A Revealed Preference Approach to the Measurement of Congestion in Travel Cost Models
Bibliographic record
Abstract
Travel cost models are regularly used to determine the value of recreational sites or particular site characteristics, yet a key site attribute, congestion, is often excluded from such analyses. One of several reasons is that congestion (unlike many other site attributes) is determined in equilibrium by the process of individuals sorting across sites, and thus presents significant endogeneity problems. This paper illustrates this source of endogeneity, describes how previous research has dealt with it by way of stated preference techniques, and describes an instrumental variables approach to address it in a revealed preference context. We demonstrate that failing to address the endogeneity of congestion will likely lead to the understatement of its costs, and possibly to the mistaken recovery of agglomeration benefits. We apply our technique to the valuation of a large recreational fishing site in Wisconsin (Lake Winnebago) which, if eliminated, would induce significant re-sorting of anglers amongst remaining sites. In our application, ignoring congestion leads to an understatement of the lake’s value by more than 50 percent.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".