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 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 (“non-target”) 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 non-target listings. We then leverage these empirical results by prescribing 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 non-target and target listings.

Keywords: sharing economy, Airbnb, tax, machine learning, causal forest, difference-in-differences, heterogeneous treatment effect, prescriptive analytics

JEL Classification: C23, D04, H23, L83