Logit Analysis of the Relationship between Interest Rate Ceiling and Micro Lending Market in Kenya
Bibliographic record
Abstract
Interest rate ceilings have been declining over the past decades as most developing countries continue liberalizing their financial policies. Prior to 2015, Kenya’s banking sector was vibrant and highly profitable. The sector loan book grew at an impressive compound annual rate of 16% in 2011 to 35% in 2015. However, after interest rate cap in 2016, there has been a general slowdown in micro lending and rise in non-performing loans. Some studies argue that the ceiling protects consumers from exploitation and guarantees access to credit while others observe the contrary. This study sought to establish the relationship between interest rate ceiling and micro lending in Kenya. It was anchored on financial accelerator effect theory and the theory of financial repression. The study relied on secondary data from Banks and Micro Entrepreneurs. Logit models were estimated to establish the relevant relationships. It was established that interest rate ceiling had significant negative association with credit supply and default rate. However, it had a significant positive association with cost of Credit. Both Nagelkerke’s R2 and Cox and Snell’s showed that the estimated model fitted well. The Wald criterion demonstrated that credit supply, costs of credit and default rate were significantly different from zero. Thus, the independent variables were significantly affected by interest rate ceiling. It is recommended that banks pursuing policy of increasing credit supply and reducing cost of credit should advocate for the repeal of interest rate ceiling while those interested in reducing default rate should advocate for its retention.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".