Title: Replication Data for: Effects of a large scale social media advertising campaign on holiday travel and COVID-19 infections: a cluster randomized controlled trial

Type Dataset Emily Breza, Fatima Cody Stanford, Marcela Alsan, Burak Alsan, Abhijit Banerjee, Arun G. Chandrasekhar, Sarah Eichmeyer, Traci Glushko, Paul Goldsmith-Pinkham, Kelly Holland, Emily Hoppe, Mohit Karnani, Sarah Liegl, Tristan Loisel, Lucy Ogbu-Nwobodo, Benjamin A. Olken, Carlos Torres, Pierre-Luc Vautrey, Erica Warner, Susan Wootton, Esther Duflo (2021): Replication Data for: Effects of a large scale social media advertising campaign on holiday travel and COVID-19 infections: a cluster randomized controlled trial. Harvard Dataverse. Dataset. https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/4EK4KX

Authors: Emily Breza (Harvard University) ; Fatima Cody Stanford (Massachusetts General Hospital, Harvard Medical School) ; Marcela Alsan (Harvard Kennedy School of Government) ; Burak Alsan (Online Care Group) ; Abhijit Banerjee (MIT) ; Arun G. Chandrasekhar (Stanford University) ; Sarah Eichmeyer (Ludwig Maximilian University of Munich) ; Traci Glushko (Bozeman Health Deaconess Hospital) ; Paul Goldsmith-Pinkham (Yale University) ; Kelly Holland (Lynn Community Health Center) ; Emily Hoppe (John Hopkins University) ; Mohit Karnani (MIT) ; Sarah Liegl (St. Anthony North Family Medicine) ; Tristan Loisel (Paris School of Economics) ; Lucy Ogbu-Nwobodo (Harvard Medical School, Massachusetts General Hospital, McLean Hospital) ; Benjamin A. Olken (MIT) ; Carlos Torres (Harvard Medical School, Massachusetts General Hospital for Children) ; Pierre-Luc Vautrey (MIT) ; Erica Warner (Massachusetts General Hospital, Harvard Medical School) ; Susan Wootton (McGovern Medical School at the University of Texas Health Science Center at Houston) ; Esther Duflo (MIT) ; Vautrey, Pierre-Luc (MIT) ;

Links

• Item record in Abdul Latif Jameel Poverty Action Lab Dataverse
• Digital object URL

Summary

This package contains replication data for: "Doctors’ and Nurses’ Social Media Ads Reduced Holiday Travel and COVID-19 infections: A cluster randomized controlled trial in 13 States". It also contains the IRB protocol documents. It contains 9 datasets: -clean_cases.csv: zip level Covid-19 cases -county_covariates.dta: county level covariates -county_pop2019.dta countains county population in 2019 -Election2020.dta: election data from 2020 -fb_movement_data.dta: Facebook mobility data -randomized_sample_thanksgiving.xlsx: zip and county treatment status during the Thanksgiving campaign -randomized_sample_christmas.xlsx: zip and county treatment status during the Christmas campaign -us-counties.csv: county level Covid-19 data -randomized_zip.csv: treatment randomizations generated by zip_randomization.R for Randomization Inference The code, produced in R, contains both cleaning and analysis code. For further details on the data or how to run the code, please see the readme file. The abstract of the paper is as follows: During the COVID-19 epidemic, many health professionals started using mass communication on social media to relay critical information and persuade individuals to adopt preventative health behaviors. Our group of clinicians and nurses developed and recorded short video messages to encourage viewers to stay home for the Thanksgiving and Christmas Holidays. We then conducted a two-stage clustered randomized controlled trial in 820 counties (covering 13 States) in the United States of a large-scale Facebook ad campaign disseminating these messages. In the first level of randomization, we randomly divided the counties into two groups: high intensity and low intensity. In the second level, we randomly assigned zip codes to either treatment or control such that 75% of zip codes in high intensity counties received the treatment, while 25% of zip codes in low intensity counties received the treatment. In each treated zip code, we sent the ad to as many Facebook subscribers as possible (11,954,109 users received at least one ad at Thanksgiving and 23,302,290 users received at least one ad at Christmas). The first primary outcome was aggregate holiday travel, measured using mobile phone location data, available at the county level: we find that average distance travelled in high-intensity counties decreased by -0.993 percentage points (95% CI -1.616, -0.371, p-value 0.002) the three days before each holiday. The second primary outcome was COVID-19 infection at the zip-code level: COVID-19 infections recorded in the two-week period starting five days post-holiday declined by 3.5 percent (adjusted 95% CI [-6.2 percent, -0.7 percent], p-value 0.013) in intervention zip codes compared to control zip codes.

More information

• DOI: 10.7910/DVN/4EK4KX

Subjects

• Medicine, Health and Life Sciences, Social Sciences

Dates

• Publication date: 2021
• Submitted: June 22, 2021
• Updated: July 28, 2021

Rights

• info:eu-repo/semantics/openAccess
• https://creativecommons.org/publicdomain/zero/1.0/ CC0 Waiver

Format

electronic resource