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Using Multiple Pretreatment Periods to Improve Difference-in-Differences and Staggered Adoption Designs

Author

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  • Egami, Naoki
  • Yamauchi, Soichiro

Abstract

While a difference-in-differences (DID) design was originally developed with one pre- and one posttreatment period, data from additional pretreatment periods are often available. How can researchers improve the DID design with such multiple pretreatment periods under what conditions? We first use potential outcomes to clarify three benefits of multiple pretreatment periods: (1) assessing the parallel trends assumption, (2) improving estimation accuracy, and (3) allowing for a more flexible parallel trends assumption. We then propose a new estimator, double DID, which combines all the benefits through the generalized method of moments and contains the two-way fixed effects regression as a special case. We show that the double DID requires a weaker assumption about outcome trends and is more efficient than existing DID estimators. We also generalize the double DID to the staggered adoption design where different units can receive the treatment in different time periods. We illustrate the proposed method with two empirical applications, covering both the basic DID and staggered adoption designs. We offer an open-source R package that implements the proposed methodologies.

Suggested Citation

  • Egami, Naoki & Yamauchi, Soichiro, 2023. "Using Multiple Pretreatment Periods to Improve Difference-in-Differences and Staggered Adoption Designs," Political Analysis, Cambridge University Press, vol. 31(2), pages 195-212, April.
    • Handle: RePEc:cup:polals:v:31:y:2023:i:2:p:195-212_3
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    Cited by:

    1. Jiafeng Gu, 2024. "The Effects of Straw Burning Bans on the Use of Cooking Fuels in China," Energies, MDPI, vol. 17(24), pages 1-19, December.
    2. Eichfelder, Sebastian & Nguyen, Hang T. T., 2025. "Corporate tax incidence and tax avoidance: Evidence from the German Business Tax Reform 2008," arqus Discussion Papers in Quantitative Tax Research 309, arqus - Arbeitskreis Quantitative Steuerlehre.
    3. repec:osf:socarx:dg5ps_v1 is not listed on IDEAS
    4. Ping Li & Lijing Xu & Xuesong Gu & Yiduo Chen, 2025. "Emission Reduction Effect of Carbon Trading Policy Based on Multi-Period DID and Synergy Effect," Sustainability, MDPI, vol. 17(11), pages 1-29, May.
    5. Strezhnev, Anton, 2024. "Group-specific linear trends and the triple-differences in time design," SocArXiv dg5ps, Center for Open Science.
    6. Sardar Bonabi & Sarah Bana & Vijay Gurbaxani & Tingting Nian, 2025. "Beyond Code: The Multidimensional Impacts of Large Language Models in Software Development," Papers 2506.22704, arXiv.org, revised Jun 2025.
    7. Li, Zhiyi & Hu, Boqiang & Bao, Yifei & Wang, Yifei, 2025. "Supply chain digitalization, green technology innovation and corporate energy efficiency," Energy Economics, Elsevier, vol. 142(C).
    8. Jonas M. Mikhaeil & Christopher Harshaw, 2025. "In Defense of the Pre-Test: Valid Inference when Testing Violations of Parallel Trends for Difference-in-Differences," Papers 2510.26470, arXiv.org, revised Jan 2026.
    9. Silvia De Nicol`o & Beatrice Biondi & Mario Mazzocchi, 2026. "Three's a crowd: Identification challenges in the triple difference model with spillover effects," Papers 2601.15764, arXiv.org.

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