Human behaviour, cultural narratives and the economy during and after Covid-19

This project focuses on the Culture-Based Development (CBD) aspects of the effect of the Covid-19 pandemic. It analyses cultural biases created through narratives and conveying old social constructs that generate socio-economic inequalities during and after the pandemic. The unfolding of the Covid-19 pandemic is an unprecedented, unanticipated global phenomenon that allows us to improve our understanding about how a sudden shock amplifies existing inequalities and socio-economic divides across space. In a seminal piece, Scheidel (2017) poses the argument that “catastrophic levellers” taking tens of millions of lives undiscriminatory, such as the medieval Great Plagues, the Russian revolutions or the World Wars, no longer exist in industrial societies. However, the Covid-19 global pandemic is a ‘leveller’ that discriminates according to pre-existing, pre-pandemic cultural and economic fault lines. Such discrimination is revealed in a spatial pattern that reflects clusters of people with the ‘left-behind’ feelings and that were already expressed by the population in the form of radical voting in the UK and across Europe. In a series of working papers, we investigate the cultural underpinnings of these inequalities, including: (i) The relationship between Ethnic and poverty-related inequality with morbidity and mortality in front of the new Covid-19 Great Leveller; (ii) Aggregate measures of mental health and the progressively radicalizing narratives related to voting behaviour; (iii) Mental health sensitivity to fiscal and monetary policy interventions; (iv) Variations in the mental health resilience across space and cultures in response to different public policies; (v) Cultural devaluation of life in response to various policies that remove the lockdown; (vi) Magnified socio-economic inequalities during the Covid-19 pandemic and their impact on future radicalization and voting both in the UK and the EU. We apply a suite of relevant econometric methods, such as interrupted time-series analysis, difference-in-differences, data decomposition and instrumental variable approaches. We also use in-house expertise in data-mining, signal processing and the design of machine learning instruments to condition and make sense of the data.