Effect of Apportioned Federal Revenue on Economic Growth: The Nigerian Experience
Bibliographic record
Abstract
The major objective of income distribution to the federal, state and local governments in Nigeria is to achieve economic growth which leads to economic development. This ultimate aim of governance in Nigeria appears not to have been achieved due to alleged corruption and mismanagement of the monthly allocated funds. Thus, this study investigates the effect of revenue apportioned to the three levels of government on economic growth in Nigeria. The study employs annual time series data which cover a period from 1981-2016 and have been collected from CBN Statistical Bulletin, 2016 edition. Ordinary Least Square (OLS) method is used to perform the multi-regression analysis with the aid of e-views version 9. The findings of the study reveal that the federally apportioned revenue to the federal government (FAFG) has a significant positive impact on RGDP while FALG has a robust significant positive impact on RGDP. The result also indicates that FASG has a significant negative influence on RGDP. This leads to a conclusion that mismanagement of funds by the state governments is a cause for concern. Therefore, the study suggests, among others, that revenue sharing formula in the country should be based more on impact of expenditure incurred on executed projects (long term and short term) by each tier of government than on any other parameter to achieve fairness and efficiency in public service delivery at all levels of governance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".