Public Responses to Agricultural Disasters: Rethinking the Role of Government
Bibliographic record
Abstract
We provide a broad overview of the role and history of federal disaster relief in U.S. agriculture. We discuss various economic arguments that may be used as justification for such disaster relief and subsidized insurance programs. In general, we find no persuasive argument that market failure justifies subsidized risk management activities by the government. Important exceptions exist in the case of catastrophic damages to public infrastructure, invasive and communicable disease threats, and the hazards posed by accidental or deliberate contamination of food supplies in that the presence of significant transactions costs may inhibit private market solutions. We also consider a panel VAR analysis of the dynamic interrelationships among market returns and farm program payments conveyed under three different types of programs—disaster assistance, crop insurance, and all other direct payments. An important finding is that disaster and insurance payments appear to imply higher subsequent levels of market income risk in agriculture. This finding is consistent with arguments that subsidized disaster assistance and insurance may lead to greater risk in agriculture. Nous présentons un large aperçu du rôle et de l'historique du programme fédéral d'assistance en cas de catastrophe agricole aux États‐Unis. Nous analysons différents arguments économiques qui peuvent justifier ces programmes d'aide et d'assurance subventionnés. En général, nous ne trouvons aucun argument convaincant comme quoi une défaillance de marché justifie des activités de gestion du risque subventionnées par le gouvernement. Cependant, des exceptions importantes existent pour les cas de dommages catastrophiques à des infrastructures publiques; de menaces de maladies contagieuses et invasives; et de dangers associés à la contamination accidentelle ou délibérée de la chaîne alimentaire, auquel cas les coûts de transaction importants pourraient inhiber les solutions du marché privé. Nous considérons également une analyse panel VAR des relations entre les rendements de marché et les paiements versés en vertu de trois types de programme: assistance en cas de catastrophe, assurance récolte et tout autre type de paiement direct. Nous en arrivons à la conclusion importante que les paiements d'assurance et d'aide aux sinistrés semblent mener à des niveaux de risque relatif au revenu marchand plus élevés dans le secteur de l'agriculture. Ceci concorde avec les arguments voulant que les programmes subventionnés d'assurance et d'assistance en cas de catastrophe mènent à une augmentation des risques dans le secteur de l'agriculture.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".