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

• 成果类型: Article
• 署名作者: Guo, Xue; Cheng, Zhi (Aaron); Pavlou, Paul A.
• 署名单位: University System of Georgia; Georgia State University; University of London; London School Economics & Political Science; University of Miami
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.2022.0307
• 发表日期: 2025
• 关键词: occupational-mobility; technological-change; Sharing economy; INEQUALITY; tasks; IMPACT; FUTURE; GROWTH; taxi; jobs

摘要

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 selfemployment opportunities for workers, stressing the need to reconsider online gig platforms as a means to reshape existing industries and stimulate entrepreneurial endeavors.