The Labor Economics of Paid Crowdsourcing

TL;DR

This paper models paid crowdsourcing labor supply, estimates workers' reservation wages, and examines how incentives influence worker behavior, revealing rational responses and target earning behaviors among crowd workers.

Contribution

It introduces a novel method for estimating reservation wages and applies it to crowd workers, providing insights into their labor supply and incentive responses.

Findings

Reservation wages are approximately log normally distributed with a median of $1.38/hour.

Labor supply elasticity at the median wage is 0.43.

Many workers respond rationally to incentives, but some set earnings targets.

Abstract

Crowdsourcing is a form of "peer production" in which work traditionally performed by an employee is outsourced to an "undefined, generally large group of people in the form of an open call." We present a model of workers supplying labor to paid crowdsourcing projects. We also introduce a novel method for estimating a worker's reservation wage--the smallest wage a worker is willing to accept for a task and the key parameter in our labor supply model. It shows that the reservation wages of a sample of workers from Amazon's Mechanical Turk (AMT) are approximately log normally distributed, with a median wage of $1.38/hour. At the median wage, the point elasticity of extensive labor supply is 0.43. We discuss how to use our calibrated model to make predictions in applied work. Two experimental tests of the model show that many workers respond rationally to offered incentives. However, a non-trivial fraction of subjects appear to set earnings targets. These "target earners" consider not just the offered wage--which is what the rational model predicts--but also their proximity to earnings goals. Interestingly, a number of workers clearly prefer earning total amounts evenly divisible by 5, presumably because these amounts make good targets.