Using Event Studies as an Outcome in Causal Analysis

Authors

Affiliation

Dmitry Arkhangelsky

CEMFI

Kazuharu Yanagimoto

CEMFI

Tom Zohar

CEMFI

• Latest WP
• CEMFI WP
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Abstract

We study a causal inference problem with group-level outcomes, which are themselves parameters identified from microdata. We formalize these outcomes using population moment conditions and demonstrate that one-step Generalized Method of Moments (GMM) estimators are generally inconsistent due to an endogenous weighting bias, where policy affects the implicit GMM weights. In contrast, two-stage Minimum Distance (MD) estimators perform well when group sizes are sufficiently large. While MD estimators can still be inconsistent in small groups due to a policy-induced sample selection, we demonstrate that this can be addressed by incorporating auxiliary population information. An empirical application illustrates the practical importance of these findings.

Important table & figure

Figure B.2 (a): Child penalties by age at first childbirth

Figure II (b): Effect of the childcare provision expansion on CP (MD)

BibTeX citation

@online{arkhangelsky2025,
  title = {Using Event Studies as an Outcome in Causal Analysis},
  author = {Arkhangelsky, Dmitry and Yanagimoto, Kazuharu and Zohar, Tom},
  date = {2025-01-29},
  eprint = {2403.19563},
  eprinttype = {arXiv},
  eprintclass = {econ},
  doi = {10.48550/arXiv.2403.19563},
  url = {http://arxiv.org/abs/2403.19563},
  langid = {english},
  pubstate = {prepublished},
  keywords = {Economics - General Economics,Quantitative Finance - Economics}
}