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How sharing is the “sharing economy”? Evidence from 97 Airbnb markets
Open accessAbstract
Digital platforms such as Airbnb have become a major economic and political force in recent years, presenting themselves as a “sharing economy”–a new, more just way of organizing social and economic activity–while functioning as owners and managers of proprietary markets. These platforms have in recent years been subject to variegated but growing regulations, begging questions of how these affect their platform markets. This paper examines these claims by a large-scale international comparative analysis of the revenue distribution of Airbnb markets in 97 cities and regions, focusing on the level and evolution of revenue inequality, and estimating the racial and gender revenue gaps by using machine learning classification of host profile pictures. Examining 834,722 listings, 513,785 hosts, and 13,466,854 reviews, the paper finds an average Gini coefficient of 0.68, implying that a majority of the market revenue tends to go to about 10% of the hosts. The level of centralization varies significantly across cities, but is consistently growing over time, with government regulation appearing as a counteracting factor, which however only temporarily slows down the growing dominance of a small minority of large-scale hosts. The paper furthermore finds large gender and race revenue gaps, as Black hosts receive on average 22% less revenue for their listings, and women an average of 12% less. These findings contribute important data to ongoing academic and policy debates, as well as a starting point for further research on inequality in the sharing economy, and how it can be regulated.
Overview
CoChat Summary
TL;DR
Analyzing 97 Airbnb markets, this paper finds the platform is highly unequal and increasingly centralized, contradicting “sharing economy” claims. It reports an average Gini coefficient of 0.68, with Black hosts receiving 22% less revenue and women 12% less on average.
Key findings
- Across 97 cities and regions, the average Gini coefficient is 0.68 (median 0.68, SD 0.069); about 10% of hosts receive roughly 50% of market revenue, and in the most unequal cities (Columbus, Austin, Prague) a large share goes to just 1% of hosts.
- Inequality rose over time in all nine cities with data since 2015; regulation did not significantly alter the long-term trend, though San Francisco’s 2018 registration deal caused a temporary spike and drop before returning to growing inequality.
- Black hosts receive on average 22% less revenue, and women 12% less; gaps are larger in some cities, e.g., Black hosts in Cape Town receive 57.9% less and in Rio de Janeiro 47.1% less, while women in Rio receive 32.4% less.
- Black hosts are underrepresented relative to city demographics—e.g., in Cape Town 4.8% of Airbnb hosts are classified as Black versus 38.6% of the population, while 74.4% of hosts are classified as White versus 1.4% of the population, an over 5,300% overrepresentation of White hosts.
Methods
- Uses InsideAirbnb scraped data for 97 cities/regions; the abstract reports 834,722 listings, 513,785 hosts, and 13,466,854 reviews, with 2019 data for current inequality and historic data since 2015 for nine cities.
- Relative revenue is proxied as number of reviews × listing price, summed per host; hosts with no reviews are excluded; inequality is measured via Lorenz curves, Gini coefficients, and power-law fitting.
- Race and gender are classified from host profile pictures using Clarifai (discarding 26% with no face and 22% with multiple faces); OLS regressions use revenue normalized to city average, baseline White male, validated with Genderize (91.5% classified by first name; 92% agreement for women).
Limitations
- The paper states the relative-revenue proxy may bias estimates—e.g., if small/personal hosts receive more reviews, inequality may be underestimated—and assumes profile photos show the actual host; hosts using logos or other photos may be missed.
- The paper states race and gender operationalization is limited: race is socially constructed and not merely skin color, gender is treated as binary, and the analysis is descriptive rather than explanatory, not disentangling direct and indirect discrimination.
- Evident from the text: historic analysis covers only nine cities, and race/gender gap analysis focuses on the 14 cities with the most hosts due to limited classification resources.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
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Statistics
Figures
![Fig 1 A log-log plot of the probability distribution function of the revenues of the 456,489 hosts in all 97 cities, showing the distribution of revenue over the population of hosts. This is fit with a power law distribution (dashed line), with α = 1.840. This analysis was carried out using powerlaw [41] based on the approach suggested in Clauset et al [42].](/paper-figures/1eb9d739129846acb670bc00ee804838/how-sharing-is-the-sharing-economy-evidence-from-97-airbnb-markets-figure-1.jpg)
Verification
Claims the paper makes
0 Valid · 0 Not valid · 10 Not checked · 76 Own results and interpretations
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In every city except Beijing, female hosts receive less revenue than male hosts.”
- Also stated in: § Conclusion · hedged
“While women are overrepresented as hosts, they receive less revenue than men in almost all cities.”
§ How sharing is the “sharing economy”? Evidence from 97 Airbnb markets > Results > Race and gender gaps
plainstates an association- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § Conclusion · hedged
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the host revenue approximates a power-law distribution, expressed as a straight line on a log-log plot.”
§ How sharing is the “sharing economy”? Evidence from 97 Airbnb markets > Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Surprisingly, while cities with more regulation tend to have lower levels of inequality, regulation against Airbnb does not significantly seem to impact the long-term evolution of the Gini coefficient.”
§ How sharing is the “sharing economy”? Evidence from 97 Airbnb markets > Results > Evolution of inequality
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Turning to the question of revenue gaps, the paper found large gaps between genders and racial groups.”
§ How sharing is the “sharing economy”? Evidence from 97 Airbnb markets > Conclusion
plainstates an association- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Amsterdam overall provides an interesting counterexample to the trends, where both Black, Hispanic-Latino, Southeastern Asian and East Asian hosts receive significantly more revenue than White hosts.”
§ How sharing is the “sharing economy”? Evidence from 97 Airbnb markets > Results > Race and gender gaps
plainstates an association- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
35 more claims
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Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ How sharing is the “sharing economy”? Evidence from 97 Airbnb markets > Results
“The Lorenz curves for the three cities with lowest, median and highest Gini coefficients, respectively. The Lorenz curve shows the cumulative fraction of the population on the x-axis, and the cumulative fraction of the revenue taken by this population on the y-axis. For instance, looking at Prague, that the line is at y = 0.3 at x = 0.8 tells us that 80% of the hosts receive around 30% of the revenue.”
§ How sharing is the “sharing economy”? Evidence from 97 Airbnb markets > Results
“| Czech Republic | Prague | 10,727 | 4,833 | 256,109 | 2.22 | 23.88 | 0.82 |”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ How sharing is the “sharing economy”? Evidence from 97 Airbnb markets > Results > Race and gender gaps
“On Airbnb, in contrast, only 4.8% are classified as Black, and 74.4% are classified as White”
§ How sharing is the “sharing economy”? Evidence from 97 Airbnb markets > Results > Race and gender gaps
“| Cape Town | 4.8% | 1.9% | 1.0% | 5.6% | 11.2% | 0.8% | 74.7% | 58.5% | 41.5% |”
Citations
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Airbnb and urban population change: an empirical analysis of the case of Stockholm, Sweden
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An enhanced decision-making framework for predicting future trends of sharing economy
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Evaluation of factors having an impact on the development of the sharing economy
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Metadata
- Authors
- Törnberg, P.
- Affiliations
- University of Amsterdam (NL)
- Corresponding author
- Törnberg, Petter
- ORCID
- Törnberg, Petter
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2022-04-21
- Volume
- 17
- Issue
- 4
- Pages
- e0266998
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC9022852
- OpenAlex
- W4224242071
- Semantic Scholar
- f28f2e7050c3614d5ffbf3baf61cf556059e1a4e
- Cited by
- 24
- References
- 37
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 4 · 2023: 9 · 2022: 1
- FWCI
- 3.05
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- UvA-DARE (University of Amsterdam) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (VI.Veni.201S.006)
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › Sharing Economy and Platforms
- Social Sciences › Sociology and Political Science › Digital Economy and Work Transformation
- Social Sciences › Business, Management and Accounting › Strategy and Management › Digital Platforms and Economics
- Keywords
- sharing economy, Airbnb, platform markets, Gini coefficient, market centralization, platform regulation, digital platforms, peer-to-peer accommodation
- MeSH terms
- Cities, Female, Humans, Male, Government Regulation
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