Revisiting growth dynamics in G7: an econometric critique of AI, education and industrialization interactions
Bibliographic record
Abstract
Purpose This study explores the key determinants of GDP growth in G7 countries (Canada, France, Germany, Italy, Japan, the UK and the United States). The analysis spans annual data from 2010 to 2023, emphasizing the roles of AI innovation, education, industrialization, governance and trade in shaping economic growth within G7 economies. Design/methodology/approach A dynamic panel data regression model is applied using the Arellano and Bond (1991) generalized method of moments (GMM) estimator to address endogeneity, autocorrelation and heteroskedasticity. Robustness checks include re-estimations with robust standard errors and Driscoll–Kraay standard errors to correct for cross-sectional dependence and further strengthen the validity of the results. Findings The results reveal that industrial value added, trade openness and investment growth significantly and positively influence GDP growth in G7 economies. Conversely, AI patent applications and government expenditure on education have negative effects, which may reflect short-term inefficiencies, resource diversion or misalignment between spending and labor market demands. Originality/value This study contributes to the literature by providing updated empirical evidence on growth drivers in advanced economies. It highlights the importance of balancing innovation and education spending with effective policy frameworks to maximize their long-term contribution to 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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".