Bibliographic record
Abstract
The dawn of the 21st century has changed the scene of the world economy and shifted the center of economic growth from the Western hemisphere to the Asian Continent. The global financial crisis of 2008 and the European debt crisis of 2010 have exposed the inherent weakness of G-7 economies, which are facing the challenges of twin deficits, falling productivity, rising debts, and an aging population. In contrast, the emerging economies particularly, China, India, and Brazil, have shown robust economic growth from 2000 to 2010. The objective of this paper is to measure the changes taking place in the world economy from 2000 to 2010 and their impact on asset allocation, employment, poverty, and allocation of resources. For this purpose, the author has selected total of 14 countries as a sample and divided them into two groups: G-7 and E-7. The G-7group Includes the USA, Germany, France, UK, Italy, Japan, and Canada while E-7 contains China, India, Brazil, Russia, Indonesia, Turkey, and Pakistan.
 The methodology used in the study is to compare different economic and financial indicators of these two groups of countries in order to obtain desired results. Mostly time series and cross-sectional secondary data, collected from the database of IMF, World Bank, US Federal reserves, relevant international research journals, and books, have been used. Different statistical techniques such as trend analysis and ratio analysis have been applied to measure the changes in selected variables. The results of the study are very encouraging. We found that increase in the national income of emerging economies has brought trickle-down effects and reduced poverty and inequality level in emerging economies. Only in China, about 400 million people have come out of the poverty trap due to rising employment opportunities. The share of E-7 economies in the world R & D has increased from14% in 2001 to 20.1% in 2007, while the share of G-7 economies has declined by 2.4% during the same period.
 The study found the declining trend of productivity levels in G-7 countries vis-à-vis E-7 countries. The anti-migrants policy of G-7 countries is one of the contributing factors to the decline of productivity. This study endorses the views of Blanchard (1997), David Romer (2001), Yuan Langwiler (2004), David C. Colander (2004), Andrew Glyn(2006), and John Hawksworth (2007) who contended that the USA will no more be a world economic power and China will likely to overtake her as world economic leader by mid of 21st century.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".