Bibliographic record
Abstract
An increasingly popular position holds that although markets can be important contributors to development, they can also undermine it. Evidence for capitalism’s effect on development is ambiguous and mixed. We should therefore be cautious and modest advocates of markets. I call this view “two cheers for capitalism.” This paper empirically investigates this view. I find that citizens in countries that became more capitalist over the last quarter century became substantially wealthier, healthier, more educated, and politically freer. Citizens in countries that became significantly less capitalist over this period endured stagnating income, shortening life spans, smaller gains in education, and increasingly oppressive political regimes. The data unequivocally evidence capitalism’s superiority for development and merit its unqualified endorsement. Full-force cheerleading for capitalism is well deserved and three cheers are in order instead of two. I conclude with a few thoughts about why some academics insist on pretending otherwise.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
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".