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Power calculations for regression-discontinuity designs

Author

Listed:
  • Matias Cattaneo

    (University of Michigan)

  • Rocio Titiunik

    (University of Michigan)

  • Gonzalo Vazquez-Bare

    (University of California, Santa Barbara)

Abstract

In this article, we introduce two commands, rdpow and rdsampsi, that conduct power calculations and survey sample selection when using local polyno- mial estimation and inference methods in regression-discontinuity designs. rdpow conducts power calculations using modern robust bias-corrected local polynomial inference procedures and allows for new hypothetical sample sizes and bandwidth selections, among other features. rdsampsi uses power calculations to compute the minimum sample size required to achieve a desired level of power, given estimated or user-supplied bandwidths, biases, and variances. Together, these commands are useful when devising new experiments or surveys in regression-discontinuity designs, which will later be analyzed using modern local polynomial techniques for estimation, inference, and falsification. Because our commands use the community- contributed (and R) package rdrobust for the underlying bandwidths, biases, and variances estimation, all the options currently available in rdrobust can also be used for power calculations and sample-size selection, including preintervention covariate adjustment, clustered sampling, and many bandwidth selectors. Finally, we also provide companion R functions with the same syntax and capabilities.

Suggested Citation

  • Matias Cattaneo & Rocio Titiunik & Gonzalo Vazquez-Bare, 2019. "Power calculations for regression-discontinuity designs," Stata Journal, StataCorp LP, vol. 19(1), pages 210-245, March.
  • Handle: RePEc:tsj:stataj:v:19:y:2019:i:1:p:210-245
    DOI: 10.1177/1536867X19830919
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    2. Bach, Laurent & Guillouzouic, Arthur & Malgouyres, Clément, 2021. "Does holding elections during a Covid-19 pandemic put the lives of politicians at risk?," Journal of Health Economics, Elsevier, vol. 78(C).
    3. Yörük, Barış K., 2023. "Does public policy affect attitudes? Evidence from age-based health insurance coverage policies in the United States," Journal of Economic Behavior & Organization, Elsevier, vol. 205(C), pages 287-302.
    4. Bagues, Manuel & Campa, Pamela, 2021. "Can gender quotas in candidate lists empower women? Evidence from a regression discontinuity design," Journal of Public Economics, Elsevier, vol. 194(C).
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    8. Stommes, Drew & Aronow, P. M. & Sävje, Fredrik, 2023. "On the Reliability of Published Findings Using the Regression Discontinuity Design in Political Science," I4R Discussion Paper Series 22, The Institute for Replication (I4R).
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    16. Anthony Harris & Anthony D'Agostino & Sara Litke-Farzaneh & Beryl Seiler & Matt Sloan, "undated". "Morocco Land Productivity Project: Evaluation Design Report," Mathematica Policy Research Reports f3fc788501b64608b17e1cb23, Mathematica Policy Research.
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    18. Vanessa Cirulli & Giuliano Resce & Marco Ventura, 2021. "Co-payment exemption and healthcare consumption. Quasi-experimental evidence from Italy," Working Papers in Public Economics 203, University of Rome La Sapienza, Department of Economics and Law.
    19. Bouke Klein Teeselink & Martijn J. van den Assem & Dennie van Dolder, 2023. "Does Losing Lead to Winning? An Empirical Analysis for Four Sports," Management Science, INFORMS, vol. 69(1), pages 513-532, January.
    20. Tu T. Nguyen & Barış K. Yörük, 2020. "Aging out of dependent coverage and the effects on the use of inpatient medical care," International Journal of Health Economics and Management, Springer, vol. 20(4), pages 381-390, December.

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