Author
Listed:
- 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)
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.
Suggested Citation
Xue Guo & Zhi (Aaron) Cheng & Paul A. Pavlou, 2025.
"Skill-Biased Technical Change, Again? Online Gig Platforms and Local Employment,"
Information Systems Research, INFORMS, vol. 36(3), pages 1354-1374, September.
Handle:
RePEc:inm:orisre:v:36:y:2025:i:3:p:1354-1374
DOI: 10.1287/isre.2022.0307
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