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A Big Data Approach to Optimal Sales Taxation

Author

Listed:
  • Christian Baker

    (Department of Economics, Brigham Young University)

  • Jeremy Bejarano

    (Department of Economics, University of Chicago)

  • Richard W. Evans

    (Department of Economics, Brigham Young University)

  • Kenneth L. Judd

    (Hoover Institution, Stanford University)

  • Kerk L. Phillips

    (Department of Economics, Brigham Young University)

Abstract
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. Our approach allows for more dimensions of heterogeneity than has been previously possible, incorporates potential model uncertainty and policy objective uncertainty, and relaxes 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. We calibrate our model to the United States and test the effects of a differentiated optimal commodity tax versus a flat tax and the effect of exempting a broad class of goods (services) from commodity taxation. We find that only a potentially small amount of tax revenue is lost for a given societal welfare level by departing from an optimal differentiated sales tax schedule to a uniform flat tax and that there is only a small loss in revenue from exempting a class of goods such as services in the United States.

Suggested Citation

  • Christian Baker & Jeremy Bejarano & Richard W. Evans & Kenneth L. Judd & Kerk L. Phillips, 2014. "A Big Data Approach to Optimal Sales Taxation," BYU Macroeconomics and Computational Laboratory Working Paper Series 2014-03, Brigham Young University, Department of Economics, BYU Macroeconomics and Computational Laboratory.
    • Handle: RePEc:byu:byumcl:201403
    as

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    References listed on IDEAS

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    Cited by:

    1. Islam, Nizamul & Colombino, Ugo, 2018. "The case for NIT+FT in Europe. An empirical optimal taxation exercise," Economic Modelling, Elsevier, vol. 75(C), pages 38-69.

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    More about this item

    Keywords

    lOptimal tax; sales tax; commodity tax; big data; robustness;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • E62 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Fiscal Policy; Modern Monetary Theory
    • H21 - Public Economics - - Taxation, Subsidies, and Revenue - - - Efficiency; Optimal Taxation

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