MétaCan
Menu
Back to cohort
Record W4404118609 · doi:10.1177/00207152241285759

Free-market institutions and income inequality: Did the link persist around the world even in times of falling within-country inequality, 2000–2021?

2024· article· en· W4404118609 on OpenAlexvenueno aff
Tibor Rutar

Bibliographic record

VenueInternational Journal of Comparative Sociology · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicIncome, Poverty, and Inequality
Canadian institutionsnot available
Fundersnot available
KeywordsFalling (accident)InequalityEconomicsEconomic inequalityLink (geometry)Demographic economicsIncome inequality metricsLabour economicsDevelopment economics

Abstract

fetched live from OpenAlex

High or rising economic inequality can exacerbate political inequalities and is plausibly linked with some social harms, such as health problems and declines in happiness and trust. Within-country income inequality increased sharply across most of the world since the 1980s. One prominent critical sociological account of this occurrence points toward institutions of free-market capitalism, or “neoliberalism,” as a key cause that unleashed inequality during the globalization age. This article empirically operationalizes free-market institutions with the use of Fraser Institute’s index of economic freedom and examines the issue with fixed-effects regressions in a novel dataset of 130 countries between the years 2000 and 2021. It finds a substantial positive correlation between the two variables in the developing, though not the developed, world. This finding is robust to a variety of alternative specifications. Moreover, across specifications, modest size of government and freedom of international trade stand out as the two clear components of economic freedom driving the aggregate relationship. Finally, mediation analysis suggests there also exists an indirect ameliorative relationship between economic freedom and inequality through the conduit of economic development.

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 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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.181
Threshold uncertainty score0.744

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.077
GPT teacher head0.399
Teacher spread0.322 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Comparative SociologySame topicIncome, Poverty, and InequalityFrench-language works237,207