Digital Discrimination: The Case of Airbnb.com
Open accessAbstract
Online marketplaces often contain information not only about products, but also about the people selling the products. In an effort to facilitate trust, many platforms encourage sellers to provide personal profiles and even to post pictures of themselves. However, these features may also facilitate discrimination based on sellers’ race, gender, age, or other aspects of appearance. In this paper, we test for racial discrimination against landlords in the online rental marketplace Airbnb. Using a new data set combining pictures of all New York City landlords on Airbnb with their rental prices and information about quality of the rentals, we show that non-black hosts charge approximately 12% more than black hosts for the equivalent rental. These effects are robust when controlling for all information visible in the Airbnb marketplace. These findings highlight the prevalence of discrimination in online marketplaces, suggesting an important unintended consequence of a seemingly-routine mechanism for building trust.
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African-American guests are discriminated against on Airbnb, and African-American hosts earn about 12% less than White hosts for similar listings.
“African-American guests (and hosts) are discriminated against on Airbnb in a way that would be infeasible under different design choices (Edelman and Luca 2014, Edelman, Luca, and Svirsky 2016). Looking at listings in New York City, Edelman and Luca (2014) find that AfricanAmerican hosts earn about 12% less than White hosts for similar listings.”
From Designing Online Marketplaces: Trust and Reputation Mechanisms (2016) · cites this paper as Edelman and Luca 2014
Black hosts in New York, on average, must ask 12% less than non-black hosts for lodgings of similar quality.
“Black hosts in New York, on average, must ask 12% less than non-black hosts for lodgings of similar quality (Edelman & Luca, 2014).”
From Trust and power in Airbnb's digital rating and reputation system (2025) · cites this paper as Edelman & Luca, 2014
Evaluative infrastructure can accentuate discrimination based on pre-existing distinctions.
“Secondly, instead of opening up possibilities for knowledge creation, evaluative infrastructure can accentuate discrimination based on pre-existing distinctions (Gillespi, 2010: Edelman & Luca, 2014).”
From Evaluative infrastructures: Accounting for platform organization (2017) · cites this paper as Edelman & Luca, 2014
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Ladegaard, I. (2018) · The Sociological Review · doi:10.1177/0038026118758538 · Cited by 51 · see citing works
Lampinen, A., Huotari, K., & Cheshire, C. (2015) · Interaction design & architecture(s)/ID&A Interaction design & architecture(s) · doi:10.55612/s-5002-024-001 · Cited by 48 · see citing works
- Authors
- Edelman, B. G., & Luca, M.
- Affiliations
- Microsoft (United States) (US)
- Harvard University (US)
- Corresponding author
- Luca, Michael
- Venue
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Published
- 2014
- ISSN
- 1556-5068 (ISSN-L)
- OpenAlex
- W2113764854
- Cited by
- 471
- References
- 16
- Citations by year
- 2026: 7 · 2025: 23 · 2024: 28 · 2023: 24 · 2022: 63 · 2021: 52 · 2020: 50 · 2019: 70 · 2018: 55 · 2017: 49 · 2016: 31 · 2015: 16 · 2014: 2
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- SSRN Electronic Journal (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Names, Identity, and Discrimination Research
- Social Sciences › Business, Management and Accounting › Marketing › Sharing Economy and Platforms
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- racial discrimination, Airbnb, landlords, rental pricing, appearance-based discrimination, New York City, housing discrimination
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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