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On Minimizing the Risk of Bias in Randomized Controlled Trials in Economics

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  • Alex Eble
  • Peter Boone
  • Diana Elbourne

Abstract

Estimation of empirical relationships is prone to bias. Economists have carefully studied sources of bias in structural and quasi-experimental approaches, but the randomized control trial (RCT) has only begun to receive such scrutiny. In this paper, we argue that several lessons from medicine, derived from analysis of thousands of RCTs establishing a clear link between certain practices and biased estimates, can be used to reduce the risk of bias in economics RCTs. We identify the subset of these lessons applicable to economics and use them to assess risk of bias in estimates from economics RCTs published between 2001 and 2011. In comparison to medical studies, we find most economics studies do not report important details on study design necessary to assess risk of bias. Many report practices that suggest risk of bias, though this does not necessarily mean bias resulted. We conclude with suggestions on how to remedy these issues.

Suggested Citation

  • Alex Eble & Peter Boone & Diana Elbourne, 2017. "On Minimizing the Risk of Bias in Randomized Controlled Trials in Economics," The World Bank Economic Review, World Bank, vol. 31(3), pages 687-707.
    • Handle: RePEc:oup:wbecrv:v:31:y:2017:i:3:p:687-707.
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    File URL: http://hdl.handle.net/10.1093/wber/lhw034
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    3. Orla Doyle, 2017. "The First 2,000 Days and Child Skills: Evidence from a Randomized Experiment of Home Visiting," Working Papers 201715, School of Economics, University College Dublin.
    4. Pascaline Dupas & Edward Miguel, 2016. "Impacts and Determinants of Health Levels in Low-Income Countries," NBER Working Papers 22235, National Bureau of Economic Research, Inc.

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    JEL classification:

    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments

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