Investigating the nonlinear nexus between natural resources, digitization, economic policy uncertainty, and financial structure in Canada
Bibliographic record
Abstract
Abstract The relationship between natural resource abundance and financial structure remains a contested issue, with the existing literature divided between the resource curse and resource blessing hypotheses. However, few studies examine how external macro-institutional factors shape this nexus. This study fills that gap by investigating how digitization (TNI), economic policy uncertainty (EPU), regulatory quality (RQ), and inflation (INF) influence the impact of natural resources (NRs) on financial structure, using Canada as a representative case of a developed, resource-rich economy. Quarterly data from 1990 to 2022 are analyzed using Wavelet Quantile Regression (WQR) and validated with Quantile-on-Quantile Regression (QQR) to account for asymmetries and nonlinearity. The results support the resource blessing hypothesis, indicating that natural resources have a positive impact on financial structure, particularly when complemented by digitization and policy stability. These findings suggest that Canada’s institutional capacity and digital transformation efforts enhance its ability to leverage natural wealth. The study recommends policies that integrate digital infrastructure and institutional quality improvements to strengthen financial systems in resource-dependent economies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".