The Longitudinal Health, Income, and Employment Model (LHIEM): a discrete-time microsimulation model for policy analysis

TL;DR

The paper introduces LHIEM, a path-dependent microsimulation model for analyzing long-term health, income, and employment policies in the U.S., capable of tracking accumulated outcomes over time.

Contribution

LHIEM is a novel, flexible, path-dependent discrete-time microsimulation that models health, income, and employment dynamics for policy evaluation.

Findings

LHIEM effectively predicts health expenditures and income over multiple years.

The model demonstrates utility in evaluating health care financing policies.

LHIEM captures the impact of policy changes on long-term individual outcomes.

Abstract

Dynamic microsimulation has long been recognized as a powerful tool for policy analysis, but in fact most major health policy simulations lack path dependency, a critical feature for evaluating policies that depend on accumulated outcomes such as retirement savings, wealth, or debt. We propose the Longitudinal Health, Income and Employment Model (LHIEM), a path-dependent discrete-time microsimulation that predicts annual health care expenditures, family income, and health status for the U.S. population over a multi-year period. LHIEM advances the population from year to year as a Markov chain with modules capturing the particular dynamics of each predictive attribute. LHIEM was designed to assess a health care financing proposal that would allow individuals to borrow from the U.S. government to cover health care costs, requiring careful tracking of medical expenditures and medical debt over time. However, LHIEM is flexible enough to be used for a range of modeling needs related to predicting health care spending and income over time. In this paper, we present the details of the model and all dynamic modules, and include a case study to demonstrate how LHIEM can be used to evaluate proposed policy changes.