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      Who Do You Trust? The Consequences of Partisanship and Trust for Public Responsiveness to COVID-19 Orders

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      Perspectives on Politics
      Cambridge University Press (CUP)

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          Abstract

          Non-uniform compliance with public policy by citizens can undermine the effectiveness of government, particularly during crises. Mitigation policies intended to combat the novel coronavirus offer a real-world measure of citizen compliance, allowing us to examine the determinants of asymmetrical responsiveness. Analyzing county-level cellphone data, we leverage staggered roll-out to estimate the causal effect of stay-at-home orders on mobility using a difference-in-differences strategy. We find movement is significantly curtailed, and examination of descriptive heterogeneous effects suggests the key roles that partisanship and trust play in producing irregular compliance. We find that Republican-leaning counties comply less than Democratic-leaning ones, which we argue underlines the importance of trust in science and acceptance of large-scale government policies for compliance. However, this partisan compliance gap shrinks when directives are given by Republican leaders, suggesting citizens are more trusting of co-partisan leaders. Furthermore, we find that higher levels of social trust increase compliance; yet these gains attenuate or intensify depending upon community-level partisan sentiments. Our study provides a real-world, behavioral measure that demonstrates the influence of partisanship, social trust, and their interaction on citizen welfare. Finally, we argue that our results speak to how trust in government may impact successful containment of the COVID-19 pandemic.

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          An interactive web-based dashboard to track COVID-19 in real time

          In December, 2019, a local outbreak of pneumonia of initially unknown cause was detected in Wuhan (Hubei, China), and was quickly determined to be caused by a novel coronavirus, 1 namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak has since spread to every province of mainland China as well as 27 other countries and regions, with more than 70 000 confirmed cases as of Feb 17, 2020. 2 In response to this ongoing public health emergency, we developed an online interactive dashboard, hosted by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, Baltimore, MD, USA, to visualise and track reported cases of coronavirus disease 2019 (COVID-19) in real time. The dashboard, first shared publicly on Jan 22, illustrates the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries. It was developed to provide researchers, public health authorities, and the general public with a user-friendly tool to track the outbreak as it unfolds. All data collected and displayed are made freely available, initially through Google Sheets and now through a GitHub repository, along with the feature layers of the dashboard, which are now included in the Esri Living Atlas. The dashboard reports cases at the province level in China; at the city level in the USA, Australia, and Canada; and at the country level otherwise. During Jan 22–31, all data collection and processing were done manually, and updates were typically done twice a day, morning and night (US Eastern Time). As the outbreak evolved, the manual reporting process became unsustainable; therefore, on Feb 1, we adopted a semi-automated living data stream strategy. Our primary data source is DXY, an online platform run by members of the Chinese medical community, which aggregates local media and government reports to provide cumulative totals of COVID-19 cases in near real time at the province level in China and at the country level otherwise. Every 15 min, the cumulative case counts are updated from DXY for all provinces in China and for other affected countries and regions. For countries and regions outside mainland China (including Hong Kong, Macau, and Taiwan), we found DXY cumulative case counts to frequently lag behind other sources; we therefore manually update these case numbers throughout the day when new cases are identified. To identify new cases, we monitor various Twitter feeds, online news services, and direct communication sent through the dashboard. Before manually updating the dashboard, we confirm the case numbers with regional and local health departments, including the respective centres for disease control and prevention (CDC) of China, Taiwan, and Europe, the Hong Kong Department of Health, the Macau Government, and WHO, as well as city-level and state-level health authorities. For city-level case reports in the USA, Australia, and Canada, which we began reporting on Feb 1, we rely on the US CDC, the government of Canada, the Australian Government Department of Health, and various state or territory health authorities. All manual updates (for countries and regions outside mainland China) are coordinated by a team at Johns Hopkins University. The case data reported on the dashboard aligns with the daily Chinese CDC 3 and WHO situation reports 2 for within and outside of mainland China, respectively (figure ). Furthermore, the dashboard is particularly effective at capturing the timing of the first reported case of COVID-19 in new countries or regions (appendix). With the exception of Australia, Hong Kong, and Italy, the CSSE at Johns Hopkins University has reported newly infected countries ahead of WHO, with Hong Kong and Italy reported within hours of the corresponding WHO situation report. Figure Comparison of COVID-19 case reporting from different sources Daily cumulative case numbers (starting Jan 22, 2020) reported by the Johns Hopkins University Center for Systems Science and Engineering (CSSE), WHO situation reports, and the Chinese Center for Disease Control and Prevention (Chinese CDC) for within (A) and outside (B) mainland China. Given the popularity and impact of the dashboard to date, we plan to continue hosting and managing the tool throughout the entirety of the COVID-19 outbreak and to build out its capabilities to establish a standing tool to monitor and report on future outbreaks. We believe our efforts are crucial to help inform modelling efforts and control measures during the earliest stages of the outbreak.
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            The Origins and Consequences of Affective Polarization in the United States

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              Cool dudes: The denial of climate change among conservative white males in the United States

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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Perspectives on Politics
                Perspect. polit.
                Cambridge University Press (CUP)
                1537-5927
                1541-0986
                April 16 2021
                : 1-27
                Article
                10.1017/S1537592721000049
                5c740b2a-b4f1-4567-a3a2-c436860f44e9
                © 2021

                https://www.cambridge.org/core/terms

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