30.10 Key Assumptions of Staggered DiD Designs

  1. Rollout Exogeneity
    Treatment assignment and timing should be uncorrelated with potential outcomes.

    • Evidence: Regress adoption on pre-treatment variables. And if you find evidence of correlation, include linear trends interacted with pre-treatment variables (Hoynes and Schanzenbach 2009)
    • Evidence: (Deshpande and Li 2019, 223)
      • Treatment is random: Regress treatment status at the unit level to all pre-treatment observables. If you have some that are predictive of treatment status, you might have to argue why it’s not a worry. At best, you want this.
      • Treatment timing is random: Conditional on treatment, regress timing of the treatment on pre-treatment observables. At least, you want this.

  2. No Confounding Events
    Ensure no other policies or shocks coincide with the staggered treatment rollout.

  3. Exclusion Restrictions

    • No Anticipation: Treatment timing should not affect outcomes prior to treatment.
    • Invariance to History: Treatment duration shouldn’t matter; only the treated status matters (often violated).

  4. Standard DID Assumptions

    • Parallel Trends (Conditional or Unconditional)
    • Random Sampling
    • Overlap (Common Support)
    • Effect Additivity

References

Deshpande, Manasi, and Yue Li. 2019. “Who Is Screened Out? Application Costs and the Targeting of Disability Programs.” American Economic Journal: Economic Policy 11 (4): 213–48.
Hoynes, Hilary W, and Diane Whitmore Schanzenbach. 2009. “Consumption Responses to in-Kind Transfers: Evidence from the Introduction of the Food Stamp Program.” American Economic Journal: Applied Economics 1 (4): 109–39.