The Impact of Stock Market Development on Economic Growth: The Case of Botswana
Bibliographic record
Abstract
This study examines the impacts of the stock market development on economic growth using Botswana as a case study. The study uses times series data covering a decade from 2006 to 2016. The method of analysis used is the Auto regressive distributed lag (ARDL) bounds model. The stock market capitalization ratio (MCR) was used as a proxy for market size while value of shares traded ratio (ST) and Turnover ratio (TR) were used as a proxy for liquidity, collectively representing stock market development. Real gross domestic product (GDP) growth rate was used to represent economic growth .The results show that market capitalization and turnover ratio have a negative correlation with economic growth, while the value of shares traded has a strong positive correlation with economic growth. This result implies that liquidity has propensity to stimulate economic growth in Botswana. The results of this study also found that there exists no causality relationship between stock market development and economic growth. The government should make policies that boost the interest of domestic investors in Botswana as this might spur investors’ interest and boost stock market activity which will improve liquidity and therefore stimulate economic growth.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".