November 4, 2016
Florian Scheuer, Stanford University and NBER, and Iván Werning, MIT and NBER
Scheuer and Werning show that the Diamond and Mirrlees (1971) linear tax model contains the Mirrlees (1971) nonlinear tax model as a special case. In this sense, the Mirrlees model is an application of Diamond-Mirrlees. The researchers also derive the optimal tax formula in Mirrlees from the Diamond-Mirrlees formula. In the Mirrlees model, the relevant compensated crossprice elasticities are zero, providing a situation where an inverse elasticity rule holds. The researchers provide four extensions that illustrate the power and ease of their approach, based on Diamond-Mirrlees, to study nonlinear taxation. First, they consider annual taxation in a lifecycle context. Second, they include human capital investments. Third, they incorporate more general forms of heterogeneity into the basic Mirrlees model. Fourth, they consider an extensive margin labor force participation decision, alongside the intensive margin choice. In all these cases, the relevant optimality condition is easily obtained as an application of the general Diamond-Mirrlees tax formula.
Emmanuel Saez, University of California at Berkeley and NBER, and Stefanie Stantcheva, Harvard University and NBER
This paper develops a theory of optimal capital taxation that expresses optimal tax formulas in sufficient statistics following the methodology of optimal labor income taxation. Saez and Stantcheva first consider a simple model with utility functions linear in consumption and featuring heterogeneous utility for wealth. In this case, there are no transitional dynamics, the steady-state is reached immediately and has finite elasticities of capital with respect to the net-of-tax rate. This allows for a simple and transparent optimal tax analysis with formulas expressed in terms of empirical elasticities and social preferences (as in the optimal labor income tax theory). These formulas have the advantage of being easily taken to the data to simulate optimal taxes, which the researchers do using U.S. tax return data on labor and capital incomes. Second, they show how these results can be extended to a much broader class of utility functions and models. The same types of formulas carry over.
Raj Chetty; David Grusky and Maximilian Hell, Stanford University; Nathaniel Hendren, Harvard University and NBER; Robert Manduca, Harvard University; and Jimmy Narang, University of California at Berkeley
Dmitry Taubinsky, Dartmouth College and NBER, and Alex Rees-Jones, University of Pennsylvania
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, Taubinsky and Rees-Jones show theoretically that the efficiency costs of taxation are amplified by 1) individual differences in underreaction and 2) the degree to which attention is increasing with the size of the tax rate. To empirically assess the importance of these issues, the researchers implement an online shopping experiment in which 2,998 consumers matching the U.S. adult population on key demographics purchase common household products, facing tax rates that vary in size and salience. The researchers find that: 1) there are significant individual differences in underreaction to taxes. Accounting for this heterogeneity increases the efficiency cost of taxation estimates by at least 200%, as compared to estimates generated from a representative agent model. 2) Tripling existing sales tax rates roughly doubles consumers' attention to taxes. The results provide new insights into the mechanisms and determinants of boundedly rational processing of not-fully-salient incentives, and the researchers' general approach provides a framework for robust behavioral welfare analysis.
John Beshears, David Laibson, and Brigitte C. Madrian, Harvard University and NBER; James J. Choi, Yale University and NBER; Christopher D. Clayton, Harvard University; and Christopher Harris, Cambridge University
Matthew C. Weinzierl, Harvard University and NBER
Weinzierl studies how U.S. survey respondents' views on distributive justice are shown to differ in two specific, related ways from what is conventionally assumed in modern optimal tax research. A large share of respondents, and in some cases a large majority, resist the full equalization of inequality due to brute luck that standard analyses would recommend. Related, a similar share prefer a classical benefit-based logic for the assignment of taxes over the conventional logic of diminishing marginal social welfare. Moreover, these two views are linked: respondents who more strongly resist equalization are more likely to prefer the classical benefit-based principle. Together, these results suggest that a large share of the American public views the allocation of pre-tax incomes as relevant to optimal tax policy andat least in partjustly deserved unless proven otherwise, judgments that are inconsistent with standard welfarist objectives.
Brian G. Knight, Brown University and NBER, and Nathan M. Schiff, Shanghai University of Finance and Economics
Public universities in the United States typically charge much higher tuition to non-residents. Perhaps due, at least in part, to these differences in tuition, roughly 75 percent of students nationwide attend in-state institutions. While distinguishing between residents and non-residents is consistent with welfare maximization by state governments, it may lead to economic inefficiencies from a national perspective, with potential welfare gains associated with reducing the gap between in-state and out-of-state tuition. Knight and Schiff first formalize this idea in a simple model. While a social planner maximizing national welfare does not distinguish between residents and non-residents, state governments set higher tuition for non-residents. The welfare gains from reducing this tuition gap can be characterized by a sufficient statistic relating out-of-state enrollment to the tuition gap. The researchers then estimate this sufficient statistic via a border discontinuity design using data on the geographic distribution of student residences by institution.