Stacking Subsidies in Factor Markets: Evidence from Market Experiments
Bibliographic record
Abstract
Government policies employ different support programs such as subsidies to reduce risks, increase efficiency in markets, and enhance societal welfare. In markets such as ethanol markets, where multiple agents receive subsidy, it is often difficult to determine whether recipients of these support programs will transfer some of their payments to other agents in the market. In this study, we use laboratory market experiments to understand subsidy incidence in markets where both buyers and sellers receive subsidies, and there are few buyers relative to sellers. Our results show that when subsidizing both sides of the market, framing effects matter, and when markets are buyer concentrated, subsidy distributions generally tend to favor buyers. With a per-unit subsidy of 20 tokens to both sides and an equal number of buyers and sellers in the market, we find that buyers increase their earnings by 13.4% while seller earnings decrease by 16.1%. On a per-schedule basis, buyer earnings in the concentrated market are similar to what we observed in the competitive market.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".