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Record W4224433999 · doi:10.3386/w29963

Is the Price Right? The Role of Economic Tradeoffs in Explaining Reactions to Price Surges

2022· report· en· W4224433999 on OpenAlexaboutno aff
Julio Elías, Nicola Lacetera, Mario Macis

Bibliographic record

VenueNational Bureau of Economic Research · 2022
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsMonetary economicsMacroeconomicsInternational economics

Abstract

fetched live from OpenAlex

Public authorities often introduce price controls following price surges, potentially causing inefficiencies and exacerbating shortages. A survey experiment with 7, 612 Canadian and US respondents shows that unregulated price surges raise moral objections and widespread disapproval. However, acceptance increases, and demand for regulation decreases when participants are prompted to consider economic tradeoffs between controlled and unregulated prices, whereby incentives from higher prices lead to additional supply and enhance access to goods. Moreover, highlighting these tradeoffs 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 non-utilitarian values held by the general population.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.077
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.077
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0040.005
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.413
GPT teacher head0.446
Teacher spread0.033 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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