Date of Award

May 2024

Degree Type

Dissertation

Degree Name

Doctor of Philosophy

Department

Economics

First Advisor

Kevin Thom

Committee Members

Scott Drewianka, John Heywood, Jangsu Yoon

Keywords

ADHD, aging, divorce, household wealth, machine learning, mental health

Abstract

This dissertation presents three chapters about understudied characteristics of the older population. As the United States and other developed countries' populations age, more dedicated research is needed to understand and implement policies to improve the welfare of this demographic group. Though there is a vast literature on various life-cycle outcomes of the elderly, gaps remain. Two such aspects have been examined here: marital stability and mental health.

Chapter 1 investigates how changes in household wealth affect the likelihood of divorce among older adults aged 50 and above in the United States. Using panel data from the Health and Retirement Study (HRS) from 1992 to 2018, I first establish a descriptive non-linear relationship between wealth, and divorce probabilities. The likelihood is higher for lower levels of wealth and monotonically declines, but only upto the median - beyond that it remains fairly flat. Extreme changes in wealth, both positive and negative, are associated with higher probability of divorce, as compared to moderate changes. To estimate the causal effects, I use two separate plausibly exogenous shocks to wealth, one from the stock market and the other from the housing market. A \$10,000 predicted increase in real wealth driven by the S&P 500 contemporaneously raises divorce chances by 262 percent, marginally significant with a p-value of 0.06. A similar sized lagged housing market driven wealth increase also boosts divorce likelihood by 300 percent, significant at 1 percent.

Attention Deficit and Hyperactivity Disorder (ADHD) is a common mental health condition, usually diagnosed during childhood and often found to be persistent into adulthood as well. Motivated by a lack of reliable, large-scale measure of observable ADHD status among older adults, in chapter 2 my co-authors and I develop a method to estimate the likelihood of ADHD among older individuals in the HRS. A small subsample in the HRS is asked diagnostic questions related to ADHD. We use a series of machine learning models to predict ADHD status in this subsample as a function of observables available for tens of thousands more HRS respondents. Our main results indicate that an 11 percentage point increase in ADHD likelihood (average difference between those meeting v.s. failing to meet diagnostic criteria) is associated with a 6.7 percentage point lower probability of working for pay, an 11 percent reduction in earnings conditional on working, an 11 percent reduction in household wealth at retirement and 0.1 percentage point increase in the risk of first marriage ending in divorce. Linking the HRS data with the Social Security Administration (SSA) records, we show substantial differences in the earnings trajectories of low v.s. high ADHD-risk adults. These differences emerge in the 30s and grow over the life-cycle. The present discounted value of average earnings differences between these groups over the ages 22-65 is substantial (approximately $180,000).

In chapter 3, I examine whether polygenic risk for ADHD predicts divorce, leveraging genetic data on approximately 9,600 Americans from the HRS. I track individuals from ages 20-50 and estimate the association between a polygenic score for ADHD and marital dissolution risk over this period. A higher genetic risk is associates with a higher divorce probability. The genetic influence persists adjusting for demographics and exhibits heterogeneity by birth cohort. Interactions with specific forms of environment suggest little gene-environment interplay. By linking ADHD genetic propensity to dissolution, this analysis demonstrates utility of polygenic scores for elucidating social outcomes related to neuropsychiatric conditions. Incorporating genetic data on older adults provides unique evidence regarding ADHD’s potential lifelong impacts on family relationships. Findings emphasize the value of genetic tools to inform social topics where establishing causality is challenging.

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