Bibliographic record
Abstract
We address the poverty trap rationale for aid to Africa. We calibrate models that embody typical explanations for stagnation: coordination failures, ineffective mix of occupational choices and imperfect capital markets, and insufficient human capital accumulation coupled with high fertility. Calibration is ideally suited for this evaluation given the paucity of high-quality data, the high degree of model nonlinearity, and the need for conducting counterfactual policy experiments. We find that calibrations that yield multiple equilibria -- one being prosperity and the other stagnation -- are not particularly robust in capturing the African situation. This tempers optimism about foreign aid typically prescribed based on models of multiplicity. Moreover, conditional on multiplicity, the calibrated models indicate that the cost of policy interventions needed to trigger development in stagnant economies is small. The lack of reforms in Africa, despite the low estimated costs, suggests political hurdles to reform. It is not clear that foreign aid would be able to circumvent these. Taken together, we conclude that the case for foreign aid to Africa is weak.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".