Potential Effects Of Eliminating The Individual Mandate Penalty In California
Bibliographic record
Abstract
The tax penalty for noncompliance with the Affordable Care Act's individual mandate is to be eliminated starting in 2019. We investigated the potential impact of this change on enrollees' decisions to purchase insurance and on individual-market premiums. In a survey of enrollees in the individual market in California in 2017, 19 percent reported that they would not have purchased insurance had there been no penalty. We estimated that premiums would increase by 4-7 percent if these enrollees were not in the risk pool. The percentages of enrollees who would forgo insurance were higher among those with lower income and education, Hispanics, and those who had been uninsured in the prior year, relative to the comparison groups. Compared to older enrollees and those with two or more chronic conditions, respectively, younger enrollees and those with no chronic conditions were also more likely to say that they would not have purchased insurance. Eliminating the mandate penalty alone is unlikely to destabilize the California individual market but could erode coverage gains, especially among groups whose members have historically been less likely to be insured.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 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".