ECONOMIC GROWTH IN G-7 COUNTRIES: AN EMPIRICAL ANALYSIS OF ITS DETERMINANTS
Bibliographic record
Abstract
This study is an exploration of the key factors of economic growth among G-7 economies, including Canada, France, Germany, Italy, Japan, United Kingdom and United States, between the years 2000 and 2023. As economic giants of the world, a study on the sources of growth in these countries provides a good knowledge on the enhancement of long-term growth and development policies. The study identifies and estimates the role of investment, balance of trade, the participation of labor force, human capital index as a proxy of health and education and high-technology exports through dynamic panel-data estimation technique by means of the Generalized Method of Moments (GMM). The methodology takes into consideration country-heterogeneity as well as time dynamics in order to provide a sound and reliable outcome. The results affirm that investment and human capital are invariably robust factors in driving GDP growth with balance of trade being positively supportive as well. Also, the labor force participation and high-technology exports, which are statistically less significant in this model but show positive upward trends, have a potential in the future with the help of adaptive and innovation-friendly policies. Comprehensively, the findings point to the long-term viability of investment, education, and technology development on promoting inclusive, resilient, and sustainable growth in advanced 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".