Working Paper: NBER ID: w24416
Authors: Arindrajit Dube; Jeff Jacobs; Suresh Naidu; Siddharth Suri
Abstract: On-demand labor platforms make up a large part of the “gig economy.” We quantify the extent of monopsony power in one of the largest on-demand labor platforms, Amazon Mechanical Turk (MTurk), by measuring the elasticity of labor supply facing the requester (employer) using both observational and experimental variation in wages. We isolate plausibly exogenous variation in rewards using a double-machine-learning estimator applied to a large dataset of scraped MTurk tasks. We also re-analyze data from 5 MTurk experiments that randomized payments to obtain corresponding experimental estimates. Both approaches yield uniformly low labor supply elasticities, around 0.1, with little heterogeneity.
Keywords: monopsony; online labor markets; Amazon Mechanical Turk; labor supply elasticity
JEL Codes: J01; J42
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| monopsony power (J42) | labor supply elasticity (J20) |
| task rewards (J33) | likelihood of task acceptance (J29) |
| task rewards (J33) | labor supply elasticity (J20) |
| task rewards (J33) | monopsony power (J42) |