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

The role of online gig platforms in local labor markets has recently been debated with two competing predictions: online gig platforms either complement local employment by matching service demand and supply and creating job opportunities, or substitute local employment by obsoleting workers with certain skills. Yet, there has been inadequate analysis and evidence for the labor movement amid the growing online gig economy. Drawing upon the literature on Skill-Biased Technical Change and online gig platforms, we study the role of TaskRabbit—a large gig platform that matches freelancer labor to local demand of domestic tasks (e.g., cleaning) online and requires services offline—on the local employment of housekeeping occupations. Exploiting the staggered expansion pattern of TaskRabbit into U.S. cities over time, we identify a disproportionate decrease in the number of workers in the traditional housekeeping businesses after TaskRabbit entry. Notably, this decrease is driven by a significant decline in the employment of middle-skilled workers (i.e., first-line managers, supervisors) whose labor tasks could easily be automated by the matching algorithms of TaskRabbit, rather than low-skilled manual workers (i.e., janitors, cleaners). Interestingly, we show evidence that gig platforms may not crowd out middle-skilled housekeeping workers by laying them off or shifting them to other occupations, but rather they encourage local entrepreneurial activities by boosting self-employment in the same industry. The findings suggest that online gig platforms like TaskRabbit may not simply be viewed as skill-biased; instead, they exert a labor redistribution effect that reconciles labor automation (i.e., substitution) with labor augmentation (i.e., complementarity), calling for reconsidering the online gig economy as a means to stimulating local labor markets