Bibliographic record
Abstract
Abstract Natural disasters pose a significant and rapidly growing burden to society, causing over a million deaths and worldwide economic losses in the trillions of dollars in the last twenty years. Concerned over the extent to which their populations are exposed to disaster risk, policymakers in disaster-prone countries strive to increase the penetration of disaster insurance from its relatively low current level and wish to arrest the increasing share of public liability for private losses arising from rising public expenditures on disaster recovery. Although evidence regarding disaster risk and insurance suggests that individuals respond to their economic incentives when deciding on the degree to which to expose their property and other to risk from a recurrent disaster, potential inefficiencies in private insurance markets can distort these individual incentives and result in underinsurance and excessive exposure. Current research into whether such apparent market inefficiencies are primarily attributed to the behavior of private market participants or to the adverse incentives arising from current programs of disaster aid, regulation and other public policies is of fundamental importance to attaining these policy objectives. This article critically assesses the current state of mainstream economic and political research into disasters, public policy, and household behavior toward disaster risk. Findings of the most important and influential empirical and theoretical studies over the last 30 years are described, as well as limits on the robustness and interpretation of these findings arising from the characteristics of economic data on disasters and potential bias in measuring the determinants of disaster insurance coverage. Also discussed are both theoretical and empirical evidence that moral hazard on the part of households, insurance firms, and elected officials results in misallocations of private coverage; and it is demonstrated that, exactly contrary to the objectives of public policy, current programs of disaster aid in the presence of moral hazard create incentives for households to minimize, rather than maximize, market coverage of their exposure to disaster risk. The conclusion presents and proves a proposition, original to this article, that any compensatory public aid program is necessarily a source of economic inefficiency and, conditional on net losses, decreases economic 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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".