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Regression Discontinuity Applications with Rounding Errors in the Running Variable

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  • Yingying Dong

    (Department of Economics, University of California-Irvine)

Abstract

Many empirical applications of regression discontinuity (RD) models use a running variable that is rounded and hence is discrete, e.g., age in years, or birth weight in ounces. This paper shows that standard RD estimation using a rounded discrete running variable leads to inconsistent estimates of treatment effects, even when the true functional form relating the outcome and the running variable is known and is correctly specified. This paper provides simple formulas to correct for this discretization bias. The proposed approach does not require instrumental variables, but instead uses information regarding the distribution of rounding errors, which is easily obtained and often close to uniform. The proposed approach is applied to estimate the effect of Medicare on insurance coverage in the US, and to investigate the retirement-consumption puzzle in China, utilizing the Chinese mandatory retirement policy.

Suggested Citation

  • Yingying Dong, 2012. "Regression Discontinuity Applications with Rounding Errors in the Running Variable," Working Papers 111206, University of California-Irvine, Department of Economics.
    • Handle: RePEc:irv:wpaper:111206
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    File URL: https://www.economics.uci.edu/files/docs/workingpapers/2011-2012/dong-06.pdf
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    Cited by:

    1. Davezies, Laurent & Le Barbanchon, Thomas, 2017. "Regression discontinuity design with continuous measurement error in the running variable," Journal of Econometrics, Elsevier, vol. 200(2), pages 260-281.
    2. Olivier Bargain & Delphine Boutin, 2021. "Minimum Age Regulation and Child Labor: New Evidence from Brazil," The World Bank Economic Review, World Bank, vol. 35(1), pages 234-260.
    3. Michal Kolesár & Christoph Rothe, 2018. "Inference in Regression Discontinuity Designs with a Discrete Running Variable," American Economic Review, American Economic Association, vol. 108(8), pages 2277-2304, August.
    4. Yingying Dong & Dennis Yang, 2016. "Mandatory Retirement and the Consumption Puzzle: Prices Decline or Quantities Decline?," Upjohn Working Papers 16-251, W.E. Upjohn Institute for Employment Research.
    5. Takahide Yanagi, 2014. "The Effect of Measurement Error in the Sharp Regression Discontinuity Design," KIER Working Papers 910, Kyoto University, Institute of Economic Research.
    6. Arthur Lewbel, 2012. "An Overview of the Special Regressor Method," Boston College Working Papers in Economics 810, Boston College Department of Economics.
    7. Ciani, Emanuele, 2016. "Retirement, pension eligibility and home production," Labour Economics, Elsevier, vol. 38(C), pages 106-120.
    8. Daniel Burkhard, 2015. "Consumption smoothing at retirement: average and quantile treatment effects in the regression discontinuity design," Diskussionsschriften dp1512, Universitaet Bern, Departement Volkswirtschaft.

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    More about this item

    Keywords

    Regression discontinuity; Rounding; Rounding errors; Discrete running variable;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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