Privacy concerns in insurance markets: Implications for market equilibria and customer utility
Bibliographic record
Abstract
Abstract We analyze insurance market outcomes and customer utility under asymmetric information when customers have heterogeneous privacy concerns and access to a screening technology that permits their private information to be revealed. If the market outcome without the technology is of the Rothschild–Stiglitz type, so too is the market outcome with the technology for those who do not submit to the screening technology and thus retain their private information. Low‐risk customers who reveal their private information are better off and those who do not reveal their risk type are no worse off, resulting in a Pareto improvement. If, however, the market outcome without the technology is of the Wilson–Miyazaki–Spence type, the market may no longer exhibit cross‐subsidies after the screening technology is introduced. In this case, low‐risk customers who reveal their risk type are better off, but this is at the expense of those who do not reveal their risk type, who are worse off due to intensified adverse selection. The negative externality on those who do not reveal their risk type can outweigh the utility gains of those low‐risk customers who do reveal their risk type, resulting in lower expected welfare. In this case, a privacy law would improve expected welfare.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".