Tax-Induced Inequalities in the Sharing Economy

Abstract

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 charge an "occupancy tax'" (which is common for hotels) to Airbnb bookings. In this study, we investigate the effects of the occupancy tax policy on Airbnb listings, using a combination of a generalized causal forest methodology and a difference-in-differences framework. Further, given the considerable variety among listings on Airbnb, we also estimate the heterogeneous treatment effects of the tax. One key finding is that the tax reduces listing revenues substantially, but Airbnb hosts are reluctant to respond by reducing the listing prices. A second important result is that the tax policy is more favorable to commercial hosts with multiple properties or entire-space ("target'') listings, versus residential hosts with single shared-space ("non-target'') listings. We further show evidence that this heterogeneous treatment effect is caused by customers' discriminatory tax aversion against residential hosts. Stemming from these empirical results we prescribe how hosts should optimally set prices in response to the occupancy tax and also identify the discriminatory tax rates that would equalize the tax's effect across target and non-target listings.

Keywords: sharing economy, Airbnb, tax, machine learning, causal forest, difference-in-differences

JEL Classification: C23, D04, H23, L83