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Optimal Allocation of Interviews to Baseline and Endline Surveys in Place-Based Randomized Trials and Quasi-Experiments

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  • Donald P. Green
  • Winston Lin
  • Claudia Gerber

Abstract

Background: Many place-based randomized trials and quasi-experiments use a pair of cross-section surveys, rather than panel surveys, to estimate the average treatment effect of an intervention. In these studies, a random sample of individuals in each geographic cluster is selected for a baseline (preintervention) survey, and an independent random sample is selected for an endline (postintervention) survey. Objective: This design raises the question, given a fixed budget, how should a researcher allocate resources between the baseline and endline surveys to maximize the precision of the estimated average treatment effect? Results: We formalize this allocation problem and show that although the optimal share of interviews allocated to the baseline survey is always less than one-half, it is an increasing function of the total number of interviews per cluster, the cluster-level correlation between the baseline measure and the endline outcome, and the intracluster correlation coefficient. An example using multicountry survey data from Africa illustrates how the optimal allocation formulas can be combined with data to inform decisions at the planning stage. Another example uses data from a digital political advertising experiment in Texas to explore how precision would have varied with alternative allocations.

Suggested Citation

  • Donald P. Green & Winston Lin & Claudia Gerber, 2018. "Optimal Allocation of Interviews to Baseline and Endline Surveys in Place-Based Randomized Trials and Quasi-Experiments," Evaluation Review, , vol. 42(4), pages 391-422, August.
    • Handle: RePEc:sae:evarev:v:42:y:2018:i:4:p:391-422
    DOI: 10.1177/0193841X18799128
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    1. Martin S. Ridout & Clarice G. B. Demétrio & David Firth, 1999. "Estimating Intraclass Correlation for Binary Data," Biometrics, The International Biometric Society, vol. 55(1), pages 137-148, March.
    2. Heinz Welsch, 2007. "Macroeconomics and Life Satisfaction: Revisiting the “Misery Index”," Journal of Applied Economics, Taylor & Francis Journals, vol. 10(2), pages 237-251, November.
    3. Yang L. & Tsiatis A. A., 2001. "Efficiency Study of Estimators for a Treatment Effect in a Pretest-Posttest Trial," The American Statistician, American Statistical Association, vol. 55, pages 314-321, November.
    4. Gerber, Alan S. & Gimpel, James G. & Green, Donald P. & Shaw, Daron R., 2011. "How Large and Long-lasting Are the Persuasive Effects of Televised Campaign Ads? Results from a Randomized Field Experiment," American Political Science Review, Cambridge University Press, vol. 105(1), pages 135-150, February.
    5. Luke W. Miratrix & Jasjeet S. Sekhon & Bin Yu, 2013. "Adjusting treatment effect estimates by post-stratification in randomized experiments," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(2), pages 369-396, March.
    6. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769.
    7. McKenzie, David, 2012. "Beyond baseline and follow-up: The case for more T in experiments," Journal of Development Economics, Elsevier, vol. 99(2), pages 210-221.
    8. D. Firth & K. E. Bennett, 1998. "Robust models in probability sampling," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(1), pages 3-21.
    9. David Dorn & Justina Fischer & Gebhard Kirchgässner & Alfonso Sousa-Poza, 2007. "Is It Culture or Democracy? The Impact of Democracy and Culture on Happiness," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 82(3), pages 505-526, July.
    10. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881.
    11. Richard Williams, 2012. "Using the margins command to estimate and interpret adjusted predictions and marginal effects," Stata Journal, StataCorp LP, vol. 12(2), pages 308-331, June.
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