Capital Markets & Economic Growth: A Tale of BRICS Countries
Bibliographic record
Abstract
The GDP growth of any economy acts as a proxy of the overall growth of that economy. This paper attempts to investigate the significance of economic growth based on economic factors. Several factors contribute to the economic growth. Moreover, foreign direct investment (FDI) bridges the gap of saving and investment in the capital formation and thus supports for economic growth. In this paper, we examine the significant effect of the economic indicators on the GDP growth and the extent of influence. The focus of this paper is also to check if the significant economic indicators of GDP growth is consistent across the economies. We consider the data of fifteen economic indicators for BRICS countries over a period of 1990 to 2018. For this purpose, we employ Karl Pearson’s correlation and regression model. The result reveals that final consumption expenditure, gross capital formation, general government final consumption expenditure affect the GDP growth of Brazil, gross capital formation, general government final consumption expenditure affect the GDP growth of Russia whereas the foreign direct investment, gross capital formation (annual % growth), gross savings (% of GDP) affect the GDP growth of China. The household final consumption expenditure per capita growth and gross capital formation affect significantly the GDP growth of India. Final consumption expenditure, household final consumption expenditure, gross savings (% of GDP), affects South Africa’s GDP growth. The result of this paper has important implications for the policy makers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".