Department of Human Geography, Amsterdam Institute for Social Science Research, University of Amsterdam, Amsterdam, The Netherlands.
PLoS One. 2022 Apr 21;17(4):e0266998. doi: 10.1371/journal.pone.0266998. eCollection 2022.
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.
近年来，数字平台（如 Airbnb）已成为一股重要的经济和政治力量，它们将自己定位为“共享经济”——一种新的、更公平的组织社会和经济活动的方式，同时也是专有市场的所有者和管理者。近年来，这些平台受到了各种形式但日益严格的监管，这引发了人们对这些监管如何影响其平台市场的质疑。本文通过对 Airbnb 市场在 97 个城市和地区的收入分配进行大规模的国际比较分析，对这些说法进行了检验，重点关注收入不平等的程度和演变，并通过对主机个人资料图片进行机器学习分类来估计种族和性别收入差距。该研究共分析了 834722 条房源、513785 位房东和 13466854 条评论，发现平均基尼系数为 0.68，这意味着市场收入的大部分倾向于大约 10%的房东。集中化程度在不同城市之间差异显著，但随着时间的推移一直呈上升趋势，政府监管似乎是一个抗衡因素，但只能暂时减缓少数大规模房东日益占据主导地位的趋势。此外，该研究还发现了较大的性别和种族收入差距，黑人房东的房源平均收入比白人房东低 22%，女性房东则低 12%。这些发现为正在进行的学术和政策辩论提供了重要数据，也为共享经济中的不平等以及如何对其进行监管的进一步研究提供了起点。
