Testing for Asymmetric Information Using “Unused Observables” in Insurance Markets: Evidence from the U.K. Annuity Market
Bibliographic record
Abstract
Abstract This article tests for asymmetric information in the U.K. annuity market of the 1990s by trying to identify “unused observables,” attributes of individual insurance buyers that are correlated both with subsequent claims experience and with insurance demand but that insurance companies did not use to set insurance prices. Unlike the widely used positive correlation test for asymmetric information, which searches for a positive correlation between insurance demand and risk experience, the unused observables test is not confounded by heterogeneity in individual preference parameters that may affect insurance demand. We identify residential location as an unused observable in the U.K. annuity market of this period. Even though residential location was observed by all market participants, the decision not to condition prices on it created the same types of market inefficiencies that arise when annuity buyers have private information about mortality risk. Our findings raise questions about how insurance companies select the set of buyer attributes that they use in setting policy prices. In the decade following the period that we study, U.K. insurance companies changed their pricing practices and began to condition annuity prices on a buyer's postcode.
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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 | 0.015 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".