Investigating the Link between Economic Complexity Index and Monetary Policy Lending Rates in Selected Sub-Saharan African Countries
Bibliographic record
Abstract
This article investigates if there is a link between economic complexity index and monetary policy lending rates in selected Sub-Saharan African countries.Economic complexity index (ECI) as a measure of productive capabilities and a mix of sophisticated products that countries export, has been found to influence some economic indicators such as economic growth and inequality.Little attention has been paid to ECI's link to lending rates in monetary policy bank lending rate transmission mechanism.In this paper, the ECI-lending rate nexus has been investigated using a panel autoregressive distribution lag methodology.Results indicated a long-run significant relationship with the Kao and Johansen combined cointegration.It was further illustrated in the long-un that ECI estimates have a negative and significant impact on monetary policy lending rates.The series could correct to equilibrium at a significant rate of 25%.These results provided new insights needed for appropriate development economic policy to reduce monetary policy lending rates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".