What Determines Enterprise Borrowing from Self Help Groups? An Interpretable Supervised Machine Learning Approach

Despite several advantages associated with borrowing from micro-finance institutions, such as self-help groups (SHGs), many enterprises in developing countries continue to rely on informal lenders. Using machine learning techniques on a novel village-enterprise matched dataset from India, we predict an enterprise’s choice of credit source as a function of three key mechanisms: supply-side factors, infrastructural facilities and socio-demographic characteristics. Proximity to markets and social norms of the village, proxied by high literacy rates and sex ratios, play important roles in credit uptake from SHGs. However, the absence of financial access points, such as commercial or cooperative bank branches, is not prohibitive.