Lessons for Macroeconomic Policy from Nigeria Amid the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has had severe impacts on Nigeria’s macroeconomy and the livelihoods of households. Summary: The COVID-19 pandemic has had severe impacts on the macroeconomy and the livelihoods of households globally. For Nigeria which saw its first case in February 2020, the economic contraction was severe and sustained leading to a recession in the third quarter of 2020. Consequently, the Nigerian government has increased its spending plans – to counteract the effect of the pandemic on the income and spending of households and firms – which has been delivered through cash transfers, tax rebates, loans, loan guarantees among other mediums. Discussions around the efficacy of the macroeconomic policy responses deployed have begun to gain traction as a fiscal year has elapsed since the pandemic started and the policies were put in place. This research and policy brief examines the macroeconomic landscape and policy interventions in Nigeria with the objective of developing lessons not only for Nigeria but for other developing economies. The aim is that lessons from Nigeria can guide economic policy makers in developing countries to create a sustained economic recovery.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".