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Annual Review of Political Science

Volume 19, 2016

Misunderstandings About the Regression Discontinuity Design in the Study of Close Elections*

Abstract

Recently, the regression discontinuity (RD) design has become increasingly popular among social scientists. One prominent application is the study of close elections. We explicate several methodological misunderstandings widespread across disciplines by revisiting the controversy concerning the validity of RD design when applied to close elections. Although many researchers invoke the local or as-if-random assumption near the threshold, it is more stringent than the required continuity assumption. We show that this seemingly subtle point determines the appropriateness of various statistical methods and changes our understanding of how sorting invalidates the design. When multiple-testing problems are also addressed, we find that evidence for sorting in US House elections is substantially weaker and highly dependent on estimation methods. Finally, we caution that despite the temptation to improve the external validity, the extrapolation of RD estimates away from the threshold sacrifices the design's advantage in internal validity.

Keyword(s): as-if-random assumption, continuity, extrapolation, multiple testing, placebo test, sorting
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/content/journals/10.1146/annurev-polisci-032015-010115
2016-05-11
2024-04-18
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Literature Cited

  1. Angrist J, Rokkanen M. 2015. Wanna get away? RD identification away from the cutoff. J. Am. Stat. Assoc. 1105121331–44
  2. Benjamini Y, Hochberg Y. 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57:1289–300 [Google Scholar]
  3. Benjamini Y, Yekutieli D. 2001. The control of the false discovery rate in multiple testing under dependency. Ann. Stat. 29:41165–88 [Google Scholar]
  4. Boas T, Hidalgo D, Richardson N. 2014. The spoils of victory: campaign donations and government contracts in Brazil. J. Polit. 76:2415–29 [Google Scholar]
  5. Butler DM. 2009. A regression discontinuity design analysis of the incumbency advantage and tenure in the U.S. House.. Elect. Stud. 28:1123–28 [Google Scholar]
  6. Calonico S, Cattaneo M, Titiunik R. 2014. Robust nonparametric confidence intervals for regression-discontinuity designs. Econometrica 82:62295–326 [Google Scholar]
  7. Calonico S, Cattaneo M, Titiunik R. 2015. : an package for robust nonparametric inference in regression-discontinuity designs. TheJournal 7:138–51 [Google Scholar]
  8. Cattaneo MD, Frandsen B, Titiunik R. 2015a. Randomization inference in the regression discontinuity design: an application to party advantages in the U.S. Senate.. J. Causal Inference 3:11–24 [Google Scholar]
  9. Cattaneo MD, Keele L, Titiunik R, Vazquez-Bare G. 2015b. Interpreting regression discontinuity designs with multiple cutoffs Work. pap., Univ. Mich., Penn. State Univ.
  10. Cattaneo MD, Titiunik R, Vazquez-Bare G. 2015c. Comparing inference approaches in RD designs: a reexamination of the effect of Head Start on child mortality Work. pap., Univ. Mich.
  11. Caughey D, Sekhon JS. 2011. Elections and the regression discontinuity design: lessons from close U. S. House races. 1942–2008 Polit. Anal. 19:4385–408 [Google Scholar]
  12. Dinas E. 2014. Does choice bring loyalty? Electoral participation and the development of party identification. Am. J. Polit. Sci. 58:2449–65 [Google Scholar]
  13. Dunning T. 2008. Improving causal inference: strengths and limitations of natural experiments. Polit. Res. Q. 61:2282–93 [Google Scholar]
  14. Dunning T, Nilekani J. 2013. Ethnic quotas and political mobilization: caste, parties, and distribution in Indian village councils. Am. Polit. Sci. Rev. 107:35–56 [Google Scholar]
  15. Eggers A, Fowler A, Hainmueller J, Hall A, Snyder J. 2015a. On the validity of the regression discontinuity design for estimating electoral effects: new evidence from over 40,000 close races. Am. J. Polit. Sci. 59:1259–74 [Google Scholar]
  16. Eggers AC, Freier R, Grembi V, Nannicini T. 2015b. Regression discontinuity designs based on population thresholds: pitfalls and solutions DIW Berlin Disc. Pap. No. 1503
  17. Eggers AC, Hainmueller J. 2009. MPs for sale? Returns to office in postwar British politics. Am. Polit. Sci. Rev. 103:4513 [Google Scholar]
  18. Erikson R, Folke O, Snyder J. 2015. A gubernatorial helping hand? How governors affect presidential elections. J. Polit. 77:2491–504 [Google Scholar]
  19. Erikson R, Rader K. 2013. Much ado about nothing: RDD and the incumbency advantage Presented at Annu. Eur. Polit. Sci. Assoc. Conv., 3rd, Barcelona, June 20–22
  20. Fan J, Gijbels I. 1996. Local Polynomial Modelling and Its Applications Boca Raton, FL: Chapman & Hall/CRC
  21. Folke O, Snyder J. 2012. Gubernatorial midterm slumps. Am. J. Polit. Sci. 56:4931–48 [Google Scholar]
  22. Fouirnaies A, Hall A. 2014. The size and sources of the financial incumbency advantage: evidence from American legislatures. J. Polit. 76:3711–24 [Google Scholar]
  23. Friedman J, Holden R. 2009. The rising incumbent reelection rate: What's gerrymandering got to do with it?. J. Polit. 71:2593–611 [Google Scholar]
  24. Galasso V, Nannicini T. 2011. Competing on good politicians. Am. Polit. Sci. Rev. 105:179–99 [Google Scholar]
  25. Gerber AS, Kessler DP, Meredith M. 2011. The persuasive effects of direct mail: a regression discontinuity based approach. J. Polit. 73:01140–55 [Google Scholar]
  26. Gerber ER, Hopkins DJ. 2011. When mayors matter: estimating the impact of mayoral partisanship on city policy. Am. J. Polit. Sci. 55:2326–39 [Google Scholar]
  27. Grimmer J, Hersh E, Feinstein B, Carpenter D. 2011. Are close elections random? Work. pap., Stanford Univ., Yale Univ., Harvard Univ.
  28. Hainmueller J, Hall AB, Snyder JM. 2015. Assessing the external validity of election RD estimates: an investigation of the incumbency advantage. J. Polit. 77:3707–20 [Google Scholar]
  29. Holbein J, Hillygus S. 2016. Making young voters: the impact of preregistration on youth turnout. Am. J. Polit. Sci. In press
  30. Imbens G, Kalyanaraman K. 2012. Optimal bandwidth choice for the regression discontinuity estimator. Rev. Econ. Stud. 79:3933–59 [Google Scholar]
  31. Imbens GW, Lemieux T. 2008. Regression discontinuity designs: a guide to practice. J. Econom. 142:2615–35 [Google Scholar]
  32. Keele L, Titiunik R. 2015. Geographic boundaries as regression discontinuities. Polit. Anal. 23:1127–55 [Google Scholar]
  33. Keele L, Titiunik R. 2016. Natural experiments based on geography. Polit. Sci. Res. Methods 4:165–95 [Google Scholar]
  34. Krasno JS, Green DP. 2008. Do televised presidential ads increase voter turnout? Evidence from a natural experiment. J. Polit. 70:1245–61 [Google Scholar]
  35. Lee D, Lemieux T. 2010. Regression discontinuity designs in economics. J. Econ. Lit. 48:281–355 [Google Scholar]
  36. Lee D, Moretti E, Butler M. 2004. Do voters affect or elect policies? Evidence from the US House. Q. J. Econ. 119:3807–59 [Google Scholar]
  37. Lee DS. 2008. Randomized experiments from non-random selection in U.S. House elections. J. Econom. 142:2675–97 [Google Scholar]
  38. Legewie J. 2013. Terrorist events and attitudes toward immigrants: a natural experiment. Am. J. Sociol. 118:51199–245 [Google Scholar]
  39. McCrary J. 2008. Manipulation of the running variable in the regression discontinuity design: a density test. J. Econom. 142:2698–714 [Google Scholar]
  40. Pierce L, Rogers T, Snyder JA. 2016. Losing hurts: the happiness impact of partisan electoral loss. J. Exp. Polit. Sci. In press
  41. Posner DN. 2004. The political salience of cultural difference: why Chewas and Tumbukas are allies in Zambia and adversaries in Malawi. Am. Polit. Sci. Rev. 98:4529–45 [Google Scholar]
  42. Samii C. 2013. Perils or promise of ethnic integration? Evidence from a hard case in Burundi. Am. Polit. Sci. Rev. 107:3558–73 [Google Scholar]
  43. Skovron C, Titiunik R. 2015. A practical guide to regression discontinuity Work. pap., Dep. Polit. Sci., Univ. Mich.
  44. Snyder J. 2005. Detecting manipulation in U.S. House elections. Work. pap. 2–15, Haas School Bus., Univ. Calif. Berkeley
  45. Thistlewaite D, Campbell D. 1960. Regression-discontinuity analysis: an alternative to the ex-post-facto experiment. J. Educ. Psychol. 51:6309–17 [Google Scholar]
  46. Wing C, Cook TD. 2013. Strengthening the regression discontinuity design using additional design elements: a within-study comparison. J. Policy Anal. Manag. 32:4853–77 [Google Scholar]
/content/journals/10.1146/annurev-polisci-032015-010115
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Misunderstandings About the Regression Discontinuity Design in the Study of Close Elections*
Annual Review of Political Science 19, 375 (2016); https://doi.org/10.1146/annurev-polisci-032015-010115
/content/journals/10.1146/annurev-polisci-032015-010115
/content/journals/10.1146/annurev-polisci-032015-010115
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