Attention Variation and Welfare: Theory and Evidence from a Tax Salience Experiment
By Dmitry Taubinsky, Alex Rees-Jones. 2016
219 citations recorded by the source corpus.
This paper shows that accounting for variation in mistakes can be crucial for welfare analysis. Focusing on consumer underreaction to not-fully-salient sales taxes, we show theoretically that the efficiency costs of taxation are amplified by differences in underreaction across individuals and across tax rates. To empirically assess the importance of these issues, we implement an online shopping experiment in which 2,998 consumers purchase common household products, facing tax rates that vary in size and salience. We replicate prior findings that, on average, consumers underreact to non-salient sales taxes—consumers in our study react to existing sales taxes as if they were only 25% of their size. However, we find significant individual differences in this underreaction, and accounting for this heterogeneity increases the efficiency cost of taxation estimates by at least 200%. Tripling existing sales tax rates nearly doubles consumers’ attention to taxes, and accounting for this endogeneity increases efficiency cost estimates by 336%. Our results provide new insights into the mechanisms and determinants of boundedly rational processing of not-fully-salient incentives, and our general approach provides a framework for robust behavioral welfare analysis.
View Paper: https://www.semanticscholar.org/paper/8018dd20102a8dc15e6f4152fe111ff3a54d9a83
Citations in the corpus
- The Impact of Salience on Investor Behavior: Evidence from a Natural Experiment (2019, unknown): “For example, Chetty, Looney, and Kroft (2009) find that consumer demand responds more strongly to sales tax when it is salient and included in the posted price, compared to when it is added at the register (see also Taubinsky and Rees-Jones 2017).”
- A Behavioral New Keynesian Model (2020, unknown): “(Taubinsky and Rees-Jones (2017)), attention parameters are being progressively better measured (see Gabaix (2017) for a survey), and one can hope that the same thing will happen for macro parameters of attention.”