Skill-biased technical change, again? Online gig platforms and local employment

Guo, Xue, Cheng, Aaron ORCID: 0000-0002-2070-3761 and Pavlou, Paul A. (2024) Skill-biased technical change, again? Online gig platforms and local employment. Information Systems Research. ISSN 1047-7047

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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 effect heterogeneity 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.

Item Type:	Article
Additional Information:	© 2024 INFORMS
Divisions:	Management
Subjects:	T Technology > T Technology (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science H Social Sciences > HD Industries. Land use. Labor H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
Date Deposited:	13 Aug 2024 13:54
Last Modified:	20 Dec 2024 00:56
URI:	http://eprints.lse.ac.uk/id/eprint/124538

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