The Skills Space in Informal Work: Insights from Bangalore Slums
基于班加罗尔贫民窟近1500名工人的微观数据，结合计量经济学和机器学习，分析非正规工人的技能构成及其与工资水平和稳定性的关系，发现个人与社会技能、英语和计算机素养的重要性及性别差异。
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.