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

Xue Guo, Aaron Cheng and Paul A. Pavlou

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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.

Keywords: online gig platforms; employment; skill-biased technical change; difference-in-differences; generalized synthetic control (search for similar items in EconPapers)
JEL-codes: J01 J50 R14 (search for similar items in EconPapers)
Date: 2024-11-13
New Economics Papers: this item is included in nep-pay
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Published in Information Systems Research, 13, November, 2024. ISSN: 1047-7047

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