Acceptability of App-Based Contact Tracing for COVID-19: Cross-Country Survey Study.

BACKGROUND: The COVID-19 pandemic is the greatest public health crisis of the last 100 years. Countries have responded with various levels of lockdown to save lives and stop health systems from being overwhelmed. At the same time, lockdowns entail large socioeconomic costs. One exit strategy under consideration is a mobile phone app that traces the close contacts of those infected with COVID-19. Recent research has demonstrated the theoretical effectiveness of this solution in different disease settings. However, concerns have been raised about such apps because of the potential privacy implications. This could limit the acceptability of app-based contact tracing in the general population. As the effectiveness of this approach increases strongly with app uptake, it is crucial to understand public support for this intervention. OBJECTIVE: The objective of this study is to investigate the user acceptability of a contact-tracing app in five countries hit by the pandemic. METHODS: We conducted a largescale, multicountry study (N=5995) to measure public support for the digital contact tracing of COVID-19 infections. We ran anonymous online surveys in France, Germany, Italy, the United Kingdom, and the United States. We measured intentions to use a contact-tracing app across different installation regimes (voluntary installation vs automatic installation by mobile phone providers) and studied how these intentions vary across individuals and countries. RESULTS: We found strong support for the app under both regimes, in all countries, across all subgroups of the population, and irrespective of regional-level COVID-19 mortality rates. We investigated the main factors that may hinder or facilitate uptake and found that concerns about cybersecurity and privacy, together with a lack of trust in the government, are the main barriers to adoption. CONCLUSIONS: Epidemiological evidence shows that app-based contact tracing can suppress the spread of COVID-19 if a high enough proportion of the population uses the app and that it can still reduce the number of infections if uptake is moderate. Our findings show that the willingness to install the app is very high. The available evidence suggests that app-based contact tracing may be a viable approach to control the diffusion of COVID-19.

Bonus versus penalty: How robust are the effects of contract framing?

We study the relative effectiveness of contracts that are framed either in terms of bonuses or penalties. In one set of treatments, subjects know at the time of effort provision whether they have achieved the bonus/avoided the penalty. In another set of treatments, subjects only learn the success of their performance at the end of the task. We fail to observe a contract framing effect in either condition: effort provision is statistically indistinguishable under bonus and penalty contracts.

Peer Effects in Pro-Social Behavior: Social Norms or Social Preferences?

We compare social preference and social norm based explanations for peer effects in a three-person gift-exchange experiment. In the experiment a principal pays a wage to each of two agents, who then make effort choices sequentially. In our baseline treatment we observe that the second agent's effort is influenced by the effort choice of the first agent, even though there are no material spillovers between agents. This peer effect is predicted by the Fehr-Schmidt (1999) model of social preferences. As we show from a norms-elicitation experiment, it is also consistent with social norms compliance. A conditional logit investigation of the explanatory power of payoff inequality and elicited norms finds that the second agent's effort is best explained by the social preferences model. In further experiments we find that the peer effects change as predicted by the social preferences model. Again, a conditional logit analysis favors an explanation based on social preferences, rather than social norms. Our results suggest that, in our context, the social preferences model provides a parsimonious explanation for the observed peer effect.