Titelaufnahme

Titel
Attrition in randomized control trials : using tracking information to correct bias / Teresa Molina (Millan Nova School of Business and Economics and IZA), Karen Macours (Paris School of Economics and INRA) ; IZA, Institute of Labor Economics
VerfasserMolina Millán, Teresa In der Gemeinsamen Normdatei der DNB nachschlagen In Wikipedia suchen nach Teresa Molina Millán ; Macours, Karen In der Gemeinsamen Normdatei der DNB nachschlagen In Wikipedia suchen nach Karen Macours
KörperschaftForschungsinstitut zur Zukunft der Arbeit In der Gemeinsamen Normdatei der DNB nachschlagen In Wikipedia suchen nach Forschungsinstitut zur Zukunft der Arbeit
ErschienenBonn, Germany : IZA Institute of Labor Economics, April 2017
Ausgabe
Elektronische Ressource
Umfang1 Online-Ressource (83 Seiten) : Diagramme, Karten
SerieDiscussion paper ; no. 10711
URNurn:nbn:de:hbz:5:2-121931 Persistent Identifier (URN)
Zugriffsbeschränkung
 Das Dokument ist frei verfügbar.
Volltexte
Attrition in randomized control trials [1.68 mb]
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Zusammenfassung

This paper starts from a review of RCT studies in development economics, and documents many studies largely ignore attrition once attrition rates are found balanced between treatment arms. The paper analyzes the implications of attrition for the internal and external validity of the results of a randomized experiment with balanced attrition rates, and proposes a new method to correct for attrition bias. We rely on a 10-years longitudinal data set with a final attrition rate of 10 percent, obtained after intensive tracking of migrants, and document the sensitivity of ITT estimates for schooling gains and labour market outcomes for a social program in Nicaragua. We find that not including those found during the intensive tracking leads to an overestimate of the ITT effects for the target population by more than 35 percent, and that selection into attrition is driven by observable baseline characteristics. We propose to correct for attrition using inverse probability weighting with estimates of weights that exploit the similarities between missing individuals and those found during an intensive tracking phase. We compare these estimates with alternative strategies using regression adjustment, standard weights, bounds or proxy information.