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On the Performance of the Neyman Allocation with Small Pilots

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  • Yong Cai
  • Ahnaf Rafi
Abstract
The Neyman Allocation is used in many papers on experimental design, which typically assume that researchers have access to large pilot studies. This may be unrealistic. To understand the properties of the Neyman Allocation with small pilots, we study its behavior in an asymptotic framework that takes pilot size to be fixed even as the size of the main wave tends to infinity. Our analysis shows that the Neyman Allocation can lead to estimates of the ATE with higher asymptotic variance than with (non-adaptive) balanced randomization. In particular, this happens when the outcome variable is relatively homoskedastic with respect to treatment status or when it exhibits high kurtosis. We provide a series of empirical examples showing that such situations can arise in practice. Our results suggest that researchers with small pilots should not use the Neyman Allocation if they believe that outcomes are homoskedastic or heavy-tailed. Finally, we examine some potential methods for improving the finite sample performance of the FNA via simulations.

Suggested Citation

  • Yong Cai & Ahnaf Rafi, 2022. "On the Performance of the Neyman Allocation with Small Pilots," Papers 2206.04643, arXiv.org, revised Jun 2024.
    • Handle: RePEc:arx:papers:2206.04643
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    File URL: http://arxiv.org/pdf/2206.04643
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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. Gharad Bryan & Dean Karlan & Jonathan Zinman, 2015. "Referrals: Peer Screening and Enforcement in a Consumer Credit Field Experiment," American Economic Journal: Microeconomics, American Economic Association, vol. 7(3), pages 174-204, August.
    3. 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.
    4. David McKenzie, 2017. "Identifying and Spurring High-Growth Entrepreneurship: Experimental Evidence from a Business Plan Competition," American Economic Review, American Economic Association, vol. 107(8), pages 2278-2307, August.
    5. Nava Ashraf & Dean Karlan & Wesley Yin, 2006. "Tying Odysseus to the Mast: Evidence From a Commitment Savings Product in the Philippines," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 121(2), pages 635-672.
    6. Max Cytrynbaum, 2021. "Optimal Stratification of Survey Experiments," Papers 2111.08157, arXiv.org, revised Aug 2023.
    7. Vaart,A. W. van der, 2000. "Asymptotic Statistics," Cambridge Books, Cambridge University Press, number 9780521784504, September.
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    Cited by:

    1. Yuehao Bai & Azeem M. Shaikh & Max Tabord-Meehan, 2024. "A Primer on the Analysis of Randomized Experiments and a Survey of some Recent Advances," Papers 2405.03910, arXiv.org.

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