SEPARATING MORAL HAZARD FROM ADVERSE SELECTION AND LEARNING IN AUTOMOBILE INSURANCE: LONGITUDINAL EVIDENCE FROM FRANCE
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The identification of information problems in different markets is a challenging issue in the economic literature. In this paper, we study the identification of moral hazard from adverse selection and learning about risk within the context of a multi-period dynamic model. We extend the model of Abbring, Chiappori, and Pinquet (2003, Journal of the European Economic Association, 1, 767–820) to include learning about risk and insurance coverage choice over time. We derive testable empirical implications for panel data. We then perform tests using longitudinal data from France during the period 1995–1997. We find evidence of moral hazard among a sub-group of policyholders with less driving experience (less than 15 years). Policyholders with fewer than five years of experience have a combination of learning about risk and moral hazard, whereas no residual information problem is found for policyholders with more than 15 years of experience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it