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Attrition in Randomized Control Trials: Using Tracking Information to Correct Bias

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  • Molina Millán, Teresa

    (Universidad de Alicante)

  • Macours, Karen

    (Paris School of Economics)

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.

Suggested Citation

  • Molina Millán, Teresa & Macours, Karen, 2017. "Attrition in Randomized Control Trials: Using Tracking Information to Correct Bias," IZA Discussion Papers 10711, Institute of Labor Economics (IZA).
  • Handle: RePEc:iza:izadps:dp10711
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    Cited by:

    1. Teresa Molina Millán & Tania Barham & Karen Macours & John A Maluccio & Marco Stampini, 2019. "Long-Term Impacts of Conditional Cash Transfers: Review of the Evidence," World Bank Research Observer, World Bank Group, vol. 34(1), pages 119-159.
    2. Ubfal, Diego & Arraiz, Irani & Beuermann, Diether & Frese, Michael & Maffioli, Alessandro & Verch, Daniel, 2019. "The Impact of Soft-Skills Training for Entrepreneurs in Jamaica," IZA Discussion Papers 12325, Institute of Labor Economics (IZA).
    3. Sarojini Hirshleifer & Dalia Ghanem & Karen Ortiz-Becerra, 2019. "Testing for Attrition Bias in Field Experiments," Working Papers 201919, University of California at Riverside, Department of Economics, revised Aug 2019.
    4. Molina Millán, Teresa & Macours, Karen & Maluccio, John A. & Tejerina, Luis, 2020. "Experimental long-term effects of early-childhood and school-age exposure to a conditional cash transfer program," Journal of Development Economics, Elsevier, vol. 143(C).
    5. Teresa Molina-Millan & Tania Barham & Karen Macours & John A. Maluccio & Marco Stampini, 2016. "Long-term Impacts of Conditional Cash Transfers in Latin America: Review of the Evidence," IDB Publications (Working Papers) 96136, Inter-American Development Bank.
    6. Barham, Tania & Macours, Karen & 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.
    7. Ham, Andrés & Michelson, Hope C., 2018. "Does the form of delivering incentives in conditional cash transfers matter over a decade later?," Journal of Development Economics, Elsevier, vol. 134(C), pages 96-108.

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    More about this item

    Keywords

    survey non response; sample selectivity; randomized controlled trial; inverse probability weights;
    All these keywords.

    JEL classification:

    • 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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