Taxpayer Bias in Perceived Income Distributions

Daniel Díez-Alonso
Authors registered in the RePEc Author Service: Daniel Díez Alonso

MPRA Paper from University Library of Munich, Germany

Abstract: Contrary to the predictions of classical models, poor people tend to display lower support for redistribution owing to a biased perception about the income distribution in the society. This paper explores the potential effects of income taxes on perceived income distributions and income rank. I present a theoretical model that maps the information from the tax schedule onto informative signals that taxpayers use to infer their perceived position in the income distribution. Using probabilistic regression analysis on the General Social Survey, I find evidence supporting that the changes in perceptions of income ranks observed in the USA in the last decades were influenced by changes in the federal income tax system. To identify causality in a controlled environment, I then test the main predictions of the theoretical model by randomizing tax systems in two online experiments conducted on Amazon Mechanical Turk with American workers. Results of a large pilot identify statistically significant differences between individuals facing a proportional tax system with a unique average tax rate and those facing a progressive tax system with increasing marginal tax rates. Compared to no tax information (control), facing the progressive tax system used in the experiment induces a 12% higher perceived average income level and a 25% lower perceived probability of being above the average income level, while the proportional system does not generate significant differences. These findings encourage further research to identify the exact elements in a tax schedule that generate a bias that can affect support for redistributive policies.

Keywords: income tax; perceived position; income position; redistribution; income bias (search for similar items in EconPapers)
JEL-codes: D03 D31 D63 H24 (search for similar items in EconPapers)
Date: 2020-09-30, Revised 2021-01-10
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