Extreme Changes in Changes

Yuya Sasaki, Yulong Wang

Research output: Contribution to journalArticlepeer-review


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 article 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 article is accompanied by a Stata command.

Original languageEnglish (US)
Pages (from-to)812-824
Number of pages13
JournalJournal of Business and Economic Statistics
Issue number2
StatePublished - 2024


  • Extreme quantile
  • Pareto exponent
  • Quantile treatment effect

ASJC Scopus subject areas

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty


Dive into the research topics of 'Extreme Changes in Changes'. Together they form a unique fingerprint.

Cite this