Economic Factors Behind the Pandemic Deaths: A Regional Perspective

We focus the analysis on the regional factors meddling through the effects of Covid 19 on a territorial basis. In the first part of the paper, we explore mortality COVID rate from the outbreak of the pandemic up to September 2020, when uncertainty was global on how to react to the virus, across the European regions. The main objective of this part is to explore the influence on Covid mortality of the nature of the Health Systems and the role of the regions in responding the pandemic. In the second part we translate some of the hypothesis in the empirical arena. We use for this purpose a rich data set of NUTS-2 from Eurostat . We adjust for the Social Insurance (SIS) or the National Health Service nature (NHS) of the Health systems, and the Regional Authority index (RAI) on the degree of decentralization from the OECD fiscal federalism network. In addition, we correlate to mortality EQI, the quality of the regional government index (basically the sense of corruption). Other than these institutional country aspects we delve into the impact of variables such as the size of the population, age structure, per capita GDP, the density of the NUTS and some other spatial factors. We find that at the beginning of the health crisis, with the chaotic irruption of the infection, the uncertainty of the policies to be applied and with some regional random responses, those richer, more populated NUTS and countries with a hierarchical NHS did show worse mortality ratios, with low significance for RAI. We focus on the differences in the global mortality in our NUTS2 sample between 2020 and 2021. In 2021, started massive vaccination, the Covid evolution was better understood, and decentralization-based policies were instrumented in some countries. In these models, RAI is statistically significant, but not the nature of the systems anymore. It looks like an idiosyncratic, close to the problem, regional answer to the pandemics diluted the differences of the national health systems. The quality index of the regional government is also highly correlated to mortality changes. In the third part of the paper, we zoom in the reality of a single country (Spain) by analyzing territorial heterogeneity. We compute for the Spanish provinces (NUTS3) the total number of Quality Adjusted Life Years calculated the QALYs lost in the three defined periods (initial lockdown, centralized management and decentralized management) given the gender and the age structure of each province. We calculate then the number of QALYs lost with coronavirus, given absolute mortality and provincial relative age-related health damage.