Natural Resources, Institutions Quality, and Economic Growth; A Cross-Country Analysis
Bibliographic record
Abstract
Natural resources as a source of wealth can increase prosperity or impede economic growth. Empirical studies with different specifications and dataare also mixed on whether natural resources are curse or blessing. In fact, the variety of model specifications, measurements, and samples in the empirical literature makes it difficult to generalize the results. In this study, a growth model including natural resources is developed to estimate the effect of natural resource dependency on economic growth, using different measures of natural resources and controlling for the quality of institutions in 149 countries during 1996-2010. The results show that natural resource abundance, proxied by per capita natural wealth, has a positive and significant effect on GDP growth. However, the impact of natural resource dependency on GDP growth depends on the type of natural resources and the quality of institutions. Fuel dependency, for example, can be considered a strong curse, as it has no effect on GDP growth, and agriculture and food dependency a weak curse, as it can increase GDP growth in the presence of good institutional qualities. Results also show that among different indexes used for institutional qualities, government effectiveness, regulatory quality, and rule of law are more effective in avoiding the negative effect of resource dependency. The thresholds above which different types of institutional qualities can turn a curse to a blessing are also estimated for different types of natural resource dependency.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| 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".