Pricing Regulation and Imperfect Competition on the Massachusetts Health Insurance Exchange
Bibliographic record
Abstract
We analyze consumer demand and model the effect of pricing regulation under imperfect competition using data from the Massachusetts health insurance exchange. We identify consumer demand using coarse insurer pricing strategies. There is substantial heterogeneity in preferences by consumer type, with younger consumers twice as price sensitive as older consumers. As a result, older consumers face higher markups over costs. Modified community rating links prices for consumers that differ in both costs and preferences. Constrained prices are not simply the population-weighted average of unconstrained prices, because community rating changes the marginal consumer firms face. Tightening rating regulations transfers resources from low cost to high cost consumers, but also reduces firm profits and increases overall consumer surplus. We use our model to examine other insurance regulations. For instance, minimum loss ratios (designed to limit firm profits) will also alter the transfers between consumers. Moreover, risk adjustment will be insufficient to equalize prices across consumer types, as markups still differ. As a result, without a mandate, the market can unravel due to differences in preferences alone
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".