Monopsony in Online Labor Markets
- (pp. 33-46)
Abstract
Despite the seemingly low switching and search costs of on-demand labor markets like Amazon Mechanical Turk, we find substantial monopsony power, as measured by the elasticity of labor supply facing the requester (employer). We isolate plausibly exogenous variation in rewards using a double machine learning estimator applied to a large dataset of scraped MTurk tasks. We also reanalyze data from five MTurk experiments that randomized payments to obtain corresponding experimental estimates. Both approaches yield uniformly low labor supply elasticities, around 0.1, with little heterogeneity. Our results suggest monopsony might also be present even in putatively "thick" labor markets.Citation
Dube, Arindrajit, Jeff Jacobs, Suresh Naidu, and Siddharth Suri. 2020. "Monopsony in Online Labor Markets." American Economic Review: Insights, 2 (1): 33-46. DOI: 10.1257/aeri.20180150Additional Materials
JEL Classification
- C44 Operations Research; Statistical Decision Theory
- J22 Time Allocation and Labor Supply
- J23 Labor Demand
- J42 Monopsony; Segmented Labor Markets