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Bunching Estimation of Elasticities Using Stata

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Abstract

A continuous distribution of agents that face a piecewise-linear schedule of incentives results in a distribution of responses with mass points located where the slope (kink) or intercept (notch) of the schedule changes. Bunching methods use these mass points to estimate an elasticity parameter, which summarizes agents' responses to incentives. This article introduces the command bunching, which implements new non-parametric and semi-parametric identification methods for estimating elasticities developed by Bertanha et al. (2021). These methods rely on weaker assumptions than currently made in the literature and result in meaningfully different estimates of the elasticity in various contexts.

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  • Marinho Bertanha & Andrew H. McCallum & Alexis Payne & Nathan Seegert, 2021. "Bunching Estimation of Elasticities Using Stata," Finance and Economics Discussion Series 2021-006, Board of Governors of the Federal Reserve System (U.S.).
    • Handle: RePEc:fip:fedgfe:2021-06
    DOI: 10.17016/FEDS.2021.006
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    Cited by:

    1. Bertanha, Marinho & McCallum, Andrew H. & Seegert, Nathan, 2023. "Better bunching, nicer notching," Journal of Econometrics, Elsevier, vol. 237(2).

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    More about this item

    Keywords

    Bunching; Bunchbounds; Bunchtobit; Bunchfilter; Partial identification; Censored regression; Income elasticity; Tax;
    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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