  The literature on regression kink designs develops identification results for
average effects of continuous treatments (Card, Lee, Pei, and Weber, 2015),
average effects of binary treatments (Dong, 2018), and quantile-wise effects of
continuous treatments (Chiang and Sasaki, 2019), but there has been no
identification result for quantile-wise effects of binary treatments to date.
In this paper, we fill this void in the literature by providing an
identification of quantile treatment effects in regression kink designs with
binary treatment variables. For completeness, we also develop large sample
theories for statistical inference and a practical guideline on estimation and
inference.
