Economic Growth Effects of the Interaction of Natural Resources and Institutional Quality by Source: Empirical Evidence from Africa
Bibliographic record
Abstract
The purpose of this study was to investigate the relationship between natural resources by source, institutional quality by source, and economic growth in Africa using multiple co-integration analysis, the ARDL technique, and VECM granger causality. The findings show that various resources contribute to economic growth in different ways. Mineral rents (MR) have a negative impact on growth, while forest rents (FR) have a beneficial impact. The findings also show that forest rents contribute more to growth in the rule-of-law (ROL) model than in the market openness (MO) model. Among the institutional quality (IQ) variables, the rule of law has the most significant impact on the continent's economic growth. Furthermore, when IQ was added as an interaction variable in the models, both resources (MR and FR) ended up contributing favourably to development. The study recommends that resource-rich countries must specifically concentrate on improving the rule of law since robust outcomes are generated when interacted with natural resources.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".