Most Consumers Don’t Buy Hybrids: Is Rational Choice a Sufficient Explanation?
Bibliographic record
Abstract
Although federal regulation of vehicle fuel economy is often seen as environmental policy, over 70% of the estimated benefits of the 2017–2025 federal standards are savings in consumer expenditures on gasoline. Rational-choice economists question the counting of these benefits since studies show that the fuel efficiency of a car is reflected in its price at sale and resale. We contribute to this debate by exploring why most consumers in the United States do not purchase a proven fuel-saving innovation: the hybrid-electric vehicle (HEV). A database of 110 vehicle pairs is assembled where a consumer can choose a hybrid or gasoline version of virtually the same vehicle. Few choose the HEV. A total cost of ownership model is used to estimate payback periods for the price premiums associated with the HEV choice. In a majority of cases, a rational-choice explanation is sufficient to understand consumer disinterest in the HEV. However, in a significant minority of cases, a rational-choice explanation is not readily apparent, even when non-pecuniary attributes (e.g., performance and cargo space) are considered. Future research should examine, from a behavioral economics perspective, why consumers do not choose HEVs when pricing and payback periods appear to be favorable.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".