How to make universal, voluntary testing for COVID-19 work? A behavioural economics perspective.

Testing is widely seen as one core element of a successful strategy to curtail the COVID-19 pandemic and many countries have increased their efforts to provide testing at large scale. As most democratic governments refrain from enacting mandatory testing, a key emerging challenge is to increase voluntary participation. Using behavioural economics insights complemented with data from a novel survey in the US and a survey experiment in Luxembourg, we examine behavioural factors associated with the individual willingness to get tested (WTT). In our analysis, individual characteristics that correlate positively with WTT include age, altruism, conformism, the tendency to abide by government-imposed rules, concern about contracting COVID-19, and patience. Risk aversion, unemployment, and conservative political orientation correlate negatively with WTT. Building on and expanding these insights may prove fruitful for policy to effectively raise people's propensity to get tested.

COVID-19 in Europe: Dataset at a sub-national level.

The COVID-19 pandemic has hit humanity, straining health care systems, economies, and governments worldwide. In one of the responses to the pandemic, a big global effort has been mounted to collect, analyze, and make data publicly available. However, many of the existing COVID-19 public datasets are (i) aggregated at country level, and (ii) tend not to bring the COVID-19-specific data coupled with socio-demographic, economic, public policy, health, pollution and environmental factors, all of which may be key elements to study the transmission of the SARS-CoV-2 and its severity. To aid the evaluation of the determinants and impact of the COVID-19 pandemic at a large scale, we present here a new dataset with socio-demographic, economic, public policy, health, pollution and environmental factors for the European Union at the small regions level (NUTS3). The database is freely accessible at http://dx.doi.org/10.17632/2ghxnrkr9p.4. This dataset can help to monitor the COVID-19 mortality and infections at the sub-national level and enable analysis that may inform future policymaking.