Income Distribution in the Latin American Southern Cone during the First Globalization Boom and Beyond
Bibliographic record
Abstract
Latin America is the most unequal region in the world and there is intense debate concerning the explanations and timing of such high levels of income inequality. Latin America was also the region, not including European Offshoots, which experienced the most rapid growth during the first globalization boom. It can, therefore, be taken as an interesting case of study regarding how globalization forces impinged on growth and income distribution in peripheral regions. This article presents a first estimate of income inequality in the Southern Cone of South America (Brazil 1872 and 1920, Chile 1870 and 1920, Uruguay 1920) and some assumptions concerning Argentina (1870 and 1920), and Uruguay (1870). We find an increasing trend towards inequality between 1870 and 1920, which can be explained as a process of inequality both within individual countries and among countries. This trend is discussed along three lines: the relationship between inequality and per capita income levels; the dynamics of the expansion to new areas; and movements of relative factor prices and of the terms of trade. During the current globalization process inequality remained apparently stable, as a result of contradictory movements: within-country inequality increased, especially in the three countries with the highest per capita income; on the other hand, between-country inequality was reduced due to the process of club-convergence among the Southern Cone countries. Divergence with core countries was deepened. Some implicit results seem to show that state-led industrialization was featured by decreasing inequality, both within and among countries.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".