COVID-19 Vaccine Passport and International Traveling: The Combined Effect of Two Nudges on Americans' Support for the Pass.

Immunity passports have the potential to allow large-scale international traveling to resume. However, they can only become an effective tool if they are widely supported by the general public. We carry out a double blind randomized online experiment with a sample of N=4000 Americans to study (i) whether two nudges can increase the level of support for a COVID pass for international traveling, (ii) the relationship between the effects of the nudges, and (iii) if these nudges have a negative spillover on the intention to get vaccinated. We find that both nudges increase the support for the COVID pass and that their impact is stronger when they are used together. Moreover, we find that the two nudges do not negatively affect intentions to get vaccinated. Our findings have important implications for policymakers and for the nascent literature on the interaction between multiple nudges.

How do Google searches for symptoms, news and unemployment interact during COVID-19? A Lotka-Volterra analysis of google trends data.

In this paper I exploit Google searches for the topics "symptoms", "unemployment" and "news" as a proxy for how much attention people pay to the health and economic situation and the amount of news they consume, respectively. I then use an integrable nonautonomous Lotka-Volterra model to study the interactions among these searches in three U.S. States (Mississippi, Nevada and Utah), the District of Columbia and in the U.S. as a whole. I find that the results are very similar in all areas analyzed, and for different specifications of the model. Prior to the pandemic outbreak, the interactions among health searches, unemployment searches and news consumption are very weak, i.e. an increase in searches for one of these topics does not affect the amount of searches for the others. However, from around the beginning of the pandemic these interactions intensify greatly, suggesting that the pandemic has created a tight link between the health and economic situation and the amount of news people consume. I observe that from March 2020 unemployment predates searches for news and for symptoms. Consequently, whenever searches for unemployment increase, all the other searches decrease. Conversely, when searches for any of the other topics considered increase, searches for unemployment also increase. This underscores the importance of mitigating the impact of COVID-19 on unemployment to avoid that this issue swallows all others in the mind of the people.

Odi et Amo: A nudge to reduce the consumption of single-use carrier bags.

In this article we show that it is possible to reduce carrier bags consumption without imposing additional costs on supermarket customers. To this end, we exploit the fact that people shopping in a certain area are likely to have homogeneous preferences on certain dimensions (e.g., sport teams). We implement a simple treatment in a supermarket in Naples, Italy: whenever a customer of the supermarket buys a single-use carrier bag we donate a small sum to an institution that is likely to be perceived negatively by the customers of the supermarket (an "anti-charity"). Vice versa, whenever a customer does not purchase a bag, we donate the same amount to an association that is likely to be perceived positively by the customers of the supermarket (a "charity"). We measure the impact of the treatment with respect to the previous weeks, the corresponding weeks of the previous year and we look at the persistence of the effect. Our results show that this treatment causes a significant reduction in bags consumption without imposing monetary costs on supermarket customers. Moreover, we show that our nudge can easily be used by regulators at virtually no cost by creating multi-city tournaments. We explain how these tournaments could be arranged.

The scale of COVID-19 graphs affects understanding, attitudes, and policy preferences.

Mass media routinely present data on coronavirus disease 2019 (COVID-19) diffusion with graphs that use either a log scale or a linear scale. We show that the choice of the scale adopted on these graphs has important consequences on how people understand and react to the information conveyed. In particular, we find that when we show the number of COVID-19 related deaths on a logarithmic scale, people have a less accurate understanding of how the pandemic has developed, make less accurate predictions on its evolution, and have different policy preferences than when they are exposed to a linear scale. Consequently, merely changing the scale the data is presented on can alter public policy preferences and the level of worry about the pandemic, despite the fact that people are routinely exposed to COVID-19 related information. Providing the public with information in ways they understand better can help improving the response to COVID-19, thus, mass media and policymakers communicating to the general public should always describe the evolution of the pandemic using a graph on a linear scale, at least as a default option. Our results suggest that framing matters when communicating to the public.