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Managing Threats to Randomization

Managing Threats to Randomization. Threat (1): Spillovers. If people in the control group get treated, randomization is no more perfect Choose the appropriate unit of randomization to minimize the risk Monitor the implementation May be interesting to analyze as such Measure the spillovers

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Managing Threats to Randomization

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  1. Managing Threats to Randomization

  2. Threat (1): Spillovers • If people in the control group get treated, randomization is no more perfect • Choose the appropriate unit of randomization to minimize the risk • Monitor the implementation • May be interesting to analyze as such • Measure the spillovers • Spillovers may reflect a good program with a large effect or an important take-up

  3. Spillover: An Example • Deworming • Previous studies randomize deworming treatment within schoools • Suppose that deworming prevents transmission of disease, what problems does this create for evaluation? • Suppose externalities are local, how can we measure total impact? Adapted from Threats and Analysis, Shawn Cole, J-PAL.

  4. Adapted from Threats and Analysis, Shawn Cole, J-PAL.

  5. Measuring total impact in the presence of spillovers • Design unit of randomization so that it encompasses the spillovers • E.g. if we expect externalities are all within the school: • Randomization at the school level allows for estimation of overall impact Adapted from Threats and Analysis, Shawn Cole, J-PAL.

  6. Measuring total impact in the presence of spillovers Adapted from Threats and Analysis, Shawn Cole, J-PAL.

  7. Threat (2): Attrition • Failure to collect outcome data from some individuals who were part of the original sample. • Random attrition will only reduce a study's statistical power • Attrition that is correlated with the treatment may bias estimates. • People who benefited from the program left more • People who did benefit from the program staid more • Even if attrition rates are similar, people who dropped out may be different

  8. Fighting Attrition • Track participants after they leave the program. • Collect good information in the baseline on how to follow people (for example the names of neighbors and relatives) • Report attrition levels in the treatment and comparison groups and compare attritors with non-attritors using baseline data (when available). • Bound the potential bias of your effect

  9. Attrition bias: an example • Problem to be addressed: • Too weak (undernourished) children don’t come to school • Intervention: • You start a school feeding program and want to do an evaluation: Treatment & Control Group • Expectation: • Weak children start going to school more if they live next to a treatment school • Outcome of interest • Weight of children who attend school • Measurement: • You go to all the schools (T & C) and measure everyone who is in school on a given day • Will the treatment-control difference in weight be over-stated or understated? (due to attrition bias) Adapted from Threats and Analysis, Shawn Cole, J-PAL.

  10. Adapted from Threats and Analysis, Shawn Cole, J-PAL.

  11. Attrition Bias: If only children >20kg come to school Treatment-control difference becomes understated! Adapted from Threats and Analysis, Shawn Cole, J-PAL.

  12. Threat (3): Non-compliance • Treatment units do not actually take up the program, or are not served • May lead you to underestimate the impact • You can try to prevent it by monitoring the implementation closely • But you can see the effect on the treated • Multiplying the observed effect by the inverse proportion of people who complied • And non-compliance often teaches a lot about the program…

  13. Conclusions • If properly designed and conducted, RCTs provide the most credible assessment of the impact of a program: with other methods, difficult to be sure that we control for all selection bias → risk of overestimation of impact • Results from RCTs are easy to understand and much less subject to methodological quibbles • Credibility + Ease of understanding =>More likely to convince policymakers and funders of effectiveness (or lack thereof) of program

  14. Conclusions (cont.) • However, in some cases RCTs are simply not feasible • Must be aware of the limitations of any method used • What are the implicit assumptions, the potential bias etc.

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