Subsidy removal and its effect on inflation in Nigeria? A critique
Bibliographic record
Abstract
The study investigates the impact of fuel subsidy removal on inflation trajectory in Nigeria. Monthly time series data on inflation, domestic fuel price (proxied by pms price), exchange rate, money supply and fiscal policy (proxied by government spending) covering the period 2014M01 to 2023m05, were utilized for the study. The period coincided with full fuel subsidy intervention by the fiscal authority without any structural break, policy reforms or partial subsidy removal, thus, the justification for the period selection. Following some econometric diagnostic tests, a traditional Vector Autoregressive (VAR) model was employed to establish the ex-ante and ex-post inflation trajectory in pre and post subsidy removal era in Nigeria. The ex-ante result reveals significant positive response of inflation due to shocks to domestic fuel price in 11 months periods, though, transitory with about two months lag. Using the simulation-scenario analysis, the ex-post results shows the trajectory path of inflation due to subsidy removal which suggests 9 months acceleration in inflation in the future after the month the policy was announced. Also, the study establishes different scenarios of domestic fuel price and how inflation responds to such. Similarly, the study sets inflation threshold across several scenarios developed in this study and found that inflation would begin to decelerate from the month of February 2024 after 9 months of consistent upward trend from June 2023. Finally, we recommend a holistic policy collaboration between the fiscal authority and the CBN in addressing the ‘’known and expected’’ inflationary pressure coming from shocks to domestic fuel price due to subsidy removal. The fiscal authority should also roll out permanent measures to address the welfare implications of subsidy removal in Nigeria, while keeping eyes on inflation trajectory as well.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".