Last updated: 2020-07-28

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Knit directory: UKsocialLicence/

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Rmd 2132776 StephanLewandowsky 2020-03-29 Start workflowr project.

Social Licensing of Privacy-Encroaching Policies to Address the COVID-19 Pandemic

This is a “live” document that records analyses of a global project dedicated to examining the public’s acceptance of privacy-encroaching tracking policies. This document is updated whenever new data or analyses become available.

Last update: Tue Jul 28 10:14:47 2020

Project information

Authors: Simon Dennis, Stephan Lewandowsky, Philipp Lorenz-Spreen, Klaus Oberauer, Yasmina Okan, Rob Goldstone, Yang Cheng-Ta, Yoshihisa Kashima, Amy Perfors, Josh White, Paul Garrett, Nic Geard, Daniel Little, Lewis Mitchell, Martin Tomko, Anastasia Kozyreva, Stefan Herzog, Ralph Hertwig, Thorsten Pachur, Muhsin Yesilada, Marcus Butavicius

Summary of project: The nature of the COVID-19 pandemic may require governments to use big data technologies to help contain its spread. Countries that have managed to “flatten the curve”, (e.g., Singapore), have employed collocation tracking through mobile Wi-Fi, GPS, and Bluetooth as a strategy to mitigate the impact of COVID-19. Through collocation tracking, Government agencies may observe who you have been in contact with and when this contact occurred, thereby rapidly implementing appropriate measures to reduce the spread of COVID-19. The effectiveness of collocation tracking relies on the willingness of the population to support such measures, implying that government policy-making should be informed by the likelihood of public compliance. Gaining the social license — broad community acceptance beyond formal legal requirements — for collocation tracking requires the perceived public health benefits to outweigh concerns of personal privacy, security, and any potential risk of harm.

This project involves a longitudinal cross-cultural study to trace people’s attitudes towards different tracking-based policies during the crisis. At present, we are planning multiple waves in Australia, Germany, and the U.K. We will have at least one wave in the U.S. and Switzerland, and other countries are expected to participate as well.

We aim to understand (1) the factors that influence the social license around governmental use of location tracking data in an emergency, (2) how this may change over time, and (3) how it may differ across cultures. We will present participants with one of two vignettes describing mild or severe Government tracking methods that may reduce the spread of COVID-19, and then question participants’ attitudes towards the proposed methods.

Further details and a discussion of this project can be found on the /r/BehSciResearch subreddit.

Background and approach to analysis

The COVID-19 crisis has challenged all sectors of society, including science. The present crisis demands an all-out scientific response if it is to be mastered with minimal damage. This means that we, as a community of scientists, need to think about how we can adapt to the moment in order to be maximally beneficial. How can we quickly and reliably deliver an evidence base for the many, diverse questions that behavioural science can inform: minimizing the negative impacts of isolation, providing support for vulnerable groups who have depended on face-to-face interaction, coping with stress, effective remote delivery of work and teaching, combatting misinformation, getting communication and messaging right, fostering the development of resilient new cultural practices, to name but a few.

In short, we need “science without the drag” — that is, high-quality robust science that operates at an immensely accelerated pace. Ulrike Hahn, Nick Chater, David Lagnado and I put our initial thoughts onto PsyarXiv. This project seeks to take a first step towards converting those thoughts into practice. A discussion of those steps can be found at the /r/BehSciMeta subreddit.

The first step is that the analysis will be posted “live” online, as it progresses, in near real time on this webpage.

Overview of method

All countries used an identical design and nearly identical surveys (translated as necessary). Sampling regime differed between countries and is described with the analyses.

Study design: Between-subjects design with 1 factor (government mobile tracking severity, instantiated by scenario) and two levels (mild and severe). Tracking method descriptions primarily differed by i) whether participation was optional or mandatory, and ii) if tracking data could be used to inform individuals when they may have been exposed to COVID-19, or to enforce individualized lockdown laws through fines and arrests.

The mild scenario was as follows:

Tracking COVID-19 Transmission

The COVID-19 pandemic has rapidly become a worldwide threat. Containing the virus’ spread is essential to minimise the impact on the healthcare system, the economy, and save many lives. The U.K. Government might consider using smartphone tracking data to identify and contact those who may have been exposed to people with COVID-19. This would help reduce community spread by identifying those most at risk and allowing health services to be appropriately targeted. Only people who downloaded a government app and agreed to be tracked and contacted would be included in the project. The more people download and use this app, the more effectively the Government would be able to contain the spread of COVID-19. Data would be stored in an encrypted format on a secure server accessible only to the U.K. Government. Data would only be used to contact those who might have been exposed to COVID-19.

The severe scenario was as follows:

Tracking COVID-19 Transmission

The COVID-19 pandemic has rapidly become a worldwide threat. Containing the virus’ spread is essential to minimise the impact on the healthcare system, the economy, and save many lives. The U.K. Government might consider using phone tracking data supplied by telecommunication companies to identify and contact those who may have been exposed to people with COVID-19. This would help reduce community spread by identifying those most at risk and allowing health services to be appropriately targeted. All people using a mobile phone would be included in the project, with no possibility to opt-out. Data would be stored in an encrypted format on a secure server accessible only to the U.K. Government which may use the data to locate people who were violating lockdown orders and enforce them with fines and arrests where necessary. Data would also be used to inform the appropriate public health response and to contact those who might have been exposed to COVID-19. Individual quarantine orders could be made on the basis of this data.

Principal measured variables: Participant’s attitudes towards government tracking methods were assessed. People’s perceived threat from COVID-19 and their basic political worldviews were measured. Basic demographics were also collected.

Available analyses

United Kingdom Wave 1: 28 and 29 March 2020.

United Kingdom Wave 2: 16 April 2020. Added Bluetooth scenario and new questions about immunity passports.

Australia Wave 1: Early April 2020.

Australia Wave 2: Mid April 2020.

Australia Wave 3: Early May 2020.

Australia Wave 4: 23-25 June 2020.

United States Wave 1: 6 and 7 April 2020

Germany Wave 1: 30-31 March

Germany Wave 2: 17-22 April

Taiwan

Spain Wave 1: 27 April-2 May 2020

Switzerland Wave 1: 8 - 28 April 2020

Italy Wave 1: 4-5 July 2020


sessionInfo()
R version 3.6.3 (2020-02-29)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18362)

Matrix products: default

locale:
[1] LC_COLLATE=English_United Kingdom.1252 
[2] LC_CTYPE=English_United Kingdom.1252   
[3] LC_MONETARY=English_United Kingdom.1252
[4] LC_NUMERIC=C                           
[5] LC_TIME=English_United Kingdom.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] workflowr_1.6.1

loaded via a namespace (and not attached):
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[13] promises_1.1.0  whisker_0.4     rmarkdown_2.2   tools_3.6.3    
[17] stringr_1.4.0   glue_1.4.1      httpuv_1.5.2    xfun_0.14      
[21] yaml_2.2.1      compiler_3.6.3  htmltools_0.4.0 knitr_1.28