The Contribution of China, India and Brazil to Narrowing North-South Differences in GDP/capita, World Trade Shares, and Market Capitalization
Bibliographic record
Abstract
This paper focuses on the contribution to recent narrowing of the gap between Northern and Southern economies in GDP/capita, shares in world trade and market capitalization attributable both jointly and single to China, India, and Brazil (the three currently largest rapidly growing Southern economies).We report North South differences in GDP/capita which (depending slightly on definition of North and South, as well as price deflators used) fall from 22 to 15.9 in constant USD between 1990 and 2009, changing Northern and Southern shares in world trade which fall for the North from 82.3% to 64.4% and rise for the South from 17.7% to 35.6%, and a changing North -South gap in stock market capitalizations from 27.6 to 3.3 over the same time.In contrast the North -China gap falls from 57.2 to 13.1 between 1990 and 2009, and India from 70.4 to 38.1 using market exchange rates and from 23.4 to 5.5 for China and from 20.7 to 11.4 for India using PPP rates.We calculate the portions of North -South gap change after 1990 which is accounted for by growth individually and jointly of China, India, and Brazil.Our calculations show that the majority of the change occurs from growth in these three economies, and the most from China.We suggest that the conventional view of a North -South bipolar world may need recasting into a tripolar world of the North, the Large South, and the rest of the South.In this, world manufacturing activity, trade, and even more rapidly, market capitalization are gravitating towards the Large Three, with a narrowing South -Large Three gap as well as a shrinking North -Large Three gap.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".