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Record W2094726995 · doi:10.1080/13504851.2011.556585

International price dispersions of the Big Mac and economic integration

2011· article· en· W2094726995 on OpenAlexaboutno aff
Yukio Fukumoto

Bibliographic record

VenueApplied Economics Letters · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsnot available
Fundersnot available
KeywordsPrice dispersionBig dataPrice indexEconomicsPer capitaIndex (typography)BusinessEconometricsComputer scienceSociology

Abstract

fetched live from OpenAlex

McDonald's Big Mac hamburger is available all over the world, and its recipe and quality are almost identical around the world and have changed little over time. However, Big Mac prices are widely disparate even across countries with more similar per capita incomes. This fact would be amazing because world tariff levels have been falling steadily and world trade volumes have been increasing dramatically in recent decades. We pay attention to the situation of international goods markets' integration and attempt to clarify why the deviations from absolute Big Mac parity are common across countries. To summarize our main results, global price dispersion of the Big Mac did not decrease although per capita income dispersion reduced. On the other hand, price dispersions of the Big Mac decreased within countries where trade ties are strong. These results suggest that Big Mac prices of countries in the world converge to several levels and that existence of trading bloc accounts for deviations from absolute Big Mac parity to some extent.

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)0.001
metaresearch head score (Gemma)0.007
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 score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.006
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0120.001

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 head0.053
GPT teacher head0.185
Teacher spread0.132 · 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

Citations3
Published2011
Admission routes1
Has abstractyes

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