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Record W2612969947 · doi:10.1093/oxrep/grz017

Inequality: A hidden cost of market power

2019· article· en· W2612969947 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueOxford Review of Economic Policy · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic theories and models
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsInequalityCompetition (biology)Market powerEconomic inequalityDistribution (mathematics)Income distributionPower (physics)Sample (material)Demographic economicsMarket economy

Abstract

fetched live from OpenAlex

Abstract This paper explores the impact of competition on inequality by developing a new model to illustrate how higher profits from market power, and associated higher prices, could influence the distribution of wealth and income. We analyse data from eight OECD countries—Canada, France, Germany, Korea, Japan, Spain, the United Kingdom, and the United States. In an average country in the sample, market power increases the wealth of the richest 10 per cent by between 12 and 21 per cent for a range of reasonable assumptions about savings behaviour, while it reduces the income of the poorest 20 per cent by 11 per cent or more. The results contribute to the economic literature on the origins of inequality, suggesting that lack of competition may be one source of economic inequality.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.863
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.026
GPT teacher head0.273
Teacher spread0.247 · 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