The Unintended Consequences of Regulation in the Presence of Multiple Unpriced Externalities: Evidence from the Transportation Sector
Bibliographic record
Abstract
In transportation systems with unpriced congestion, allowing single-occupant low-emission vehicles in high occupancy vehicle (HOV) lanes to encourage their adoption exacerbates congestion costs for carpoolers. The resulting welfare effects of the policy are negative, with environmental benefits overwhelmingly dominated by the increased congestion costs. Exploiting the introduction of the Clean Air Vehicle Stickers policy in California with a regression discontinuity design, our results imply a best-case cost of $124 per ton of reductions in greenhouse gases, $606,000 dollars per ton of nitrogen oxides reduction, and $505,000 dollars per ton of hydrocarbon reduction, exceeding those of other options readily available to policymakers. For reasons discussed in Harberger (1974), the estimation of the overall welfare effects of government interventions to correct externalities is more challenging * Bento: Cornell University, Charles H Dyson School of Applied Economics and Management. 424 Warren Hall, Ithaca, NY 14853 (e-mail: amb396@cornell.edu); Kaffine: University of Colorado Boulder Department of Economics, Econ 11, Boulder, CO 80309 (email: daniel.kaffine@colorado.edu); Roth: University of California, Irvine, Department of Economics, 3151 Social Science Plaza, Irvine, CA 92697 (email: kroth1@uci.edu); Zaragoza-Watkins: University of California at Berkeley, Agricultural and Resource Economics, 207 Giannini Hall, Berkeley, CA 94720 (email: mdzwatkins@berkeley.edu). The authors thank Richard Arnott, Lucas Davis, Mary Evans, Matthew Freedman, Kenneth Gillingham, Sumeet Gulati, Ryan Kellogg, Shanjun Li, Jordan Matsudaria, Justin McCrary, Erich Muehlegger, James Sallee, and Hendrik Wolff for helpful comments as well as seminar participants at AERE-Seattle 2011, Colorado School of Mines, Columbia, EAERE 2011, Inter-American Development Bank, Oregon State University, Stanford, TREE Seminar Series, UC-Berkeley, University of Connecticut, UEA-Miami 2011, WCERE-Montreal 2010, and Yale. Finally we thank Arthur T Degaetano for weather data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".