The Skills Space in Informal Work: Insights from Bangalore Slums

Abstract

We develop a framework for mapping and analysing informal worker skills using microdata from nearly 1500 workers residing in the slums of Bangalore, India. Alongside econometric modelling, we employ machine learning techniques to explore relationships between skills crowdsourced from respondents. We find that informal workers rely on a host of task, language, personal and social skills. Further, we identify skill claims associated with both levels and stability of wage earnings. Our results include insights on gender disparities in skill claims, importance of English and computer literacy and the central role of personal and social skills in the Indian informal labour market.