Revisiting the governance-growth nexus: Evidence from the world’s largest economies
Bibliographic record
Abstract
This study delves into the symmetric effects of governance on economic growth for the world’s ten largest economies, employing a model augmented with well-known growth, governance, and control predictors to inform model specification. Using panel and time-series techniques, both collectively and individually, the initial results reveal that governance predictors and growth postulate a long-run symmetric nexus. Applying the autoregressive distributed lags (ARDL) model, the results show that although governance predictors positively impact the economic growth of the panel both in the short and long runs, growth is weakly sensitive to governance predictors. The results of the ARDL estimates for cross-country show that Canada’s growth is highly sensitive to governance predictors, followed by France, showing moderate sensitivity. Moreover, the findings support the notion that the US, China, Germany, India, the UK, Brazil, and Italy exhibit weak sensitivity to governance predictors. Besides, the error-correction results demonstrate a high speed of adjustment of the short-run symmetries of the panel to its long-run equilibrium. Since economic growth swiftly responds to the rise and fall of governance predictors, specific policy adjustments are required to maintain sustainable and long-run 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".