Effectiveness of the national credit act of South Africa in reducing household debt: a Johansen cointegration and VECM analysis
Bibliographic record
Abstract
The rise in unsecured lending has cast doubt on the effectiveness of the National Credit Act in South Africa. Reckless lending was seen rising since 2006 and plateauing in 2009. Could this be evidence of the effectiveness of the National Credit Act (NCA) curbing reckless lending household debts? This study embarks on finding whether reckless lending was present in the Pre-NCA period running from 1994 to the end of 2nd quarter of 2007 when the NCA was enacted. Further in this study, the effectiveness of NCA in curbing reckless lending in the Post-NCA period starting from the 3rd quarter of 2007 to the 2nd quarter of 2014. Using the Johansen Cointegration analysis and Vector Error Correction Model, long run and short run Granger causality tests are done with the household debt as a dependent and debt service coverage ratio, household debt to disposable income ratio and disposable income as independents. The results from the tests done provide convincing evidence that reckless lending indeed was present in the Pre-NCA period and there is evidence showing the curbing of reckless lending in the Post-NCA period.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".