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Better Bunching, Nicer Notching

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Abstract

We study the bunching identification strategy for an elasticity parameter that summarizes agents' response to changes in slope (kink) or intercept (notch) of a schedule of incentives. A notch identifies the elasticity but a kink does not, when the distribution of agents is fully flexible. We propose new non-parametric and semi-parametric identification assumptions on the distribution of agents that are weaker than assumptions currently made in the literature. We revisit the original empirical application of the bunching estimator and find that our weaker identification assumptions result in meaningfully different estimates. We provide the Stata package bunching to implement our procedures.

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  • Marinho Bertanha & Andrew H. McCallum & Nathan Seegert, 2021. "Better Bunching, Nicer Notching," Finance and Economics Discussion Series 2021-002, Board of Governors of the Federal Reserve System (U.S.).
    • Handle: RePEc:fip:fedgfe:2021-02
    DOI: 10.17016/FEDS.2021.002
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    Cited by:

    1. Andreas R. Kostøl & Andreas S. Myhre, 2021. "Labor Supply Responses to Learning the Tax and Benefit Schedule," American Economic Review, American Economic Association, vol. 111(11), pages 3733-3766, November.
    2. Neryvia Pillay Bell, 2020. "Taxpayer responsiveness to taxation: Evidence from bunching at kink points of the South African income tax schedule," WIDER Working Paper Series wp-2020-68, World Institute for Development Economic Research (UNU-WIDER).
    3. Alinaghi, Nazila & Creedy, John & Gemmell, Norman, 2020. "Do Couples Bunch More? Evidence from Partnered and Single Taxpayers in New Zealand," Working Paper Series 9366, Victoria University of Wellington, Chair in Public Finance.
    4. Marinho Bertanha & Andrew H. McCallum & Alexis Payne & Nathan Seegert, 2022. "Bunching estimation of elasticities using Stata," Stata Journal, StataCorp LP, vol. 22(3), pages 597-624, September.
    5. Pablo Gutierrez Cubillos, 2022. "Dividend tax credits and the elasticity of taxable income: evidence from small businesses," Working Papers 630, ECINEQ, Society for the Study of Economic Inequality.
    6. Carolina Caetano & Gregorio Caetano & Hao Fe & Eric R. Nielsen, 2021. "A Dummy Test of Identification in Models with Bunching," Finance and Economics Discussion Series 2021-068, Board of Governors of the Federal Reserve System (U.S.).
    7. Aronsson, Thomas & Jenderny, Katharina & Lanot, Gauthier, 2021. "Maximum Likelihood Bunching Estimators of the ETI," Umeå Economic Studies 987, Umeå University, Department of Economics.

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    More about this item

    Keywords

    Partial identification; Censored regression; Bunching; Notching;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • H24 - Public Economics - - Taxation, Subsidies, and Revenue - - - Personal Income and Other Nonbusiness Taxes and Subsidies
    • J20 - Labor and Demographic Economics - - Demand and Supply of Labor - - - General

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