On the Effectiveness of Uniform Subsidies in Increasing Market Consumption
Bibliographic record
Abstract
We study the problem faced by a central planner trying to increase the consumption of a good, such as new malaria drugs in Africa. The central planner allocates subsidies to its producers, subject to a budget constraint and endogenous market response. The policy most commonly implemented in practical applications of this problem is uniform, in the sense that it allocates the same per-unit subsidy to every firm, primarily because of its simplicity and perceived fairness. Surprisingly, we identify sufficient conditions of the firms’ marginal costs such that uniform subsidies are optimal, even if the firms’ efficiency levels are arbitrarily different. Moreover, this insight is usually preserved even if the central planner is uncertain about the specific market conditions. Further in many cases, uniform subsidies simultaneously attain the best social welfare solution. Additionally, simulation results in relevant settings where uniform subsidies are not optimal suggest that they induce a nearly optimal market consumption. On the other hand, if the firms face a fixed cost of entry to the market, then the performance of uniform subsidies can be significantly worse, suggesting the need for an alternative policy in this setup. This paper was accepted by Yossi Aviv, operations management.
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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 | 0.007 | 0.042 |
| 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 | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".