Regression Discontinuity Methods

Abstract

No AccessJan 2022Regression Discontinuity MethodsAuthors/Editors: Paul Glewwe, Petra ToddPaul GlewweSearch for more papers by this author, Petra ToddSearch for more papers by this authorhttps://doi.org/10.1596/978-1-4648-1497-6_ch14AboutView ChaptersFull TextPDF (0.9 MB) ToolsAdd to favoritesDownload CitationsTrack Citations ShareFacebookTwitterLinked In Abstract: Examines how regression discontinuity (RD) methods can evaluate the impacts of certain types of programs or policies by explaining the intuition for this approach and then providing a more rigorous explanation of the assumptions needed to apply RD estimation. Some programs have specific rules determining participants’ eligibility, such as the value of some individual, family, or community characteristic. In such situations, it may prove possible to estimate the impact of the program using RD methods, which can provide estimates of the impacts of certain types of programs or policies that, under certain conditions, approximate a randomized controlled trial (RCT). In particular, cases provide the best data in which eligibility depends on a continuous variable for which a cutoff point separates the eligible from the ineligible. Until recently, these methods worked mainly in the context of evaluating effects of education interventions in the United States, but now they increasingly apply in both education and noneducation programs in developing countries. ReferencesBuddelmeyer, Hielke and Emmanuel Skoufias. 2004. “An Evaluation of the Performance of Regression Discontinuity Design on Progresa.” Policy Research Working Paper 3386, World Bank, Washington, DC. LinkGoogle ScholarCameron, Colin and Pravin Trivedi. 2005. Microeconometrics: Methods and Applications. New York: Cambridge University Press. CrossrefGoogle ScholarChay, Kenneth, Patrick McEwan, and Miguel Urquiola. 2005. “The Central Role of Noise in Evaluating Interventions That Use Test Scores to Rank Schools.” American Economic Review 95 (4): 1237–58. CrossrefGoogle ScholarEfron, Bradley and Robert Tibshirani. 1993. An Introduction to the Bootstrap. New York: Chapman and Hall. CrossrefGoogle ScholarFan, J. 1992. “Design-Adaptive Nonparametric Regression.” Journal of the American Statistical Association 87 (420): 998–1004. CrossrefGoogle ScholarHahn, Jinyong, Petra Todd, and Wilbert van der Klaauw. 1999. “Evaluating the Effect of an Antidiscrimination Law Using a Regression Discontinuity Design.” NBER Working Paper 7131, National Bureau of Economic Research, Cambridge, MA. CrossrefGoogle ScholarHahn, Jinyong, Petra Todd, and Wilbert van der Klaauw. 2001. “Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design.” Econometrica 69 (1): 201–09. CrossrefGoogle ScholarImbens, Guido W. and Karthik Kalyanaraman. 2012. “Optimal Bandwidth Choice for the Regression Discontinuity Estimator.” Review of Economic Studies 79 (3): 933–59. CrossrefGoogle ScholarLavy, Victor. 2009. “Performance Pay and Teachers’ Effort, Productivity, and Grading Ethics.” American Economic Review 99 (5): 1979–2011. CrossrefGoogle ScholarLee, David and Thomas Lemieux. 2010. “Regression Discontinuity Design in Economics.” Journal of Economic Literature 48 (2): 281–355. CrossrefGoogle ScholarLucas, Adrienne and Isaac Mbiti. 2014. “Effects of School Quality on Student Achievement: Discontinuity Evidence from Kenya.” American Economic Journal: Applied Economics 6 (3): 234–63. CrossrefGoogle ScholarThistlethwaite, Donald L. and Donald T. Campbell. 1960. “Regression-Discontinuity Analysis: An Alternative to the Ex Post Facto Experiment.” Journal of Educational Psychology 51 (6): 309–17. CrossrefGoogle ScholarTrochim, William M. K. 1984. Research Design for Program Evaluation: The Regression-Discontinuity Approach. Beverly Hills: Sage Publications. Google ScholarUrquiola, Miguel and Eric Verhoogen. 2009. “Class-Size Caps, Sorting, and the Regression-Discontinuity Design.” American Economic Review 99 (1): 179–215. CrossrefGoogle Scholarvan der Klaauw, Wilbert. 2002. “Estimating the Effect of Financial Aid Offers on College Enrollment: A Regression Discontinuity Approach.” International Economic Review 43 (4): 1249–87. CrossrefGoogle Scholar Previous chapterNext chapter FiguresreferencesRecommendeddetails View Published: January 2022ISBN: 978-1-4648-1497-6e-ISBN: 978-1-4648-1498-3 Copyright & Permissions Related TopicsEducationMacroeconomics and Economic GrowthScience and Technology Development KeywordsIMPACT EVALUATIONMONITORING AND EVALUATIONM&EPERFORMANCE EVALUATIONEVALUATION APPROACHESREGRESSION ANALYSISPOLICY DESIGN AND IMPLEMENTATIONRANDOMIZED CONTROLLED TRIALSRCTSEDUCATIONAL OUTCOMES PDF DownloadLoading ...