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Manipulation of the Running Variable in the Regression Discontinuity Design: A Density Test

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  • Justin McCrary

Abstract

Standard sufficient conditions for identification in the regression discontinuity design are continuity of the conditional expectation of counterfactual outcomes in the running variable. These continuity assumptions may not be plausible if agents are able to manipulate the running variable. This paper develops a test of manipulation related to continuity of the running variable density function. The methodology is applied to popular elections to the House of Representatives, where sorting is neither expected nor found, and to roll-call voting in the House, where sorting is both expected and found.

Suggested Citation

  • Justin McCrary, 2007. "Manipulation of the Running Variable in the Regression Discontinuity Design: A Density Test," NBER Technical Working Papers 0334, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberte:0334
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    18. Hijzen, Alexander & Mondauto, Leopoldo & Scarpetta, Stefano, 2013. "The Perverse Effects of Job-Security Provisions on Job Security in Italy: Results from a Regression Discontinuity Design," IZA Discussion Papers 7594, Institute of Labor Economics (IZA).
    19. YANAGI, Takahide & 柳, 貴英, 2015. "Regression Discontinuity Designs with Nonclassical Measurement Error," Discussion Papers 2015-09, Graduate School of Economics, Hitotsubashi University.
    20. Cazor Katz, Andre & Acuña, Hector & Carrasco, Diego & Carrasco, Martín, 2017. "Transferencias como Canal de Ventaja Electoral: El Caso de Chile [Discretionary Government Transfers to Catch Votes: The Case of Chile]," MPRA Paper 83668, University Library of Munich, Germany.
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    24. William Rhodes & Sarah Kuck Jalbert, 2013. "Regression Discontinuity Design in Criminal Justice Evaluation," Evaluation Review, , vol. 37(3-4), pages 239-273, June.

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    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables

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