Comparative Analysis of the Literature on Economic Growth in the Perspective of Advanced and Emerging Economies
Bibliographic record
Abstract
In this paper we have critically analyzed previous literature on economic growth with special reference to advanced and emerging economies in order to understand what research so far has been made by different researchers on various determinants and what they have opinion about the growth of these economies in future.The objective of this study is to investigate the phenomenon why do some countries record fast economic growth and why some other countries have stagnant situation in spite of all efforts, policy initiatives, latest technology, and human capital.For this purpose, we specifically selected G-7 countries and E-7 (Emerging economies).Then we analyze their specific economic indicators such as human capital, technology, aging population and its likely financial burden on the respective economies, ratio of working population to total population, manufacturing capacity and export potential.The advanced countries included in this study are the United States, United Kingdom, Germany, France, Canada, and Italy and Japan while emerging economies included into this study is China, India, Brazil, Russian Federation, Indonesia, Turkey, and Pakistan.After critical analysis of literature we conclude that economists have dismal view about the economic growth of advanced countries in future due to mounting high level of debt, income inequality, less revenue generating space, aging population and growing burden of social spending.In contrast, the economists who conducted research on emerging economies are very much optimistic about their consistent economic growth in future because they have younger working population, less debt burden, growing per capita income and living standard, big consumer markets, expanding middle class, increasing exports, and fiscal discipline.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".