Extreme Changes in Changes*

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 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.政策分析师通常关注针对极端结局个体的干预效应评估，例如出生体重极低的婴儿。现有的变化量变化法（changes-in-changes, CIC）估计量专为中等分位数场景设计，对这类子群体的估算效果欠佳。本文提出一种新型CIC估计量，可精准估算极端分位数下的干预效应。基于该估计量的渐近正态性，本文同时提出一种易于实现的统计推断方法。基于模拟实验结果，本文建议针对极端分位数场景使用本文提出的极端CIC估计量，而中等分位数场景则应采用传统CIC估计量。运用本文提出的方法，本文针对处于最危重状况的婴儿，研究1993年劳动所得税抵免（Earned Income Tax Credit, EITC）改革带来的收入增长对其出生体重的影响。本文配套提供可直接使用的Stata命令。