Testing the Relationship between Government Revenue and Expenditure: Evidence from Nigeria
Bibliographic record
Abstract
The paper examines the revenue-spending hypothesis for Nigeria using macro data from 1970 to 2011. Correlation analysis, granger causality test, regression analysis, lag regression model, vector error correction model and impulse response analysis were the techniques used for analysis. The paper found that revenue and expenditure are highly correlated and that causality runs from revenue to expenditure in Nigeria. The vector error correction model also confirms that there is a significant long run relationship between revenue and expenditure implying that disequilibrium in expenditure can be corrected in the long run through policies that adjust oil and non-oil sector revenues. The lagged regression model showed that the positive relationship between revenue and expenditure reverts to negative at lag five thereby justifying the need for the use of medium term expenditure framework to monitor expenditure patterns in the short to medium term. The paper concludes that short term shocks from crude oil price passes through oil revenue to affect expenditure. This has led to swings in public expenditure pattern with sustained increase of recurrent expenditure over capital that has consequences for economic growth. Putting policies in place to enhance the performance of the non-oil sector and adopting expenditure framework that accounts for possible decline in crude oil prices was conceived as useful in enhancing a healthy revenue-expenditure relationship in Nigeria.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".