Orientation: As lack of access to credit hinders small, micro and medium enterprises (SMMEs) success and lending technologies being conduits transmit credit access, more focus must be on the effect of lending technologies on credit rationing.Research purpose: To analyse the extent of credit rationing amongst SMMEs based on lender and firm characteristics.Motivation for the study: In South Africa, SMMEs are funded by different lenders using different lending technologies, but little is known about which ones are more effective.Research approach/design and method: The study takes a quantitative approach. In this study, 321 SMMEs are sampled from 1486 small businesses on the registers of the Nelson Mandela Bay Business Chamber and the Border-Kei Chamber of Business in the Eastern Cape province of South Africa. Financing of SMMEs is captured with a categorical credit-rationing variable. Accordingly, a logit technique is used. The first model captures credit rationing as a binary variable. In the second model, the nature of credit rationing is disaggregated resulting in a four-measure categorical variable.Main findings: Little rationing occurs when asset-based and venture capital methods are used. Microfinance and privately owned development financial institutions have high rationing levels, similar to commercial banks, defeating the purpose of their special existence to address excluded groups. Black people-owned and female-owned businesses are the most rationed. Credit rationing decreases with firm size, but the effects are amplified by race.Practical/managerial implications: To improve SMMEs access to finance, the government should focus on allocating funds to firms using SMMEs’ credit rationing risk profiles.Contribution/value-add: Lending technology, lender type and SMME characteristics relationships indicate that SMMEs can benefit from a well-understood rationing risk profile of firms in the economy. Therefore, policies on support and regulation of the distribution of loan portfolios aligned to empirical rationing risk profiles can improve SMME growth. However, this study has used SMME data from the Eastern Cape province only, one of the nine provinces in South Africa. Thus, the provincial heterogeneity effects are not captured in this study.
An Investigation into the Effect of Small and Medium Enterprises (SMEs) on the Socio-Economic Development of Alice in Eastern Cape Province, South Africa
