Government Expenditure and Economic Growth Nexus: Evidence from Nigeria
Bibliographic record
Abstract
The need to better the lots of citizens through government expenditure has raised questions on the impact of government expenditure on the economic development and growth of nations. It is against this background that this paper examined the antecedent effect of government spending on the Nigerian economic growth. The general objective of the study is to ascertain the relationship between government expenditure and economic growth in Nigeria; specifically, the study examined: (i) the significance influence of government capital expenditure on economic growth in Nigeria and (ii) the significance influence of government recurrent expenditure on economic growth in Nigeria. The study employed ordinary least square (OLS) multiple regression analysis in estimating the specified model, with the Gross Domestic Product (GDP) as the dependent variable, while Capital Expenditure (CAPEXP) and Recurrent Expenditure (REXP) are the independent variables. Data between 1980 – 2013 were collected from secondary sources through the National Bureau of Statistics (NBS) and Central Bank of Nigeria (CBN). Results showed that in Nigeria, there exist a significant relationship between the government expenditure and economic growth. The study therefore recommends instilling fiscal discipline in government expenditures, and putting in place structural mechanisms to act as surveillance on capital spending so as to boost the nation’s human and social capital.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".