Who Supports Free Trade in Latin America?
Bibliographic record
Abstract
This paper examines individual trade policy preferences across 17 countries in Latin America. The focus is on whether skilled or unskilled workers are more likely to support liberalised trade and on whether country characteristics, such as factor endowments, alter the preferences of skilled and unskilled workers. Based on the standard Heckscher-Ohlin model and the Stolper-Samuelson theorem, wage inequality in developing countries will decrease under free trade and unskilled workers will benefit. We find that on average skilled workers are more likely than unskilled workers to support free trade in Latin American countries. Separate country regressions reveal that this pattern is only statistically significant in 8 out of 17 Latin American countries. However, there are no countries in our sample in which unskilled workers are statistically more likely to support free trade than skilled workers, not even in the lowest skill-endowed country in the sample. We also find that people from Latin American countries with higher GDP, faster growth, more cropland and a longer period of time since reform were more likely on average to support free trade.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".