Dynamic treatment effects

James J. Heckman, John Eric Humphries, Gregory Veramendi

Research output: Contribution to journalArticle

12 Scopus citations

Abstract

This paper develops robust models for estimating and interpreting treatment effects arising from both ordered and unordered multi-stage decision problems. Identification is secured through instrumental variables and/or conditional independence (matching) assumptions. We decompose treatment effects into direct effects and continuation values associated with moving to the next stage of a decision problem. Using our framework, we decompose the IV estimator, showing that IV generally does not estimate economically interpretable or policy-relevant parameters in prototypical dynamic discrete choice models, unless policy variables are instruments. Continuation values are an empirically important component of estimated total treatment effects of education. We use our analysis to estimate the components of what LATE estimates in a dynamic discrete choice model.

Original languageEnglish (US)
Pages (from-to)276-292
Number of pages17
JournalJournal of Econometrics
Volume191
Issue number2
DOIs
StatePublished - Apr 1 2016

Keywords

  • C38
  • D03
  • I12
  • I14
  • I21
  • JEL classification C32

ASJC Scopus subject areas

  • Economics and Econometrics
  • Applied Mathematics
  • History and Philosophy of Science

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    Heckman, J. J., Humphries, J. E., & Veramendi, G. (2016). Dynamic treatment effects. Journal of Econometrics, 191(2), 276-292. https://doi.org/10.1016/j.jeconom.2015.12.001