Mandated Health Insurance Benefits and the Utilization and Outcomes of Infertility Treatments
Bibliographic record
Abstract
During the last two decades, the treatment of infertility has improved dramatically. These treatments, however, are expensive and rarely covered by insurance, leading many states to adopt regulations mandating that health insurers cover them. In this paper, we explore the effects of benefit mandates on the utilization and outcomes of infertility treatments. We find that use of infertility treatments is significantly greater in states adopting comprehensive versions of these mandates. While greater utilization had little impact on the number of deliveries, mandated coverage was associated with a relatively large increase in the probability of a multiple birth. For relatively low fertility patients who responded to the expanded insurance coverage, treatment was often unsuccessful and did not result in a live birth. For relatively high fertility patients, in contrast, treatment often led to a multiple, rather than a singleton, birth. We also find evidence that the beneficial effects on the intensive treatment margin that have been proposed in other studies are relatively small. We conclude that, while benefit mandates potentially solve a problem of adverse selection in this market, these benefits must be weighed against the costs of the significant moral hazard in utilization they induce.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".