Corporate Taxation and Evasion Responses: Evidence from a Minimum Tax in Honduras

Abstract: We study corporate responses to a minimum income tax, using the universe of corporate tax filings in Honduras. The policy design allows us to separately estimate cost misreporting under profit taxation and the elasticity of reported revenue. Large corporations overreport true costs when taxed on profits. Taxing revenue leads to a substantial decrease in reported revenues: we estimate an elasticity in the range 0.35-1. The elasticity of revenue is attenuated when third-party information on the revenue of firms is available, suggesting misreporting plays an important role. Our results inform trade-offs when broadening tax bases to curb evasion.
Reducing Interference Bias in Online Marketplace Experiments Using Cluster Randomization: Evidence from a Pricing Meta-Experiment on Airbnb

Abstract: Online marketplace designers frequently run randomized experiments to measure the impact of proposed product changes. However, given that marketplaces are inherently connected, total average treatment effect (TATE) estimates obtained through individual-level randomized experiments may be biased due to violations of the stable unit treatment value assumption, a phenomenon we refer to as “interference bias.” Cluster randomization, i.e., the practice of randomizing treatment assignment at the level of “clusters” of similar individuals, is an established experiment design technique for countering interference bias in social networks, but it is unclear ex ante if it will be effective in marketplace settings. In this paper, we use a meta-experiment or “experiment over experiments” conducted on Airbnb to both provide empirical evidence of interference bias in online market settings and assess the viability of cluster randomization as a tool for reducing interference bias in marketplace TATE estimates. Results from our meta-experiment indicate that at least 19.76% of the TATE estimate produced by an individual-randomized evaluation of the platform fee increase we study is attributable to interference bias and eliminated through the use of cluster randomization. We also find suggestive, non-statistically significant evidence that interference bias in seller-side experiments is more severe in demand-constrained markets, and that the efficacy of cluster randomization at reducing interference bias increases with cluster quality.