Inequality in Online Labor Markets
Online labor markets represent the future of work for many job applicants. Online markets now support a range of occupations including nursing care, computer programming, deliveries, and ridesharing. Jobs can range from “micro-tasks” that take minutes to complete (such as Mechanical Turk) to complex projects that require multiple-week commitments for a team of freelancers. While employers and employees can find such jobs through gender- and race-blind searches, some research suggests that inequalities by race and gender persist in online settings.
Anikó Hannák, and her collaborators David Lazer and Christo Wilson, will explore how user actions, site design, and algorithmic systems interact to create inequalities in online job markets. They will use observational data to investigate the extent to which workers’ demographic features are related to employer ratings and reviews and how visible they are in search results. They will address three questions: Are there observed differences by race and gender in the social feedback workers receive? How large are the correlations between worker demographics and search rank? To what extent are worker dropout rates related to demographics and social feedback?