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Data and Code for: High Marginal Tax Rates on the Top 1%? Lessons from a...
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Updated Nov 16, 2020
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EmailClick to copy linkLink copiedCiteDirk Krueger; Fabian Kindermann (2020). Data and Code for: High Marginal Tax Rates on the Top 1%? Lessons from a Life Cycle Model with Idiosyncratic Income Risk [Dataset]. http://doi.org/10.3886/E126741V1Unique identifierhttps://doi.org/10.3886/E126741V1Dataset updatedNov 16, 2020Dataset provided byAmerican Economic AssociationAuthorsDirk Krueger; Fabian KindermannLicensehttps://opensource.org/licenses/GPL-3.0https://opensource.org/licenses/GPL-3.0
DescriptionThis paper argues that high marginal labor income tax rates on top earners are an effective tool for social insurance even when households have high labor supply elasticity, make dynamic savings decisions, and policies have general equilibrium effects. We construct a large scale overlapping generations model with uninsurable labor productivity risk, show that it has a realistic wealth distribution and numerically characterize the optimal top marginal rate. We find that marginal tax rates for top 1% earners of 79% are optimal as long as the model earnings and wealth distributions display a degree of concentration as observed in US data.
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FacebookTwitterEmailData and Code for: High Marginal Tax Rates on the Top 1%? Lessons from a Life Cycle Model with Idiosyncratic Income Risk
https://opensource.org/licenses/GPL-3.0https://opensource.org/licenses/GPL-3.0
This paper argues that high marginal labor income tax rates on top earners are an effective tool for social insurance even when households have high labor supply elasticity, make dynamic savings decisions, and policies have general equilibrium effects. We construct a large scale overlapping generations model with uninsurable labor productivity risk, show that it has a realistic wealth distribution and numerically characterize the optimal top marginal rate. We find that marginal tax rates for top 1% earners of 79% are optimal as long as the model earnings and wealth distributions display a degree of concentration as observed in US data.