Impact of fiscal policy in an intertemporal CGE model for South Africa
Bibliographic record
Abstract
This paper uses an intertemporal computable general equilibrium model to investigate the consequences of an expansive fiscal policy designed to accelerate economic growth in South Africa. A key contribution is made to existing literature on the transmission mechanism of fiscal policy in African economies. To the best of our knowledge, no published study has empirically analyzed the macroeconomic effects of fiscal policy in the context of an open, middle-income sub-Saharan African economy like South Africa using an integrated intertemporal model with such disaggregated production structure. The paper shows that an expansive fiscal policy would have a temporary impact on gross domestic product (GDP) but would translate into higher debt relative to GDP. Using increased taxation to finance the additional spending would lessen this impact but would also negatively affect macroeconomic variables. Increased investment spending would improve long-term GDP, under any financing scheme, and would decrease debt-to-GDP ratio as well as deficit-to-GDP ratio. This outcome is driven by the positive impact infrastructure has on total factor productivity. Sensitivity analysis shows that these conclusions are qualitatively similar for wide values of the elasticity of the total factor productivity to infrastructure. In fact, the conclusions hold even when comparing different financing schemes.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".