COVID-19, lockdowns and well-being: Evidence from Google Trends

Abstract

The COVID-19 pandemic and government intervention such as lockdowns may severely affect people's mental health. While lockdowns can help to contain the spread of the virus, they may result in substantial damage to population well-being. We use Google Trends data to test whether COVID-19 and the associated lockdowns implemented in Europe and America led to changes in well-being related topic search-terms. Using difference-in-differences and a regression discontinuity design, we find a substantial increase in the search intensity for boredom in Europe and the US. We also found a significant increase in searches for loneliness, worry and sadness, while searches for stress, suicide and divorce on the contrary fell. Our results suggest that people's mental health may have been severely affected by the pandemic and lockdown.

Keywords: Boredom; COVID-19; Lockdown; Loneliness; Well-being.

Fig. 1

Google Trends in boredom, loneliness and sadness before and after the stay-at-home orders. The vertical axis shows the average searches (on a scale from 0 to 100) in the days before (negative values) and after (positive values) the stay-at-home order was announced (set equal to day zero) in 2020 (red dots) and the same date in 2019 (grey dots) for nine European countries (left) and 42 US States (right). The eight US States without a lockdown are excluded from the analysis. The dots correspond to the raw averages by bins of one day, weighted by the number of inhabitants per country/State. The European countries included are: Austria, Belgium, France, Ireland, Italy, Luxembourg, Portugal, Spain and the UK.

Fig. 2

The effects of the stay-at-home orders on well-being. Each bar represents Differences-in-Differences estimates using the 2019 period as a counterfactual. All models control for a dummy that takes the value of 1 in the days after the stay-at-home order was announced, as well as country/State, year, week, day of the week fixed effects and the one-day lagged number of new deaths from Covid-19 per million. Weights are applied. Robust standard errors are plotted. Standard errors are clustered at the day level.

Fig. 3

Duration of the effects of the stay-at-home orders on boredom, loneliness and sadness. The vertical axis shows event-study estimates using the 2019 period as the counterfactual. The 4th week before the stay-at-home-order (in 2019 or 2020) is the reference period. The models include dummies for each week from three weeks before to four weeks after the stay-at-home order. Controls: country/State, year, week, day of the week fixed effects as well as the one-day lagged number of new deaths from Covid-19 per million. Weights are applied. Robust standard errors are plotted. Standard errors are clustered at the day level.