A machine learning approach to rural entrepreneurship

Abstract

This study offers a novel approach to understand the mechanisms of rural entrepreneurship by applying five alternative machine learning techniques on data obtained from the Life in Transition Survey III. Results highlight how capital constraints, age, factors related to trust and over-trust, awareness of current trends, the use of various media tools, a competitive character, institutional factors, and education are associated with the success and failure of potential entrepreneurs in rural areas who attempt to set up a business. The final predictions are achieved with accuracies ranging from seventy-two to ninety-two percent.