Measuring Schmeduling

• 成果类型: Article
• 署名作者: Rees-Jones, Alex; Taubinsky, Dmitry
• 署名单位: Cornell University; National Bureau of Economic Research; University of California System; University of California Berkeley
• 刊物名称: REVIEW OF ECONOMIC STUDIES
• ISSN/ISSBN: 0034-6527
• DOI: 10.1093/restud/rdz045
• 发表日期: 2020
• 页码: 2399-2438
• 关键词: income-tax rates; health-insurance; optimal taxation; redistribution evidence; information frictions; adverse selection; deadweight loss; taxable income; preferences; SALIENCE

摘要

What mental models do individuals use to approximate their tax schedule? Using incentivized forecasts of the U.S. Federal income tax schedule, we estimate the prevalence of the schmeduling heuristics for constructing mental representations of nonlinear incentive schemes. We find evidence of widespread reliance on the ironing heuristic, which linearizes the tax schedule using one's average tax rate. In our preferred specification, 43% of the population irons. We find no evidence of reliance on the spotlighting heuristic, which linearizes the tax schedule using one's marginal tax rate. We show that the presence of ironing rationalizes a number of empirical patterns in individuals' perceptions of tax liability across the income distribution. Furthermore, while our empirical framework accommodates a rich class of other misperceptions, we find that a simple model including only ironers and correct forecasters accurately predicts average underestimation of marginal tax rates. We replicate our finding of prevalent ironing, and a lack of other systematic misperceptions, in a controlled experiment that studies real-stakes decisions across exogenously varied tax schedules. To illustrate the policy relevance of the ironing heuristic, we show that it augments the benefits of progressive taxation in a standard model of earnings choice. We quantify these benefits in a calibrated model of the U.S. tax system.