Bibliographic record
Abstract
A variety of state and federal tax incentives have been used to subsidize gas-electric hybrid vehicles. In this paper, I estimate the incidence of these tax incentives using microdata on sales of the Toyota Prius, in order to determine who benets from these policies. I focus on three sharp changes in federal subsidies stemming from the Energy Policy Act of 2005 and assemble several pieces of evidence which indicate that consumers captured nearly all of the benet. First, subsidy exclusive transaction prices do not jump in the anticipated direction in response to large changes in federal tax incentives. Second, estimates that account for consumer heterogeneity indicate that consumers captured the majority of the gains. Third, a state panel regression on state tax incentives shows that state tax incentives have little or no eect on transaction prices, which is consistent with the federal result. The conclusion that consumers captured the subsidy poses a challenge to standard models of tax incidence. Toyota faced a binding capacity constraint when the credit was introduced. Given a xed supply, standard models predict that Toyota would capture the subsidy. I argue that consumers gained instead because Toyota believed that raising prices to clear the market would lower future demand for hybrids. I outline a stylized model in which current prices inuence future demand and show that a capacity constraint can generate the tax incidence observed in the data. I then discuss factors that could give rise to such a demand system, drawing on the theory of search among alternatives and the behavioral literature on fairness. Finally, I draw lessons for the study of tax incidence in other markets.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".