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

By Douglas L. Miller, N. Shenhav, Michel Z. Grosz. The Journal of human resources, 2019

62 citations recorded by the source corpus.

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.

Subfields: Gender Studies, Safety Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/60abe360febe62daea1d31669ff4f7ec4916c180

Citations in the corpus

• The Long-Term Effects of Universal Preschool in Boston (2021, unknown): “Non-experimental studies of the federal Head Start program find initial test score gains that subsequently fade out, but positive effects often reappear for long-term outcomes (Currie and Thomas, 1995; Garces et al., 2002; Ludwig and Miller, 2007; Deming, 2009; Miller et al., 2019; Bailey et al., 20”