Skill-Biased Technical Change, Again? Online Gig Platforms and Local Employment

Xue Guo (), Zhi (Aaron) Cheng () and Paul A. Pavlou ()
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Xue Guo: Robinson College of Business, Georgia State University, Atlanta, Georgia 30303
Zhi (Aaron) Cheng: Department of Management, London School of Economics and Political Science, London WC2A 2AE, United Kingdom
Paul A. Pavlou: Miami Herbert Business School, University of Miami, Miami, Florida 33146

Information Systems Research, 2025, vol. 36, issue 3, 1354-1374

Abstract: Online gig platforms have the potential to influence employment in existing industries. Popular press and academic research offer two competing predictions: First, online gig platforms may reduce the supply of incumbent workers by intensifying competition and obsoleting certain skills of workers; or, second, they may boost the supply of workers by increasing client-worker matching efficiency and creating new employment opportunities for workers. Yet, there has been limited understanding of the labor movements amid the rise of online gig platforms. Extending the skill-biased technical change literature, we study the impact of TaskRabbit—a location-based gig platform that matches freelance workers to local demand for domestic tasks (e.g., cleaning services)—on the local supply of incumbent, work-for-wages housekeeping workers. We also examine the heterogeneous effects across workers at different skill levels. Exploiting the staggered TaskRabbit expansion into U.S. cities, we identify a significant decrease in the number of incumbent housekeeping workers after TaskRabbit entry. Notably, this is mainly driven by a disproportionate decline in the number of middle-skilled workers (i.e., first-line managers, supervisors) whose tasks could easily be automated by TaskRabbit’s matching algorithms, but not low-skilled workers (i.e., janitors, cleaners) who typically perform manual tasks. Interestingly, TaskRabbit entry does not necessarily crowd out middle-skilled housekeeping workers, neither laying them off nor forcing them to other related occupations; rather, TaskRabbit entry supports self-employment within the housekeeping industry. These findings imply that online gig platforms may not naively be viewed as skill biased, especially for low-skilled workers; instead, they redistribute middle-skilled managerial workers whose cognitive tasks are automated by the sorting and matching algorithms to explore new self-employment opportunities for workers, stressing the need to reconsider online gig platforms as a means to reshape existing industries and stimulate entrepreneurial endeavors.

Keywords: online gig platforms; employment; skill-biased technical change; difference-in-differences; generalized synthetic control (search for similar items in EconPapers)
Date: 2025
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