Causal inference by quantile regression kink designs

A-Tier
Journal: Journal of Econometrics
Year: 2019
Volume: 210
Issue: 2
Pages: 405-433

Authors (2)

Chiang, Harold D. (not in RePEc) Sasaki, Yuya (Vanderbilt University)

Score contribution per author:

2.011 = (α=2.01 / 2 authors) × 2.0x A-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

Abstract

The quantile regression kink design (QRKD) is proposed by empirical researchers, but its causal interpretation remains unknown. We show that the QRKD estimand measures a weighted average of heterogeneous marginal effects at respective conditional quantiles of outcome given a designed kink point. We also derive limit processes for the QRKD estimator to conduct statistical inference on heterogeneous treatment effects using the QRKD. Applying our methods to the Continuous Wage and Benefit History Project (CWBH) data, we find heterogeneous positive causal effects of unemployment insurance benefits on unemployment durations. These effects are larger for individuals with longer unemployment durations.

Technical Details

RePEc Handle
repec:eee:econom:v:210:y:2019:i:2:p:405-433
Journal Field
Econometrics
Author Count
2
Added to Database
2026-01-29