This paper analyses the consequences on demographic changes observed in the municipalities of Abruzzo region after the earthquake of L'Aquila in 2009. Relying on Italian National Institute of Statistics (Istat) data on the population in the municipalities, we aim to understand how population growth changed after the earthquake, analysing population dynamics both in the municipalities affected by the earthquake and in the other municipalities of the Abruzzo region. We apply a spatial regression model to understand the associations between population growth, the earthquake, and some demographic and economic variables, taking into account the spatial autocorrelation. Our findings confirm the importance of space and the close association of the population dynamics after the earthquake with the population age structure, both in the pre‐existing situation and in the subsequent dynamics. Population trends in the Abruzzo region were closely associated with the pre‐existing vulnerabilities. The effect of the earthquake was strong in the crater municipalities in the period immediately after the shock, while it petered out over time. A destructive earthquake implies a tremendous shock, however, in places that are already suffering from demographic, social, and economic fragility, pre‐existing factors can act equally or even more strongly on the population dynamics in the medium term. Hence, in the medium period, the characteristics of the population and socio‐economic context are stronger than in the short one, and also the effect that space and territorial contiguities have on these and other nonobservable variables is stronger than in the short period.
This paper aims to analyse sentiments and emotions about migration in Italy using Twitter, by comparing the period of COVID-19 pandemic with the previous year. We take Italy as a case study because it has been severely affected by the COVID-19, it is one of the largest recipients of immigrants in Europe and, is among the few countries that implemented an amnesty for irregular migrant workers during the pandemic. We apply a text mining and sentiment analysis to the tweets with hashtags and keywords related to the migration and to the COVID-19 pandemic. Results show that tweets related to migration express a sense of emergency and also invasion. No major changes occurred in the period of the pandemic in comparison with the previous period. Indeed, both negative and positive sentiments are present in the tweets in both periods, confirming a certain polarization in the public discourse about migration.
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