The Determinants of National Savings in West African Countries: A Time Series and Dynamic Panel Data Analysis
Bibliographic record
Abstract
The objective of this paper is to analyze the determinants of national savings in West African countries, using both time series analysis and panel data over the period 1980–2020. To do so, we used the Autoregressive Distributed Lag (ARDL) model through the cointegration approach of boundary tests to check the robustness of the long-run relationship and the error correction mechanism (ECM) to capture the short-run dynamics between savings and its determinants. The results revealed that domestic income was a statistically significant determinant of national savings in the short and long run in West Africa. Based on the empirical results of the panel data, the results reveal that the current account positively influences savings in English countries in both the short and long run. On the other hand, domestic income and value added in agriculture were found to be determinants of savings in Francophone countries. It is recommended that, in order to promote savings, growth and economic development, policies aimed at improving labor productivity and the balance of trade are essential to increase savings rates in West Africa.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".