Fiscal and Monetary Policy for Decent Employment in Nigeria
Bibliographic record
Abstract
The level of unemployment in Nigeria has risen persistently, increasing the risk of the non-achievement of the SDG goal 8 – decent work and economic growth. Economists have documented that monetary and fiscal policies are effective tools for influencing economic variables such as the unemployment rate. In this study, we attempt to investigate and compare how these tools affect unemployment level in Nigeria. This study comes at an important time in Nigeria when the economy just exited a recession and is still experiencing low production and rising unemployment. This study investigates the nexus between macroeconomic policies and unemployment using the Autoregressive Distributed Lag (ARDL) estimation technique. The study finds that government capital expenditure helps to reduce unemployment in the long run only. On the other hand, the currency in circulation and the real GDP help to reduce unemployment rate in both the short and the long run. The study recommends a policy mix, which proposes that government expenditure be judiciously employed, and simultaneously, the Central Bank of Nigeria (CBN) should regulate the supply of money into the economy to not trigger inflation and unemployment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".