Published and Accepted Papers

Health State Risk Categorization: A Machine Learning Clustering Approach Using Health and Retirement Study Data, The Journal of Financial Data Science May 2022 (forthcoming)

with Dr. Dhagash Mehta

Working Papers

Upward Intergenerational Insurance for Long-Term Care (Job Market Paper)

Abstract: This paper assesses the value of Medicaid for recent retirees in insuring against long-term care risks while taking into account child-to-parent transfers. Parent retirees receive substantial transfers in the forms of informal care and financial transfers from children. To understand the role of upward intergenerational insurance for old-age health risks, I develop a dynamic model for parent-child pairs and childless retirees. A vital feature of the model is that a parent and her child interact strategically to make decisions about transfers in a non-cooperative game. I estimate and calibrate the model to match the Health and Retirement Study (HRS) data. Using the calibrated model, I calculate the insurance values of Medicaid relative to its cost for retirees and child households. Compensating variation calculations suggest that childless retirees value every dollar of Medicaid insurance at $2.20, which is twice the value for parent retirees ($1.10). Furthermore, I find that middle-income parent retirees value Medicaid insurance less than poor and wealthy parent retirees. An additional result of the paper is that child households also value Medicaid. This decomposition provides a new consideration for the efficient design of Medicaid benefits, particularly in light of a growing population aging without children.

Work in Progress

Information Technological Change and Sagging Non-Routine Cognitive Employment Growth in the 2000s

Medical Expenditure Risks and Elderly Household Consumption (with Shanke Zhao)