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Attrition in Randomized Control Trials: Using tracking information to correct bias

Karen Macours and Teresa Molina Millan ()

No 11962, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: 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.

Keywords: attrition; long-term evaluation; randomized control trials (search for similar items in EconPapers)
JEL-codes: C31 C81 C93 O12 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-dev, nep-ecm, nep-exp and nep-lab
Date: 2017-04
References: View references in EconPapers View complete reference list from CitEc
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Working Paper: Attrition in Randomized Control Trials: Using Tracking Information to Correct Bias (2017) Downloads
Working Paper: Attrition in randomized control trials: Using tracking information to correct bias (2017) Downloads
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