Determinants of Price Dynamics in African Countries
Bibliographic record
Abstract
This study analyzes the different determinants of price dynamics in Africa. We employed Bayesian Model Averaging to shed light on the primary determinants of price dynamics while taking into account the uncertainty associated with model design. Data was collected on 51 (Note 1) African countries for the chosen period from 1980-2020. The findings show that price dynamics in Africa are explained by various factors; the prices of imported foodstuffs, the production gap, government efficiency, the rule of law, English origin, and distance from the sea have a positive effect on price dynamics. Conversely, the interest rate, gold prices, millet supply, the budget balance rule, political stability and absence of violence, corruption control, and rural population have a negative effect on price dynamics in Africa. We urge the African governments to alleviate inflationary pressures and foster a more stable and prosperous economic environment for their citizens. Thus, monetary policy plays a crucial role in managing inflation in Africa. The establishment of an observatory of price dynamics is a solution to maintaining and controlling inflation in African countries.
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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 | 0.007 |
| 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 | 0.001 |
| 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".