Cheating? The Case of Producers’ Under‐Reporting Behavior in Hog Insurance in China
Bibliographic record
Abstract
Abstract Information asymmetry exists in virtually every insurance setting. The institutional arrangement of hog insurance in China offers a unique opportunity to investigate the farmer's behavior of under‐reporting the actual number of finished hogs on one hand, and the insurer's efficiency in determining the actual numbers on the other. Using data on 444 hog operators synchronized from farm production survey and insurance records, results showed that farmers report on average 11.5% fewer hogs to the insurance company. The level of under‐reporting is positively associated with the size of operation. Farmers with longer farming experience and more conservative risk attitude report more accurately. The under‐report behavior is also partially attributed to a farmer's limited capacity of accurate estimation. Due to information barrier, the insurance company is only able to recover 18.6% of the under‐report at the indemnity payment stage. Results are robust after controlling for potential sample selection problems. It is suggested that technical supports, public programs and premium incentive designs in repeated insurance should be considered to promote more accurate reports.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".