“A Structural Analysis of Opioid Misuse: Health, Labor, Policy, and Misperception of Opioid Misuse Risk” (Job Market Paper, Link)
Abstract: I study how health, labor status, and perception of the risk of opioid misuse jointly shape opioid misuse behavior and how policy can respond. I develop and estimate a dynamic model of opioid misuse and labor supply with endogenous mortality risk and misperception of the risk of opioid misuse by combining multiple restricted data sets. I decompose the effects of three aggregate changes between 2015–2019: rising opioid mortality risk, expanded state prescribing restrictions, and cross-state variation in illegal opioid prices. I find that the decline in opioid misuse rates is almost entirely explained by higher mortality risk. State restrictions on opioid prescribing reduce opioid misuse among the healthy group but push the unemployed and unhealthy toward illegal opioids, resulting in a negligible effect. The illegal opioid price plays no role. Eliminating the misperception would reduce opioid misuse by 20 percent, suggesting a new policy channel in combating the opioid epidemic.
“Externality in Sending Children Back Home: A Structural Approach to Foster Care Incentives”
This paper examines the unintended consequences of the foster care policy on children aimed at reunifying families. Although child abuse and neglect are rare, its recurrence and foster readmission are notably high. This raises concerns over the policy’s efficacy in children’s safety and health. Leveraging the Child and Caregiver Outcomes Using Linked Data (CCOULD) published by the U.S. Department of Health and Human Services, this study aims to (1) assess the impact of child maltreatment and foster care re/admission on children’s health; (2) evaluate whether Medicaid data can be used to predict future child maltreatment; (3) build a dynamic model of foster care system that flexibly captures its institutional incentive and incentive to care for children’s welfare, and (4) examine how would additional information from Medicaid change foster care system’s decision on children and how much it would change children’s welfare, measured by Medicaid expenditure.
“Identification of Dynamic Discrete Choice Models with Quasi-Hyperbolic Discounting under Finite Dependence”
In this paper, I generalize the representation theorem in Arcidiacono and Miller (2019) in dynamic discrete choice models with quasi-hyperbolic discounting. Then I provide identification result given the two-period finite dependence and exclusion restriction in a finite horizon model. Monte Carlo simulation shows that the exclusion restriction is strong enough to separate the discount factor and present bias.
“Sufficient Conditions for Identification of Dynamic Discrete Choice Models under Finite Dependence”
This paper investigates the role of finite dependence paths in identifying dynamic discrete choice models. I prove constructively that there exists a maximum number of these paths for identification in discrete state space. The number of finite dependence paths grows exponentially by the number of states and choices. This growth explains the lack of consensus on the number of paths for reliable identification. In the one-period finite dependence setting, identification of flow utilities is achieved by the full rank condition of the linear system of equations of conditional value function differences. By examining a two-period finite dependence setting as a special case of multiple-period finite dependence, I demonstrate methods to determine identification for utility primitives.
Work in Progress
``De-biased Conditional Choice Probabilities Estimation off Short Panels” (with R. A. Miller)
``Lifecycle Decisions of Labor Supply, Homeownership, Marriage, and Fertility’’ (with R. A. Miller)
