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Extreme Changes in Changes

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  • Yuya Sasaki
  • Yulong Wang

Abstract

Policy analysts are often interested in treating the units with extreme outcomes, such as infants with extremely low birth weights. Existing changes-in-changes (CIC) estimators are tailored to middle quantiles and do not work well for such subpopulations. This paper proposes a new CIC estimator to accurately estimate treatment effects at extreme quantiles. With its asymptotic normality, we also propose a method of statistical inference, which is simple to implement. Based on simulation studies, we propose to use our extreme CIC estimator for extreme, such as below 5% and above 95%, quantiles, while the conventional CIC estimator should be used for intermediate quantiles. Applying the proposed method, we study the effects of income gains from the 1993 EITC reform on infant birth weights for those in the most critical conditions. This paper is accompanied by a Stata command.

Suggested Citation

  • Yuya Sasaki & Yulong Wang, 2022. "Extreme Changes in Changes," Papers 2211.14870, arXiv.org, revised May 2023.
    • Handle: RePEc:arx:papers:2211.14870
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    References listed on IDEAS

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    1. Armelle Guillou & Peter Hall, 2001. "A diagnostic for selecting the threshold in extreme value analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 293-305.
    2. Hilary Hoynes & Doug Miller & David Simon, 2015. "Income, the Earned Income Tax Credit, and Infant Health," American Economic Journal: Economic Policy, American Economic Association, vol. 7(1), pages 172-211, February.
    3. Janet Currie, 2011. "Inequality at Birth: Some Causes and Consequences," American Economic Review, American Economic Association, vol. 101(3), pages 1-22, May.
    4. Yuya Sasaki & Yulong Wang, 2022. "Fixed-k Inference for Conditional Extremal Quantiles," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(2), pages 829-837, April.
    5. Adriana Camacho, 2008. "Stress and Birth Weight: Evidence from Terrorist Attacks," American Economic Review, American Economic Association, vol. 98(2), pages 511-515, May.
    6. William N. Evans & Craig L. Garthwaite, 2014. "Giving Mom a Break: The Impact of Higher EITC Payments on Maternal Health," American Economic Journal: Economic Policy, American Economic Association, vol. 6(2), pages 258-290, May.
    7. Victor Chernozhukov, 2005. "Extremal quantile regression," Papers math/0505639, arXiv.org.
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    Cited by:

    1. Ghanem, Dalia & Hirshleifer, Sarojini & Kédagni, Désiré & Ortiz-Becerra, Karen, 2024. "Correcting attrition bias using changes-in-changes," Journal of Econometrics, Elsevier, vol. 241(2).

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