DOI: 10.5061/dryad.3bk3j9khg
title: How partisanship and public health concerns affect individuals’ social mobility during COVID-19

description: Rampant partisanship in the United States may be the largest obstacle to
 the reduced social mobility most experts see as critical to limiting the
 spread of the COVID-19 pandemic. Analyzing a total of just over 1.1
 million responses collected daily between April 4th and September 10th
 reveals not only that partisanship is more important than public health
 concerns for explaining individuals’ willingness to stay-at-home and
 reduce social mobility, but also that the effect of partisanship has grown
 over time – especially among Republicans. All else equal, the
 relative importance of partisanship for the increasing (un)willingness of
 Republicans to stay-at-home highlights the challenge that politics poses
 for public health.
description: To obtain our data, we interview of
 1,135,638 randomly selected respondents from the Survey Monkey platform
 between April 4, 2020 and September 29, 2020. These individuals are
 randomly sampled from the approximately 2 million individuals who take
 Survey Monkey surveys every day. The
 data we analyze was collected by Survey Monkey using respondents who have
 consented to participate in the Survey Monkey audience panel and
 surveys.  The anonymized data are weighted for
 age, race, sex, education, and geography using the Census Bureau’s
 American Community Survey to reflect the demographic composition of the
 United States. An additional smoothing parameter for political party
 identification based on aggregates of SurveyMonkey research surveys is
 included. Weights are generated each day using daily interviews Monday
 through Friday, and once over combined interviews on Saturday and Sunday.
 Party identification parameters are refreshed bi-weekly. Data on COVID-19 prevalence comes
 from COVID-19 Data Repository by the Center for Systems Science and
 Engineering (CSSE) at Johns Hopkins University. These data report, on each
 day, the number of COVID-19 cases in each county in the United
 States. As a first data processing step, we
 use county population estimates from the American Community Survey to
 transform county cases to be cases per 1000 residents. We
 then calculate for each county, for each week, the change in the number of
 cases from the previous week in that county, using the median number of
 cases in each county for each week. Consider, for example, interviewing
 an individual in zip-code 37206 (in Davidson county on Nashville’s East
 Side) on April 4th. On the week of April 4th in Davidson county (in which
 the zip-code is wholly contained) the median number of cases per 1000 was
 1.001. In the week previous, the median number of cases per 1000 was .428.
 This individual, therefore, would be given the value .573 for change in
 county cases per 1000. This value is a fair representation of the fact
 that this individual, on this day, was witnessing a worsening public
 health situation in their immediate surrounding. Survey Monkey respondents self-report
 their zip-code. To assign respondents to a county we determine the county
 in which the majority of the residents of that zip-code live in. For 86%
 of respondents, their zip-code is entirely contained within one
 county. Finally, we merge the data on how the
 number of COVID-19 cases is changing in each county on each day to the
 Survey Monkey data.
description: Data was analyzed in R.  All code needed to replicate tables and
 figures are included.