Bibliographic record
Abstract
Distinguishing the role of coercive labor and political institutions from the effects of economic inequality levels and populations’ ethno-linguistic compositions in explaining the diverging patterns of development across the Americas has remained a challenging task. This paper examines whether the incentives for elite groups to enforce coercive labor and political institutions, holding other factors constant, inhibited economic development by restricting the provision of public schooling. Using 19th-century micro data from municipalities in Puerto Rico, and exploiting variation in the suitability of coffee cultivation across regions and the timing of the nineteenth century coffee boom, we find that coffee-region local governments allocated more public resources to enforce coercive labor measures and repress revolutionary movements, as documented by greater expenditures targeted towards the enforcement of coercive contracts and the size of military and government-backed paramilitary forces. These local governments also allocated fewer resources towards the provision of primary schooling - a decline of 40 percent in the provision of public primary schools and a decline in literacy rates of 25 percent. These findings are consistent with models of factor price manipulation and political repression under elite-controlled non-democratic regimes, in which the returns to labor are depressed as a result of the extraction of rents from peasants’ wages and literacy-based voting rights are restricted through limited access to schooling.
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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 | 0.007 |
| 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 | 0.009 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".