Seventy-five Years of Measuring Income Inequality in Latin America
Bibliographic record
Abstract
Drawing on a comprehensive compilation of quantile shares and inequality measures for 34 countries, including over 5,600 estimated Gini coefficient, we review the measurement of income inequality in Latin America and the Caribbean over the last seven decades. Although the evidence from the first quarter century roughly until the 1970s is too fragmentary and difficult to compare, clearer patterns emerge for last fifty years. The central feature of these patterns is a broad inverted U curve, with inequality rising in most countries prior to the 1990s, and falling during the early 21st Century, at least until the mid-2010s, when trends appear to diverge across countries. This broad pattern is modified by country specificities, with considerable variation in timing and magnitude. Whereas this broad picture emerges for income inequality dynamics, there is much more uncertainty about the exact levels of inequality in the region. The uncertainty arises from the disparity in estimates for the same country/year combinations, depending on whether they come from household surveys exclusively; from some combination of surveys and administrative tax data; and on whether they attempt to scale income aggregates to achieve consistency with National Accounts estimates. Since no single method is fully convincing at present, we are left with (often wide) ranges, or bands, of inequality as our best summaries of inequality levels. Reassuringly, however, the dynamic patterns are generally robust across the bands.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".