Wagner or Keynes for Ghana? Government Expenditure and Economic Growth Dynamics. A ‘VAR’ Approach
Bibliographic record
Abstract
This paper analysed empirically the causal relationship between government expenditure growth and GDP growth in Ghana from 1980 - 2010. The study employed vector autoregressive (VAR)/Granger causality analysis developed by Sims (1980) and Granger (1969). The cointegration results provided evidence of a unique cointegrating vector. Granger causality test conducted revealed that causality exist only from GDP growth to government expenditure growth and not the vice versa. This implication supports Wagner's law of expanding state activities for Ghana. This result means that in estimating government expenditure, GDP growth must be taken into account so as to avoid the problem of misspecification and biasness of estimates generated. The findings also suggest that government must focus on policies that would create the enabling environment for growth to thrive rather than increasing its expenditure with the aim of increasing GDP growth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".