The effects of state policy design features on take‐up and crowd‐out rates for the state children's health insurance program
Bibliographic record
Abstract
We evaluate the effects of state policy design features on SCHIP take-up rates and on the degree to which SCHIP benefits crowd out private benefits. The results indicate overall program take-up rates of approximately 10 percent. However, there is considerable heterogeneity across states, suggesting a potential role of inter-state variation in policy design. We find that several design mechanisms have significant and substantial positive effects on take-up. For example, eliminating asset tests, offering continuous coverage, simplifying the application and renewal processes, and extending benefits to parents all have sizable and positive effects on take-up rates. Mandatory waiting periods, on the other hand, consistently reduce take-up rates. In all, inter-state differences in outreach and anti-crowd-out efforts explain roughly one-quarter of the cross-state variation in take-up rates. Concerning the crowding out of private health insurance benefits, we find that between one-quarter and one-third of the increase in public health insurance coverage for SCHIP-eligible children is offset by a decline in private health coverage. We find little evidence that the policy-induced variation in take-up is associated with a significant degree of crowd out, and no evidence that the negative effect on private coverage caused by state policy choices is any greater than the overall crowding-out effect. This suggests that states are not augmenting take-up rates by enrolling children that are relatively more likely to have private health insurance benefits.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".