Stock Markets Development in Sub-Saharan Africa: Business Regulations, Governance and Fiscal Policy
Bibliographic record
Abstract
This study examines the effectiveness of the state in stimulating stock market activity in sub-Saharan Africa (SSA) using fiscal policy, governance quality and stock market as the main determinant variables. Using annual data from six selected sub-Saharan African economies and employing a dynamic panel data estimating technique, we find that government effectiveness stimulates capitalization while business regulations decrease it in SSA. In addition, we find that final consumption expenditure, interest rate spread and credit to the state increase capitalization whereas credit to the private sector and inflation had adverse effects. With respect to business regulations, our study reveals that starting a business, closing it and enforcing contracts engender stock market activity in SSA. Among the several variables that stimulate stock market activity; only foreign direct investment (FDI) did increase capitalization. Thus, the study concludes that since not all government institutions and business regulations are critical to stock market development, various governments should be careful and selective in their economic stimulants if they want to develop their stock markets.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".