What would households pay for a reduction of automobile traffic? Evidence from nine German cities
Bibliographic record
Abstract
This paper quantifies the marginal willingness to pay for a reduction of automobile traffic. By using a new structural approach in a hedonic framework by Bishop and Timmins 2019 we are able to avoid common issues in hedonic studies using instrumental variables. Our analysis is based on data from nine large cities in Germany between 2016 and 2019 and includes 533,402 detailed observations at the apartment level as well as for various points of interest. To the best of our knowledge this is the first paper to conduct this analysis for Germany. We estimate that the average willingness to pay for a reduction of traffic by city and per year ranges between €30.3-59.2 for a 10% reduction, €93.8-158.3 for a 20% reduction and €190.6-252 for a 30% reduction. The highest willingness to pay for a reduction of traffic is observed in Frankfurt am Main, the lowest in Leipzig. Further, we compute the expected gains for a reduction of traffic at the city level. In addition to the willingness to pay for a reduction of traffic, this considers the composition of the road network as well as for the number of households. Accordingly, these expected gains amount to €163,970-1,019,454€ for a 10% reduction, €484,023-3,261,837 for a 20% reduction, and €1,018,240-6,727,148 for a 30% reduction. The highest expected gains for a reduction of traffic is observed in Munich, the lowest in Leipzig
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".