Unemployment and Worker Participation in the Gig Economy: Evidence from an Online Labor Market

• 成果类型: Article
• 署名作者: Huang, Ni; Burtch, Gordon; Hong, Yili; Pavlou, Paul A.
• 署名单位: Arizona State University; Arizona State University-Tempe; University of Minnesota System; University of Minnesota Twin Cities; University of Houston System; University of Houston
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.2019.0896
• 发表日期: 2020
• 页码: 431-448
• 关键词: MIGRATION; BUSINESS; MODEL

摘要

The gig economy has low barriers to entry, enabling flexible work arrangements and allowing workers to engage in contingent employment, whenever, and in some cases, such as online labor markets, wherever, workers desire. The growth of the gig economy has been partly attributed to technological advancements that enable flexible work environments. In this study, we consider the role of an alternative driver, economic downturns, and associated financial stressors in the offline economy, for example, unemployment. As the exact nature of the relationship between online labor supply and offline unemployment is not immediately clear, in this work, we seek to quantify the relationship, exploring heterogeneity across a variety of county-specific characteristics. We study these relationships in the context of a leading online labor market, combining data on the participation of workers residing in counties across the United States with county-level data on unemployment from the Bureau of Labor Statistics. Our results demonstrate a positive and significant association between local (county) unemployment in the traditional offline labor market and the supply of online workers residing in the same county, as well as significantly larger volumes of online project bidding activity from workers in the same county. Specifically, we estimate that a 1% increase in county unemployment is associated with a 21.8% increase in the volume of county residents actively working online at the platform. Furthermore, our results suggest significant heterogeneity in the relationship, such that a significantly larger supply of online labor manifests when unemployment occurs in counties characterized by better internet access, younger and more educated populations, and populations whose social ties are dispersed over a wider geographic area. We discuss the theoretical and practical implications for workers, online labor markets, and policy makers.