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Optimal data collection for randomized control trials

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  • Pedro Carneiro
  • Sokbae (Simon) Lee
  • Daniel Wilhelm

Abstract

In a randomized control trial, the precision of an average treatment e ffect estimator can be improved either by collecting data on additional individuals, or by collecting additional covariates that predict the outcome variable. We propose the use of pre-experimental data such as a census, or a household survey, to inform the choice of both the sample size and the covariates to be collected. Our procedure seeks to minimize the resulting average treatment e ect estimator's mean squared error, subject to the researcher's budget constraint. We rely on an orthogonal greedy algorithm that is conceptually simple, easy to implement (even when the number of potential covariates is very large), and does not require any tuning parameters. In two empirical applications, we show that our procedure can lead to substantial gains of up to 58%, either in terms of reductions in data collection costs or in terms of improvements in the precision of the treatment eff ect estimator, respectively.The original version of the working paper, posted on 01 April, 2016, is available here.

Suggested Citation

  • Pedro Carneiro & Sokbae (Simon) Lee & Daniel Wilhelm, 2016. "Optimal data collection for randomized control trials," CeMMAP working papers 15/16, Institute for Fiscal Studies.
    • Handle: RePEc:azt:cemmap:15/16
    DOI: 10.1920/wp.cem.2016.1516
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    References listed on IDEAS

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    1. Jinyong Hahn & Keisuke Hirano & Dean Karlan, 2011. "Adaptive Experimental Design Using the Propensity Score," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(1), pages 96-108, January.
    2. Miriam Bruhn & David McKenzie, 2009. "In Pursuit of Balance: Randomization in Practice in Development Field Experiments," American Economic Journal: Applied Economics, American Economic Association, vol. 1(4), pages 200-232, October.
    3. 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.
    4. Lucas C. Coffman & Muriel Niederle, 2015. "Pre-analysis Plans Have Limited Upside, Especially Where Replications Are Feasible," Journal of Economic Perspectives, American Economic Association, vol. 29(3), pages 81-98, Summer.
    5. repec:feb:artefa:0110 is not listed on IDEAS
    6. John A. List, 2011. "Why Economists Should Conduct Field Experiments and 14 Tips for Pulling One Off," Journal of Economic Perspectives, American Economic Association, vol. 25(3), pages 3-16, Summer.
    7. Kari Lock Morgan & Donald B. Rubin, 2015. "Rerandomization to Balance Tiers of Covariates," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1412-1421, December.
    8. Pedro Carneiro & Oswald Koussihouèdé & Nathalie Lahire & Costas Meghir & Corina Mommaerts, 2015. "Decentralizing education resources: school grants in Senegal," CeMMAP working papers CWP15/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. John List & Sally Sadoff & Mathis Wagner, 2011. "So you want to run an experiment, now what? Some simple rules of thumb for optimal experimental design," Experimental Economics, Springer;Economic Science Association, vol. 14(4), pages 439-457, November.
    10. Daniel S. Hamermesh, 2013. "Six Decades of Top Economics Publishing: Who and How?," Journal of Economic Literature, American Economic Association, vol. 51(1), pages 162-172, March.
    11. Bhattacharya, Debopam & Dupas, Pascaline, 2012. "Inferring welfare maximizing treatment assignment under budget constraints," Journal of Econometrics, Elsevier, vol. 167(1), pages 168-196.
    12. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881.
    13. Oriana Bandiera & Iwan Barankay & Imran Rasul, 2011. "Field Experiments with Firms," Journal of Economic Perspectives, American Economic Association, vol. 25(3), pages 63-82, Summer.
    14. Benjamin A. Olken, 2015. "Promises and Perils of Pre-analysis Plans," Journal of Economic Perspectives, American Economic Association, vol. 29(3), pages 61-80, Summer.
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