Nexus of Fiscal Instability and Developmental Outcomes in Nigeria
Bibliographic record
Abstract
This paper examines the effect of fiscal instability component on the fluctuation in welfare indicator for 45 years. Descriptive statistics reveals that fiscal component and real GDP per capital are largely unstable and Hodrick-Prescott filter (HPF) is employed as a smoothing measure of the long-term component. Descriptive statistics reveals that lesser government revenue had been committed to the development purposes compared with recurrent expenditure since the beginning of the fourth republic in Nigeria. Using ARDL model, the study found that, there exist a long-run association among the variable of interest as one percent increase in the rate of instability in recurrent expenditure led to an approximate of 30% reduction in the fluctuation of the welfare indicator while instability in the capital expenditure led to 36% increase in the fluctuation of the GDP per capital. In the short-run however, 1% increase in the immediate lagged value of cyclical capital expenditure had significantly increase the fluctuation in the current welfare index by 54% but such effect is reduced to 43% in two-year lagged. Also, one percent increase in the immediate lagged value of instability in the recurrent government expenditure had significantly reduced the fluctuation in the GDP per capital by 21% but only 9% of such reduction was off set in the two-year lagged. The study therefore, recommended greater control of instability in the fiscal components through diversification revenue base should be emphasized in other to stabilize the fluctuation of the welfare indicator in the short-run and long-run.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".