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

The gig economy maintains low barriers to entry, enabling flexible working arrangements and allowing individuals to engage in contingent work, whenever (and in some cases, such as online labor markets, wherever) they desire. The growth of the gig economy has often been attributed to technological advancements that underpin digital platforms. In this study, we consider the likely effect of an alternative driver, financial stressors, namely job loss in the offline labor market. Specifically, we seek to quantify the relationship between offline unemployment and participation in the online gig economy, noting that it is not altogether clear how the supply of labor in online labor markets will respond to unemployment shocks in the local economy. And we further explore the moderating roles of county-specific characteristics. We study the questions in the context of an online labor market, Freelancer, merging data on the participation of workers residing in respective counties across the United States with county-level data on unemployment and mass layoff statistics from the Bureau of Labor Statistics. Leveraging three distinct identification strategies (instrumental variables, a difference-in-differences analysis based on a large-scale natural experiment, and a subsample analysis of plausibly exogenous variation in unemployment arising from localized mass-layoff events), we demonstrate robust evidence that local unemployment in the traditional offline labor market leads to a significant increase in both the number of active workers residing in the same county, and increases in associated bidding activity in the online labor market. At the same time, our relative time estimates suggest that, to some degree, these effects decayed with recovery from the financial crisis, suggesting that many workers who initially shifted to the gig economy ultimately returned to traditional employment. We also report evidence of significant heterogeneities in the response to offline unemployment, such that greater gig economy participation results from unemployment in counties characterized by better Internet access, as well as those having younger or more educated populations, and populations whose social ties are dispersed over a wider geographic area. We discuss the implications for workers, online labor markets, and policymakers.