  This study analyzes the impact of the COVID-19 pandemic on the subjective
well-being as measured through Twitter data indicators for Japan and Italy. It
turns out that, overall, the subjective well-being dropped by 11.7% for Italy
and 8.3% for Japan in the first nine months of 2020 compared to the last two
months of 2019 and even more compared to the historical mean of the indexes.
Through a data science approach we try to identify the possible causes of this
drop down by considering several explanatory variables including, climate and
air quality data, number of COVID-19 cases and deaths, Facebook Covid and flu
symptoms global survey, Google Trends data and coronavirus-related searches,
Google mobility data, policy intervention measures, economic variables and
their Google Trends proxies, as well as health and stress proxy variables based
on big data. We show that a simple static regression model is not able to
capture the complexity of well-being and therefore we propose a dynamic elastic
net approach to show how different group of factors may impact the well-being
in different periods, even over a short time length, and showing further
country-specific aspects. Finally, a structural equation modeling analysis
tries to address the causal relationships among the COVID-19 factors and
subjective well-being showing that, overall, prolonged mobility
restrictions,flu and Covid-like symptoms, economic uncertainty, social
distancing and news about the pandemic have negative effects on the subjective
well-being.
