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arXiv:1712.05796cs.CY

A Data-Driven Analysis of Workers' Earnings on Amazon Mechanical Turk

Kotaro Hara, Abi Adams, Kristy Milland, Saiph Savage, Chris Callison-Burch, Jeffrey Bigham

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Abstract

A growing number of people are working as part of on-line crowd work, which has been characterized by its low wages; yet, we know little about wage distribution and causes of low/high earnings. We recorded 2,676 workers performing 3.8 million tasks on Amazon Mechanical Turk. Our task-level analysis revealed that workers earned a median hourly wage of only ~\$2/h, and only 4% earned more than \$7.25/h. The average requester pays more than \$11/h, although lower-paying requesters post much more work. Our wage calculations are influenced by how unpaid work is included in our wage calculations, e.g., time spent searching for tasks, working on tasks that are rejected, and working on tasks that are ultimately not submitted. We further explore the characteristics of tasks and working patterns that yield higher hourly wages. Our analysis informs future platform design and worker tools to create a more positive future for crowd work.

arXiv comments
Conditionally accepted for inclusion in the 2018 ACM Conference on Human Factors in Computing Systems (CHI'18) Papers program
Journal reference
DOI
Subjects
cs.CYComputers and Society cs.HCHuman-Computer Interaction

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