Analysis of Government Expenditure and Sectoral Employment in the Post-apartheid South Africa: Application of ARDL Model
Bibliographic record
Abstract
The current study has been designed to analyse the interactions between real government spending and job creation in South Africa focusing on five major economic sectors, namely construction, financial, manufacturing, mining, and retail sectors. The main objective of the study was to determine how job creation in different economic sectors responds to changes in real government spending. To achieve this objective, the study used five different autoregressive distributed lag (ARDL) models to analyse the long-run and shot-run relationships between government spending and employment rate in each of the aforementioned five economic sectors. The sample period consisted of quarterly observations starting from the first quarter of 1994 to last quarter of 2015. The study found a long-run relationship between government spending and job creation in the mining sector but there was no evidence of long-run relationships between government spending and jobs creation in construction, financial, manufacturing, and retail sectors. The short-run analysis showed that government spending could create jobs in all five sectors. This paper concluded that increasing government spending can only create short-term jobs but does not create lasting jobs in most sectors, except the mining sector. To increase the number of durable jobs, the South African government should therefore increase spending on mining sector.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 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".