[论文解读] Service Providers of the Sharing Economy: Who Joins and Who Benefits?

Qing Ke|arXiv (Cornell University)|Sep 22, 2017

Sharing Economy and Platforms参考文献 24被引用 6

一句话总结

本研究利用美国211,000个房源和188,000名房东的关联数据，分析了 Airbnb 房东的参与情况，发现低收入和高教育水平地区拥有更多房东，但高收入地区房源表现更佳——尽管弱势群体参与度更高，但其获得的经济收益仍存在显著的社会经济差异。

ABSTRACT

Many "sharing economy" platforms, such as Uber and Airbnb, have become increasingly popular, providing consumers with more choices and suppliers a chance to make profit. They, however, have also brought about emerging issues regarding regulation, tax obligation, and impact on urban environment, and have generated heated debates from various interest groups. Empirical studies regarding these issues are limited, partly due to the unavailability of relevant data. Here we aim to understand service providers of the sharing economy, investigating who joins and who benefits, using the Airbnb market in the United States as a case study. We link more than 211 thousand Airbnb listings owned by 188 thousand hosts with demographic, socio-economic status (SES), housing, and tourism characteristics. We show that income and education are consistently the two most influential factors that are linked to the joining of Airbnb, regardless of the form of participation or year. Areas with lower median household income, or higher fraction of residents who have Bachelor's and higher degrees, tend to have more hosts. However, when considering the performance of listings, as measured by number of newly received reviews, we find that income has a positive effect for entire-home listings; listings located in areas with higher median household income tend to have more new reviews. Our findings demonstrate empirically that the disadvantage of SES-disadvantaged areas and the advantage of SES-advantaged areas may be present in the sharing economy.

研究动机与目标

• 理解社会经济地位（SES）如何影响美国 Airbnb 房东在共享经济中的参与和收益。
• 调查不同房源类型（如整套房源与私人房间）及随时间变化的参与模式是否存在差异。
• 评估社会经济地位占优或不利的地区在共享经济参与中是否获得更大收益。
• 通过识别访问和结果方面的差异，为政策制定和平台设计提供依据。

提出的方法

• 将211,000个 Airbnb 房源与188,000名房东在普查区层面关联其人口统计、社会经济、住房及旅游数据。
• 使用多元回归模型分析社会经济因素（收入、教育）与房东参与及表现之间的关联。
• 以新获得的评价数量作为市场成功程度的代理指标，衡量房源表现。
• 通过多年时间序列分析，评估社会经济关联随时间的变化。
• 由于个体房东数据有限，采用普查区层面聚合方法，在精确度与噪声之间取得平衡。

实验结果

研究问题

• RQ1 RQ1：社会经济地位（SES）特征如何与共享经济中服务提供者的参与相关联？这种关联是否因房源类型（如整套房源与私人房间）而异？
• RQ2 RQ2：这些 SES 关联随时间如何变化？
• RQ3 RQ3：SES 特征如何与共享经济中的实际收益（如房源表现）相关联？

主要发现

• 普查区中位家庭收入越低，其 Airbnb 房东数量越多，表明社会经济地位不利地区参与度更高。
• 拥有学士学位或更高学历的居民比例越高，其房东数量也越多，表明教育是参与的重要预测因子。
• 对于整套房源，房东所在地区中位家庭收入越高，新获得的评价数量越多，表明富裕地区市场表现更佳。
• 即使控制其他因素后，收入对房源表现的积极影响依然存在，表明社会经济地位占优地区从共享经济中获得更大收益。
• 研究结果揭示了一个悖论：尽管低 SES 地区贡献了更多房东，但高 SES 地区获得的回报更高，表明不同社会经济群体间收益分配不均。