Bibliographic record
Abstract
By international standards, gross domestic product (GDP) per capita in Latin America is low: around one fourth of that of the United States. Moreover, in the last five decades, Latin America has failed to catch-up in wealth to the level of the United States while other countries at similar or even lower stages of development have been successful. The failure to attain higher levels of relative income represents what I call the development problem in Latin America. Using a development accounting framework, I find that the bulk of the difference in GDP per capita between Latin America and the United States is accounted for by low GDP per hour and, in particular, low total factor productivity (TFP) in Latin America. I calculate that to explain the difference in GDP per hour, TFP in Latin America must be around 60 percent of that in the United States. I then consider a model with heterogeneous production units where institutions and policy distortions lead to a 60 percent productivity ratio between Latin America and the United States. Removing the barriers to productivity can increase long-run GDP per hour in Latin America by a factor of 4 relative to that of the United States. This increase is equivalent to 70-years worth of U.S. post WW-II development.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.035 | 0.004 |
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".