Poverty, redistribution, and the middle class: redistribution via probability distributions vs. redistribution via the linear income tax system

Guillermina Jasso

    Research output: Contribution to journalArticlepeer-review

    Abstract

    It has been known for a long time that (1) when graphs of income amount on income relative rank for two income distributions intersect twice, three “transfer groups” are generated, with the poorest and richest both gaining under the same alternative income distribution and the middle group losing; and (2) the linear income tax system satisfies three fundamental principles of tax justice, namely, that as pretax income increases, three quantities should also increase—posttax income, tax amount, and tax rate. This paper links those two ideas, suggesting that the linear income tax system may be the natural and most effective way to guard against poverty reduction policies which, while helping the poorest, as urged by Rawls, may harm the middle, contributing to the weakening of the middle class, thought at least since Aristotle to be the backbone of society. This paper illustrates the two approaches with one initial distribution and three alternative final distributions, contrasting their minimum, median, proportion below the mean, and inequality. It also shows how to guard the linear income tax system against violating the tax amount principle of tax fairness when there is an injection of resources (e.g., from deficit spending or oil revenues) and how to empirically estimate the parameters (e.g., the marginal tax rate) of the linear income system that the population will regard as fair.

    Original languageEnglish (US)
    Article number1334925
    JournalFrontiers in Sociology
    Volume8
    DOIs
    StatePublished - 2023

    Keywords

    • Pareto distribution
    • income fairness and tax fairness
    • inequality
    • linear income tax system
    • lognormal distribution
    • middle class
    • poverty
    • redistribution

    ASJC Scopus subject areas

    • General Social Sciences

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