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

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  • Guo, Xue
  • Cheng, Aaron
  • Pavlou, Paul A.

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.

Suggested Citation

  • Guo, Xue & Cheng, Aaron & Pavlou, Paul A., 2024. "Skill-biased technical change, again? Online gig platforms and local employment," LSE Research Online Documents on Economics 124538, London School of Economics and Political Science, LSE Library.
  • Handle: RePEc:ehl:lserod:124538
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    File URL: http://eprints.lse.ac.uk/124538/
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    More about this item

    Keywords

    online gig platforms; employment; skill-biased technical change; difference-in-differences; generalized synthetic control;
    All these keywords.

    JEL classification:

    • R14 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Land Use Patterns
    • J01 - Labor and Demographic Economics - - General - - - Labor Economics: General
    • J50 - Labor and Demographic Economics - - Labor-Management Relations, Trade Unions, and Collective Bargaining - - - General

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