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A 2 million-person, campaign-wide field experiment shows how digital advertising affects voter turnout

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Abstract

We present the results of a large, US$8.9 million campaign-wide field experiment, conducted among 2 million moderate- and low-information persuadable voters in five battleground states during the 2020 US presidential election. Treatment group participants were exposed to an 8-month-long advertising programme delivered via social media, designed to persuade people to vote against Donald Trump and for Joe Biden. We found no evidence that the programme increased or decreased turnout on average. We found evidence of differential turnout effects by modelled level of Trump support: the campaign increased voting among Biden leaners by 0.4 percentage points (s.e. = 0.2 pp) and decreased voting among Trump leaners by 0.3 percentage points (s.e. = 0.3 pp) for a difference in conditional average treatment effects of 0.7 points (t1,035,571 = −2.09; P = 0.036; \(\widehat{{\rm{DIC}}}=0.7\) points; 95% confidence interval = −0.014 to 0). An important but exploratory finding is that the strongest differential effects appear in early voting data, which may inform future work on early campaigning in a post-COVID electoral environment. Our results indicate that differential mobilization effects of even large digital advertising campaigns in presidential elections are likely to be modest.

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Fig. 1: Average treatment effects and CATEs of treatment on 2020 turnout under three inverse probability-weighted regression specifications.
Fig. 2: Heterogeneous effects of treatment on voting, in-person voting and early voting by Trump support under three inverse probability-weighted regression specifications.
Fig. 3: 2020 turnout rates by one-point bins of Trump support score and condition.
Fig. 4: Treatment Assignment Flow Chart.
Fig. 5: Balance on pre-treatment covariates.
Fig. 6: Examples of typical advertisement content run in Acronym’s persuasion program.

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Data availability

An anonymized replication dataset is available via Dataverse at https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/YMKVA1. TargetSmart generously agreed to make replication data available for this paper. By downloading replication data, researchers agree to use the data only for academic research, agree not to share the data with outside parties and agree not to attempt to re-identify individuals in the dataset in order to download the data.

Code availability

Replication scripts are available via Dataverse at https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/YMKVA1.

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Acknowledgements

We thank D. Green, F. Sävje, M. Michelson, B. Sinclair, E. Porter, L. Vavreck, Y. Velez, J. Kalla and D. Broockman for generous early feedback. We also thank E. Franklin Fowler and the Wesleyan Media Project for generously sharing data. We received no specific external funding for this work.

Author information

Author notes

  1. These authors contributed equally: Minali Aggarwal, Jennifer Allen, Alexander Coppock, Dan Frankowski, Solomon Messing, Kelly Zhang.

Authors and Affiliations

  1. Department of Political Science, Yale University, New Haven, CT, USA

    Minali Aggarwal & Alexander Coppock

  2. MIT Sloan School of Management, Boston, MA, USA

    Jennifer Allen

  3. Boxydog, Minneapolis, MN, USA

    Dan Frankowski

  4. McCourt School of Public Policy, Georgetown University, Washington, DC, USA

    Solomon Messing

  5. Busara Center for Behavioral Economics, Nairobi, Kenya

    Kelly Zhang

  6. Condorsay, New York, NY, USA

    James Barnes

  7. Macmillan Publishers, New York, NY, USA

    Andrew Beasley

  8. Greenlight, New York, NY, USA

    Harry Hantman

  9. Department of Politics, New York University, New York, NY, USA

    Sylvan Zheng

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Contributions

S.M. and J.B. conceived of the initial research. S.M. designed and implemented the original experiment. A.B. and H.H. administered the experiment. D.F., M.A., K.Z., S.Z., J.A. and S.M. contributed to data collection and curation. M.A., J.A. and A.C. analysed the results of the experiment. A.C. and S.M. drafted the initial manuscript and figures. All authors contributed to the revision and editing of the manuscript.

Corresponding author

Correspondence to Solomon Messing.

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Competing interests

All researchers were employed by Acronym or were contractors thereof during the 2020 election cycle. J.A., S.Z. and H.H. have a substantial financial interest in Facebook. Acronym played a role in conceptualizing and designing the study presented here: all of the authors except A.C. contributed to the design and/or implementation of the experiment while employed by Acronym from January 2020 to January 2021. After the termination of their employment in January 2021, but before working on this manuscript, the other primary authors signed explicit third-party data-sharing agreements on 8 February 2021 to allow data access while they conducted the scientific analysis and reporting presented here, with other institutions. Acronym and the authors agreed in writing to the publication of this manuscript in advance of manuscript analysis and preparation. The authors agreed to provide Acronym with a draft of the manuscript before publication.

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Nature Human Behaviour thanks the anonymous reviewers for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Matching of treatment voters to Facebook users, a description of the advertisement content, a description of the advertisement testing programme, analysis of the overall Facebook advertisement environment, an overview of the PAP, PAP deviations, randomization code and Supplementary Tables 1–3 and Figs. 1–4.

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Aggarwal, M., Allen, J., Coppock, A. et al. A 2 million-person, campaign-wide field experiment shows how digital advertising affects voter turnout. Nat Hum Behav 7, 332–341 (2023). https://doi.org/10.1038/s41562-022-01487-4

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