Determinants of Average Lending Rates among Selected Commercial Banks in Kenya
Itimu, Samuel Mwaura
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Despite liberalization of Kenya’s financial sector in 1991, Kenya has become less competitive in terms of affordability of financial services and access to loans compared to countries such as South Africa and Malaysia whose private sector credit to GDP ratio are above 100 percent compared to Kenya’s private sector lending which stands at 40% of GDP. Financial sector liberalization has led to increased financial services access as evidenced by CBK data where 26.4% of the population in 2006 could access financial services compared to 66.9% in 2013(CBK) Newsletter No.1 December 2014.This remarkable growth is as a result of financial innovation including mobile banking, agency banking and credit information sharing which has translated to economic growth but has not led to matched increased access to credit. High cost of credit and operational inefficiency among commercial banks in Kenya limits the access of loans to the private sector and individuals which ultimately slows economic growth and development. The study is on the probable determinants of average lending rates among commercial banks in Kenya which include Bank Specific factors such as Non-performing loans, Operating costs, capital adequacy, and Bank size and liquidity risk. Also industry factors such as Kenya Banks Reference Rate and Central Bank Rate are included in the study. The effect of Credit information Sharing and Government Domestic borrowing on lending rates among commercial banks and intervening variable inflation be studied. The study employed a descriptive research design and the population consisted of eleven listed commercial Banks in Kenya. The Target population was staff of the eleven listed banks working in Credit and Risk and Compliance departments. A sample size of 33 was derived from three staff from credit and risk and compliance departments of each of the listed commercial banks in Kenya. Purposive sampling technique was used to collect data. Secondary data was collected from published journals and financial statements. The financial statements for the year 2012, 2013, 2014, 2015 and 3rdquarter of 2016 were used. Correlation and multiple regression analysis was used to analyze the nature and degree of relationship between the independent and dependent variables. Statistical package for social sciences was utilized to aid in data analysis. Summary of findings on the objectives was done, conclusion and recommendations to various stakeholders made.