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.

摘要

我们使用居住在印度班加罗尔贫民窟的近 1500 名工人的微数据开发了一个框架，用于绘制和分析非正式工人的技能。除了计量经济学建模外，我们还采用机器学习技术来探索从受访者那里获得的众包技能之间的关系。我们发现非正式工人依赖于一系列任务、语言、个人和社交技能。此外，我们确定了与工资收入水平和稳定性相关的技能要求。我们的结果包括对技能要求方面的性别差异、英语和计算机素养的重要性以及个人和社交技能在印度非正式劳动力市场中的核心作用的见解。