Financial Constraints and Firm Capital Structure in Kenya
Bibliographic record
Abstract
Empirical evidence suggests that capital structure varies across firms facing different levels of information asymmetry, however, this evidence contradict the prediction of pecking order hypothesis. Although debt capacity constraints offer some explanation for this discrepancy, it fails to explain the behavior of small high growth firms who do not issue debt even with no debt capacity constraints. Against this backdrop, this study investigated the effects of financial constraints on firm capital structure in Kenya. This was implemented by interacting a financial constraints dummy with the right-hand side variables of pecking order test equation to allow for any variation of capital structure across financial constraints regimes. The results show that constrained firms use less internal funds and have less cash than unconstrained firms. Pecking order theory was not supported. However, allowing financial constraints regimes in pecking order equation improved the fit of the model and produced results that are consistent with pecking order prediction. Financing behavior varies with financial constraints status. The wider the wedge between the cost of debt and the opportunity cost of internal funds, the higher the value transferred to debt-holders and the lower the debt utilization. To improve firm access to capital, policies should be geared towards reducing the wedge between the cost of external and internal funds.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.006 | 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".