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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".