Non-Price Equilibria for Non-Marketed Goods
Bibliographic record
Abstract
As part of the Resources for the Future Frontiers of Environmental Economics collection of papers, we consider the problem of general equilibrium feedback effects in non-price space as they relate to non-market valuation. Our overall objective is to examine the extent to which non-price equilibria arising from both simple and complex sorting behavior can be empirically modeled and the resulting differences in partial and general equilibrium welfare measures quantified. After motivating the problem, in general, we consider the specific context of congestion in recreation demand applications, which we classify as the outcome of a simple sorting equilibrium. Using both econometric and computable general equilibrium (CGE) models, we examine the conceptual and computational challenges associated with this class of problems and present findings on promising solution avenues. We demonstrate the relevance of accounting for congestion effects in recreation demand with an application to lake visits in Iowa. Our econometric and CGE results confirm that, for some plausible counterfactual scenarios, substantial differences exist between partial and general equilibrium welfare estimates. We conclude the paper by describing tasks that are needed to move forward research in this area.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.040 | 0.003 |
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".