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In a recession such as COVID-19 pandemic with important consequences in the labour market, it is important to design policies that really address factors behind the employment loss for each demographic subgroup. The aim of this paper is to decompose and identify these factors for each demographic group, comparing the effects of the Great Recession with the COVID-19 pandemic. Although segregation was saving job loss for young and female workers in the Great Recession, these groups were concentrated in the most affected sectors during the COVID-19, increasing their job loss. However, a policy which just focuses on recovering employment from these sectors will not solve the employment problems behind young workers since they are being used as reserved workers fired in the downturn and hired in the upturns. We attempt to measure also the effect of Expediente de Regularización de Empleo Temporal (ERTE) subsidies on employment.

In this paper we present a forecast of the impact of measures to stem Covid-19 on the Spanish economy at a highly disaggregate level, using input-output techniques. Our estimations cover the period 2020-2021, and we consider two scenarios depending on the possibility of a second wave of massive infections in the autumn of 2020. In 2020, the lockdown of the population and the shutdown of a large part of the production system for several weeks are a supply-side shock that will be followed by a demand-side shock whose impact is expected to be even larger. In 2021 there will be some recovery, although we believe that it will not be sufficient for offsetting the initial negative shock. al negative shock.

This paper investigates the pattern of COVID-19 contagion in Portuguese municipalities from March 23rd to April 5th (the exponential phase). We have recurred to spatial autocorrelation models to discuss how the conglomeration of highly infectious spaces has also contributed to infecting neighbouring municipalities. We have used several indicators for the contagion of COVID-19 from the number of infectious individuals to rates of infectious. As explicative variables, additionally to spatial proximity, we also considered population density, the share of the elderly population as well as the length of municipal perimeter/border. Our results show that highly dense municipalities tended to contaminate close areas. Lengthier perimeters also showed a positive effect on the contagious indicators for a given municipality.