Workers at Risk
To reduce the epidemic spreading, the Italian Government adopted several social distancing measures including two consecutive sectoral lockdowns, on March 11 and March 25. Each decree lists the sectors that were forced to close their workplaces.Footnote 15 Other sectors, such as the health and food industries as well as their value chains, were kept open; it was also strongly advised to work from remote for those who could (including primary to college education). In this section, we describe the lockdown in terms of employment, with a particular regard to workers exposed to risks because working in proximity to other people or because of their age and gender.Footnote 16 Note that our focus is on sectors because the Government enacted shutdown policies at this level. While we do not take a stand on whether this is the optimal observational unit on which adopting this kind of policy, it is reasonable under the assumption that occupations within each sector act under perfect complementarity. That is, within each sector, a certain amount of input in terms of occupations is needed in order for the sector to produce output, where these amounts are given by the sector-specific occupational employment weights.Footnote 17
The first decree has left 2.7 million of workers at home, which amounts to around 11.6% of total employment. It specifically targeted some service sectors, “Accommodation and food service activities” and “Arts, sports and recreational activities”.Footnote 18 After March 25, only a few main sectors were fully operative: “Energy and gas”, Water supply and waste management”, “Transportation and storage”, “Finance and insurance”, “Public administration” and, rather obviously, the “Health and social assistance” and the agricultural sectors (note that a very small agricultural subsector involved in forestry activities has been put under lockdown).
To understand better whether sectors that were forced to close were also more exposed to risks, we first correlate the sectoral lockdowns with the physical proximity index in a simple univariate OLS regression (Table A3 in the Appendix). The first lockdown targeted sectors where workers’ physical proximity was particularly high, while this was much less so if we consider the March 25 lockdown that involved many more sectors. However, the physical proximity index does not give information as to whether a certain profession requires one to carry out tasks in physical proximity to co-workers or to interact frequently with external customers and clients. These two sub-dimensions of physical proximity are important since they imply a different degree of worker exposure to contagion risk. It is easier to implement safety measures that minimize contagion risk—e.g., trace and isolate infected workers and co-workers who have been in contact with them—when workers operate in physical proximity to other co-workers in the same firm. However, it could be harder to adopt measures to contain and control the spread of COVID-19 infections when workers carry out tasks that imply interaction with the public. Thus, we also correlate the sectoral lockdowns with the proximity to co-workers and proximity to the public indexes (Panel B and Panel C of Table A3 in the Appendix, respectively). The lockdown sectors, on average, are characterized by higher values of the proximity to the public index than the proximity to co-workers one.
The analysis above, however, is uninformative about whether these policies implicitly also targeted workers at high risk. Table 2 reports the correlations between the shares of workers at high risk (dependent variables) on the sectoral lockdown dummies (independent variable; column 1) that we recover from OLS regressions: in the other columns, we report different specifications, in turn excluding the health industry and weighting the regressions by sectoral employment.Footnote 19 The observations indicate the number of sectors at the 4-digit level for which the lockdown decision have been taken by the governmental decrees. Panel A of Table 2 reports as dependent variable the share of workers in the top tercile of the physical proximity index, defined as in Eq. (1). Results suggest that the first decree involved sectors with a relatively higher percentage of workers at high risk, having involved workers in the restaurants and accommodation industries and in some retail stores; such percentage is higher by up to 54% points. The second decree was not significantly associated with a high share of workers at risk as it targeted many more sectors: notably, also the R-squared drops from 0.32 in column (3) to 0.02 in column (6). The correlation—still positive and significant—is lower when looking at the share of workers that score high in the proximity to co-workers index as outcome variable (Table 2, Panel B), whereas it has a similar size when considering the share of workers in the top tercile of the proximity to the public index distribution (Table 2, Panel C). Thus, the first decree targeted those sectors where it is more difficult to adopt measures to prevent contagion and stop the spread of the infection.
Table 3 shows another important dimension of health risk related to workers’ demographics: we focus on the percentage of male workers above the age of 50 by sector. The regression's results indicate that there was a negative association between the sectors locked-down and their percentage of above-50 male workers; namely, the sectors that stayed open had a higher share of workers who were male above the age of 50. The second lockdown, by targeting many sectors, hit indistinctively sectors regardless of their percentage of above-50 male workers.
Despite the evidence that many workers at risk are in essential sectors (e.g., about one third of all school instructors in Italy are above 50 years of age), optimal policies should take the age dimension in consideration due to the high susceptibility of this population (Favero et al. 2020; Brotherhood et al. 2020; Rampini 2020; Glover et al. 2020). To this end, Acemoglu et al. (2020), with an application to the US, show that optimal policies differentially targeting risk/age groups of population significantly outperform optimal uniform policies and that the oldest group would have more advantages. However, to our knowledge, relaxation of the lockdown measures in Italy and other European countries adopted so far did not target the age of workers.
Working from home
Some workers are less at risk of COVID-19 infections than others because they carry out tasks that can be done at home. Moreover, these workers remained plausibly operative during the sectoral lockdowns. By lowering social interactions, they also put less pressure on public health at the first peak of the epidemic. Table 1 showed that the sectors with the greater share of workers that could work from home are “Energy”, “Finance”, “Public Administration” and “Professional services”, not the sectors affected by the lockdown decrees. Given the share of those who can work from home (Table 1), there could be up to 3 million persons who worked from home in essential (i.e., open) sectors and not in workplaces during the first wave of the pandemic.Footnote 20
To formally test the hypothesis that lockdown involved sectors with a lower percentage of workers who can easily work from remote, we run a regression similar to those reported in Tables 2 and 3. On the left hand side, we now have the percentage of workers in the sector whose job is among those with the highest chance to be performed from remote. In this case, we expect to see a negative association between such percentage of workers and the lockdown measures. The results in Table 4 confirm this hypothesis: on average, the sectors that were shut down by the two decrees, had a lower share of workers with a high possibility to work from remote. This goes from about 29% after March 11 decree, to 18% after the March 25 adoption of the second decree.Footnote 21 The much broader scope of this latter lockdown, also reflected in a lower R-squared (see columns (3) and (6)), may have contributed few workers with a lower need to work from a specific workplace to keep working from home.