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Attrition in randomized control trials: Using tracking information to correct bias

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  • Teresa Molina Millan
  • Karen Macours
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 nal 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 nd 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.

Suggested Citation

  • Teresa Molina Millan & Karen Macours, 2017. "Attrition in randomized control trials: Using tracking information to correct bias," NOVAFRICA Working Paper Series wp1702, Universidade Nova de Lisboa, Nova School of Business and Economics, NOVAFRICA.
    • Handle: RePEc:unl:novafr:wp1702
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    4. Macours, Karen & Barham, Tania & Maluccio, John, 2017. "Are Conditional Cash Transfers Fulfilling Their Promise? Schooling, Learning, and Earnings After 10 Years," CEPR Discussion Papers 11937, C.E.P.R. Discussion Papers.
    5. Clint Harris & Jon Eckhardt & Brent Goldfarb, 2024. "A Survey Selection Correction using Nonrandom Followup with an Application to the Gender Entrepreneurship Gap," Papers 2404.17693, arXiv.org.
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    • O1 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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