Producer Compensation under Government Programs: What Should the Magnitude Be?
Bibliographic record
Abstract
When policies are changed it is not uncommon for losers to be compensated. Economic theory and quantitative analysis are useful in determining the efficiency gains/losses associated with a policy change, but are little help in deciding what the approach to compensation should be. The amount of compensation varies, depending on, in part, the political clout of the parties being negatively affected by a policy change—compensation is what politicians and the sector demanding compensation can agree on. We formulate four approaches to producer compensation within the context of the Ontario Tobacco Transition Program where producers would have suffered losses in the absence of compensation. The approaches range from providing zero-compensation to providing compensation based on the entire value of the tobacco quota. The Canadian government chose to compensate producers for the termination of the tobacco quota program based on an approach that far exceeded other possible compensation approaches. Importantly, efficiency is not affected by the compensation approach.
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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 | 0.034 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".