This Stata dataset contains 366 daily observations pertaining to the period running from the 24th of February 2020 to the 23rd of February 2021 (one year). First, we collected the daily distribution of COVID-19 positive cases in the 107 Italian second-level institutional bodies (i.e., provinces) and of performed swabs and recorded deaths in the country’s 19 regions and 2 Autonomous provinces, provided by the Italian Civil Protection. Furtherly, we make use of the Containment and Health Index, developed by the University of Oxford’s Blavatnik School of Government, tracing the government response to the pandemic outbreak over time. Moreover, we gathered the number of daily controls and fines imposed to citizens due to disrespecting the restrictive measures aimed at containing the Coronavirus spread, made available by the Italian Ministry of the Interior. Plus, we employ Google’s Community Mobility Reports, capturing movement trends across different categories of places at the province level. Additionally, we include the regional-level scores of bonding and bridging social capital, which may play a role in explaining citizens’ compliance. Lastly, we complement these sources with a number of variables describing the demographic characteristics of the analysed provinces (i.e., activity rate, density, population, ratio of over-65s to the total population), taken from the Italian National Institute of Statistics (Istat). Some dummies portraying the restrictions adopted in particular periods (i.e., lockdown, red and orange zones) are also computed. We also provide Stata codes for ten regression models. Models A1 to A3 are Hausman-Taylor panel regressions of provincial positivity rate; Model B is a fixed-effects panel regression of time spent in residential areas; Models C1 to C3 are similar to Models A1 to A3 but estimated through Negative Binomial fixed-effects panel regressions, employing the count of provincial cases as dependent variable; Models D1 to D3 are Negative Binomial fixed-effects panel regressions of the regional deaths count. For more information, see Panarello D. and Tassinari G., "One year of COVID-19 in Italy: are containment policies enough to shape the pandemic pattern?", Socio-Economic Planning Sciences (forthcoming).

One year of COVID-19 in Italy: Policies, Health, Mobility and Structural information

Demetrio Panarello
;
Giorgio Tassinari
2021

Abstract

This Stata dataset contains 366 daily observations pertaining to the period running from the 24th of February 2020 to the 23rd of February 2021 (one year). First, we collected the daily distribution of COVID-19 positive cases in the 107 Italian second-level institutional bodies (i.e., provinces) and of performed swabs and recorded deaths in the country’s 19 regions and 2 Autonomous provinces, provided by the Italian Civil Protection. Furtherly, we make use of the Containment and Health Index, developed by the University of Oxford’s Blavatnik School of Government, tracing the government response to the pandemic outbreak over time. Moreover, we gathered the number of daily controls and fines imposed to citizens due to disrespecting the restrictive measures aimed at containing the Coronavirus spread, made available by the Italian Ministry of the Interior. Plus, we employ Google’s Community Mobility Reports, capturing movement trends across different categories of places at the province level. Additionally, we include the regional-level scores of bonding and bridging social capital, which may play a role in explaining citizens’ compliance. Lastly, we complement these sources with a number of variables describing the demographic characteristics of the analysed provinces (i.e., activity rate, density, population, ratio of over-65s to the total population), taken from the Italian National Institute of Statistics (Istat). Some dummies portraying the restrictions adopted in particular periods (i.e., lockdown, red and orange zones) are also computed. We also provide Stata codes for ten regression models. Models A1 to A3 are Hausman-Taylor panel regressions of provincial positivity rate; Model B is a fixed-effects panel regression of time spent in residential areas; Models C1 to C3 are similar to Models A1 to A3 but estimated through Negative Binomial fixed-effects panel regressions, employing the count of provincial cases as dependent variable; Models D1 to D3 are Negative Binomial fixed-effects panel regressions of the regional deaths count. For more information, see Panarello D. and Tassinari G., "One year of COVID-19 in Italy: are containment policies enough to shape the pandemic pattern?", Socio-Economic Planning Sciences (forthcoming).
2021
Demetrio Panarello; Giorgio Tassinari
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/895485
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