Costly (Dis)Agreement: Optimal Intervention, Income Redistribution, and Transfer Efficiency of Output Quotas in the Presence of Cheating
Bibliographic record
Abstract
This study builds on previous work by Giannakas and Fulton (2003, 2000) on the economics of output quotas in the presence of cheating by examining the efficiency of the policy in transferring income to producers as well as the optimal regulatory response to enforcement costs and farmer noncompliant behavior in a decentralized policy making environment. Analytical results show that enforcement costs and cheating change the transfer efficiency of output quotas, the level of intervention that transfers a given surplus to producers, the socially optimal income redistribution, and the social welfare from intervention. The incidence of the policy is shown to depend on the relative political preferences of the policy makers and the policy enforcers making the consideration of the decentralized policy making structure critical in analyzing output quotas in the presence of cheating.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".