Hyunji Kim · Risk Management and Healthcare Policy 2025 · pooled cross-sectional observational study · n=4,524,753

The Impact of Individual Mandate and Income on Private Health Insurance Enrollment: A State-Level Analysis on Individual Behavior Change

Cited 0 times in the scientific literature.

Level 4 - case-series / case-control

Repeated cross-sectional observational study; level assigned by design analogy for health policy research.

OpenAlex W4409271282 · doi:10.2147/rmhp.s501240 · record verified 2026-08-29

What was done

Researchers evaluated the association between state-level individual health insurance mandates and private health insurance enrollment following the repeal of the federal individual mandate penalty. Using pooled cross-sectional data from the Integrated Public Use Microdata Series (IPUMS) USA from 2019 to 2021, the study analyzed 4,524,753 non-elderly adults (aged 19 to 64) across the United States, including four states and Washington, D.C., that retained individual mandates. Logistic regression models and marginal effects were calculated to examine the relationship between state mandate policies, individual income, their interaction, and the probability of having private health insurance coverage.

What was found

State-level individual mandates and higher individual income were both independently associated with an increased likelihood of private health insurance enrollment. An interaction analysis showed that the positive impact of the mandate was more pronounced among lower-income individuals: for every $10,000 increase in individual income, the probability of enrollment under the mandate decreased by 0.885%.

Why it matters

These findings suggest that state-level individual mandates can help sustain private health insurance coverage after federal penalty repeals. Because the mandate exerts the greatest enrollment pressure on lower-income populations, complementary financial supports such as subsidies may be necessary to prevent disproportionate economic burdens.

Limits

The study used a cross-sectional design, which limits causal inference. Specific odds ratios, baseline enrollment rates, and confidence intervals were omitted from the abstract. Additionally, potential unmeasured confounders across states—such as differences in state Medicaid expansion status, local premium subsidies, and regional economic conditions—were not detailed in the abstract.

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