Toward a geographic understanding of the sharing economy: Systemic biases in UberX and TaskRabbit

Jacob Thebault-Spieker, Loren Terveen, Brent J Hecht

Research output: Contribution to journalArticlepeer-review

68 Scopus citations


Despite the geographically situated nature of most sharing economy tasks, little attention has been paid to the role that geography plays in the sharing economy. In this article, we help to address this gap in the literature by examining how four key principles from human geography-distance decay, structured variation in population density, mental maps, and "the Big Sort" (spatial homophily)-manifest in sharing economy platforms. We find that these principles interact with platform design decisions to create systemic biases in which the sharing economy is significantly more effective in dense, high socioeconomic status (SES) areas than in low-SES areas and the suburbs. We further show that these results are robust across two sharing economy platforms: UberX and TaskRabbit. In addition to highlighting systemic sharing economy biases, this article more fundamentally demonstrates the importance of considering well-known geographic principles when designing and studying sharing economy platforms.

Original languageEnglish (US)
Article number21
JournalACM Transactions on Computer-Human Interaction
Issue number3
StatePublished - Apr 2017

Bibliographical note

Funding Information:
This research was supported by NSF Grants IIS-1707296, IIS-1218826, IIS-0808692 and a Microsoft FUSE Sharing Economy Research Award.

Publisher Copyright:
© 2017 ACM.


  • Geography residential segregation big sort mental maps distance decay population density location-aware computing
  • Mobile crowdsourcing
  • Sharing economy


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