Lockdown, essential sectors, and Covid-19: Lessons from Italy

Abstract

This paper investigates how economic activity impacted Covid-19 infections and all-cause mortality. To this purpose, we exploit the distribution of essential sectors, which were exempted from a national lockdown enacted in Italy during the first wave of the pandemic, across provinces and rich administrative data in a difference-in-differences framework. We find that a standard deviation increase in essential workers per built square kilometre leads to 1.1 additional daily cases and 0.32 additional daily deaths per 100,000 inhabitants. Back of the envelope calculations suggest that about one third (47,000) of the Covid-19 cases and about 13% (13,000) of deaths between March and May of 2020 can be attributed to the less stringent lockdown for these sectors. The effect is heterogeneous across sectors. Finally, we find that the local health system played a relevant role in reducing fatalities with a higher number of general practitioners and hospital beds per capita being associated with a lower mortality.

Keywords: Covid-19; Essential sectors; Lockdown.

Fig. 1

Density across Provinces. Note: Hundreds of individuals per built square kilometre based on social security administrative and national statistics data.

Fig. 2

Density of Essential Workers and its Effect over Time on Infections and Mortality. Note: Estimates for the effect of the density of essential workers before and after the policy implementation as described in Eq. (2) for 2020 on Covid-19 infections and mortality. Density of workers in essential sectors is measured as the number of workers (in hundreds) employed in essential sectors in 2019 per built square kilometre. Panel (a) reports effects for the number of reported cases for 100,000 inhabitants while Panel (b) reports the effect on number of deaths per 100,000 inhabitants. Regression based on daily data for 106 Italian provinces between the 25th of February and the 3rd of May 2020. Dates collected in three days groups to improve readability. The regression includes a 4th order polynomial trend from the first registered Covid-19 case in the province, and date (three days groups) and province fixed effects. The period between 5th and the 7th of March is used as a reference period. Observations weighted by the population in the province at the start of 2020. Confidence intervals at 95% based on standard errors clustered at the province level reported.

Fig. 3

Effect of Density of Essential Sectors on Covid-19 Infections and Mortality by Sector: Effect of Standard Deviation Change. Note: Estimates for the effect of density of essential workers in different sectors on Covid-19 infections and mortality. Density of workers in essential sectors is measured as the number of workers (in hundreds) employed in essential sectors in 2019 per built square kilometre. Panel (a) reports effects for the number of reported cases for 100,000 inhabitants while Panel (b) reports the effect on number of deaths per 100,000 inhabitants. Reported coefficients and standard errors computed for a standard deviation change in the density of workers in a specific sector. We report the p-value for a F-test for the equality of coefficients at the bottom of each graph. The regression includes a 4th order polynomial trend from the first registered Covid-19 case in the province, and date and province fixed effects. Regression based on daily data for 106 Italian provinces between the 25th of February and the 3rd of May 2020. Observations weighted by the population in the province at the start of 2020. Confidence intervals at 95% based on standard errors clustered at the province level reported. Services to firms and ind. includes: Financial and Insurance activities; Wholesale and Retail Trade; Professional, Scientific and Technical Activities. Other category includes: Agriculture, Forestry and Fishing; Water Supply; Sewerage, Waste Management and Remediation Activities; Other Service Activities; Construction; Electricity, Gas, Steam and Air Conditioning Supply; Information and Communication; Education; Public Administration and Defence; Compulsory Social Security; Mining and Quarrying.

Fig. 4

Effect of Density of Essential Workers on Mortality in 2019. Note: Estimates for the effect of the density of essential workers before and after the policy implementation as described in Eq. (2) for 2019 on mortality. We report the effect of density of essential sector workers on number of deaths per 100,000 inhabitants. Density of workers in essential sectors is measured as the number of workers (in hundreds) employed in essential sectors in 2018 per built square kilometre. Regression based on daily data for 106 Italian provinces between the 25th of February and the 3rd of May 2019. Dates collected in three days groups to improve readability. The period between 5th and the 7th of March is used as a reference period. The regression includes a 4th order polynomial trend from the first registered Covid-19 case in the province, and date (three days groups) and province fixed effects. Observations weighted by the population in the province at the start of 2019. Confidence intervals at 95% based on standard errors clustered at the province level reported.

Fig. C.1

Average Density of Essential Sectors. Note: Figure plots the average density of essential workers by sectors across Italian provinces. Density is measured as the number of workers in essential sectors (in hundreds) in 2019 per built square kilometre. Data weighted by population in 2020. Services to firms and ind. includes: Financial and Insurance activities; Wholesale and Retail Trade; Professional, Scientific and Technical Activities. Other category includes: Agriculture, Forestry and Fishing; Water Supply; Sewerage, Waste Management and Remediation Activities; Other Service Activities; Construction; Electricity, Gas, Steam and Air Conditioning Supply; Information and Communication; Education; Public Administration and Defence; Compulsory Social Security; Mining and Quarrying.

Fig. C.2

Effect of Density of Essential Sectors on Covid-19 Infections and Mortality: Interaction with Time Trend. Note: Predicted polynomials for the number of new Covid-19 infections in Panel (a) and daily deaths in Panel (b). Polynomials estimated by using the equation used in Column (3) of Table 2 with the addition of a fourth order polynomial interacted with the density of workers in essential sector. The prediction includes the coefficient of the interaction between the density in essential sectors and the post 03/22 dummy, the polynomial trend and the interaction between the polynomial trend and the density of essential sector. The prediction with higher density of essential workers sets the density at one standard deviation (441 workers per built km2). Estimation based on the whole sample between the 25th of February and the 3rd of March. Observations weighted by the population in the province on the 1st of January 2020.

Fig. C.3

Effect of Density of Essential Sectors on Covid-19 Infections and Mortality by Sector: Coefficients. Note: Estimates for the effect of density of essential workers in different sectors on Covid-19 infections and mortality. Density of workers in essential sectors is measured as the number of workers (in hundreds) employed in essential sectors in 2019 per built square kilometre. Panel (a) reports effects for the number of reported cases for 100,000 inhabitants while Panel (b) reports the effect on number of deaths per 100,000 inhabitants. The regression includes a 4th order polynomial trend from the first registered Covid-19 case in the province, and date and province fixed effects. Regression based on daily data for 106 Italian provinces between the 25th of February and the 3rd of May 2020. Observations weighted by the population in the province at the start of 2020. Confidence intervals at 95% based on standard errors clustered at the province level reported. Services to firms and ind. includes: Financial and Insurance activities; Wholesale and Retail Trade; Professional, Scientific and Technical Activities. Other category includes: Agriculture, Forestry and Fishing; Water Supply; Sewerage, Waste Management and Remediation Activities; Other Service Activities; Construction; Electricity, Gas, Steam and Air Conditioning Supply; Information and Communication; Education; Public Administration and Defence; Compulsory Social Security; Mining and Quarrying.

Fig. C.4

Effect of Density of Essential Sectors on Covid-19 Infections and Mortality by Sector (more detailed disaggregation): Effect of a Standard Deviation Change. Note: Estimates for the effect of density of essential workers in different sectors on Covid-19 infections and mortality. Density of workers in essential sectors is measured as the number of workers (in hundreds) employed in essential sectors in 2019 per built square kilometre. Panel (a) reports effects for the number of reported cases for 100,000 inhabitants while Panel (b) reports the effect on number of deaths per 100,000 inhabitants. Reported coefficients and standard errors computed for a standard deviation change in the density of workers in a specific sector. The regression includes a 4th order polynomial trend from the first registered Covid-19 case in the province, and date and province fixed effects. Regression based on daily data for 106 Italian provinces between the 25th of February and the 3rd of May 2020. Observations weighted by the population in the province at the start of 2020. Confidence intervals at 95% based on standard errors clustered at the province level reported. Other category includes: Agriculture, Forestry and Fishing; Water Supply; Sewerage, Waste Management and Remediation Activities; Other Service Activities; Construction; Electricity, Gas, Steam and Air Conditioning Supply; Information and Communication; Education; Public Administration and Defence; Compulsory Social Security; Mining and Quarrying.

Fig. C.5

Effect of Density of Essential Sectors on Covid-19 Infections and Mortality: Sensitivity of Estimates to Single Provinces. Note: Estimates for the effect of density of essential workers on Covid-19 infections and mortality. Each dot reports the coefficient of our difference-in-differences variable in the main equation by excluding the province reported on the x axis. Label reports one province every seven provinces for the sake of clarity. Density of workers in essential sectors is measured as the number of workers (in hundreds) employed in essential sectors in 2019 per built square kilometre. Panel (a) reports effects for the number of reported cases for 100,000 inhabitants while Panel (b) reports the effect on number of deaths per 100,000 inhabitants. Red line reports the corresponding OLS baseline estimate of Column 3 of Table 2. The regression includes a 4th order polynomial trend from the first registered Covid-19 case in the province, and date and province fixed effects. Regression based on daily data for 106 Italian provinces between the 25th of February and the 3rd of May 2020. Observations weighted by the population in the province at the start of 2020. Confidence intervals at 95% based on standard errors clustered at the province level reported. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)