Italy was the first non-Asian country to experience the rapid and extensive spread of COVID-19. Based on data provided by the Italian Civil Protection Department (2020),Footnote 3 Fig. 1 shows the dynamics of positive cases, hospitalizations, and deaths from the 24th of February onwards.
The dynamics of positive cases and hospitalized people became significant by the end of February, with an exponential trend reaching a peak in the second half of March; afterwards, the respective variations took a declining path. Deaths followed a similar path, with approximately a 10-day delay, although levels were still significant at the end of April.
A first measure taken by the national government to prevent the outbreak was implemented on the 30th of January, before the virus was officially detected in the country. This involved blocking all flights to and from China and declaring a state of emergency, thus allowing for higher discretional policies. On the 21st of February, when a cluster of cases was detected in the Lombardy region, the government decided to declare “red areas” and tried to isolate some small municipalities. Nevertheless, the virus spread throughout the northeast of the country, and on the 23rd of February, Italy became the European country with the highest number of infected people recorded.
From the beginning of March, the Italian government reacted to the emergency through a series of increasingly stringent rules for social distancing. Italy has been the first European country to implement significant restrictions to citizens’ mobility and personal freedom. The first measure at the national level was announced and signed by the Prime Minister, Giuseppe Conte, on March 4 and became effective the day after. The main restriction concerned the suspension of school activities for all grades.Footnote 4
On March 8, the Italian government signed another extraordinary restriction act for Lombardy and another 14 northern provinces (i.e., Modena, Parma, Piacenza, Reggio Emilia, Rimini, Pesaro–Urbino, Alessandria, Asti, Novara, Verbano–Cusio–Ossola, Vercelli, Padova, Treviso, and Venezia). This measure became effective the day after, although the national press spread the news the day before the act was signed. On March 12, the day after the World Health Organization declared a “pandemic” and with the virus already spreading to other regions and provinces, the Italian government extended the same measures to the whole country.Footnote 5 The measures involved the shutdown of all commercial and retail business activities, except for those considered basic necessities. Even food services such as bars and restaurants were closed, with the exception of take-away services. Furthermore, mobility was restricted to going to work, shopping for food, and emergencies.
The vertical lines in Fig. 1 correspond to the starting dates of the national lockdown measures. The third vertical line on the graph, on March 26,Footnote 6 corresponds to the last containment measure adopted: the closure of all “non-essential” economic activities. The enforcement of this lockdown had a fuzzy evolution: a first version of the decree was announced on March 21, published on March 22, and then modified after a meeting with workers’ unions and entrepreneur representatives.Footnote 7 After this measure, only 53% of firms were allowed to remain open (Centra et al. 2020).
Many studies have tried to forecast the contagion dynamics in Italy (Remuzzi and Remuzzi 2020; Grasselli et al. 2020; Fanelli and Piazza 2020), or in Italy and other countries (see among others, Zhang et al. 2020). Some studies have also focused on the lockdown’s effect, trying to evaluate the impact in terms of saved lives and contagion reduction (Lavezzo et al. 2020; Hsiang et al. 2020). Casella (2020) compares two types of restrictive measures: the tight lockdown adopted in China and the significant but less severe measures adopted in the Lazio region (the closure of schools and the main lockdown). He develops a control-oriented model capturing the control-relevant dynamics to homogenize territories. He concludes that suppression strategies can be effective if enacted very early, while mitigation strategies are prone to failure.
Pedersen and Meneghini (2020) implement a SIQR (Susceptible, Infectious, Quarantined, Recovered) model through which they evaluate the effect of lockdown measures in the north of Italy using data until March 19. They conclude that restriction measures slowed down the exponential growth rate but did not incisively reduce the spread of COVID-19. Giordano et al. (2020) propose a SIDARTHE (Susceptible, Infected, Diagnosed, Ailing, Recognized, Threatened, Healed, Extinct) model able to predict the epidemic’s trend. Considering the period from February 20 to April 5, they analyze how the progressive restrictions have affected the spread of the epidemic. They found that lockdown measures had a moderate effect, probably due to their incremental nature. The main conclusion of the paper is that lockdown measures have to be combined with widespread testing and contact tracing to defeat the virus. The document redacted by Direzione Centrale Studi e Ricerche INPS (DCSR – INPS 2020) tries to quantify the effect of the third lockdown measure by exploiting spatial variation in the degree of closure of economic activities. This report claims that the reduction in COVID-19 cases started from the day the decree was introduced, without any delay. In any case, all of these studies, except Casella (2020), suffer from the same set of limitations in terms of the specification of the detection delay that was stressed above. Furthermore, except for the DCSR-INPS study, they are more focused on the forecasting of possible future scenarios and none performed a retrospective analysis of the features of the different kinds of restrictive measures.
Finally, what the literature has understated is that measures have both direct impacts due to the specific measures adopted and the particular dates on which they are enforced, and indirect effects for which things can be different and the distinction between lockdowns fuzzy. A prominent example is the announcement effect. Indeed, COVID-19’s reproduction number also depends on individual behaviors such as avoiding skin contact between people or hand washing, which can be modified by the perception and knowledge of the virus. Both the announcement and implementation of restrictive measures can have a relevant impact on these, in particular in a country that has been one of the most affected by the novel coronavirus.
Figure 2 reports the Google Trends in Italy for “Coronavirus Italia” from mid-January to mid-April 2020.Footnote 8 The red line corresponds to the announcement date of the corresponding restrictive measures, whose actual introduction corresponds to the blue line. The first peak in Google searches corresponds to the date of air traffic closure between China and the state of emergency announcement. The second peak is recorded at the announcement and implementation of “red-zones” in some northern municipalities. The next peak occurs on the 4th of March, when the first national lockdown was announced. From this day onwards, the Google searches increased up to the implementation of the subsequent lockdown in the northern regions and started to decline on March 12, when the second lockdown was implemented at the national level. The upsurge of interest in the phenomenon related to the announcement of the previous restrictive measures might have affected the epidemic’s path independently from the direct impact of the specific measures.
The same increased awareness might have other indirect effects through a massive shift of white-collar workers towards smart working (see Bonacini et al. 2020) and the decision of many firms to reduce their overall activities because of the incoming fall in final demand. Figure 3 displays the trends in electricity consumption in Italy from February 3 to April 9, 2020. Blue lines correspond to the dates when the three national lockdowns were implemented. The reduction in electricity consumption begins with the first lockdown, but it decreases sharply after the second (main) lockdown. Thus, standard economic activities seem to have decreased their electricity consumption already after the first lockdowns, although the shutdown was imposed only on a minority of economic activities—mainly schools, food facilities, and some retail, leisure, and cultural activities. The last lockdown, which imposed the closure of all (remaining) non-essential activities, seems to have had a lesser impact on energy consumption, which even showed a slight increase some days later.
All these descriptive indicators reinforce the need for a non-epidemic econometric strategy to deepen the detection delay issue and to assess the effects of the different lockdowns by also inspecting possible indirect and side effects. This is what we try to do in the next section.