Purpose The purpose of this study was to examine micro and small scale enterprises’ growth determinants operating in Benishangul-Gumuz Regional State of Ethiopia as emerging region. Design/methodology/approach The study adopted an explanatory research design with arrangement of primary data collection via a cross-sectional survey questionnaire followed by mixed research approach. The sample of this study was 220 enterprises determined by Yamane’s formula and selected using proportional stratified random sampling technique. Findings The result of regression analysis revealed that initial investment, access to land, access to finance, location, sectoral engagement, market linkage, and business experience are significant in explaining growth in one hand. On the other side, however, gender, education, ownership, formal recording, and financial management practice are found to be insignificant variables in determining enterprises’ growth. Research limitations/implications More evidence is needed on micro and small scale enterprises’ growth determinants before any generalization of the results can be made. In addition, the empirical tests were conducted only on 220 entrepreneurs since 2018. Therefore, the results of the study cannot be assumed to extend beyond this group of entrepreneurs to different study periods. Practical implications The study might help the entrepreneurs in addressing the factors affecting growth to take actions toward developing their performance and in turn contribute to employment, export participation, poverty alleviation, and women empowerment. Originality/value This paper adds to the literature on the determinants of micro- and small-scale enterprises’ growth. In particular, it tests the impact of initial investment, access to land, access to finance, location, sectoral engagement, market linkage, business experience, education, ownership structure, and financial management practice on growth of enterprises.

No abstract

The purpose of this study was to examine factors that determine micro- and small-scale enterprises’ financing preference in line with pecking order theory and access to credit in Benishangul-Gumuz Regional State of Ethiopia. The study used primary data collected using cross-sectional survey questionnaire. The sample of this study was 296 enterprises selected using proportional stratified random sampling technique. The data were analyzed using descriptive and logistic regression analysis. The results of probit estimation revealed that business experience, collateral, gender, motivation and enterprises’ sectoral engagement affect financing preference. To investigate access to credit determinants, only enterprises that need to raise capital through credit were considered. The empirical results, therefore, revealed that business experience, size, sectoral engagement, collateral, interest rate, loan repayment period, and preparation of business plan, financial reporting, location and educational background of entrepreneurs affect access to credit of enterprises. Before any generalization of the results can be made, more evidence is needed on enterprises’ financing preference and access to credit determinants for the fact that the empirical tests were conducted only on 296 entrepreneurs since 2019. Therefore, the findings are valid and practicable only for the entrepreneurs under the study and the results cannot be assumed to extend beyond this group of entrepreneurs to different study periods. The study makes an original contribution to the literature of small business finance by investigating determinants of micro- and small-scale enterprises’ financing preference and access to credit in Benishangul Gumuz Regional State of Ethiopia as a developing country.

After financial crisis of 2007-08, where many international corporations and financial institutions needed a bail out by government to remain in business, financial solvency and stability have become top most priorities for the financial sector globally (Chotalia, 2014). For this reason, Prediction of financial distress has gained a great deal of interest by researchers in finance recently (Al-Saleh & Al-Kandari, 2012). In view of this, there exist a large number of models and associated ratios propounded by various authors in predicting how healthy a company's financial condition is (Ahmed & Alam, 2015). However, the discriminant analysis as given by Altman is most effective and accurate among different techniques to predict financial distress (Bal, 2015). Accordingly, the purpose of this study was to evaluate financial distress conditions of selected Ethiopian micro finance institutions (MFIs) by applying Edward Altman's revised Z-score model using secondary data for the period 2011 to 2015. To this end, the study revealed that 94 % of MFIs are in the safe zone and 6% in the grey zone among the selected institutions. The finding of the study also indicates fluctuation in Z-score of the institutions from period to period. In view of this, the paper is expected to be used as input for policy makers and practitioners as long as it provides empirical evidence on financial distress condition of MFIs. Moreover, along with the theoretical contribution, this study is expected to contribute insights for academicians as literature for further studies in the area of MFIs and state of financial distress.

In developing economies, stable foreign direct investment inflow is used as a means of realization of private sector growth and sustainable development goals. However, there is variability in inflows to African region in general and its economic bloc groupings in particular overtime across countries. In this regard, numerous empirical studies have been carried out on the determinants of investment inflow variability using different datasets on developing countries despite the studies have produced paradoxical findings. The aim of this study is, therefore, to empirically identify factors that determine variability of foreign direct investment inflows to COMESA member countries using panel data estimators. The study used explanatory research design with arrangement of secondary data, ex post control over variables, unbalanced short panel inclined with quantitative approach. The data were acquired from world development and governance indicators of World Bank for a period of 15 years ranging from 2002 to 2016 for 17 countries. Econometric model estimation procedures and diagnostic tests for classical linear regression model assumptions were carried out before making valid analysis. Accordingly, empirical evidence of the study revealed that infrastructure, government effectiveness, economic growth, control over corruption, trade openness, political stability, human capital and financial development have statistically positive effect on the inflow. However, external debt, inflation and regulatory quality failed to show significant effect. Therefore, member countries should take measures to narrow-up bottlenecks of financial development, improve infrastructure, scale-up trade integration, improve human capital quality, work to bring better political stability and to control corruption in order to boost-up stable inflows.