The nonparametric identification of treatment effects in duration models

Jaap H. Abbring*, Gerard J. Van den Berg

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

277 Citations (Scopus)

Abstract

This paper analyzes the specification and identification of causal multivariate duration models. We focus on the case in which one duration concerns the point in time a treatment is initiated and we are interested in the effect of this treatment on some outcome duration. We define "no anticipation of treatment" and relate it to a common assumption in biostatistics. We show that (i) no anticipation and (ii) randomized treatment assignment can be imposed without restricting the observational data. We impose (i) but not (ii) and prove identification of models that impose some structure. We allow for dependent unobserved heterogeneity and we do not exploit exclusion restrictions on covariates. We provide results for both single-spell and multiple-spell data. The timing of events conveys useful information on the treatment effect.

Original languageEnglish
Pages (from-to)1491-1517
Number of pages27
JournalEconometrica
Volume71
Issue number5
DOIs
Publication statusPublished - Sept-2003
Externally publishedYes

Keywords

  • program evaluation
  • bivariate duration analysis
  • selectivity bias
  • hazard rate
  • partial likelihood
  • unobserved heterogeneity
  • anticipation
  • PROPORTIONAL HAZARD MODEL
  • COMPETING RISKS MODEL
  • CAUSAL INFERENCE
  • IDENTIFIABILITY
  • EMPLOYMENT
  • MORTALITY
  • LIFETIMES
  • FAILURE
  • IMPACT
  • TWIN

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