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Learning the effect of persuasion via difference-in-differences

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  • Sung Jae Jun
  • Sokbae (Simon) Lee

Abstract

The persuasion rate is a key parameter for measuring the causal effect of a directional message on influencing the recipient’s behavior. Its identification has relied on exogenous treatment or the availability of credible instruments, but the requirements are not always satisfied in observational studies. Therefore, we develop a novel econometric framework for the average persuasion rate on the treated and other related parameters by using the difference-in-differences approach. The average treatment effect on the treated is a standard parameter in difference-in-differences, but we show that it is an overly conservative measure in the context of persuasion. For estimation and inference, we propose regression-based approaches as well as semiparametrically efficient estimators. Beginning with the two-period case, we extend the framework to staggered treatment settings, where we show how to conduct richer analyses like the event-study design. We investigate the British election and the Chinese curriculum reform as empirical examples.

Suggested Citation

  • Sung Jae Jun & Sokbae (Simon) Lee, 2024. "Learning the effect of persuasion via difference-in-differences," CeMMAP working papers 24/24, Institute for Fiscal Studies.
    • Handle: RePEc:azt:cemmap:24/24
    DOI: 10.47004/wp.cem.2024.2424
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    References listed on IDEAS

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    1. Liyang Sun & Jesse M. Shapiro, 2022. "A Linear Panel Model with Heterogeneous Coefficients and Variation in Exposure," Journal of Economic Perspectives, American Economic Association, vol. 36(4), pages 193-204, Fall.
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    3. Clément de Chaisemartin & Xavier D’Haultfœuille, 2023. "Two-way fixed effects and differences-in-differences with heterogeneous treatment effects: a survey," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 1-30.
    4. Daniel Ackerberg & Xiaohong Chen & Jinyong Hahn & Zhipeng Liao, 2014. "Asymptotic Efficiency of Semiparametric Two-step GMM," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(3), pages 919-943.
    5. Goodman-Bacon, Andrew, 2021. "Difference-in-differences with variation in treatment timing," Journal of Econometrics, Elsevier, vol. 225(2), pages 254-277.
    6. Sun, Liyang & Abraham, Sarah, 2021. "Estimating dynamic treatment effects in event studies with heterogeneous treatment effects," Journal of Econometrics, Elsevier, vol. 225(2), pages 175-199.
    7. Liyang Sun & Jesse M. Shapiro, 2022. "A linear panel model with heterogeneous coe!cients and variation in exposure," Economics Virtual Symposium 2022 01, Stata Users Group.
    8. Roth, Jonathan & Sant’Anna, Pedro H.C. & Bilinski, Alyssa & Poe, John, 2023. "What’s trending in difference-in-differences? A synthesis of the recent econometrics literature," Journal of Econometrics, Elsevier, vol. 235(2), pages 2218-2244.
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