探索COVID-19在意大利的本地动态:当地劳动力市场以及贝加莫和布雷西亚的案例

Marco Lomuscio
{"title":"探索COVID-19在意大利的本地动态:当地劳动力市场以及贝加莫和布雷西亚的案例","authors":"Marco Lomuscio","doi":"10.5947/jeod.2021.002","DOIUrl":null,"url":null,"abstract":"In containing and mitigating the diffusion of COVID-19, countries are not fully able to pursue local test, trace and isolate strategies due to difficulties in detecting place-based infections and positive asymptomatic cases. This paper explores whether and to what extent local labour markets, functional areas defined by employment self-containment indexes and labour mobility data, can grasp the spatial dynamics of COVID-19 diffusion. Local labour markets capture most of the socio-economic interactions of working and residential populations and identify areas in which people are more likely to engage in frequent, face-to-face contacts with neighbours, colleagues, friends and relatives. Through an exploratory spatial data analysis and the estimation of a spatial autoregressive model, this paper examined a sample of 441 municipalities and 20 local labour markets. These territorial units belong to the Lombard provinces of Bergamo and Brescia (Italy), among the worst affected areas of the country in respect to both reported deaths and confirmed infections in the early stages of the pandemic. The findings suggest that municipal variations in mortality rates in 2020 correlate with a range of statistics for local labour markets, namely self-containment indexes, labour market dynamics and commuting behaviours. Overall, this paper shows that local labour markets are a useful scale of analysis in detecting the geography of COVID-19 diffusion in the target sample, and verifies the possibility of capturing the spatial dynamics of the epidemic on a smaller territorial scale than NUTS-3 regions do. Copyright © 2021 The Authors.","PeriodicalId":206501,"journal":{"name":"European Economics: Labor & Social Conditions eJournal","volume":"65 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Exploring Local Dynamics of COVID-19 in Italy: Local Labour Markets and the Cases of Bergamo and Brescia\",\"authors\":\"Marco Lomuscio\",\"doi\":\"10.5947/jeod.2021.002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In containing and mitigating the diffusion of COVID-19, countries are not fully able to pursue local test, trace and isolate strategies due to difficulties in detecting place-based infections and positive asymptomatic cases. This paper explores whether and to what extent local labour markets, functional areas defined by employment self-containment indexes and labour mobility data, can grasp the spatial dynamics of COVID-19 diffusion. Local labour markets capture most of the socio-economic interactions of working and residential populations and identify areas in which people are more likely to engage in frequent, face-to-face contacts with neighbours, colleagues, friends and relatives. Through an exploratory spatial data analysis and the estimation of a spatial autoregressive model, this paper examined a sample of 441 municipalities and 20 local labour markets. These territorial units belong to the Lombard provinces of Bergamo and Brescia (Italy), among the worst affected areas of the country in respect to both reported deaths and confirmed infections in the early stages of the pandemic. The findings suggest that municipal variations in mortality rates in 2020 correlate with a range of statistics for local labour markets, namely self-containment indexes, labour market dynamics and commuting behaviours. Overall, this paper shows that local labour markets are a useful scale of analysis in detecting the geography of COVID-19 diffusion in the target sample, and verifies the possibility of capturing the spatial dynamics of the epidemic on a smaller territorial scale than NUTS-3 regions do. Copyright © 2021 The Authors.\",\"PeriodicalId\":206501,\"journal\":{\"name\":\"European Economics: Labor & Social Conditions eJournal\",\"volume\":\"65 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European Economics: Labor & Social Conditions eJournal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5947/jeod.2021.002\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Economics: Labor & Social Conditions eJournal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5947/jeod.2021.002","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

在遏制和减缓COVID-19传播的过程中,由于难以发现地方感染和无症状阳性病例,各国无法完全采取当地检测、追踪和隔离战略。本文探讨了当地劳动力市场、由就业自我遏制指数和劳动力流动数据定义的功能区能否以及在多大程度上掌握COVID-19扩散的空间动态。当地劳动力市场涵盖了工作人口和居住人口的大部分社会经济互动,并确定了人们更有可能与邻居、同事、朋友和亲戚进行频繁面对面接触的领域。通过探索性的空间数据分析和空间自回归模型的估计,本文研究了441个城市和20个地方劳动力市场的样本。这些领土单位属于伦巴第省的贝加莫省和布雷西亚省(意大利),就报告的死亡人数和在大流行病早期确诊的感染人数而言,这两个省属于该国受影响最严重的地区。研究结果表明,2020年各城市死亡率的变化与当地劳动力市场的一系列统计数据相关,即自我遏制指数、劳动力市场动态和通勤行为。总体而言,本文表明,在检测目标样本中COVID-19扩散的地理位置时,当地劳动力市场是一个有用的分析尺度,并验证了在比nut -3区域更小的领土尺度上捕获流行病空间动态的可能性。版权所有©2021作者。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Exploring Local Dynamics of COVID-19 in Italy: Local Labour Markets and the Cases of Bergamo and Brescia
In containing and mitigating the diffusion of COVID-19, countries are not fully able to pursue local test, trace and isolate strategies due to difficulties in detecting place-based infections and positive asymptomatic cases. This paper explores whether and to what extent local labour markets, functional areas defined by employment self-containment indexes and labour mobility data, can grasp the spatial dynamics of COVID-19 diffusion. Local labour markets capture most of the socio-economic interactions of working and residential populations and identify areas in which people are more likely to engage in frequent, face-to-face contacts with neighbours, colleagues, friends and relatives. Through an exploratory spatial data analysis and the estimation of a spatial autoregressive model, this paper examined a sample of 441 municipalities and 20 local labour markets. These territorial units belong to the Lombard provinces of Bergamo and Brescia (Italy), among the worst affected areas of the country in respect to both reported deaths and confirmed infections in the early stages of the pandemic. The findings suggest that municipal variations in mortality rates in 2020 correlate with a range of statistics for local labour markets, namely self-containment indexes, labour market dynamics and commuting behaviours. Overall, this paper shows that local labour markets are a useful scale of analysis in detecting the geography of COVID-19 diffusion in the target sample, and verifies the possibility of capturing the spatial dynamics of the epidemic on a smaller territorial scale than NUTS-3 regions do. Copyright © 2021 The Authors.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
The Skill-Specific Impact of Past and Projected Occupational Decline Persistent Monetary Policy in a Model with Labor Market Frictions High Discounts and Low Fundamental Surplus: An Equivalence Result for Unemployment Fluctuations Exploring Local Dynamics of COVID-19 in Italy: Local Labour Markets and the Cases of Bergamo and Brescia Poison or Cure? a Study on the Periodic Sponge Effect in Denmark’s Tourism Industry
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1