Financial Development and Income Inequality in the Selected Southern African Development Community Countries
Bibliographic record
Abstract
Financial development is widely regarded as another conduit through which income inequality can be reduced. The study empirically examines the relationship between financial development and income inequality in selected Southern African Development Community (SADC) countries, employing the Generalised Method of Moments technique for the period 1980 to 2016. Based on the inequality-decreasing hypothesis, a model which links financial sector development and inequality was estimated. Empirical results revealed that financial sector development overall does have an impact on income inequality in the selected SADC countries. An interesting observation from the empirical results is that the actual dimension of financial development plays a significant role in determining the relationship between financial development and income inequality in the SADC region. The impact of financial depth on income inequality is not obvious in the study, depending on the variable used. On the relationship between financial system stability and income inequality, results reveal that a stable financial system is beneficial to the poor. Financial efficiency does not appear to have a significant role in reducing income inequality in the selected SADC countries. The findings imply that a specific approach to financial sector development rather than a blanket approach is desirable.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".