Interplay between Finance and Institutions in the Development Process of the Industrial Sector: Evidence from South Africa
Bibliographic record
Abstract
Despite the importance of the financial system and quality of institutions to the attainment of economic development goals, the mediating role of institutions in how finance influences the development of the industrial sector across countries has not been given adequate attention in the literature. Therefore, this study assessed the moderating role of institutions in the relationship between finance and industrial development of South Africa for the period 1984–2021. To evaluate the long-run relationship among the variables, the combined cointegration test of Bayer and Hanck was used, while fully modified least squares, dynamic least squares and canonical cointegrating regression were employed to estimate elasticity relationships. The findings of the study revealed that finance impacts industrial development positively in South Africa, but this positive impact is diminished by the quality of institutions in the country. Therefore, the financial system in South Africa needs to be rooted in a high-quality institutional structure for its beneficial impact on the industrial sector to be reinforced for sustainable development. Moreover, there is a need for more reforms in the financial system to promote efficiency that would translate growth in finance into more inclusive growth gains in the industrial sector.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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".