Corruption, Development and the Curse of Natural Resources
Bibliographic record
Abstract
Abstract. Sachs and Warner (1995) found a negative relationship between natural resources and economic growth, concluding that natural resources are a curse. This explanation for poor economic growth is now widely accepted. We provide an alternative econometric framework for evaluating the resource curse. We focus on resource rents and rent-seeking behaviour, arguing that rent seeking affects corruption and that, in turn, impacts well-being. Our measure of well-being is the Human Development Index, although we find similar results for per capita GDP. While resource abundance does not directly impact economic development, we find that natural resources are associated with rent seeking that negatively affects well-being, with results robust to various model specifications and sensitivity analyses. Résumé. Sachs et Warner (1995) ont observé une relation négative entre les ressources naturelles et la croissance économique et ils en ont conclu que les ressources naturelles étaient une malédiction. Cette explication de la faible croissance économique est maintenant largement acceptée. Nous offrons un cadre économétrique pour évaluer différemment cette malédiction des ressources. Nous nous concentrons sur les rentes tirées des ressources et sur la recherche de rente, en faisant valoir que la recherche de rente affecte la corruption, qui à son tour nuit au bien-être. Notre mesure du bien-être est l'indice de développement humain, même si nous trouvons des résultats similaires pour le PIB par habitant. Bien que l'abondance des ressources n'ait pas d'impact direct sur le développement économique, nous constatons que les ressources naturelles sont associées à la recherche de rente qui a une incidence négative sur le bien-être, comme en attestent nos résultats empiriques selon les diverses spécifications du modèle et des analyses de sensibilité.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".