| dc.description.abstract | Interest rate ceilings have been declining over the past several decades globally as most of the
developing countries continue liberalizing their financial policies. Prior to 2015, Kenya’s
banking sector was vibrant and highly profitable. Sector loan book grew at an impressive
compound annual growth rate of 16% from 2011 and 2015, Micro lending to gross domestic
product was 35%. However, after the interest rate cap in August 2016, there has been a general
slowdown to micro lending, January, 2016 (16.8% growth); August, 2016, (5.4 %growth) and
December, 2016, (4.3% growth). Non-performing loans increased across the sector from an
industry average of 5.2% before 2015 to an average of 7.9% as at September 2016. Studies
relating to the interest rate ceiling and micro lending have given mixed results, some argue that it
protects consumers from exploitation by guaranteeing access to credit at reasonable interest rates
while others observe the contrary. It is on this basis that the study sought to establish the
relationship between interest rate ceiling and micro lending. The objective of this study was to
establish the effect of interest rate ceiling on micro lending market in Migori County, Kenya.
The study was anchored on financial accelerator effect theory. Secondary data were collected
from Bank Supervision Reports, published papers, official websites of commercial banks, and
records kept by micro entrepreneurs. The data were on Credit Supply, Cost of Credit and Default
rate covering 31 months from December, 2014 to June, 2017. The collected data were edited,
coded and entered for analysis using the Statistical Package for Social Sciences (Version 17.0)
computer package. The research findings were presented in tables for clarity. Pearson
Correlation was used to asses bivariate association between the study variables and logistic
regression model estimated to establish the existing relationships. The study revealed that there
was a negative significant association between interest rate ceiling and credit supply (r = −.718, p
< .001), and that of default rate (r = −.795, p < .001). However, its association with Cost of
Credit yielded a positive significant result, (r = .642, p < .001). A binary logistics regression
analysis fitted the three variables together with interest rate ceiling to predict the effect on micro
lending. -2 Log likelihood (-2LL) 23.434, 25.894 and 19705 indicating that fitting the model was
better than constant only model. Nagelkerke’s 2 R results .577, .481, and .683, all indicated a
greater improvement from null model to fitted model. Cox and Snell’s R-Square for the
estimated models shows that 42%, 34.4% and 50.3 % variations in Credit Supply, Cost of Credit
and Default rate respectively is explained by the logistic model. Binary logistic classification and
prediction indicates an improvement of 87.1 %, 83.9%, and 90.3 % from null model to fitted
model. The Wald criterion demonstrated that Credit Supply, Wald = 11.743, df = 1, p= 0.001,
Cost of Credit Wald = 9.962, df = 1, p= 0.001 and default rate Wald = 12.231, df = 1, p= 0.001,
were significantly different from Zero thus the three independent variables were significantly
affected by the interest rate ceiling. The study therefore recommends that Banks pursuing the
policy of increasing credit supply and reducing cost of credit should advocate for the repeal of
interest rate ceiling. However, those interested in reducing default rate should advocate for its
retention. The findings of this study will be of great use to the researchers for future studies,
government & lending institutions in formulating credit policies so as to promote the uptake of
credit facilities in Migori County and even Kenya as a whole. | en_US |
