Economic Growth, Regional Savings and FDI in Sub-Saharan Africa: Trivariate Causality and Error Correction Modeling Approach
Bibliographic record
Abstract
Empirical studies examining the dynamic causal relationship between key macroeconomic variables using varied forms of bivariate causality methodology abound in the macroeconomic and finance literature. Causal inference based on such bivariate causality approach however, has been criticized for its inherent likelihood to draw causal inference or attribute causation to variables in scenarios where an omitted variable might have a better claim; Lutkephol (1982), Umberto Triacca (1998). This study is modeled to reduce this inherent weakness by employing trivariate causality methodology through error correction approach. Using aggregate data on Sub-Sahara Africa spanning the period 1977 to 2010, this study finds joint uni-directional causal relationship running from FDI and Gross Regional Savings growth to regional GDP growth. Empirical results further document additional uni-directional joint causal relationship stemming from GDP growth and Gross Regional Savings to growth in FDI inflow into the sub-region.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".