Section1.1. Methodology: Instrumental forest analysis.

Section1.1. Methodology: Instrumental forest analysis.</p

Section2.2.

Rank-Weighted Average Treatment Effect (RATE) for insurance-effect analysis. (PDF)</p

Variable importance scores for all covariates in each analysis where percentages (bold indicates importance > 20% of the mean importance).

Variable importance scores for all covariates in each analysis where percentages (bold indicates importance > 20% of the mean importance).</p

Overall effects of health insurance using instrumental forest.

Overall effects of health insurance using instrumental forest.</p

Distribution of baseline characteristics over the eight lottery draws.

Distribution of baseline characteristics over the eight lottery draws.</p

Forest plot for subgroups’ conditional average treatment effects of lottery selection and health insurance on amount of out-of-pocket spending ($).

Forest plot for subgroups’ conditional average treatment effects of lottery selection and health insurance on amount of out-of-pocket spending ($).</p

Forest plot for subgroups’ conditional average treatment effects of lottery selection and health insurance on number of prescription drugs.

Forest plot for subgroups’ conditional average treatment effects of lottery selection and health insurance on number of prescription drugs.</p

Forest plot for subgroups’ conditional average treatment effects of lottery selection and health insurance on physical component score.

Forest plot for subgroups’ conditional average treatment effects of lottery selection and health insurance on physical component score.</p

RATE estimates and standard errors.

Existing evidence regarding the effects of Medicaid expansion, largely focused on aggregate effects, suggests health insurance impacts some health, healthcare utilization, and financial hardship outcomes. In this study we apply causal forest and instrumental forest methods to data from the Oregon Health Insurance Experiment (OHIE), to explore heterogeneity in the uptake of health insurance, and in the effects of (a) lottery selection and (b) health insurance on a range of health-related outcomes. The findings of this study suggest that the impact of winning the lottery on the health insurance uptake varies among different subgroups based on age and race. In addition, the results generally coincide with findings in the literature regarding the overall effects: lottery selection (and insurance) reduces out-of-pocket spending, increases physician visits and drug prescriptions, with little (short-term) impact on the number of emergency department visits and hospital admissions. Despite this, we detect quite weak evidence of heterogeneity in the effects of the lottery and of health insurance across the outcomes considered.</div

Forest plot for subgroups’ conditional average treatment effects of lottery selection and health insurance on mental component score.

Forest plot for subgroups’ conditional average treatment effects of lottery selection and health insurance on mental component score.</p