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Record W4412598701 · doi:10.5267/j.ac.2025.7.001

An analysis of the dynamics of petroleum prices and inflation in Malawi

2025· article· en· W4412598701 on OpenAlexvenueno aff
Fredrick Mangwaya Banda, Andrew Munthopa Lipunga

Bibliographic record

VenueAccounting · 2025
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsnot available
Fundersnot available
KeywordsInflation (cosmology)EconomicsPetroleumDynamics (music)MacroeconomicsMonetary economicsEnvironmental scienceKeynesian economicsEconometricsGeologyPsychologyPhysicsPaleontologyTheoretical physics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.272
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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