associated with educational attainment, we calculate polygenic scores (PGSs) for respondents in the HRS derived from a recent genome-wide association study (GWAS) for years of schooling. We find evidence that both individual genetic endowment and the state-level educational environment contribute to individual schooling and lifetime earnings, with limited evidence for any interaction between them. The exception is completion of a secondary degree, where we find that individuals educated in states with higher average educational attainment during their primary schooling years were more likely to obtain a GED or high school degree—regardless of genotype—whereas individuals raised in states with below average educational attainment were approximately 7 to 27 percent less likely to obtain a secondary degree than individuals with similar PGSs in higher achieving states.
Discussant(s)
Jason M. Fletcher
,
University of Wisconsin-Madison
Patrick Turley
,
Massachusetts General Hospital and Broad Institute
Jonathan Beauchamp
,
Harvard University
Owen Thompson
,
University of Wisconsin-Milwaukee
JEL Classifications
  • I0 - General