Events

Miss Yulu Tang
Ph.D. Candidate of Economics
Harvard University

Globally, migrant workers often cluster with hometown peers in the same sectors and locations. This paper quantifies two countervailing effects of clustering: learning benefits and labor market congestion costs, using data on millions of migrant workers from a large food delivery platform in China. First, I provide direct evidence of knowledge spillovers. New workers learn from their peers’ past delivery experiences, decreasing search time by 10%. Using quasi-random variation in workers’ location choices induced by entry bonuses, I show that having one hometown peer nearby increases new workers’ productivity and earnings by 2%.Second, I quantify a cost of clustering due to correlated shocks. Migrant workers supply six additional hours weekly during adverse hometown shocks (floods and pandemic lockdowns). Due to inelastic consumer demand, clustering causes workers to compete for deliveries during such periods, and real wages decrease by 10% on average. I use a model to quantify the trade-off between the learning benefits and congestion costs. Worker utility increases (due to learning) and then falls (due to congestion) with clustering. Eliminating congestion costs through insurance doubles equilibrium clustering and increases productivity by 30%.