services like hairdressers). Places that are not associated to an economic
activity (mostly outdoors) also have a weight greater than one million. Most
categories in business and professional services receive fewer visits, but
still much more than manufacturing and construction. Finally, “upstream”
industries such as utilities and wholesale trade do not have any associated
Foursquare category and so do not have any weight; Based on Foursquare places
manually matched to NAICS, agriculture and mining appear non-existent in the
New York metropolitan area.

Based on this information, we choose $\tau_{k}=1$ for each of the seven
customer-facing industries mentioned above, and select $\tau_{k}=0$ for all
other industries.

Now, even though we can use the contacts extracted from the data covering the
period from 02/12/20 to 06/01/20 for this kind of analysis, we cannot use them
directly in the simulations. Indeed, note that the data from March to June
naturally incorporates all the societal changes that occurred during those
days: some people were not present in the workplace layer because they were
fired or working from home; others stopped going to certain venues in the
community, etc. As such, if we were to use the real data from this period, we
would not be able to couple the epidemic module with the economic module that
estimates who should be fired/hired. Similarly, we would not be able to
estimate any fear of infection since it would already be contained in the
data.

Instead, we initialize the community and workplace contacts using data from
the period 2020/02/13 to 2020/03/11, which we take as representative of the
pre-pandemic situation. Then, at each step, contacts belonging to the
corresponding day of the week are sampled from this set. For instance, on any
Monday in the simulation we would select the contacts from 2020/02/17,
2020/02/24, 2020/03/02 or 2020/03/09. In this way, we can run simulations
under “normal” conditions (i.e. without the effects of the pandemic),
incorporating non-pharmaceutical interventions into the model (for instance,
by removing all links associated to a certain type of industry), including the
effect of the firing process by removing individuals from the workplace layer,
etc.

###  S3.2 Shocks

####  S3.2.1 Local supply shocks

To stop the spread of COVID-19, the New York Governor announced in March 2020
that all non-essential businesses statewide must close in-office personnel
functions.888Part of the New York metropolitan area is actually in New Jersey,
which had slightly different restrictions from New York State. For simplicity,
we just assume that the restrictions imposed by New York State are valid for
the entire metropolitan area and were applied at the same time. This
announcement included a document explaining which businesses were considered
essential.999 https://www.governor.ny.gov/news/governor-cuomo-issues-guidance-
essential-services-under-new-york-state-pause-executive-order Unlike the
lockdown announcements from other governments, the definition of an essential
business in New York was not linked to industry classification codes.
Nevertheless, by using the description of NAICS industries and carefully
examining the essential businesses described in New York’s lockdown
announcement, we classified industries as essential or non-essential according
to New York guidelines.

To build New York’s list of essential industries, we used the BLS 2019
industry employment data for the NY-NJ-PA metropolitan area. There are
employment data for 218 industries at the 4-digit NAICS level. Additionally,
there are employment data for 5 industries101010 These industries are:
Fishing, hunting and trapping; Mining, except oil and gas; Construction of
buildings; Paper manufacturing; and Truck transportation  at the 3-digit NAICS
level that is not available at the 4-digit level. This gives a total of $223$
industries to classify as essential or non-essential. By carefully reading the
description of these industries and the New York government’s mandate, we
assigned an essential score of 1 to those industries that met the criteria for
essentialness and 0 otherwise111111 All industries were assigned a binary
essential score, except ”Restaurants and other eating places”. Restaurants
were considered essential only for take-out and/or delivery. We assigned this
industry an essential score of $0.1$. . We then aggregated the essential score
of industries to the 2-digit NAICS classification level. We did this
aggregation by taking the employment weighted average of each industry
subclass. The procedure is fully documented in the replication materials.

We interpret the essential score of industry $k$, $\text{ESS}_{k}$, as the
fraction of workers from industry $k$ that are allowed to work on-site
according to the New York government regulations. We then define the supply
shock at time $t$ as $s_{k,t}^{\text{local}}=1-\text{ESS}_{k}$, if
restrictions were in place at $t$ (in our baseline calibration, this is
between March 16 and May 15, 2020), and $s_{k,t}^{\text{local}}=0$ if
restrictions were not in place.

The last column in Table S3 shows the essential score of the 20 2-digit NAICS
industries. We can see that a few industries are completely essential
(agriculture, mining, utilities, finance, management, health and public
administration), only one is completely non-essential (arts, entertainment and
recreation) and all the others are partly essential.

####  S3.2.2 Supply shocks in the Rest of the US

Closures have been very different across states and imposed at different
times. To get an estimate of supply shocks in the rest of the US, we rely on
the data that have been made publicly available in Ref. [38]. The authors
examined executive orders issued in each State and, using a methodology
similar to the one outlined above, gave an essential score to each 2-digit
NAICS industry in each State. Weighing the data by state-industry employment
shares, we compute an average essential score for each industry. Our estimates
lead to essential scores that are generally higher than the ones in NY,
especially for construction and manufacturing, which are 94% and 86% essential
respectively (compared to 49% and 56%). Given the different times at which
restrictions were imposed throughout the U.S., for simplicity we impose
restrictions in the rest of the US at the same time as we impose restrictions
in NY.

####  S3.2.3 Rest of US pandemic indicator

While the number of deaths $D_{t}$ in the NY metro area is produced
endogenously by the epidemic module, the time series of deaths in the Rest of
the US, $D_{t}^{\text{rest}}$ is given exogenously. We compute it using data
from the New York Times dataset.121212https://github.com/nytimes/COVID-19-data

####  S3.2.4 Government and other demand shocks

Both in New York and in the Rest of the US, we model changes in government
demand and demand for investment and net exports (“other final demand”)
exogenously. We let changes in government demand $\mathcal{G}_{t}$ be given by
$\mathcal{G}_{t}=-0.05$ for all days $t$ after the time restrictions are
imposed. This corresponds to assuming a small 5% increase in government demand
throughout the pandemic period. At the same time, we impose a 30% shock
$\mathcal{S}_{t}$ to other final demand, i.e. $\mathcal{S}_{t}=0.30$. These
shocks to government and other final demand lead to changes in realized
government consumption, investment and exports that are in line with the data
(Figure 2A).

###  S3.3 Synthetic population

Our synthetic population is composed of 416,442 individuals grouped into
153,547 households.131313The size of the synthetic population is determined by
the number of Cuebiq users, see Sections S3.3.1 and S3.3.6. Each synthetic
individual has two unique ids: one id is a unique identifier, the other id
corresponds to a Cuebiq user (not all individuals in the synthetic population
are Cuebiq users). In addition, each individual is characterized by eight
variables and attributes: household id, the census tract where they live,
their age, income, employment status, occupation, industry where they work,
and whether they can work from home. Of these, only the employment status
changes over time, while we assume that the other attributes are fixed.

In the following sections, from Section S3.3.1 to Section S3.3.4, we explain
how we construct these quantities. In Section S3.3.5 we show summary
statistics and a comparison between the synthetic population and the real New
York population. Next, in Section S3.3.6, we explain how we match Cuebiq users
to synthetic individuals. Finally, in Section S3.3.7, we explain how we
initialize model variables based on the characteristics of synthetic
individuals. As a reference through this section, Figure S2A summarizes the
correlations between variables that are obtained by construction.141414Note
that a distance of two in this graph could also imply some correlation. For
example, age and industry may be correlated because both are correlated to
income.

Figure S2: Correlation between variables of individuals and matching between
synthetic individuals and Cuebiq users. A: a link between variables indicates
that these variables are correlated by construction. When variables form a
clique, e.g. income, industry and occupation, it means that they are all
determined jointly. B: synthetic individuals are matched to Cuebiq users
(mobility traces) mainly using information about the census tract where they
live, but also based on the industry they work for and on whether they can
work from home.

####  S3.3.1 Age, household, census tract

We generate as many adults in each census tract as there are Cuebiq users
whose “home” is in the census tract, plus a few synthetic adults (1% of all
adults) that do not have an associated Cuebiq user but are needed to better
match the statistical properties of household size.

We assign an age to synthetic adults, create synthetic children, and group
them into households using the procedure documented in Ref. [31]
(Supplementary Information Section 1). For New York, this procedure uses a
combination of demographic tables from the ACS, similar to the ones used in
the rest of this paper, as well as detailed microdata from IPUMS USA and other
data sources. Microdata are used to compute the conditional distribution of a
variable given a condition. For instance, microdata can be used to compute the
probability that a spouse has a given age conditional on the age of the other
spouse.

The approach, documented in detail in Scheme S1 in Ref. [31], is to

  1. 1.

Sample household size from ACS demographic tables

  2. 2.

Determine the age of a reference adult household member using the conditional
distribution of that age conditional on household size

  3. 3.

Determine the composition of the household by sampling from the conditional
distribution of the existence of a given relation to the reference adult
member determined above. Relations include: husband or wife, son, daughter,
son-in-law, daughter-in-law, brother, sister, brother-in-law, sister-in-law,
mother, father, mother-in-law, father-in-law, niece, nephew, grandchild, and
others.

  4. 4.

For each selected household member that has a relation of the type determined
above with the reference household member, determine their properties sampling
from a conditional distribution that depends on the reference member. For
instance, the age of a spouse is sampled conditionally on the age of the
reference member, to ensure that the age difference is in line with data.

To ensure a more detailed spatial representation of the household structure,
we resample household characteristics across census tracts. For instance, if
the age of a household reference person is above median in a given census
tract, we sample households with higher age of reference person with slightly
larger probability.

As a convention, in this paper we assign to the adult with highest income in
the household the title of “household head”.

####  S3.3.2 Employment status and industry.

We assign employment status to individuals based on their census tract and
their age using ACS Table B23001, reporting the number of individuals by age
group151515 While age in the synthetic population is a variable taking integer
values between 0 and 103, only the following age groups are available in Table
23001:
$[18,19],(19,21],(21,24],(24,29],(29,34],(34,44],(44,54],(54,59],(59,61],(61,64],(64,69],(69,74],(74+]$.
We simply give our agents the same age groups as in Table 23001, assuming that
all agents within each age group have the same employment rate.  and census
tract in each of the following categories: out of the labor force, unemployed,
employed.161616 The employed category includes armed forces employees.  We
group individuals that are unemployed or out of the labor force into a unique
category “Not employed”. All individuals below 18 years old are, by
construction, not employed. We end up, for each census tract and each age
group, with a share of employed individuals. We take this share as the
parameter of a Bernoulli distribution, and assign employment status to all
agents in the same age group and census tract by drawing from such a Bernoulli
distribution. To all agents that are employed, we then assign one of the 20
NAICS 2-digit industries based on the same procedure, using ACS table C24030
to get industry shares of employment for each census tract. We then assign an
industry to all employed agents within each census tract drawing from a
categorical distribution (i.e., the multivariate version of a Bernoulli
distribution) whose vector of probabilities is given by the industry shares of
employment.

####  S3.3.3 Occupation and possibility to work from home

The occupation of a worker is strongly linked to the industry they work for.
For instance, it is unlikely to find art performers in agriculture, while
production occupations are likely in manufacturing. At the same time,
occupations are strongly linked to geography, as, for example, most
inhabitants of Manhattan are more likely to be engaged in knowledge-related
occupations than inhabitants from other parts of the New York metro area. The
ACS does not provide a cross tabulation for industries and occupations at the
census tract level. To obtain a distribution of occupations that is jointly
correlated with industry and geography, we proceed as follows.

We obtain the joint distribution of occupations and industries from the BLS
Occupational Employment Statistics (OES), table natsector_M2019_dl.171717
https://www.bls.gov/oes/special.requests/oesm19in4.zip For each of 20
industries, we know the occupational share of 22 occupations, which correspond
to the most aggregated occupations in the SOC hierarchy (see Table S4).181818
Not all occupations exist in any given industry.  It should be noted that
these data are valid for the US as a whole; however, we assume that the
occupational composition of industries in NY is not too different from the one
in the rest of the US (see Section S3.3.5 for a validation of this
assumption). To obtain the distribution of occupations for each census tract,
we use ACS table C24010, giving the share of employed workers for each of the
22 main occupations in each census tract.

For all individuals that have been assigned a given industry, we then draw
their occupation respecting both the relation between occupation and industry
and that between occupation and geography. Focusing on a given industry, we
first draw the number of employed workers in a given occupation from a
multinomial distribution whose probability vector is given by the occupational
share of that industry. We then assign the occupations to specific agents
living in a census tract with a probability that is proportional to the number
of employed workers in that occupation in their census tract.

Let us consider an example. Assume that 100 individuals are employed in
industry $X$, and that workers in industry $X$ are either performing
occupation $Y$ (60 workers) or occupation $Z$ (40 workers). We then draw the
number of workers in each occupation from a multinomial distribution with
probability vector $[0.6,0.4]$, obtaining, for instance, that 58 workers are
employed in occupation $Y$ and 42 in $Z$.191919 In this example, it could also
make sense to draw the occupations respecting the exact shares, i.e. 60 and 40
workers. However, this is not possible when certain occupations are very rare
but still possible, as rounding to integers would imply that no agents are
ever assigned such occupations.  Assume further that the metro area has two
regions, $A$ and $B$, and that the share of inhabitants of A who work in
occupation $Y$ is twice that of inhabitants of B. So, to assign the 58 workers
of $Y$, we select inhabitants of A with a probability that is twice as much as
that of inhabitants of B.

code | occupation_title | remote_labor_index
---|---|---
11-0000 | Management Occupations | 0.71
13-0000 | Business and Financial Operations Occupations | 0.77
15-0000 | Computer and Mathematical Occupations | 0.72
17-0000 | Architecture and Engineering Occupations | 0.63
19-0000 | Life, Physical, and Social Science Occupations | 0.49
21-0000 | Community and Social Service Occupations | 0.59
23-0000 | Legal Occupations | 0.54
25-0000 | Education, Training, and Library Occupations | 0.59
27-0000 | Arts, Design, Entertainment, Sports, and Media Occupations | 0.64
29-0000 | Healthcare Practitioners and Technical Occupations | 0.34
31-0000 | Healthcare Support Occupations | 0.18
33-0000 | Protective Service Occupations | 0.19
35-0000 | Food Preparation and Serving Related Occupations | 0.34
37-0000 | Building and Grounds Cleaning and Maintenance Occupations | 0.18
39-0000 | Personal Care and Service Occupations | 0.28
41-0000 | Sales and Related Occupations | 0.65
43-0000 | Office and Administrative Support Occupations | 0.54
45-0000 | Farming, Fishing, and Forestry Occupations | 0.09
47-0000 | Construction and Extraction Occupations | 0.21
49-0000 | Installation, Maintenance, and Repair Occupations | 0.19
51-0000 | Production Occupations | 0.17
53-0000 | Transportation and Material Moving Occupations | 0.18
Table S4: Remote labor index across 22 occupations.

Finally, we determine whether a worker is able to work from home using the
remote labor index (RLI) introduced in Ref. [2]. This index is bounded between
0 and 1 and captures the proportion of an occupation’s work activities that
can be performed from home. When $\text{RLI}_{o}=0$ none of occupation $o$’s
work activities can be performed at home, while $\text{RLI}_{o}=1$ indicates
that all of the work activities related to occupation $o$ can be performed
from home. Del Rio Chanona et al. [2] calculated the remote labor index for
occupations in the Standard Occupation Classification (SOC) system at the
detailed level. We aggregate this index into the SOC system at the major
level, which has 22 occupational categories. Table S4 shows the remote labor
index for each of the 22 occupations that we consider. As can be seen, workers
in the service industries are much more likely to be able to work from home.

We interpret the Remote Labor Index as the probability that a worker can work
from home. For each worker in occupation $o$, we decide whether they can work
from home or not by drawing from a Bernoulli distribution with success
probability $\text{RLI}_{o}$. We do this at the beginning of the simulation
and assume that a worker’s capability to work from home remains constant
throughout the simulation.

####  S3.3.4 Income

We assign income to agents in such a way that income is correlated with
industry, occupation, age and geography. As a general problem, different data
sources use different definitions and conventions on income, and so it is
difficult to compare income levels across datasets. To address this issue, we
proceed in steps changing the relative income of different socio-economic
groups, and then renormalize income levels at the end to the level indicated
in the Consumption Expenditure Survey.202020
https://www.bls.gov/cex/2019/msas/norteast.pdf  (We normalize to these values
for consistency with our initialization of the consumption patterns, see
Section S3.5.)

We derive income for each industry-occupation pair from the same BLS OES data
that we used to derive the share of occupations within industries. Information
on income is presented in terms of quantiles: the BLS provides 10th, 25th,
50th (median), 75th and 90th quantiles.212121A few data points for the 90th
quantile, and one for the 75th quantile, are missing for some combinations of
industry and occupation. In these cases, we impute the missing quantiles so
that the ratio to the median is the same as the average ratio of the quantile
to the median for the occupation-industry pairs for which this information is
available. Because we cannot sample incomes from quantiles, we fit a log-
normal distribution for each set of quantiles corresponding to an occupation-
industry combination.222222There is a debate on whether the best fit to income
distributions is with a log-normal or with a Pareto distribution, with some
consensus that the Pareto distribution is more appropriate for the tail and
the log-normal for the rest of the distribution. Because here we are more
interested in the body of the distribution, we choose a log-normal functional
form. See e.g. Ref. [43] for some pointers to the literature. In the vast
majority of cases, the log-normal provides an excellent fit. (Results
available in the replication materials.) We then assign income to all employed
individuals drawing from the log-normal distribution whose parameters
correspond to the occupation-industry pair of that individual.

The next step is to adjust income levels by age.232323In principle,
differences in income across age could be explained by the different
probability to be employed in different industries and occupations by age.
Because we could not find data to make the assignment of industry and
occupation dependent on age, this correlation is not present in our synthetic
population. We use the Current Population Survey table “Median usual weekly
earnings of full-time wage and salary workers by age, race, Hispanic or Latino
ethnicity, and sex, not seasonally
adjusted”242424https://www.bls.gov/webapps/legacy/cpswktab3.htm to derive 2019
income levels in the following age groups: $[18,19],(19,24],$
$(24,34],(34,44],(44,54],(54,64],(64+)$. In Q4, mean weekly earnings for these
age groups were, respectively, $\$494,\$616,\$876,$
$\$1047,\$1039,\$1053,\$942$. We rescale incomes across age groups so that
they are proportional to these values.

After that, we adjust individual incomes so that the resulting household
incomes, as averaged over each census tract, match the mean household income
in the same census tract as obtained from Census table S1901 (2019, 5-year ACS
average).

So far, we only assigned an income to employed individuals. As a final step,
we need to assign an income to individuals who are out of the labor force.
This is a difficult step, as it is difficult to find data sources that
consistently compare their income to that of workers, and even for workers
there are other sources of income than wages and salaries. For instance,
focusing on retirement income, Bee and Mitchell [44] find that using different
data sources and definitions of income leads to a 30% increase in retirement
income. As the majority of non-employed individuals who still receive a
substantial income is likely to be composed by retirees, we decide to focus
solely on this group. In line with our attempt to be consistent with the
Consumption Expenditure Survey, we choose mean retirement income in such a way
that the mean income of individuals above 65 years old is about 60% of that of
individuals between 55 and 64 years.252525
https://www.bls.gov/cex/2019/combined/age.pdf  Given mean income, we assign
income values based on a log-normal distribution calibrated over the universe
of workers (i.e., not specific industries or occupation). We also make sure
that retirement income is rescaled by mean household income across census
tracts, obtained from table S1901 as above.

Our final step, as mentioned at the beginning, is to normalize all incomes so
that the mean income in the synthetic population corresponds to the 2019 mean
income for the New York metro area as indicated in the Consumption Expenditure
Survey.

####  S3.3.5 Summary statistics

In this section we show summary statistics for the variables in the synthetic
population. When possible, we also compare these summary statistics to data,
as a way to validate our approach.

Figure S3: Percentage of the real and synthetic populations. A: percentage at
the census tract level. On average, each census tract is 0.02% of both the
real and synthetic population. The Pearson correlation coefficient between the
census tract shares in the real and synthetic populations is 0.60. We use a
density kernel to show the areas with highest density of points. B: percentage
at the county level. Each county makes up on average 4-5% of both the real and
synthetic populations, although some counties such as Queens and Kings are
much more populous than others. The Pearson correlation coefficient between
the county shares in the real and synthetic populations is 0.92.

We begin in Figure S3, by showing the shares of both the real and synthetic
populations, both across census tracts and counties. There are 23 counties and
approximately 4300 census tracts, so, on average, each county makes up 4-5% of
the population, and each tract makes up 0.02% of the population. However, as
it is immediate from the figure, there is a lot of heterogeneity, as some
counties and tracts are much more populous than others. For instance, Queens
and Kings have many more inhabitants than Putnam or Hunterdon.

The population shares are not identical in the real and synthetic populations,
as can be seen from the fact that not all points are aligned on the identity
line. This is because we build our synthetic population based on the number of
Cuebiq users in each census tract, and the number of Cuebiq users is not
exactly proportional to the population of the census tract. Despite this
limitation, there is a good correlation between the real and synthetic
population shares, with a Pearson correlation coefficient $\rho=0.60$ in the
case of census tracts, and $\rho=0.92$ in the case of counties. The main
outlier in the case of counties is Manhattan (New York county), that makes up
around 8% of the real population and only around 5% of the synthetic
population. This is partly due to Cuebiq users leaving New York when the
pandemic began (we only retain users that we could identify for the full
period, i.e. that were observed in the last two months of the period (May-June
2020) at least once; see Ref. [16]).

Figure S4: Marginal distributions of the main population attributes. We
compare distributions in the real population (dark grey) and in the synthetic
population (light grey).

Figure S4 shows the marginal distributions of the main attributes of synthetic
individuals: age, household size, employment status, industry, occupation, and
income. It compares distributions in the real population (dark grey) and in
the synthetic population (light grey).

Starting with age (panel A), we can see that the age distribution in the
synthetic population closely resembles that in the real population. The only
exceptions are intermediate years, with a slightly larger share of individuals
between 25 and 39 in the real population compared to the synthetic population,
and a slightly smaller share between 40 and 54. It is difficult to compare the
relative size of different age groups as age intervals comprise different
numbers of years, but we can see that the bulk of the distribution is
concentrated in intermediate years, as in most age distributions in advanced
economies.

Panel B shows that household size is very similar in the synthetic and real
population. Most households are composed by one or two individuals, but around
2% of households is composed by seven individuals or more.

As we can see in panel C, a bit more than 60% adults are employed, with the
remaining share being non-employed. This is true both in the real and in the
synthetic population.

Panel D shows the percentage of workers employed in each of the 20 2-digit
NAICS industries that we consider. We can see that health is the industry that
employs most workers, followed by education, retail and professional services.
Unsurprisingly, given the structure of the New York economy, agriculture and
mining employ a tiny share of workers. (Management also employs few workers,
but this is due to the NAICS industries definition.) The real and synthetic
populations correspond almost perfectly.

Panel E shows the percentage of workers employed in the 22 main SOC
occupations that we consider. Occupations such as Office and Administrative
Support, Sales and Related, Management, Business and Financial Operations,
Educational Instruction and Library are among the most frequent occupations,
while specialized occupations such as Architecture and Engineering, Life,
Physical and Social Science, Legal, are less common. Here, the match between
the synthetic and real populations is less good than in the previous panels.
This is due to the algorithm that we use to assign occupations respecting both
the relation to industry and geography, as explained in Section S3.3.3. In
particular, it is mostly due to the assumption that the occupational
composition of industries in New York is the same as in the rest of the US.
For instance, we can see in the figure that our algorithm overestimates the
fraction of Office and Administrative Support workers and underestimates the
fraction of workers in Management occupations, relative to the real
population. This is likely because managers of US corporations are likely to
reside in New York, while Office and Administrative Support workers are more
likely to reside in less expensive areas. Yet, we see that this shortcoming is
limited to a few industries, as the percentage of workers is similar in the
real and synthetic populations in the majority of cases.

Panels F and G show the distribution of both personal and household income. We
see that the peak of the distribution is below $50,000 in both cases, but the
distribution has a long tail, extending above $250,000. We compare household
income in the real and synthetic populations in panel H.262626 We rescale
incomes from Table S1901 so that the mean income corresponds to the one in our
dataset. This is because mean income in the New York metro area is $116604 in
the ACS and $103011 in the Consumption Expenditure Survey (our benchmark).  We
see that the correspondence is generally good, except for the very low income
categories, which the synthetic population underestimates. This is due to the
problem mentioned in Section S3.3.4, that it is difficult to assign an income
to individuals out of the labor force, which arguably comprise the majority of
individuals in those income categories. As we cannot accurately compare income
for part time and occasional work and unemployment benefits to standard labor
income, we cannot properly account for these low-income categories.

Figure S5: Maps of census tracts by mean household income. Panel A shows the
New York metro area as a whole, while panel B zooms in on New York City. To
ease visualization, we limit mean census-tract household incomes to $250,000.

After looking at marginal distributions in Figure S4, we now show joint
distributions. We begin with joint distributions of geography and other
attributes in Figures S5 and S6. Figure S5 shows maps of census-tract mean
household income. We can see that while high-income areas are clustered in a
few zones, they are spread in the metro area, beyond New York City. The Bronx
and the Queens are among the lowest-income areas. These data come from the
synthetic population but, as we explained in Section S3.3.4, the spatial
income distribution is the same as in the real population because we
renormalized all incomes to the mean income of each census tract.272727 In
principle, this normalization may not be necessary, as long as differences in
industry and occupation explain differences in income across census tracts.
Indeed, we find that the Pearson correlation coefficient between mean
household income in the real and synthetic population, across census tracts
and before renormalization, is $\rho=0.58$. To more accurately match
geography, we prefer to nonetheless proceed with the renormalization described
above, but this correlation suggests that the population building workflow
yields consistent results.

Figure S6: Variables by geography. A: employment rate across census tracts.
The Pearson correlation correlation coefficient between the real and synthetic
populations is $\rho=0.52$. We use a density kernel to show the areas with
highest density of points. B: County share of employment for a few selected
counties and industries. There is almost perfect correlation between the model
and the data ($\rho=1.00$). C: County share of employment for a few selected
counties and occupations. The correlation is still very good ($\rho=0.87$).

Figure S6 shows the relations between geography and other variables. Panel A
shows the employment rate across census tracts. The mean employment rate is a
bit above 0.60 in both the real and synthetic population, and the bulk of the
distribution is concentrated on employment rates between 55 and 70 in the real
population, and 50 and 75 in the synthetic population. The larger dispersion
in the synthetic population is likely due to sampling. In any case, there is a
good correlation ($\rho=0.52$) between employment rates in the real and
synthetic population at this very small spatial level.

Panel B shows the percentage of employment of a given industry in a given
county, for a few selected industries and counties. We can see that there is
some specialization, with some counties having a much larger share of
employment in a given industry than other counties. This corresponds to the
situations where one marker of a given color is very distant from the other
markers. For instance, almost 25% of Bronx workers are employed in Health,
compared to 10-17% in the other selected counties. Likewise, about 13% of
workers in Passaic are employed in manufacturing, against 3% in the other
counties. Unsurprisingly, 13% of workers in Manhattan are employed by the
financial industry, against 3% in the other counties. The synthetic population
captures almost perfectly this variability that exists in the real population
($\rho=1.00$).

Panel C is similar to panel B, but it focuses on occupations. Here, we see for
example that Union county is specialized in Transportation and Material Moving
occupations, Somerset in Computer and Mathematical occupations, etc. In this
case, the correlation between employment shares in the real and synthetic
populations is not perfect, but still good ($\rho=0.87$). This is again due to
the algorithm explained in Section S3.3.3.

Figure S7: Comparisons of joint relations in the real and synthetic
populations. A: employment and age. B: income and industry. C: income and
occupation.

After showing joint relations between geography and other variables, in Figure
S7 we show other joint relations. In panel A we look at the employment rate
across age groups. As we sampled from the joint distribution of employment and
age, the employment rate is almost the same in the real and synthetic
population. We see that the employment rate is around 80% between 24 and 54
years old, decreasing at the other ends of the age distribution.

Panel B shows mean individual income across industries. We compare the data
for the US as a whole from the BLS dataset that we used in Section S3.3.3 with
data for New York in our synthetic population (we do not have real data on
this joint relation for New York). We see that the industries that pay their
workers the least are accommodation-food, agriculture, retail and
entertainment, while industries paying their workers the most are management,
professional services, utilities and information. This ranking is the same
(except for a few exceptions) both in the real data valid for the US and in
the synthetic data valid for New York. As expected, income levels are higher
in New York.

Panel C is analogous to panel B, but for occupations. It includes “Retired”
agents, who are not employed, for comparison. We see that the least paid
occupations are Food Preparation and Serving Related, Healthcare Support,
Cleaning and Maintenance; Retired individuals also receive very low income.
Conversely, the highest paid occupations are Management, Legal, Computer and
Mathematical, and Architecture and Engineering.

Figure S8: Industry-Occupation cross tabulation. A: heat map showing the
occupational share of each industry in the synthetic population. B: heat map
showing median personal income for each industry-occupation pair in the
synthetic population. Grey squares indicate that there are fewer than 10
individuals working in an industry-occupation pair (so that results would be
too dependent on stochastic realizations of the log-normal distribution).

As a final illustration of the properties of the synthetic population, Figure
S8 shows a cross tabulation of industries and occupations in the synthetic
population. Panel A shows the occupational share of each industry (rows sum to
100). We see that some occupations are spread across all industries more or
less equally. For instance, there are some workers in Management, Business and
Financial Operations, Sales and Related, Office and Administrative Support
occupations in almost all industries, although these occupations are slightly
more concentrated in Information, Finance, Real Estate, Professional services
and Management. Other occupations are concentrated in a few industries. For
instance, Farming, Forestry and Fishing Occupations take up the majority of
jobs in Agriculture, Production occupations in Manufacturing, Transportation
and Material Moving in Transportation. Certain occupations are almost not
represented in certain industries (white squares).

Panel B shows the same cross tabulation, but the intensity of color indicates
median personal income of workers in each industry-occupation pair. We see
that income changes more strongly with the occupation than with the industry
(there is more variability across columns than across rows within the same
column), but certain industries pay their workers more than others, even for
the same occupation.

####  S3.3.6 Matching to Cuebiq users

Our synthetic population features 319,487 adults, and we have 316,070 Cuebiq
users. Ideally, we want to match synthetic individuals and Cuebiq users so
that each Cuebiq user is matched to a synthetic individual that has the same
characteristics as the real user. To do so, we match synthetic individuals and
Cuebiq users based on the census tract, the industry, the possibility to work
from home and the employment status (see the schematic in Figure S2B).

As a first step, we restrict the samples of synthetic individuals and Cuebiq
users so that synthetic individuals living in a census tract are matched to
Cuebiq users whose “home” location is in that census tract. As we showed in
Section S3.3.5, our synthetic population captures a lot of the variability
across census tracts, so already a correct matching based on geography is
likely to provide an overall accurate matching.

To further refine this matching, we look at the number of stays of Cuebiq
users at their workplace. We rank the Cuebiq users in a census tract by visits
to the workplace (which can be zero) and assign to the non-employed synthetic
agents the Cuebiq users with minimal number of stays in the workplace. Because
workplace data are available since February 2020, when most employed people
were normally going to work in person, we are unlikely to be conflating agents
who can work from home and unemployed agents.

As another refinement, we assign synthetic agents that cannot work from home
and that work in essential industries to Cuebiq users whose number of stays in
the workplace decreased the least in April and May compared to February and
March. More precisely, we assign to each agent a score of “essentialness-in-
person”, given by the product of the essential score of the industry it works
for (from the last column in Table S3) and an indicator function that is 0
when the agent can work from home and 1 when the agent must work in person. We
then match the synthetic individuals with the highest values of this score
with the Cuebiq users whose ratio between “visits to the workplace in April-
May” and “visits to the workplace in February-March” is highest. For instance,
we are likely to match a synthetic nurse, who works in-person in an essential
industry, to a Cuebiq user who did not stop physically going to work in April
and May. At the same time, we could match a synthetic financial analyst to a
Cuebiq user who stopped going to the office in April and May.

####  S3.3.7 Initialization of model variables

The synthetic population is one of the key building blocks of both the
epidemic and economic modules. All our efforts to get the joint relations
between socio-economic attributes right is crucial for the results where we
marginalize on some variables. For instance, getting the relation between
industry and income right is crucial to explain differences in employment
across income groups, and getting the relation between age and employment is
very important to explain differences in infection rates across ages. Apart
from its relevance for the results, information from the synthetic population
is used to directly initialize some of the variables of the epidemic and
economic module.

In terms of the epidemic module, we use it to initialize $\omega_{ij}^{S}$ and
$\omega_{ij}^{H}$, as defined in Section S1.

For the economic module, we use information on employment, industry, and
ability to work from home to create lists of both in-person and from-home
workers for each industry, and to initialize $l_{k,0}^{\text{P}}$ and
$l_{k,0}^{\text{H}}$, for all industries $k$.282828As we do not have a
synthetic population for the rest of the US, we obtain industry employment
data from the ACS and rescale initial employment levels to be consistent with
the size of the NY synthetic population. In other words, given that there are
almost 200 thousand workers in the NY synthetic population, compared to 9.6
million in the real population, we rescale the total number of workers in each
industry in the rest of the US by a factor equal to 200,000/9,600,000.  As we
explain in more detail in Section S3.5, we use information on age and income
to group consumers into groups $g$ with the same consumption patterns.

###  S3.4 Input-output

We first obtain input-output data for the US as a whole (Section S3.4.1), and
then follow the literature on regionalization to break it down into a New
York-Rest of the US input-output system (Section S3.4.2). Finally, in Section
S3.4.3 we explain how we use this information to initialize model variables.

####  S3.4.1 Symmetric industry input-output tables

Our input-output data is taken from the US Bureau of Economic Analysis (BEA)
and based on the most recent year (2019). Our model is initialized using data
on industries, but the BEA provides input-output data as a mix between
industries and commodities. Roughly, a commodity is defined as the main output
of an industry, but a firm that is classified in a main industry may also
produce commodities that are classified in another industry. For instance, a
hotel that serves dinner to its customers also produces restaurant services;
if hotels and restaurants are listed as separate industries, this fact needs
to be taken into account.

We derive industry by industry tables from the Make and Use Tables (after
redefinitions, at basic prices) provided in the BEA Supplemental Estimate
Tables292929 https://www.bea.gov/industry/input-output-accounts-data . To
derive the input-output tables, we follow the industry technology assumption
outlined in Ref. [45] which we briefly summarize below.

Let $V$ denote the _industry_ $\times$ _commodity_ make matrix and $U$ the
_commodity_ $\times$ _industry_ use matrix. We define $q$ and $g$ as the
vectors of commodity and industry specific outputs, respectively. Vector $g$
can contain an industry’s production of scrap, $h$. We let $x=g-h$ denote
industry output excluding scrap. We further use a circumflex to indicate a
diagonal matrix, e.g. $\hat{q}=\text{diag}(q)$.

By defining the transformation matrix

$\displaystyle T$ $\displaystyle=g\hat{x}^{-1}V\hat{q}^{-1},$ (39)

we can derive the matrices of intermediate consumption $Z$ and the technical
coefficient matrix $A$ as

$\displaystyle Z$ $\displaystyle=TU,$ (40) $\displaystyle A$
$\displaystyle=Zx^{-1}.$ (41)

Further to trade of intermediate goods, the other key component of input-
output tables is final demand. The BEA provides information on final demand as
disaggregated in several sub-components: private consumption, government
consumption (defense and non-defense), investment in residential and non-
residential structures, machinery and other investment goods, exports and
imports. We aggregate these sub-components into three final demand categories:
private consumption, government consumption, and “other final demand”. At this
point, we have data on final demand by commodity, i.e. we have a matrix
$f^{c}$ whose rows correspond to different commodities and whose columns are
the three main categories of final demand listed above. We obtain final demand
by industry $f$ applying the same transformation matrix $T$:

$f=Tf^{c}.$

Our desired number of industries is 20 (2-digit NAICS industries), as this is
the finest breakdown of industry information at the census tract level in the
ACS. However, the BEA does not provide input-output information at this level
of aggregation. Indeed, it either provides information at the level of 15
industries/commodities, or at the level of 3-digit NAICS codes, which comprise
71 industries/commodities. Therefore, we perform the steps above with 71
industries/commodities, and then aggregate gross output $x$, intermediate
consumption $Z$ and final demand $f$ to our target 20 industries. We finally
re-compute the technical coefficients matrix $A$ using the same formula as
above.

####  S3.4.2 Regionalization

Since there exists no input-output data on the NY MSA, we use standard non-
survey techniques to derive the regional relationships from national tables
[18, ch.8]. Specifically, our regional tables are obtained by using the Flegg
et al. Location Quotient (FLQ) due to its superior performance over simpler
alternative methods [26].303030We provide a short summary of our approach here
but refer to Refs. [46], [18] and the recent literature on industrial ecology
virtual laboratories [47] for a more detailed and critical discussion.  We
then obtain the input-output relations for the rest of the US (RoUS) as a
residual from the NY MSA regional tables.

Our goal is to split the intermediate consumption matrix $Z$ into matrices of
the value of intermediate goods flowing (i) from industries in NY to
industries in NY, $Z^{\text{NY}->\text{NY}}$; (ii) from industries in NY to
industries in the RoUS, $Z^{\text{NY}->\text{RoUS}}$; (iii) from industries in
the RoUS to industries in NY, $Z^{\text{RoUS}->\text{NY}}$; (iv) from
industries in the RoUS to industries in RoUS, $Z^{\text{RoUS}->\text{RoUS}}$.
Moreover, we want to divide final demand $f$ into final demand of: (i) agents
in NY to industries in NY, $f^{\text{NY}->\text{NY}}$; (ii) agents in RoUS to
industries in NY, $f^{\text{NY}->\text{RoUS}}$; (iii) agents in NY to
industries in RoUS, $f^{\text{RoUS}->\text{NY}}$; (iv) agents in RoUS to
industries in RoUS, $f^{\text{RoUS}->\text{RoUS}}$. (In all cases, arrows
indicate the direction of the goods and services, from the producer to the
consumer.)

Let us start from intermediate consumption. Our starting point is that
industries in NY produce goods and services with the same shares of inputs
(“recipes”) as in the rest of the US (and as in the US as a whole). Thus, if a
certain input is not produced in NY, it must be imported from elsewhere in the
US. For instance, a manufacturing firm needing inputs from the mining industry
needs to import those inputs from the rest of the US. Defining
$A^{\text{NY}->\text{NY}}$ as the technical coefficient matrix for industries
located in New York demanding intermediate goods from industries also located
in NY, and $A^{\text{RoUS}->\text{NY}}$ as the equivalent technical
coefficient matrix for imports from the RoUS, this assumption is translated in
the constraint

$A^{\text{NY}->\text{NY}}+A^{\text{RoUS}->\text{NY}}=A.$ (42)

Additionally, considering industries located in the RoUS, the following
constraint must hold:

$A^{\text{NY}->\text{RoUS}}+A^{\text{RoUS}->\text{RoUS}}=A.$ (43)

It is intuitive that coefficients in $A^{\text{RoUS}->\text{NY}}$ are likely
to be much larger than coefficients in $A^{\text{NY}->\text{RoUS}}$. Indeed, a
larger share of inputs for firms in NY come from the RoUS than for firms in
the RoUS from NY, if only by a matter of size (the NY economy is about 7% of
that of the US a whole). This is likely to be heterogeneous by industries:
firms in the RoUS may use information and financial services from NY quite
often, while firms in NY may satisfy most of their intermediate demand for
information and financial services from the NY industries. To take these
effects into account, we proceed as follows.

We let

$A_{kl}^{\text{NY}->\text{NY}}=\rho_{kl}^{\text{NY}}A_{kl}.$ (44)

Thus, $\rho_{kl}^{\text{NY}}$ indicates by how much intra-regional trade flows
differ from national ones. If the (normalized) industry-industry flows within
a region are identical to the national flows, we have
$\rho_{kl}^{\text{NY}}=1$. As mentioned, typically $\rho_{kl}^{\text{NY}}<1$
since intra-regional flows tend to be smaller than their national equivalents.

To arrive at the estimate of $\rho_{kl}^{\text{NY}}$, we first define the
Simple Location Quotient (SLQ) and Cross-Industry Location Quotient (CILQ) for
which we write

$SLQ_{k}=\frac{y_{k}^{\text{r}}/y^{\text{r}}}{y_{k}^{\text{n}}/y^{\text{n}}}$
(45) $CILQ_{kl}=\frac{SLQ_{k}}{SLQ_{l}},$ (46)

respectively. $y_{k}^{\text{r}}$ denotes the regional GDP of industry $k$ in a
generic region $r$ and $y_{k}^{\text{n}}$ is the same at the national level.
Moreover, $y^{\text{r}}=\sum_{k}y_{k}^{\text{r}}$ and
$y^{n}=\sum_{k}y_{k}^{n}$.313131We obtain data from the BEA regional accounts:
https://apps.bea.gov/itable/iTable.cfm?ReqID=70&step=1&acrdn=5. As the GDP of
some industries is not given for year 2019 due to confidentiality issues, we
impute that GDP based on values in previous years. In principle,
$y_{k}^{\text{NY}}$ and $y_{k}^{\text{US}}$ should be gross output rather than
GDP, but using GDP is equivalent as the intermediate share of industry $k$
cancels out between the numerator and the denominator.

The SLQ is a measure of regional concentration of industries. If $SLQ_{k}>1$,
sector $k$ is more concentrated at the regional compared to the national
level, in the sense that industry $k$’s contribution to GDP is larger in the
region than it is in the country. For example, in the case of the information
and financial industries in NY, $SLQ_{k}>1$, while for agriculture and
manufacturing we have $SLQ_{k}<1$.

The CILQ, in addition, takes into account that the relative importance might
be different for sellers and buyers. $CILQ_{kl}>1$ implies that GDP of
regional sector $k$ is larger than GDP of regional sector $l$ than it is at
the national levels. Thus, the demand of $l$ can be fully met by the supply of
$k$. On the other hand, if $CILQ_{kl}<1$, parts of $l$’s demand need to be
imported. For example, $CILQ_{kl}<1$ when $l$ is information or finance: as
these sectors are really big in NY compared to the RoUS, their intermediate
demand for other goods and services must be at least in part satisfied by
industries outside of NY. Conversely, $CILQ_{kl}>1$ if $l$ is agriculture or
manufacturing: as these industries are small in NY, their intermediate demand
can be fully satisfied by NY industries.

We can now define the FLQ equivalently to Ref. [48] as

$FLQ_{kl}=\begin{cases}\lambda\cdot CILQ_{kl}&\mbox{if }k\neq l,\\\
\lambda\cdot SLQ_{k}&\mbox{if }k=l,\end{cases}$ (47)

where $\lambda=[\log_{2}(1+y^{r}/y^{n})]^{\delta}$ and $\delta\in[0,1)$. The
FLQ combines the SLQ and CILQ and also takes into account that the region size
affects the modification of technical coefficients. Smaller regions will need
to import more from other regions and thus $\lambda$ will be smaller. To what
extent region size affects regional coefficients can be controlled by the
parameter $\delta$. Since there is no general consensus on the estimation of
$\delta$, we choose $\delta=0.15$, which avoids problems with negative demand.

We then substitute

$\rho_{kl}^{\text{r}}=\begin{cases}FLQ_{kl}&\mbox{if }FLQ_{kl}<1,\\\
1&\mbox{if }FLQ_{kl}\geq 1,\end{cases}$ (48)

in Eq. (44) to obtain our estimate of regional technological coefficients. By
applying these formulas with region $r$ corresponding to NY, $r=NY$, we find
the technical coefficient matrix $A^{\text{NY}->\text{NY}}$. We then obtain
$A^{\text{RoUS}->\text{NY}}$ from Eq. (42).

Next, we obtain GDP of industry $k$ in the RoUS, $y_{k}^{\text{RoUS}}$, as a
residual from total GDP and GDP in NY, i.e.
$y_{k}^{\text{RoUS}}=y_{k}^{\text{n}}-y_{k}^{\text{NY}}$. Identifying region
$r$ with the RoUS, we use Eq. (48) to obtain coefficients
$\rho_{kl}^{\text{RoUS}}$ and $A^{\text{RoUS}->\text{RoUS}}$,323232In this
case, we assume $\lambda=1$ to avoid negative final demand for certain NY
industries. and Eq. (43) to obtain the residual $A^{\text{NY}->\text{RoUS}}$.
We finally calculate intermediate consumption $Z^{\text{NY}->\text{NY}}$ from
Eq. (41), i.e.
$Z^{\text{NY}->\text{NY}}=A^{\text{NY}->\text{NY}}x^{\text{NY}}$,333333We
obtain gross output in NY, $x^{\text{NY}}$, from GDP in NY, $y^{\text{NY}}$,
by assuming that the shares of intermediate goods is the same in NY as in the
US as a whole, i.e.
$x_{k}^{\text{NY}}=\frac{y_{k}^{\text{NY}}}{y_{k}^{\text{US}}}x_{k}^{\text{US}}$.
and proceed similarly to obtain the other intermediate consumption matrices.

Figure S9: Shrinking of technical coefficients. Each entry represents the
value $\rho_{kl}$ that multiplies each technical coefficient from US-wide
input-output tables, to account for regionalization. Values $\rho_{kl}=1$
imply no shrinking (dark blue), while smaller values of $\rho_{kl}$ (light
blue and white) imply strong shrinking.

Figure S9 shows the shrinkage coefficients $\rho_{kl}^{\text{NY}}$ in the top
left block and $\rho_{kl}^{\text{RoUS}}$ in the bottom right block. It also
shows $1-\rho_{kl}^{\text{NY}}$ in the bottom left, and
$1-\rho_{kl}^{\text{RoUS}}$ in the top right. It is immediately clear that, in
relative terms, industries in NY depend much more on industries in the RoUS
than industries in the RoUS depend on industries in NY, as is natural given
region sizes. In line with the narrative that we kept so far, NY industries
such as information or finance need to import a lot of intermediate goods and
services from the RoUS because they are so large in NY that local suppliers
cannot meet the demand. The only exception is demand for their own products,
which they can meet because they are large. Conversely, intermediate demand by
agriculture and mining is fully satisfied by industries in NY, because these
industries are so small in NY that supply is more than enough; the only
exception is again agricultural and mining inputs that need to be imported
from the RoUS.

We follow the same procedure to derive regional final demand categories. Let
$f_{k}^{r}$ and $f_{k}^{n}$ be the regional and national final demand values
and $f^{r}=\sum_{k}f_{k}^{r}$ and $f^{n}=\sum_{k}f_{k}^{n}$. To estimate the
regional purchase coefficient, we write

$\frac{f_{k}^{r}}{f^{r}}=\rho_{k}^{f}\frac{f_{k}^{n}}{f^{n}},$ (49)

where $\rho^{f}$ is obtained from

$\rho_{k}^{f}=\begin{cases}FLQ_{kk}&\mbox{if }FLQ_{kk}<1,\\\ 1&\mbox{if
}FLQ_{kk}\geq 1.\end{cases}$ (50)

To obtain final demand by New Yorkers to industries in NY,
$f^{\text{NY}->\text{NY}}$, we simply apply Eq. (50) with $r=NY$. This means,
for instance, that a good part of NY households’ consumption of manufacturing
products is satisfied by firms located outside of NY, as manufacturing is
small in NY relative to the RoUS. The same applies to other components of
final demand (e.g. NY plants will invest in machinery produced elsewhere in
the US). We then calculate final demand by agents in NY to industries in RoUS,
$f^{\text{RoUS}->\text{NY}}$, as the residual, i.e.
$f^{\text{RoUS}->\text{NY}}=f^{\text{NY}}-f^{\text{NY}->\text{NY}}$. We
finally compute demand by agents in RoUS to both industries in NY and in RoUS
as a residual from total output, i.e.
$f_{k}^{\text{NY}->\text{RoUS}}=x_{k}^{\text{NY}}-\sum_{l}Z_{kl}^{\text{NY}->\text{NY}}-\sum_{l}Z_{kl}^{\text{NY}->\text{RoUS}}-f_{k}^{\text{NY}->\text{NY}}$
(we distribute final demand across private consumption, government consumption
and other final demand following the same shares of these components of final
demand as in the US as a whole). We proceed similarly to obtain
$f^{\text{RoUS}->\text{RoUS}}$.

For consistency, we also compute value added of each industry both in NY and
RoUS as the difference between the relevant component of $x$ and the column
sum of $Z$, e.g.
$va_{k}^{\text{NY}}=x_{k}^{\text{NY}}-\sum_{k}Z_{kl}^{\text{NY}->\text{NY}}-\sum_{k}Z_{kl}^{\text{RoUS}->\text{NY}}$.

Figure S10: New York (NY) - Rest of the US (RoUS) input output system. Entries
represent flows from the row of origin to the column of destination. They are
colored based on flow value, in million USD; negative values are colored grey.
From top-left to bottom-right, each block delimited by black lines represents:
(i) the value of intermediate goods flowing from industries in NY to
industries in NY, $Z^{\text{NY}->\text{NY}}$; the value of intermediate goods
flowing from industries in NY to industries in RoUS,
$Z^{\text{NY}->\text{RoUS}}$; final demand of agents in NY to industries in
NY, $f^{\text{NY}->\text{NY}}$; final demand of agents in RoUS to industries
in NY, $f^{\text{NY}->\text{RoUS}}$; the same flows as above, but flowing from
the RoUS, $Z^{\text{RoUS}->\text{NY}}$, $Z^{\text{RoUS}->\text{RoUS}}$,
$f^{\text{RoUS}->\text{NY}}$, $f^{\text{RoUS}->\text{RoUS}}$; value added
generated by industries located in NY, $va_{\text{NY}}$ and by industries
located in the RoUS, $va_{\text{RoUS}}$.

Figure S10 shows the entire NY-RoUS input-output system. The intensity of blue
in a cell indicates the magnitude of an intermediate flow or of a component of
final demand or value added. (Some components are negative, e.g. because the
US import more of a given good from the Rest of the World than they export.)
This representation has the advantage of showing compactly the contributions
of the various components to the input-output system, and the relative sizes
of industries and regions.

####  S3.4.3 Initialization of model variables

As shown in Table S2, we use the derived input-output system to initialize
several model variables:

  * •

$Z_{k,l,0}$, intermediate consumption by industry $l$ of goods and services
produced by $k$, is obtained from the matrices $Z^{\text{NY}->\text{NY}}$,
$Z^{\text{NY}->\text{RoUS}}$, $Z^{\text{RoUS}->\text{NY}}$,
$Z^{\text{RoUS}->\text{RoUS}}$, depending on whether $k$ or $l$ are located in
NY or in the RoUS. This results in a $40\times 40$ intermediate consumption
matrix.

  * •

$c_{k,0}^{\text{local}},G_{k,0}^{\text{local}},f_{k,0}^{\text{local}}$ are
obtained from the private consumption, government consumption and other final
demand components of $f^{\text{NY}->\text{NY}}$ and
$f^{\text{RoUS}->\text{NY}}$, and
$c_{k,0}^{\text{rest}},G_{k,0}^{\text{rest}},f_{k,0}^{\text{rest}}$ are
obtained from the same components of $f^{\text{NY}->\text{RoUS}}$ and
$f^{\text{RoUS}->\text{RoUS}}$. Each of these six variables form a vector with
length 40.

  * •

$x_{k,0}$, gross output of industry $k$, is the row sum of the input-output
system shown in Figure S10, i.e. it is obtained by summing intermediate and
final consumption. This results in a vector of gross output of length 40.

  * •

$A_{k,l}$, the $k,l$ coefficient of the technical coefficients matrix, is
obtained by $A_{k,l}=Z_{k,l}/x_{l}$. As $Z$, also $A$ is a $40\times 40$
matrix.

  * •

So far, we only listed realized variables. All demand variables, namely
intermediate orders $O_{k,l,0}$, private consumption demand
$c_{k,0}^{d,\text{local}},c_{k,0}^{d,\text{rest}}$, government consumption
demand, $G_{k,0}^{d,\text{local}},G_{k,0}^{d,\text{rest}}$, and other final
demand, $f_{k,0}^{d,\text{local}},f_{k,0}^{d,\text{rest}}$, are initially set
to their realized counterparts. This corresponds to assuming that before the
pandemic all demand was exactly satisfied. Demand matrices and vectors have
the same dimensions as their realized counterparts (e.g., orders $O$ are a
$40\times 40$ matrix).

###  S3.5 Consumption expenditures

The only dataset that has information on consumption disaggregated by age,
income and other characteristics is the Consumer Expenditure Survey (from now
on, CE). This is a survey, meaning that they ask respondents what they
consumed, using a classification system that is just specific to this survey.
The CE is produced by the BLS.

The other main dataset on consumption expenditures is produced by the BEA,
which also uses it for input-output tables and national accounts. This dataset
is called Personal Consumption Expenditures (PCE). It uses a different
classification system than the CE, but there exists a crosswalk produced by
the BLS and by the BEA to match these two classifications. Differently from
CE, PCE is obtained by directly considering receipt data of wholesalers,
retailers and other producers and other data sources to estimate actual
consumption, so it is not a survey.

In general, the CE and PCE are different. There is a whole literature
comparing them, see e.g. Ref. [49]. Overall, expenditure levels in CE seem to
be around 60% of PCE, and this varies substantially across expenditure items.
For instance, purchases of motor vehicles are very similar, but purchases of
furniture in CE are 64% of those in PCE, and purchases of personal care
services are 35% in CE compared to PCE.

In our approach, we consider PCE as our benchmark, and attribute consumption
categories in CE to the closest PCE categories to disaggregate consumption by
age and income. We finally obtain household consumption of goods and services
produced by a given industry by mapping PCE categories to NAICS commodities
and finally to NAICS industries.

####  S3.5.1 Consumer expenditure survey (CE)

We obtain data for consumption disaggregated by age and income from the
consumer expenditure survey (CE). These data are available in several formats,
we decide to use tables available on the BLS
website.343434https://www.bls.gov/cex/tables.htm Specifically, we focus on the
cross-tabulated tables “Age of reference person by income before taxes” for
years 2018-2019: these tables provide socio-demographic information as well as
income and consumption across many expenditure items for certain joint age-
income groups. (Age refers to the head of the household –simply the respondent
to the survey–, while income is the sum of all individual incomes within the
household.) Specifically, information is available for all combinations of age
brackets $<25$ years old, $25-34$ years old, $35-44$ years old, $45-54$ years
old, $55-64$ years old, $>65$ years old, and income brackets $<\$15,000$,
$\$15,000-\$29,999$, $\$30,000-\$39,999$, $\$40,000-\$49,999$,
$\$50,000-\$69,999$, $\$70,000-\$99,999$, $>\$100,000$. This gives a total of
$6\times 7=42$ age-income combinations.353535The combination $<25$ years old -
$>\$100,000$ is actually missing in the dataset as not enough households could
be found with these characteristics. We thus impute it by simply assuming
that, for each consumption category, the ratio between the consumption of
$<25$ years old - $>\$100,000$ and that of $<25$ years old -
$\$70,000-\$99,999$ is the same as the ratio between the consumption of
$25-34$ years old - $>\$100,000$ and that of $25-34$ years old -
$\$70,000-\$99,999$.

Out of all the information provided in these tables, we focus on the number of
households for each of the 42 age-income combinations, and on the average
yearly consumption of households in each age-income combination for many
expenditure items. These items are classified according to a system developed
by the BLS named UCC, which comprises several hierarchical levels. For
instance, going down the hierarchy from the top, it is possible to know
consumption of food, of food at home, of “meats, poultry, fish and eggs” and
of beef.

####  S3.5.2 Personal consumption expenditures (PCE)

Personal consumption expenditures data are provided by the BEA as part of the
U.S. national accounts. These data are used to compute GDP from the
expenditure approach, together with information on investment, exports, etc.
Differently from the CE, these data are not obtained as part of a survey, but
rather estimated from several sources that include receipts from several
industries. The BEA has its own hierarchical classification of expenditure
items. We focus on the level of aggregation provided in “Table 2.4.5. Personal
Consumption Expenditures by Type of Product” (year 2019), which comprises 76
expenditure items.

####  S3.5.3 Mapping CE to PCE

Sometimes consumption categories in PCE are easily comparable to those in CE.
To consider the same example as above, from top to bottom of the PCE
hierarchy, it is possible to know consumption of “Food and beverages purchased
for off-premises consumption”, “Food and nonalcoholic beverages purchased for
off-premises consumption”, “Food purchased for off-premises consumption”,
“Meats and poultry” and “Beef and veal”. In other cases, however, items in PCE
are not comparable to items in CE. For instance, CE does not provide as
detailed information on consumption of financial services as PCE does.
Moreover, expenditure in CE underestimates expenditure in PCE: aggregate CE
consumption is 60% of PCE consumption (which is consistent with national
accounts).

Thus, we proceed as follows. For each of the 76 expenditure items in PCE, we
assign one or more expenditure items in CE. A CE expenditure item can be
assigned to multiple PCE items. For instance, PCE categories “Sporting
equipment, supplies, guns, and ammunition” and “Sports and recreational
vehicles” do not exist independently in CE, and so we assign the CE category
“Other entertainment supplies, equipment, and services” to both. This matching
is based on the CE-PCE concordance file available on the BLS
website.363636https://www.bls.gov/cex/pce-concordance-2017.xlsx

We then normalize consumption for each age-income category and each PCE
expenditure item so that total consumption for that item corresponds to the
value provided in the BEA table 2.4.5 for year 2019. Therefore, information
coming from CE is used to tell the consumption shares of PCE expenditure items
across age-income categories, while the total consumption values are
consistent with those provided by the BEA.

This process does not preserve age-income shares of total consumption, in the
sense that dividing total consumption of any given age-income category by
total consumption across all categories after this mapping will in general
give different results than in PCE. This is unavoidable: as mentioned above,
certain consumption items are underrepresented in CE, so actual shares of
total consumption cannot be the same. However, the Pearson correlation
coefficient between age-income shares of total consumption in CE and in PCE
(after we perform our mapping) is 0.98, suggesting that this is a minor issue.

####  S3.5.4 Mapping PCE categories to NAICS industries

At this point, we have a table with average yearly consumption for all
combinations of 76 PCE expenditure items and 42 age-income categories. What we
need, however, is a table with average yearly consumption for good and
services produced by all 20 2-digit NAICS industries, again for the 42 age-
income categories that we consider. This requires performing three steps:
mapping the 76 PCE expenditure items to the 71 3-digit NAICS commodities;
mapping the NAICS commodities to the corresponding 71 NAICS industries;
aggregating the 71 3-digit NAICS industries to the 20 2-digit NAICS
industries.

To perform the first step, we use the bridge provided by the BEA, again for
year 2019.373737https://apps.bea.gov/industry/xls/underlying-
estimates/PCEBridge_1997-2019_SUM.xlsx This is rather straightforward, except
for the need to attribute trade and transport margins to the relevant NAICS
commodities. To do so, we attribute margins so that total consumption of each
of the 71 NAICS commodities matches the data provided by the BEA.

For the second step, we derive the transformation matrix $T$ as detailed in
Section S3.4.1. This transformation attributes consumption of a commodity to
the industry that produced it. The most relevant example is education and
health: a significant portion of consumption of these commodities is
attributed to Public Administration, reflecting public schools and hospitals.

Finally, we aggregate all 71 3-digit NAICS industries to the 20 2-digit NAICS
industries using the official concordance. Our final dataset is a table with
20 rows and 42 columns, reflecting 2-digit NAICS industries and age-income
categories. This table is supplemented by a row which gives the total number
of households in each age-income category.

####  S3.5.5 Regional table

The approach as described so far applies to the U.S. as a whole. Indeed, the
data that we used to convert CE consumption into expenditure on NAICS
industries are only available at the country level.

To adapt consumption by age and income to the NY MSA, we use both data from
the regional input-output tables that we constructed (Section S3.4) and from
the synthetic population that we built (Section S3.3).

First, we change the number of households in each age-income category to
reflect the number of households in the NY MSA (as modeled by our synthetic
population), as opposed to the wider US. To do so, recall that our synthetic
population only comprises a bit more than 400 thousand individuals, a
representative sample of the real population that comprises more than 19
million individuals. Moreover, the synthetic population features 160 thousand
households, which again are smaller than the real 7.1 million
households.383838See, e.g.
https://censusreporter.org/profiles/31000US35620-new-york-newark-jersey-city-
ny-nj-pa-metro-area/. To address this issue, we rescale the number of
households in the synthetic population for each age-income category by a
factor of approximately 7100/160. The population share of each age-income
category, that is, the number of households in that age-income category
divided by the total number of households, is quite similar in our NY
synthetic population than in the CE data, in the sense that the Pearson
correlation coefficient of population shares across age-income groups is 0.88.
Of course, as income in the NY MSA is higher than in the rest of the US, the
highest income categories are over-represented. Moreover, as discussed in
Section S3.3.5 and shown in Figure S4H the lowest income categories are under-
represented.

The second step is to adapt spending so that overall consumption on each
industry matches consumption by New Yorkers as derived in the regional input-
output tables. In principle, changing the population structure and keeping the
average spending per household constant should already allow recovering a
total consumption that is consistent with input-output tables. Indeed,
spending for most industries as computed directly from the consumption table
is about 82% as spending obtained from the regional input-output tables. The
remaining discrepancy can be explained by many factors, including different
spending patterns in NY than in the rest of the US, different real population
structure than in our synthetic population, etc. To be consistent with
regional input-output tables, we simply rescale consumption on each industry
$k$ by each age-income category $g$ so that it matches total consumption
$c_{k,0}^{\text{local}}$, i.e.

$\sum_{g}c_{g,k,0}^{d,\text{local}}=c_{k,0}^{\text{local}}$ (51)

Figure S11: Consumption by age and income in the NY MSA. Each entry is yearly
consumption for goods and services produced by any given industry by
households within a certain age and income range (yo=years old; k$=thousands
dollars). The numbers on the right of the plot show the number of synthetic
households in the NY MSA (in thousands, normalized to the real population)
that belong to the age-income category on the same row.

In Figure S11, we show the resulting dataset. Two main things are worth
noting. First, overall consumption is larger for certain industries, such as
real estate, health, manufacturing, and zero for management and
construction.393939This is because, for instance, home renovation is
classified as investment, not as consumption. (This pattern could also be seen
on the final demand columns of Figure S10.) Second, most columns are
characterized by a periodic pattern by which the shade of blue becomes darker
every seven cells. This is because the total consumption of goods and services
increases with income. The only exceptions to this pattern are real estate and
health, which tend to have higher consumption among the young (at the top of
the heat map) and the elderly (at the bottom of the heat map), respectively.

Figure S12: Consumption by age and by income in the NY MSA. Each entry is the
percentage share of consumption of goods and services produced by a given
industry for a certain income or age group. (In other words, rows sum to 100.)

To better understand how age and income affect consumption, in Figure S12 we
plot industry consumption by income (panel A), aggregating age categories, and
by age (panel B), aggregating income categories. In both panels, to focus on
the different spending patterns of different income or age groups, we
normalize rows to 100. Looking at panel A, we can see a clear pattern that
poorer households spend a much larger share of their income on real estate
than richer households. Richer households tend to spend more on education,
entertainment and accommodation and food, although differences are not
enormous. For most industries, there is not much difference across income
groups, meaning that households tend to spend the same share of their income
on those industries irrespective of their overall income level. Looking at
panel B, we see that the only industry for which the younger spend more than
the older is real estate. The elder spend more in finance and health.

##  Supplementary Information S4 Calibration

Both the epidemic and the economic modules have some model-wide parameters
that we must set to a specific value. For some of these parameters –mostly
epidemic– we are able to find a reference for their value, and simply set them
ex-ante. Other parameters need to be calibrated ex-post, using an Approximate
Bayesian Computation (ABC) rejection algorithm. In the following Section S4.1
we address calibration in the epidemic module, listing which parameters can be
directly calibrated and which need to be calibrated using ABC. We then discuss
the economic parameters in Section S4.2. Finally, in Section S4.3, we explain
the ABC procedure and show our results.

###  S4.1 Epidemic parameters

Many of the parameters of the epidemic module are clinical. These include
length of incubation period, proportion of asymptomatic, probability to infect
if asymptomatic as compared to a symptomatic case, infection fatality ratio by
age group, etc. We list these parameters in Table S5, showing their value and
the reference from which they have been taken.

Parameters | Description | Age group | Value | Ref.
---|---|---|---|---
$r$ | relative infectiousness of asymptomatic individuals | - | 50% | $\dagger$
$k$ | proportion of pre-symptomatic transmission | - | 50% | [50]
$\epsilon$ | incubation period | - | 5 days | [51]
$p_{a}$ | symptomatic probability | 0-9 | 0.181 | [52]
|  | 10-19 | 0.181 |
|  | 20-29 | 0.225 |
|  | 30-39 | 0.225 |
|  | 40-49 | 0.300 |
|  | 50-59 | 0.300 |
|  | 60-69 | 0.360 |
|  | 70-79 | 0.360 |
|  | $\geq 80$ | 0.646 |
$\chi$ | susceptibility | 0-18 | 0.56 | [53]
|  | $\geq 19$ | 1.00 |
$\gamma$ | pre-symptomatic period | - | 2 days | [54]
$\mu$ | time to isolation | - | 2.5 days |
$\delta$ | days from isolation to death | - | 12.5 | [50]
IFR | infection fatality ratio | 0-9 | 0.00161% |  [55]‡
|  | 10-19 | 0.00695% |
|  | 20-29 | 0.0309% |
|  | 30-39 | 0.0844% |
|  | 40-49 | 0.161% |
|  | 50-59 | 0.595% |
|  | 60-69 | 1.93% |
|  | 70-79 | 4.28% |
|  | $\geq 80$ | 7.80% |
$T_{n}$ | Notification of death | - | 7 days | [50]
$\theta$ | outdoor transmissibility | - | 0.05 | [56]
Table S5: Baseline set of parameters. $\dagger$: assumed ;$*$: calibrated to
the generation time $T_{g}$; ${\ddagger}$ Only applied to symptomatic
individuals. As such, a correction factor of 1/p is applied to all age groups.

The epidemic module has three remaining free parameters: (1) the number of
infected individuals on the first day for which we have data to build the
interaction network; (2) the transmissibility, $\beta$; (3) fear of infection
$\phi^{EPI}$.

We begin the simulation on 02/12/20, when it was estimated that there were
already several infected individuals in New York. In particular, we use the
estimates provided by the GLEAM model [57], according to which there were 165
exposed individuals. To initialize the system into such a state, one could
select that number of agents randomly from the simulation and move them into
the latent compartment. However, this would not resemble the real evolution of
the epidemic, which does not infect people at random but instead follows the
path imposed by the behavior of individuals. For this reason, we initialize
the system with 10 latent individuals and let the simulation evolve. Once the
estimated number of individuals is observed, the system starts to run in
calendar time from 02/12/2020 to 06/30/2020 (each step corresponds to 1 day),
so that $t=0$ is 02/12/2020. This allows us to start the simulation on
calendar time with the estimated number of latent individuals without having
to select them at random. This also reduces the horizontal drift
characteristic of stochastic epidemiological models, although aligning the
simulations at such early time still yields a significant temporal uncertainty
resulting in large confidence intervals [58].

The other two parameters, $\beta$ and $\phi^{EPI}$, are estimated using ABC,
together with the economic parameters. The ABC procedure uses both economic
and epidemic data to define the quantities that the model must match. The
epidemic data that we use for calibration are the weekly estimated number of
deaths as a consequence of COVID-19 [59].

###  S4.2 Economic parameters

The parameters that we calibrate in the economic module are the fear of
unemployment $\phi^{U}$, the consumer demand reallocation parameter $\Delta
s$, the speeds of hiring and firing $\gamma_{H}$ and $\gamma_{F}$, and the
fear of infection $\phi^{ECO}$. We actually do not directly calibrate
$\phi^{ECO}$, because fear of infection in the economic module should be
proportional to the same quantity in the epidemic module (see Materials and
Methods). We instead calibrate the proportionality factor $\tilde{\phi}$, and
then compute $\phi^{ECO}=\tilde{\phi}\cdot\phi^{EPI}$.

We calibrate these parameters to match six official statistics, mostly coming
from national accounts. We choose to consider official sources as they are
most likely not biased towards any particular industry, and this makes them
most directly comparable to our model that is initialized on national and
regional accounts. Additionally, we focus on statistics that are likely to
make parameter identification possible, i.e. variables that our model would
predict very differently when using different parameter configurations.

These six statistics are: NY employment, NY Gross Domestic Product (GDP), US
GDP, US Investments+Net exports (“Other final demand”), US consumption of
goods and services produced by customer-facing industries, US consumption of
goods and services produced by industries that do not require contact with
customers. (When possible, we consider NY statistics, but these are not
available for other final demand and for consumption disaggregated by
industry. So, in this case we must compare to US data.) In all cases, we
compare the value of these variables in the second quarter of 2020 with the
last quarter of 2019, both in the model and in the data. (In the model, this
means that we compare to initial steady state values.) In the data, these
variables declined, respectively, by 18.9%, 11.4%, 10.2%, 18.8%, 20.7% and
3.4%.404040Sources: QCEW for employment in NY, BEA regional accounts for GDP
in NY (GDP in current dollars, table SQGDP2, updated December 23, 2020, NY
state – the data for the metro area are only available annually, so we use NY
state as a proxy), BEA national accounts for US GDP and other final demand
(Table 1.1.5, last revised on February 25, 2021), BEA underlying detail for US
consumption by commodity (Table 2.4.5U, last revised on February 25, 2021),
transformed to consumption by industry using the same procedure as in Section
S3.5.4, for US consumption of customer-facing and no-customer-facing
industries.

###  S4.3 Approximate Bayesian Computation

We run 100,000 parameter combinations chosen uniformly at random in the most
promising region of the parameter space (which has been identified by running
preliminary simulations), shown in Figure S13. We run each parameter
combination with a different random seed. We then select only the combinations
that yield errors no larger than (in absolute value):

  * •

0.012 weekly deaths per 1000 synthetic individuals. Compared to the peak of
the epidemic wave, this is a 3% error.

  * •

One percentage point for employment and GDP in NY. Here and below, we compare
economic statistics produced by the model, as averaged over all days in
Q2-2020, with the official statistics listed in Section S4.2. Letting
$\mathcal{T}$ be Q2-2020, $T=|\mathcal{T}|=90$ be the number of days in
Q2-2020, $x_{t}$ be an economic quantity at time $t$, and
$\hat{x}_{\mathcal{T}}$ an official economic statistic in Q2-2020, we require

$\frac{1}{T}\sum_{t=1}^{T}x_{t}-\hat{x}_{\mathcal{T}}<0.01.$

  * •

Two percentage points for US-wide quantities except other final demand, i.e.
GDP and consumption of both customer-facing and no-customer-facing industries.

  * •

Four percentage points for other final demand, which is not a primary focus of
this study.

The choice of thresholds is mostly arbitrary in Approximate Bayesian
Computation. We choose these thresholds to stress that, from an epidemic point
of view, we want to match the number of deaths very closely, while from an
economic point of view we aim at matching NY statistics most strictly, while
we allow more slack for US-wide statistics, as we model the rest of the US at
a coarser level.

Figure S13: Posterior of epidemic and economic parameters. These histograms
show the full range of sampled parameters on the horizontal axis. One can
interpret the priors as flat over these ranges.

Based on these thresholds, we select 361 parameter combinations. Figure S13
shows the distribution of the eight parameters across the selected
combinations. Under Approximate Bayesian Computation, this is interpreted as
the posterior distribution of the parameters, obtained without the need to
evaluate the likelihood. We use all these accepted parameter combinations to
generate uncertainty in the results, according to the Bayesian nature of ABC.
In particular, we sample each of these selected parameter combinations
uniformly at random and run them with the same random seed that has been used
in the calibration. As we perform 200 simulation runs for each counterfactual,
this is sufficient.

We see that the distribution of some parameters is clearly peaked. These
parameters include the transmissibility $\beta$, the fear of infection in the
epidemic and economic modules, $\phi^{\text{EPI}}$ and $\phi^{\text{ECO}}$,
and the fear of unemployment $\phi^{U}$. In other cases, such as the
proportionality factor $\tilde{\phi}$, reallocation $\Delta s$ and firing
speed $\gamma_{H}$, ABC fails to identify specific parameter values and
returns a flat posterior. This is mostly because the economic statistics are
averaged over Q2-2020 and so are not much sensitive to parameters such as
$\gamma_{H}$ and $\gamma_{F}$ that determine the speed of the transient.
Moreover, a pairplot (not shown) suggests that for almost all parameter
combinations there is virtually no correlation between parameter values,
except for two cases. First, we find that $\phi^{\text{EPI}}$ and
$\tilde{\phi}$ are negatively correlated. This is explained by the fact that
it is the parameter $\phi^{\text{ECO}}=\phi^{\text{EPI}}\cdot\tilde{\phi}$
that determines economic outcomes, and so it is hard to distinguish between
combinations of $\phi^{\text{EPI}}$ and $\tilde{\phi}$ that give the same
$\phi^{\text{ECO}}$. Second, we find that $\Delta s$ and $\phi^{U}$ are
positively correlated. This is explained by the fact that consumption of goods
and services by customer-facing industries declined much more than by non-
customer-facing industries (Figure 2A). So, because when $\phi^{U}$ is high
consumption demand declines a lot for both types of industries, one needs a
high value of reallocation $\Delta s$ to explain the small decline in non-
customer-facing consumption. These correlations suggest why the distribution
of certain parameters are not peaked, and at the same time elucidate certain
mechanisms of the model.

##  Supplementary Information S5 Supplementary results on the empirical
scenario

Here we report additional results on the scenario that was used to describe
the economic and epidemic effects of the first wave of the COVID-19 pandemic
in the New York metro area. In this scenario we

  * •

Started protective measures on 2020-03-16.

  * •

Closed customer-facing and other non-essential economic activities such as
large parts of manufacturing and construction.

  * •

Closed schools and mandated work from home.

  * •

Calibrated fear of infection to its baseline value.

###  S5.1 Economic results

Figure S14: Household consumption by industry in the empirical scenario. For
each industry, we show average realized household consumption throughout the
simulation period as compared to its steady state level. Industries are ranked
from the ones in which consumption was reduced the least (at the top) to the
ones in which it was reduced the most (at the bottom). Error bars indicate
2.5-97.5 percentiles.

Figure S14 shows that household consumption declines the most among customer-
facing industries, although it declines for manufacturing as well. To explain
why it also declines for manufacturing (demand for manufacturing goods does
not decline in the model since manufacturing is not a customer-facing
industry), note that here we are reporting realized consumption, which also
depends on supply. As half of the manufacturing industry was considered non-
essential in NY (Table S3), and 14% of manufacturing was considered non-
essential in the Rest of the US, not all demand could be satisfied. Another
observation is that consumption of some industries, such as finance and
insurance and utilities, in some simulations increased above the pre-pandemic
level. This is not in contrast with the assumption that industries cannot
produce more than the pre-pandemic level, because household consumption is
only part of total demand. Therefore, with a consumption of intermediate goods
that is lower than the pre-pandemic level, household consumption can be
higher.

Figure S15 shows how employment changed across occupations. Employment
declined across all occupations, but it declined most strongly among
occupations that could not be performed from home and that are particularly
common in the industries that were not considered essential (Figure S8A). From
Figure S7 we also see that the most affected occupations tend to be the ones
with a highest percentage of low-income workers.

Figure S15: Employment by occupation in the empirical scenario. For each
occupation, we show average employment throughout the simulation period as
compared to its steady state level. Industries are ranked from the ones in
which employment was reduced the least (at the top) to the ones in which it
was reduced the most (at the bottom). Error bars indicate 2.5-97.5
percentiles. Figure S16: Employment by census tract in the empirical scenario
throughout the New York metro area. Census tracts are colored depending on
employment in April 2020 compared to steady state values. Census tracts
colored purple are the ones in which employment was reduced the most, while
census tracts colored yellow are the ones in which it was reduced the least.
Note that we crop the color bar so that census tracts in which employment was
lower than 76% of the pre-pandemic level are still colored purple, and census
tracts in which it was higher than 88% of the pre-pandemic level are still
colored yellow.

Figure S16 shows changes in employment throughout the NY metro area. We see
that there is a lot of heterogeneity across census tracts, with a weak pattern
of decreasing employment as the distance from Manhattan increases.

####  S5.1.1 Validation data

##### TTR data on employment and consumption by income.

Track the Recovery (TTR, [3]) is a project by Opportunity Insights, a lab
based at Harvard University. It uses private-sector data to track several
economic statistics in real-time, across demographic groups, and for very
granular geographical levels. We downloaded their data414141Repository:
https://github.com/OpportunityInsights/EconomicTracker. Last access:
27/01/2021. and focused on two key datasets: their combined employment
indicator and consumption data by Affinity. For employment, we have data at
the county level, which we aggregate at the NY Metropolitan Statistical Area
(MSA) level by taking a weighted mean where weights are proportional to
county-level employment. For consumption, data are only available at the state
level. Because 1/3 of the NY MSA population come from New Jersey State, and
2/3 come from New York State, we aggregate consumption data by using these
population shares as weights. For both employment and consumption, raw data
are given as percentage changes from the initial day (2020-01-14); for
comparison to our simulations we rebase them so that the initial day has value
100 (this transformation does not affect the interpretation of the data at
all). Employment and consumption data are further disaggregated across
household income levels. For employment data, low-income households have
annual income less than 27k$, while high-income households earn more than 60k$
per household (these incomes are not “high” given the cost of life in NY, but
for comparison we need to follow the convention in Ref. [3] that classifies
households by income at the US level). For consumption data, low-income
households are those who earn less than 46k$ a year, while high-income
households are those that earn more than 78k$.424242As detailed in Section
S3.5, our household income brackets are: $<$15k$, 15-29k$, 30-39k$, 40-49k$,
50-69k$, 70-99k$, $>$100k$. These do not perfectly overlap with the income
brackets in TTR data, so we consider the lowest three income brackets
($<$15k$, 15-29k$, 30-39k$) as corresponding to low-income households, and the
last two income brackets (70-99k$, $>$100k$) as corresponding to high-income
households.

##### QCEW data on industry employment.

The Quarterly Census on Employment and Wages (QCEW) is a data release by the
Bureau of Labor Statistics (BLS) that gives monthly data on employment, worker
wages and number of establishments, at various levels of spatial aggregation.
QCEW data are particularly reliable as they are calculated based on detailed
administrative information received by states and counties from all private
sector employers. We extract data from January 2020 to June 2020, for the NY
MSA, FIPS code C3562. Industry-level data are only available for private
employment. Given that we are particularly interested to use these data to
compare the predictions of our model regarding differentials in employment
across industries, this is unlikely to be a major limitation. A more serious
issue is that, due to confidentiality concerns, the QCEW does not provide
industry-level data when the number of employees in a given industry is too
small. For instance, it does not provide any information for NAICS code 323:
Printing and Related Support Activities, presumably because in the NY MSA
there are too few printing manufacturing firms and it might be possible to
recover firm-level information from industry-level data. This would not be an
issue as this paper focuses on quite aggregated 2-digit NAICS industries, but
the QCEW extends missing information at more aggregate level. For instance,
all 3-digit subsectors of NAICS industry 32 except 323 are available so, if
the QCEW was providing information on industry 32, it would be possible to
obtain information on industry 323 by taking the difference between employment
in 32 and employment in all 3-digit subsectors except 323. This is a problem,
as for these reasons the QCEW does not provide any information for several
important aggregates, such as manufacturing (31-33), wholesale trade (42),
transportation (48-49), administrative services (56). To deal with this issue,
we build 2-digit industrial aggregates summing from the subsectors for which
data are available, and check using US-level data that the subsectors for
which data are not available are a very small fraction (often 1-2%) of total
industry employment.

###  S5.2 Epidemic results

Figure S17: Time series of prevalence and deaths. A: prevalence over time, in
the data (seroprevalence surveys) and in the model. B: weekly deaths over
time, in the data and in the model. In both panels, error bands indicate the
2.5-97.5 percentiles.

Figure S17 shows time series for prevalence and deaths. We see that the model
predicts a time evolution for prevalence that is consistent with data, with
high uncertainty about the final value of prevalence. The uncertainty over the
time series for deaths is much smaller, and we see that the model can
successfully track weekly data except in the first two weeks (in Figure 2 we
were only showing monthly aggregation). Note that the prevalence is not
directly proportional to the number of deaths since it is possible to have a
lot of infections in the younger age groups without significantly increasing
the number of deaths.

Figure S18: Time series of contacts in the epidemic module. A: average weight
of community contacts per infected individual. B: average weight of workplace
contacts per infected individual. In both cases, the values are computed by
adding up the weight of all the contacts that infected individuals have with
susceptible ones, divided by the total size of the population. Error bands
indicate the 2.5-97.5 percentiles.

Figure S18 shows the average weight of contacts in either the community or the
workplace layer from infected to susceptible individuals, both in the real
data and in our model. Note that in the real data an individual who chose to
work from home does not have any connections. Instead, in the data fed to the
model (as explained in Section S3.1.3) the link will exist. As such, to
properly compare both situations, we only add the weight of those contacts
that are materialized (in the model, if either individual works from home the
contact cannot take place). As we can see, the model correctly captures the
trend of the workplace contacts. For the community, instead, we observe an
important peak of contacts in the model yields right before the closure
imposed on 03/16 in comparison with the real data. This is probably related to
voluntary stay-at-home behavior following the declaration of the National
Emergency on 03/13. Indeed, as seen reported in [16], the number of contacts
in the community on the weekend from 03/14 to 03/15 is smaller than expected
even though most places had not been force to close yet. This behavior cannot
be reproduced by the fear of infection that we have introduced since it only
depends on the number of reported deaths and not on any exogenous events.

Note also that there is not an important surge of contacts in the real data
following the reopening of economic activities. This is probably due to the
slow pace at which the riskier venues reopened. For instance, restaurants were
only allowed to operate for delivery, and indoor dining came back in
September. Similarly, retail stores in New Jersey reopened on May 18 but only
for curbside pickup. Thus, even though in the economic module the restrictions
to operate are lifted, the epidemic module assumes that most industries are
still closed until the end of the simulation period [60, 61].

Figure S19: Contacts in the workplace. Ratio between workplace contacts with
infectious individuals before and after the imposition of protective measures,
in the model and in the data. This figure has the same interpretation as
Figure 2E.

Our last results strictly on validation are shown in Figure S19. Here we show
the same plot as Figure 2E, but for workplace contacts. We find that these are
extremely well correlated between model and data, with a correlation
coefficient of 0.88. Workplace contacts declined less in Transportation and
Health than Entertainment and Accommodation-Food, in line with the
essentialness of industries. Workplace contacts declined a lot also in
Professional services, Education and Finance, reflecting the switch to work
from home in these industries.

Our next set of results on the empirical scenario cannot strictly be
considered as validation, as we will show some results that we could not
quantitatively compare to real-world data because such data do not exist.
However, as we will discuss in Section S5.2.1, we can qualitatively find
support for the following results.

Figure S20: Infections by origin and by socio-economic characteristics. A:
Time series of infections disaggregated by origin (workplace, community,
school and household layers). The dashed vertical line indicates the day when
we impose protective measures (2020-03-16). B: Time series of workplace plus
community infections disaggregated by the industry where the infection took
place. Time series are normalized by total workplace and community infections.
C: Percentage of all workers across some income levels and occupations that
has been infected in the workplace before 2020-03-16 and after 2020-03-16.

Figure S20 shows results on infections by origin and by socio-economic
characteristics. Our model predicts that, before protective measures were
implemented in mid-March, most infections occurred in the workplace and
community layers (Figure S20A). After these chains of transmission were
severed by social distancing, household transmission took the highest share of
infections. At the industry level (Figure S20B), we see a large share of
infections in entertainment and accommodation-food before 2020-03-16,
especially during weekends, while this share declined after 2020-03-16. In the
second period, retail acquired the largest share of infections, as it required
in-person interactions and remained partly essential (e.g., consider grocery
stores and pharmacies). We also find a general pattern that high-income
workers engaged in knowledge-related and business occupations got more
infected than low-income workers engaged in physical occupations before
2020-03-16 (Figure S20C). This is because they work in denser census tracts
that we assume are at higher risk of infection [16]. The reverse pattern holds
after 2020-03-16, because many high-income workers switch to working from
home, while low-income workers in essential occupations must keep working in
person.

Figure S21: Infections by the industry in which the infection happened in the
empirical scenario. We sort industries by NAICS code, and show results
distinguishing between the community and workplace layers. Error bars indicate
2.5-97.5 percentiles and refer to the total of infections, not just infections
in the community layer.

Figure S21 shows infections across industries, distinguishing between
infections in the community and workplace layers. It is possible to see that
for some industries most infections happen in the community layer (retail
trade, arts, entertainment, and recreation, accommodation and food services),
while for other industries a majority of infections occur in the workplace
layer (health care and social assistance, finance and insurance, professional,
scientific and technical services). In a few cases such as construction and
manufacturing, infections occur only in the workplace, as these economic
activities are not customer-facing.

Figure S22: Infections by age by origin in the empirical scenario. For each
age band, we show the prevalence at the end of the simulation, distinguishing
between the origin of infections. Error bars indicate 2.5-97.5 percentiles and
refer to the total of infections, not just infections in the household layer.

Figure S22 shows the percentage of people in each age band that got infected,
distinguishing by the origin of infections. We see that children (agents up to
17 years old) got some infections in school, but most infections in the
household (likely due to the relatively early closure of schools). Across all
age groups infections in the household are the majority (likely related to the
stay-at-home order which increased time spent in the household while reduced
contacts everywhere else), but for intermediate age groups infections are
almost equally divided with the workplace and community layers. The fact that
intermediate age groups got infected the most is consistent with empirical
evidence discussed in Section S5.2.1

####  S5.2.1 Empirical evidence

The empirical evidence on rates of infection across socio-economic
characteristics is mixed. Indeed, it is well known that infections confirmed
by a PCR test are largely biased, especially in the early period of the
pandemic that we are looking at. So, we need to look at serological tests, but
the ones available for the New York area are not representative of the whole
population. Therefore, we will also consider results for other countries when
these are more likely to be representative.

##### Sources.

Case counts (confirmed by a PCR test) are available by county using the New
York Times dataset. The New York City (NYC) Health Authority
(https://github.com/nychealth/coronavirus-data) provides additional data on
case counts, also disaggregated by age. Refs. [62] and [63] report results on
serological tests among NYC health care workers, while [64] offered
serological tests to shoppers in several counties in New York state. Ref. [65]
analyzes serological tests to look at prevalence among occupations. Ref. [66]
performed a large-scale serological study among patients on dialysis across
all the United States. Ref. [67] studies the outcomes of serological tests in
Spain during the first wave: this is the only study that is representative of
the population. Finally, Ref. [68] provides a detailed breakdown of infections
confirmed by PCR tests across occupations for the whole population of Norway.

##### Infections by age.

PCR-positives in New York City, as obtained by the NYC Health Authority, show
a consistently higher positivity rate as age is increased. By the first week
of June, 2.3% of New Yorkers aged more than 75 tested positive, which compares
to only 1.6% of adults aged 45-54 and 0.7% of young adults aged 18-24. But
these results are biased by the fact that only people with severe symptoms
were tested in the first wave of the epidemic. Looking at serological tests,
both [62] and [66] found decreasing prevalence with increasing age, while [63]
and [64] find the highest prevalence in middle ages. The results in [67]
depend on the type of test used, but generally find a higher prevalence at
higher ages. Overall, these results lend some support to our results on age
prevalence (Figure S22).

##### Infections by occupation.

The studies limited to healthcare workers in New York show higher infection
rates among service and maintenance workers than among nurses or physicians,
but the relative difference varies between studies [62, 63]. Ref. [65] finds
the highest prevalence among prison staff and the lowest among laboratory
technician, but there is no specific patterns and the set of occupations is
not representative of occupations – the classification is non-standard. Ref.
[67] finds the highest prevalence among healthcare workers, an intermediate
prevalence among workers in transportation, the police, and teleworkers, and
the lowest prevalence among retailers and cleaners. This, however, was true
for the first wave; in the second wave, cleaners had a much larger prevalence
than teachers, and close to the healthcare workers. Ref. [68] finds higher
prevalence among healthcare workers and bus and tram drivers and lower
prevalence among waiters and personal services in the first wave, but higher
prevalence among workers in the hospitality sector in the second wave. We find
this evidence on occupations to be too mixed to be compared to our results.

##### Infections by income.

Several studies consider race and, because race is strongly correlated with
income, argue about income on the basis of race. For instance, studies showing
that blacks and hispanics have higher prevalence than whites also imply that
prevalence was high among low-income people. Here, we prefer to look at
prevalence by income directly. A NYC Health study shows that ZIP codes with
higher poverty rates have higher prevalence according to antibody tests:
https://github.com/nychealth/coronavirus-data/blob/master/totals/antibody-by-
poverty.csv. This is in line with the results about race. The Spanish study,
which is representative of the full population, does not show a clear trend on
infections by income, with higher-income individuals being more hit in the
first wave and less in the second wave [67]. In this case, these results are
supportive of our finding that rich individuals in NY got more infected before
the lockdown and less infected after the lockdown (Figure S20C).

##  Supplementary Information S6 Supplementary results on the counterfactual
scenarios

Here we report additional results on the counterfactuals. These include:

  * •

Starting protective measures on 2020-02-17 (early), 2020-03-16 (baseline) and
2020-03-30 (late).

  * •

Closing all non-essential industries as in the NY lockdown (“Non-essential”),
only closing non-essential customer-facing industries (“Cust.-facing 100%”),
only closing 25-50-75% of these industries (uniformly across industries), and
leaving all economic activities open (“All open”). Recall that in all cases
the essential part of industries is always left open. For instance, when we
say that 100% of the retail industry is closed, we only refer to non-essential
shops, constituting only 35% of the retail industry (Table S3).

  * •

Closing schools and mandating work from home; opening schools keeping work
from home mandatory; and opening schools and making work from home voluntary

  * •

Calibrating fear of infection to 0.1 (low), 1 (baseline), and 10 (high) times
its baseline value.

To show the generality of our results, in this supplementary materials we
report more results in one plot than in the main paper. This may be at the
cost of legibility – for instance, while in Figure 3 we only vary fear two
factors at a time (with shape and color to reflect all factor combinations),
here we will vary three factors at a time (including size).

###  S6.1 Aggregate results

Figure S23: Deaths and unemployment across counterfactual scenarios, including
partial closures of customer-facing industries. For each scenario, we show the
aggregate unemployment rate as averaged throughout the simulation period, and
the cumulative number of deaths. Dashed lines indicate the empirical
unemployment and number of deaths. Scenarios are distinguished by the strength
of behavior change, as exemplified by the fear of infection parameter (color);
by the day when protective measures –closures of economic activities, school
closures, imposition of work from home– were started (symbol); by the specific
closure of economic activities (size). Compared to Figure 3A, we consider a
partial closure of customer-facing industries: We allow for these industries
to be 25% closed, 50% closed, 75% closed. Error bars indicate 2.5-97.5
percentiles.

Figure S23 is similar to Figure 3A, but contains more information. First, it
considers the variation of all three factors – closures, fear of infection and
start of protective measures – at a time. Second, it includes the scenarios in
which non-essential customer-facing economic activities are partly (25-50-75%)
closed.

The results are in line with Figure 3A: strong behavior change reduces deaths
but increases unemployment as much as strict closures; closing all non-
essential rather than just customer-facing industries greatly increases
unemployment while marginally reducing deaths; and waiting to start protective
measures is in most cases counterproductive both for epidemic and economic
outcomes. This figure also shows other combinations of scenarios that were not
shown in the main text. For instance, it is interesting to look at the
combination of high fear of infection and an early start of protective
measures (blue squares). In this case, leaving all economic activities open
(even partially) leads to both more deaths and more unemployment than closing
only customer-facing industries. The results for the scenarios that involve
partial closure of customer-facing industries interpolate almost linearly
between the cases in which these industries are completely open or completely
closed.

Figure S24: Deaths and unemployment across counterfactual scenarios, varying
school closures and imposition of work from home. For each scenario, we show
the aggregate unemployment rate as averaged throughout the simulation period,
and the cumulative number of deaths. Dashed lines indicate the empirical
unemployment and number of deaths. Scenarios are distinguished by the strength
of behavior change, as exemplified by the fear of infection parameter (color);
by the specific closure of economic activities (size); and by whether schools
were closed and/or work from home imposed (symbol). In all cases, we impose
protective measures on 2020-02-17 (Early), so that they have maximal effect
and we can better see the effect of school closures and work from home. Error
bars indicate 2.5-97.5 percentiles.

Figure S24 shows results when school and work from home policies are changed.
Compared to the baseline in which schools are closed and work from home is
imposed, we see a much increase in deaths due to relaxing the work from home
mandate than to closing schools (the distance between squares and circles is
much smaller than the distance between circles and triangles).

Figure S25: Distribution of deaths and unemployment across simulation runs.
For each of the scenarios in Figure 3A, we show a violin plot and a point
distribution for both the mean unemployment rate and the cumulative number of
deaths.

Finally, Figure S25 shows how results are distributed across simulation runs
for the scenarios shown in Figure 3A. As discussed in the main text, when
comparing different counterfactuals we only report mean results: reporting
error bars would make the plot more complicated without adding much useful
information, as what matters most is the difference across scenarios rather
than absolute values. For instance, it would not be much informative to say
that unemployment could vary between 7% and 10% under baseline fear of
infection and no closures, and between 9% and 11% when fear of infection is
again baseline but customer-facing industries are closed (as in the two
leftmost examples in the top row of Figure S25). This would suggest that there
is substantial overlap between the two scenarios. However, using a paired
Welch t-test, one can reject the hypothesis that there is no difference in
means with p-value $<10^{-6}$. More systematically, we find that all pairs of
scenarios shown in Figure S25 have highly statistically significant
differences in means, despite overlaps between the distributions.

###  S6.2 Disaggregate economic results

Figure S26: Employment and consumption by income across counterfactual
scenarios. We select three combinations of fear of infection and start of
protective measures, and for each of these combinations we study all six
possibilities for closures of economic activities. Both employment and
consumption are aggregated across industries and averaged throughout the
simulation period, and then compared to their pre-pandemic level. We
distinguish between aggregate employment and consumption, and employment and
consumption by low-income individuals and households (those earning less than
27k$, to keep the same convention as in the validation part of the paper) and
by high-income individuals and households (those earning more than 60k$).
Error bands indicate 2.5-97.5 percentiles.

Figure S26 focuses on three scenarios selected for illustration. In all cases,
employment and consumption go down as fear of infection is increased;
employment of low-income individuals is below employment of high-income
individuals (except in the leftmost cases in the leftmost panel), and this
difference widens with stricter closures of economic activities; consumption
by low-income households decreases less than consumption by high-income
households, with little difference when different closure policies are
considered. When fear of infection is high, we see an “inverted-V” shape for
both employment and consumption, in line with the aggregate result in Figure
S23 that in this case opening all economic activities leads to more
unemployment than keeping customer-facing activities closed.

Figure S27: Employment and consumption by industry across counterfactual
scenarios. We focus on the empirical scenario to select fear of infection
(baseline) and the start of protective measures (baseline), and we study all
six possibilities for closures of economic activities. Both employment and
consumption are averaged throughout the simulation period, and then compared
to their pre-pandemic level. Here and in following plots, we do not show error
bands to enhance visibility.

Figure S27 focuses on the empirical scenario, only changing the possible
closures of economic activities. It shows that the difference across
industries in terms of employment and consumption becomes smaller when
customer-facing industries are opened, but accommodation-food and
entertainment remain among the ones most strongly hit. The reason why public
administration employment remains equal to its pre-pandemic level is that most
demand for public administration services comes from the government, which we
assume to increase its demand by 5%. This means that the public administration
industry is never demand-constrained.

Figure S28: Employment by occupation across counterfactual scenarios. We focus
on the empirical scenario to select fear of infection (baseline) and the start
of protective measures (baseline), and we study all six possibilities for
closures of economic activities. For each occupation, employment is averaged
throughout the simulation period, and then compared to its pre-pandemic level.
To enhance readability, we consider combinations of color and line type, so
there is nothing specific to occupations represented by dashed vs. solid
lines.

Figure S28 is similar to Figure S27, but it shows occupational employment. The
results are in line with what can be expected given the occupational
composition of industries (Figure S8A). For instance, food preparation and
serving related occupations are the worst hit across all six scenarios
considered here, in line with the fact that the accommodation-food industry
was the second most hit across all these scenarios.

###  S6.3 Disaggregate epidemic results

Figure S29: Workplace infections prevalence by income across counterfactual
scenarios. We select three combinations of fear of infection and start of
protective measures, and for each of these combinations we study all six
possibilities for closures of economic activities. Workplace infections
prevalence is defined as the percentage of all workers of a given type that
have been infected in the workplace at the end of the simulation period. We
distinguish between prevalence of low-income workers (those in the first
quintile of the income distribution) and of high-income workers (those in the
top quintile of the income distribution). Error bands indicate 2.5-97.5
percentiles.

Figure S29 shows that high-income workers get more infected in the workplace
than low-income workers when fear of infection is low and work from home is
imposed very late (left panel), while the reverse is true when work from home
is imposed early (right panel). Closing customer-facing industries reduces
infection rates among low-income workers the most.

Figure S30: Prevalence by origin across counterfactual scenarios. We select
three combinations of fear of infection and start of protective measures, and
for each of these combinations we study all six possibilities for closures of
economic activities. Prevalence of infections of a given origin is defined as
the percentage of all individuals in the population that have been infected in
the corresponding layer at the end of the simulation period. We distinguish
between workplace, community, school and household infections. Error bands
indicate 2.5-97.5 percentiles.

Figure S30 shows the origin of infections across the same scenarios as in
Figure S29. In all cases the main contribution is from the household layer and
the smallest contribution is from the school layer. Community infections are
larger than workplace infections, although the difference is larger when
customer-facing industries are open.

Figure S31: Infections by industry of infection across counterfactual
scenarios. We focus on the empirical scenario to select fear of infection
(baseline) and the start of protective measures (baseline), and we study all
six possibilities for closures of economic activities. We show the number of
community and workplace infections per 1000 individuals that took place in any
given industry for each scenario.

In line with this result, Figure S31 shows that infection rates decline the
most in retail, accommodation-food, education and entertainment when customer-
facing industries are closed, although these industries remain the main
overall source of infections.

Figure S32: Workplace infections prevalence by occupation across
counterfactual scenarios. We focus on the empirical scenario to select fear of
infection (baseline) and the start of protective measures (baseline), and we
study all six possibilities for closures of economic activities. Workplace
infections prevalence is defined as the percentage of all workers of a given
occupation that have been infected in the workplace at the end of the
simulation period. To enhance readability, we consider combinations of color
and line type, so there is nothing specific to occupations represented by
dashed vs. solid lines.

Finally, Figure S32 shows workplace infections by occupation. We see that
production, installation, maintenance and repair, transportation and material
moving occupations are among the most hit, as these occupations cannot be
performed from home. Closing down construction and manufacturing, as in the
“non-essential” scenarios, reduces infection rates among these occupations the
most.