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 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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".