The Impact of the ACA on Premiums: Evidence from the Self-Employed
Bibliographic record
Abstract
This article examines the impact of the Affordable Care Act on premiums by studying a segment of the nongroup market, the self-employed. Because self-employed health insurance premiums are deductible, tax data contain comprehensive individual-level information on the premiums paid by this group prior to the establishment of health insurance exchanges. We compare these prior premiums to reference silver premiums available on the exchanges and find that exchange premiums are 4.2 percent higher on average among the entire sample but 42.3 percent lower on average after taxes and subsidies. We also examine which type of exchange coverage would cost less than the individual's prior health insurance premiums and find that almost 60 percent of families could purchase bronze plans for less than their prior premiums, though only about a quarter could purchase platinum plans. After taxes and subsidies, the fractions increase to over 85 percent for bronze plans and over half for platinum plans.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".