Paper Title:
Causal Duration Analysis with Diff-in-Diff
Published on:
8 May 2024
Primary Category:
Econometrics
Paper Authors:
Ben Deaner,
Hyejin Ku
Proposes duration diff-in-diff to avoid bias in standard diff-in-diff with duration data
Allows flexible modeling while retaining simplicity and transparency
Applies methods to Austrian unemployment policy, avoids specifying hazard model
Finds significant positive impact on duration, parallel trends not rejected
Duration analysis of unemployment benefit policy changes
This paper proposes methods to adapt difference-in-differences analysis to settings with duration outcomes, which indicate whether an individual has entered an absorbing state. It retains the simplicity of diff-in-diff while avoiding bias. The approach is applied to study an Austrian policy extending unemployment benefits, using a transparent cross-cohort design.
Simplified analysis of case-cohort study with interval-censored data
Local effects of continuous exposure
Using staggered treatment adoption to identify causal effects under non-parallel trends
Dynamic effects of treatment recommendations that individuals may not follow
Evaluating causes of continuous effects
Identification of causal effects in an instrumental variables model
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