Analysis of the Impact Fiscal Fundamentals on Unemployment in Nigeria. Imperatives for Covid 19, Era
Bibliographic record
Abstract
The study examined the impact of fiscal fundamental on unemployment rate in Nigeria from 1980 to 2020 focusing on COVID-19 imperatives. The research work embraces OLS estimating techniques to estimate the relationship between the variables. The result of the analysis revealed that government expenditure had positive and significant effect on the rate of unemployment. Also government revenue had a positive but insignificant impact on unemployment during. The implication of these findings for COVID-19 is that the narrative which is obtained from the analysis needs to be changed. Government revenue should be made to have significant impact on unemployment. The pandemic has led to a lot of job lost and the unemployment rate in Nigeria has risen by about 55% peaking at 36% youth unemployment rate as at last quarter of 2020. The study therefore, recommends that government should refocus expenditure and revenue in the country in such a way it will target development of infrastructural facilities so as to increase productivity and in turn facilitate employment generation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".