Tax-Induced Inequalities in the Sharing Economy

• 成果类型: Article
• 署名作者: Cui, Yao; Davis, Andrew M.
• 署名单位: Cornell University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2021.4277
• 发表日期: 2022
• 页码: 7202-7220
• 关键词: sharing economy; Airbnb; tax; Machine Learning; causal forest; difference-in-differences; heterogeneous treatment effect; Prescriptive Analytics

摘要

The growth of sharing economy marketplaces like Airbnb has generated discussions on their socioeconomic impact and lack of regulation. As a result, most major cities in the United States have started to collect an occupancy tax for Airbnb bookings. In this study, we investigate the heterogeneous treatment effects of the occupancy tax policy on Airbnb listings, using a combination of a generalized causal forest methodology and a difference-in-differences framework. While we find that the introduction of the tax significantly reduces both listing revenues and sales, more importantly, these effects are disproportionately more pronounced for residential hosts with single shared-space (nontarget) listings versus commercial hosts with multiple properties or entire-space (target) listings. We further show that this unintended consequence is caused by customers' discriminatory tax aversion against nontarget listings. We then leverage these empirical results by prescribing how hosts should optimally set prices in response to the occupancy tax and identify the discriminatory tax rates that would equalize the tax's effect across nontarget and target listings.