Financial Flexibility Prevalence Revisited with Evidence from South Africa
Bibliographic record
Abstract
Financial flexibility occurs when companies borrow less than expected and is widely practiced. A commonly used model to establish the presence of financial flexibility is based on the determinants of the leverage model, which was developed some time ago and is composed of various factors that determine a company’s leverage use. Governmental borrowing and financial sector development in the meantime were shown to be key drivers of corporate borrowing. We add these two factors to the original model to establish how the prevalence of financial flexibility is affected by these inclusions into the model. South Africa is used as a locality for the study because of its relatively recent financial sector development and increased governmental borrowing. The results of the study show that financial flexibility is more prevalent when these factors are considered in a South African context. Previous studies have paradoxically shown a lower financial flexibility prevalence in South Africa when compared to a developed market such as the UK, which is contradictory to developing market debt conservatism. In this study, we show that when accounting for financial sector development and governmental borrowing, financial flexibility is widely prevalent in a South African context, at similar levels to that of a developed economy. The primary implication of the study’s findings is that financial flexibility may have been underreported in developed markets in prior studies.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".