Micro and Small Enterprises (MSEs) sector contributes 20% to the GDP of the Kenyan economy. The vision of micro finance is to promote the growth of micro enterprises. In pursuit of this vision, the rapid growth of Micro finance institutions (MFIs) has made MSEs access to credit more than doubled from 7.5% in 2006 to 17.9% in 2009. Despite this increase, a recent study has shown that over 50% of MSEs continue to have a deteriorating performance with 3 in every 5 MSEs failing within months of establishment. This brings to question the effectiveness of the role of micro finance in promoting growth of micro enterprises. The objective of this study was therefore to evaluate the effect of provision of micro finance on the performance of youth micro enterprises under K-REP program in Kisii County, Kenya. The study employed a cross sectional survey design. A sample of 86 youth micro enterprises was selected from a population of 110 youth micro enterprises using simple random sampling technique. Primary quantitative data were collected by use of structured questionnaires and analysed by use of descriptive statistics, multiple regression analysis and Pearson correlation coefficient. The empirical results revealed that loan had the largest significant effect on performance of micro enterprises with a beta coefficient of 0.385, followed by training in micro enterprise investment with a beta coefficient of 0.281 and Savings mobilization had the least but significant effect with a beta coefficient of 0.272. Based on the research findings, the study concludes that provision of micro finance has a significant effect on the performance of youth micro enterprises in Kenya. Therefore provision of micro finance to the youth to engage in micro enterprise activities will help spur economic development and alleviate youth unemployment, in line with Kenya's vision 2030.

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 R 2 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.

Financial inclusion brings closer financial services at affordable costs to sections of disadvantaged and low-income segments of society. There have been many formidable challenges in financial inclusion such challenges include; bridging the gap between the sections of society that are financially excluded within the ambit of the formal financial system, providing financial literacy and strengthening credit delivery mechanisms so as to promote financial economic growth. A nation can grow economically and socially if its weaker section can turn out to be financially independent. Kenya strives to become a regional financial hub with vibrant, efficient and globally competitive financial system to drive savings and investments by the year 2030, where financial inclusion has been identified as a key driver. However, there is paucity of information on the contribution of financial inclusion on performance of micro finance banks in Kenya. It is on this basis that the study sought to determine the effect of financial inclusion on financial performance of micro finance banks in Kenya. The study was guided by Expectations Theory, Contracting Cost Theory and Market Hypothesis Theory. A census study was carried out for all the twelve (12) microfinance banks in Kenya. The study relied on secondary data covering the period 2015-2019 and this was obtained from audited financial statements of the microfinance banks. Correlational research design was adopted. Random and Fixed effects panel data models were estimated to establish the relationships. Choice of the best model between the two was done using the Hausman test where random effect was selected. Post estimation tests including multicollinearity, autocorrelation and heteroscekedastity work conducted. An insignificant negative relationship was established between firm size and return on assets (Coef= -.0014368, p >0.05). Similarly, the study established insignificant negative relationship between interest rates and financial performance as measured by return on asset p >0.05). However, there was a significant positive relationship between operational efficiency and return on asset as a measure of financial performance (Coef= .394119, p <0.05). The study concluded that operational efficiency affected financial performance of micro finance banks in Kenya hence recommended that microfinance institutions should leverage on operational efficiency in order to make profit for shareholders.

Low capital formation in Kenya, averaging rate 20.13% of GDP over the period 2006-2017 has kept unemployment rate above 39% with more than 65 per cent of people living on less than $ 2 a day. Yet previous studies do not have a clear answer to the question of whether increasing bilateral aid/multilateral aidenhances capital formation or not. This study's purpose was to investigate the effect of multilateral aid and bilateral aid on capital formation in Kenya. The study was anchored by Solow (1956) model. Autoregressive distributed lag estimates for data over 1974-2017 suggested that multilateral aid has positive insignificant effect on capital formation while bilateral aid has negative significant effect after one year. Error correction mechanism model estimates suggest that bilateral aid has positive significant effect on capital formation in the short-run during the programme year but becomes negative thereafter. The results were robust for impulse response analyses. The study concluded that bilateral aid retards capital formation in the long run but enhances it in the short-run during the first year.Soliciting for more bilateral aid was recommended in order accelerate capital formation in Kenya in the short-run.

Dynamic capital structure is the way firms make adjustments towards the target capital structure which is proxied by debt equity ratio. There has been variation of debt equity ratio of firms at the Nairobi Security Exchange (NSE) in an effort to achieving targeted leverage that would yield targeted revenues and profits for firms. Despite this, most firms still operate at sub optimal level and experience losses. Studies in this respect have attributed the sub optimal operations to monetary facets such as inflation rate, exchange rate and interest rate. However, the studies have yielded mixed results leaving the effect of monetary facets on the dynamic capital structure unresolved. It is on this basis the study sought to establish effect of the monetary facets on dynamic capital structure of selected commercial banks listed at the NSE. The study was anchored on market timing theory and guided by correlational research design. The target population was eight tier one banks at the NSE. Secondary data spanning ten years from 2010 to 2019 were obtained from commercial banks audited financial statements while data on monetary facets was obtained from Central Bank of Kenya database and audited financial statements of the banks. Panel data methodology was adopted to estimate Random and Fixed Effect Models and the Hausman test used to select the appropriate model. Whereas exchange rate had insignificant positive effect, interest rate had significant positive effect on the dynamic capital structure. Inflation however, had significant negative effect on the same. Therefore, to enhance performance of banks, hedging interest rate and inflation rate risks is necessary.