Geographical Discrimination in the Gig Economy
It is shown that, after controlling for observable workers’ characteristics and their job bids, foreign job-seekers are 42 percent less likely to win contracts from Spanish employers, which represent about two-thirds of all employers in the platform.
Abstract
This study seeks to empirically test the narrative of a frictionless global market for digital labor. The empirical strategy is based on the examination of internal data from Nubelo, one of the largest Spanish-language online labor platforms. The results suggest that information-related frictions long observed in traditional labor markets are exacerbated in online platforms, resulting in discrimination based on country of origin. We show that, after controlling for observable workers’ characteristics and their job bids, foreign job-seekers are 42 percent less likely to win contracts from Spanish employers, which represent about two-thirds of all employers in the platform. We attribute this result to the activation of social stereotypes that orient employers’ hiring decisions, in the absence of verifiable information about the quality of individual workers. We draw implications for platform design and the governance of online labor contracts.