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Learning the Effect of Persuasion via Difference-In-Differences

Sung Jae Jun, Sokbae Lee

arXiv 18 Oct 2024 · Econometrics

arXiv:2410.14871 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We develop a difference-in-differences framework to measure the persuasive impact of informational treatments on behavior. We introduce two causal parameters, the forward and backward average persuasion rates on the treated, which refine the average treatment effect on the treated. The forward rate excludes cases of "preaching to the converted," while the backward rate omits "talking to a brick wall" cases. We propose both regression-based and semiparametrically efficient estimators. The framework applies to both two-period and staggered treatment settings, including event studies, and we demonstrate its usefulness with applications to a British election and a Chinese curriculum reform.

Citation extraction

50
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103
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distinct cited
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main-text words

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Jun, Sung Jae and Sokbae Lee (2023) Identifying the effect of persuasion self1.00073100%
2Callaway, Brantly and Pedro H.C. Sant’Anna (2021) Difference-in-Differences with Multiple Time Periods1.00063100%
3Cantoni, Davide, Yuyu Chen, David Y Yang, Noam Yuchtman, and Y Jane… (2017) Curriculum and ideology0.874112100%
4Ladd, Jonathan McDonald and Gabriel S. Lenz (2009) Exploiting a Rare Communication Shift to Document the Persuasive Power of the News Media0.87482100%
5Roth, Jonathan, Pedro HC Sant’Anna, Alyssa Bilinski, and John Poe (2023) What’s trending in difference-in-differences? A synthesis of the recent econometrics literature0.84333100%
6Hainmueller, Jens (2012) Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies0.81142100%
7Newey, Whitney K (1994) The asymptotic variance of semiparametric estimators0.81142100%
8Pearl, Judea (1999) Probabilities of causation: three counterfactual interpretations and their identification0.81142100%
9DellaVigna, Stefano and Ethan Kaplan (2007) The Fox News effect: Media bias and voting0.73732100%
10Dawid, A. Philip, David L. Faigman, and Stephen E. Fienberg (2014) Fitting Science Into Legal Contexts: Assessing Effects of Causes or Causes of Effects?0.64422100%

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Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Better Understanding Triple Differences Estimators0.40511