Free-market institutions and income inequality: Did the link persist around the world even in times of falling within-country inequality, 2000–2021?
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".