Is the Price Right? The Role of Economic Trade-Offs in Explaining Reactions to Price Surges
Bibliographic record
Abstract
Public authorities often introduce price controls following price surges, potentially causing inefficiencies and exacerbating shortages. A survey experiment with 7,612 Canadian and U.S. respondents shows that unregulated price surges raise moral objections and widespread disapproval. However, acceptance increases and demand for regulation declines when participants are prompted to consider economic trade-offs between controlled and unregulated prices, whereby incentives from higher prices lead to additional supply and enhance access to goods. Moreover, highlighting these trade-offs reduces polarization in moral judgments between supporters and opponents of unregulated pricing. Textual analysis of responses to open-ended questions provides further insights into our findings, and an incentivized donation task demonstrates consistency between stated preferences and real-stakes behavior. Although economic trade-offs do influence public support for price control policies, the evidence indicates that even when the potential gains in economic efficiency from unregulated prices are explicit, a significant divide persists between the utilitarian views that standard economic thinking implies and the nonutilitarian values held by the general population. This paper was accepted by Dorothea Kübler, behavioral economics and decision analysis. Funding: This work was supported by Johns Hopkins “Catalyst Award” and Business of Health Initiative (HBHI) and the Sandra Rotman Centre for Health Sector Strategy at the University of Toronto. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.04555 .
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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 | 0.011 | 0.106 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".