Abstract:
Due to the COVID-19 pandemic several countries introduced various restrictions on the mobility of their citizens. As a result, the periods of various quarantine preventives were suspected to result in the disruption of social networks. Stay-at-home policies, closures of public places, schools, workplaces, etc. had an impact on both economies and citizens well-being. Indeed, well-being is an important and an interdisciplinary issue connected also with economics. The aim of the presented research was to analyse the causal impact of lockdowns on well-being, taking under the consideration the particular stress on mental health issues. However, at the same time not only lockdowns were impacting well-being, but the pandemic affected several economic conditions, which themselves influenced well-being. In this study it was tried to approximate those effects by the Internet search queries. In particular, Google Trends data were used. The second, important, feature of this research was the application of the Bayesian structural time series to model causality, instead of the commonly used the difference-in-differences method. Indeed, there are various arguments favouring this novel Bayesian method over the previously used methods. Acknowledgements: The research was supported by the program “Excellence Initiative – Research University (IDUB)” through the project BOB-IDUB-622-89/2021 – Nowe Idee POB III IDUB.
Keywords: causal impact, economy, lockdown, mental health, time-series, well-being
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