The paradox of resource-richness: unraveling the effects on financial markets in natural resource abundant economies
Bibliographic record
Abstract
Abstract In the contemporary global landscape, understanding the nexus between financial inclusion and natural resource abundance is crucial, especially for resource-rich nations. This study uses diagnostic tests and method of moments quantile regression to examines this interplay across Australia, Brazil, Canada, China, India, Russia, and the United States. We find that achieving financial inclusion is significantly challenging for countries that heavily rely on natural resources. Diversified income sources and equitable wealth distribution are essential to mitigate these challenges. Additionally, we identify a positive correlation between economic development and financial inclusion, highlighting the mutually reinforcing relationship between growth and inclusivity. Our research also reveals a notable link between adopting renewable energy and improving financial inclusion, suggesting that environmental responsibility and financial accessibility are intertwined. Foreign direct investment has nuanced impacts on financial inclusion, adding depth to our understanding. Overall, stable income from natural resources and diversified economic development emerge as key promoters of financial inclusion. These insights advocate for regionally specific policies and lay a solid foundation for future research and informed policymaking that address financial inclusion challenges and advance sustainable development. Graphical abstract
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".