No abstract

The COVID-19 pandemic has required societal behavioral change in order to slow the spread of the virus. Making people keep proper distance when in public and wear face masks have become a priority for governments around the world. Following previous literature on the effects of behaviorally informed text messages on promoting healthy behavior, São Paulo's innovation in government lab implemented a text message (SMS)-based intervention informed by behavioral insights aimed at encouraging people to stay at home, wear face masks, and maintain 2 meters distance from others. Specifically, five different SMS frameworks were analyzedreciprocity towards health workers, social norms, civic duty, risk perception, and self-efficacy/ collective identity. A field experiment was run in which beliefs about the pandemic, awareness of safe distancing, and social distancing behaviors were measured via a telephone survey. Results indicate that individuals who received text messages became better informed about the correct distance they should keep from others, and more likely to wear a mask. Respondents who received the 'civic duty' frame, designed to prime a sense of duty to protect family and friends, were consistently better informed and more likely to always wear a mas than other frames, although this difference is small. Specifically, those who received the 'civic duty' message were 12.75% more likely to report the right distance to keep from others and 3% more likely to report always wearing a mask compared to those who did not received a text message. When looking at differences across groups, it was found that men express more risky behavior than women; older individuals go out less and wear a mask more; respondents in richer areas less likely to leave home for work, but more likely to exercise or walk the dog; and, one's proximity to the disease affects belief and behavior, although differently depending on the relationship's degree and on the severity of the illness. These results have informed the scale up of this intervention: a second text message intervention of 3 text messages to over 2.7 million citizens, a combined effort between the city Sao Paulo, the Inter-American Development Bank (IDB) and the Partnership for Healthy Cities..

The COVID-19 pandemic is a global crisis that has forced governments around the world to implement large-scale interventions such as school closures and national lockdowns. Previous research has shown that partisanship plays a major role in explaining public attitudes towards these policies and beliefs about the intensity of the crisis. However, it remains unclear whether and how partisan differences in policy support relate to partisan gaps in beliefs about the number of deaths that the pandemic will cause. Do individuals who forecast fewer COVID-19 deaths show less agreement with preventive measures? How does partisanship correlate with people’s beliefs about the intensity of the crisis and their support for COVID-19 policies? Here, we sought to answer these questions by performing a behavioral experiment in Argentina (Experiment 1, N = 640) and three quasi-replication studies in Uruguay (Experiment 2, N = 372), Brazil (Experiment 3, N = 353) and the United States (Experiment 4, N = 630). In all settings, participants forecasted the number of COVID-19 deaths in their country after considering either a high or low number, and then rated their agreement with a series of interventions. This anchoring procedure, which experimentally induced a large variability in the forecasted number of deaths, did not modify policy preferences. Instead, each experiment provided evidence that partisanship was a key indicator of the optimism of forecasts and the degree of support for COVID-19 policies. Remarkably, we found that the number of forecasted deaths was robustly uncorrelated with participants’ agreement with preventive measures designed to prevent those deaths. We discuss these empirical observations in the light of recently proposed theories of tribal partisan behavior. Moreover, we argue that these results may inform policy making as they suggest that even the most effective communication strategy focused on alerting the public about the severity of the pandemic would probably not translate into greater support for COVID-19 preventive measures.

The COVID-19 pandemic is a global crisis that has forced governments around the world to implement large-scale interventions such as school closures and national lockdowns. Previous research has shown that partisanship plays a major role in explaining public attitudes towards these policies and beliefs about the severity of the crisis. However, the cognitive roots of this phenomenon remain poorly understood. In principle, partisan gaps in policy support could emerge from cost-benefit analyses from individuals with dissimilar perceptions about the severity of the pandemic, as proposed by rational models of partisan behavior. Alternatively, polarized responses may be driven by social identity motives that are unrelated to individual beliefs, as predicted by theories of tribal partisanship. Here, we tested the predictions of these two models across four experiments (N=1980) performed in four different countries (Argentina, Uruguay, Brazil, and the United States). Participants forecasted the number of COVID-19 deaths in their country after considering either a high or low number. Then, they rated their agreement with a series of interventions. This anchoring procedure, which experimentally induced a large variability in the forecasted number of deaths, did not modify policy preferences. Instead, we observed that partisanship independently modulated the optimism of forecasts and participants’ support for COVID-19 policies. These results, which are against the predictions of the rational partisanship model, have strong policy implications. In particular, our findings suggest that communication strategies aimed at informing the public about the severity of the pandemic will not substantially change levels of support for COVID-19 interventions.