Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
4-2018
Abstract
A growing number of people are working as part of on-line crowd work. Crowd work is often thought to be low wage work. However, we know little about the wage distribution in practice and what causes low/high earnings in this setting. 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. While the average requester pays more than $11/h, lower-paying requesters post much more work. Our wage calculations are influenced by how unpaid work is accounted for, 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 platform design and worker tools to create a more positive future for crowd work.
Keywords
Crowdsourcing, Amazon Mechanical Turk, Hourly wage
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing
Publication
CHI '18: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, Montreal, April 21-26
First Page
449:1
Last Page
14
ISBN
9781450356206
Identifier
10.1145/3173574.3174023
Publisher
ACM
City or Country
New York
Citation
HARA, Kotaro; ADAMS, Abigail; MILLAND, Kristy; SAVAGE, Saiph; CALLISON-BURCH, Chris; and BIGHAM, Jeffrey P..
A data-driven analysis of workers' earnings on Amazon Mechanical Turk. (2018). CHI '18: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, Montreal, April 21-26. 449:1-14.
Available at: https://ink.library.smu.edu.sg/sis_research/4209
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3173574.3174023
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons