Control Function Methods

Abstract

Listen

Translate article

No AccessJan 2022Control Function 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_ch16AboutView ChaptersFull TextPDF (0.9 MB) ToolsAdd to favoritesDownload CitationsTrack Citations ShareFacebookTwitterLinked In Abstract: Stresses that control function estimators explicitly recognize that nonrandom selection into a program may give rise to an endogeneity problem—nonrandom selection could cause participation to correlate with unobserved factors that influence the outcome variable. When randomized controlled trials prove unusable to estimate program impacts, different types of estimators can work for nonexperimental data. An important issue when applying the various methods remains whether they assume that program participation could depend on unobserved factors. If this proves possible, one alternative remains instrumental variables (IV) methods and control function methods. Economists have used IV methods for many decades, but control function methods remain a relatively new approach. Control function methods remove bias caused by selection on unobservables by explicitly modeling the selection process and how it relates to the observed outcomes. The major challenge in applying these methods for estimating program impacts comes from the need to separately identify the intercept of the control function from the treatment effect. ReferencesAndrews, Donald and Marcia Schafgans. 1998. “Semiparametric Estimation of the Intercept of a Sample Selection Model.” Review of Economic Studies 65 (3): 497–518. CrossrefGoogle ScholarGreene, William. 2012. Econometric Analysis, Seventh Edition. Upper Saddle River, NJ: Prentice Hall. Google ScholarHeckman, James. 1979. “Sample Selection Bias as a Specification Error.” Econometrica 47 (1): 153–61. CrossrefGoogle ScholarHeckman, James. 1980. “Addendum to Sample Selection Bias as Specification Error.” In Evaluation Studies Review Annual, edited by Stromsdorfer, E and G Frakas. San Francisco: Sage. Google ScholarHeckman, James. 1990. “Varieties of Selection Bias.” American Economic Review 80 (2): 313–18. Google ScholarHeckman, James and Bo Honoré. 1990. “The Empirical Content of the Roy Model.” Econometrica 58 (5): 1121–49. CrossrefGoogle ScholarHeckman, James, Hidehiko Ichimura, Jeffrey Smith, and Petra Todd. 1998. “Characterizing Selection Bias Using Experimental Data.’’ Econometrica 66 (5): 1017–98. CrossrefGoogle ScholarHeckman, James and Salvador Navarro. 2004. “Using Matching, Instrumental Variables, and Control Functions to Estimate Economic Choice Models.” Review of Economics and Statistics 86 (1): 30–57. CrossrefGoogle ScholarHeckman, James and Richard Robb. 1985. “Alternative Methods for Evaluating the Impact of Interventions.’’ In Longitudinal Analysis of Labor Market Data, edited by Heckman, James and Burton Singer, 156–264. Cambridge: Cambridge University Press. CrossrefGoogle ScholarHeckman, James and Richard Robb. 1986. “Alternative Methods for Solving the Problem of Selection Bias in Evaluating the Impact of Treatments on Outcomes.” In Drawing Inferences from Self-Selected Samples, edited by Wainer, H, 63–108. New York: Springer-Verlag. CrossrefGoogle ScholarIchimura, Hideko and Petra Todd. 2007. “Implementing Nonparametric and Semiparametric Estimators.” In Handbook of Econometrics, Vol. 6B, edited by Heckman, J and E Leamer. Amsterdam: Elsevier. CrossrefGoogle ScholarKlein, Roger and Richard Spady. 1993. “An Efficient Semiparametric Estimator for Binary Response Models.” Econometrica 61 (2): 387–421. CrossrefGoogle ScholarRoy, Andrew D. 1951. “Some Thoughts on the Distribution of Earnings.” Oxford Economic Papers 3 (2): 135–46. CrossrefGoogle ScholarSapelli, Claudio and Bernardita Vial. 2002. “The Performance of Private and Public Schools in the Chilean Voucher System.” Cuardenos do Economía 39 (118): 423–54. Google ScholarWooldridge, Jeffrey. 2010. Econometric Analysis of Cross Section and Panel Data, 2nd edition. Cambridge, MA: MIT Press. Google Scholar Previous chapterNext chapter FiguresreferencesRecommendeddetails View Published: January 2022ISBN: 978-1-4648-1497-6e-ISBN: 978-1-4648-1498-3 Copyright & Permissions Related TopicsMacroeconomics and Economic GrowthScience and Technology Development KeywordsIMPACT EVALUATIONMONITORING AND EVALUATIONM&EPERFORMANCE EVALUATIONEVALUATION APPROACHESREGRESSION ANALYSISPOLICY DESIGN AND IMPLEMENTATIONRANDOMIZED CONTROLLED TRIALSRCTSCONTROL FUNCTION METHODS PDF DownloadLoading ...