Saving-Investment Correlation and Capital Mobility in Sub-Saharan African Countries: A Reappraisal through Inward and Outward Capital Flows’ Correlation
Bibliographic record
Abstract
This paper analyses the Feldstein-Horioka puzzle in 15 sub-Saharan African countries accounting for the correlation between inward and outward capital flows. Applying cross section, panel data, and even time series analyses, we show that our results are consistent with previous studies related to developing countries. More interesting, we confirm, for sub-Saharan African countries, the recent hypothesis of Georgepoulos and Hejazi (2009) that the Feldstein-Horioka home bias is unrelated to the correlation between inward and outward capital flows for developing countries. Although the saving-investment coefficient weakens in the correlation adjusted regression, we show that the coefficient on Flows, the variable which accounts for the correlation between inward and outward capital flows is always positive and insignificant. We argue that the downward movement in the saving-investment coefficient is due the omission of some factors (foreign aid and trade openness) which are relevant for developing countries in the framework of the Feldstein-Horioka analysis. We also state that our results are more likely to reflect the poor financial structure of the countries in our sample. Therefore, we suggest that policymakers in Sub-Sahara Africa should put more emphasis in creating and developing efficient financial market which could favor portfolio diversification.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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".