The Impact of Internally Generated Revenue on Economic Development in Nigeria
Bibliographic record
Abstract
The study investigated the impact of internally generated revenue (IGR) on economic development of Nigeria. The inability of States and Local governments in Nigeria to generate enough revenue to cope with their expenditure responsibilities has been a serious challenge. The improper use of IGR and corruption have remained a setback to economic development in Nigeria, hence the clamour from the citizens. This study made use of ex-post facto research design to specifically examine the impact of total IGR (TIGR), Federal Government Independent Revenue (FGIR), States IGR (SIGR) and Local IGR (LIGR) Governments IGR on the Real Gross Domestic Product (RGDP i.e. proxy for economic development) of the country. The time series data employed covered a period from 1981 to 2016 and were gathered from the Central Bank of Nigeria (CBN) Statistical Bulletin. The statistical tool used for the data analysis was the multi-regression and t-test for test of hypotheses. The findings of the study revealed that TIGR, SIGR and LIGR have robust and significant positive impact (p-value = 0.000 < 0.05) on RGDP, while FGIR also indicated positive and significant influence on RGDP. There was an existence of high correlation between the dependent and independent variables. The study concluded that the positive impact of IGR is not out of place but the physical evidence is apparently lacking and therefore government policies that could eradicate sharp practices in the government system are required. The study also recommends that government official with corruption history should not be allowed to continue to handle responsibilities rather; people with outstanding integrity should be given opportunity to occupy government positions that are sensitive and could help achieve economic development objectives.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".