Competing under Information Heterogeneity: Evidence from Auto Insurance
Bibliographic record
Abstract
Abstract This article studies competition under information heterogeneity in selection markets and examines the impact of public information regulations aimed at reducing information asymmetries between competing firms. We develop a novel model and introduce new empirical strategies to analyse imperfect competition in markets where firms have heterogeneous information about consumers, vary in cost structures, and offer differentiated products. Using data from the Italian auto insurance market, we find substantial differences in the precision of risk ratings across insurers, and those with less accurate risk-rating algorithms tend to have more efficient cost structures. We assess the equilibrium effects of giving firms equal access to aggregated risk information from a centralized bureau. This policy significantly reduces prices by increasing competition, leading to a 15.7% boost in consumer surplus, almost reaching the efficiency benchmark where firms have full knowledge of consumers’ true risk. Aggregating information through the bureau favours low-risk consumers and reduces average costs by 12 euros per contract through more efficient insurer–insuree matching.
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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 | 0.005 | 0.026 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".