Microcredit Determinants and Portfolio Quality of Microfinance Institutions in Kenya

Abstract

Micro credit plays a major role in development strategies. This is in view of its direct relationship to both poverty alleviation and improvement of the living standards. At the same time, low access to credit and gender inequalities in developing societies inhibit economic growth and development. Further, societies that discriminate based on gender have lower credit accessibility, greater poverty, slower economic growth, weaker governance, and a lower standard of living. Micro credit gives access to services to average earners wishing to access money to improve income-generating activities. Financial services of this nature are offered to those that depend on their small-scale economic activities and businesses who are considered highly risky by the mainstream commercial banks. Literature shows that many small enterprises and low-income earners always find it difficult to access financing in the mainstream commercial banks partly because of the stringent measures taken by commercial banks to shield themselves from non-performing loans. This study therefore seeks to investigate the effect of microcredit determinants on portfolio quality of microfinance institutions in Kenya. The study is anchored on financial intermediation theory supported by information asymmetry theory and the modern portfolio theory. The study will adopt descriptive survey research design with the population comprising all the 57 microfinance institutions in Kenya. Primary data will be collected using semi structured questionnaire through drop and pick method. Face and content validity of the questionnaire will be ascertained by supervisor, lecturers and peers. Reliability of the questionnaire will be tested using Cronbach’s alpha. Data analysis will be aided by SPSS Version 23.0. Quantitative data will be analysed using descriptive statistics as well as inferential analysis such as correlation and regression analysis. Qualitative data will be analysed using conceptual content analysis. Coefficient of determination (R2) will be used to test the significance of the model F-statistic Data will be presented in tables, charts and graphs.