An analysis of the dynamics of petroleum prices and inflation in Malawi
Bibliographic record
Abstract
Owing to the immense negative effects brought about by inflation on the economies globally, politicians and policymakers are preoccupied with finding ways of controlling inflation. This study, therefore, set out to find out how prices of petroleum products, namely; diesel, paraffin, and petrol, affect inflation in Malawi. It employs the autoregressive distributed lag (ARDL) model using time series data on inflation and prices of petroleum products collected from the Reserve Bank of Malawi (RBM) and Malawi Energy Regulatory Authority (MERA). The empirical findings show that the price of petrol, the price of diesel, and the price of paraffin have a statistically positive effect on inflation in Malawi, both in the short run and the long run. These findings imply that increases in the prices of these commodities will lead to increases in inflation both in the short run and the long run. The study, therefore, recommends that policymakers need to make sure that prices of petroleum products are kept as low as possible to control inflation in Malawi. This can take the form of ensuring the existence of huge foreign exchange reserves to be used to stabilize the foreign exchange market, to ensure that the Malawi Kwacha does not depreciate anyhow.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".