IMPACT OF GOVERNMENT REVENUE ON ECONOMIC GROWTH IN NIGERIA
Bibliographic record
Abstract
Revenue generated by government ought to reflect economic growth in any economy, unfortunately in Nigeria, there is insistent detach between huge revenue generation from both oil and non-oil which could not reflect sustainable economic development path. The study considered the impact of government revenue on economic growth in Nigeria Using an ex-post facto research strategy, this study looked at how public authority revenue affected economic growth in Nigeria. Information derived from secondary sources and published in the 2024 Statistical Bulletin by the Central Bank of Nigeria (CBN) covering the years 1981–2023. Government Revenue was used as an independent variable to represent both oil and non-oil revenue, while GDP was used as a dependent variable to represent economic growth. Preliminary analysis was carried out on the sourced data to ensure that the data are stationary and there is co-integration between the observed variables. The data was analyzed with the help of Fully Modified Ordinary Least Squares, Dynamic Ordinary Least Squares, DOLS and Canonical Cointegrating Regression. Meanwhile, Dynamic Ordinary Least Squares, (DOLS) was chosen because it produced the largest R-squared value among the three competing estimation methods. The outcome of the study showed that both oil and non-oil revenue impacted the economic growth in Nigeria. The study suggested that government should put to good use money derived from various sources such as to fund essential services, invest in infrastructures so as to impact economic growth
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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 | 0.001 |
| Scholarly communication | 0.002 | 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".