2021
DOI: 10.1162/rest_a_00910
|View full text |Cite
|
Sign up to set email alerts
|

Inferring Tax Compliance from Pass-Through: Evidence from Airbnb Tax Enforcement Agreements

Abstract: Tax enforcement is especially costly when market participants are difficult to observe. The benefits of enforcement depend crucially on pre-enforcement compliance. We derive an upper bound on pre-enforcement compliance from the pass-through of newlyenforced taxes. Using data on Airbnb listings and the platform's voluntary collection agreements, we find that taxes are paid on, at most, 24 percent of Airbnb transactions prior to enforcement. We also find that demand for Airbnb listings is inelastic, driving thre… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

1
17
0

Year Published

2022
2022
2023
2023

Publication Types

Select...
8

Relationship

0
8

Authors

Journals

citations
Cited by 19 publications
(18 citation statements)
references
References 13 publications
1
17
0
Order By: Relevance
“…Recent empirical work seems to show, for example, that the enforcement 90 For further discussion, see Cui and Hashimzade, supra note 82. of sales and hotel taxes on transactions on AirBnB resulted in higher listing prices, with most of the burden of the taxes passed onto final consumers. 91 Yet findings like this are consistent with both the underlying assumptions and the policy intent of a DST designed as a tax on LSR. In terms of underlying assumptions, the empirical evidence supports the supposition that in AirBnB's business model, landlords are subsidized, and profit is made from charging guests.…”
Section: B Pass-through Of Positive Marginal Costssupporting
confidence: 70%
“…Recent empirical work seems to show, for example, that the enforcement 90 For further discussion, see Cui and Hashimzade, supra note 82. of sales and hotel taxes on transactions on AirBnB resulted in higher listing prices, with most of the burden of the taxes passed onto final consumers. 91 Yet findings like this are consistent with both the underlying assumptions and the policy intent of a DST designed as a tax on LSR. In terms of underlying assumptions, the empirical evidence supports the supposition that in AirBnB's business model, landlords are subsidized, and profit is made from charging guests.…”
Section: B Pass-through Of Positive Marginal Costssupporting
confidence: 70%
“…On that basis, Bibler, Teltser, and Tremblay (2018), with data on 27 US major metropolitan areas and more than 860,000 properties active between August 2014 and September 2017, estimate that a 10% tax reduces prices charged by hosts by 2.4% and increases prices paid by guests in 7.6%; so, given that demand is more inelastic than supply, the tax burden falls mainly on guests. Hence, the authors assert that tax enforcement is an ineffective policy lever for interest groups seeking to reduce local Airbnb activity.…”
Section: Methodsmentioning
confidence: 99%
“…Online markets are increasingly used for trading home-improvement services (Initiative D21, 2015(Initiative D21, , 2016. One challenge is to ensure the tax compliance of platform users (e.g., Alm and Melnik, 2010;Bibler et al, 2020). 2 We study sellers' behavior in two regulatory environments often seen in online platforms.…”
Section: Introductionmentioning
confidence: 99%
“…Third, we add to the literature on third-party reporting and tax compliance (e.g., Kleven et al, 2011;Pomeranz, 2015;Naritomi, 2019) by showing that even in a developed country like Germany, in which incentives for third-party reporting exist, a substantial fraction of sellers intend to evade. Fourth, we contribute to the recent literature examining how access to evasion opportunities (or the elimination thereof) affects prices (Doerrenberg and Duncan, 2019;Asatryan and Gomtsyan, 2020;Bibler et al, 2020) by providing an estimate of the price difference in a given transaction. Fifth, we contribute to the literature on tax compliance in online markets (e.g., Bibler et al, 2020) by showing that sellers' types and behaviors are different in the two markets.…”
Section: Introductionmentioning
confidence: 99%