Generalized Social Marginal Welfare Weights for Optimal Tax Theory

This paper proposes a theory of optimal taxation using the tax reform approach and generalized social marginal welfare weights to capture social preferences for redistribution. A tax system is optimal if no budget neutral small reform can increase a weighted sum of (money metric) gains and losses across individuals. However, the weights used for aggregating gains and losses are not derived from a standard social welfare function based on individual utilities but instead directly specified to reflect society’s views for justice. Optimum tax formulas take the same form as standard welfarist tax formulas by simply substituting standard marginal social welfare weights with those generalized marginal social welfare weights. We show how the use of suitable generalized social welfare weights can help resolve most of the puzzles of the traditional welfarist approach while retaining constrained Pareto efficiency. In contrast to the welfarist approach, generalized welfare weights can be specified to (1) provide a rich theory of optimal taxation even absent any behavioral responses, (2) treat differently “deserved income” vs. “undeserved income,” (3) treat differently “deserving transfer beneficiaries” vs. “free loaders”, (4) rule out the use of tags unless they can make a Pareto improvement. We show how the most prominent alternatives to utilitarianism such as Libertarianism, Rawlsianism, Equality of Opportunity, Fair Income Taxation, Poverty alleviation, can be re-cast within our theory. Hence, generalized welfare weights can be derived from social justice principles, leading to a normative theory of taxation. Generalized welfare weights can also be derived from estimating actual social preferences of the public, leading to a positive theory of taxation. We use a simple online survey to illustrate this latter approach. ∗Stefanie Stantcheva, MIT, [email protected]; Emmanuel Saez, University of California, Department of Economics, 530 Evans Hall #3880, Berkeley, CA 94720, [email protected]. We thank Marc Fleurbaey, Louis Kaplow, Ben Lockwood, Thomas Piketty, Florian Scheuer, Matthew Weinzierl, and numerous conference participants for useful discussions and comments. We acknowledge financial support from NSF Grant SES-1156240, the MacArthur Foundation, and the Center for Equitable Growth at UC Berkeley.