MétaCan
Menu
Back to cohort
Record W4310163514 · doi:10.33423/jabe.v24i5.5620

Falling Prices: Does This Cause Purchases to Be Delayed or Speed Up? Evidence From the Gasoline Market

2022· article· en· W4310163514 on OpenAlexvenueno aff
Hans Schumann, Harmeet Singh

Bibliographic record

VenueJournal of Applied Business and Economics · 2022
Typearticle
Languageen
FieldEnergy
TopicEnergy, Environment, and Transportation Policies
Canadian institutionsnot available
Fundersnot available
KeywordsDeflationEconomicsInflation (cosmology)Anticipation (artificial intelligence)Monetary economicsRecessionKeynesian economicsGreat recessionPoint (geometry)Falling (accident)Empirical evidenceMacroeconomicsMonetary policy

Abstract

fetched live from OpenAlex

When teaching macroeconomics, students intuitively know why macroeconomists stress the dangers of inflation, but question why economists will say deflation is worse. To explain macroeconomists will almost always point to Japan’s “Lost decade”, a spiral of declining economic activity intertwined with declining prices. Their claim is that the deflation was a principle driver for the deepening recession as declining prices could cause consumers not to purchase more (as the law of demand would normally expect) but rather less in anticipation of even lower prices to come. This paper looked at the empirical evidence from the energy sector, specifically gasoline sales during the 2013-2015 time period and verified that there is evidence that some US consumers did indeed delay purchases even if they ultimately bought more.

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)0.002
metaresearch head score (Gemma)0.015
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 score0.022
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0150.002

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 head0.031
GPT teacher head0.232
Teacher spread0.202 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Applied Business and EconomicsSame topicEnergy, Environment, and Transportation PoliciesFrench-language works237,207