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Selection into Identification in Fixed Effects Models, with Application to Head Start

Author

Listed:
  • Douglas L. Miller
  • Na’ama Shenhav
  • Michel Grosz
Abstract
Many papers use fixed effects (FE) to identify causal impacts. We document that when treatment status only varies within some FE groups (for example, families, for family fixed effects), FE can induce nonrandom selection of groups into the identifying sample. To address this, we introduce a reweighting-on-observables estimator that can help recover the average treatment effect for policy-relevant populations. We apply these insights to reexamine the long-term effects of Head Start in the PSID and the CNLSY and find that the reweighted estimates are frequently smaller than the FE estimates. This underscores concerns with the external validity of FE estimates. The tools that we propose can strengthen the validity of this approach.

Suggested Citation

  • Douglas L. Miller & Na’ama Shenhav & Michel Grosz, 2023. "Selection into Identification in Fixed Effects Models, with Application to Head Start," Journal of Human Resources, University of Wisconsin Press, vol. 58(5), pages 1523-1566.
    • Handle: RePEc:uwp:jhriss:v:58:y:2023:i:5:p:1523-1566
    Note: DOI: https://doi.org/10.3368/jhr.58.5.0520-10930R1
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    References listed on IDEAS

    as
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    More about this item

    JEL classification:

    • I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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