Graduation Year

2022

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Economics

Major Professor

Andrei Barbos, Ph.D.

Committee Member

Padmaja Ayyagari, Ph.D.

Committee Member

Giulia La Mattina, Ph.D.

Committee Member

Lu Lu, Ph.D.

Committee Member

Gabriel Picone, Ph.D.

Keywords

Health Economics, Opioid Epidemic, Disability Benefits, Domestic Violence

Abstract

Strong empirical evidence points towards a significantly higher prevalence of opioid consumption among people receiving disability benefits (DB) than in the general population of the United States. However, no previous research established a causal relationship between the decision to award DB to applicants and their subsequent opioid use. We aim to contribute towards filling this gap. There are channels through which awarding DB may both increase and depress opioid consumption, and thus, ex-ante, the sign of a potential causal relationship is ambiguous. To correct for the treatment endogeneity, since an individual’s age at the time of the decision on an application impacts discontinuously at certain age cutoffs the award decision, we employ a fuzzy Regression Discontinuity model with three age cutoffs used for identification. We find that awarding DB increases the likelihood of using opioids by about 27-30 percentage points. This suggests that the positive association between DB receipt and opioid consumption is likely to be causal.

Drug control policies improve social welfare by curbing substance abuse or overdose. However, some of their potential unintended consequences, like the effects on drug-related crimes, remain underexplored. To the best of my knowledge, the existing literature has not established any causal relationships between drug control policies and domestic violence. As one of the largest state-level policies, the Mandatory Access (MA) Prescription Drug Monitoring Programs (PDMPs) have been shown to be effective at decreasing opioid use. This paper is the first to causally examine whether MA PDMPs impact the prevalence of intimate partner violence. Additionally, it complements the current literature by examining its effects on multiple types of child maltreatment crimes. I employ a difference-in-differences model on offense-level crime data and find that MA PDMPs can significantly decrease the offenses of intimate partner assaults as well as child assault and intimidation. The results are robust under multiple robustness checks.

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