Nonlinear Pricing Under Regulation: Comparing Cap Rules and Taxes in the Laboratory
Bibliographic record
Abstract
Se reporta un experimento que contrasta los impactos de un impuesto y un límite de cantidad en un mercado con un solo producto y dos consumidores con preferencias privadas. Se discuten los efectos sobre la canasta de elección y el excedente del consumidor. La política regulatoria varía según el tratamiento experimental. Con regulación, el objetivo es reducir a la mitad el tamaño de la opción grande original. En comparación al grupo sin intervención, los vendedores que enfrentan un límite de cantidad intentan servir a los compradores por separado con una frecuencia similar. Con un impuesto, es menos probable que los vendedores ofrezcan dos alternativas. El excedente del consumidor no es afectado cuando hay un límite de cantidad, mientras que los compradores con alta preferencia por el producto ven su excedente disminuido por el impuesto. Estos resultados tienen implicaciones para la formulación de políticas en la industria minorista de alimentos y otras en las que las autoridades pretenden regular el consumo y al mismo tiempo proteger el excedente del consumidor.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".