Research
Understanding the formation of risk preferences is crucial for elucidating the roots of economic, social, and health inequalities. However, this area remains inadequately explored. This study employs a risk preference measure directly linked to the labor market to examine whether previous experiences with high unemployment rates influence current risk decision-making among the elderly, and whether this impact varies by genotype. The findings indicate that individuals with low genetic predispositions for risk tolerance are more significantly influenced by historical fluctuations in unemployment rates than those with high genetic predispositions for risk tolerance. Consequently, this paper identifies genetic endowment as a crucial moderating factor that shapes how past experiences impact current decision-making processes. This disparity in how past experiences shape risk preferences based on genetic predisposition may further amplify inequalities in health, wealth, income, and other outcomes associated with risk preferences.
The Effect of Public Health Insurance on Risk Aversion
Understanding the drivers of risk preferences is crucial, as these preferences shape decisions with significant economic and social implications. This study examines the impact of Medicare health insurance on individual risk preferences, employing a regression discontinuity design around the age 65 eligibility cutoff. Using data from the Health and Retirement Study (HRS), we find that Medicare enrollment significantly reduces risk aversion, supporting the theory that lower background risk encourages greater risk tolerance. Our findings show that this shift minimally affects health behaviors but substantially increases financial risk-taking, particularly in stockholding. These results underscore the broader economic influence of health insurance policies, suggesting that public health insurance can promote economic growth by indirectly shaping risk preferences.
This study investigates the role of the interplay between genetic endowment and education in health formation, with a focus on addressing health disparities in later life stages. Leveraging data from the UK's Raise of School Leaving Age policy as a natural experiment, coupled with innovative methodology in gene-environment model estimation, I explore the impact of genetic predispositions and educational attainment on the prevalence of chronic diseases and medical conditions among the elderly population.
The paper reveals that genetic endowment significantly influences the likelihood of developing heart attacks, strokes, cancer, and type 2 diabetes in later life. Furthermore, the paper shows that education serves as a protective factor against adverse health outcomes associated with genetic predispositions, particularly concerning heart attacks and cancer. This study contributes to advancing our understanding of health formation processes and highlights the significance of tailored interventions and policies in reducing health inequalities and promoting overall population health.
A New Method to Study Gene-Environment Interaction in Empirical Economics Models
Many socio-economic surveys have started to include genetic data about their respondents, which has lead to new studies that investigate how environments and choices interact with genetic endowments to form important economic, behavioral, or health outcomes. To cope with the high dimensionality of genetic data, researchers often summarize individual genetic information using an index for genetic predisposition called a polygenic score (PGS). The index exploits information from genome-wide association studies (GWAS), which establish robust correlations between genes and determinants of economic wellbeing, health, and inequality: including preferences, smoking, obesity, and education. The GWAS correlations are then used to construct a PGS for a given outcome, which then often serves as a variable in empirical economic models. This paper revisits the validity of the usage of PGSs in the framework of the widely used gene-environment models and in the non-interacted models. First, I demonstrate that gene-environment (GxE) interactions can distort the PGS index and thereby skew the results of important parameters of GxE studies. To correct the bias that stems from omitted GxE interaction in the GWAS, I propose a new two-step method to estimate GxE models and their non-interacted counterparts. The new method requires only information from a GWAS to select the relevant genetic variables in the first step, and in the second step it estimates the full GxE model jointly. Unlike the standard method, this procedure does not rely on the GWAS estimates, which are often derived from a different population than in the survey used for the main empirical specification. Hence, the new method does not suffer from biases that stem from using GWAS estimates in the PGS index. In the empirical application I show that measurement error bias can significantly distort inference based on the standard GxE modelling approach. By not relying on the GWAS estimates, the new method expands the scope of the current survey-based studies that aim to incorporate genetic data into social research. The new method allows the study of outcomes for which suitable GWAS are not yet available.