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A Big Data Approach to Optimal Sales Taxation

Christian Baker, Jeremy Bejarano, Richard W. Evans, Kenneth L. Judd, Kerk L. Phillips

In this study, we characterize and demonstrate a solution method for an optimal commodity (sales) tax problem consisting of multiple goods, heterogeneous agents, and a nonconvex policy maker optimization problem. The contribution of our approach is to allow for more dimensions of heterogeneity than has been previously possible, to incorporate potential model uncertainty and policy objective uncertainty, and to relax some of the assumptions in the previous literature that were necessary to generate a convex optimization problem for the policy maker. Our solution technique involves creating a large database of optimal responses by different individuals for different policy parameters and using \big data” techniques to compute policy maker objective values over these individuals.

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