PRELIMINARY DRAFT: PLEASE DO NOT CITE WITHOUT PERMISSION
Bibliographic record
Abstract
Notches — where small changes in behavior lead to large changes in a tax or subsidy — figure prominently in many policies, but have been rarely examined by economists. In this paper, we analyze a class of notches associated with policies in the United States and Canada aimed at improving vehicle fuel economy. We provide several pieces of evidence showing that automakers respond to notches in fuel economy policy by precisely manipulating fuel economy ratings so as to just qualify for more favorable treatment. We then describe the welfare consequences of this behavior, derive a welfare summary statistic applicable to many contexts, and draw lessons for corrective taxation in the presence of notches. Finally, we use a comparison of the amount of strategic manipulation that occurs surrounding tax notches to the amount surrounding presentation notches in fuel economy label rules as a way of estimating consumer willingness to pay for fuel economy. ∗The author’s would like to thank David Good, Robert Mull, and seminar participants at the University of Michigan for helpful comments.
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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 | 0.003 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.645 | 0.324 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".