How Do Latin American Migrants in the U.S. Stand on Schooling Premium? What Does it Reveal About Education Quality in Their Home Countries?
Bibliographic record
Abstract
Indicators for quality of schooling are not only relatively new in the world but also unavailable for a sizable share of the world's population. In their absence, some proxy measures have been devised. One simple but powerful idea has been to use the schooling premium for migrant workers in the U.S. (Bratsberg and Terrell, 2002). In this paper we extend this idea and compute measures for the schooling premium of immigrant workers in the U.S. over a span of five decades. Focusing on those who graduated from either secondary or tertiary education in Latin American countries, we present comparative estimates of the evolution of such premia for both schooling levels. The results show that the schooling premia in Latin America have been steadily low throughout the whole period of analysis. The results stand after controlling for selective migration in different ways. This contradicts the popular belief in policy circles that the education quality of the region has deteriorated in recent years. In contrast, schooling premium in India shows an impressive improvement in recent decades, especially at the tertiary level.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".