Heterogeneous (Mis)Perceptions of Energy Costs: Implications for Measurement and Policy Design
Bibliographic record
Abstract
Quantifying heterogeneity in consumers’ misperceptions of product costs is crucial for policy design. We illustrate this point in the energy context and the design of Pigouvian policies. We estimate non-parametric distributions of perceptions of energy costs in the U.S. appliance market using a revealed preference approach. We show that the average degree of misperception is misleading— while the largest share of consumers correctly perceives energy costs, a significant share undervalues them, and smaller shares either significantly overvalues or completely ignores them. We show that setting a tax based on mean misperception deviates substantially from the optimal tax that accounts for heterogeneous misperceptions. While correctly characterizing misperception is crucial for setting optimal Pigouvian taxes for externalities, it is less important for setting optimal standards. We find that standards can largely outperform taxes. Standards’ advantage is they reduce variance in energy operating costs relative to taxes, which internalizes distortionary effects from misperceptions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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".