
handle: 10419/171003 , 10419/171118
AbstractProviding estimable sufficient statistics to give policy prescriptions has become a widespread approach, a well-known limitation of which is the endogeneity of sufficient statistics to the policy. Using optimal tax policy as our field of application, we highlight a new source of endogeneity. It arises under multidimensional heterogeneity, because optimal tax formulas are then expressed as functions of weighted means of sufficient statistics computed at the individual level and the weights are endogenous to the tax policy. We analytically show that ignoring these composition effects leads to underestimate the optimal linear tax and, under a restrictive set of assumptions, the optimal nonlinear tax as well. In the latter case, we use an improved tax perturbation approach to study composition effects without these assumptions. Numerical simulations on US data suggest the optimal tax rate may be underestimated by 6 p.p. for high incomes levels. We also relate our tax perturbation method to the first-order mechanism design method, both methods having hitherto been used separately to derive optimal tax schedules.
H.H2.H23 - Externalities • Redistributive Effects • Environmental Taxes and Subsidies, multidimensional screening problems, ddc:330, allocation perturbation, optimal taxation, composition effects, [QFIN] Quantitative Finance [q-fin], tax perturbation, Optimal taxation, sufficient statistics, H21, [SHS.ECO] Humanities and Social Sciences/Economics and Finance, H.H2.H21 - Efficiency • Optimal Taxation
H.H2.H23 - Externalities • Redistributive Effects • Environmental Taxes and Subsidies, multidimensional screening problems, ddc:330, allocation perturbation, optimal taxation, composition effects, [QFIN] Quantitative Finance [q-fin], tax perturbation, Optimal taxation, sufficient statistics, H21, [SHS.ECO] Humanities and Social Sciences/Economics and Finance, H.H2.H21 - Efficiency • Optimal Taxation
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 33 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
