2022
DOI: 10.1080/00343404.2022.2035708
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Resilience to health shocks and the spatial extent of local labour markets: evidence from the Covid-19 outbreak in Italy

Abstract: Table A.1: Descriptive statistics Mean SD Minimum Maximum Observations mortality growth 0.313 1.540 -1.000 39.000 intensive margin 0.334 0.189 0.000 3.898 extensive margin 0.012 0.013 0.000 0.339 internal mobility 0.114 0.053 0.000 0.403 coastal 0.078 0.268 0.000 1.000 mountainous 0.732 0.443 0.000 1.000 ln density 4.718 1.406 -0.266 9.411 ln house m 2 pc 3.763 0.134 3.266 4.450 share males 0.496 0.017 0.414 0.650 share over75 0.119 0.042 0.025 0.435 share cohab over65 0.360 0.124 0.075 1.781 hospital beds pc … Show more

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Cited by 5 publications
(6 citation statements)
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“…First, in distinguishing between the resident and worker populations of neighbourhoods, we are able to unpack the channels through which urban density facilitates the spread of the virus across neighbourhoods through social and economic interactions. Thus we contribute to the emerging literature documenting the critical role played by industrial and employment densities in spreading the virus (Almagro and Orane-Hutchinson 2022;Ascani et al 2021a;Di Porto et al 2022), and recent studies on the role of labour mobility on the spread of COVID-19 (Ascani et al 2021b;Borsati et al 2023), by providing first evidence of the differential impact of the local labour structure on COVID-19 transmission with respect to where people live and where they work, which has so far received limited attention. This also distinguishes this paper from recent work by Almagro et al (2023), using cell-phone mobility data to study the effect of greater out-of-home mobility and within-home crowding on the risk of COVID-19 hospitalization.…”
Section: Introductionmentioning
confidence: 77%
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“…First, in distinguishing between the resident and worker populations of neighbourhoods, we are able to unpack the channels through which urban density facilitates the spread of the virus across neighbourhoods through social and economic interactions. Thus we contribute to the emerging literature documenting the critical role played by industrial and employment densities in spreading the virus (Almagro and Orane-Hutchinson 2022;Ascani et al 2021a;Di Porto et al 2022), and recent studies on the role of labour mobility on the spread of COVID-19 (Ascani et al 2021b;Borsati et al 2023), by providing first evidence of the differential impact of the local labour structure on COVID-19 transmission with respect to where people live and where they work, which has so far received limited attention. This also distinguishes this paper from recent work by Almagro et al (2023), using cell-phone mobility data to study the effect of greater out-of-home mobility and within-home crowding on the risk of COVID-19 hospitalization.…”
Section: Introductionmentioning
confidence: 77%
“…Similarly, the lockdown strategy introduced in Italy at the beginning of the first wave has been shown to have reduced the spread of the virus away from provinces that were first hit (Bourdin et al 2021). Complementary evidence is offered by the recent strand of research looking specifically at the relationship between labour mobility and the spread of COVID-19, pointing to a significant role of individuals' mobility as well as the position of municipalities within a network of commuting flows on disease transmission and depth of the shock (Ascani et al 2021b;Borsati et al 2023). After the onset of the pandemic, the role played by density was not shaped solely by policy.…”
Section: The Role Of Public Health Policiesmentioning
confidence: 97%
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“…They also affirm the correlation between epidemic distribution and economic factors. Borsati et al (2023) investigate the link between worker mobility and disease spread, demonstrating that a reduction in commuting could have substantially decreased the number of deaths during the first wave of the pandemic in 2020. Cerqua and Letta (2022) employ machine learning to show that the economic repercussions of the pandemic varied widely across Italy, independent of the epidemiological trends during the first wave.…”
Section: Related Literaturementioning
confidence: 99%
“…With the growing interest in explaining the causes and consequences of crises on regions, regional studies have progressively used the concept of resilience (Davies 2011 ; Bathelt et al 2013 ; Eraydın 2013 , 2016 ; Dijkstra et al 2015 ; Crescenzi et al 2016 ; Cuadrado-Roura et al 2016 ; Martin et al 2016 ; Doğruel et al 2018 ; Brinks and Ibert 2020 ; Gong et al 2020 ; Houston 2020 ; Kapitsinis 2020 ; Klimanov et al 2020 ; Goschin and Constantin 2021 ; Tupy et al 2020 ; Borsati et al 2022 ; Kim et al 2022 ; Li et al 2022 ).…”
Section: Introductionmentioning
confidence: 99%