Economic diversification and institutional quality as growth factors in oil-dependent economies
Bibliographic record
Abstract
Amid global hydrocarbon dependence and the growing challenges of the energy transition, issues of economic diversification and institutional quality are becoming crucial for the sustainable development of oil-exporting countries. This study aims to estimate the impact of economic diversification on GDP growth in ten oil-exporting countries over the period 1990–2023, accounting for the moderating role of institutional quality. Using panel data for Canada, Iraq, Kazakhstan, Kuwait, Nigeria, Norway, Russia, Saudi Arabia, the United Arab Emirates, and the United States, the analysis employs fixed-effects models and dynamic systemic GMMs to address endogeneity. The results show that an increase in the diversification index by one standard deviation (0.168) increases GDP growth by 0.75 percentage points, with the effect being 2.4 times higher in countries with strong institutions than in countries with weak institutions. A threshold level of oil dependence was identified at 25% of GDP, above which the negative consequences of the "resource curse" begin to predominate. A time-lapse analysis revealed an increase in the diversification effect over time: from 2.134 in the 1990s to 5.234 in 2020–2023, highlighting its growing importance in the context of the global energy transition. A decomposition of the effects shows that a reduction in macroeconomic volatility accounts for 35.2% of the total effect, technological externalities for 28.7%, human capital development for 21.3%, and institutional improvements for 14.8%. These results underscore the need to combine economic reforms with institutional transformation to overcome resource dependence and ensure sustainable economic growth.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| 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".