How stable and predictable are welfare estimates using recreation demand models?
Bibliographic record
Abstract
Abstract Economic analyses of environmental policy projects typically use pre‐existing estimates of welfare measures that are then transferred over time to the policy relevant periods. Understanding how stable and predictable these welfare estimates are over time is important for applying them in policy. Yet, revealed preference models of recreation demand have received few temporal stability assessments compared to other nonmarket valuation methods. We use a large administrative panel dataset on campground reservations covering 10 years to study temporal stability and predictability of environmental quality welfare estimates. Welfare estimates are statistically different across years in 62% of the comparisons, and this ranges from 47%–71% depending on modeling assumptions. Using an event study design, we find evidence that week‐specific welfare estimates are stable after an initial adjustment week in response to a change in environmental quality. Our findings further reveal that using 2 years of data in the modeling compared to a single year improves the prediction of future welfare measure estimates substantially, but further prediction improvements are modest when including more than 2 years of data. Predictions of welfare estimates are more consistent when using data closer in time to the prediction year. We discuss the implications of our results for using revealed preference studies in policy analysis.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".