The Economic Geography of Human Capital in Twentieth-Century Latin America
Bibliographic record
Abstract
In this paper we present results for educational achievement in the different economic regions of Latin America (Big countries: Mexico and Brazil; Southern Cone; Andean countries; Central America; and others) during the twentieth century. The variables we use to measure education are average years of education, literacy, average years in primary school, average years in secondary school, and average years in university. To attain a broader perspective on the relationship of education with human capital and with welfare and wellbeing we relate the educational measures to life expectancy and other human capital variables and GDP per capita. We then use regressions to examine the impact of race and ethnicity on education, and of education on economic growth and levels of GDP per capita.The most significant results we wish to emphasize are related to the importance of race and racial fractionalization in explaining regional differences in educational achievement. Southern Cone countries, with a higher density of white population, present the highest levels of education in average terms, while countries from Central America and Brazil, with a higher proportion of Indigenous Americans and/or blacks, have the lowest levels. In most countries the major improvements in educational achievement are: the expansion of primary education during the first half of the twentieth century, and the expansion of secondary education after 1950. In all cases, average years in university are low, despite improvements in university quality during the last decades of the century when professors exiled during dictatorships returned to their countries of origin. International comparisons (continental averages for years of education weighted by country population size) place twentieth-century Latin America in an intermediate position between the USA and Europe at the top, and countries from Asia and Africa at the bottom.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".